data analytics case study examples

10 Real World Data Science Case Studies Projects with Example

Top 10 Data Science Case Studies Projects with Examples and Solutions in Python to inspire your data science learning in 2023.

BelData science has been a trending buzzword in recent times. With wide applications in various sectors like healthcare , education, retail, transportation, media, and banking -data science applications are at the core of pretty much every industry out there. The possibilities are endless: analysis of frauds in the finance sector or the personalization of recommendations on eCommerce businesses.  We have developed ten exciting data science case studies to explain how data science is leveraged across various industries to make smarter decisions and develop innovative personalized products tailored to specific customers.

Walmart Sales Forecasting Data Science Project

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Data science case studies in retail , data science case study examples in entertainment industry , data analytics case study examples in travel industry , case studies for data analytics in social media , real world data science projects in healthcare, data analytics case studies in oil and gas, what is a case study in data science, how do you prepare a data science case study, 10 most interesting data science case studies with examples.

So, without much ado, let's get started with data science business case studies !

With humble beginnings as a simple discount retailer, today, Walmart operates in 10,500 stores and clubs in 24 countries and eCommerce websites, employing around 2.2 million people around the globe. For the fiscal year ended January 31, 2021, Walmart's total revenue was $559 billion showing a growth of $35 billion with the expansion of the eCommerce sector. Walmart is a data-driven company that works on the principle of 'Everyday low cost' for its consumers. To achieve this goal, they heavily depend on the advances of their data science and analytics department for research and development, also known as Walmart Labs. Walmart is home to the world's largest private cloud, which can manage 2.5 petabytes of data every hour! To analyze this humongous amount of data, Walmart has created 'Data Café,' a state-of-the-art analytics hub located within its Bentonville, Arkansas headquarters. The Walmart Labs team heavily invests in building and managing technologies like cloud, data, DevOps , infrastructure, and security.

Walmart is experiencing massive digital growth as the world's largest retailer . Walmart has been leveraging Big data and advances in data science to build solutions to enhance, optimize and customize the shopping experience and serve their customers in a better way. At Walmart Labs, data scientists are focused on creating data-driven solutions that power the efficiency and effectiveness of complex supply chain management processes. Here are some of the applications of data science  at Walmart:

i) Personalized Customer Shopping Experience

Walmart analyses customer preferences and shopping patterns to optimize the stocking and displaying of merchandise in their stores. Analysis of Big data also helps them understand new item sales, make decisions on discontinuing products, and the performance of brands.

ii) Order Sourcing and On-Time Delivery Promise

Millions of customers view items on Walmart.com, and Walmart provides each customer a real-time estimated delivery date for the items purchased. Walmart runs a backend algorithm that estimates this based on the distance between the customer and the fulfillment center, inventory levels, and shipping methods available. The supply chain management system determines the optimum fulfillment center based on distance and inventory levels for every order. It also has to decide on the shipping method to minimize transportation costs while meeting the promised delivery date.

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iii) Packing Optimization 

Also known as Box recommendation is a daily occurrence in the shipping of items in retail and eCommerce business. When items of an order or multiple orders for the same customer are ready for packing, Walmart has developed a recommender system that picks the best-sized box which holds all the ordered items with the least in-box space wastage within a fixed amount of time. This Bin Packing problem is a classic NP-Hard problem familiar to data scientists .

Whenever items of an order or multiple orders placed by the same customer are picked from the shelf and are ready for packing, the box recommendation system determines the best-sized box to hold all the ordered items with a minimum of in-box space wasted. This problem is known as the Bin Packing Problem, another classic NP-Hard problem familiar to data scientists.

Here is a link to a sales prediction data science case study to help you understand the applications of Data Science in the real world. Walmart Sales Forecasting Project uses historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and you must build a model to project the sales for each department in each store. This data science case study aims to create a predictive model to predict the sales of each product. You can also try your hands-on Inventory Demand Forecasting Data Science Project to develop a machine learning model to forecast inventory demand accurately based on historical sales data.

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Amazon is an American multinational technology-based company based in Seattle, USA. It started as an online bookseller, but today it focuses on eCommerce, cloud computing , digital streaming, and artificial intelligence . It hosts an estimate of 1,000,000,000 gigabytes of data across more than 1,400,000 servers. Through its constant innovation in data science and big data Amazon is always ahead in understanding its customers. Here are a few data analytics case study examples at Amazon:

i) Recommendation Systems

Data science models help amazon understand the customers' needs and recommend them to them before the customer searches for a product; this model uses collaborative filtering. Amazon uses 152 million customer purchases data to help users to decide on products to be purchased. The company generates 35% of its annual sales using the Recommendation based systems (RBS) method.

Here is a Recommender System Project to help you build a recommendation system using collaborative filtering. 

ii) Retail Price Optimization

Amazon product prices are optimized based on a predictive model that determines the best price so that the users do not refuse to buy it based on price. The model carefully determines the optimal prices considering the customers' likelihood of purchasing the product and thinks the price will affect the customers' future buying patterns. Price for a product is determined according to your activity on the website, competitors' pricing, product availability, item preferences, order history, expected profit margin, and other factors.

Check Out this Retail Price Optimization Project to build a Dynamic Pricing Model.

iii) Fraud Detection

Being a significant eCommerce business, Amazon remains at high risk of retail fraud. As a preemptive measure, the company collects historical and real-time data for every order. It uses Machine learning algorithms to find transactions with a higher probability of being fraudulent. This proactive measure has helped the company restrict clients with an excessive number of returns of products.

You can look at this Credit Card Fraud Detection Project to implement a fraud detection model to classify fraudulent credit card transactions.

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Let us explore data analytics case study examples in the entertainment indusry.

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Netflix started as a DVD rental service in 1997 and then has expanded into the streaming business. Headquartered in Los Gatos, California, Netflix is the largest content streaming company in the world. Currently, Netflix has over 208 million paid subscribers worldwide, and with thousands of smart devices which are presently streaming supported, Netflix has around 3 billion hours watched every month. The secret to this massive growth and popularity of Netflix is its advanced use of data analytics and recommendation systems to provide personalized and relevant content recommendations to its users. The data is collected over 100 billion events every day. Here are a few examples of data analysis case studies applied at Netflix :

i) Personalized Recommendation System

Netflix uses over 1300 recommendation clusters based on consumer viewing preferences to provide a personalized experience. Some of the data that Netflix collects from its users include Viewing time, platform searches for keywords, Metadata related to content abandonment, such as content pause time, rewind, rewatched. Using this data, Netflix can predict what a viewer is likely to watch and give a personalized watchlist to a user. Some of the algorithms used by the Netflix recommendation system are Personalized video Ranking, Trending now ranker, and the Continue watching now ranker.

ii) Content Development using Data Analytics

Netflix uses data science to analyze the behavior and patterns of its user to recognize themes and categories that the masses prefer to watch. This data is used to produce shows like The umbrella academy, and Orange Is the New Black, and the Queen's Gambit. These shows seem like a huge risk but are significantly based on data analytics using parameters, which assured Netflix that they would succeed with its audience. Data analytics is helping Netflix come up with content that their viewers want to watch even before they know they want to watch it.

iii) Marketing Analytics for Campaigns

Netflix uses data analytics to find the right time to launch shows and ad campaigns to have maximum impact on the target audience. Marketing analytics helps come up with different trailers and thumbnails for other groups of viewers. For example, the House of Cards Season 5 trailer with a giant American flag was launched during the American presidential elections, as it would resonate well with the audience.

Here is a Customer Segmentation Project using association rule mining to understand the primary grouping of customers based on various parameters.

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In a world where Purchasing music is a thing of the past and streaming music is a current trend, Spotify has emerged as one of the most popular streaming platforms. With 320 million monthly users, around 4 billion playlists, and approximately 2 million podcasts, Spotify leads the pack among well-known streaming platforms like Apple Music, Wynk, Songza, amazon music, etc. The success of Spotify has mainly depended on data analytics. By analyzing massive volumes of listener data, Spotify provides real-time and personalized services to its listeners. Most of Spotify's revenue comes from paid premium subscriptions. Here are some of the examples of case study on data analytics used by Spotify to provide enhanced services to its listeners:

i) Personalization of Content using Recommendation Systems

Spotify uses Bart or Bayesian Additive Regression Trees to generate music recommendations to its listeners in real-time. Bart ignores any song a user listens to for less than 30 seconds. The model is retrained every day to provide updated recommendations. A new Patent granted to Spotify for an AI application is used to identify a user's musical tastes based on audio signals, gender, age, accent to make better music recommendations.

Spotify creates daily playlists for its listeners, based on the taste profiles called 'Daily Mixes,' which have songs the user has added to their playlists or created by the artists that the user has included in their playlists. It also includes new artists and songs that the user might be unfamiliar with but might improve the playlist. Similar to it is the weekly 'Release Radar' playlists that have newly released artists' songs that the listener follows or has liked before.

ii) Targetted marketing through Customer Segmentation

With user data for enhancing personalized song recommendations, Spotify uses this massive dataset for targeted ad campaigns and personalized service recommendations for its users. Spotify uses ML models to analyze the listener's behavior and group them based on music preferences, age, gender, ethnicity, etc. These insights help them create ad campaigns for a specific target audience. One of their well-known ad campaigns was the meme-inspired ads for potential target customers, which was a huge success globally.

iii) CNN's for Classification of Songs and Audio Tracks

Spotify builds audio models to evaluate the songs and tracks, which helps develop better playlists and recommendations for its users. These allow Spotify to filter new tracks based on their lyrics and rhythms and recommend them to users like similar tracks ( collaborative filtering). Spotify also uses NLP ( Natural language processing) to scan articles and blogs to analyze the words used to describe songs and artists. These analytical insights can help group and identify similar artists and songs and leverage them to build playlists.

Here is a Music Recommender System Project for you to start learning. We have listed another music recommendations dataset for you to use for your projects: Dataset1 . You can use this dataset of Spotify metadata to classify songs based on artists, mood, liveliness. Plot histograms, heatmaps to get a better understanding of the dataset. Use classification algorithms like logistic regression, SVM, and Principal component analysis to generate valuable insights from the dataset.

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Below you will find case studies for data analytics in the travel and tourism industry.

Airbnb was born in 2007 in San Francisco and has since grown to 4 million Hosts and 5.6 million listings worldwide who have welcomed more than 1 billion guest arrivals in almost every country across the globe. Airbnb is active in every country on the planet except for Iran, Sudan, Syria, and North Korea. That is around 97.95% of the world. Using data as a voice of their customers, Airbnb uses the large volume of customer reviews, host inputs to understand trends across communities, rate user experiences, and uses these analytics to make informed decisions to build a better business model. The data scientists at Airbnb are developing exciting new solutions to boost the business and find the best mapping for its customers and hosts. Airbnb data servers serve approximately 10 million requests a day and process around one million search queries. Data is the voice of customers at AirBnB and offers personalized services by creating a perfect match between the guests and hosts for a supreme customer experience. 

i) Recommendation Systems and Search Ranking Algorithms

Airbnb helps people find 'local experiences' in a place with the help of search algorithms that make searches and listings precise. Airbnb uses a 'listing quality score' to find homes based on the proximity to the searched location and uses previous guest reviews. Airbnb uses deep neural networks to build models that take the guest's earlier stays into account and area information to find a perfect match. The search algorithms are optimized based on guest and host preferences, rankings, pricing, and availability to understand users’ needs and provide the best match possible.

ii) Natural Language Processing for Review Analysis

Airbnb characterizes data as the voice of its customers. The customer and host reviews give a direct insight into the experience. The star ratings alone cannot be an excellent way to understand it quantitatively. Hence Airbnb uses natural language processing to understand reviews and the sentiments behind them. The NLP models are developed using Convolutional neural networks .

Practice this Sentiment Analysis Project for analyzing product reviews to understand the basic concepts of natural language processing.

iii) Smart Pricing using Predictive Analytics

The Airbnb hosts community uses the service as a supplementary income. The vacation homes and guest houses rented to customers provide for rising local community earnings as Airbnb guests stay 2.4 times longer and spend approximately 2.3 times the money compared to a hotel guest. The profits are a significant positive impact on the local neighborhood community. Airbnb uses predictive analytics to predict the prices of the listings and help the hosts set a competitive and optimal price. The overall profitability of the Airbnb host depends on factors like the time invested by the host and responsiveness to changing demands for different seasons. The factors that impact the real-time smart pricing are the location of the listing, proximity to transport options, season, and amenities available in the neighborhood of the listing.

Here is a Price Prediction Project to help you understand the concept of predictive analysis which is widely common in case studies for data analytics. 

Uber is the biggest global taxi service provider. As of December 2018, Uber has 91 million monthly active consumers and 3.8 million drivers. Uber completes 14 million trips each day. Uber uses data analytics and big data-driven technologies to optimize their business processes and provide enhanced customer service. The Data Science team at uber has been exploring futuristic technologies to provide better service constantly. Machine learning and data analytics help Uber make data-driven decisions that enable benefits like ride-sharing, dynamic price surges, better customer support, and demand forecasting. Here are some of the real world data science projects used by uber:

i) Dynamic Pricing for Price Surges and Demand Forecasting

Uber prices change at peak hours based on demand. Uber uses surge pricing to encourage more cab drivers to sign up with the company, to meet the demand from the passengers. When the prices increase, the driver and the passenger are both informed about the surge in price. Uber uses a predictive model for price surging called the 'Geosurge' ( patented). It is based on the demand for the ride and the location.

ii) One-Click Chat

Uber has developed a Machine learning and natural language processing solution called one-click chat or OCC for coordination between drivers and users. This feature anticipates responses for commonly asked questions, making it easy for the drivers to respond to customer messages. Drivers can reply with the clock of just one button. One-Click chat is developed on Uber's machine learning platform Michelangelo to perform NLP on rider chat messages and generate appropriate responses to them.

iii) Customer Retention

Failure to meet the customer demand for cabs could lead to users opting for other services. Uber uses machine learning models to bridge this demand-supply gap. By using prediction models to predict the demand in any location, uber retains its customers. Uber also uses a tier-based reward system, which segments customers into different levels based on usage. The higher level the user achieves, the better are the perks. Uber also provides personalized destination suggestions based on the history of the user and their frequently traveled destinations.

You can take a look at this Python Chatbot Project and build a simple chatbot application to understand better the techniques used for natural language processing. You can also practice the working of a demand forecasting model with this project using time series analysis. You can look at this project which uses time series forecasting and clustering on a dataset containing geospatial data for forecasting customer demand for ola rides.

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7) LinkedIn 

LinkedIn is the largest professional social networking site with nearly 800 million members in more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking around 1 billion interactions per month. The data science team at LinkedIn works with this massive pool of data to generate insights to build strategies, apply algorithms and statistical inferences to optimize engineering solutions, and help the company achieve its goals. Here are some of the real world data science projects at LinkedIn:

i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems

LinkedIn Recruiter helps recruiters build and manage a talent pool to optimize the chances of hiring candidates successfully. This sophisticated product works on search and recommendation engines. The LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The results delivered have to be relevant and specific. The initial search model was based on linear regression but was eventually upgraded to Gradient Boosted decision trees to include non-linear correlations in the dataset. In addition to these models, the LinkedIn recruiter also uses the Generalized Linear Mix model to improve the results of prediction problems to give personalized results.

ii) Recommendation Systems Personalized for News Feed

The LinkedIn news feed is the heart and soul of the professional community. A member's newsfeed is a place to discover conversations among connections, career news, posts, suggestions, photos, and videos. Every time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be displayed on the feed by sorting through posts and ranking the most relevant results on top. The algorithms help LinkedIn understand member preferences and help provide personalized news feeds. The algorithms used include logistic regression, gradient boosted decision trees and neural networks for recommendation systems.

iii) CNN's to Detect Inappropriate Content

To provide a professional space where people can trust and express themselves professionally in a safe community has been a critical goal at LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and abusive behavior on their platform. Any form of spam, harassment, inappropriate content is immediately flagged and taken down. These can range from profanity to advertisements for illegal services. LinkedIn uses a Convolutional neural networks based machine learning model. This classifier trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate." The inappropriate list consists of accounts having content from "blocklisted" phrases or words and a small portion of manually reviewed accounts reported by the user community.

Here is a Text Classification Project to help you understand NLP basics for text classification. You can find a news recommendation system dataset to help you build a personalized news recommender system. You can also use this dataset to build a classifier using logistic regression, Naive Bayes, or Neural networks to classify toxic comments.

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Pfizer is a multinational pharmaceutical company headquartered in New York, USA. One of the largest pharmaceutical companies globally known for developing a wide range of medicines and vaccines in disciplines like immunology, oncology, cardiology, and neurology. Pfizer became a household name in 2010 when it was the first to have a COVID-19 vaccine with FDA. In early November 2021, The CDC has approved the Pfizer vaccine for kids aged 5 to 11. Pfizer has been using machine learning and artificial intelligence to develop drugs and streamline trials, which played a massive role in developing and deploying the COVID-19 vaccine. Here are a few data analytics case studies by Pfizer :

i) Identifying Patients for Clinical Trials

Artificial intelligence and machine learning are used to streamline and optimize clinical trials to increase their efficiency. Natural language processing and exploratory data analysis of patient records can help identify suitable patients for clinical trials. These can help identify patients with distinct symptoms. These can help examine interactions of potential trial members' specific biomarkers, predict drug interactions and side effects which can help avoid complications. Pfizer's AI implementation helped rapidly identify signals within the noise of millions of data points across their 44,000-candidate COVID-19 clinical trial.

ii) Supply Chain and Manufacturing

Data science and machine learning techniques help pharmaceutical companies better forecast demand for vaccines and drugs and distribute them efficiently. Machine learning models can help identify efficient supply systems by automating and optimizing the production steps. These will help supply drugs customized to small pools of patients in specific gene pools. Pfizer uses Machine learning to predict the maintenance cost of equipment used. Predictive maintenance using AI is the next big step for Pharmaceutical companies to reduce costs.

iii) Drug Development

Computer simulations of proteins, and tests of their interactions, and yield analysis help researchers develop and test drugs more efficiently. In 2016 Watson Health and Pfizer announced a collaboration to utilize IBM Watson for Drug Discovery to help accelerate Pfizer's research in immuno-oncology, an approach to cancer treatment that uses the body's immune system to help fight cancer. Deep learning models have been used recently for bioactivity and synthesis prediction for drugs and vaccines in addition to molecular design. Deep learning has been a revolutionary technique for drug discovery as it factors everything from new applications of medications to possible toxic reactions which can save millions in drug trials.

You can create a Machine learning model to predict molecular activity to help design medicine using this dataset . You may build a CNN or a Deep neural network for this data analyst case study project.

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9) Shell Data Analyst Case Study Project

Shell is a global group of energy and petrochemical companies with over 80,000 employees in around 70 countries. Shell uses advanced technologies and innovations to help build a sustainable energy future. Shell is going through a significant transition as the world needs more and cleaner energy solutions to be a clean energy company by 2050. It requires substantial changes in the way in which energy is used. Digital technologies, including AI and Machine Learning, play an essential role in this transformation. These include efficient exploration and energy production, more reliable manufacturing, more nimble trading, and a personalized customer experience. Using AI in various phases of the organization will help achieve this goal and stay competitive in the market. Here are a few data analytics case studies in the petrochemical industry:

i) Precision Drilling

Shell is involved in the processing mining oil and gas supply, ranging from mining hydrocarbons to refining the fuel to retailing them to customers. Recently Shell has included reinforcement learning to control the drilling equipment used in mining. Reinforcement learning works on a reward-based system based on the outcome of the AI model. The algorithm is designed to guide the drills as they move through the surface, based on the historical data from drilling records. It includes information such as the size of drill bits, temperatures, pressures, and knowledge of the seismic activity. This model helps the human operator understand the environment better, leading to better and faster results will minor damage to machinery used. 

ii) Efficient Charging Terminals

Due to climate changes, governments have encouraged people to switch to electric vehicles to reduce carbon dioxide emissions. However, the lack of public charging terminals has deterred people from switching to electric cars. Shell uses AI to monitor and predict the demand for terminals to provide efficient supply. Multiple vehicles charging from a single terminal may create a considerable grid load, and predictions on demand can help make this process more efficient.

iii) Monitoring Service and Charging Stations

Another Shell initiative trialed in Thailand and Singapore is the use of computer vision cameras, which can think and understand to watch out for potentially hazardous activities like lighting cigarettes in the vicinity of the pumps while refueling. The model is built to process the content of the captured images and label and classify it. The algorithm can then alert the staff and hence reduce the risk of fires. You can further train the model to detect rash driving or thefts in the future.

Here is a project to help you understand multiclass image classification. You can use the Hourly Energy Consumption Dataset to build an energy consumption prediction model. You can use time series with XGBoost to develop your model.

10) Zomato Case Study on Data Analytics

Zomato was founded in 2010 and is currently one of the most well-known food tech companies. Zomato offers services like restaurant discovery, home delivery, online table reservation, online payments for dining, etc. Zomato partners with restaurants to provide tools to acquire more customers while also providing delivery services and easy procurement of ingredients and kitchen supplies. Currently, Zomato has over 2 lakh restaurant partners and around 1 lakh delivery partners. Zomato has closed over ten crore delivery orders as of date. Zomato uses ML and AI to boost their business growth, with the massive amount of data collected over the years from food orders and user consumption patterns. Here are a few examples of data analyst case study project developed by the data scientists at Zomato:

i) Personalized Recommendation System for Homepage

Zomato uses data analytics to create personalized homepages for its users. Zomato uses data science to provide order personalization, like giving recommendations to the customers for specific cuisines, locations, prices, brands, etc. Restaurant recommendations are made based on a customer's past purchases, browsing history, and what other similar customers in the vicinity are ordering. This personalized recommendation system has led to a 15% improvement in order conversions and click-through rates for Zomato. 

You can use the Restaurant Recommendation Dataset to build a restaurant recommendation system to predict what restaurants customers are most likely to order from, given the customer location, restaurant information, and customer order history.

ii) Analyzing Customer Sentiment

Zomato uses Natural language processing and Machine learning to understand customer sentiments using social media posts and customer reviews. These help the company gauge the inclination of its customer base towards the brand. Deep learning models analyze the sentiments of various brand mentions on social networking sites like Twitter, Instagram, Linked In, and Facebook. These analytics give insights to the company, which helps build the brand and understand the target audience.

iii) Predicting Food Preparation Time (FPT)

Food delivery time is an essential variable in the estimated delivery time of the order placed by the customer using Zomato. The food preparation time depends on numerous factors like the number of dishes ordered, time of the day, footfall in the restaurant, day of the week, etc. Accurate prediction of the food preparation time can help make a better prediction of the Estimated delivery time, which will help delivery partners less likely to breach it. Zomato uses a Bidirectional LSTM-based deep learning model that considers all these features and provides food preparation time for each order in real-time. 

Data scientists are companies' secret weapons when analyzing customer sentiments and behavior and leveraging it to drive conversion, loyalty, and profits. These 10 data science case studies projects with examples and solutions show you how various organizations use data science technologies to succeed and be at the top of their field! To summarize, Data Science has not only accelerated the performance of companies but has also made it possible to manage & sustain their performance with ease.

FAQs on Data Analysis Case Studies

A case study in data science is an in-depth analysis of a real-world problem using data-driven approaches. It involves collecting, cleaning, and analyzing data to extract insights and solve challenges, offering practical insights into how data science techniques can address complex issues across various industries.

To create a data science case study, identify a relevant problem, define objectives, and gather suitable data. Clean and preprocess data, perform exploratory data analysis, and apply appropriate algorithms for analysis. Summarize findings, visualize results, and provide actionable recommendations, showcasing the problem-solving potential of data science techniques.

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Top 20 Analytics Case Studies in 2024

Although the potential of Big Data and business intelligence are recognized by organizations, Gartner analyst Nick Heudecker says that the failure rate of analytics projects is close to 85%. Uncovering the power of analytics improves business operations, reduces costs, enhances decision-making , and enables the launching of more personalized products.

In this article, our research covers:

How to measure analytics success?

What are some analytics case studies.

According to  Gartner CDO Survey,  the top 3 critical success factors of analytics projects are:

• Creation of a data-driven culture within the organization,
• Data integration and data skills training across the organization,
• And implementation of a data management and analytics strategy.

The success of the process of analytics depends on asking the right question. It requires an understanding of the appropriate data required for each goal to be achieved. We’ve listed 20 successful analytics applications/case studies from different industries.

During our research, we examined that partnering with an analytics consultant helps organizations boost their success if organizations’ tech team lacks certain data skills.

*Vendors have not shared the client name

For more on analytics

If your organization is willing to implement an analytics solution but doesn’t know where to start, here are some of the articles we’ve written before that can help you learn more:

• AI in analytics: How AI is shaping analytics
• Edge Analytics in 2022: What it is, Why it matters & Use Cases
• Application Analytics: Tracking KPIs that lead to success

Finally, if you believe that your business would benefit from adopting an analytics solution, we have data-driven lists of vendors on our analytics hub and analytics platforms

We will help you choose the best solution tailored to your needs:

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month. Cem's work has been cited by leading global publications including Business Insider , Forbes, Washington Post , global firms like Deloitte , HPE, NGOs like World Economic Forum and supranational organizations like European Commission . You can see more reputable companies and media that referenced AIMultiple. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider . Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Top 10 real-world data science case studies.

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Frequently Asked Questions

Real-world data science case studies differ significantly from academic examples. While academic exercises often feature clean, well-structured data and simplified scenarios, real-world projects tackle messy, diverse data sources with practical constraints and genuine business objectives. These case studies reflect the complexities data scientists face when translating data into actionable insights in the corporate world.

Real-world data science projects come with common challenges. Data quality issues, including missing or inaccurate data, can hinder analysis. Domain expertise gaps may result in misinterpretation of results. Resource constraints might limit project scope or access to necessary tools and talent. Ethical considerations, like privacy and bias, demand careful handling.

Lastly, as data and business needs evolve, data science projects must adapt and stay relevant, posing an ongoing challenge.

Real-world data science case studies play a crucial role in helping companies make informed decisions. By analyzing their own data, businesses gain valuable insights into customer behavior, market trends, and operational efficiencies.

These insights empower data-driven strategies, aiding in more effective resource allocation, product development, and marketing efforts. Ultimately, case studies bridge the gap between data science and business decision-making, enhancing a company's ability to thrive in a competitive landscape.

Key takeaways from these case studies for organizations include the importance of cultivating a data-driven culture that values evidence-based decision-making. Investing in robust data infrastructure is essential to support data initiatives. Collaborating closely between data scientists and domain experts ensures that insights align with business goals.

Finally, continuous monitoring and refinement of data solutions are critical for maintaining relevance and effectiveness in a dynamic business environment. Embracing these principles can lead to tangible benefits and sustainable success in real-world data science endeavors.

Data science is a powerful driver of innovation and problem-solving across diverse industries. By harnessing data, organizations can uncover hidden patterns, automate repetitive tasks, optimize operations, and make informed decisions.

In healthcare, for example, data-driven diagnostics and treatment plans improve patient outcomes. In finance, predictive analytics enhances risk management. In transportation, route optimization reduces costs and emissions. Data science empowers industries to innovate and solve complex challenges in ways that were previously unimaginable.

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Data Analytics Case Study Guide (Updated for 2024)

What are data analytics case study interviews.

When you’re trying to land a data analyst job, the last thing to stand in your way is the data analytics case study interview.

One reason they’re so challenging is that case studies don’t typically have a right or wrong answer.

Instead, case study interviews require you to come up with a hypothesis for an analytics question and then produce data to support or validate your hypothesis. In other words, it’s not just about your technical skills; you’re also being tested on creative problem-solving and your ability to communicate with stakeholders.

This article provides an overview of how to answer data analytics case study interview questions. You can find an in-depth course in the data analytics learning path .

How to Solve Data Analytics Case Questions

Check out our video below on How to solve a Data Analytics case study problem:

With data analyst case questions, you will need to answer two key questions:

• What metrics should I propose?
• How do I write a SQL query to get the metrics I need?

In short, to ace a data analytics case interview, you not only need to brush up on case questions, but you also should be adept at writing all types of SQL queries and have strong data sense.

These questions are especially challenging to answer if you don’t have a framework or know how to answer them. To help you prepare, we created this step-by-step guide to answering data analytics case questions.

We show you how to use a framework to answer case questions, provide example analytics questions, and help you understand the difference between analytics case studies and product metrics case studies .

Data Analytics Cases vs Product Metrics Questions

Product case questions sometimes get lumped in with data analytics cases.

Ultimately, the type of case question you are asked will depend on the role. For example, product analysts will likely face more product-oriented questions.

Product metrics cases tend to focus on a hypothetical situation. You might be asked to:

Investigate Metrics - One of the most common types will ask you to investigate a metric, usually one that’s going up or down. For example, “Why are Facebook friend requests falling by 10 percent?”

Measure Product/Feature Success - A lot of analytics cases revolve around the measurement of product success and feature changes. For example, “We want to add X feature to product Y. What metrics would you track to make sure that’s a good idea?”

With product data cases, the key difference is that you may or may not be required to write the SQL query to find the metric.

Instead, these interviews are more theoretical and are designed to assess your product sense and ability to think about analytics problems from a product perspective. Product metrics questions may also show up in the data analyst interview , but likely only for product data analyst roles.

Data Analytics Case Study Question: Sample Solution

Let’s start with an example data analytics case question :

You’re given a table that represents search results from searches on Facebook. The query column is the search term, the position column represents each position the search result came in, and the rating column represents the human rating from 1 to 5, where 5 is high relevance, and 1 is low relevance.

Each row in the search_events table represents a single search, with the has_clicked column representing if a user clicked on a result or not. We have a hypothesis that the CTR is dependent on the search result rating.

Write a query to return data to support or disprove this hypothesis.

search_results table:

search_events table

Step 1: With Data Analytics Case Studies, Start by Making Assumptions

Hint: Start by making assumptions and thinking out loud. With this question, focus on coming up with a metric to support the hypothesis. If the question is unclear or if you think you need more information, be sure to ask.

Answer. The hypothesis is that CTR is dependent on search result rating. Therefore, we want to focus on the CTR metric, and we can assume:

• If CTR is high when search result ratings are high, and CTR is low when the search result ratings are low, then the hypothesis is correct.
• If CTR is low when the search ratings are high, or there is no proven correlation between the two, then our hypothesis is not proven.

Step 2: Provide a Solution for the Case Question

Hint: Walk the interviewer through your reasoning. Talking about the decisions you make and why you’re making them shows off your problem-solving approach.

Answer. One way we can investigate the hypothesis is to look at the results split into different search rating buckets. For example, if we measure the CTR for results rated at 1, then those rated at 2, and so on, we can identify if an increase in rating is correlated with an increase in CTR.

First, I’d write a query to get the number of results for each query in each bucket. We want to look at the distribution of results that are less than a rating threshold, which will help us see the relationship between search rating and CTR.

This CTE aggregates the number of results that are less than a certain rating threshold. Later, we can use this to see the percentage that are in each bucket. If we re-join to the search_events table, we can calculate the CTR by then grouping by each bucket.

Step 3: Use Analysis to Backup Your Solution

Hint: Be prepared to justify your solution. Interviewers will follow up with questions about your reasoning, and ask why you make certain assumptions.

Answer. By using the CASE WHEN statement, I calculated each ratings bucket by checking to see if all the search results were less than 1, 2, or 3 by subtracting the total from the number within the bucket and seeing if it equates to 0.

I did that to get away from averages in our bucketing system. Outliers would make it more difficult to measure the effect of bad ratings. For example, if a query had a 1 rating and another had a 5 rating, that would equate to an average of 3. Whereas in my solution, a query with all of the results under 1, 2, or 3 lets us know that it actually has bad ratings.

Product Data Case Question: Sample Solution

In product metrics interviews, you’ll likely be asked about analytics, but the discussion will be more theoretical. You’ll propose a solution to a problem, and supply the metrics you’ll use to investigate or solve it. You may or may not be required to write a SQL query to get those metrics.

We’ll start with an example product metrics case study question :

Let’s say you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slow decrease in the average number of comments per user from January to March in this city.

The company has been consistently growing new users in the city from January to March.

What are some reasons why the average number of comments per user would be decreasing and what metrics would you look into?

Step 1: Ask Clarifying Questions Specific to the Case

Hint: This question is very vague. It’s all hypothetical, so we don’t know very much about users, what the product is, and how people might be interacting. Be sure you ask questions upfront about the product.

Answer: Before I jump into an answer, I’d like to ask a few questions:

• Who uses this social network? How do they interact with each other?
• Has there been any performance issues that might be causing the problem?
• What are the goals of this particular launch?
• Has there been any changes to the comment features in recent weeks?

For the sake of this example, let’s say we learn that it’s a social network similar to Facebook with a young audience, and the goals of the launch are to grow the user base. Also, there have been no performance issues and the commenting feature hasn’t been changed since launch.

Step 2: Use the Case Question to Make Assumptions

Hint: Look for clues in the question. For example, this case gives you a metric, “average number of comments per user.” Consider if the clue might be helpful in your solution. But be careful, sometimes questions are designed to throw you off track.

Answer: From the question, we can hypothesize a little bit. For example, we know that user count is increasing linearly. That means two things:

• The decreasing comments issue isn’t a result of a declining user base.
• The cause isn’t loss of platform.

We can also model out the data to help us get a better picture of the average number of comments per user metric:

• January: 10000 users, 30000 comments, 3 comments/user
• February: 20000 users, 50000 comments, 2.5 comments/user
• March: 30000 users, 60000 comments, 2 comments/user

One thing to note: Although this is an interesting metric, I’m not sure if it will help us solve this question. For one, average comments per user doesn’t account for churn. We might assume that during the three-month period users are churning off the platform. Let’s say the churn rate is 25% in January, 20% in February and 15% in March.

Step 3: Make a Hypothesis About the Data

Hint: Don’t worry too much about making a correct hypothesis. Instead, interviewers want to get a sense of your product initiation and that you’re on the right track. Also, be prepared to measure your hypothesis.

Answer. I would say that average comments per user isn’t a great metric to use, because it doesn’t reveal insights into what’s really causing this issue.

That’s because it doesn’t account for active users, which are the users who are actually commenting. A better metric to investigate would be retained users and monthly active users.

What I suspect is causing the issue is that active users are commenting frequently and are responsible for the increase in comments month-to-month. New users, on the other hand, aren’t as engaged and aren’t commenting as often.

Step 4: Provide Metrics and Data Analysis

Hint: Within your solution, include key metrics that you’d like to investigate that will help you measure success.

Answer: I’d say there are a few ways we could investigate the cause of this problem, but the one I’d be most interested in would be the engagement of monthly active users.

If the growth in comments is coming from active users, that would help us understand how we’re doing at retaining users. Plus, it will also show if new users are less engaged and commenting less frequently.

One way that we could dig into this would be to segment users by their onboarding date, which would help us to visualize engagement and see how engaged some of our longest-retained users are.

If engagement of new users is the issue, that will give us some options in terms of strategies for addressing the problem. For example, we could test new onboarding or commenting features designed to generate engagement.

Step 5: Propose a Solution for the Case Question

Hint: In the majority of cases, your initial assumptions might be incorrect, or the interviewer might throw you a curveball. Be prepared to make new hypotheses or discuss the pitfalls of your analysis.

Answer. If the cause wasn’t due to a lack of engagement among new users, then I’d want to investigate active users. One potential cause would be active users commenting less. In that case, we’d know that our earliest users were churning out, and that engagement among new users was potentially growing.

Again, I think we’d want to focus on user engagement since the onboarding date. That would help us understand if we were seeing higher levels of churn among active users, and we could start to identify some solutions there.

Tip: Use a Framework to Solve Data Analytics Case Questions

Analytics case questions can be challenging, but they’re much more challenging if you don’t use a framework. Without a framework, it’s easier to get lost in your answer, to get stuck, and really lose the confidence of your interviewer. Find helpful frameworks for data analytics questions in our data analytics learning path and our product metrics learning path .

Once you have the framework down, what’s the best way to practice? Mock interviews with our coaches are very effective, as you’ll get feedback and helpful tips as you answer. You can also learn a lot by practicing P2P mock interviews with other Interview Query students. No data analytics background? Check out how to become a data analyst without a degree .

Finally, if you’re looking for sample data analytics case questions and other types of interview questions, see our guide on the top data analyst interview questions .

Data Analytics Case Studies that will inspire you.

• June 16, 2023 July 21, 2023

As an explorer in the world of big data Analytics, I’ve witnessed firsthand how the strategic or sometimes even Tactical application of data analytics can shape industries, transform businesses, and revolutionize the way we do business. However, data is boring and that’s why case studies that are real-world narratives of businesses from diverse industries—manufacturing, retail, finance, logistics, telecom, and insurance are so much more interesting.

All Data analytics case studies are a testament to the transformative power of data. Like for example Siemens, a global industrial giant, has leveraged data analytics to increase production efficiency, reducing production time by an astounding 20%. Retail behemoth Amazon has harnessed data analytics to personalize customer shopping experiences, while Bank of America uses it to identify fraudulent transactions, cutting its fraud losses by half.

You will agree that it’s a transformative force reshaping the way businesses operate, make decisions, and interact with their customers. In our company, we often liken it to a goldmine. The raw data, much like the rough ore, may not appear valuable at first glance. However, with the right tools, techniques, and a touch of analytical magic, we can extract precious insights hidden within, just as miners draw valuable gold from the earth.

It takes a huge amount of effort to Unravel the immense potential of data analytics to foster innovation, enhance decision-making, and improve customer experiences. In the data realm, the possibilities are as vast as the data itself.

How Data Analytics is Revolutionizing the Manufacturing Industry

Data Analytics is Revolutionizing the Manufacturing Industry. Around 80% of companies believe that data analytics will be key to their success in the near future. Already, 65% of them are seeing positive changes from using data analytics. One of the most substantial benefits is the cost-savings and manufacturers who use data analytics save about $1.2 million each year. It’s also helping improve product quality by 20% and reduce production costs by up to 15%. Some inspiration can be drawn from how these big companies are using data analytics to transform manufacturing.

• Siemens:  Siemens is using data analytics to improve the efficiency of its production lines. The company has installed sensors on its equipment to collect data on production processes. This data is then analyzed to identify areas where efficiency can be improved. As a result of these efforts, Siemens has been able to reduce the time it takes to produce a product by 20%.
• General Electric:  General Electric is using data analytics to improve the quality of its products. The company has developed a system that uses data analytics to identify potential defects in products before they are shipped to customers. This system has helped General Electric to reduce the number of defects in its products by 50%.
• Nike:  Nike is using data analytics to improve the performance of its athletes. The company has developed a system that uses data analytics to track the performance of athletes during training. This data is then used to provide athletes with personalized training plans that help them to improve their performance.

These are just a few examples of how data analytics is being used in the manufacturing industry. As technology continues to develop, As the amount of data generated by manufacturing operations continues to grow, the potential benefits of data analytics are expected to rise proportionately. We can expect to see even more innovative and creative ways to use data analytics to improve manufacturing operations.

Retailers using data analytics to Personalize the Shopping Experience

Retail Industry is big, According to Statista , the global retail market generated 27 trillion U.S. dollars in 2022. This is expected to grow to 30 trillion U.S. dollars by 2024. The industry is also a major employment generator, according to the World Bank, the retail industry employed 627 million people in 2020. This number is expected to grow to 715 million people by 2030.

The retail industry is also a major source of innovation, as businesses are constantly finding new ways to reach customers and sell products. One of the key initiatives all major retailers are trying is personalization which is impossible without effective Data analytics.

Retailers have recognized that personalization not only enhances customer engagement but also amplifies customer lifetime value, with 67% of retailers asserting that it can heighten this value by up to 10%. Churn, a critical metric for retailers, can be substantially reduced through personalization. A few case studies which highlight the same

• Amazon:  Amazon is using data analytics to personalize the shopping experience for its customers. The company collects data on customer purchase history, browsing behavior, and search history. This data is then used to recommend products that the customer is likely to be interested in. Amazon also uses data analytics to target customers with personalized advertising.
• Target:   Target is using data analytics to predict customer behavior. The company collects data on customer purchase history, browsing behavior, and social media activity. This data is then used to predict when a customer is likely to make a purchase. Target can then send targeted marketing messages to these customers.

As retailers continue to accumulate more data on customer preferences, behaviors, and purchasing patterns, the potential benefits of personalization are expected to grow.

How Data Analytics is Helping Financial Institutions to Combat Fraud

Everyone is looking for easy money and that is the key reason why financial institutions are constantly under attack from fraudsters. In 2021, the global cost of fraud was estimated to be $5.8 trillion. Data analytics is helping financial institutions to combat fraud in a number of ways.

• Bank of America:  Bank of America is using data analytics to combat fraud. The company collects data on customer transactions, account balances, and credit scores. This data is then used to identify fraudulent transactions. Bank of America has been able to reduce its fraud losses by 50% as a result of these efforts.
• Capital One:  Capital One is using data analytics to personalize the lending experience for its customers. The company collects data on customer income, employment history, and credit scores. This data is then used to determine which customers are most likely to repay a loan. Capital One has been able to reduce its loan defaults by 20% as a result of these efforts.
• Wells Fargo:  Wells Fargo is using data analytics to improve customer service. The company collects data on customer calls, emails, and social media interactions. This data is then used to identify areas where customer service can be improved. Wells Fargo has been able to reduce the number of customer complaints by 10% as a result of these efforts.

These are just a few examples of how data analytics is being used by financial institutions to combat fraud. As technology continues to develop, we can expect to see even more innovative and creative ways to use data analytics to combat fraud one example could be the use of Generative adversarial networks (GANs) a machine learning model which can be trained to combat fraud.

How Data Analytics Helps to reduce delivery time and Improve Supply Chain

Data analytics plays a crucial role in optimizing supply chains and reducing delivery times. This is a separate center of excellence created by companies that are known as Supply chain analytics . By collecting and analyzing vast amounts of data from various touchpoints in the supply chain, companies can make more informed decisions, anticipate problems, and create efficiencies that save both time and money. A study by McKinsey found that data analytics can help to improve supply chain efficiency by up to 30%.

• Walmart:  Walmart is using data analytics to improve the efficiency of its supply chain. The company collects data on sales, inventory levels, and transportation costs. This data is then used to identify areas where efficiency can be improved. Walmart has been able to reduce its transportation costs by 10% as a result of these efforts.
• UPS:  UPS is using data analytics to improve the efficiency of its delivery operations. The company collects data on weather conditions, traffic patterns, and customer behavior. This data is then used to optimize delivery routes and times. UPS has been able to reduce its delivery times by 5% as a result of these efforts.
• Amazon:  Amazon is using data analytics to improve the efficiency of its fulfillment centers. The company collects data on product demand, inventory levels, and worker productivity. This data is then used to optimize the layout of fulfillment centers and the allocation of workers. Amazon has been able to increase the productivity of its fulfillment centers by 20% as a result of these efforts.c

The supply chain department now uses Real-time analytics, a Supply chain control tower that brings constant visibility of various KPIs of the organization.

How Telecom Companies are leveraging data analytics to improve various KPI

Telecommunication companies are increasingly turning to data analytics to boost their performance across various Key Performance Indicators (KPIs). By analyzing vast amounts of data generated from call records, network traffic, customer service interactions, and more, telecom companies are unlocking new avenues for growth and efficiency. A study by McKinsey found that data analytics can help telecom companies to reduce churn by up to 15%. There is more to it like

• AT&T:  AT&T is using data analytics to improve the customer experience. The company collects data on customer usage patterns, service requests, and satisfaction ratings. This data is then used to improve customer service, identify areas for improvement, and develop new products and services. AT&T has been able to improve its customer satisfaction ratings by 10% as a result of these efforts.
• Verizon:  Verizon is using data analytics to improve the performance of its network. The company collects data on network usage, traffic patterns, and outages. This data is then used to identify and address any bottlenecks or disruptions in the network. Verizon has been able to reduce the number of network outages by 50% as a result of these efforts.
• T-Mobile:   T-Mobile is using data analytics to target marketing campaigns. The company collects data on customer demographics, interests, and purchase history. This data is then used to create targeted marketing campaigns that are more likely to be successful. T-Mobile has been able to increase its marketing ROI by 20% as a result of these efforts.

SCIKIQ has recently launched its Telecom Analytics Centre of Excellence and offers analytics for enhancing customer service, optimizing network performance, detecting and preventing fraud, predicting maintenance needs, and ensuring revenue assurance.

How Insurance companies are using Data Analytics to Improve various processes

In an industry where precision and risk management are key, insurance companies are finding data analytics to be a game-changer. According to a study by Capgemini, 70% of insurance companies are using data analytics to improve customer experience. A few Data Analytics case studies in this industry are

• State Farm:  State Farm is using data analytics to improve the accuracy of its claims processing. The company collects data on customer claims, vehicle history, and weather conditions. This data is then used to automate the claims process and identify any potential fraud. State Farm has been able to reduce the time it takes to process claims by 50% as a result of these efforts.
• Progressive:  Progressive is using data analytics to improve the underwriting process. The company collects data on customer driving history, credit scores, and demographics. This data is then used to price policies and identify high-risk customers. Progressive has been able to reduce its losses by 10% as a result of these efforts.
• Geico:  Geico is using data analytics to personalize the customer experience. The company collects data on customer purchase history, browsing behavior, and social media activity. This data is then used to recommend products and services that the customer is likely to be interested in. Geico has been able to increase its customer retention rate by 5% as a result of these efforts.

Data analytics is key to Cyber Insurance as well. How Data Analytics is helping fight cybercrime with Cyber insurance, Download the Guide by SCIKIQ.

These Data Analytics case studies provide valuable insights into how organizations leverage data analytics to gain a competitive edge, make data-driven decisions, and achieve remarkable outcomes in their respective fields.

Explore what Data Analytics use cases can be applied to Manufacturing , Finance, Marketing, Telecom, and Banking.

By examining Data Analytics case studies & examples, we can gain inspiration, learn from best practices, and understand the transformative impact of data analytics on businesses of all sizes. Whether it’s optimizing supply chains, improving product quality, or predicting customer behavior, data analytics case studies highlight the immense potential of data-driven approaches to reshape industries and drive growth.

Explore what are the Top strategic goals of the modern CXO and how to achieve strategic goals with effective data management.

chandan Mishra

Driving efficiency: data analytics use cases in manufacturing excellence, bridging the gap between cyber insurance and effective data management, related product.

• Banking Blogs Cyber Data mangement Development Healthcare Logistics Retail SCIKIQ

LLM: Revolutionizing NLP Across Industries with Risks and Benefits

• April 12, 2024 April 12, 2024

• Agency Banking Data Governance Data mangement Development Healthcare Logistics Retail SCIKIQ SCIKIQ Supply chain Telecom

Governance of Data in Flight

• April 5, 2024 April 9, 2024

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The Convergence Blog

The convergence - an online community space that's dedicated to empowering operators in the data industry by providing news and education about evergreen strategies, late-breaking data & ai developments, and free or low-cost upskilling resources that you need to thrive as a leader in the data & ai space., data analysis case study: learn from humana’s automated data analysis project.

Lillian Pierson, P.E.

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Got data? Great! Looking for that perfect data analysis case study to help you get started using it? You’re in the right place.

If you’ve ever struggled to decide what to do next with your data projects, to actually find meaning in the data, or even to decide what kind of data to collect, then KEEP READING…

Deep down, you know what needs to happen. You need to initiate and execute a data strategy that really moves the needle for your organization. One that produces seriously awesome business results.

But how you’re in the right place to find out..

As a data strategist who has worked with 10 percent of Fortune 100 companies, today I’m sharing with you a case study that demonstrates just how real businesses are making real wins with data analysis. 

In the post below, we’ll look at:

• A shining data success story;
• What went on ‘under-the-hood’ to support that successful data project; and
• The exact data technologies used by the vendor, to take this project from pure strategy to pure success

If you prefer to watch this information rather than read it, it’s captured in the video below:

Here’s the url too: https://youtu.be/xMwZObIqvLQ

3 Action Items You Need To Take

To actually use the data analysis case study you’re about to get – you need to take 3 main steps. Those are:

• Reflect upon your organization as it is today (I left you some prompts below – to help you get started)
• Review winning data case collections (starting with the one I’m sharing here) and identify 5 that seem the most promising for your organization given it’s current set-up
• Assess your organization AND those 5 winning case collections. Based on that assessment, select the “QUICK WIN” data use case that offers your organization the most bang for it’s buck

Step 1: Reflect Upon Your Organization

Whenever you evaluate data case collections to decide if they’re a good fit for your organization, the first thing you need to do is organize your thoughts with respect to your organization as it is today.

Before moving into the data analysis case study, STOP and ANSWER THE FOLLOWING QUESTIONS – just to remind yourself:

• What is the business vision for our organization?
• What industries do we primarily support?
• What data technologies do we already have up and running, that we could use to generate even more value?
• What team members do we have to support a new data project? And what are their data skillsets like?
• What type of data are we mostly looking to generate value from? Structured? Semi-Structured? Un-structured? Real-time data? Huge data sets? What are our data resources like?

Jot down some notes while you’re here. Then keep them in mind as you read on to find out how one company, Humana, used its data to achieve a 28 percent increase in customer satisfaction. Also include its 63 percent increase in employee engagement! (That’s such a seriously impressive outcome, right?!)

Step 2: Review Data Case Studies

Here we are, already at step 2. It’s time for you to start reviewing data analysis case studies  (starting with the one I’m sharing below). I dentify 5 that seem the most promising for your organization given its current set-up.

Humana’s Automated Data Analysis Case Study

The key thing to note here is that the approach to creating a successful data program varies from industry to industry .

Let’s start with one to demonstrate the kind of value you can glean from these kinds of success stories.

Humana has provided health insurance to Americans for over 50 years. It is a service company focused on fulfilling the needs of its customers. A great deal of Humana’s success as a company rides on customer satisfaction, and the frontline of that battle for customers’ hearts and minds is Humana’s customer service center.

Call centers are hard to get right. A lot of emotions can arise during a customer service call, especially one relating to health and health insurance. Sometimes people are frustrated. At times, they’re upset. Also, there are times the customer service representative becomes aggravated, and the overall tone and progression of the phone call goes downhill. This is of course very bad for customer satisfaction.

Humana wanted to use artificial intelligence to improve customer satisfaction (and thus, customer retention rates & profits per customer).

Humana wanted to find a way to use artificial intelligence to monitor their phone calls and help their agents do a better job connecting with their customers in order to improve customer satisfaction (and thus, customer retention rates & profits per customer ).

In light of their business need, Humana worked with a company called Cogito, which specializes in voice analytics technology.

Cogito offers a piece of AI technology called Cogito Dialogue. It’s been trained to identify certain conversational cues as a way of helping call center representatives and supervisors stay actively engaged in a call with a customer.

The AI listens to cues like the customer’s voice pitch.

If it’s rising, or if the call representative and the customer talk over each other, then the dialogue tool will send out electronic alerts to the agent during the call.

Humana fed the dialogue tool customer service data from 10,000 calls and allowed it to analyze cues such as keywords, interruptions, and pauses, and these cues were then linked with specific outcomes. For example, if the representative is receiving a particular type of cues, they are likely to get a specific customer satisfaction result.

The Outcome

Customers were happier, and customer service representatives were more engaged..

This automated solution for data analysis has now been deployed in 200 Humana call centers and the company plans to roll it out to 100 percent of its centers in the future.

The initiative was so successful, Humana has been able to focus on next steps in its data program. The company now plans to begin predicting the type of calls that are likely to go unresolved, so they can send those calls over to management before they become frustrating to the customer and customer service representative alike.

What does this mean for you and your business?

Well, if you’re looking for new ways to generate value by improving the quantity and quality of the decision support that you’re providing to your customer service personnel, then this may be a perfect example of how you can do so.

Humana’s Business Use Cases

Humana’s data analysis case study includes two key business use cases:

• Analyzing customer sentiment; and
• Suggesting actions to customer service representatives.

Analyzing Customer Sentiment

First things first, before you go ahead and collect data, you need to ask yourself who and what is involved in making things happen within the business.

In the case of Humana, the actors were:

• The health insurance system itself
• The customer, and
• The customer service representative

As you can see in the use case diagram above, the relational aspect is pretty simple. You have a customer service representative and a customer. They are both producing audio data, and that audio data is being fed into the system.

Humana focused on collecting the key data points, shown in the image below, from their customer service operations.

By collecting data about speech style, pitch, silence, stress in customers’ voices, length of call, speed of customers’ speech, intonation, articulation, silence, and representatives’  manner of speaking, Humana was able to analyze customer sentiment and introduce techniques for improved customer satisfaction.

Having strategically defined these data points, the Cogito technology was able to generate reports about customer sentiment during the calls.

Suggesting actions to customer service representatives.

The second use case for the Humana data program follows on from the data gathered in the first case.

In Humana’s case, Cogito generated a host of call analyses and reports about key call issues.

In the second business use case, Cogito was able to suggest actions to customer service representatives, in real-time , to make use of incoming data and help improve customer satisfaction on the spot.

The technology Humana used provided suggestions via text message to the customer service representative, offering the following types of feedback:

• The tone of voice is too tense
• The speed of speaking is high
• The customer representative and customer are speaking at the same time

These alerts allowed the Humana customer service representatives to alter their approach immediately , improving the quality of the interaction and, subsequently, the customer satisfaction.

The preconditions for success in this use case were:

• The call-related data must be collected and stored
• The AI models must be in place to generate analysis on the data points that are recorded during the calls

Evidence of success can subsequently be found in a system that offers real-time suggestions for courses of action that the customer service representative can take to improve customer satisfaction.

Thanks to this data-intensive business use case, Humana was able to increase customer satisfaction, improve customer retention rates, and drive profits per customer.

The Technology That Supports This Data Analysis Case Study

I promised to dip into the tech side of things. This is especially for those of you who are interested in the ins and outs of how projects like this one are actually rolled out.

Here’s a little rundown of the main technologies we discovered when we investigated how Cogito runs in support of its clients like Humana.

• For cloud data management Cogito uses AWS, specifically the Athena product
• For on-premise big data management, the company used Apache HDFS – the distributed file system for storing big data
• They utilize MapReduce, for processing their data
• And Cogito also has traditional systems and relational database management systems such as PostgreSQL
• In terms of analytics and data visualization tools, Cogito makes use of Tableau
• And for its machine learning technology, these use cases required people with knowledge in Python, R, and SQL, as well as deep learning (Cogito uses the PyTorch library and the TensorFlow library)

These data science skill sets support the effective computing, deep learning , and natural language processing applications employed by Humana for this use case.

If you’re looking to hire people to help with your own data initiative, then people with those skills listed above, and with experience in these specific technologies, would be a huge help.

Step 3: S elect The “Quick Win” Data Use Case

Still there? Great!

It’s time to close the loop.

Remember those notes you took before you reviewed the study? I want you to STOP here and assess. Does this Humana case study seem applicable and promising as a solution, given your organization’s current set-up…

YES ▶ Excellent!

Earmark it and continue exploring other winning data use cases until you’ve identified 5 that seem like great fits for your businesses needs. Evaluate those against your organization’s needs, and select the very best fit to be your “quick win” data use case. Develop your data strategy around that.

NO , Lillian – It’s not applicable. ▶  No problem.

Discard the information and continue exploring the winning data use cases we’ve categorized for you according to business function and industry. Save time by dialing down into the business function you know your business really needs help with now. Identify 5 winning data use cases that seem like great fits for your businesses needs. Evaluate those against your organization’s needs, and select the very best fit to be your “quick win” data use case. Develop your data strategy around that data use case.

More resources to get ahead...

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Data Analytics Case Study Guide 2023

by Sam McKay, CFA | Data Analytics

Data analytics case studies reveal how businesses harness data for informed decisions and growth.

For aspiring data professionals, mastering the case study process will enhance your skills and increase your career prospects.

So, how do you approach a case study?

Use these steps to process a data analytics case study:

Understand the Problem: Grasp the core problem or question addressed in the case study.

Collect Relevant Data: Gather data from diverse sources, ensuring accuracy and completeness.

Apply Analytical Techniques: Use appropriate methods aligned with the problem statement.

Visualize Insights: Utilize visual aids to showcase patterns and key findings.

Derive Actionable Insights: Focus on deriving meaningful actions from the analysis.

This article will give you detailed steps to navigate a case study effectively and understand how it works in real-world situations.

By the end of the article, you will be better equipped to approach a data analytics case study, strengthening your analytical prowess and practical application skills.

Let’s dive in!

Table of Contents

What is a Data Analytics Case Study?

A data analytics case study is a real or hypothetical scenario where analytics techniques are applied to solve a specific problem or explore a particular question.

It’s a practical approach that uses data analytics methods, assisting in deciphering data for meaningful insights. This structured method helps individuals or organizations make sense of data effectively.

Additionally, it’s a way to learn by doing, where there’s no single right or wrong answer in how you analyze the data.

So, what are the components of a case study?

Key Components of a Data Analytics Case Study

A data analytics case study comprises essential elements that structure the analytical journey:

Problem Context: A case study begins with a defined problem or question. It provides the context for the data analysis , setting the stage for exploration and investigation.

Data Collection and Sources: It involves gathering relevant data from various sources , ensuring data accuracy, completeness, and relevance to the problem at hand.

Analysis Techniques: Case studies employ different analytical methods, such as statistical analysis, machine learning algorithms, or visualization tools, to derive meaningful conclusions from the collected data.

Insights and Recommendations: The ultimate goal is to extract actionable insights from the analyzed data, offering recommendations or solutions that address the initial problem or question.

Now that you have a better understanding of what a data analytics case study is, let’s talk about why we need and use them.

Why Case Studies are Integral to Data Analytics

Case studies serve as invaluable tools in the realm of data analytics, offering multifaceted benefits that bolster an analyst’s proficiency and impact:

Real-Life Insights and Skill Enhancement: Examining case studies provides practical, real-life examples that expand knowledge and refine skills. These examples offer insights into diverse scenarios, aiding in a data analyst’s growth and expertise development.

Validation and Refinement of Analyses: Case studies demonstrate the effectiveness of data-driven decisions across industries, providing validation for analytical approaches. They showcase how organizations benefit from data analytics. Also, this helps in refining one’s own methodologies

Showcasing Data Impact on Business Outcomes: These studies show how data analytics directly affects business results, like increasing revenue, reducing costs, or delivering other measurable advantages. Understanding these impacts helps articulate the value of data analytics to stakeholders and decision-makers.

Learning from Successes and Failures: By exploring a case study, analysts glean insights from others’ successes and failures, acquiring new strategies and best practices. This learning experience facilitates professional growth and the adoption of innovative approaches within their own data analytics work.

Including case studies in a data analyst’s toolkit helps gain more knowledge, improve skills, and understand how data analytics affects different industries.

Using these real-life examples boosts confidence and success, guiding analysts to make better and more impactful decisions in their organizations.

But not all case studies are the same.

Let’s talk about the different types.

Types of Data Analytics Case Studies

Data analytics encompasses various approaches tailored to different analytical goals:

Exploratory Case Study: These involve delving into new datasets to uncover hidden patterns and relationships, often without a predefined hypothesis. They aim to gain insights and generate hypotheses for further investigation.

Predictive Case Study: These utilize historical data to forecast future trends, behaviors, or outcomes. By applying predictive models, they help anticipate potential scenarios or developments.

Diagnostic Case Study: This type focuses on understanding the root causes or reasons behind specific events or trends observed in the data. It digs deep into the data to provide explanations for occurrences.

Prescriptive Case Study: This case study goes beyond analytics; it provides actionable recommendations or strategies derived from the analyzed data. They guide decision-making processes by suggesting optimal courses of action based on insights gained.

Each type has a specific role in using data to find important insights, helping in decision-making, and solving problems in various situations.

Regardless of the type of case study you encounter, here are some steps to help you process them.

Roadmap to Handling a Data Analysis Case Study

Embarking on a data analytics case study requires a systematic approach, step-by-step, to derive valuable insights effectively.

Here are the steps to help you through the process:

Step 1: Understanding the Case Study Context: Immerse yourself in the intricacies of the case study. Delve into the industry context, understanding its nuances, challenges, and opportunities.

Identify the central problem or question the study aims to address. Clarify the objectives and expected outcomes, ensuring a clear understanding before diving into data analytics.

Step 2: Data Collection and Validation: Gather data from diverse sources relevant to the case study. Prioritize accuracy, completeness, and reliability during data collection. Conduct thorough validation processes to rectify inconsistencies, ensuring high-quality and trustworthy data for subsequent analysis.

Step 3: Problem Definition and Scope: Define the problem statement precisely. Articulate the objectives and limitations that shape the scope of your analysis. Identify influential variables and constraints, providing a focused framework to guide your exploration.

Step 4: Exploratory Data Analysis (EDA): Leverage exploratory techniques to gain initial insights. Visualize data distributions, patterns, and correlations, fostering a deeper understanding of the dataset. These explorations serve as a foundation for more nuanced analysis.

Step 5: Data Preprocessing and Transformation: Cleanse and preprocess the data to eliminate noise, handle missing values, and ensure consistency. Transform data formats or scales as required, preparing the dataset for further analysis.

Step 6: Data Modeling and Method Selection: Select analytical models aligning with the case study’s problem, employing statistical techniques, machine learning algorithms, or tailored predictive models.

In this phase, it’s important to develop data modeling skills. This helps create visuals of complex systems using organized data, which helps solve business problems more effectively.

Understand key data modeling concepts, utilize essential tools like SQL for database interaction, and practice building models from real-world scenarios.

Furthermore, strengthen data cleaning skills for accurate datasets, and stay updated with industry trends to ensure relevance.

Step 7: Model Evaluation and Refinement: Evaluate the performance of applied models rigorously. Iterate and refine models to enhance accuracy and reliability, ensuring alignment with the objectives and expected outcomes.

Step 8: Deriving Insights and Recommendations: Extract actionable insights from the analyzed data. Develop well-structured recommendations or solutions based on the insights uncovered, addressing the core problem or question effectively.

Step 9: Communicating Results Effectively: Present findings, insights, and recommendations clearly and concisely. Utilize visualizations and storytelling techniques to convey complex information compellingly, ensuring comprehension by stakeholders.

Step 10: Reflection and Iteration: Reflect on the entire analysis process and outcomes. Identify potential improvements and lessons learned. Embrace an iterative approach, refining methodologies for continuous enhancement and future analyses.

This step-by-step roadmap provides a structured framework for thorough and effective handling of a data analytics case study.

Now, after handling data analytics comes a crucial step; presenting the case study.

Presenting Your Data Analytics Case Study

Presenting a data analytics case study is a vital part of the process. When presenting your case study, clarity and organization are paramount.

To achieve this, follow these key steps:

Structuring Your Case Study: Start by outlining relevant and accurate main points. Ensure these points align with the problem addressed and the methodologies used in your analysis.

Crafting a Narrative with Data: Start with a brief overview of the issue, then explain your method and steps, covering data collection, cleaning, stats, and advanced modeling.

Visual Representation for Clarity: Utilize various visual aids—tables, graphs, and charts—to illustrate patterns, trends, and insights. Ensure these visuals are easy to comprehend and seamlessly support your narrative.

Highlighting Key Information: Use bullet points to emphasize essential information, maintaining clarity and allowing the audience to grasp key takeaways effortlessly. Bold key terms or phrases to draw attention and reinforce important points.

Addressing Audience Queries: Anticipate and be ready to answer audience questions regarding methods, assumptions, and results. Demonstrating a profound understanding of your analysis instills confidence in your work.

Integrity and Confidence in Delivery: Maintain a neutral tone and avoid exaggerated claims about findings. Present your case study with integrity, clarity, and confidence to ensure the audience appreciates and comprehends the significance of your work.

By organizing your presentation well, telling a clear story through your analysis, and using visuals wisely, you can effectively share your data analytics case study.

This method helps people understand better, stay engaged, and draw valuable conclusions from your work.

We hope by now, you are feeling very confident processing a case study. But with any process, there are challenges you may encounter.

Key Challenges in Data Analytics Case Studies

A data analytics case study can present various hurdles that necessitate strategic approaches for successful navigation:

Challenge 1: Data Quality and Consistency

Challenge: Inconsistent or poor-quality data can impede analysis, leading to erroneous insights and flawed conclusions.

Solution: Implement rigorous data validation processes, ensuring accuracy, completeness, and reliability. Employ data cleansing techniques to rectify inconsistencies and enhance overall data quality.

Challenge 2: Complexity and Scale of Data

Challenge: Managing vast volumes of data with diverse formats and complexities poses analytical challenges.

Solution: Utilize scalable data processing frameworks and tools capable of handling diverse data types. Implement efficient data storage and retrieval systems to manage large-scale datasets effectively.

Challenge 3: Interpretation and Contextual Understanding

Challenge: Interpreting data without contextual understanding or domain expertise can lead to misinterpretations.

Solution: Collaborate with domain experts to contextualize data and derive relevant insights. Invest in understanding the nuances of the industry or domain under analysis to ensure accurate interpretations.

Challenge 4: Privacy and Ethical Concerns

Challenge: Balancing data access for analysis while respecting privacy and ethical boundaries poses a challenge.

Solution: Implement robust data governance frameworks that prioritize data privacy and ethical considerations. Ensure compliance with regulatory standards and ethical guidelines throughout the analysis process.

Challenge 5: Resource Limitations and Time Constraints

Challenge: Limited resources and time constraints hinder comprehensive analysis and exhaustive data exploration.

Solution: Prioritize key objectives and allocate resources efficiently. Employ agile methodologies to iteratively analyze and derive insights, focusing on the most impactful aspects within the given timeframe.

Recognizing these challenges is key; it helps data analysts adopt proactive strategies to mitigate obstacles. This enhances the effectiveness and reliability of insights derived from a data analytics case study.

Now, let’s talk about the best software tools you should use when working with case studies.

Top 5 Software Tools for Case Studies

In the realm of case studies within data analytics, leveraging the right software tools is essential.

Here are some top-notch options:

Tableau : Renowned for its data visualization prowess, Tableau transforms raw data into interactive, visually compelling representations, ideal for presenting insights within a case study.

Python and R Libraries: These flexible programming languages provide many tools for handling data, doing statistics, and working with machine learning, meeting various needs in case studies.

Microsoft Excel : A staple tool for data analytics, Excel provides a user-friendly interface for basic analytics, making it useful for initial data exploration in a case study.

SQL Databases : Structured Query Language (SQL) databases assist in managing and querying large datasets, essential for organizing case study data effectively.

Statistical Software (e.g., SPSS , SAS ): Specialized statistical software enables in-depth statistical analysis, aiding in deriving precise insights from case study data.

Choosing the best mix of these tools, tailored to each case study’s needs, greatly boosts analytical abilities and results in data analytics.

Final Thoughts

Case studies in data analytics are helpful guides. They give real-world insights, improve skills, and show how data-driven decisions work.

Using case studies helps analysts learn, be creative, and make essential decisions confidently in their data work.

Check out our latest clip below to further your learning!

Frequently Asked Questions

What are the key steps to analyzing a data analytics case study.

When analyzing a case study, you should follow these steps:

Clarify the problem : Ensure you thoroughly understand the problem statement and the scope of the analysis.

Make assumptions : Define your assumptions to establish a feasible framework for analyzing the case.

Gather context : Acquire relevant information and context to support your analysis.

Analyze the data : Perform calculations, create visualizations, and conduct statistical analysis on the data.

Provide insights : Draw conclusions and develop actionable insights based on your analysis.

How can you effectively interpret results during a data scientist case study job interview?

During your next data science interview, interpret case study results succinctly and clearly. Utilize visual aids and numerical data to bolster your explanations, ensuring comprehension.

Frame the results in an audience-friendly manner, emphasizing relevance. Concentrate on deriving insights and actionable steps from the outcomes.

How do you showcase your data analyst skills in a project?

To demonstrate your skills effectively, consider these essential steps. Begin by selecting a problem that allows you to exhibit your capacity to handle real-world challenges through analysis.

Methodically document each phase, encompassing data cleaning, visualization, statistical analysis, and the interpretation of findings.

Utilize descriptive analysis techniques and effectively communicate your insights using clear visual aids and straightforward language. Ensure your project code is well-structured, with detailed comments and documentation, showcasing your proficiency in handling data in an organized manner.

Lastly, emphasize your expertise in SQL queries, programming languages, and various analytics tools throughout the project. These steps collectively highlight your competence and proficiency as a skilled data analyst, demonstrating your capabilities within the project.

Can you provide an example of a successful data analytics project using key metrics?

A prime illustration is utilizing analytics in healthcare to forecast hospital readmissions. Analysts leverage electronic health records, patient demographics, and clinical data to identify high-risk individuals.

Implementing preventive measures based on these key metrics helps curtail readmission rates, enhancing patient outcomes and cutting healthcare expenses.

This demonstrates how data analytics, driven by metrics, effectively tackles real-world challenges, yielding impactful solutions.

Why would a company invest in data analytics?

Companies invest in data analytics to gain valuable insights, enabling informed decision-making and strategic planning. This investment helps optimize operations, understand customer behavior, and stay competitive in their industry.

Ultimately, leveraging data analytics empowers companies to make smarter, data-driven choices, leading to enhanced efficiency, innovation, and growth.

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Data for Success: 10 Inspiring Product Analytics Case Studies

Successful companies understand the value of utilizing product analytics to make informed decisions, optimize user experiences, and drive growth . From entertainment giants like Netflix to e-commerce platforms like Shopify , businesses across industries leverage product analytics to gain a competitive edge. In this blog post, we’ll explore 10 inspiring case studies showcasing the power of product analytics.

Real-world examples of how data-driven insights transformed businesses

1. Netflix ‘s Content Recommendation System: Personalized Engagement Delve into the realm of data-driven innovation as you uncover the inner workings of Netflix ‘s cutting-edge recommendation algorithm. Through meticulous analysis of user data, this algorithm breathes life into personalized entertainment, decoding individual preferences, viewing history, and interactions to craft a seamless streaming experience, resulting in a profound boost in user engagement and unwavering retention rates. This fusion of data and innovation is a testament to the power of harnessing user insights to revolutionize the entertainment industry, showcasing unparalleled content curation. Read the case here >>

2. Airbnb ‘s Dynamic Pricing Strategy : Revenue Optimization Experience the revolution of dynamic pricing, where data-driven insights and innovative hospitality transform travel. Airbnb uses real-time data to shape pricing, aligning with demand, local events , and seasons. This ensures hosts maximize earnings while keeping guests satisfied. Travelers find prices tailored to their preferences and budget, building transparency and trust. This fresh pricing approach balances host profitability and guest affordability, redefining hospitality through data-guided strategies. Read the case here >>

3. Spotify ‘s Music Personalization: Tailored Playlists Explore the world of personalized music through Spotify’s ingenious algorithm. By analyzing users’ listening behavior, Spotify crafts personalized playlists that uniquely resonate. These curated musical journeys transcend genres, leading to delightful discoveries and cherished rediscoveries. Through this innovative blend of data analysis and musical intuition, Spotify creates longer listening sessions and heightened user satisfaction, showcasing the transformative power of finely tuned data in crafting auditory experiences. Read the case here >>

4. Shopify ‘s Conversion Rate Optimization: Enhanced E-commerce Sales Dive into e-commerce optimization with Shopify’s advanced analytics. Every click, scroll, and interaction in this digital marketplace leaves insights. Shopify ‘s analytics tools uncover valuable data, enabling businesses to decode customer behavior, spot bottlenecks, and enhance the sales funnel . Armed with these insights, businesses adeptly tackle conversion rate challenges , refining user experiences for persuasion. As they fine-tune websites, adjusting the layout, navigation, product presentation, and checkout, a tangible improvement in sales and revenue emerges. This narrative showcases how data-driven choices reshape e-commerce, orchestrating growth one insight at a time. Read the case here >>

5. Uber ‘s Surge Pricing Algorithm: Efficient Demand Management Explore the world of dynamic pricing through Uber’s lens. Uber’s data-driven surge pricing in urban transportation is an optimization exemplar. The algorithm identifies demand spikes during peak hours, special events, or adverse weather. It then adjusts fares, balancing rider expectations and driver incentives to align supply with demand. This equilibrium ensures reliable rides for riders and encourages drivers into high-demand areas. This data symphony showcases efficiency, aligning rider and driver interests and boosting Uber ‘s peak-time revenue. Read the case here >>

6. Coca-Cola ‘s Freestyle Machines: Flavor Innovation Experience the realm of beverage innovation where Coca-Cola’s data-driven insights create a symphony of flavors and precise inventory. The Freestyle machines showcase how data fuels innovation and efficiency. By analyzing customer preferences, consumption patterns, and flavor combinations, Coca-Cola crafts new blends for evolving tastes. These inventive mixes tantalize taste buds and highlight data-creativity synergy. Beyond flavor, data guides inventory management. Freestyle machines’ real-time data grasp popular beverages by location, optimizing inventory to match demand. This fusion of data and beverage artistry quenches thirst and demonstrates how data sparks innovation, improves offerings, and refines operational excellence. Read the case here >>

7. Fitbit ‘s User Engagement Enhancement: Health Tech Insights Enter the health and fitness tech world, where Fitbit’s mastery of product analytics shines as a guide for evolving insights. In the dynamic wearable landscape, understanding user preferences shapes resonating experiences. With various sensors and data collection tools , Fitbit deciphers patterns like steps, heart rate, sleep, and workouts. This data portrays users’ fitness journeys, refining features based on goals and needs. By empowering users, Fitbit creates an engaged ecosystem. Data insights drive product innovation, enhancing the journey towards better health. Read the case here >>

8. Facebook ‘s News Feed Customization: Tailored Engagement Enter the realm of social media dynamics, where Facebook’s data mastery shines in tailoring content consumption. The News Feed is a virtual hub for sharing, interacting, and exploring in this digital arena. Using diverse data streams, from interactions to browsing habits, Facebook employs algorithms to curate personalized content symphonies. This approach lets users discover posts, stories, and updates that personally resonate, fostering community connections beyond demographics. As users dive into this sea of tailored content, engagement thrives, cementing the platform in their daily lives. This showcases the convergence of data and interaction, with Facebook’s insights orchestrating seamless digital journeys. Read the case here >>

9. Slack’ s Collaboration Revolution: Data-Driven Features Enter the world of workplace collaboration, where Slack’s data-driven innovation shines. Effective communication and collaboration are pivotal for modern productivity. Slack pioneers this realm, utilizing product analytics to understand user interactions, preferences, and challenges. This treasure trove guides Slack’s evolution, enabling seamless feature integration to meet users’ needs. With real-time data guiding them, Slack enhances messaging, integrates third-party tools, and refines the user experience. As teams work on the platform, every action shapes refined user journeys. The outcome is a harmonious work rhythm, embodying the idea that data-guided innovation creates user-centered excellence. Read the case here >>

10. Supercell ‘s Monetization Mastery: Community and Revenue Growth Step into the dynamic mobile gaming world, where Supercell shines as a data-driven gaming leader. In mobile gaming, engagement and monetization go hand in hand, and Supercell excels by using product analytics to create experiences that deeply resonate with players. Every interaction, from swipes to cleared levels, generates data that Supercell transforms into valuable insights. This understanding of player behavior is the foundation of their community engagement strategy. Supercell curates content updates aligned with player preferences, sparking excitement and leading to irresistible in-game purchases. This harmonious blend of data insights and game design propels community engagement while ensuring player satisfaction generates revenue. In the dynamic realm of mobile gaming, Supercell ‘s expertise in product analytics illustrates how carefully orchestrated data shapes digital experiences, fosters enduring player connections, and cultivates thriving gaming ecosystems. Read the case here >>

These case studies showcase the transformative impact of product analytics across various sectors. By harnessing the power of data, companies can better understand their customers, optimize processes, and ultimately achieve their business goals. Each case study link takes you to an in-depth analysis of how these companies implemented product analytics to drive success.

As technology evolves and data becomes more accessible, these examples provide a glimpse into the vast potential of product analytics. Stay tuned to the ever-evolving landscape of data-driven insights that continue to shape how businesses operate and deliver value to their customers.

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FDS Proof Point

National Broadband Map: A Case Study on Open Innovation for National Policy

The National Broadband Map is a tool that provide consumers nationwide reliable information on broadband internet connections. This case study describes how crowd-sourcing, open source software, and public engagement informs the development of a tool that promotes government transparency.

Federal Communications Commission

National Renewable Energy Laboratory API Case Study

This case study describes the launch of the National Renewable Energy Laboratory (NREL) Developer Network in October 2011. The main goal was to build an overarching platform to make it easier for the public to use NREL APIs and for NREL to produce APIs.

National Renewable Energy Laboratory

Open Energy Data at DOE

This case study details the development of the renewable energy applications built on the Open Energy Information (OpenEI) platform, sponsored by the Department of Energy (DOE) and implemented by the National Renewable Energy Laboratory (NREL).

open data , data sharing , Federal Data Strategy

Pairing Government Data with Private-Sector Ingenuity to Take on Unwanted Calls

The Federal Trade Commission (FTC) releases data from millions of consumer complaints about unwanted calls to help fuel a myriad of private-sector solutions to tackle the problem. The FTC’s work serves as an example of how agencies can work with the private sector to encourage the innovative use of government data toward solutions that benefit the public.

Federal Trade Commission

data cleaning , Federal Data Strategy , open data , data sharing

Profile in Data Sharing - National Electronic Interstate Compact Enterprise

The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the federal government and states support children who are being placed for adoption or foster care across state lines. It greatly reduces the work and time required for states to exchange paperwork and information needed to process the placements. Additionally, NEICE allows child welfare workers to communicate and provide timely updates to courts, relevant private service providers, and families.

Profile in Data Sharing - National Health Service Corps Loan Repayment Programs

The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the Health Resources and Services Administration collaborates with the Department of Education to make it easier to apply to serve medically underserved communities - reducing applicant burden and improving processing efficiency.

Profile in Data Sharing - Roadside Inspection Data

The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the Department of Transportation collaborates with the Customs and Border Patrol and state partners to prescreen commercial motor vehicles entering the US and to focus inspections on unsafe carriers and drivers.

Profiles in Data Sharing - U.S. Citizenship and Immigration Service

The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the U.S. Citizenship and Immigration Service (USCIS) collaborated with the Centers for Disease Control to notify state, local, tribal, and territorial public health authorities so they can connect with individuals in their communities about their potential exposure.

SBA’s Approach to Identifying Data, Using a Learning Agenda, and Leveraging Partnerships to Build its Evidence Base

Through its Enterprise Learning Agenda, Small Business Administration’s (SBA) staff identify essential research questions, a plan to answer them, and how data held outside the agency can help provide further insights. Other agencies can learn from the innovative ways SBA identifies data to answer agency strategic questions and adopt those aspects that work for their own needs.

Small Business Administration

process redesign , Federal Data Strategy

Supercharging Data through Validation as a Service

USDA's Food and Nutrition Service restructured its approach to data validation at the state level using an open-source, API-based validation service managed at the federal level.

data cleaning , data validation , API , data sharing , process redesign , Federal Data Strategy

The Census Bureau Uses Its Own Data to Increase Response Rates, Helps Communities and Other Stakeholders Do the Same

The Census Bureau team produced a new interactive mapping tool in early 2018 called the Response Outreach Area Mapper (ROAM), an application that resulted in wider use of authoritative Census Bureau data, not only to improve the Census Bureau’s own operational efficiency, but also for use by tribal, state, and local governments, national and local partners, and other community groups. Other agency data practitioners can learn from the Census Bureau team’s experience communicating technical needs to non-technical executives, building analysis tools with widely-used software, and integrating efforts with stakeholders and users.

open data , data sharing , data management , data analysis , Federal Data Strategy

The Mapping Medicare Disparities Tool

The Centers for Medicare & Medicaid Services’ Office of Minority Health (CMS OMH) Mapping Medicare Disparities Tool harnessed the power of millions of data records while protecting the privacy of individuals, creating an easy-to-use tool to better understand health disparities.

Centers for Medicare & Medicaid Services

geospatial , Federal Data Strategy , open data

The Veterans Legacy Memorial

The Veterans Legacy Memorial (VLM) is a digital platform to help families, survivors, and fellow veterans to take a leading role in honoring their beloved veteran. Built on millions of existing National Cemetery Administration (NCA) records in a 25-year-old database, VLM is a powerful example of an agency harnessing the potential of a legacy system to provide a modernized service that better serves the public.

Veterans Administration

data sharing , data visualization , Federal Data Strategy

Transitioning to a Data Driven Culture at CMS

This case study describes how CMS announced the creation of the Office of Information Products and Data Analytics (OIPDA) to take the lead in making data use and dissemination a core function of the agency.

data management , data sharing , data analysis , data analytics

PDF (10 pages)

U.S. Department of Labor Case Study: Software Development Kits

The U.S. Department of Labor sought to go beyond merely making data available to developers and take ease of use of the data to the next level by giving developers tools that would make using DOL’s data easier. DOL created software development kits (SDKs), which are downloadable code packages that developers can drop into their apps, making access to DOL’s data easy for even the most novice developer. These SDKs have even been published as open source projects with the aim of speeding up their conversion to SDKs that will eventually support all federal APIs.

Department of Labor

open data , API

U.S. Geological Survey and U.S. Census Bureau collaborate on national roads and boundaries data

It is a well-kept secret that the U.S. Geological Survey and the U.S. Census Bureau were the original two federal agencies to build the first national digital database of roads and boundaries in the United States. The agencies joined forces to develop homegrown computer software and state of the art technologies to convert existing USGS topographic maps of the nation to the points, lines, and polygons that fueled early GIS. Today, the USGS and Census Bureau have a longstanding goal to leverage and use roads and authoritative boundary datasets.

U.S. Geological Survey and U.S. Census Bureau

data management , data sharing , data standards , data validation , data visualization , Federal Data Strategy , geospatial , open data , quality

USA.gov Uses Human-Centered Design to Roll Out AI Chatbot

To improve customer service and give better answers to users of the USA.gov website, the Technology Transformation and Services team at General Services Administration (GSA) created a chatbot using artificial intelligence (AI) and automation.

General Services Administration

AI , Federal Data Strategy

resources.data.gov

An official website of the Office of Management and Budget, the General Services Administration, and the Office of Government Information Services.

This section contains explanations of common terms referenced on resources.data.gov.

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Home / Blog / Big Data & Analytics / Data Analytics Case Studies: Real-World Examples of Business Insights and Success

• June 24, 2023

Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 18+ years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.Schema

Table of Content

Enhancing Customer Experience through Data Analytics

Driving business growth with data insights, improving healthcare outcomes with data analytics, harnessing social media analytics for marketing success, transforming retail through data analytics.

Businesses from all sectors of the economy are realising the enormous importance of data analytics in generating insights and success in today's data-driven environment. Data analytics has developed into a potent tool for accelerating growth and gaining a competitive edge, from optimising processes to enhancing consumer experiences and making knowledgeable business decisions. We will examine actual case studies that demonstrate the revolutionary effect of data analytics in various corporate scenarios as part of this blog series. These case studies emphasise the difficulties encountered, the analytical methods used, and the observable results obtained. Join us as we examine these motivational instances of how businesses have used data analytics to reveal insightful information and achieve outstanding results. With the help of these case studies, we hope to motivate and inform organisations about the possibilities of data analytics while also providing them with useful advice and best practises for their own data-driven journeys. Discover the tales behind the data analytics success stories that have transformed sectors and advanced enterprises as we set out on a journey of exploration.

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Delivering a quality customer experience is essential for success in today's cutthroat business environment. In this endeavour, data analytics has emerged as a game-changer, enabling businesses to gather insightful knowledge on the behaviour, preferences, and demands of their customers. Organisations can deliver targeted advertising, personalise customer experiences, and improve e-commerce conversion rates by leveraging the power of data analytics. We examine how market giants like Netflix, Facebook, and Amazon have used data analytics to revolutionise the customer experience through real-world case studies. These instances demonstrate the practical advantages of data-driven methods, such as raised user involvement, better advertising efficiency, and raised client satisfaction.

1. The Netflix Recommendation Engine: Personalization at Scale

With the help of data analytics, Netflix, a well-known streaming service, has completely changed how we consume entertainment by providing tailored suggestions to millions of customers worldwide. The Netflix recommendation engine uses complex algorithms to analyse user behaviour, watching history, and preferences in order to suggest relevant content that is catered to each user's preferences. This case study examines how Netflix's data analytics capabilities have boosted customer happiness by enhancing user engagement, extending viewing sessions, and transforming the customer experience.

2. Targeted Advertising: How Facebook Utilizes Data Analytics

The social media behemoth Facebook mainly relies on data analytics to power its specialised advertising campaigns. Facebook uses sophisticated data analytics tools to provide personalised adverts to its large user base by examining user demographics, interests, and online behaviour. This case study looks at how Facebook's data analytics platform helps marketers reach their target market more efficiently, which boosts click-through rates, boosts conversion rates, and boosts return on ad spend. It demonstrates the effectiveness of data analytics in enhancing marketing campaigns and providing users with pertinent material.

3. Improving E-commerce Conversion Rates: Amazon's Data-driven Approach

The leader in global e-commerce, Amazon, uses data analytics to enhance conversion rates and optimise its website. Amazon uses data-driven methods such as personalised product recommendations, dynamic pricing, and targeted promotions to improve the shopping experience by examining user browsing behaviour, purchase histories, and product preferences. This case study looks at how Amazon's data analytics activities have enhanced customer loyalty, customer satisfaction, and sales. It demonstrates how data analytics affects e-commerce performance and the direction of online purchasing in the future.

Each of these case studies exemplifies how data analytics can dramatically improve the consumer experience. Organisations may provide personalised experiences, efficiently target their marketing efforts, and improve conversion rates by utilising data-driven insights, ultimately leading to business growth and success.

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Organisations are increasingly relying on data analytics to spur growth and acquire a competitive edge in today's data-driven business environment. This topic examines actual cases of businesses using data insights to drive business growth. These case studies demonstrate the revolutionary power of data analytics in fostering corporate success, from optimising pricing tactics to focusing on certain client segments and making data-driven decisions. Utilising the abundance of data at their disposal, businesses may develop insightful understandings, spot possibilities, and come to wise judgements that spur growth, raise customer satisfaction, and boost profitability. These instances demonstrate the enormous potential of data analytics as a tactical tool for fostering commercial expansion in a sector that is undergoing fast change.

1. Pricing optimisation: Uber's flexible pricing policy

The ride-sharing platform Uber makes use of data analytics to dynamically adjust its pricing. Uber adjusts its fares in real-time to balance supply and demand by analysing a number of variables, including rider demand, driver availability, and traffic conditions. This case study examines how Uber's data-driven pricing strategy has increased profits while simultaneously enhancing consumer happiness by supplying dependable and easily available transportation options during peak hours.

2. Market segmentation: Customer Targeting at Coca-Cola

Coca-Cola, a major global beverage company, uses data analytics to efficiently identify and target particular client categories. Coca-Cola customises its marketing initiatives and product offerings for various market segments by researching consumer preferences, purchasing trends, and demographic information. This case study examines how Coca-Cola has maintained its market leadership while connecting with a variety of consumer groups thanks to its data-driven market segmentation approach.

3. Data-Driven Decision Making: Netflix's Content Acquisition Strategy

The streaming media platform Netflix extensively depends on data analytics to inform its content purchase choices. Netflix determines material that resonates with its audience and makes data-informed decisions about content production, licencing, and distribution by examining user viewing trends, preferences, and comments. This case study looks at how Netflix has been able to build a fascinating library of series and films, draw in and keep customers, and compete successfully in the fiercely competitive streaming market.

These case studies demonstrate how data insights have a dramatic effect on fostering business expansion. Organisations can target particular client segments, optimise pricing strategies, and make decisions that are in line with customer preferences by utilising the power of data analytics. In today's data-driven market, the capacity to use data-driven insights offers a competitive advantage, improves customer happiness, and spurs business growth.

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Healthcare organisations may now improve patient care, streamline operations, and achieve better clinical results thanks to data analytics, which is revolutionising the sector. This topic examines actual cases of how data analytics are changing healthcare delivery. These case studies demonstrate the ability of data analytics in guiding evidence-based decision-making and enhancing patient outcomes, from predictive modelling to identify at-risk patients and avoid adverse events to analysing large-scale healthcare data to reveal trends and patterns. Healthcare professionals may pinpoint problem areas, personalise treatments, and put preventative measures in place for diseases by utilising data insights. These instances demonstrate how data analytics has the power to transform healthcare, saving lives and raising the standard of treatment overall.

1. Predictive Analytics in Disease Prevention: IBM Watson's Healthcare Solutions

Healthcare solutions from IBM Watson are at the cutting edge of using predictive analytics to stop diseases and enhance patient outcomes. IBM Watson analyses enormous volumes of healthcare data to find trends, forecast dangers, and enable early intervention. It does this by utilising artificial intelligence and machine learning. This case study demonstrates how predictive analytics is used to prevent disease, assisting healthcare professionals in proactively identifying people who are at a high risk of contracting particular diseases and developing focused preventive interventions. Healthcare practitioners can benefit from useful insights provided by IBM Watson's predictive analytics capabilities, which range from cancer screening and diagnosis to cardiovascular risk assessment. Predictive analytics can help healthcare organisations move from a reactive to a proactive mode, increasing patient outcomes and easing the burden on the system as a whole.

2. Fraud Detection in Healthcare Insurance: UnitedHealth Group's Analytics

Analytics have been successfully used by UnitedHealth Group, a top provider of healthcare insurance, to identify and stop fraudulent activity in healthcare insurance claims. With healthcare fraud on the rise, it is essential for insurance firms to use data analytics to spot and stop fraudulent behaviour. This case study demonstrates how UnitedHealth Group makes use of cutting-edge analytics tools like anomaly detection and predictive modelling to find suspicious trends and fraudulent activity in claims data. UnitedHealth Group is able to spot prospective fraudsters, stop fraudulent claims, and safeguard the integrity of their insurance operations by analysing enormous amounts of structured and unstructured data, including medical records and billing data. UnitedHealth Group highlights the value of data-driven strategies in reducing fraud threats and securing the healthcare industry through their strong analytics skills.

3. Real-time Patient Monitoring: Philips' Healthcare Data Analytics

With the help of their healthcare data analytics solutions, Philips, a world leader in healthcare technology, has achieved tremendous strides in real-time patient monitoring. Philips enables healthcare providers to continually monitor patients' vital signs, track their medical conditions, and spot potential threats in real-time by utilising the Internet of Things (IoT) and sophisticated analytics algorithms. This case study demonstrates how Philips' data analytics capabilities enable healthcare practitioners to take prompt, well-informed decisions that promote patient safety and improve patient outcomes. Philips allows remote monitoring, early diagnosis of deterioration, and proactive intervention through the integration of wearables, sensors, and cloud-based analytics systems. This reduces hospital readmissions and boosts patient satisfaction. Real-time patient monitoring shows how data analytics can have a transformative effect on healthcare delivery, enabling more individualised, effective, and efficient patient care.

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Social media has developed into a potent tool for businesses to engage with their target audience in the current digital era. This case study looks at how businesses may use social media analytics to learn important things about customer trends, preferences, and behaviour. Businesses can better understand their target market by analysing social media data, including engagement metrics, sentiment analysis, and demographic data, and then adjusting their marketing strategy as necessary. Social media analytics provide actionable insights for efficient marketing decision-making, from identifying influencers and tracking brand reputation to tracking campaign performance and gauging customer sentiment. This case study presents successful instances of businesses utilising social media analytics to enhance their marketing initiatives, raise brand awareness, and succeed in marketing in the digital environment.

1. Social Listening and Sentiment Analysis: Nike's Social Media Strategy

Nike, a leader in the worldwide sportswear market, has included social listening and sentiment analysis into their data-driven social media strategy. This case study looks at how Nike tracks conversations, trends, and sentiments related to their brand across numerous social media channels using sophisticated analytics technologies. Nike receives important insights into client opinions, preferences, and experiences by analysing the massive volume of social media data. This helps Nike better understand their target audience and adjust their marketing strategy accordingly. Nike can determine customer sentiment towards their goods, marketing initiatives, and brand reputation through sentiment research, allowing them to spot areas for development and capitalise on favourable reviews. Nike is able to make well-informed decisions, improve customer engagement, and forge closer ties with their audience because to their data-driven approach to social media management. The case study demonstrates how social listening and sentiment analysis were beneficial in establishing Nike's social media strategy and fostering favourable brand perception in the online environment.

2. Influencer Marketing: How Glossier Leverages Data Analytics

Popular beauty company Glossier has successfully incorporated influencer marketing into their overall marketing plan. In-depth analysis of Glossier's use of data analytics to find and work with influencers that complement their brand image and target audience is provided in this case study. To find the best influencers for their campaigns, Glossier uses data analytics tools to examine influencer metrics, engagement rates, and audience demographics. Glossier's influencer collaborations are guaranteed to reach their intended target group and have the most impact thanks to this data-driven strategy. Additionally, Glossier uses data analytics to evaluate key performance indicators like brand mentions, website traffic, and revenue attributable to influencer collaborations to gauge the success of their influencer marketing efforts. Glossier's influencer marketing strategy uses data analytics to help them make data-driven decisions, improve their campaigns, and boost their return on investment. In this case study, Glossier's influencer marketing activities are enhanced by data analytics, allowing them to engage with their audience and promote brand exposure and growth.

3. Social Media Engagement and Conversion: Airbnb's Data-driven Campaigns

Through their data-driven campaigns, Airbnb, a top online marketplace for vacation rentals, has mastered the art of social media engagement and conversion. This case study looks at how Airbnb uses data analytics to power effective social media campaigns that improve conversions and bookings while also generating high levels of engagement. Airbnb gathers and examines a sizable amount of data using social media analytics tools in order to obtain insights into user behaviour, preferences, and trends. Using this information, they may provide personalised and targeted social media content that appeals to their target demographic. Additionally, Airbnb makes use of data analytics to determine the most efficient social media channels and marketing techniques for reaching their target audience. Airbnb optimises their social media efforts in real-time, making data-informed tweaks to maximise engagement and conversion rates through A/B testing and ongoing monitoring of campaign performance indicators. Understanding their audience, personalising their content, and utilising data analytics have all helped Airbnb build very effective social media campaigns that generate significant engagement and eventually help their company expand. This case study demonstrates how social media marketing decisions made using data can provide outstanding results for businesses like Airbnb.

The retail sector has seen a revolution thanks to data analytics, which has allowed businesses to restructure their operations and make data-driven decisions. In-depth analysis of data analytics' impact on the retail industry, including how it has improved consumer experiences, optimised inventory management, and increased profitability, is provided in this case study. Retailers can analyse enormous amounts of data to learn important insights about customer preferences, buying patterns, and market trends by utilising modern analytics tools and techniques. Retailers can use this data to customise product offerings, marketing efforts, and pricing strategies to better suit customer requests. Retailers can also optimise inventory levels, estimate demand properly, and streamline their supply chains thanks to data analytics, which lowers costs and boosts operational effectiveness. Using technology like RFID, beacons, and facial recognition to monitor consumer behaviour and personalise interactions, retailers may also use data analytics to improve the in-store experience. Retailers may acquire a competitive edge in a rapidly changing and dynamic industry by using the power of data analytics, fostering business expansion, and ensuring long-term success.

1. Inventory Optimization: Zara's Agile Supply Chain Analytics

The well-known apparel retailer Zara has had amazing success by using data analytics to streamline its inventory control and build an adaptable supply chain. Zara can precisely estimate demand and modify its inventory levels by analysing real-time data on consumer preferences, market trends, and sales performance. Zara is able to maintain ideal stock levels as a result, lowering the possibility of overstocking or stockouts and cutting down on storage expenses. By using a data-driven strategy, Zara is able to react fast to shifting consumer preferences and market trends, ensuring that its stores are supplied with the appropriate goods at the appropriate time. Zara has established itself as a leader in inventory optimisation through advanced analytics approaches like predictive modelling and demand forecasting , allowing them to offer the newest fashion trends to customers with incredible speed and efficiency.

2. Customer Journey Analysis: Sephora's Personalized Shopping Experience

Global beauty store Sephora has adopted data analytics to improve client journeys and provide a tailored purchasing experience. Sephora learns about unique preferences, past purchases, and browsing habits through sophisticated user data collecting and analysis. This enables them to offer each consumer specialised recommendations, specific product ideas, and focused promos. Sephora can comprehend the customer's journey across several touchpoints, such as their online interactions, social media participation, and in-store visits, by utilising data analytics. Through the creation of seamless, personalised experiences, Sephora is able to increase client loyalty and increase revenue. With data analytics, Sephora keeps innovating in the beauty retail sector, giving its customers a unique and enjoyable shopping experience.

3. Real-time Analytics in Brick-and-Mortar Stores: Walmart's Store Operations

One of the biggest retail chains in the world, Walmart, uses real-time data to improve customer satisfaction and optimise shop operations. Walmart receives real-time insights into store performance, customer traffic patterns, and product availability by utilising data from a variety of sources, including point-of-sale systems, inventory management systems, and IoT devices. As a result, they are able to optimise store layout, employee levels, and inventory replenishment using data. For instance, Walmart might pinpoint high-traffic areas in the shop and thoughtfully position well-liked products there to boost visibility and sales. Walmart can track product availability using real-time analytics, ensuring that shelves are consistently stocked and minimising the likelihood of out-of-stock situations. By harnessing the power of data analytics in their brick-and-mortar stores, Walmart optimizes its operations, improves customer satisfaction, and maximizes profitability.

The case studies that are covered in this blog show how data analytics can transform corporate insights and success. Data analytics has emerged as a crucial resource for businesses across many sectors, from increasing consumer experiences to fostering business growth. These case studies highlight how businesses like Netflix , Uber, and Nike have used data analytics to gain a competitive advantage, make informed decisions, and produce outstanding results. Businesses may find untapped opportunities, streamline processes, personalise services, and boost performance by utilising data. The success stories of these businesses provide other organisations with motivation and inspiration to use data analytics and realise their full potential. As data continues to grow in volume and complexity, businesses that invest in data analytics capabilities and cultivate a data-driven culture will be well-positioned to thrive in the ever-evolving business landscape.

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14 Big Data Examples Showing The Great Value of Smart Analytics In Real Life At Restaurants, Bars, and Casinos

“You can have data without information, but you cannot have information without data.” – Daniel Keys Moran

When you think of big data, you usually think of applications related to banking, healthcare analytics , or manufacturing. After all, these are some pretty massive industries with many examples of big data analytics, and the rise of business intelligence software is answering what data management needs. However, the usage of data analytics isn’t limited to only these fields. While data science is a relatively new field, more and more industries are jumping on the data gold rush.

In this post, we will help you put the power of big data into perspective by offering a range of real-world applications of big data for multiple industries. Let's dive into it!  

What Is An Example Of Big Data? Discover 14 Real World Success Cases

The best examples of big data can be found both in the public and private sectors. From targeted advertising, education, and already mentioned massive industries (healthcare, manufacturing, or banking) to real-life scenarios in guest service or entertainment.

What’s the motive? Well, as we will explore here, bustling entertainment and hospitality entities, including casinos, restaurants, and bars that are embracing the power of digital data, including it in their management reporting practice, and predicting customer behaviors and patterns, are reaping the rewards of increased efficiency, improved customer experiences, and ultimately, a significant boost in profits.

While these industries are traditionally slow in adopting innovations, some front-runners are leading the pack. And while a mere 22% of marketers state that they have a data-driven marketing strategy that is achieving significant results - by leveraging the right insights in the right way, success is inevitable. And when you consider that by the year 2025, 181 zettabytes of data will be generated, the potential for data-driven organizational growth in the hospitality sector is enormous.

Big data can serve to deliver benefits in some surprising areas. Here, we’ll examine 14 big data use cases that are changing the face of the entertainment and hospitality industries as well as other industries, including banking and education, while also enhancing your daily life in the process.

1) Big Data Is Making Fast Food Faster

The first of our big data examples is in fast food. You pull up to your local McDonald’s or Burger King and notice that there’s a really long line in front of you. You start drumming your fingers on the wheel, lamenting the fact that your “fast food” excursion is going to be anything but, and wondering if you should drive to the Wendy’s a block away instead.

However, before you have time to think about your culinary crisis too deeply, you notice that a few cars ahead of you have already gone through. The line is moving much quicker than expected… what gives? You shrug it off, drive up to the window, and place your order.

Behind the scenes

What you may not have realized is that big data has just helped you get your hands on those fries and burgers a little bit earlier. Some fast-food chains are now monitoring their drive-through lanes and changing their menu features (you know, the ones on the LCD screen as opposed to the numbers on the board) in response. Here’s how it works: if the line is really backed up, the features will change to reflect items that can be quickly prepared and served to move through the queue faster. If the line is relatively short, then the features will display higher-margin menu items that take a bit more time to prepare.

Now that’s some smart fast food.

2) Augmented Furniture Shopping

Next in our list of big data applications, we have an example from the furniture industry. You just moved into your dream apartment. You already have a vision of what you want your interior decor to look like, but you are not sure if all your crazy ideas will go together. You’ve been to dozens of shops, but you haven’t brought yourself up to buy anything because you are afraid it might not look great in your new space. 

Until one day, a good friend of yours talked to you about the IKEA app. There, you can browse for your favorite furniture style and virtually place the objects in your house to see how they would look in person! And not just that, you can also choose from a range of wall colors to see what matches your style better. You can finally see how that beautiful sofa or coffee table will look without the need to buy and then return a bunch of items, all from the comfort of your own home. 

Source : Architectmagazine.com 

IKEA has always been known for providing the best experiences for its customers. The retail giant uses both qualitative and psychographic data to understand its customer’s behaviors on a deeper level and offer them the best experience. For instance, they observed that most of their clients go to the store with their kids, often making it harder for them to shop. To solve this issue, they implemented supervised play areas so that parents could shop without distractions. 

In 2017, the company wanted to take its shopping experience one step further by creating an augmented reality app that allowed users to test a product without leaving their homes. The app automatically scales products in real-time based on room dimensions with a 98% accuracy. The issue they faced at the moment was that people needed to close the app and go into IKEA’s shopping app or website to buy the desired product. 

A few years later, with the advancement of AR technology, the retail enterprise decided to mutate its app into a new version called IKEA Studio. This time it reimagined the whole virtual experience by allowing users to plan an entire space with different pieces of furniture, shelving systems, decorations, and even wall colors. The designs can then be exported in both 3D and 2D to share with family and friends. 

While this proved to be especially useful in COVID-19, using augmented reality powered by big data has also allowed IKEA to boost its sustainability actions. By preventing customers from driving to the store location and buying or searching for their needed items, the company can focus more on diminishing its environmental footprint by optimizing its shipment and packaging processes—definitely amongst the greatest big data applications in the modern shopping world.

3) Self-serve Beer And Big Data

Another great big data example in real life. You walk into your favorite bar. The bartender, instead of asking you, “What’ll you have?” hands you a little plastic card instead.

“Uhhh… what’s this?” you ask. He spreads his hands. “Well, the folks upstairs wanted to try out this new system. Basically, you pour all your own beer – you just swipe this card first.”

Your eyebrows raise. “So, basically, I am my own bartender from now on?”

The bartender snorts and shakes his head. “I mean, I’ll still serve you if you’d like. But with this system, you can try as little or as much of a beer as you want. Want a quarter glass of that new IPA you’re not sure about? Go right ahead. Want only half a glass of stout because you’re a bit full from dinner? Be my guest. It’ll all get automatically added to your tab, and you pay for it at the end, just like always.”

You nod, starting to get the picture. “And if I want to mix two different beers together”

“No,” the bartender says. “Never do that.”

You might think this scenario is from some weird beer-based science fiction book, but in reality, it’s already happening. An Israeli company by the name of Weissberger has enabled self-serve beer through two pieces of equipment:

• “Flow meters” which are attached to all the taps/kegs in the bar
• A router that collects all this flow data and sends it to the bar’s computer

By using this system, a lot of cool things can be made possible. For example, you can let customers pour their own beer in a “self-serve” style fashion. However, there are other profitable possibilities as well that come from the use of big data. Bar owners can use these flow meters to see which beers are selling when, according to the time of day, the day of the week, and so on. Then, they can use this data to create specials that take advantage of customer behavior.

They can also use this data to:

• Order new kegs at the right time since they know more accurately how much beer they are serving
• See if certain bartenders are more “generous” with their pours than others
• See if certain bartenders are giving free pours to themselves or their buddies

In Europe, the brewing company Carlsberg found that 70% of their beer sold in city bars was bought between 8-10 p.m., while only 40% of their beer sold in suburban bars was bought in that period. Using this data, they could develop market-specific prices and discounts.

Carlsberg also found that when customers were given a magnetic card and allowed to self-pour beer, they ended up consuming 30% more beer than before. This increased consumption came from customers trying small amounts of beer that they wouldn’t have bought before when they were limited to buying a full pint or larger.

4) Consumers Are Deciding The Overall Menu

Have you ever seen those marketing campaigns companies use where consumers help them “pick the next flavor?” Doritos and Mountain Dew have both used this strategy with varying levels of success. However, the underlying philosophy is sound: let the customers pick what they want and supply that!

Well, big data is letting customers speak even more directly (without having to go to a web page). An article titled “ The Big Business of Big Data ” examines some of the possibilities.

One of our big data analytics examples is that of Tropical Smoothie Cafe. In 2013, they took a slight risk and introduced a veggie smoothie to their previously fruit-only smoothie menu. By keeping track of their data, Tropical Smoothie Cafe found that the veggie smoothie was soon one of their best sellers, and they introduced other versions of vegetable smoothies as a result.

Things get deeper: Tropical Smoothie Cafe was able to use big data to see at what times during the day consumers were buying the most vegetable smoothies. Then, they could use time-specific marketing campaigns (such as “happy hours”) to get consumers in the door during those times.

5) Personalized Movie Suggestions On Netflix 

Moving on with our list of industry examples of big data, we have streaming services. It’s finally Friday. You sit down on your couch after a hard work week, ready to watch a movie while drinking a beer or a glass of wine. You don’t know which movie or TV show to watch, but Netflix has you covered. The app offers a range of options from all your favorite genres based on what you usually like to watch. In just a few minutes, you have picked a perfect movie and are ready to start enjoying your night. 

Being a large enterprise, Netflix deals with massive amounts of data from its over 150 million subscribers. With the streaming industry becoming increasingly competitive, the subscription-based company uses all this information to their advantage to offer targeted experiences to their customers. According to Data And Analytics Network, the data they collect includes: 

• Viewing day, time, device, and location 
• Keywords and number of searches 
• The number of times you paused, rewound, fast-forwarded, and rewatched content 
• Browsing and scrolling patterns 
• And even how much time a user takes to finish a movie or a TV show 

By applying a series of algorithms to the massive amounts of customer data they possess, Netflix can predict what the user will watch next and offer a range of options based on the aforementioned data. This method has proven to be very successful for Netflix, as 80% of the content being steamed is based on their recommendations algorithm. 

But this is not all. Netflix also selects the cover image that is being shown in certain movies or TV shows, depending on the user profile. It does this by using Artwork Visual Analysis (AVA) “a collection of tools and algorithms designed to surface high-quality imagery from videos. It can predict which merchandising still will resonate most with individual users based on their age and general preferences”. Like this, you will likely see a different promotion on your favorite TV show than the one your mom or friend will see on their own profiles. 

6) Big Data Makes Your Next Casino Visit More Fun

Another interesting use of big data examples in real life is with casinos. You walk into the MGM Grand in Las Vegas, excited for a weekend of gambling and catching up with old friends. Immediately, you notice a change. Those slot machines that you played endlessly on your last visit have moved from their last spot in the corner to a more central location right at the entrance. Entranced by fond memories of spinning numbers and free drinks, you walk right on over.

“Our job is to figure out how to optimize the selection of games so that people have a positive experience when they walk through the door… We can understand how games perform, how well guests receive them, and how long they should be on the floor.”

This quote is from Lon O’Donnell, MGM’s first-ever director of corporate slot analytics. An article titled “ Casinos Bet Large with Big Data ” expands on how MGM uses data analysis tools to measure performance and make better business decisions. Think about business from a casino’s point of view for a moment. Casinos have an interesting relationship with their customers. Of course, in the long run, they want you to lose more money than you win – otherwise, they wouldn’t be able to make a profit. However, if you lose a large amount of money on any one visit, you might have such a bad experience that you stop going altogether… which is bad for the casino. On the flip side, they also want to avoid situations where you “hit it big”, as that costs them a lot of money.

Basically, the ideal situation for a casino is when you lose more than you win over the long run, but you don’t lose a horrendous amount in any one visit. Right now, MGM is using big data to make sure that happens. By analyzing the data from individual slot machines, for example, they can tell which machines are paying out what and how often.

They can also tell things like:

• Which machines aren’t being played and need to be replaced or relocated
• Which machines are the most popular (and at what times)
• Which areas of the casino pull in the most profits (and which areas need to be rearranged)

7) We Missed You!

The next of our examples of companies using big data applies to restaurants. Imagine this: you’re relaxing at home, trying to decide which restaurant to eat at with your spouse. You live in NYC and work long hours, and there are just so many options. The decision takes longer than it should; you’ve had a long week, and your brain is fried.

Suddenly, an email arrives in your inbox. Delaying your food choices for a moment (and ignoring the withering glare of your spouse as you zone out of the conversation), you see an email from Fig & Olive, your favorite Mediterranean joint that you were a regular at but haven’t been able to visit in more than a month. The subject line says, “We Miss You!” and when you open it, you’re greeted with a message that communicates two points:

• Fig & Olive is wondering why you haven’t been in for a while.
• They want to give you a free order of crostini because they just miss you so much!

“Honey”, you exclaim, “I know where we’re going!”

The 7-unit NY-based Fig & Olive has been using guest management software to track their guest's ordering habits and to deliver targeted email campaigns. For example, the “We Miss You!” campaign generated almost 300 visits and $36,000 in sales – a seven times return on the company’s investment into big data.

8) The MagicBand

The MagicBand is almost as whimsical as it sounds, as it’s a data-driven innovation that’s been pioneered by the ever-dreamy Disneyland.

Now, imagine visiting a Disneyland park with your friend, partner, or children and each being given a wrist device on entry - one that provides you with key information on queuing times, entertainment start times, and suggestions tailored for you by considering your personality and your preferences. Oh, and one of your favorite Disney mascots greeting you by name. It would make your time at the park all the more, well, magical, right?

Enter MagicBand.

With an ever-growing roster of adrenaline-pumping rides, refreshment stands, arcades, bars, restaurants, and experiences within its four walls - and some 58 million people visiting its various parks every year - this hospitality brand uses big data to enhance its customer experience and remain relevant in a competitive marketplace.

Developed with RFID technology, the MagicBand interacts with thousands of sensors strategically placed around its various amusement parks, gathering colossal stacks of big customer data and processing it to not only significantly enhance its customer experience but gain a wealth of insights that serve to benefit its long-term business intelligence strategy , in addition to its overall operational efficiency - truly a big data testament to the power of business analytics tools in today’s hyper-connected world.

9) Checking In And Out With Your Smartphone

These days, a great deal of us humans are literally glued to our smartphones. While once solely developed for the making and receiving of calls and basic text messages, today’s telecommunication offerings are essentially miniature computers, processing streams of big data and breaking down geographical barriers in the process.

When you go to a hotel, often you’re excited, meaning you’ll want to check into your room, freshen up and enjoy the facilities, or head out and explore. However, sluggish service and long queues can end up seriously eating into your time. Moreover, once you have passed the check-in desk, you risk losing your key - creating a costly and inconvenient nightmare.

That said, what if you could use your smartphone as your key, and what if you could check in and out autonomously, order room service, and pre-order drinks and services through a mobile app. Well, you can at Hilton hotels.

At the end of 2017, the acclaimed hotel brand rolled out its mobile key and service technology to 10 of its most prominent UK branches, and due to its success, this innovation has spread internationally and will be included in its portfolio of 4,000 plus in the near future. In addition to making the hotel hospitality experience more autonomous, the insights collected through the application will help make the hotel’s consumer drinking and dining experience more bespoke.

This cutting-edge big data example from Hilton highlights the fact that by embracing the power of information as well as the connectivity of today's digital world, it’s possible to transform your customer experience and communicate your value proposition across an almost infinite raft of new consumer channels.

And, as things develop, we expect to see more hotels, bars, pubs, and restaurants utilizing this technology in the not-so-distant future.

10) A Nostalgic Shift

Amusement arcades were all the rage decades ago, but due to the evolution of digital gaming, many traditional entertainment centers outside the bright lights of Sin City simply couldn’t compete with immersive consoles, resulting in a host of closures.

But with a sprinkling of nostalgia and the perfect coupling of old and new, you might have noticed that the amusement arcade has somewhat of a renaissance. It seems that those who grew up in a time when arcades reigned supreme are craving a nostalgic trip down memory lane, taking their children for good old retro family experiences. You might have also noticed if you’re amongst those people, that while there are all of the offerings you remember as a child, there are a sprinkling of cutting-edge new amusements and tech-driven developments that make the whole experience more fun, fluid, and easy to navigate.

A shining example of an amusement arcade chain that has stood the test of time is an Australian brand named Timezone.

By leveraging the big data available to the business, Timezone gained invaluable insights into customer spending habits, visitation times, preferred amusement, and geographical proximity to their various branches. By gathering this information, the brand has been able to tailor each branch to its local customers while capitalizing on consumer trends to fortify its long-term strategy.

Speaking to BI Australia, Timezone’s Kane Fong , explained:

“By leveraging the big data available to the organizations, Timezone gained invaluable insights on customer spending habits, visitation times, preferred amusement, and geographical proximity to their various branches. In gathering this information, the brand has been able to tailor each branch to its local customers while capitalizing on consumer trends to fortify its long-term business strategy.”

11) Reducing school drop-outs with big data

As you’ve learned from the previous examples, big data has permeated several industries, and the education system is no exception. For decades, academic institutions have tried to give their students the best environment and tools to complete their courses successfully. However, despite their best efforts, college dropout remains among the biggest challenges for colleges in the United States and across the globe. 

In fact, according to recent research, the USA experiences 40% of college dropouts , with only 41% of students graduating after four years without delay. Due to such conditions, educational organizations started to turn to big data to find a solution to these concerning rates. That is where “Course Signals” from Purdue University was born. A system to predict which students are more likely to not complete their courses. 

Behind the scenes 

In 2007, Purdue University generated a predictive model that analyzed individual student grades, demographic information, historical academic performance, and more to help teachers and administrators identify students who are at risk of not finalizing their courses at an early stage. The system categorized each student within a “risk group” with colors red, yellow, and green, mimicking traffic sign lights. 

To use the system, the teacher needs to manually run it and analyze the student signals to offer personalized feedback and resources to those struggling the most. What makes this initiative so valuable is that the feedback can be provided in real-time, starting from the second week of a course, meaning students can receive the support they need from the beginning. It is believed that the predictive model helped Purdue achieve a 21% increase in retention of students who took at least one course using the system. A successful application of big data in education! 

12) Enhancing the musical experience at Spotify 

The next real-life example of data analytics comes from Spotify. The way we consume music has changed throughout the years thanks to the rise of new technologies. In just a few years, we jumped from owning our favorite artist's CDs to listening to their new albums on our iPods or other portable devices. Today, it is all about streaming music in popular apps such as Spotify or Apple Music, which compete day to day to offer the best experience to their users. 

Imagine you are about to take a road trip. You connect your phone to your car speaker through Bluetooth but realize you are tired of listening to the same 50 songs on your playlist. You would love to meet some new artists in the same genres you usually listen to but don’t know where to start looking. Your friend, who is currently sitting next to you in the car, tells you to look at your Spotify’s “discover” feature. Speechless, you go into your app and find a complete section suggesting new artists and playlists with music based on your own preferences. Happy with this discovery, you spend the next three hours listening to some awesome new music! 

For years now, Spotify has put improving customer experience at the center of their work. In 2012, they launched their “Discover” feature, which offered new artists, songs, and playlists to users based on their historical preferences. Eventually, the feature mutated into “Discover Weekly”, which offered the same format but every week. This brought immense value for music lovers who wanted to explore new tunes and artists to listen to but also opened the doors for several smaller artists to a wider audience. Within the first five years of implementation, users spent 2.3 billion hours listening to music from the Discover Weekly playlists. 

This is just one of the multiple initiatives Spotify has developed using big data. Among some of the most popular ones, we can find “Wrapped”, which gives users a roundup of their year through the music they listened to. Every December, all Spotify users can see how many hours of music they listened to that year, their favorite artists, and the most listened-to song, among other things. Through the years, “Wrapped” has turned into the most expected event as users share their findings on their social media. One of the best examples of data analytics in daily life!

13) Wimbledon improves fan experience with data 

Moving on with our examples of big data in everyday life, we will cover sports. It's finally time to take a holiday, so you and your partner decide to visit London. While searching for fun activities, you notice that, during your stay, the Wimbledon tennis championship will be carried out. You are not precisely an expert in the sport, but after the pandemic, you developed a new interest in it, and this is the ultimate event to go to for any fanatic. So, you decide to purchase two tickets.

After a long wait, the day is finally there. You are sitting at Centre Court to watch an exciting match. The issue is you are not really that familiar with the players, and you wonder who is more likely to win and other relevant information. So, you decide to go into their website and encounter “Win Factor”, a section that provides fans with all the necessary data to follow the match, get to know the players, and even make predictions about who you think will win. The section is so complete that you decide to recommend it to other friends who also like tennis so they can follow it in real-time with you. The experience ends up being everything you expected! 

In 2022, after the pandemic and a Netflix hit show increased interest in F1, many other sporting industries felt challenged to increase the fan experience and engagement. This is what happened at Wimbledon, where organizers realized that many of the fans that were attending the event didn’t know a lot about the players and did not watch other tennis matches the rest of the year, putting a toll on engagement. That is how the idea to develop Win Factor came to life. 

Win Factor is a tool that aggregates data from several different sources to offer fans a range of stats, including strengths and weaknesses about players, match predictions, profiles for rising stars, and much more. The tool was generated as an effort from the organization to get fans “closer to the sport” and increase their level of engagement. In fact, fans can even make their own predictions based on the information they just learned, making it even more exciting for them. 

The idea was developed by the Wimbledon organization in collaboration with a long-term partner of 33 years, IBM. The technology giant uses artificial intelligence to gather detailed insights online and in the court to provide fans with this enhanced experience. It is definitely a great initiative to boost this traditional and widely regarded sport! 

14) Personalized coffee at Starbucks

Last but not least, in our list of examples of big data analytics, we have an application related to everyone's favorite drink, coffee. You are an avid Starbucks drinker. After various weeks of collecting stars in their Rewards Program, you are finally entitled to your free reward. Since you are in a good mood, you decide you want to try something new. However, you have been drinking the same black coffee for the past five years and don’t know what to try next. So, you decide to open your Starbucks app and find a range of recommendations that, surprisingly, you think you’ll like. Decided, you go into your closest store and get a free iced coffee that you really enjoy because it is a hot summer day. You never got one of those before and are happy you decided to try something new.  

Behind the scene 

With an estimated 90 million weekly transactions in their 25,000 stores worldwide, Starbucks is an undisputed leader in the industry. That is because they have been able to boost customer experience and engagement through the use of various big data-related initiatives. The most popular and successful is their reward program. 

Starbucks's reward program is integrated into the company’s app, allowing users to gather stars whenever they buy a product. Customers who manage to gather around 100 stars can collect a reward, including free drinks or pastries. What makes this program so successful is the fact that, from the 17 million Starbucks App users, 13 million are using the program, allowing the company to gather massive amounts of data regarding customer preferences. The company uses that data to then offer personalized suggestions to customers about what new products they could try using a complex cloud-based AI engine, going as far as suggesting products based on the customer's current location, that day’s weather, or if it's a holiday. 

The company also uses big data to determine new store locations. They do this by using mapping and online BI tools , like datapine, to determine proximity to other Starbucks locations, demographics, traffic, and more. So, if you are wondering if two stores that are very close to each other compete, the data has already told them they won’t. A great big data in business example!

Key Takeaways From Big Data Applications 

Big data is changing how we eat, drink, play, and gamble to make our lives as consumers easier, more personal, and more entertaining.

What’s even more amazing is that we’re only at the beginning of the adoption of big data in the hospitality and entertainment industries. As we as humans evolve the way we gather, organize, and analyze data, more incredible big data applications will emerge in the near and distant future. We are living in exciting times.

This also permeates into the business area, where organizations of all sizes turn to powerful online data analysis tools to boost their data-driven efforts and ensure sustainable growth.

For more mind-blowing big data applications in real-world situations, explore our insights into big data in healthcare , logistics , and even American football .

If you want to understand your data analysis in detail, you can try our online data visualization tool for a 14-day free trial !

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4 Examples of Business Analytics in Action

• 15 Jan 2019

Data is a valuable resource in today’s ever-changing marketplace. For business professionals, knowing how to interpret and communicate data is an indispensable skill that can inform sound decision-making.

“The ability to bring data-driven insights into decision-making is extremely powerful—all the more so given all the companies that can’t hire enough people who have these capabilities,” says Harvard Business School Professor Jan Hammond , who teaches the online course Business Analytics . “It’s the way the world is going.”

Before taking a look at how some companies are harnessing the power of data, it’s important to have a baseline understanding of what the term “business analytics” means.

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What Is Business Analytics?

Business analytics is the use of math and statistics to collect, analyze, and interpret data to make better business decisions.

There are four key types of business analytics: descriptive, predictive, diagnostic, and prescriptive. Descriptive analytics is the interpretation of historical data to identify trends and patterns, while predictive analytics centers on taking that information and using it to forecast future outcomes. Diagnostic analytics can be used to identify the root cause of a problem. In the case of prescriptive analytics , testing and other techniques are employed to determine which outcome will yield the best result in a given scenario.

Related : 4 Types of Data Analytics to Improve Decision-Making

Across industries, these data-driven approaches have been employed by professionals to make informed business decisions and attain organizational success.

Check out the video below to learn more about business analytics, and subscribe to our YouTube channel for more explainer content!

Business Analytics vs. Data Science

It’s important to highlight the difference between business analytics and data science . While both processes use big data to solve business problems they’re separate fields.

The main goal of business analytics is to extract meaningful insights from data to guide organizational decisions, while data science is focused on turning raw data into meaningful conclusions through using algorithms and statistical models. Business analysts participate in tasks such as budgeting, forecasting, and product development, while data scientists focus on data wrangling , programming, and statistical modeling.

While they consist of different functions and processes, business analytics and data science are both vital to today’s organizations. Here are four examples of how organizations are using business analytics to their benefit.

Business Analytics Examples

According to a recent survey by McKinsey , an increasing share of organizations report using analytics to generate growth. Here’s a look at how four companies are aligning with that trend and applying data insights to their decision-making processes.

1. Improving Productivity and Collaboration at Microsoft

At technology giant Microsoft , collaboration is key to a productive, innovative work environment. Following a 2015 move of its engineering group's offices, the company sought to understand how fostering face-to-face interactions among staff could boost employee performance and save money.

Microsoft’s Workplace Analytics team hypothesized that moving the 1,200-person group from five buildings to four could improve collaboration by increasing the number of employees per building and reducing the distance that staff needed to travel for meetings. This assumption was partially based on an earlier study by Microsoft , which found that people are more likely to collaborate when they’re more closely located to one another.

In an article for the Harvard Business Review , the company’s analytics team shared the outcomes they observed as a result of the relocation. Through looking at metadata attached to employee calendars, the team found that the move resulted in a 46 percent decrease in meeting travel time. This translated into a combined 100 hours saved per week across all relocated staff members and an estimated savings of $520,000 per year in employee time.

The results also showed that teams were meeting more often due to being in closer proximity, with the average number of weekly meetings per person increasing from 14 to 18. In addition, the average duration of meetings slightly declined, from 0.85 hours to 0.77 hours. These findings signaled that the relocation both improved collaboration among employees and increased operational efficiency.

For Microsoft, the insights gleaned from this analysis underscored the importance of in-person interactions and helped the company understand how thoughtful planning of employee workspaces could lead to significant time and cost savings.

2. Enhancing Customer Support at Uber

Ensuring a quality user experience is a top priority for ride-hailing company Uber. To streamline its customer service capabilities, the company developed a Customer Obsession Ticket Assistant (COTA) in early 2018—a tool that uses machine learning and natural language processing to help agents improve their speed and accuracy when responding to support tickets.

COTA’s implementation delivered positive results. The tool reduced ticket resolution time by 10 percent, and its success prompted the Uber Engineering team to explore how it could be improved.

For the second iteration of the product, COTA v2, the team focused on integrating a deep learning architecture that could scale as the company grew. Before rolling out the update, Uber turned to A/B testing —a method of comparing the outcomes of two different choices (in this case, COTA v1 and COTA v2)—to validate the upgraded tool’s performance.

Preceding the A/B test was an A/A test, during which both a control group and a treatment group used the first version of COTA for one week. The treatment group was then given access to COTA v2 to kick off the A/B testing phase, which lasted for one month.

At the conclusion of testing, it was found that there was a nearly seven percent relative reduction in average handle time per ticket for the treatment group during the A/B phase, indicating that the use of COTA v2 led to faster service and more accurate resolution recommendations. The results also showed that customer satisfaction scores slightly improved as a result of using COTA v2.

With the use of A/B testing, Uber determined that implementing COTA v2 would not only improve customer service, but save millions of dollars by streamlining its ticket resolution process.

Related : How to Analyze a Dataset: 6 Steps

3. Forecasting Orders and Recipes at Blue Apron

For meal kit delivery service Blue Apron, understanding customer behavior and preferences is vitally important to its success. Each week, the company presents subscribers with a fixed menu of meals available for purchase and employs predictive analytics to forecast demand , with the aim of using data to avoid product spoilage and fulfill orders.

To arrive at these predictions, Blue Apron uses algorithms that take several variables into account, which typically fall into three categories: customer-related features, recipe-related features, and seasonality features. Customer-related features describe historical data that depicts a given user’s order frequency, while recipe-related features focus on a subscriber’s past recipe preferences, allowing the company to infer which upcoming meals they’re likely to order. In the case of seasonality features, purchasing patterns are examined to determine when order rates may be higher or lower, depending on the time of year.

Through regression analysis—a statistical method used to examine the relationship between variables—Blue Apron’s engineering team has successfully measured the precision of its forecasting models. The team reports that, overall, the root-mean-square error—the difference between predicted and observed values—of their projection of future orders is consistently less than six percent, indicating a high level of forecasting accuracy.

By employing predictive analytics to better understand customers, Blue Apron has improved its user experience, identified how subscriber tastes change over time, and recognized how shifting preferences are impacted by recipe offerings.

Related : 5 Business Analytics Skills for Professionals

4. Targeting Consumers at PepsiCo

Consumers are crucial to the success of multinational food and beverage company PepsiCo. The company supplies retailers in more than 200 countries worldwide , serving a billion customers every day. To ensure the right quantities and types of products are available to consumers in certain locations, PepsiCo uses big data and predictive analytics.

PepsiCo created a cloud-based data and analytics platform called Pep Worx to make more informed decisions regarding product merchandising. With Pep Worx, the company identifies shoppers in the United States who are likely to be highly interested in a specific PepsiCo brand or product.

For example, Pep Worx enabled PepsiCo to distinguish 24 million households from its dataset of 110 million US households that would be most likely to be interested in Quaker Overnight Oats. The company then identified specific retailers that these households might shop at and targeted their unique audiences. Ultimately, these customers drove 80 percent of the product’s sales growth in its first 12 months after launch.

PepsiCo’s analysis of consumer data is a prime example of how data-driven decision-making can help today’s organizations maximize profits.

Developing a Data Mindset

As these companies illustrate, analytics can be a powerful tool for organizations seeking to grow and improve their services and operations. At the individual level, a deep understanding of data can not only lead to better decision-making, but career advancement and recognition in the workplace.

“Using data analytics is a very effective way to have influence in an organization,” Hammond says . “If you’re able to go into a meeting, and other people have opinions, but you have data to support your arguments and your recommendations, you’re going to be influential.”

Do you want to leverage the power of data within your organization? Explore Business Analytics —one of our online business essentials courses —to learn how to use data analysis to solve business problems.

This post was updated on March 24, 2023. It was originally published on January 15, 2019.

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• Top Courses

Google Data Analytics Capstone: Complete a Case Study

This course is part of Google Data Analytics Professional Certificate

Taught in English

Some content may not be translated

Instructor: Google Career Certificates

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Sponsored by Google

446,635 already enrolled

(13,880 reviews)

Recommended experience

Beginner level

  No prior experience with spreadsheets or data analytics is required. All you need is high-school level math and a curiosity about how things work.

What you'll learn

Differentiate between a capstone project, case study, and a portfolio.

Identify the key features and attributes of a completed case study.

Apply the practices and procedures associated with the data analysis process to a given set of data.

Discuss the use of case studies/portfolios when communicating with recruiters and potential employers.

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Build your Data Analysis expertise

• Learn new concepts from industry experts
• Gain a foundational understanding of a subject or tool
• Develop job-relevant skills with hands-on projects
• Earn a shareable career certificate from Google

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

There are 4 modules in this course

This course is the eighth and final course in the Google Data Analytics Certificate. You’ll have the opportunity to complete a case study, which will help prepare you for your data analytics job hunt. Case studies are commonly used by employers to assess analytical skills. For your case study, you’ll choose an analytics-based scenario. You’ll then ask questions, prepare, process, analyze, visualize and act on the data from the scenario. You’ll also learn about useful job hunting skills, common interview questions and responses, and materials to build a portfolio online. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course, learners will: - Learn the benefits and uses of case studies and portfolios in the job search. - Explore real world job interview scenarios and common interview questions. - Discover how case studies can be a part of the job interview process. - Examine and consider different case study scenarios. - Have the chance to complete your own case study for your portfolio.

Learn about capstone basics

A capstone is a crowning achievement. In this part of the course, you’ll be introduced to capstone projects, case studies, and portfolios, and will learn how they help employers better understand your skills and capabilities. You’ll also have an opportunity to explore the online portfolios of real data analysts.

What's included

3 videos 5 readings 1 quiz 1 discussion prompt 1 plugin

3 videos • Total 14 minutes

• Introducing the capstone project • 4 minutes • Preview module
• Rishie: What employers look for in data analysts • 2 minutes
• Best-in-class • 7 minutes

5 readings • Total 100 minutes

• Course 8 overview: Set your expectations • 20 minutes
• Explore portfolios • 20 minutes
• Your portfolio and case study checklist • 20 minutes
• Revisit career paths in data • 20 minutes
• Next steps • 20 minutes

1 quiz • Total 20 minutes

• Data journal: Prepare for your project • 20 minutes

1 discussion prompt • Total 10 minutes

• Introduce yourself • 10 minutes

1 plugin • Total 10 minutes

• Refresher: Your Google Data Analytics Certificate roadmap • 10 minutes

Optional: Build your portfolio

In this part of the course, you’ll review two possible tracks to complete your case study. You can use a dataset from one of the business cases provided or search for a public dataset to develop a business case for an area of personal interest. In addition, you'll be introduced to several platforms for hosting your completed case study.

3 videos 9 readings 1 quiz 4 discussion prompts 1 plugin

3 videos • Total 7 minutes

• Get started with your case study • 3 minutes • Preview module
• Unlimited potential with analytics case studies • 1 minute
• Share your portfolio • 2 minutes

9 readings • Total 150 minutes

• Introduction to building your portfolio • 10 minutes
• Choose your case study track • 20 minutes
• Track A details • 10 minutes
• Case Study 1: How does a bike-share navigate speedy success? • 20 minutes
• Case Study 2: How can a wellness company play it smart? • 20 minutes
• Track B details • 10 minutes
• Case Study 3: Follow your own case study path • 20 minutes
• Resources to explore other case studies • 20 minutes
• Create your online portfolio • 20 minutes

1 quiz • Total 60 minutes

• Hands-On Activity: Add your portfolio to Kaggle • 60 minutes

4 discussion prompts • Total 40 minutes

• Case Study 1: How does a bike-share navigate speedy success? • 10 minutes
• Case Study 2: How can a wellness company play it smart? • 10 minutes
• Case Study 3: Follow your own case study path • 10 minutes
• Optional: Share your portfolio with others • 10 minutes
• Capstone roadmap • 10 minutes

Optional: Use your portfolio

Your portfolio is meant to be seen and explored. In this part of the course, you’ll learn how to discuss your portfolio and highlight specific skills in interview scenarios. You’ll also create and practice an elevator pitch for your case study. Finally, you’ll discover how to position yourself as a top applicant for data analyst jobs with useful and practical interview tips.

6 videos 7 readings 1 quiz

6 videos • Total 27 minutes

• Discussing your portfolio • 4 minutes • Preview module
• Scenario video: Introductions • 7 minutes
• Scenario video: Case study • 5 minutes
• Scenario video: Problem-solving • 3 minutes
• Scenario video: Negotiating terms • 3 minutes
• Nathan: VetNet and giving advice to vets • 3 minutes

7 readings • Total 110 minutes

• Introduction to sharing your work • 10 minutes
• The interview process • 20 minutes
• Scenario video series introduction • 20 minutes
• What makes a great pitch • 10 minutes
• Top tips for interview success • 10 minutes
• Prepare for interviews with Interview Warmup • 20 minutes
• Negotiate your contract • 20 minutes
• Self-Reflection: Polish your portfolio • 20 minutes

Put your certificate to work

Earning your Google Data Analytics Certificate is a badge of honor. It's also a real badge. In this part of the course, you'll learn how to claim your certificate badge and display it in your LinkedIn profile. You'll also be introduced to job search benefits that you can claim as a certificate holder, including access to the Big Interview platform and Byteboard interviews.

3 videos 4 readings 2 quizzes 1 discussion prompt 1 plugin

3 videos • Total 5 minutes

• Congratulations on completing your Capstone Project! • 1 minute • Preview module
• From all of us ... • 1 minute
• Explore professional opportunities • 3 minutes

4 readings • Total 80 minutes

• Showcase your work • 20 minutes
• Claim your Google Data Analytics Certificate badge • 20 minutes
• Sign up to the Big Interview platform • 20 minutes
• Expand your data career expertise • 20 minutes

2 quizzes • Total 4 minutes

• End-of-program checklist • 2 minutes
• Did you complete a case study? • 2 minutes
• Connect with Google Data Analytics Certificate graduates • 10 minutes
• End-of-program survey • 10 minutes

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I enjoyed the course. Getting to know the basics of SQL, Tableau, and R was a challenge at first but was explained in great detail and definitaly helped that it was a streamlined process.

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I found a new passion in data analytics. I already signed up for a data analytics boot camp to further develop my data analytics team. Thank you to the amazing Google team that taught the courses.

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An elevator pitch gives potential employers a quick, high-level understanding of your professional experience. What are the key considerations when creating an elevator pitch? Select all that apply.

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Qualitative case study data analysis: an example from practice

Affiliation.

• 1 School of Nursing and Midwifery, National University of Ireland, Galway, Republic of Ireland.
• PMID: 25976531
• DOI: 10.7748/nr.22.5.8.e1307

Aim: To illustrate an approach to data analysis in qualitative case study methodology.

Background: There is often little detail in case study research about how data were analysed. However, it is important that comprehensive analysis procedures are used because there are often large sets of data from multiple sources of evidence. Furthermore, the ability to describe in detail how the analysis was conducted ensures rigour in reporting qualitative research.

Data sources: The research example used is a multiple case study that explored the role of the clinical skills laboratory in preparing students for the real world of practice. Data analysis was conducted using a framework guided by the four stages of analysis outlined by Morse ( 1994 ): comprehending, synthesising, theorising and recontextualising. The specific strategies for analysis in these stages centred on the work of Miles and Huberman ( 1994 ), which has been successfully used in case study research. The data were managed using NVivo software.

Review methods: Literature examining qualitative data analysis was reviewed and strategies illustrated by the case study example provided. Discussion Each stage of the analysis framework is described with illustration from the research example for the purpose of highlighting the benefits of a systematic approach to handling large data sets from multiple sources.

Conclusion: By providing an example of how each stage of the analysis was conducted, it is hoped that researchers will be able to consider the benefits of such an approach to their own case study analysis.

Implications for research/practice: This paper illustrates specific strategies that can be employed when conducting data analysis in case study research and other qualitative research designs.

Keywords: Case study data analysis; case study research methodology; clinical skills research; qualitative case study methodology; qualitative data analysis; qualitative research.

• Case-Control Studies*
• Data Interpretation, Statistical*
• Nursing Research / methods*
• Qualitative Research*
• Research Design

Data Analytics in Healthcare: 7 Real-World Examples and Use Cases

• Data Science ,   Healthcare
• 31 Aug, 2020
• No comments Share

Analytics application cases in healthcare

Predicting palliative care patients risk: Penn Medicine

Optimization of clinical space usage: texas children's hospital.

• An online scheduling tool was leveraged to allow self-scheduling through the web.
• The hospital also established a template for allocating scheduling time in four-hour blocks. Appointments of different duration were allocated to different time blocks. All the unfilled appointments were distributed in a 72-hour time zone to close the gap.
• Weekend appointments and extended hospital hours were added.
• An annual revenue increased by $8.3 million with 53 thousand appointments respectively
• 30 thousand online schedules
• 39 percent patient satisfaction rate growth

Applying machine learning to predict operation duration and disease risk probability: Lucile Packard Children’s Hospital Stanford

• Identify patients at clinical decline risk
• Prevent central line-associated bloodstream infections
• Predict surgical operation duration

Operation room delay reduction: The University of Chicago Medical Center

Daily emergency room visits prediction: envision physician services, monitoring patient state deterioration: ysbyty gwynedd, leveraging data to create covid-19 mortality model: agilon health.

• create a COVID-19 model for approximately 125,000 individuals that were assigned with risk scores.
• increase one partner location’s telehealth appointments from none in the first week to 2,200 in weeks 12 and 13, aligning with social distancing and overall pandemic policies.

What are the other opportunities of data analytics in healthcare?

9 of the Best Data Analytics Portfolios on the Web

Seeking some inspiration for your data analytics portfolio?

Whether you’re a newly qualified data analyst or a seasoned data scientist, you’ll need a portfolio that pops. While data analytics portfolios are traditionally about highlighting your work, they also need to show off your personality, your communication skills, and your personal brand.

In this post, we highlight our top nine data analytics portfolios from around the web. This includes screenshots, tips, and examples for how you can show your best side. While your portfolio should naturally include some strong projects, how you present yourself and your work is just as crucial as the content you’re sharing.

We’ll start by explaining why a data analytics portfolio is so important. If you want, you can skip straight to the good stuff using the menu below.

• Harrison Jansma
• Naledi Hollbruegge
• Anubhav Gupta
• Jessie-Raye Bauer
• Maggie Wolff
• Data analyst portfolio FAQ

First, though…

What’s the point of a data analytics portfolio?

As the first thing an employer sees, a strong data analytics portfolio needs to highlight your best work.

Given the complexity of data analytics, it might seem that a visual portfolio isn’t the best approach. The detail of data analytics projects can indeed be a bit mundane at times, but this is why a strong portfolio is so vital. Creating an engaging narrative is far more effective than simply linking to pre-existing code (although that’s required, too, of course).

Rather than simply telling people what you do, use visuals (where possible) to bring your work to life. After all, storytelling is a key skill for data analytics, a field where facts and figures are used to weave a narrative. Taking inspiration from the following, you’ll soon see how you can combine words, projects, and visuals to create a portfolio that shines.

1. Harrison Jansma

Who is harrison jansma.

Harrison Jansma is a US-based data process manager at Capital One. His website shows a clear passion for automating tedious tasks using tech. He also has a presence on  Medium .

What makes Harrison Jansma’s data analytics portfolio so great?

Harrison’s data analytics portfolio is a good example of how to use a portfolio to show off your personality. While he includes some sample projects of his work, just as much focus goes into creating a sense of his personal brand, using fun graphics, choice words, and a taste of his interests.

Right on the homepage, we see a large photograph of Harrison’s friendly face, and a short quote: ‘Contemplative coder and analyst. Inspired by tough problems.’ This description is intriguing and drives us on. Below, Harrison highlights his interest areas and his three most recent projects as exemplars.

This is a clever approach. Sometimes it’s hard to know which projects to share. Incorporating exemplar projects on your homepage is a good way to highlight them while having the option for people to see more if they want to.

It’s also worth noting that Harrison doesn’t get into detailed case studies on his website. Instead, he links directly to his projects. This is quite common practice.

What can we learn from Harrison Jansma?

These days, it’s important to cultivate your personal brand. Harrison Jansma shows us how to introduce personality to your portfolio. He even has a page including pictures of his dog! While it’s your choice how much you want to share about yourself, Harrison’s approach humanizes him while remaining unobtrusive and professional.

Key takeaway

Showcase your best work, with an option for viewers to read more. And if you add a dash of personality, your portfolio examples needn’t be super slick.

View Harrison Jansma’s full portfolio website

2. Naledi Hollbruegge

Who is naledi hollbruegge.

Naledi is a freelance consulting analyst and social researcher based in the UK. She believes that data has the power to make the world a better place, and wants to play her part in that process. Sounds pretty admirable!

What makes Naledi Hollbruegge’s data analytics portfolio so great?

There’s a clear drive in Naledi’s portfolio to find clients. With this in mind, the first thing Naledi flags is her ability to carry out all the key jobs of a data analyst (collecting, processing, and visualizing data). She then dives right in with a quick introduction followed by some project samples.

This portfolio is a prime example of good storytelling. First, Naledi tells us what she can do. Next, she demonstrates it with some projects that highlight those skills, adding an extra layer to the tale. While remaining professional, she also gives us a taster of her interests.

For instance, she has a clear focus on social justice. This is shown with her link to a Tableau project exploring perceptions of discrimination . Another project explores  girls’ rights and well-being . Naledi has implicitly shown us her ethics, strengthening the value of her business proposition. This is something to consider when creating your portfolio projects.

What can we learn from Naledi Hollbruegge?

Naledi demonstrates how to use your portfolio to tell a story. She achieves this brilliantly with a combination of personal statements and supporting projects. In addition to her portfolio,  she also maintains a blog where she writes about her interests. Combined, these aspects all tell us that she believes in the power of data analytics to change the world. That would certainly make us want to hire her!

For some, data analytics is just a day job. That’s fine. But combining client work with personal projects  will show that data analytics is more than just a professional interest—it’s something you’re dedicated to.

View Naledia Holbruegge’s full portfolio website

3. Tim Hopper

Who is tim hopper.

Tim Hopper is a data scientist, machine learning engineer , and cybersecurity software developer based in the US.

What makes Tim Hopper’s data analytics portfolio so great?

Tim’s is a great example of a multimedia portfolio. Rather than bombarding us with a list of his past projects, he’s given us a taste of his interests and expertise using a variety of different methods. This includes a combination of humor, podcasts, articles, and videos that tell us what he does and show us how he works. The details of his projects, meanwhile, are available on his GitHub (which he links to in numerous places on his website).

On his homepage, Tim quickly grabs our attention with unobtrusive but bold visuals, and a headline that tells us his key skills: ‘Machine Learning. Cybersecurity. Python. Software Engineering. Math Jokes.’ This provides a nice taste of his experience, as well as his personality. Notice that the top menu includes links to Tim’s podcasts, talks, and articles, as well as other sites of interest. He also links out to his social media: Twitter, LinkedIn, and GitHub. These offer more in-depth information about his professional background.

Interestingly, Tim doesn’t use his portfolio to discuss specific projects. This is an admittedly bold move. Instead, he sells himself as a thought leader and data influencer , writing about his experiences and sharing talks and podcasts about his time as a data scientist.

This is a high-risk but high pay-off strategy, and Tim executes it well. While this approach is better suited to more experienced data scientists, that doesn’t mean we can’t learn from it. Using articles, videos, and podcasts gives him a legitimate excuse to enrich his portfolio with additional media. His website is also laden with personality. He has a sense of humor, which is an appealing quality in itself.

What can we learn from Tim Hopper?

Tim shows us that you don’t need a traditional portfolio to make an impact. By creating a distinctive personal brand, you can boost your profile with more than just sample projects. By including his articles, videos, and podcasts, Tim has given us direct proof that he is a competent communicator and data scientist. If we want to find out more, we can contact him via his numerous social media platforms.

Think about the different ways you can spice up your portfolio. While we always recommend including sample projects (which Tim hasn’t done) you can definitely still enhance your offering by evidencing your other interests. Create a unique offering that nobody else can compete with.

View Tim Hopper’s full portfolio website

4. Ger Inberg

Who is ger inberg.

Ger is a Dutch freelance data scientist with a background in software engineering. He has a flair for data visualization and machine learning.

What makes Ger Inberg’s data analytics portfolio so great?

Ger Inberg’s portfolio is a standard website, created using a WordPress template. When we see this, we might wonder why he hasn’t used the opportunity to demonstrate his web design skills. But no matter…After a brief introduction, Ger’s portfolio projects are the very next thing we arrive at.

Using a simple but effective menu, it’s clear at a glance where Ger’s skills lie: data visualization, machine learning, and web development. Viewers can also filter projects by clicking on the relevant topic. But then, Ger directs us to a page where we can view data visualization apps that he’s created using R-Shiny (an R package for interactive web apps). Now we see his web design expertise in action! By showing data right in the app, he’s demonstrating both his visualization and web development skills—a great combination that highlights his expertise without shouting about it.

What can we learn from Ger Inberg?

Something noteworthy about Ger’s portfolio is that he has chosen interesting and topical datasets for his portfolio projects. These include things like global life expectancy, the spread of the coronavirus, and even an overview of which major cities are dominated by digital nomads (i.e. those who use tech to work remotely—a bit like Ger himself!)

While data analytics portfolios need to include code and other technical information, it’s good to balance granular detail with interesting datasets, and something a bit more interactive and visual.

Keep things visual if you can. Creating dedicated interactive apps or dashboards will show your coding capabilities (if you have them) as well as your ability to create memorable visualizations.

View Ger Inberg’s full portfolio website

5. James Le

Who is james le.

James Le is a data scientist, machine learning researcher, journalist, and podcaster. His enthusiasm is infectious—his portfolio makes it clear that he eats, sleeps, and breathes data science!

What makes James Le’s data analytics portfolio so great?

James Le’s website is nothing if not comprehensive, detailing all his data science exploits. While this could easily be overwhelming, he neatly breaks his website down to help visitors focus on what they’re looking for. Namely, his journalism, his academic research, and—in our case—his data analytics expertise.

Clicking through to James’ data analytics portfolio, the header is immediately attention-grabbing. The headline ‘The sexiest job of the 21st century’ tells us that he doesn’t take himself too seriously. His portfolio remains professional, though. He also has a separate coding portfolio, as we see below.

Once again, James uses a headline that isn’t afraid to show a little personality. This helps bring to life what could otherwise be quite dry content. What do we mean? Well, most of James’ projects are code-based, linking directly to files on GitHub. However, he’s still managed to keep his portfolio popping by using bright splash images. These provide a nice visual front-end. To illustrate this, the first image below is what we see on James’ website. The second is the notebook document that we click through to on GitHub.

What can we learn from James Le?

James does a fantastic job of presenting all his projects using Jupyter Notebook and R-Notebook. These formats (a bit like interactive MS Word documents) combine interactive code with text and visual elements to present data analytics work clearly and consistently. Readers know what they’re getting.

If data visualization isn’t your strongpoint, create portfolio projects using Jupyter Notebook or R Notebook. These tools are designed for presenting data analytics findings. You can host them on GitHub and hide the link behind a more appealing visual image on your portfolio.

View James Le’s full portfolio website

6. Yan Holtz

Who is yan holtz.

Yan is a data analysis and visualization specialist. He works as a software engineer at Datadog, a global cloud-monitoring service headquartered in the USA.

What makes Yan Holtz’s data analytics portfolio so great?

For style and substance combined, Yan Holtz’s data analytics portfolio is something to aim for. From the moment you land on his homepage, the interactive design (by Yan himself) grabs attention and shows off his skills.

See those geometric shapes on the homepage? They’re not just pretty to look at. They’re also dynamic and interactive, responding to the movement of the mouse. This is technical stuff; Yan’s clearly no novice. Of course, your own portfolio doesn’t need these fancy extras, but it highlights what you can achieve if you’re feeling ambitious.

What’s more, this is not a case of style over substance. It would have been easy for Yan to simply create a slick front-end that links out to other websites, such as GitHub. Instead, for each project, he’s created an appealing pop-up, offering a clear overview of what he’s worked on. As a reader, this makes for a satisfying user experience. Once we’re done, we can finally explore his projects in more detail via an app, or on GitHub.

Lastly, Yan also offers a broad range of project types from genotype sequencing, to where surfers travel. He’s implicitly telling us that (although he specializes in data visualization) he can work with all kinds of datasets. He tops this all off with customer testimonials, something many portfolios neglect to include.

What can we learn from Yan Holtz?

Yan’s eye for detail is what makes his portfolio a winner for us. He’s executed the entire thing with seemingly-effortless panache. This shows what a difference it makes if you invest extra time into your portfolio. While the level of interactivity on Yan’s website is by no means necessary, it’s a nice demonstration that a little extra focus can make a big difference. But it’s not just the code—even just the testimonials, or keeping his case studies self-contained (rather than linking directly to projects on GitHub) makes an impact.

Show that you have an eye for detail. Pay attention to your design, the language you use (and the spelling!) as well as the projects you’re promoting.

View Yan Holtz’s full portfolio website

7. Anubhav Gupta

Who is anubhav gupta.

Anubhav Gupta is a data analyst and graduate from the School of Information at UC Berkeley. He’s worked at several global cybersecurity companies.

What makes Anubhav Gupta’s data analytics portfolio so great?

What makes Anubhav’s portfolio stand out is how compact it is. All the supporting information is contained on one short web page—two quick scrolls from top to bottom. The benefit of this approach is that nobody will get bored or lose track of where they’re at.

The first thing we see is a clear, unfussy headline. This tells us everything we need to know—who Anubhav is and what he does.

Next, Anubhav introduces himself in a little more detail—still nothing heavy, he’s saved the details for his resume—but it gives us a taste of his interests, experience, and personality.

Finally, Anubhav dives right in with his projects. His projects take a slightly different approach from many of the others we’ve looked at. He primarily focuses on his roles, e.g. as a product manager or machine learning engineer, rather than the project content. He saves the detail for his case study pages. Each of these makes good use of headings, images, and neat layout to keep the messaging clear, compelling, and consistent.

What can we learn from Anubhav Gupta?

Despite having the skills to create a ‘flashy’ portfolio, Anubhav has gone for clarity and precision first and foremost. He’s provided bite-sized information which nevertheless covers everything that it needs to. Ultimately, his portfolio shows us that less is sometimes more and that a little humility goes a long way. Remember, it often demonstrates greater confidence to include a smaller handful of projects rather than stuffing in everything along with the kitchen sink. Sometimes less is more!

Keep your portfolio simple. A few short, confident sentences about who you are and a couple of sample projects are all you need.

View Anubhav Gupta’s full portfolio website

8. Jessie-Raye Bauer

Who is jessie-raye bauer.

Jessie-Raye Bauer is a data scientist working at Apple. She has a Ph.D. from The University of Texas at Austin and is trained in cognitive psychology and statistics.

What makes Jessie-Raye Bauer’s data analytics portfolio so great?

Jessie-Raye Bauer’s portfolio is an interesting example of where a career in data analytics can take you. Just like many of the other portfolios we’ve explored, Jessie-Raye focuses on her skills, adding a dash of personality. However, her portfolio is distinctly academic in feel. You might expect a data scientist of Jessie-Raye’s caliber to show off more. But she doesn’t need to. As a data scientist at Apple, she is at a point in her career where her experience largely speaks for itself.

Unlike many data science portfolios, Jessie-Raye hasn’t included links to projects in the traditional sense. Instead of linking right to GitHub projects or using traditional case studies, Jessie-Raye has chosen to showcase her work via her blog. This is more appropriate for her skill level. The complexity of the work she’s doing lends itself well to the detailed medium of a blog. It’s also in keeping with her more academic background.

A blog is an interesting way of exploring the data analytics journey alongside the author. By blogging, readers can share experiences, rather than merely reading about completed projects. This is always more engaging and insightful. Jessie-Raye also uses topics that interest her personally. For instance, one blog post is all about how to create your own Fitbit API , which begins with an explanation that she is a new Fitbit owner.

What can we learn from Jessie-Raye Bauer?

There’s nothing overly ambitious about Jessie-Raye’s portfolio. It’s even been built using a template. This tells us that those at the top of their game (who, it should be noted, are also those who often have hiring power) aren’t necessarily focused on how slick or flashy your portfolio is. As we can see from Jessie-Raye Bauer, it’s ultimately the content that matters.

View Jessie-Raye Bauer’s full portfolio website

Consider alternative options for showcasing your work. Could you use a blog? An app? Heck, could you even create a visual data analytics essay, a  bit like this one ? Consider what novel approaches you can take that will help you to stand out.

9. Maggie Wolff

Who is maggie wolff.

Last, but by no means least, Maggie is a seasoned data scientist and product analytics aficionado currently working for American Express Global Business Travel.

What makes Maggie Wolff’s data analytics portfolio so great?

Maggie has created an excellent example of a clear, attractive, accessible GitHub portfolio site. Don’t confuse the simplicity for the work of a rookie—this is a thoughtful site, showing a bit about her, her CV/resume, portfolio projects, and her related passions of the talks she gives as well as some blog articles exploring her journey into data science.

What can we learn from Maggie Wolff?

It’s easy to get wrapped up in the design and layout of your own site, wanting to show things off in the coolest way possible. But ease off, and make sure that your website is simple to negotiate around. It’s vital when constructing your site to think about the user , in this case, potential hiring managers and future contacts.

View Maggie Wolff’s data portfolio site

While it might not be one of the flashiest examples out there, that is precisely the point—Maggie’s data analyst portfolio is effective. For someone who is involved in so many things, it lays them out easily and accessibly.

10. Data analyst portfolio FAQ

What should i put in my data analyst portfolio.

Your data analyst portfolio should showcase your skills and experience in the field. This can include projects you’ve completed, data visualizations you’ve created, and analyses you’ve conducted. It’s important to ensure that your portfolio demonstrates your ability to work with real-world data and solve complex problems using data analysis techniques.

Do data analysts need portfolios?

While a data analyst portfolio isn’t strictly necessary, it can be a valuable tool in showcasing your skills and experience to potential employers. A well-crafted portfolio can help you stand out from other candidates and demonstrate your ability to work with real-world data. Additionally, creating a portfolio can help you develop your skills and gain practical experience in data analysis.

Can I make 100k as a data analyst?

Yes, it is possible to make 100k as a data analyst, but it depends on a number of factors, such as your level of experience, the size and location of the company you work for, and the specific job responsibilities. Generally, more senior roles, such as data science manager or data architect, are likely to pay higher salaries than entry-level data analyst positions.

How do I start a data portfolio?

To start a data portfolio, begin by identifying projects or analyses that showcase your skills and experience in data analysis. This can include analyzing publicly available data sets or completing projects for non-profit organizations or local businesses. Use data visualization tools, such as Tableau or Power BI, to create visually compelling representations of your findings. Finally, ensure that your portfolio is well-organized and easy to navigate, with clear descriptions of each project and your role in completing it.

And that’s the end of our list! If you’re considering a new career path in data analytics, why not check out our list of the best online data analytics courses to get your career-changing journey on its way, or get a taster with our free, five-day data analytics short course? You can also find more portfolio inspiration below:

• How to build your data analytics portfolio from scratch
• 9 Project ideas for your data analytics portfolio
• 10 Great places to find free datasets for your next project

• Open access
• Published: 16 April 2024

How does the external context affect an implementation processes? A qualitative study investigating the impact of macro-level variables on the implementation of goal-oriented primary care

• Ine Huybrechts   ORCID: orcid.org/0000-0003-0288-1756 1 , 2 ,
• Anja Declercq 3 , 4 ,
• Emily Verté 1 , 2 ,
• Peter Raeymaeckers 5   na1 &
• Sibyl Anthierens 1   na1

on behalf of the Primary Care Academy

Implementation Science volume  19 , Article number:  32 ( 2024 ) Cite this article

Metrics details

Although the importance of context in implementation science is not disputed, knowledge about the actual impact of external context variables on implementation processes remains rather fragmented. Current frameworks, models, and studies merely describe macro-level barriers and facilitators, without acknowledging their dynamic character and how they impact and steer implementation. Including organizational theories in implementation frameworks could be a way of tackling this problem. In this study, we therefore investigate how organizational theories can contribute to our understanding of the ways in which external context variables shape implementation processes. We use the implementation process of goal-oriented primary care in Belgium as a case.

A qualitative study using in-depth semi-structured interviews was conducted with actors from a variety of primary care organizations. Data was collected and analyzed with an iterative approach. We assessed the potential of four organizational theories to enrich our understanding of the impact of external context variables on implementation processes. The organizational theories assessed are as follows: institutional theory, resource dependency theory, network theory, and contingency theory. Data analysis was based on a combination of inductive and deductive thematic analysis techniques using NVivo 12.

Institutional theory helps to understand mechanisms that steer and facilitate the implementation of goal-oriented care through regulatory and policy measures. For example, the Flemish government issued policy for facilitating more integrated, person-centered care by means of newly created institutions, incentives, expectations, and other regulatory factors. The three other organizational theories describe both counteracting or reinforcing mechanisms. The financial system hampers interprofessional collaboration, which is key for GOC. Networks between primary care providers and health and/or social care organizations on the one hand facilitate GOC, while on the other hand, technology to support interprofessional collaboration is lacking. Contingent variables such as the aging population and increasing workload and complexity within primary care create circumstances in which GOC is presented as a possible answer.

Conclusions

Insights and propositions that derive from organizational theories can be utilized to expand our knowledge on how external context variables affect implementation processes. These insights can be combined with or integrated into existing implementation frameworks and models to increase their explanatory power.

Peer Review reports

Contributions to literature

Knowledge on how external context variables affect implementation processes tends to be rather fragmented. Insights on external context in implementation research often remain limited to merely describing macro-context barriers and facilitators.

Organizational theories contribute to our understanding on the impact of external context to an implementation process by explaining the complex interactions between organizations and their environments.

Findings can be utilized to help explain the mechanism of change in an implementation process and can be combined with or integrated into existing implementation frameworks and models to gain a broader picture on how external context affects implementation processes.

In this study, we integrate organizational theories to provide a profound analysis on how external context influences the implementation of complex interventions. There is a growing recognition that the context in which an intervention takes place highly influences implementation outcomes [ 1 , 2 ]. Despite its importance, researchers are challenged by the lack of a clear definition of context. Most implementation frameworks and models do not define context as such, but describe categories or elements of context, without capturing it as a whole [ 2 , 3 ]. Studies often distinguish between internal and external context: micro- and meso-level internal context variables are specific to a person, team, or organization. Macro-level external context variables consist of variables on a broader, socio-economic and policy level that are beyond one’s control [ 4 ].

Overall, literature provides a rather fragmented and limited perspective on how external context influences the implementation process of a complex intervention. Attempts are made to define, categorize, and conceptualize external context [ 5 , 6 ]. Certain implementation frameworks and models specifically mention external context, such as the conceptual model of evidence-based practice implementation in public service sectors [ 7 ], the Consolidated Framework for Implementation Research [ 8 ], or the i-PARiHS framework [ 9 ]. However, they remain limited to identifying and describing external context variables. Few studies are conducted that specifically point towards the actual impact of macro-level barriers and facilitators [ 10 , 11 , 12 ] but only provide limited insights in how these shape an implementation process. Nonetheless, external contextual variables can be highly disruptive for an organization’s implementation efforts, for example, when fluctuations in funding occur or when new legislation or technology is introduced [ 13 ]. In order to build a more comprehensive view on external context influences, we need an elaborative theoretical perspective.

Organizational theories as a frame of reference

To better understand how the external context affects the implementation process of a primary care intervention, we build upon research of Birken et al. [ 13 ] who demonstrate the explanatory power of organizational theories. Organizational theories can help explain the complex interactions between organizations and their environments [ 13 ], providing understanding on the impact of external context on the mechanism of change in an implementation process. We focus on three of the theories Birken et al. [ 8 ] put forward: institutional theory, resource dependency theory, and contingency theory. We also include network theory in recognition of the importance of interorganizational context and social ties between various actors, especially in primary care settings which are characterized by a multitude of diverse actors (meaning: participants of a process).

These four organizational theories demonstrate the ways in which organizations interact with their external environment in order to sustain and fulfill their core activities. All four of them do this with a different lens. Institutional theory states that an organization will aim to fulfil the expectations, values, or norms that are posed upon them in order to achieve a fit with their environment [ 14 ]. This theory helps to understand the relationships between organizations and actors and the institutional context in which they operate. Institutions can broadly be defined as a set of expectations for social or organizational behavior that can take the form formal structures such as regulatory entities, legislation, or procedures [ 15 ]. Resource dependency theory explains actions and decisions of organizations in terms of their dependence on critical and important resources. It postulates that organizations will respond to their external environment to secure the resources they need to operate [ 16 , 17 ]. This theory helps to gain insight in how fiscal variables can shape the adoption of an innovation. Contingency theory presupposes that an organizations’ effectiveness depends on the congruence between situational factors and organizational characteristics [ 18 ]. External context variables such as social and economic change and pressure can impact the way in which an innovation will be integrated. Lastly, network theory in its broader sense underlines the strength of networks: collaborating in networks can establish an effectiveness in which outcomes are achieved that could not be realized by individual organizations acting independently. Networks are about connecting or sharing information, resources, activities, and competences of three or more organizations aiming to achieve a shared goal or outcome [ 19 , 20 ]. Investigating networks helps to gain understanding of the importance of the interorganizational context and how social ties between organizations affect the implementation process of a complex intervention.

Goal-oriented care in Flanders as a case

In this study, we focus on the implementation of the approach goal-oriented care (GOC) in primary care in Flanders, the Dutch-speaking region in Belgium. Primary care is a highly institutionalized and regulated setting with a high level of professionalism. Healthcare organizations can be viewed as complex adaptive systems that are increasingly interdependent [ 21 ]. The primary care landscape in Flanders is characterized by many primary care providers (PCPs) being either self-employed or working in group practices or community health centers. They are organized and financed at different levels (federal, regional, local). In 2015–2019, a primary care reform was initiated in Flanders in which the region was geographically divided into 60 primary care zones that are governed by care councils. The Flemish Institute of Primary Care was created as a supporting institution aiming to strengthen the collaboration between primary care health and welfare actors. The complex and multisectoral nature of primary care in Flanders forms an interesting setting to gain understanding in how macro-level context variables affect implementation processes.

The concept of GOC implies a paradigm shift [ 22 ] that shifts away from a disease or problem-oriented focus towards a person-centered focus that departs from “what matters to the patient.” Boeykens et al. [ 23 ] state in their concept analysis that GOC could be described as a healthcare approach encompassing a multifaceted, dynamic, and iterative process underpinned by the patient’s context and values. The process is characterized by three stages: goal elicitations, goal setting, and goal evaluation in which patients’ needs and preferences form the common thread. It is an approach in which PCPs and patients collaborate to identify personal life goals and to align care with those goals [ 23 ]. An illustration of how this manifests at individual level can be found in Table 1 . The concept of GOC was incorporated in Flemish policies and included in the primary care reform in 2015–2019. It has gained interest in research and policy as a potential catalyst for integrated care [ 24 ]. As such, the implementation of GOC in Flanders provides an opportunity to investigate the external context of a complex primary care intervention. Our main research question is as follows: what can organizational theories tell us about the influence of external context variables on the implementation process of GOC?

We assess the potential of four organizational theories to enrich our understanding of the impact of external context variables on implementation processes. The organizational theories assessed are as follows: institutional theory, resource dependency theory, network theory, and contingency theory. Qualitative research methods are most suitable to investigate such complex matters, as they can help answer “how” and “why” questions on implementation [ 25 ]. We conducted online, semi-structured in-depth interviews with various primary care actors. These actors all had some level of experience at either meso- or micro-level with GOC implementation efforts.

Sample selection

For our purposive sample, we used the following inclusion criteria: 1) working in a Flemish health/social care context in which initiatives are taken to implement GOC and 2) having at least 6 months of experience. For recruitment, we made an overview of all possible stakeholders that are active in GOC by calling upon the network of the Primary Care Academy (PCA) Footnote 1 . Additionally, a snowballing approach was used in which respondents could refer to other relevant stakeholders at the end of each interview. This leads to respondents with different backgrounds (not only medical) and varying roles, such as being a staff member, project coordinator, or policy maker. We aimed at a maximum variation in the type of organizations which were represented by respondents, such as different governmental institutions and a variety of healthcare/social care organizations. In some cases, paired interviews were conducted [ 26 ] if the respondents were considered complementary in terms of expertise, background, and experience with the topic. An information letter and a request to participate was send to each stakeholder by e-mail. One reminder was sent in case of nonresponse.

Data collection

Interviews were conducted between January and June 2022 by a sociologist trained in qualitative research methods. Interviewing took place online using the software Microsoft Teams and were audio-recorded and transcribed verbatim. A semi-structured interview guide was used, which included (1) an exploration of the concept of GOC and how the respondent relates to this topic, (2) questions on how GOC became a topic of interest and initiatives within the respondent’s setting, and (3) the perceived barriers and facilitators for implementation. An iterative approach was used between data collection and data analysis, meaning that the interview guide underwent minor adjustments based on proceeding insights from earlier interviews in order to get richer data.

Data analysis

All data were thematically analyzed, both inductively and deductively, supported by the software NVivo 12©. For the inductive part, implicit and explicit ideas within the qualitative data were identified and described [ 27 ]. The broader research team, with backgrounds in sociology, medical sciences, and social work, discussed these initial analyses and results. The main researcher then further elaborated this into a broad understanding. This was followed by a deductive part, in which characteristics and perspectives from organizational theories were used as sensitizing concepts, inspired by research from Birken et al. [ 13 ]. This provided a frame of reference and direction, adding interpretive value to our analysis [ 28 ]. These analyses were subject of peer debriefing with our cooperating research team to validate whether these results aligned with their knowledge of GOC processes. This enhances the trustworthiness and credibility of our results [ 29 , 30 ]. Data analysis was done in Dutch, but illustrative quotes were translated into English.

In-depth interviews were performed with n = 23 respondents (see Table 2 ): five interviews were duo interviews, and one interview took place with n = 3 respondents representing one organization. We had n = 6 refusals: n = 3 because of time restraints, n = 1 did not feel sufficiently knowledgeable about the topic, n = 1 changed professional function, and there was n = 1 nonresponse. Respondents had various ways in which they related towards the macro-context: we included actors that formed part of external context (e.g., the Flemish Agency of Care and Health), actors that facilitate and strengthen organizations in the implementation of GOC (e.g., the umbrella organization for community health centers), and actors that actively convey GOC inside and outside their setting (e.g., an autonomous and integral home care service). Interviews lasted between 47 and 72 min. Table 3 gives an overview on the main findings of our deductive analysis with their respective links to the propositions of each of the organizational theories that we applied as a lens.

Institutional theory: laying foundations for a shift towards GOC

For the implementation of GOC in primary care, looking at the data with an institutional theory lens helps us understand the way in which primary care organizations will respond to social structures surrounding them. Institutional theory describes the influence of institutions, which give shape to organizational fields: “organizations that, in the aggregate, constitute a recognized area of institutional life [ 31 ], p. 148. Prevailing institutions within primary care in Flanders can affect how organizations within such organizational fields fulfil their activities. Throughout our interviews, we recognized several dynamics that are being described in institutional theory.

First of all, the changing landscape of primary care in Flanders (see 1.2) was often brought up as a dynamic in which GOC is intertwined with other changes. Respondents mention an overall tendency to reform primary care to becoming more integrated and the ideas of person-centered care becoming more upfront. These expectations in how primary care should be approached seem to affect the organizational field of primary care: “You could tell that in people’s minds they are ready to look into what it actually means to put the patient, the person central. — INT01” Various policy actors are committed to further steer towards these approaches: “the government has called it the direction that we all have to move towards. — INT23” It was part of the foundations for the most recent primary care reform, leading to the creation of demographic primary care zones governed by care councils and the Flemish Institute of Primary Care as supporting institution.

These newly established actors were viewed by our respondents as catalysts of GOC. They pushed towards the aims to depart from local settings and to establish connections between local actors. Overall, respondents emphasized their added value as they are close to the field and they truly connect primary care actors. “They [care councils] have picked up these concepts and have started working on it. At the moment they are truly the incubators and ecosystems, as they would call it in management slang. — INT04” For an innovation such as GOC to be diffused, they are viewed as the ideal actors who can function as a facilitator or conduit. They are uniquely positioned as they are closely in contact with the practice field and can be a top-down conduit for governmental actors but also are able to address the needs from bottom-up. “In this respect, people look at the primary care zones as the ideal partners. […] We can start bringing people together and have that helicopter view: what is it that truly connects you? — INT23” However, some respondents also mentioned their difficult governance structure due to representation of many disciplines and organizations.

Other regulatory factors were mentioned by respondents were other innovations or changes in primary care that were intentionally linked to GOC: e.g., the BelRAI Footnote 2 or Flemish Social Protection Footnote 3 . “The government also provides incentives. For example, family care services will gradually be obliged to work with the BelRAI screener. This way, you actually force them to start taking up GOC. — INT23” For GOC to be embedded in primary care, links with other regulatory requirements can steer PCPs towards GOC. Furthermore, it was sometimes mentioned that an important step would be for the policy level to acknowledge GOC as quality of care and to include the concept in quality standards. This would further formalize and enforce the institutional expectation to go towards person-centered care.

Currently, a challenge on institutional level as viewed by most respondents is that GOC is not or only to a limited extent incorporated in the basic education of most primary care disciplines. This leads to most of PCPs only having a limited understanding of GOC and different disciplines not having a shared language in this matter. “You have these primary health and welfare actors who each have their own approach, history and culture. To bring them together and to align them is challenging. — INT10” The absence of GOC as a topic in basic education is mentioned by various respondents as a current shortcoming in effectively implementing GOC in the wider primary care landscape.

Overall, GOC is viewed as our respondents as a topic that has recently gained a lot interest, both by individual PCPS, organizations, and governmental actors. The Flemish government has laid some foundations to facilitate this change with newly created institutions and incentives. However, other external context variables can interfere in how the concept of GOC is currently being picked up and what challenges arise.

Resource dependency theory: in search for a financial system that accommodates interprofessional collaboration

Another external context variable that affects how GOC can be introduced is the financial system that is at place. To analyze themes that were raised during the interviews with regard to finances, we utilized a resource dependency perspective. This theory presumes that organizations are dependent on financial resources and are seeking ways to ensure their continued functioning [ 16 , 17 ]. To a certain extent, this collides with the assumptions of institutional theory that foregrounds organization’s conformity to institutional pressures [ 32 ]. Resource dependency theory in contrast highlights differentiation of organizations that seek out competitive advantages [ 32 ].

In this context, respondents mention that their interest and willingness to move towards a GOC approach are held back by the current dominant system of pay for performance in the healthcare system. This financial system is experienced as restrictive, as it does not provide any incentive to PCPs for interprofessional collaboration, which is key for GOC. A switch to a flat fee system (in which a fixed fee is charged for each patient) or bundled payment was often mentioned as desirable. PCPs and health/social care organizations working in a context where they are financially rewarded for a trajectory or treatment of a patient in its entirety ensure that there is no tension with their necessity to obtain financial resources, as described in the resource dependency theory. Many of our respondents voice that community health centers are a good example. They cover different healthcare disciplines and operate with a fixed price per enrolled patient, regardless of the number of services for that patient. This promotes setting up preventive and health-promoting actions, which confirms our finding on the relevance of dedicated funding.

At the governmental level, the best way to finance and give incentives is said to be a point of discussion: “For years, we have been arguing about how to finance. Are we going to fund counsel coordination? Or counsel organization? Or care coordination? — INT04” Macro-level respondents do however mention financial incentives that are already in place to stimulate interprofessional collaboration: fees for multidisciplinary consultation being the most prominent. Other examples were given in which certain requirements were set for funding (e.g., Impulseo Footnote 4 , VIPA Footnote 5 ) that stimulate actors or settings in taking steps towards more interprofessional collaboration.

Nowadays, financial incentives to support organizations to engage in GOC tend to be project grants. However, a structural way to finance GOC approaches is currently lacking, according to our respondents. As a consequence, a long-term perspective for organizations is lacking; there is no stable financing and organizations are obliged to focus on projects instead of normalizing GOC in routine practice. According to a resource dependency perspective, the absence of financial incentives for practicing GOC hinders organizations in engaging with the approach, as they are focused on seeking out resources in order to fulfil their core activities.

A network-theory perspective: the importance of connectedness for the diffusion of an innovation

Throughout the interviews, interorganizational contextual elements were often addressed. A network theory lens states that collaborating in networks can lead to outcomes that could not be realized by individual organizations acting independently [ 19 , 20 ]. Networks consist of a set of actors such as PCPs or health/social care organizations along with a set of ties that link them [ 33 ]. These ties can be state-type ties (e.g., role based, cognitive) or event-type ties (e.g., through interactions, transactions). Both type of ties can enable a flow in which information or innovations can pass, as actors interact [ 33 ]. To analyze the implementation process of GOC and how this is diffused through various actors, a network theory perspective can help understand the importance of the connection between actors.

A first observation throughout the interviews in which we notice the importance of networks was in the mentioning of local initiatives that already existed before the creation of the primary care zones/care councils. In the area around Ghent, local multidisciplinary networks already organized community meetings, bringing together different PCPs on overarching topics relating to long-term care for patients with chronic conditions. These regions have a tradition of collaboration and connectedness of PCPs, which respondents mention to be highly valuable: “This ensures that we are more decisive, speaking from one voice with regards to what we want to stand for. — INT23” Respondents voice that the existence of such local networks has had a positive effect on the diffusion of ideas such as GOC, as trust between different actors was already established.

Further mentioning of the importance of networks could be found in respondents acknowledging one of the presumptions of network theory: working collaboratively towards a specific objective leads to outcomes that cannot be realized independently. This is especially true for GOC, an approach that in essence requires different disciplines to work together: “When only one GP, nurse or social worker starts working on it, it makes no sense. Everyone who is involved with that person needs to be on board. Actually, you need to finetune teams surrounding a person — INT11.” This is why several policy-level respondents mentioned that emphasis was placed on organizing GOC initiatives in a neighborhood-oriented way, in which accessible, inclusive care is aimed at by strengthening social cohesion. This way, different types of PCPs got to know each other through these sessions an GOC and would start to get aligned on what it means to provide GOC. However, in particular, self-employed PCPs are hard to reach. According to our respondents, occupational groups and care councils are suitable actors to engage these self-employed PCPs, but they are not always much involved in such a network .

To better connect PCPs and health/social care organizations, the absence of connectedness through the technological landscape is also mentioned. Current technological systems and platforms for documenting patient information do not allow for aligning and sharing between disciplines. In Flanders, there is a history of each discipline developing its own software, which lacks centralization or unification: “For years, they have decided to just leave it to the market, in such a way that you ended up with a proliferation of software, each discipline having its own package. — INT06” Most of the respondents mentioning this were aware that Flanders government is currently working on a unified digital care and support platform and were optimistic about its development.

Contingency theory: how environmental pressure can be a trigger for change

Our interviews were conducted during a rather dynamic and unique period of time in which the impact of social change and pressure was clearly visible: the Flemish primary care reform was ongoing which leads to the creation of care councils and VIVEL (see 3.1.1), and the COVID crisis impacted the functioning of these and other primary care actors. These observed effects of societal changes are reminiscent of the assumptions that are made in contingency theory. In essence, contingency theory presupposes that “organizational effectiveness results from fitting characteristics of the organization, such as its structure, to contingencies that reflect the situation of the organization [ 34 ], p. 1.” When it comes to the effects of the primary care reform and the COVID crisis, there were several mentions on how primary care actors reorganized their activities to adapt to these circumstances. Representatives of care councils/primary care zones whom we interviewed underlined that they were just at the point where they could again engage with their original action plans, not having to take up so many COVID-related tasks anymore. On the one hand, the COVID crisis had however forced them to immediately become functional and has also contributed that various primary care actors quickly got to know them. On the other hand, the COVID crisis has also kept them from their core activities for a while. On top of that, the crisis has also triggered a change the overall view towards data sharing. Some respondents mention a rather protectionist approach towards data sharing, while data sharing has become more normalized during the COVID crisis. This discussion was also relevant for the creation of a unified shared patient record in terms of documenting and sharing patient goals.

Other societal factors that were mentioned having an impact on the uptake of GOC are the demographic composition of a certain area. It was suggested that areas that are characterized by a patient population with more chronic care needs will be more likely to steer towards GOC as a way of coping with these complex cases. “You always have these GPs who blow it away immediately and question whether this is truly necessary. They will only become receptive to this when they experience needs for which GOC can be a solution — INT11.” On a macro-level, several respondents have mentioned how a driver for change is to have the necessity for change becoming very tangible. As PCPs are confronted with increasing numbers of patients with complex, chronic needs and their work becomes more demanding, the need for change becomes more acute. This finding is in line with what contingency theory underlines: changes in contingency (e.g., the population that is increasingly characterized by aging and multimorbidity) are an impetus for change for health/social care organizations to resolve this by adopting a structure that better fits the current environmental characteristics [ 34 ].

Our research demonstrates the applicability of organizational theories to help explain the impact that macro-level context variables have on an implementation process. These insights can be integrated into existing implementation frameworks and models to add the explanatory power of macro-level context variables, which is to date often neglected. The organizational theories demonstrate the ways in which organizations interact with their external environment in order to sustain and fulfill their core activities. As demonstrated in Fig. 1 , institutional theory largely explains how social expectations in the form of institutions lead towards the adoption or implementation of innovation, such as GOC. However, other organizational theories demonstrate how other macro-context elements on different areas can either strengthen or hamper the implementation process.

How organizational theories can help explain the way in which macro-level context variables affect implementation of an intervention

Departing from the mechanisms that are postulated by institutional theory, we observed that the shift towards GOC is part of a larger Flemish primary care reform in which and new institutions have been established and polices have been drawn up to go towards more integrated, person-centered care. To achieve this, governmental actors have placed emphasis on socialization of care, the local context, and establishing ties between organizations in order to become more complementary in providing primary health care [ 35 ]. With various initiatives surrounding this aim, the Flemish government is steering towards GOC. This is reminiscent of the mechanisms that are posed within institutional theory: organizations adapt to prevailing norms and expectations and mimic behaviors that are surrounding them [ 15 , 36 ].

Throughout our data, we came across concrete examples of how institutionalization takes place. DiMaggio and Powell [ 31 ] describe the subsequent process of isomorphism: organizations start to resemble each other as they are conforming to their institutional environment. A first mechanism through which this change occurs is coercive isomorphism and is clearly noticeable in our data. This type of isomorphism results from both formal and informal pressure coming from organizations from which a dependency relationship exists and from cultural expectations in the society [ 31 ]. Person-centered, GOC care is both formally propagated by governmental institutions and procedures and informally expected by current social tendencies. Care councils within primary care zones explicitly propagate and disseminate ideas and approaches that are desirable on policy level. Another form of isomorphism is professional isomorphism and relates to our finding that incorporation of GOC in basic education is currently lacking. The presumptions of professional isomorphism back up the importance of this: values, norms, and ideas that are developed during education are bound to find entrance within organizations as professionals start operating along these views.

Although many observations in our data back up the assumptions of institutional theory, it should be noticed that new initiatives such as the promotion of person-centered care and GOC can collide with earlier policy trends. Martens et al. [ 12 ] have examined the Belgian policy process relating three integrated care projects and concluded that although there is a strong support for a change towards a more patient-centered system, the current provider-driven system and institutional design complicate this objective. Furthermore, institutional theory tends to simplify actors as passive adopters of institutional norms and expectations and overlook the human agency and sensemaking that come with it [ 37 ]. For GOC, it is particularly true that PCPs will actively have to seek out their own style and fit the approach in their own way of working. Moreover, GOC was not just addressed as a governmental expectation but for many PCPs something they inherently stood behind.

Resources dependency theory poses that organizations are dependent on critical resources and adapt their way of working in response to those resources [ 17 ]. From our findings, it seems that the current financial system does not promote GOC, meaning that the mechanisms that are put forward in resources dependency theory are not set in motion. A macro-level analysis of barriers and facilitators in the implementation of integrated care in Belgium by Danhieux et al. [ 10 ] also points towards the financial system and data sharing as two of the main contextual determinants that affect implementation.

Throughout our data, the importance of a network approach was frequently mentioned. Interprofessional collaboration came forward as a prerequisite to make GOC happen, as well as active commitment on different levels. Burns, Nembhard, and Shortell [ 38 ] argue that research efforts on implementing person-centered, integrated care should have more focus on the use of social networks to study relational coordination. In terms of interprofessional collaboration, to date, Belgium has a limited tradition of working team-based with different disciplines [ 35 ]. However, when it comes to strengthening a cohesive primary care network, the recently established care councils have become an important facilitator. As a network governance structure, they resemble mostly a Network Administrative Organization (NAO): a separate, centralized administrative entity that is externally governed and not another member providing its own services [ 19 ]. According to Provan and Kenis [ 19 ], this type of governance form is most effective in a rather dense network with many participants, when the goal consensus is moderately high, characteristics that are indeed representative for the Flemish primary care landscape. This strengthens our observation that care councils have favorable characteristics and are well-positioned to facilitate the interorganizational context to implement GOC.

Lastly, the presumptions within contingency theory became apparent as respondents talked about how the need for change needs to become tangible for PCPs and organizations to take action, as they are increasingly faced with a shortage of time and means and more complex patient profiles. Furthermore, De Maeseneer [ 39 ] affirms our findings that the COVID-19 crisis could be employed as an opportunity to strengthen primary health care, as health becomes prioritized and its functioning becomes re-evaluated. Overall, contingency theory can help gain insight in how and why certain policy trends or decisions are made. A study of Bruns et al. [ 40 ] found that modifiable external context variables such as interagency collaboration were predictive for policy support for intervention adoption, while unmodifiable external context variable such as socio-economic composition of a region was more predictive for fiscal investments that are made.

Strengths and limitations

This study contributes to our overall understanding of implementation processes by looking into real-life implementation efforts for GOC in Flanders. It goes beyond a mere description of external context variables that affect implementation processes but aims to grasp which and how external context variables influence implementation processes. A variety of respondents from different organizations, with different backgrounds and perspectives, were interviewed, and results were analyzed by researchers with backgrounds in sociology, social work, and medical sciences. Results can not only be applied to further develop sustainable implementation plans for GOC but also enhance our understanding of how the external context influences and shapes implementation processes. As most research on contextual variables in implementation processes has until now mainly focused on internal context variables, knowledge on external context variables contributes to gaining a bigger picture of the mechanism of change.

However, this study is limited to the Flemish landscape, and external context variables and their dynamics might differ from other regions or countries. Furthermore, our study has examined and described how macro-level context variables affect the overall implementation processes of GOC. Further research is needed on the link between outer and inner contexts during implementation and sustainment, as explored by Lengninck-Hall et al. [ 41 ]. Another important consideration is that our sample only includes the “believers” in GOC and those who are already taking steps towards its implementation. It is possible that PCPs themselves or other relevant actors who are more skeptical about GOC have a different view on the policy and organizational processes that we explored. Furthermore, data triangulations in which this data is complemented with document analysis could have expanded our understanding and verified subjective perceptions of respondents.

Insights and propositions that derive from organizational theories can be utilized to expand our knowledge on how external context variables affect implementation processes. Our research demonstrates that the implementation of GOC in Flanders is steered and facilitated by regulatory and policy variables, which sets in motion mechanisms that are described in institutional theory. However, other external context variables interact with the implementation process and can further facilitate or hinder the overall implementation process. Assumptions and mechanisms explained within resource dependency theory, network theory, and contingency theory contribute to our understanding on how fiscal, technological, socio-economic, and interorganizational context variables affect an implementation process.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to confidentiality guaranteed to participants but are available from the corresponding author on reasonable request.

The Primary Care Academy (PCA) is a research and teaching network of four Flemish universities, six university colleges, the White and Yellow Cross (an organization for home nursing), and patient representatives that have included GOC as one of their main research domains.

BelRAI, the Belgian implementation of the interRAI assessment tools; these are scientific, internationally validated instruments enabling an assessment of social, psychological, and physical needs and possibilities of individuals in different care settings. The data follows the person and is shared between care professionals and care organizations.

The Flemish Social Protection is a mandatory insurance established by the Flemish government to provide a range of concessions to individuals with long-term care and support needs due to illness or disability.

Impulseo, financial support for general practitioners who start an individual practice or join a group practice

VIPA, grants for the realization of sustainable, accessible, and affordable healthcare infrastructure

Abbreviations

• Goal-oriented care

Primary care provider

Primary Care Academy

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Acknowledgements

We are grateful for the partnership with the Primary Care Academy (academie-eerstelijn.be) and want to thank the King Baudouin Foundation and Fund Daniël De Coninck for the opportunity they offer us for conducting research and have impact on the primary care of Flanders, Belgium. The consortium of the Primary Care Academy consists of the following: lead author: Roy Remmen—[email protected]—Department of Primary Care and Interdisciplinary Care, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; Emily Verté—Department of Primary Care and Interdisciplinary Care, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium, and Department of Family Medicine and Chronic Care, Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussel, Belgium; Muhammed Mustafa Sirimsi—Centre for Research and Innovation in Care, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; Peter Van Bogaert—Workforce Management and Outcomes Research in Care, Faculty of Medicine and Health Sciences, University of Antwerp, Belgium; Hans De Loof—Laboratory of Physio-Pharmacology, Faculty of Pharmaceutical Biomedical and Veterinary Sciences, University of Antwerp, Belgium; Kris Van den Broeck—Department of Primary Care and Interdisciplinary Care, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; Sibyl Anthierens—Department of Primary Care and Interdisciplinary Care, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; Ine Huybrechts—Department of Primary Care and Interdisciplinary Care, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; Peter Raeymaeckers—Department of Sociology, Faculty of Social Sciences, University of Antwerp, Belgium; Veerle Bufel—Department of Sociology, Centre for Population, Family and Health, Faculty of Social Sciences, University of Antwerp, Belgium; Dirk Devroey—Department of Family Medicine and Chronic Care, Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussel; Bert Aertgeerts—Academic Centre for General Practice, Faculty of Medicine, KU Leuven, Leuven, and Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven; Birgitte Schoenmakers—Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven, Belgium; Lotte Timmermans—Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven, Belgium; Veerle Foulon—Department of Pharmaceutical and Pharmacological Sciences, Faculty Pharmaceutical Sciences, KU Leuven, Leuven, Belgium; Anja Declercq—LUCAS-Centre for Care Research and Consultancy, Faculty of Social Sciences, KU Leuven, Leuven, Belgium; Dominique Van de Velde, Department of Rehabilitation Sciences, Occupational Therapy, Faculty of Medicine and Health Sciences, University of Ghent, Belgium, and Department of Occupational Therapy, Artevelde University of Applied Sciences, Ghent, Belgium; Pauline Boeckxstaens—Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, University of Ghent, Belgium; An De Sutter—Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, University of Ghent, Belgium; Patricia De Vriendt—Department of Rehabilitation Sciences, Occupational Therapy, Faculty of Medicine and Health Sciences, University of Ghent, Belgium, and Frailty in Ageing (FRIA) Research Group, Department of Gerontology and Mental Health and Wellbeing (MENT) Research Group, Faculty of Medicine and Pharmacy, Vrije Universiteit, Brussels, Belgium, and Department of Occupational Therapy, Artevelde University of Applied Sciences, Ghent, Belgium; Lies Lahousse—Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ghent, Belgium; Peter Pype—Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, University of Ghent, Belgium, End-of-Life Care Research Group, Faculty of Medicine and Health Sciences, Vrije Universiteit Brussel and Ghent University, Ghent, Belgium; Dagje Boeykens—Department of Rehabilitation Sciences, Occupational Therapy, Faculty of Medicine and Health Sciences, University of Ghent, Belgium, and Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, University of Ghent, Belgium; Ann Van Hecke—Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, University of Ghent, Belgium, University Centre of Nursing and Midwifery, Faculty of Medicine and Health Sciences, University of Ghent, Belgium; Peter Decat—Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, University of Ghent, Belgium; Rudi Roose—Department of Social Work and Social Pedagogy, Faculty of Psychology and Educational Sciences, University Ghent, Belgium; Sandra Martin—Expertise Centre Health Innovation, University College Leuven-Limburg, Leuven, Belgium; Erica Rutten—Expertise Centre Health Innovation, University College Leuven-Limburg, Leuven, Belgium; Sam Pless—Expertise Centre Health Innovation, University College Leuven-Limburg, Leuven, Belgium; Anouk Tuinstra—Expertise Centre Health Innovation, University College Leuven-Limburg, Leuven, Belgium; Vanessa Gauwe—Department of Occupational Therapy, Artevelde University of Applied Sciences, Ghent, Belgium; Didier ReynaertE-QUAL, University College of Applied Sciences Ghent, Ghent, Belgium; Leen Van Landschoot—Department of Nursing, University of Applied Sciences Ghent, Ghent, Belgium; Maja Lopez Hartmann—Department of Welfare and Health, Karel de Grote University of Applied Sciences and Arts, Antwerp, Belgium; Tony Claeys—LiveLab, VIVES University of Applied Sciences, Kortrijk, Belgium; Hilde Vandenhoudt—LiCalab, Thomas University of Applied Sciences, Turnhout, Belgium; Kristel De Vliegher—Department of Nursing–Homecare, White-Yellow Cross, Brussels, Belgium; and Susanne Op de Beeck—Flemish Patient Platform, Heverlee, Belgium.

This research was funded by fund Daniël De Coninck, King Baudouin Foundation, Belgium. The funder had no involvement in this study. Grant number: 2019-J5170820-211,588.

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Peter Raeymaeckers and Sibyl Anthierens have contributed equally to this work and share senior last authorship.

Authors and Affiliations

Department of Family Medicine and Population Health, University of Antwerp, Doornstraat 331, 2610, Antwerp, Belgium

Ine Huybrechts, Emily Verté & Sibyl Anthierens

Department of Family Medicine and Chronic Care, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Jette/Brussels, Belgium

Ine Huybrechts & Emily Verté

LUCAS — Centre for Care Research and Consultancy, KU Leuven, Minderbroedersstraat 8/5310, 3000, Leuven, Belgium

Anja Declercq

Center for Sociological Research, Faculty of Social Sciences, KU Leuven, Parkstraat 45/3601, 3000, Leuven, Belgium

Department of Social Work, University of Antwerp, St-Jacobstraat 2, 2000, Antwerp, Belgium

Peter Raeymaeckers

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• , Emily Verté
• , Muhammed Mustafa Sirimsi
• , Peter Van Bogaert
• , Hans De Loof
• , Kris Van den Broeck
• , Sibyl Anthierens
• , Ine Huybrechts
• , Peter Raeymaeckers
• , Veerle Bufel
• , Dirk Devroey
• , Bert Aertgeerts
• , Birgitte Schoenmakers
• , Lotte Timmermans
• , Veerle Foulon
• , Anja Declerq
• , Dominique Van de Velde
• , Pauline Boeckxstaens
• , An De Sutter
• , Patricia De Vriendt
• , Lies Lahousse
• , Peter Pype
• , Dagje Boeykens
• , Ann Van Hecke
• , Peter Decat
• , Rudi Roose
• , Sandra Martin
• , Erica Rutten
• , Sam Pless
• , Anouk Tuinstra
• , Vanessa Gauwe
• , Leen Van Landschoot
• , Maja Lopez Hartmann
• , Tony Claeys
• , Hilde Vandenhoudt
• , Kristel De Vliegher
•  & Susanne Op de Beeck

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IH wrote the main manuscript text. AD, EV, PR, and SA contributed to the different steps of the making of this manuscript. All authors reviewed the manuscript.

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Correspondence to Ine Huybrechts .

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Huybrechts, I., Declercq, A., Verté, E. et al. How does the external context affect an implementation processes? A qualitative study investigating the impact of macro-level variables on the implementation of goal-oriented primary care. Implementation Sci 19 , 32 (2024). https://doi.org/10.1186/s13012-024-01360-0

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What is quality in long covid care? Lessons from a national quality improvement collaborative and multi-site ethnography

• Trisha Greenhalgh   ORCID: orcid.org/0000-0003-2369-8088 1 ,
• Julie L. Darbyshire 1 ,
• Cassie Lee 2 ,
• Emma Ladds 1 &
• Jenny Ceolta-Smith 3  

BMC Medicine volume  22 , Article number:  159 ( 2024 ) Cite this article

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Long covid (post covid-19 condition) is a complex condition with diverse manifestations, uncertain prognosis and wide variation in current approaches to management. There have been calls for formal quality standards to reduce a so-called “postcode lottery” of care. The original aim of this study—to examine the nature of quality in long covid care and reduce unwarranted variation in services—evolved to focus on examining the reasons why standardizing care was so challenging in this condition.

In 2021–2023, we ran a quality improvement collaborative across 10 UK sites. The dataset reported here was mostly but not entirely qualitative. It included data on the origins and current context of each clinic, interviews with staff and patients, and ethnographic observations at 13 clinics (50 consultations) and 45 multidisciplinary team (MDT) meetings (244 patient cases). Data collection and analysis were informed by relevant lenses from clinical care (e.g. evidence-based guidelines), improvement science (e.g. quality improvement cycles) and philosophy of knowledge.

Participating clinics made progress towards standardizing assessment and management in some topics; some variation remained but this could usually be explained. Clinics had different histories and path dependencies, occupied a different place in their healthcare ecosystem and served a varied caseload including a high proportion of patients with comorbidities. A key mechanism for achieving high-quality long covid care was when local MDTs deliberated on unusual, complex or challenging cases for which evidence-based guidelines provided no easy answers. In such cases, collective learning occurred through idiographic (case-based) reasoning , in which practitioners build lessons from the particular to the general. This contrasts with the nomothetic reasoning implicit in evidence-based guidelines, in which reasoning is assumed to go from the general (e.g. findings of clinical trials) to the particular (management of individual patients).

Not all variation in long covid services is unwarranted. Largely because long covid’s manifestations are so varied and comorbidities common, generic “evidence-based” standards require much individual adaptation. In this complex condition, quality improvement resources may be productively spent supporting MDTs to optimise their case-based learning through interdisciplinary discussion. Quality assessment of a long covid service should include review of a sample of individual cases to assess how guidelines have been interpreted and personalized to meet patients’ unique needs.

Study registration

NCT05057260, ISRCTN15022307.

Peer Review reports

The term “long covid” [ 1 ] means prolonged symptoms following SARS-CoV-2 infection not explained by an alternative diagnosis [ 2 ]. It embraces the US term “post-covid conditions” (symptoms beyond 4 weeks) [ 3 ], the UK terms “ongoing symptomatic covid-19” (symptoms lasting 4–12 weeks) and “post covid-19 syndrome” (symptoms beyond 12 weeks) [ 4 ] and the World Health Organization’s “post covid-19 condition” (symptoms occurring beyond 3 months and persisting for at least 2 months) [ 5 ]. Long covid thus defined is extremely common. In UK, for example, 1.8 million of a population of 67 million met the criteria for long covid in early 2023 and 41% of these had been unwell for more than 2 years [ 6 ].

Long covid is characterized by a constellation of symptoms which may include breathlessness, fatigue, muscle and joint pain, chest pain, memory loss and impaired concentration (“brain fog”), sleep disturbance, depression, anxiety, palpitations, dizziness, gastrointestinal problems such as diarrhea, skin rashes and allergy to food or drugs [ 2 ]. These lead to difficulties with essential daily activities such as washing and dressing, impaired exercise tolerance and ability to work, and reduced quality of life [ 2 , 7 , 8 ]. Symptoms typically cluster (e.g. in different patients, long covid may be dominated by fatigue, by breathlessness or by palpitations and dizziness) [ 9 , 10 ]. Long covid may follow a fairly constant course or a relapsing and remitting one, perhaps with specific triggers [ 11 ]. Overlaps between fatigue-dominant subtypes of long covid, myalgic encephalomyelitis and chronic fatigue syndrome have been hypothesized [ 12 ] but at the time of writing remain unproven.

Long covid has been a contested condition from the outset. Whilst long-term sequelae following other coronavirus (SARS and MERS) infections were already well-documented [ 13 ], SARS-CoV-2 was originally thought to cause a short-lived respiratory illness from which the patient either died or recovered [ 14 ]. Some clinicians dismissed protracted or relapsing symptoms as due to anxiety or deconditioning, especially if the patient had not had laboratory-confirmed covid-19. People with long covid got together in online groups and shared accounts of their symptoms and experiences of such “gaslighting” in their healthcare encounters [ 15 , 16 ]. Some groups conducted surveys on their members, documenting the wide range of symptoms listed in the previous paragraph and showing that whilst long covid is more commonly a sequel to severe acute covid-19, it can (rarely) follow a mild or even asymptomatic acute infection [ 17 ].

Early publications on long covid depicted a post-pneumonia syndrome which primarily affected patients who had been hospitalized (and sometimes ventilated) [ 18 , 19 ]. Later, covid-19 was recognized to be a multi-organ inflammatory condition (the pneumonia, for example, was reclassified as pneumonitis ) and its long-term sequelae attributed to a combination of viral persistence, dysregulated immune response (including auto-immunity), endothelial dysfunction and immuno-thrombosis, leading to damage to the lining of small blood vessels and (thence) interference with transfer of oxygen and nutrients to vital organs [ 20 , 21 , 22 , 23 , 24 ]. But most such studies were highly specialized, laboratory-based and written primarily for an audience of fellow laboratory researchers. Despite demonstrating mean differences in a number of metabolic variables, they failed to identify a reliable biomarker that could be used routinely in the clinic to rule a diagnosis of long covid in or out. Whilst the evidence base from laboratory studies grew rapidly, it had little influence on clinical management—partly because most long covid clinics had been set up with impressive speed by front-line clinical teams to address an immediate crisis, with little or no input from immunologists, virologists or metabolic specialists [ 25 ].

Studies of the patient experience revealed wide geographical variation in whether any long covid services were provided and (if they were) which patients were eligible for these and what tests and treatments were available [ 26 ]. An interim UK clinical guideline for long covid had been produced at speed and published in December 2020 [ 27 ], but it was uncertain about diagnostic criteria, investigations, treatments and prognosis. Early policy recommendations for long covid services in England, based on wide consultation across UK, had proposed a tiered service with “tier 1” being supported self-management, “tier 2” generalist assessment and management in primary care, “tier 3” specialist rehabilitation or respiratory follow-up with oversight from a consultant physician and “tier 4” tertiary care for patients with complications or complex needs [ 28 ]. In 2021, ring-fenced funding was allocated to establish 90 multidisciplinary long covid clinics in England [ 29 ]; some clinics were also set up with local funding in Scotland and Wales. These clinics varied widely in eligibility criteria, referral pathways, staffing mix (some had no doctors at all) and investigations and treatments offered. A further policy document on improving long covid services was published in 2022 [ 30 ]; it recommended that specialist long covid clinics should continue, though the long-term funding of these services remains uncertain [ 31 ]. To build the evidence base for delivering long covid services, major programs of publicly funded research were commenced in both UK [ 32 ] and USA [ 33 ].

In short, at the time this study began (late 2021), there appeared to be much scope for a program of quality improvement which would capture fast-emerging research findings, establish evidence-based standards and ensure these were rapidly disseminated and consistently adopted across both specialist long covid services and in primary care.

Quality improvement collaboratives

The quality improvement movement in healthcare was born in the early 1980s when clinicians and policymakers US and UK [ 34 , 35 , 36 , 37 ] began to draw on insights from outside the sector [ 38 , 39 , 40 ]. Adapting a total quality management approach that had previously transformed the Japanese car industry, they sought to improve efficiency, reduce waste, shift to treating the upstream causes of problems (hence preventing disease) and help all services approach the standards of excellence achieved by the best. They developed an approach based on (a) understanding healthcare as a complex system (especially its key interdependencies and workflows), (b) analysing and addressing variation within the system, (c) learning continuously from real-world data and (d) developing leaders who could motivate people and help them change structures and processes [ 41 , 42 , 43 , 44 ].

Quality improvement collaboratives (originally termed “breakthrough collaboratives” [ 45 ]), in which representatives from different healthcare organizations come together to address a common problem, identify best practice, set goals, share data and initiate and evaluate improvement efforts [ 46 ], are one model used to deliver system-wide quality improvement. It is widely assumed that these collaboratives work because—and to the extent that—they identify, interpret and implement high-quality evidence (e.g. from randomized controlled trials).

Research on why quality improvement collaboratives succeed or fail has produced the following list of critical success factors: taking a whole-system approach, selecting a topic and goal that fits with organizations’ priorities, fostering a culture of quality improvement (e.g. that quality is everyone’s job), engagement of everyone (including the multidisciplinary clinical team, managers, patients and families) in the improvement effort, clearly defining people’s roles and contribution, engaging people in preliminary groundwork, providing organizational-level support (e.g. chief executive endorsement, protected staff time, training and support for teams, resources, quality-focused human resource practices, external facilitation if needed), training in specific quality improvement techniques (e.g. plan-do-study-act cycle), attending to the human dimension (including cultivating trust and working to ensure shared vision and buy-in), continuously generating reliable data on both processes (e.g. current practice) and outcomes (clinical, satisfaction) and a “learning system” infrastructure in which knowledge that is generated feeds into individual, team and organizational learning [ 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ].

The quality improvement collaborative approach has delivered many successes but it has been criticized at a theoretical level for over-simplifying the social science of human motivation and behaviour and for adopting a somewhat mechanical approach to the study of complex systems [ 55 , 56 ]. Adaptations of the original quality improvement methodology (e.g. from Sweden [ 57 , 58 ]) have placed greater emphasis on human values and meaning-making, on the grounds that reducing the complexities of a system-wide quality improvement effort to a set of abstract and generic “success factors” will miss unique aspects of the case such as historical path dependencies, personalities, framing and meaning-making and micropolitics [ 59 ].

Perhaps this explains why, when the abovementioned factors are met, a quality improvement collaborative’s success is more likely but is not guaranteed, as a systematic review demonstrated [ 60 ]. Some well-designed and well-resourced collaboratives addressing clear knowledge gaps produced few or no sustained changes in key outcome measures [ 49 , 53 , 60 , 61 , 62 ]. To identify why this might be, a detailed understanding of a service’s history, current challenges and contextual constraints is needed. This explains our decision, part-way through the study reported here, to collect rich contextual data on participating sites so as to better explain success or failure of our own collaborative.

Warranted and unwarranted variation in clinical practice

A generation ago, Wennberg described most variation in clinical practice as “unwarranted” (which he defined as variation in the utilization of health care services that cannot be explained by variation in patient illness or patient preferences) [ 63 ]. Others coined the term “postcode lottery” to depict how such variation allegedly impacted on health outcomes [ 64 ]. Wennberg and colleagues’ Atlas of Variation , introduced in 1999 [ 65 ], and its UK equivalent, introduced in 2010 [ 66 ], described wide regional differences in the rates of procedures from arthroscopy to hysterectomy, and were used to prompt services to identify and address examples of under-treatment, mis-treatment and over-treatment. Numerous similar initiatives, mostly based on hospital activity statistics, have been introduced around the world [ 66 , 67 , 68 , 69 ]. Sutherland and Levesque’s proposed framework for analysing variation, for example, has three domains: capacity (broadly, whether sufficient resources are allocated at organizational level and whether individuals have the time and headspace to get involved), evidence (the extent to which evidence-based guidelines exist and are followed), and agency (e.g. whether clinicians are engaged with the issue and the effect of patient choice) [ 70 ].

Whilst it is clearly a good idea to identify unwarranted variation in practice, it is also important to acknowledge that variation can be warranted . The very act of measuring and describing variation carries great rhetorical power, since revealing geographical variation in any chosen metric effectively frames this as a problem with a conceptually simple solution (reducing variation) that will appeal to both politicians and the public [ 71 ]. The temptation to expose variation (e.g. via visualizations such as maps) and address it in mechanistic ways should be resisted until we have fully understood the reasons why it exists, which may include perverse incentives, insufficient opportunities to discuss cases with colleagues, weak or absent feedback on practice, unclear decision processes, contested definitions of appropriate care and professional challenges to guidelines [ 72 ].

Research question, aims and objectives

Research question.

What is quality in long covid care and how can it best be achieved?

To identify best practice and reduce unwarranted variation in UK long covid services.

To explain aspects of variation in long covid services that are or may be warranted.

Our original objectives were to:

Establish a quality improvement collaborative for 10 long covid clinics across UK.

Use quality improvement methods in collaboration with patients and clinic staff to prioritize aspects of care to improve. For each priority topic, identify best (evidence-informed) clinical practice, measure performance in each clinic, compare performance with a best practice benchmark and improve performance.

Produce organizational case studies of participating long covid clinics to explain their origins, evolution, leadership, ethos, population served, patient pathways and place in the wider healthcare ecosystem.

Examine these case studies to explain variation in practice, especially in topics where the quality improvement cycle proves difficult to follow or has limited impact.

The LOCOMOTION study

LOCOMOTION (LOng COvid Multidisciplinary consortium Optimising Treatments and services across the NHS) was a 30-month multi-site case study of 10 long covid clinics (8 in England, 1 in Wales and 1 in Scotland), beginning in 2021, which sought to optimise long covid care. Each clinic offered multidisciplinary care to patients referred from primary or secondary care (and, in some cases, self-referred), and held regular multidisciplinary team (MDT) meetings, mostly online via Microsoft Teams, to discuss cases. A study protocol for LOCOMOTION, with details of ethical approvals, management, governance and patient involvement has been published [ 25 ]. The three main work packages addressed quality improvement, technology-supported patient self-management and phenotyping and symptom clustering. This paper reports on the first work package, focusing mainly on qualitative findings.

Setting up the quality improvement collaborative

We broadly followed standard methodology for “breakthrough” quality improvement collaboratives [ 44 , 45 ], with two exceptions. First, because of geographical distance, continuing pandemic precautions and developments in videoconferencing technology, meetings were held online. Second, unlike in the original breakthrough model, patients were included in the collaborative, reflecting the cultural change towards patient partnerships since the model was originally proposed 40 years ago.

Each site appointed a clinical research fellow (doctor, nurse or allied health professional) funded partly by the LOCOMOTION study and partly with clinical sessions; some were existing staff who were backfilled to take on a research role whilst others were new appointments. The quality improvement meetings were held approximately every 8 weeks on Microsoft Teams and lasted about 2 h; there was an agenda and a chair, and meetings were recorded with consent. The clinical research fellow from each clinic attended, sometimes joined by the clinical lead for that site. In the initial meeting, the group proposed and prioritized topics before merging their consensus with the list of priority topics generated separately by patients (there was much overlap but also some differences).

In subsequent meetings, participants attempted to reach consensus on how to define, measure and achieve quality for each priority topic in turn, implement this approach in their own clinic and monitor its impact. Clinical leads prepared illustrative clinical cases and summaries of the research evidence, which they presented using Microsoft Powerpoint; the group then worked towards consensus on the implications for practice through general discussion. Clinical research fellows assisted with literature searches, collected baseline data from their own clinic, prepared and presented anonymized case examples, and contributed to collaborative goal-setting for improvement. Progress on each topic was reviewed at a later meeting after an agreed interval.

An additional element of this work package was semi-structured interviews with 29 patients, recruited from 9 of the 10 participating sites, about their clinic experiences with a view to feeding into service improvement (in the other site, no patient volunteered).

Our patient advisory group initially met separately from the quality improvement collaborative. They designed a short survey of current practice and sent it to each clinic; the results of this informed a prioritization exercise for topics where they considered change was needed. The patient-generated list was tabled at the quality improvement collaborative discussions, but patients were understandably keen to join these discussions directly. After about 9 months, some patient advisory group members joined the regular collaborative meetings. This dynamic was not without its tensions, since sharing performance data requires trust and there were some concerns about confidentiality when real patient cases were discussed with other patients present.

How evidence-informed quality targets were set

At the time the study began, there were no published large-scale randomized controlled trials of any interventions for long covid. We therefore followed a model used successfully in other quality improvement efforts where research evidence was limited or absent or it did not translate unambiguously into models for current services. In such circumstances, the best evidence may be custom and practice in the best-performing units. The quality improvement effort becomes oriented to what one group of researchers called “potentially better practices”—that is, practices that are “developed through analysis of the processes of care, literature review, and site visits” (page 14) [ 73 ]. The idea was that facilitated discussion among clinical teams, drawing on published research where available but also incorporating clinical experience, established practice and systematic analysis of performance data across participating clinics would surface these “potentially better practices”—an approach which, though not formally tested in controlled trials, appears to be associated with improved outcomes [ 46 , 73 ].

Adding an ethnographic component

Following limited progress made on some topics that had been designated high priority, we interviewed all 10 clinical research fellows (either individually or, in two cases, with a senior clinician present) and 18 other clinic staff (five individually plus two groups of 5 and 8), along with additional informal discussions, to explore the challenges of implementing the changes that had been agreed. These interviews were not audiotaped but detailed notes were made and typed up immediately afterwards. It became evident that some aspects of what the collaborative had deemed “evidence-informed” care were contested by front-line clinic staff, perceived as irrelevant to the service they were delivering, or considered impossible to implement. To unpack these issues further, the research protocol was amended to include an ethnographic component.

TG and EL (academic general practitioners) and JLD (a qualitative researcher with a PhD in the patient experience) attended a total of 45 MDT meetings in participating clinics (mostly online or hybrid). Staff were informed in advance that there would be an observer present; nobody objected. We noted brief demographic and clinical details of cases discussed (but no identifying data), dilemmas and uncertainties on which discussions focused, and how different staff members contributed.

TG made 13 in-person visits to participating long covid clinics. Staff were notified in advance; all were happy to be observed. Visits lasted between 5 and 8 h (54 h in total). We observed support staff booking patients in and processing requests and referrals, and shadowed different clinical staff in turn as they saw patients. Patients were informed of our presence and its purpose beforehand and given the opportunity to decline (three of 53 patients approached did). We discussed aspects of each case with the clinician after the patient left. When invited, we took breaks with staff and used these as an opportunity to ask them informally what it was like working in the clinic.

Ethnographic observation, analysis and reporting was geared to generating a rich interpretive account of the clinical, operational and interpersonal features of each clinic—what Van Maanen calls an “impressionist tales” [ 74 ]. Our work was also guided by the principles set out by Golden-Biddle and Locke, namely authenticity (spending time in the field and basing interpretations on these direct observations), plausibility (creating a plausible account through rich persuasive description) and criticality (e.g. reflexively examining our own assumptions) [ 75 ]. Our collection and analysis of qualitative data was informed by our own professional backgrounds (two general practitioners, one physical therapist, two non-clinicians).

In both MDTs and clinics, we took contemporaneous notes by hand and typed these up immediately afterwards.

Data management and analysis

Typed interview notes and field notes from clinics were collated in a set of Word documents, one for each clinic attended. They were analysed thematically [ 76 ] with attention to the literature on quality improvement and variation (see “ Background ”). Interim summaries were prepared on each clinic, setting out the narrative of how it had been established, its ethos and leadership, setting and staffing, population served and key links with other parts of the local healthcare ecosystem.

Minutes and field notes from the quality improvement collaborative meetings were summarized topic by topic, including initial data collected by the researchers-in-residence, improvement actions taken (or attempted) in that clinic, and any follow-up data shared. Progress or lack of it was interpreted in relation to the contextual case summary for that clinic.

Patient cases seen in clinic, and those discussed by MDTs, were summarized as brief case narratives in Word documents. Using the constant comparative method [ 77 ], we produced an initial synthesis of the clinical picture and principles of management based on the first 10 patient cases seen, and refined this as each additional case was added. Demographic and brief clinical and social details were also logged on Excel spreadsheets. When writing up clinical cases, we used the technique of composite case construction (in which we drew on several actual cases to generate a fictitious one, thereby protecting anonymity whilst preserving key empirical findings [ 78 ]); any names reported in this paper are pseudonyms.

Member checking

A summary was prepared for each clinic, including a narrative of the clinic’s own history and a summary of key quality issues raised across the ten clinics. These summaries included examples from real cases in our dataset. These were shared with the clinical research fellow and a senior clinician from the clinic, and amended in response to feedback. We also shared these summaries with representatives from the patient advisory group.

Overview of dataset

This study generated three complementary datasets. First, the video recordings, minutes, and field notes of 12 quality improvement collaborative meetings, along with the evidence summaries prepared for these meetings and clinic summaries (e.g. descriptions of current practice, audits) submitted by the clinical research fellows. This dataset illustrated wide variation in practice, and (in many topics) gaps or ambiguities in the evidence base.

Second, interviews with staff ( n  = 30) and patients ( n  = 29) from the clinics, along with ethnographic field notes (approximately 100 pages) from 13 in-person clinic visits (54 h), including notes on 50 patient consultations (40 face-to-face, 6 telephone, 4 video). This dataset illustrated the heterogeneity among the ten participating clinics.

Third, field notes (approximately 100 pages), including discussions on 244 clinical cases from the 45 MDT meetings (49 h) that we observed. This dataset revealed further similarities and contrasts among clinics in how patients were managed. In particular, it illustrated how, for the complex patients whose cases were presented at these meetings, teams made sense of, and planned for, each case through multidisciplinary dialogue. This dialogue typically began with one staff member presenting a detailed clinical history along with a narrative of how it had affected the patient’s life and what was at stake for them (e.g. job loss), after which professionals from various backgrounds (nursing, physical therapy, occupational therapy, psychology, dietetics, and different medical specialties) joined in a discussion about what to do.

The ten participating sites are summarized in Table  1 .

In the next two sections, we explore two issues—difficulty defining best practice and the heterogeneous nature of the clinics—that were key to explaining why quality, when pursued in a 10-site collaborative, proved elusive. We then briefly summarize patients’ accounts of their experience in the clinics and give three illustrative examples of the elusiveness of quality improvement using selected topics that were prioritized in our collaborative: outcome measures, investigation of palpitations and management of fatigue. In the final section of the results, we describe how MDT deliberations proved crucial for local quality improvement. Further detail on clinical priority topics will be presented in a separate paper.

“Best practice” in long covid: uncertainty and conflict

The study period (September 2021 to December 2023) corresponded with an exponential increase in published research on long covid. Despite this, the quality improvement collaborative found few unambiguous recommendations for practice. This gap between what the research literature offered and what clinical practice needed was partly ontological (relating what long covid is ). One major bone of contention between patients and clinicians (also evident in discussions with our patient advisory group), for example, was how far (and in whom) clinicians should look for and attempt to treat the various metabolic abnormalities that had been documented in laboratory research studies. The literature on this topic was extensive but conflicting [ 20 , 21 , 22 , 23 , 24 , 79 , 80 , 81 , 82 ]; it was heavy on biological detail but light on clinical application.

Patients were often aware of particular studies that appeared to offer plausible molecular or cellular explanations for symptom clusters along with a drug (often repurposed and off-label) whose mechanism of action appeared to be a good fit with the metabolic chain of causation. In one clinic, for example, we were shown an email exchange between a patient (not medically qualified) and a consultant, in which the patient asked them to reconsider their decision not to prescribe low-dose naltrexone, an opioid receptor antagonist with anti-inflammatory properties. The request included a copy of a peer-reviewed academic paper describing a small, uncontrolled pre-post study (i.e. a weak study design) in which this drug appeared to improve symptoms and functional performance in patients with long covid, as well as a mechanistic argument explaining why the patient felt this drug was a plausible choice in their own case.

This patient’s clinician, in common with most clinicians delivering front-line long covid services, considered that the evidence for such mechanism-based therapies was weak. Clinicians generally felt that this evidence, whilst promising, did not yet support routine measurement of clotting factors, antibodies, immune cells or other biomarkers or the prescription of mechanism-based therapies such as antivirals, anti-inflammatories or anticoagulants. Low-dose naltroxone, for example, is currently being tested in at least one randomized controlled trial (see National Clinical Trials Registry NCT05430152), which had not reported at the time of our observations.

Another challenge to defining best practice was the oft-repeated phrase that long covid is a “diagnosis by exclusion”, but the high prevalence of comorbidities meant that the “pure” long covid patient untainted by other potential explanations for their symptoms was a textbook ideal. In one MDT, for example, we observed a discussion about a patient who had had both swab-positive covid-19 and erythema migrans (a sign of Lyme disease) in the weeks before developing fatigue, yet local diagnostic criteria for each condition required the other to be excluded.

The logic of management in most participating clinics was pragmatic: prompt multidisciplinary assessment and treatment with an emphasis on obtaining a detailed clinical history (including premorbid health status), excluding serious complications (“red flags”), managing specific symptom clusters (for example, physical therapy for breathing pattern disorder), treating comorbidities (for example, anaemia, diabetes or menopause) and supporting whole-person rehabilitation [ 7 , 83 ]. The evidentiary questions raised in MDT discussions (which did not include patients) addressed the practicalities of the rehabilitation model (for example, whether cognitive therapy for neurocognitive complications is as effective when delivered online as it is when delivered in-person) rather than the molecular or cellular mechanisms of disease. For example, the question of whether patients with neurocognitive impairment should be tested for micro-clots or treated with anticoagulants never came up in the MDTs we observed, though we did visit a tertiary referral clinic (the tier 4 clinic in site H), whose lead clinician had a research interest in inflammatory coagulopathies and offered such tests to selected patients.

Because long covid typically produces dozens of symptoms that tend to be uniquely patterned in each patient, the uncertainties on which MDT discussions turned were rarely about general evidence of the kind that might be found in a guideline (e.g. how should fatigue be managed?). Rather they concerned particular case-based clinical decisions (e.g. how should this patient’s fatigue be managed, given the specifics of this case?). An example from our field notes illustrates this:

Physical therapist presents the case of a 39-year-old woman who works as a cleaner on an overnight ferry. Has had long covid for 2 years. Main symptoms are shortness of breath and possible anxiety attacks, especially when at work. She has had a course of physical therapy to teach diaphragmatic breathing but has found that focusing on her breathing makes her more anxious. Patient has to do a lot of bending in her job (e.g. cleaning toilets and under seats), which makes her dizzy, but Active Stand Test was normal. She also has very mild tricuspid incompetence [someone reads out a cardiology report—not hemodynamically significant].

Rehabilitation guidelines (e.g. WHO) recommend phased return to work (e.g. with reduced hours) and frequent breaks. “Tricky!” says someone. The job is intense and busy, and the patient can’t afford not to work. Discussion on whether all her symptoms can be attributed to tension and anxiety. Physical therapist who runs the breathing group says, “No, it’s long covid”, and describes severe initial covid-19 episode and results of serial chest X-rays which showed gradual clearing of ground glass shadows. Team discussion centers on how to negotiate reduced working hours in this particular job, given the overnight ferry shifts. --MDT discussion, Site D

This example raises important considerations about the nature of clinical knowledge in long covid. We return to it in the final section of the “ Results ” and in the “ Discussion ”.

Long covid clinics: a heterogeneous context for quality improvement

Most participating clinics had been established in mid-2020 to follow up patients who had been hospitalized (and perhaps ventilated) for severe acute covid-19. As mass vaccination reduced the severity of acute covid-19 for most people, the patient population in all clinics progressively shifted to include fewer “post-ICU [intensive care unit]” patients (in whom respiratory symptoms almost always dominated), and more people referred by their general practitioners or other secondary care specialties who had not been hospitalized for their acute covid-19 infection, and in whom fatigue, brain fog and palpitations were often the most troubling symptoms. Despite these similarities, the ten clinics had very different histories, geographical and material settings, staffing structures, patient pathways and case mix, as Table  1 illustrates. Below, we give more detail on three example sites.

Site C was established as a generalist “assessment-only” service by a general practitioner with an interest in infectious diseases. It is led jointly by that general practitioner and an occupational therapist, assisted by a wide range of other professionals including speech and language therapy, dietetics, clinical psychology and community-based physical therapy and occupational therapy. It has close links with a chronic fatigue service and a pain clinic that have been running in the locality for over 20 years. The clinic, which is entirely virtual (staff consult either from home or from a small side office in the community trust building), is physically located in a low-rise building on the industrial outskirts of a large town, sharing office space with various community-based health and social care services. Following a 1-h telephone consultation by one of the clinical leads, each patient is discussed at the MDT and then either discharged back to their general practitioner with a detailed management plan or referred on to one of the specialist services. This arrangement evolved to address a particular problem in this locality—that many patients with long covid were being referred by their general practitioner to multiple specialties (e.g. respiratory, neurology, fatigue), leading to a fragmented patient experience, unnecessary specialist assessments and wasteful duplication. The generalist assessment by telephone is oriented to documenting what is often a complex illness narrative (including pre-existing physical and mental comorbidities) and working with the patient to prioritize which symptoms or problems to pursue in which order.

Site E, in a well-regarded inner-city teaching hospital, had been set up in 2020 by a respiratory physician. Its initial ethos and rationale had been “respiratory follow-up”, with strong emphasis on monitoring lung damage via repeated imaging and lung function tests and in ensuring that patients received specialist physical therapy to “re-learn” efficient breathing techniques. Over time, this site has tried to accommodate a more multi-system assessment, with the introduction of a consultant-led infectious disease clinic for patients without a dominant respiratory component, reflecting the shift towards a more fatigue-predominant case mix. At the time of our fieldwork, each patient was seen in turn by a physician, psychologist, occupational therapist and respiratory physical therapist (half an hour each) before all four staff reconvened in a face-to-face MDT meeting to form a plan for each patient. But whilst a wide range of patients with diverse symptoms were discussed at these meetings, there remained a strong focus on respiratory pathology (e.g. tracking improvements in lung function and ensuring that coexisting asthma was optimally controlled).

Site F, one of the first long covid clinics in UK, was set up by a rehabilitation consultant who had been drafted to work on the ICU during the first wave of covid-19 in early 2020. He had a longstanding research interest in whole-patient rehabilitation, especially the assessment and management of chronic fatigue and pain. From the outset, clinic F was more oriented to rehabilitation, including vocational rehabilitation to help patients return to work. There was less emphasis on monitoring lung function or pursuing respiratory comorbidities. At the time of our fieldwork, clinic F offered both a community-based service (“tier 2”) led by an occupational therapist, supported by a respiratory physical therapist and psychologist, and a hospital-based service (“tier 3”) led by the rehabilitation consultant, supported by a wider MDT. Staff in both tiers emphasized that each patient needs a full physical and mental assessment and help to set and work towards achievable goals, whilst staying within safe limits so as to avoid post-exertional symptom exacerbation. Because of the research interest of the lead physician, clinic F adapted well to the growing numbers of patients with fatigue and quickly set up research studies on this cohort [ 84 ].

Details of the other seven sites are shown in Table  1 . Broadly speaking, sites B, E, G and H aligned with the “respiratory follow-up” model and sites F and I aligned with the “rehabilitation” model. Sites A and J had a high-volume, multi-tiered service whose community tier aligned with the “holistic GP assessment” model (site C above) and which also offered a hospital-based, rehabilitation-focused tier. The small service in Scotland (site D) had evolved from an initial respiratory focus to become part of the infectious diseases (ME/CFS) service; Lyme disease (another infectious disease whose sequelae include chronic fatigue) was also prevalent in this region.

The patient experience

Whilst the 10 participating clinics were very diverse in staffing, ethos and patient flows, the 29 patient interviews described remarkably consistent clinic experiences. Almost all identified the biggest problem to be the extended wait of several months before they were seen and the limited awareness (when initially referred) of what long covid clinics could provide. Some talked of how they cried with relief when they finally received an appointment. When the quality improvement collaborative was initially established, waiting times and bottlenecks were patients’ the top priority for quality improvement, and this ranking was shared by clinic staff, who were very aware of how much delays and uncertainties in assessment and treatment compounded patients’ suffering. This issue resolved to a large extent over the study period in all clinics as the referral backlog cleared and the incidence of new cases of long covid fell [ 85 ]; it will be covered in more detail in a separate publication.

Most patients in our sample were satisfied with the care they received when they were finally seen in clinic, especially how they finally felt “heard” after a clinician took a full history. They were relieved to receive affirmation of their experience, a diagnosis of what was wrong and reassurance that they were believed. They were grateful for the input of different members of the multidisciplinary teams and commented on the attentiveness, compassion and skill of allied professionals in particular (“she was wonderful, she got me breathing again”—patient BIR145 talking about a physical therapist). One or two patient participants expressed confusion about who exactly they had seen and what advice they had been given, and some did not realize that a telephone assessment had been an actual clinical consultation. A minority expressed disappointment that an expected investigation had not been ordered (one commented that they had not had any blood tests at all). Several had assumed that the help and advice from the long covid clinic would continue to be offered until they were better and were disappointed that they had been discharged after completing the various courses on offer (since their clinic had been set up as an “assessment only” service).

In the next sections, we give examples of topics raised in the quality improvement collaborative and how they were addressed.

Example quality topic 1: Outcome measures

The first topic considered by the quality improvement collaborative was how (that is, using which measures and metrics) to assess and monitor patients with long covid. In the absence of a validated biomarker, various symptom scores and quality of life scales—both generic and disease-specific—were mooted. Site F had already developed and validated a patient-reported outcome measure (PROM), the C19-YRS (Covid-19 Yorkshire Rehabilitation Scale) and used it for both research and clinical purposes [ 86 ]. It was quickly agreed that, for the purposes of generating comparative research findings across the ten clinics, the C19-YRS should be used at all sites and completed by patients three-monthly. A commercial partner produced an electronic version of this instrument and an app for patient smartphones. The quality improvement collaborative also agreed that patients should be asked to complete the EUROQOL EQ5D, a widely used generic health-related quality of life scale [ 87 ], in order to facilitate comparisons between long covid and other chronic conditions.

In retrospect, the discussions which led to the unopposed adoption of these two measures as a “quality” initiative in clinical care were somewhat aspirational. A review of progress at a subsequent quality improvement meeting revealed considerable variation among clinics, with a wide variety of measures used in different clinics to different degrees. Reasons for this variation were multiple. First, although our patient advisory group were keen that we should gather as much data as possible on the patient experience of this new condition, many clinic patients found the long questionnaires exhausting to complete due to cognitive impairment and fatigue. In addition, whilst patients were keen to answer questions on symptoms that troubled them, many had limited patience to fill out repeated surveys on symptoms that did not trouble them (“it almost felt as if I’ve not got long covid because I didn’t feel like I fit the criteria as they were laying it out”—patient SAL001). Staff assisted patients in completing the measures when needed, but this was time-consuming (up to 45 min per instrument) and burdensome for both staff and patients. In clinics where a high proportion of patients required assistance, staff time was the rate-limiting factor for how many instruments got completed. For some patients, one short instrument was the most that could be asked of them, and the clinician made a judgement on which one would be in their best interests on the day.

The second reason for variation was that the clinical diagnosis and management of particular features, complications and comorbidities of long covid required more nuance than was provided by these relatively generic instruments, and the level of detail sought varied with the specialist interest of the clinic (and the clinician). The modified C19-YRS [ 88 ], for example, contained 19 items, of which one asked about sleep quality. But if a patient had sleep difficulties, many clinicians felt that these needed to be documented in more detail—for example using the 8-item Epworth Sleepiness Scale, originally developed for conditions such as narcolepsy and obstructive sleep apnea [ 89 ]. The “Epworth score” was essential currency for referrals to some but not all specialist sleep services. Similarly, the C19-YRS had three items relating to anxiety, depression and post-traumatic stress disorder, but in clinics where there was a strong focus on mental health (e.g. when there was a resident psychologist), patients were usually invited to complete more specific tools (e.g. the Patient Health Questionnaire 9 [ 90 ], a 9-item questionnaire originally designed to assess severity of depression).

The third reason for variation was custom and practice. Ethnographic visits revealed that paper copies of certain instruments were routinely stacked on clinicians’ desks in outpatient departments and also (in some cases) handed out by administrative staff in waiting areas so that patients could complete them before seeing the clinician. These familiar clinic artefacts tended to be short (one-page) instruments that had a long tradition of use in clinical practice. They were not always fit for purpose. For example, the Nijmegen questionnaire was developed in the 1980s to assess hyperventilation; it was validated against a longer, “gold standard” instrument for that condition [ 91 ]. It subsequently became popular in respiratory clinics to diagnose or exclude breathing pattern disorder (a condition in which the normal physiological pattern of breathing becomes replaced with less efficient, shallower breathing [ 92 ]), so much so that the researchers who developed the instrument published a paper to warn fellow researchers that it had not been validated for this purpose [ 93 ]. Whilst a validated 17-item instrument for breathing pattern disorder (the Self-Evaluation of Breathing Questionnaire [ 94 ]) does exist, it is not in widespread clinical use. Most clinics in LOCOMOTION used Nijmegen either on all patients (e.g. as part of a comprehensive initial assessment, especially if the service had begun as a respiratory follow-up clinic) or when breathing pattern disorder was suspected.

In sum, the use of outcome measures in long covid clinics was a compromise between standardization and contingency. On the one hand, all clinics accepted the need to use “validated” instruments consistently. On the other hand, there were sometimes good reasons why they deviated from agreed practice, including mismatch between the clinic’s priorities as a research site, its priorities as a clinical service, and the particular clinical needs of a patient; the clinic’s—and the clinician’s—specialist focus; and long-held traditions of using particular instruments with which staff and patients were familiar.

Example quality topic 2: Postural orthostatic tachycardia syndrome (POTS)

Palpitations (common in long covid) and postural orthostatic tachycardia syndrome (POTS, a disproportionate acceleration in heart rate on standing, the assumed cause of palpitations in many long covid patients) was the top priority for quality improvement identified by our patient advisory group. Reflecting discussions and evidence (of various kinds) shared in online patient communities, the group were confident that POTS is common in long covid patients and that many cases remain undetected (perhaps misdiagnosed as anxiety). Their request that all long covid patients should be “screened” for POTS prompted a search for, and synthesis of, evidence (which we published in the BMJ [ 95 ]). In sum, that evidence was sparse and contested, but, combined with standard practice in specialist clinics, broadly supported the judicious use of the NASA Lean Test [ 96 ]. This test involves repeated measurements of pulse and blood pressure with the patient first lying and then standing (with shoulders resting against a wall).

The patient advisory group’s request that the NASA Lean Test should be conducted on all patients met with mixed responses from the clinics. In site F, the lead physician had an interest in autonomic dysfunction in chronic fatigue and was keen; he had already published a paper on how to adapt the NASA Lean Test for self-assessment at home [ 97 ]. Several other sites were initially opposed. Staff at site E, for example, offered various arguments:

The test is time-consuming, labor-intensive, and takes up space in the clinic which has an opportunity cost in terms of other potential uses;

The test is unvalidated and potentially misleading (there is a high incidence of both false negative and false positive results);

There is no proven treatment for POTS, so there is no point in testing for it;

It is a specialist test for a specialist condition, so it should be done in a specialist clinic where its benefits and limitations are better understood;

Objective testing does not change clinical management since what we treat is the patient’s symptoms (e.g. by a pragmatic trial of lifestyle measures and medication);

People with symptoms suggestive of dysautonomia have already been “triaged out” of this clinic (that is, identified in the initial telephone consultation and referred directly to neurology or cardiology);

POTS is a manifestation of the systemic nature of long covid; it does not need specific treatment but will improve spontaneously as the patient goes through standard interventions such as active pacing, respiratory physical therapy and sleep hygiene;

Testing everyone, even when asymptomatic, runs counter to the ethos of rehabilitation, which is to “de-medicalize” patients so as to better orient them to their recovery journey.

When clinics were invited to implement the NASA Lean Test on a consecutive sample of patients to resolve a dispute about the incidence of POTS (from “we’ve only seen a handful of people with it since the clinic began” to “POTS is common and often missed”), all but one site agreed to participate. The tertiary POTS centre linked to site H was already running the NASA Lean Test as standard on all patients. Site C, which operated entirely virtually, passed the work to the referring general practitioner by making this test a precondition for seeing the patient; site D, which was largely virtual, sent instructions for patients to self-administer the test at home.

The NASA Lean Test study has been published separately [ 98 ]. In sum, of 277 consecutive patients tested across the eight clinics, 20 (7%) had a positive NASA Lean Test for POTS and a further 28 (10%) a borderline result. Six of 20 patients who met the criteria for POTS on testing had no prior history of orthostatic intolerance. The question of whether this test should be used to “screen” all patients was not answered definitively. But the experience of participating in the study persuaded some sceptics that postural changes in heart rate could be severe in some long covid patients, did not appear to be fully explained by their previously held theories (e.g. “functional”, anxiety, deconditioning), and had likely been missed in some patients. The outcome of this particular quality improvement cycle was thus not a wholescale change in practice (for which the evidence base was weak) but a more subtle increase in clinical awareness, a greater willingness to consider testing for POTS and a greater commitment to contribute to research into this contested condition.

More generally, the POTS audit prompted some clinicians to recognize the value of quality improvement in novel clinical areas. One physician who had initially commented that POTS was not seen in their clinic, for example, reflected:

“ Our clinic population is changing. […] Overall there’s far fewer post-ICU patients with ECMO [extra-corporeal membrane oxygenation] issues and far more long covid from the community, and this is the bit our clinic isn’t doing so well on. We’re doing great on breathing pattern disorder; neuro[logists] are helping us with the brain fogs; our fatigue and occupational advice is ok but some of the dysautonomia symptoms that are more prevalent in the people who were not hospitalized – that’s where we need to improve .” -Respiratory physician, site G (from field visit 6.6.23)

Example quality topic 3: Management of fatigue

Fatigue was the commonest symptom overall and a high priority among both patients and clinicians for quality improvement. It often coexisted with the cluster of neurocognitive symptoms known as brain fog, with both conditions relapsing and remitting in step. Clinicians were keen to systematize fatigue management using a familiar clinical framework oriented around documenting a full clinical history, identifying associated symptoms, excluding or exploring comorbidities and alternative explanations (e.g. poor sleep patterns, depression, menopause, deconditioning), assessing how fatigue affects physical and mental function, implementing a program of physical and cognitive therapy that was sensitive to the patient’s condition and confidence level, and monitoring progress using validated patient-reported outcome measures and symptom diaries.

The underpinning logic of this approach, which broadly reflected World Health Organization guidance [ 99 ], was that fatigue and linked cognitive impairment could be a manifestation of many—perhaps interacting—conditions but that a whole-patient (body and mind) rehabilitation program was the cornerstone of management in most cases. Discussion in the quality improvement collaborative focused on issues such as whether fatigue was so severe that it produced safety concerns (e.g. in a person’s job or with childcare), the pros and cons of particular online courses such as yoga, relaxation and mindfulness (many were viewed positively, though the evidence base was considered weak), and the extent to which respiratory physical therapy had a crossover impact on fatigue (systematic reviews suggested that it may do, but these reviews also cautioned that primary studies were sparse, methodologically flawed, and heterogeneous [ 100 , 101 ]). They also debated the strengths and limitations of different fatigue-specific outcome measures, each of which had been developed and validated in a different condition, with varying emphasis on cognitive fatigue, physical fatigue, effect on daily life, and motivation. These instruments included the Modified Fatigue Impact Scale; Fatigue Severity Scale [ 102 ]; Fatigue Assessment Scale; Functional Assessment Chronic Illness Therapy—Fatigue (FACIT-F) [ 103 ]; Work and Social Adjustment Scale [ 104 ]; Chalder Fatigue Scale [ 105 ]; Visual Analogue Scale—Fatigue [ 106 ]; and the EQ5D [ 87 ]. In one clinic (site F), three of these scales were used in combination for reasons discussed below.

Some clinicians advocated melatonin or nutritional supplements (such as vitamin D or folic acid) for fatigue on the grounds that many patients found them helpful and formal placebo-controlled trials were unlikely ever to be conducted. But neurostimulants used in other fatigue-predominant conditions (e.g. brain injury, stroke), which also lacked clinical trial evidence in long covid, were viewed as inappropriate in most patients because of lack of evidence of clear benefit and hypothetical risk of harm (e.g. adverse drug reactions, polypharmacy).

Whilst the patient advisory group were broadly supportive of a whole-patient rehabilitative approach to fatigue, their primary concern was fatiguability , especially post-exertional symptom exacerbation (PESE, also known as “crashes”). In these, the patient becomes profoundly fatigued some hours or days after physical or mental exertion, and this state can last for days or even weeks [ 107 ]. Patients viewed PESE as a “red flag” symptom which they felt clinicians often missed and sometimes caused. They wanted the quality improvement effort to focus on ensuring that all clinicians were aware of the risks of PESE and acted accordingly. A discussion among patients and clinicians at a quality improvement collaborative meeting raised a new research hypothesis—that reducing the number of repeated episodes of PESE may improve the natural history of long covid.

These tensions around fatigue management played out differently in different clinics. In site C (the GP-led virtual clinic run from a community hub), fatigue was viewed as one manifestation of a whole-patient condition. The lead general practitioner used the metaphor of untangling a skein of wool: “you have to find the end and then gently pull it”. The underlying problem in a fatigued patient, for example, might be an undiagnosed physical condition such as anaemia, disturbed sleep, or inadequate pacing. These required (respectively) the chronic fatigue service (comprising an occupational therapist and specialist psychologist and oriented mainly to teaching the techniques of goal-setting and pacing), a “tiredness” work-up (e.g. to exclude anaemia or menopause), investigation of poor sleep (which, not uncommonly, was due to obstructive sleep apnea), and exploration of mental health issues.

In site G (a hospital clinic which had evolved from a respiratory service), patients with fatigue went through a fatigue management program led by the occupational therapist with emphasis on pacing, energy conservation, avoidance of PESE and sleep hygiene. Those without ongoing respiratory symptoms were often discharged back to their general practitioner once they had completed this; there was no consultant follow-up of unresolved fatigue.

In site F (a rehabilitation clinic which had a longstanding interest in chronic fatigue even before the pandemic), active interdisciplinary management of fatigue was commenced at or near the patient’s first visit, on the grounds that the earlier this began, the more successful it would be. In this clinic, patients were offered a more intensive package: a similar occupational therapy-led fatigue course as those in site G, plus input from a dietician to advise on regular balanced meals and caffeine avoidance and a group-based facilitated peer support program which centred on fatigue management. The dietician spoke enthusiastically about how improving diet in longstanding long covid patients often improved fatigue (e.g. because they had often lost muscle mass and tended to snack on convenience food rather than make meals from scratch), though she agreed there was no evidence base from trials to support this approach.

Pursuing local quality improvement through MDTs

Whilst some long covid patients had “textbook” symptoms and clinical findings, many cases were unique and some were fiendishly complex. One clinician commented that, somewhat paradoxically, “easy cases” were often the post-ICU follow-ups who had resolving chest complications; they tended to do well with a course of respiratory physical therapy and a return-to-work program. Such cases were rarely brought to MDT meetings. “Difficult cases” were patients who had not been hospitalized for their acute illness but presented with a months- or years-long history of multiple symptoms with fatigue typically predominant. Each one was different, as the following example (some details of which have been fictionalized to protect anonymity) illustrates.

The MDT is discussing Mrs Fermah, a 65-year-old homemaker who had covid-19 a year ago. She has had multiple symptoms since, including fluctuating fatigue, brain fog, breathlessness, retrosternal chest pain of burning character, dry cough, croaky voice, intermittent rashes (sometimes on eating), lips going blue, ankle swelling, orthopnoea, dizziness with the room spinning which can be triggered by stress, low back pain, aches and pains in the arms and legs and pins and needles in the fingertips, loss of taste and smell, palpitations and dizziness (unclear if postural, but clear association with nausea), headaches on waking, and dry mouth. She is somewhat overweight (body mass index 29) and admits to low mood. Functionally, she is mostly confined to the house and can no longer manage the stairs so has begun to sleep downstairs. She has stumbled once or twice but not fallen. Her social life has ceased and she rarely has the energy to see her grandchildren. Her 70-year-old husband is retired and generally supportive, though he spends most evenings at his club. Comorbidities include glaucoma which is well controlled and overseen by an ophthalmologist, mild club foot (congenital) and stage 1 breast cancer 20 years ago. Various tests, including a chest X-ray, resting and exercise oximetry and a blood panel, were normal except for borderline vitamin D level. Her breathing questionnaire score suggests she does not have breathing pattern disorder. ECG showed first-degree atrioventricular block and left axis deviation. No clinician has witnessed the blue lips. Her current treatment is online group respiratory physical therapy; a home visit is being arranged to assess her climbing stairs. She has declined a psychologist assessment. The consultant asks the nurse who assessed her: “Did you get a feel if this is a POTS-type dizziness or an ENT-type?” She sighs. “Honestly it was hard to tell, bless her.”—Site A MDT

This patient’s debilitating symptoms and functional impairments could all be due to long covid, yet “evidence-based” guidance for how to manage her complex suffering does not exist and likely never will exist. The question of which (if any) additional blood or imaging tests to do, in what order of priority, and what interventions to offer the patient will not be definitively answered by consulting clinical trials involving hundreds of patients, since (even if these existed) the decision involves weighing this patient’s history and the multiple factors and uncertainties that are relevant in her case. The knowledge that will help the MDT provide quality care to Mrs Fermah is case-based knowledge—accumulated clinical experience and wisdom from managing and deliberating on multiple similar cases. We consider case-based knowledge further in the “ Discussion ”.

Summary of key findings

This study has shown that a quality improvement collaborative of UK long covid clinics made some progress towards standardizing assessment and management in some topics, but some variation remained. This could be explained in part by the fact that different clinics had different histories and path dependencies, occupied a different place in the local healthcare ecosystem, served different populations, were differently staffed, and had different clinical interests. Our patient advisory group and clinicians in the quality improvement collaborative broadly prioritized the same topics for improvement but interpreted them somewhat differently. “Quality” long covid care had multiple dimensions, relating to (among other things) service set-up and accessibility, clinical provision appropriate to the patient’s need (including options for referral to other services locally), the human qualities of clinical and support staff, how knowledge was distributed across (and accessible within) the system, and the accumulated collective wisdom of local MDTs in dealing with complex cases (including multiple kinds of specialist expertise as well as relational knowledge of what was at stake for the patient). Whilst both staff and patients were keen to contribute to the quality improvement effort, the burden of measurement was evident: multiple outcome measures, used repeatedly, were resource-intensive for staff and exhausting for patients.

Strengths and limitations of this study

To our knowledge, we are the first to report both a quality improvement collaborative and an in-depth qualitative study of clinical work in long covid. Key strengths of this work include the diverse sampling frame (with sites from three UK jurisdictions and serving widely differing geographies and demographics); the use of documents, interviews and reflexive interpretive ethnography to produce meaningful accounts of how clinics emerged and how they were currently organized; the use of philosophical concepts to analyse data on how MDTs produced quality care on a patient-by-patient basis; and the close involvement of patient co-researchers and coauthors during the research and writing up.

Limitations of the study include its exclusive UK focus (the external validity of findings to other healthcare systems is unknown); the self-selecting nature of participants in a quality improvement collaborative (our patient advisory group suggested that the MDTs observed in this study may have represented the higher end of a quality spectrum, hence would be more likely than other MDTs to adhere to guidelines); and the particular perspective brought by the researchers (two GPs, a physical therapist and one non-clinical person) in ethnographic observations. Hospital specialists or organizational scholars, for example, may have noticed different things or framed what they observed differently.

Explaining variation in long covid care

Sutherland and Levesque’s framework mentioned in the “ Background ” section does not explain much of the variation found in our study [ 70 ]. In terms of capacity, at the time of this study most participating clinics benefited from ring-fenced resources. In terms of evidence, guidelines existed and were not greatly contested, but as illustrated by the case of Mrs Fermah above, many patients were exceptions to the guideline because of complex symptomatology and relevant comorbidities. In terms of agency, clinicians in most clinics were passionately engaged with long covid (they were pioneers who had set up their local clinic and successfully bid for national ring-fenced resources) and were generally keen to support patient choice (though not if the patient requested tests which were unavailable or deemed not indicated).

Astma et al.’s list of factors that may explain variation in practice (see “ Background ”) includes several that may be relevant to long covid, especially that the definition of appropriate care in this condition remains somewhat contested. But lack of opportunity to discuss cases was not a problem in the clinics in our sample. On the contrary, MDT meetings in each locality gave clinicians multiple opportunities to discuss cases with colleagues and reflect collectively on whether and how to apply particular guidelines.

The key problem was not that clinicians disputed the guidelines for managing long covid or were unaware of them; it was that the guidelines were not self-interpreting . Rather, MDTs had to deliberate on the balance of benefits and harms in different aspects of individual cases. In patients whose symptoms suggested a possible diagnosis of POTS (or who suspected themselves of having POTS), for example, these deliberations were sometimes lengthy and nuanced. Should a test result that is not technically in the abnormal range but close to it be treated as diagnostic, given that symptoms point to this diagnosis? If not, should the patient be told that the test excludes POTS or that it is equivocal? If a cardiology opinion has stated firmly that the patient does not have POTS but the cardiologist is not known for their interest in this condition, should a second specialist opinion be sought? If the gold standard “tilt test” [ 108 ] for POTS (usually available only in tertiary centres) is not available locally, does this patient merit a costly out-of-locality referral? Should the patient’s request for a trial of off-label medication, reflecting discussions in an online support group, be honoured? These are the kinds of questions on which MDTs deliberated at length.

The fact that many cases required extensive deliberation does not necessarily justify variation in practice among clinics. But taking into account the clinics’ very different histories, set-up, and local referral pathways, the variation begins to make sense. A patient who is being assessed in a clinic that functions as a specialist chronic fatigue centre and attracts referrals which reflect this interest (e.g. site F in our sample) will receive different management advice from one that functions as a telephone-only generalist assessment centre and refers on to other specialties (site C in our sample). The wide variation in case mix, coupled with the fact that a different proportion of these cases were highly complex in each clinic (and in different ways), suggests that variation in practice may reflect appropriate rather than inappropriate care.

Our patient advisory group affirmed that many of the findings reported here resonated with their own experience, but they raised several concerns. These included questions about patient groups who may have been missed in our sample because they were rarely discussed in MDTs. The decision to take a case to MDT discussion is taken largely by a clinician, and there was evidence from online support groups that some patients’ requests for their case to be taken to an MDT had been declined (though not, to our knowledge, in the clinics participating in the LOCOMOTION study).

We began this study by asking “what is quality in long covid care?”. We initially assumed that this question referred to a generalizable evidence base, which we felt we could identify, and we believed that we could then determine whether long covid clinics were following the evidence base through conventional audits of structure, process, and outcome. In retrospect, these assumptions were somewhat naïve. On the basis of our findings, we suggest that a better (and more individualized) research question might be “to what extent does each patient with long covid receive evidence-based care appropriate to their needs?”. This question would require individual case review on a sample of cases, tracking each patient longitudinally including cross-referrals, and also interviewing the patient.

Nomothetic versus idiographic knowledge

In a series of lectures first delivered in the 1950s and recently republished [ 109 ], psychiatrist Dr Maurice O’Connor Drury drew on the later philosophy of his friend and mentor Ludwig Wittgenstein to challenge what he felt was a concerning trend: that the nomothetic (generalizable, abstract) knowledge from randomized controlled trials (RCTs) was coming to over-ride the idiographic (personal, situated) knowledge about particular patients. Based on Wittgenstein’s writings on the importance of the particular, Drury predicted—presciently—that if implemented uncritically, RCTs would result in worse, not better, care for patients, since it would go hand-in-hand with a downgrading of experience, intuition, subjective judgement, personal reflection, and collective deliberation.

Much conventional quality improvement methodology is built on an assumption that nomothetic knowledge (for example, findings from RCTs and systematic reviews) is a higher form of knowing than idiographic knowledge. But idiographic, case-based reasoning—despite its position at the very bottom of evidence-based medicine’s hierarchy of evidence [ 110 ]—is a legitimate and important element of medical practice. Bioethicist Kathryn Montgomery, drawing on Aristotle’s notion of praxis , considers clinical practice to be an example of case-based reasoning [ 111 ]. Medicine is governed not by hard and fast laws but by competing maxims or rules of thumb ; the essence of judgement is deciding which (if any) rule should be applied in a particular circumstance. Clinical judgement incorporates science (especially the results of well-conducted research) and makes use of available tools and technologies (including guidelines and decision-support algorithms that incorporate research findings). But rather than being determined solely by these elements, clinical judgement is guided both by the scientific evidence and by the practical and ethical question “what is it best to do, for this individual, given these circumstances?”.

In this study, we observed clinical management of, and MDT deliberations on, hundreds of clinical cases. In the more straightforward ones (for example, recovering pneumonitis), guideline-driven care was not difficult to implement and such cases were rarely brought to the MDT. But cases like Mrs Fermah (see last section of “ Results ”) required much discussion on which aspects of which guideline were in the patient’s best interests to bring into play at any particular stage in their illness journey.

Conclusions

One systematic review on quality improvement collaboratives concluded that “ [those] reporting success generally addressed relatively straightforward aspects of care, had a strong evidence base and noted a clear evidence-practice gap in an accepted clinical pathway or guideline” (page 226) [ 60 ]. The findings from this study suggest that to the extent that such collaboratives address clinical cases that are not straightforward, conventional quality improvement methods may be less useful and even counterproductive.

The question “what is quality in long covid care?” is partly a philosophical one. Our findings support an approach that recognizes and values idiographic knowledge —including establishing and protecting a safe and supportive space for deliberation on individual cases to occur and to value and draw upon the collective learning that occurs in these spaces. It is through such deliberation that evidence-based guidelines can be appropriately interpreted and applied to the unique needs and circumstances of individual patients. We suggest that Drury’s warning about the limitations of nomothetic knowledge should prompt a reassessment of policies that rely too heavily on such knowledge, resulting in one-size-fits-all protocols. We also cautiously hypothesize that the need to centre the quality improvement effort on idiographic rather than nomothetic knowledge is unlikely to be unique to long covid. Indeed, such an approach may be particularly important in any condition that is complex, unpredictable, variable in presentation and clinical course, and associated with comorbidities.

Availability of data and materials

Selected qualitative data (ensuring no identifiable information) will be made available to formal research teams on reasonable request to Professor Greenhalgh at the University of Oxford, on condition that they have research ethics approval and relevant expertise. The quantitative data on NASA Lean Test have been published in full in a separate paper [ 98 ].

Abbreviations

Chronic fatigue syndrome

Intensive care unit

Jenny Ceolta-Smith

Julie Darbyshire

LOng COvid Multidisciplinary consortium Optimising Treatments and services across the NHS

Multidisciplinary team

Myalgic encephalomyelitis

Middle East Respiratory Syndrome

National Aeronautics and Space Association

Occupational therapy/ist

Post-exertional symptom exacerbation

Postural orthostatic tachycardia syndrome

Speech and language therapy

Severe Acute Respiratory Syndrome

Trisha Greenhalgh

United Kingdom

United States

World Health Organization

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Acknowledgements

We are grateful to clinic staff for allowing us to study their work and to patients for allowing us to sit in on their consultations. We also thank the funder of LOCOMOTION (National Institute for Health Research) and the patient advisory group for lived experience input.

This research is supported by National Institute for Health Research (NIHR) Long Covid Research Scheme grant (Ref COV-LT-0016).

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Trisha Greenhalgh, Julie L. Darbyshire & Emma Ladds

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Contributions

TG conceptualized the overall study, led the empirical work, supported the quality improvement meetings, conducted the ethnographic visits, led the data analysis, developed the theorization and wrote the first draft of the paper. JLD organized and led the quality improvement meetings, supported site-based researchers to collect and analyse data on their clinic, collated and summarized data on quality topics, and liaised with the patient advisory group. CL conceptualized and led the quality topic on POTS, including exploring reasons for some clinics’ reluctance to conduct testing and collating and analysing the NASA Lean Test data across all sites. EL assisted with ethnographic visits, data analysis, and theorization. JCS contributed lived experience of long covid and also clinical experience as an occupational therapist; she liaised with the wider patient advisory group, whose independent (patient-led) audit of long covid clinics informed the quality improvement prioritization exercise. All authors provided extensive feedback on drafts and contributed to discussions and refinements. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Trisha Greenhalgh .

Ethics declarations

Ethics approval and consent to participate.

LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS study is sponsored by the University of Leeds and approved by Yorkshire & The Humber—Bradford Leeds Research Ethics Committee (ref: 21/YH/0276) and subsequent amendments.

Patient participants in clinic were approached by the clinician (without the researcher present) and gave verbal informed consent for a clinically qualified researcher to observe the consultation. If they consented, the researcher was then invited to sit in. A written record was made in field notes of this verbal consent. It was impractical to seek consent from patients whose cases were discussed (usually with very brief clinical details) in online MDTs. Therefore, clinical case examples from MDTs presented in the paper are fictionalized cases constructed from multiple real cases and with key clinical details changed (for example, comorbidities were replaced with different conditions which would produce similar symptoms). All fictionalized cases were checked by our patient advisory group to check that they were plausible to lived experience experts.

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No direct patient cases are reported in this manuscript. For details of how the fictionalized cases were constructed and validated, see “Consent to participate” above.

Competing interests

TG was a member of the UK National Long Covid Task Force 2021–2023 and on the Oversight Group for the NICE Guideline on Long Covid 2021–2022. She is a member of Independent SAGE.

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Greenhalgh, T., Darbyshire, J.L., Lee, C. et al. What is quality in long covid care? Lessons from a national quality improvement collaborative and multi-site ethnography. BMC Med 22 , 159 (2024). https://doi.org/10.1186/s12916-024-03371-6

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DOI : https://doi.org/10.1186/s12916-024-03371-6

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• Post-covid-19 syndrome
• Quality improvement
• Breakthrough collaboratives
• Warranted variation
• Unwarranted variation
• Improvement science
• Ethnography
• Idiographic reasoning
• Nomothetic reasoning

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