research on business analysis

• Browse All Articles
• Newsletter Sign-Up

• 11 May 2021
• Working Paper Summaries

Time Dependency, Data Flow, and Competitive Advantage

The perishability of data has strategic implications for businesses that provide data-driven products and services. This paper illustrates how different business areas might differ with respect to the rate of decay in data value and the importance of data flow in their operations.

• 06 Apr 2020

A General Theory of Identification

Statistical inference teaches us how to learn from data, whereas identification analysis explains what we can learn from it. This paper proposes a simple unifying theory of identification, encouraging practitioners to spend more time thinking about what they can estimate from the data and assumptions before trying to estimate it.

• 09 Dec 2019
• Research & Ideas

Identify Great Customers from Their First Purchase

Using data from their very first transaction, companies can identify shoppers who will create the best long-term value, says Eva Ascarza. Open for comment; 0 Comments.

• 29 Oct 2019

Crowdsourcing Memories: Mixed Methods Research by Cultural Insiders-Epistemological Outsiders

Research on the traumatic 1947 partition of British India has most often been carried out by scholars in the humanities and qualitative social sciences. This article presents mixed methods research and analysis to explore tensions within current scholarship and to inspire new understandings of the Partition, and more generally, mass migrations and displacement.

• 30 Jun 2019

The Comprehensive Effects of Sales Force Management: A Dynamic Structural Analysis of Selection, Compensation, and Training

When sales forces are well managed, firms can induce greater performance from them. For this study, the authors collaborated with a major multinational firm to develop and estimate a dynamic structural model of sales employee responses to various management instruments like compensation, training, and recruiting/termination policies.

• 07 Jan 2019

The Better Way to Forecast the Future

We can forecast hurricane paths with great certainty, yet many businesses can't predict a supply chain snafu just around the corner. Yael Grushka-Cockayne says crowdsourcing can help. Open for comment; 0 Comments.

• 28 Nov 2018

On Target: Rethinking the Retail Website

Target is one big-brand retailer that seems to have survived and even thrived in the apocalyptic retail landscape. What's its secret? Srikant Datar discusses the company's relentless focus on online data. Open for comment; 0 Comments.

• 01 Nov 2018

Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning

Passengers arriving at international hubs often endure delays, especially at immigration and security. This study of London’s Heathrow Airport develops a system to provide real-time information about transfer passengers’ journeys through the airport to better serve passengers, airlines, and their employees. It shows how advanced machine learning could be accessible to managers.

• 29 Apr 2018

Analyzing the Aftermath of a Compensation Reduction

This study of the effects of compensation cuts in a large sales organization provides a unique lens for analyzing the link between compensation schemes, worker performance, and turnover.

• 11 Dec 2017

The Use and Misuse of Patent Data: Issues for Corporate Finance and Beyond

Corporate finance researchers who analyze patent data are at risk of making highly predictable errors. The problem arises from dramatic changes in the direction and location of technological innovation (and patenting practice) over recent decades. This paper explains the pitfalls and suggests practical steps for avoiding them.

• 21 Aug 2017
• Lessons from the Classroom

Companies Love Big Data But Lack the Strategy To Use It Effectively

Big data is a critical competitive advantage for companies that know how to use it. Harvard Business School faculty share insights that they teach to executives. Open for comment; 0 Comments.

• 06 Jul 2017

Do All Your Detailing Efforts Pay Off? Dynamic Panel Data Methods Revisited

Personal selling in the form of detailing to physicians is the main go-to-market practice in the pharmaceutical industry. This paper provides a practical framework to analyze the effectiveness of detailing efforts. The method and empirical insights can help firms allocate sales-force resources more efficiently and devise optimal routes and call-pattern designs.

• 09 Dec 2015

Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life

Michael Luca, Scott Duke Kominers and colleagues describe a number of new urban data sources and illustrate how they can be used to improve the study and function of cities.

• 09 Apr 2014

Visualizing and Measuring Software Portfolio Architectures: A Flexibility Analysis

Contemporary business environments are constantly evolving, requiring continual changes to the software applications that support a business. Moreover, during recent decades, the sheer number of applications has grown significantly, and they have become increasingly interdependent. Many companies find that managing applications and implementing changes to their application portfolio architecture is increasingly difficult and expensive. Firms need a way to visualize and analyze the modularity of their software portfolio architectures and the degree of coupling between components. In this paper, the authors test a method for visualizing and measuring software portfolio architectures using data of a biopharmaceutical firm's enterprise architecture. The authors also use the measures to predict the costs of architectural change. Findings show, first, that the biopharmaceutical firm's enterprise architecture can be classified as core-periphery. This means that 1) there is one cyclic group (the "Core") of components that is substantially larger than the second largest cyclic group, and 2) this group comprises a substantial portion of the entire architecture. In addition, the classification of applications in the architecture (as being in the Core or the Periphery) is significantly correlated with architectural flexibility. In this case the architecture has a propagation cost of 23 percent, meaning almost one-quarter of the system may be affected when a change is made to a randomly selected component. Overall, results suggest that the hidden structure method can reveal new facts about an enterprise architecture. This method can aid the analysis of change costs at the software application portfolio level. Key concepts include: This method for architectural visualization could provide valuable input when planning architectural change projects (in terms of, for example, risk analysis and resource planning). The method reveals a "hidden" core-periphery structure, uncovering new facts about the architecture that could not be gained from other visualization procedures or standard metrics. Compared to other measures of complexity, coupling, and modularity, this method considers not only the direct dependencies between components but also the indirect dependencies. These indirect dependencies provide important input for management decisions. Closed for comment; 0 Comments.

• 10 Jun 2013

How Numbers Talk to People

In their new book Keeping Up with the Quants, Thomas H. Davenport and Jinho Kim offer tools to sharpen quantitative analysis and make better decisions. Read our excerpt. Open for comment; 0 Comments.

• 25 Apr 2012
• What Do You Think?

How Will the “Age of Big Data” Affect Management?

Summing up: How do we avoid losing useful knowledge in a seemingly endless flood of data? Jim Heskett's readers offer some wise suggestions. What do you think? Closed for comment; 0 Comments.

• 05 May 2010

Is Denial Endemic to Management?

Poring over reader responses to his May column, HBS professor Jim Heskett is struck by the fact that they include behavioral, structural, and even mechanical remedies. (Forum now closed. Next forum opens June 3.) Closed for comment; 0 Comments.

• 15 Apr 2010

The Consequences of Entrepreneurial Finance: A Regression Discontinuity Analysis

What difference do angel investors make for the success and growth of new ventures? William R. Kerr and Josh Lerner of HBS and Antoinette Schoar of MIT provide fresh evidence to address this crucial question in entrepreneurial finance, quantifying the positive impact that angel investors make to the companies they fund. Angel investors as research subjects have received much less attention than venture capitalists, even though some estimates suggest that these investors are as significant a force for high-potential start-up investments as venture capitalists, and are even more significant as investors elsewhere. This study demonstrates the importance of angel investments to the success and survival of entrepreneurial firms. It also offers an empirical foothold for analyzing many other important questions in entrepreneurial finance. Key concepts include: Angel-funded firms are significantly more likely to survive at least four years (or until 2010) and to raise additional financing outside the angel group. Angel-funded firms are also more likely to show improved venture performance and growth as measured through growth in Web site traffic and Web site rankings. The improvement gains typically range between 30 and 50 percent. Investment success is highly predicated by the interest level of angels during the entrepreneur's initial presentation and by the angels' subsequent due diligence. Access to capital per se may not be the most important value-added that angel groups bring. Some of the "softer" features, such as angels' mentoring or business contacts, may help new ventures the most. Closed for comment; 0 Comments.

• 22 Aug 2005

The Hard Work of Failure Analysis

We all should learn from failure—but it's difficult to do so objectively. In this excerpt from "Failing to Learn and Learning to Fail (Intelligently)" in Long Range Planning Journal, HBS professor Amy Edmondson and coauthor Mark Cannon offer a process for analyzing what went wrong. Closed for comment; 0 Comments.

• Business Essentials
• Leadership & Management
• Credential of Leadership, Impact, and Management in Business (CLIMB)
• Entrepreneurship & Innovation
• *New* Digital Transformation
• Finance & Accounting
• Business in Society
• For Organizations
• Support Portal
• Media Coverage
• Founding Donors
• Leadership Team

• Harvard Business School →
• HBS Online →
• Business Insights →

Business Insights

Harvard Business School Online's Business Insights Blog provides the career insights you need to achieve your goals and gain confidence in your business skills.

• Career Development
• Communication
• Decision-Making
• Earning Your MBA
• Negotiation
• News & Events
• Productivity
• Staff Spotlight
• Student Profiles
• Work-Life Balance
• Alternative Investments
• Business Analytics
• Business Strategy
• Business and Climate Change
• Design Thinking and Innovation
• Digital Marketing Strategy
• Disruptive Strategy
• Economics for Managers
• Entrepreneurship Essentials
• Financial Accounting
• Global Business
• Launching Tech Ventures
• Leadership Principles
• Leadership, Ethics, and Corporate Accountability
• Leading with Finance
• Management Essentials
• Negotiation Mastery
• Organizational Leadership
• Power and Influence for Positive Impact
• Strategy Execution
• Sustainable Business Strategy
• Sustainable Investing
• Winning with Digital Platforms

Business Analytics: What It Is & Why It's Important

• 16 Jul 2019

Business analytics is a powerful tool in today’s marketplace that can be used to make decisions and craft business strategies. Across industries, organizations generate vast amounts of data which, in turn, has heightened the need for professionals who are data literate and know how to interpret and analyze that information.

According to a study by MicroStrategy , companies worldwide are using data to:

• Improve efficiency and productivity (64 percent)
• Achieve more effective decision-making (56 percent)
• Drive better financial performance (51 percent)

The research also shows that 65 percent of global enterprises plan to increase analytics spending.

In light of these market trends, gaining an in-depth understanding of business analytics can be a way to advance your career and make better decisions in the workplace.

“Using data analytics is a very effective way to have influence in an organization,” said Harvard Business School Professor Jan Hammond, who teaches the online course Business Analytics , in a previous interview . “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.”

Before diving into the benefits of data analysis, it’s important to understand what the term “business analytics” means.

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

What Is Business Analytics?

Business analytics is the process of using quantitative methods to derive meaning from data to make informed business decisions.

There are four primary methods of business analysis:

• Descriptive : The interpretation of historical data to identify trends and patterns
• Diagnostic : The interpretation of historical data to determine why something has happened
• Predictive : The use of statistics to forecast future outcomes
• Prescriptive : The application of testing and other techniques to determine which outcome will yield the best result in a given scenario

These four types of business analytics methods can be used individually or in tandem to analyze past efforts and improve future business performance.

Business Analytics vs. Data Science

To understand what business analytics is, it’s also important to distinguish it from data science. While both processes analyze data to solve business problems, the difference between business analytics and data science lies in how data is used.

Business analytics is concerned with extracting meaningful insights from and visualizing data to facilitate the decision-making process , whereas data science is focused on making sense of raw data using algorithms, statistical models, and computer programming. Despite their differences, both business analytics and data science glean insights from data to inform business decisions.

To better understand how data insights can drive organizational performance, here are some of the ways firms have benefitted from using business analytics.

The Benefits of Business Analytics

1. more informed decision-making.

Business analytics can be a valuable resource when approaching an important strategic decision.

When ride-hailing company Uber upgraded its Customer Obsession Ticket Assistant (COTA) in early 2018—a tool that uses machine learning and natural language processing to help agents improve speed and accuracy when responding to support tickets—it used prescriptive analytics to examine whether the product’s new iteration would be more effective than its initial version.

Through A/B testing —a method of comparing the outcomes of two different choices—the company determined that the updated product led to faster service, more accurate resolution recommendations, and higher customer satisfaction scores. These insights not only streamlined Uber’s ticket resolution process, but saved the company millions of dollars.

2. Greater Revenue

Companies that embrace data and analytics initiatives can experience significant financial returns.

Research by McKinsey shows organizations that invest in big data yield a six percent average increase in profits, which jumps to nine percent for investments spanning five years.

Echoing this trend, a recent study by BARC found that businesses able to quantify their gains from analyzing data report an average eight percent increase in revenues and a 10 percent reduction in costs.

These findings illustrate the clear financial payoff that can come from a robust business analysis strategy—one that many firms can stand to benefit from as the big data and analytics market grows.

Related: 5 Business Analytics Skills for Professionals

3. Improved Operational Efficiency

Beyond financial gains, analytics can be used to fine-tune business processes and operations.

In a recent KPMG report on emerging trends in infrastructure, it was found that many firms now use predictive analytics to anticipate maintenance and operational issues before they become larger problems.

A mobile network operator surveyed noted that it leverages data to foresee outages seven days before they occur. Armed with this information, the firm can prevent outages by more effectively timing maintenance, enabling it to not only save on operational costs, but ensure it keeps assets at optimal performance levels.

Why Study Business Analytics?

Taking a data-driven approach to business can come with tremendous upside, but many companies report that the number of skilled employees in analytics roles are in short supply .

LinkedIn lists business analysis as one of the skills companies need most in 2020 , and the Bureau of Labor Statistics projects operations research analyst jobs to grow by 23 percent through 2031—a rate much faster than the average for all occupations.

“A lot of people can crunch numbers, but I think they’ll be in very limited positions unless they can help interpret those analyses in the context in which the business is competing,” said Hammond in a previous interview .

Skills Business Analysts Need

Success as a business analyst goes beyond knowing how to crunch numbers. In addition to collecting data and using statistics to analyze it, it’s crucial to have critical thinking skills to interpret the results. Strong communication skills are also necessary for effectively relaying insights to those who aren’t familiar with advanced analytics. An effective data analyst has both the technical and soft skills to ensure an organization is making the best use of its data.

Improving Your Business Analytics Skills

If you’re interested in capitalizing on the need for data-minded professionals, taking an online business analytics course is one way to broaden your analytical skill set and take your career to the next level

Through learning how to recognize trends, test hypotheses, and draw conclusions from population samples, you can build an analytical framework that can be applied in your everyday decision-making and help your organization thrive.

“If you don’t use the data, you’re going to fall behind,” Hammond said . “People that have those capabilities—as well as an understanding of business contexts—are going to be the ones that will add the most value and have the greatest impact.”

Do you want to leverage the power of data within your organization? Explore our eight-week online course Business Analytics to learn how to use data analysis to solve business problems.

This post was updated on November 14, 2022. It was originally published on July 16, 2019.

About the Author

An Empirical Investigation on Business Analytics in Software and Systems Development Projects

• Open access
• Published: 20 April 2022
• Volume 25 , pages 917–927, ( 2023 )

Cite this article

You have full access to this open access article

• Muhammad Ovais Ahmad   ORCID: orcid.org/0000-0002-7885-0369 1 , 2 ,
• Iftikhar Ahmad 3 ,
• Nripendra P. Rana 4 &
• Iqra Sadaf Khan 5  

3672 Accesses

3 Citations

Explore all metrics

To create competitive advantages, companies are leaning towards business analytics (BA) to make data-driven decisions. Nevertheless, users acceptance and effective usage of BA is a key element for its success. Around the globe, organizations are increasingly adopting BA, however, a paucity of research on examining the drivers of BA adoption and its continuance is noticeable in the literature. This is evident in developing countries where a higher number of systems and software development projects are outsourced. This is the first study to examine BA continuance in the context of software and systems development projects from the perspective of Pakistani software professionals. The data was collected from 186 Pakistani software professionals working in software and systems development projects. The data were analyzed using partial least squares - structural equation modelling techniques. Our structural model explains 45% variance on BA continuance intention, 69% variance on technological compatibility, and 59% variance on perceived usefulness. Our results show that confirmation has a direct impact on BA continuance intention in software and systems projects. The study has both theoretical and practical implications for professionals in the field of business analytics.

Similar content being viewed by others

Business Analytics Continuance in Software Development Projects – A Preliminary Analysis

What Methodological Attributes Are Essential for Novice Users to Analytics? – An Empirical Study

Big Data Adoption Factors and Development Methodologies: A Multiple Case Study Analysis

Avoid common mistakes on your manuscript.

1 Introduction

Businesses are collecting a range of data to achieve greater competitiveness across the globe. Business Analytics (BA) provides insight into data with the help of business knowledge to support decision-making processes. Recently, BA has received considerable attention in various industries to gain a competitive advantage (Nam et al., 2019 ; Wang et al., 2019 ). BA can be defined as “the techniques, technologies, systems, practices, methodologies, and applications that analyze critical business data to help an enterprise better understand it's business and market and make timely business decisions” (Chen et al., 2012 , p. 1166). BA is about leveraging value from data - deemed ‘the new oil’ (Acito & Khatri, 2014 ). All types of analytics (e.g. BA, data analytics, mobile analytics, text analytics, web analytics, and network analytics) rely on data mining, statistics and artificial intelligence techniques and natural language processing (Chen et al., 2012 ). According to the IDC 2017 report (Dan et al., 2021 ), the BA software market was $54.1 billion and it will increase by 11.2% in 2022. BA helps to improve the firm’s agility and performance as well as generate greater competitiveness for an organization (Ashraf et al., 2019 ; Chiang et al., 2018 ). In software development, analytics guides practitioners in decision making throughout the software and systems development process. According to Ashraf et al. ( 2019 ), BA enhances information quality and innovative capabilities. Quality information contributes to timely decisions and better adaptability to the environment. Aydiner et al. ( 2019 ) highlighted that BA helps in improving business process performance.

BA use and adoption is a multidimensional and multi-factor phenomenon. These factors could be connected to technology (data infrastructure, data quality management), organization (managerial obstacles, analytic centralization) or environment (competition intensity). According to Gartner Research (Howson & Sallam, 2017), only 30% of businesses are using BA in their decision-making process. The absence of well-defined strategic goals is a dominating factor for organizations failing to integrate BA into their system. The majority of analytic projects fail for two main reasons: employees or users are lacking data skills and management lacking big data leadership and capabilities (Persaud, 2020 ). Recent studies have focused on BA tools rather than paying attention to users and the context in which they are used (Bawack & Ahmad, 2021 ; Conboy et al., 2020 ; Mikalef et al., 2019 ). Other studies focused on defining the concepts of BA, the effects of BA on the future of the businesses, and analysis of firms' performance and business values with the use of BA; however, there is a general lack of guidelines for companies to successfully adopt BA in their business environment (Abbasi et al., 2016 ; Acito & Khatri, 2014 ). However, only a few empirical studies have been conducted to investigate the drivers and barriers of BA-related technology adoption in an organization (Chen et al., 2012 ; Chiang et al., 2018 ), which makes it difficult to generalize the results; confirming the fact that guidelines for BA adoption are still in the early stages (Abbasi et al., 2016 ; Bawack & Ahmad, 2021 ).

For many years, BA has been viewed from three perspectives, i.e., descriptive, predictive, and prescriptive (Cao, 2017 ). Recently, BA is extended to the fourth perspective “adaptive analytics" which not only analyzes the past but is also able to respond in real-time to the actions of a user (Biesialska et al., 2020 ). Maximizing the value of BA investments requires pervasive use by the users which meets their expectations such as insights, visualization, mobility, and user engagement (Wixom et al., 2013 ). By keeping humans in the loop BA analytics is the embodiment of actual decision making. Individuals perceived these factors and influence BA adoption differently (Nam et al., 2019 ). Their needs and expectations in the use of BA is a challenge (Bawack & Ahmad, 2021 ). Such expectations affect individuals’ perceptions of how to fit BA in their jobs. This sheds light on the importance to understand what software and system development team members expect from BA and the effect this has on their desire to continue using BA. Analytics technologies reshaping the work environment and pushing individuals to learn a new set of skills to progress in their profession (Persaud, 2020 ). Further, understanding user expectations is an effective means of assessing and predicting information systems success or failure and the continuance of its use (Bhattacherjee, 2001 ; Szajna & Scamell, 1993 ). The latest literature on analytics calls for studies to explore how BA better fits with user expectations; whereas others suggest studies to understand the behavioural decisions of BA users regarding the (continuous) use of BA in organizational contexts (Bawack & Ahmad, 2021 ; Dennehy et al., 2020 ; Elhoseny et al., 2020 ; Jaklic et al., 2018 ).

This paper aims to examine how software and systems development professionals’ expectations from BA affect their perceptions and continuous use of BA? We are seeking to answer the research question: What are the factors that affect BA continuance in software and systems development projects? We used the expectation-confirmation model (ECM) (Bhattacherjee, 2001 ) as our theoretical framework to hypothesize on the behaviours of software and systems development professionals vis-a-vis their expectations from BA investments made by their organizations. The adopted model provides a basis to identify the interrelationships between the investigated factors. To this end, we collected data from software and systems development professionals in Pakistan and received a total of 186 responses. The model is empirically tested using the partial least squares approach - structural equation modelling technique.

The remainder of this paper is organized as follows: Section 2 presents the research model and formulate hypotheses. Section 3 discusses the research methodology. Section 4 provides the results and Section 5 discusses the key findings, theoretical and practical implications as well as limitations of the current study and future research directions. Finally, Section 6 concludes the paper.

2 Theoretical Development, Proposed Research Model and Hypotheses Formulation

The objective of this study is to examine BA continuance in the context of software and systems development projects from the perspective of Pakistani software professionals. The expectation-confirmation model (ECM) (Bhattacherjee, 2001 ) is used as a basis for designing empirical research. ECM is based on Expectation Confirmation Theory (ECT) that is extensively used to gauge consumer satisfaction and post-purchase behaviour. ECT elaborated the consumer process to have a repurchase intention; before purchase where he/she forms an initial expectation; after purchase where the consumer forms a perception about the performance of the purchase and make comparison with original expectation (Halilovic & Cicic, 2013 ). This highlights that customer satisfaction forms a repurchase intention whereas dissatisfaction results in discontinuation. The same logic of users’ satisfaction applies to the information systems continuance intention to use. Bhattacherjee ( 2001 ) presents ECM in the information systems field and elaborated the cognitive beliefs that influence users’ intentions to continue using information systems. Intention to continue to use of any information systems can be determined by its users' satisfaction as well as perceived usefulness for its continuous usage.

ECM posits that continuance intention to use is linked with post-perceived usefulness and users satisfaction. Perceived usefulness, as well as confirmation expectations from the initial use, contribute to user satisfaction. Additionally, perceived usefulness is also influenced by confirmation. The existing studies show that ECM is extended and validated in various contexts such as online shopping, healthcare and understanding BA continuance in agile information system development projects (Bawack & Ahmad, 2021 ; Brown et al., 2014 ; Gupta et al., 2020 ; Wang et al., 2020 ; Wu et al., 2020 ). Intention to continue the use of any information systems can be determined by users’ perceived usefulness for its continuous usage. We use ECM as the theoretical foundation to investigate continuance intention to use BA in software and systems projects. Similar to Bawack and Ahmad ( 2021 ), we added the technological compatibility construct to the ECM model. Various studies reported that technological compatibility affects users’ intention to continue using new technologies (Bawack & Ahmad, 2021 ; Ahmad et al., 2021 ; Cheng, 2020 ; Gupta et al., 2020 ). Similarly, we expect that perceived usefulness, technological compatibility and confirmation contribute towards BA continuance intention. Figure 1 summarizes our conceptual model for this research.

Proposed research model (Adapted from Bhattacherjee, 2001 )

2.1 Confirmation → Perceived Usefulness

Confirmation is the degree to which the actual use experience confirms their initial expectation (Bhattacherjee, 2001 ). It means that users’ actual use experience meets initial anticipation or expectation; which leads to their satisfaction. On the other hand, when actual use experience does not correspond or fall below the initial expectation, dissatisfaction occurs because of failing to achieve expectations. Bhattacherjee ( 2001 ) elaborated confirmation as the understanding of the anticipated benefits of IS use and dis-confirmation as performance lagging expectancy failure to achieve expectation. Therefore, we hypothesize:

H1: Confirmation has a positive effect on the perceived usefulness of BA use.

2.2 Confirmation → BA Continuous Intention

Ratification of fulfilment comes after a meaningful experience, which is a sign of alignment between expectation and confirmation (Bhattacherjee, 2001 ; Gupta et al., 2020 ). A range of studies reported a relationship of among confirmation with, perceived usefulness and satisfaction (Bhattacherjee, 2001 ; Gupta et al., 2020 ; Huang, 2019 ; Shang & Wu, 2017 ; Susanto et al., 2016 ; Tam et al., 2020 ; Venkatesh et al., 2011 ). The same logic applies to BA use. BA users compare the experience of their BA use with prior expectations. If the users expectation is confirmed, then he/she will have a positive continuous intention use with the BA. We hypothesize that confirmation might have a direct connection with continuance intention. Therefore, we hypothesize the following:

H2: Confirmation has a positive effect on BA continuance intention.

2.3 Confirmation → Technological Compatibility

Several studies suggested that satisfaction with technology influences users’ intention towards the use of technologies (Bawack & Ahmad, 2021 ; Cheng, 2020 ; Gupta et al., 2020 ). According to Bawack and Ahmad ( 2021 ), confirmation has a significant effect on technological compatibility that influence BA continuance. Similarly, we expect that if software and systems professionals find BA tools compatible with their work, they will intend to continue its use in their projects. Therefore, we hypothesize:

H3: Confirmation has a positive effect on technological compatibility.

2.4 Perceived Usefulness → Continuance Intention

Perceived Usefulness has an influence on user intention across temporal stages of information systems use and continuance use intention (Bhattacherjee, 2001 ). Perceived usefulness has a direct impact on technology continuance intention (Bhattacherjee, 2001 ; Gupta et al., 2020 ). The BA users are more likely to perceive usefulness if prescriptive and predictive expectations are met. Therefore, we hypothesize:

H4: Perceived usefulness has a positive effect on BA continuance intention.

2.5 Technological Compatibility → Continuance Intention

Users are using the information systems when it matches their needs, values and expectations (Awa et al., 2017 ; Bawack & Ahmad, 2021 ). This is true in the case of analytics as well. According to Chen et al. ( 2015 ), organizations are more likely to use big data analytics when their existing values and work practices are compatible. Compatibility is an important driver in analytics acceptance and continuance (Grubljesic & Jaklic, 2015 ; Gupta et al., 2020 ). Building on the above reasoning, we hypothesize that:

H5: Technological compatibility has a positive effect on BA continuance intention.

3 Methodology

To reach the goal of our research, a survey instrument was developed based on a well-established model - ECM. The adopted model is also empirically investigated by Bawack and Ahmad ( 2021 ) in the BA context. Figure 1 illustrates the proposed research model and associated hypotheses of our research. The survey was pre-tested with two software industry experts and four researchers. Based on the feedback, we revised the statements to have clearer wordings. Survey questions are provided in Appendix 1 Table 5 . The target respondents for this study were BA practitioners in Pakistani software and systems development companies. We contacted respondents directly through phone calls and emails. The study participating companies were using Agile software development techniques (Ahmad et al., 2018 ) in their projects.

The questionnaire was divided into three parts. In the first part, participants were provided information about the purpose of the research, its benefits as well as information about the researchers. The second part included demographic questions about the participants. The third part questions measured constructs in the research model. All of the variables related to the ECM model were measured using a seven-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree).

The inherent nature of quantitative study can result in the introduction of measurement errors. To ensure that measurement errors originated due to common method bias (Podsakoff et al., 2003 ) are properly handled, we employed several steps. Firstly, and most importantly, all the respondents were informed that the survey is anonymous and that we will not store any personally identifiable information such as name, and/or IP address etc. The respondents were also encouraged to answer as honestly as possible by stating that there are no right and wrong answers. Some of the questions were rephrased to improve understandability for the target respondents. Second, to create psychological separations of measurement, the survey questions were juxtaposed. Third, pre-testing and pilot-testing of the questionnaire help to reduce the ambiguity of items and bring more clarity to each question. Fourth, Harman’s single factor test was carried out to determine if the variance can be explained by a single factor. We achieved a score of 47%, which is less than the threshold value of 50% indicating that there is no threat of common method bias (CMB).

To evaluate our proposed model, we used Partial Least Squares Structural Equation Modeling (PLS-SEM). It is important to note that PLS-SEM is the standard and well-established approach used in quantitative research to study the relationship between variability and predictive models (Hair Jr et al., 2016 ). PLS-SEM is a variance-based SEM technique and is, therefore, the preferred technique for exploratory studies involving an investigation of relationships based on theory (Hair Jr et al., 2016 ). The PLS-SEM approach involves two steps; in the first step, evaluation of the measurement model occurs, whereas, in the second step, the structural model is evaluated (Hair Jr et al., 2017 ).

In this section, we present and discuss the results of our study including demographics of respondents, measurement model, structural model and hypothesis testing.

4.1 Respondents' Demographic

We collected data from 186 respondents belonging to diverse domains. 41% of the respondents identified themselves as software developer. 27% of the respondents selected telecommunication as their job, whereas 15% belonged to the e-commerce industry. 6% of the respondents are working as IT-Consultants, and 4% are working in a government organization. The remaining 7% worked in various other sectors. 61% of the respondents have a minimum of three years of experience using BA in their work, and the remaining respondents have one to two years of BA experience. The majority of respondents are male (85%). Further demographics of the respondents are provided in Table 1 .

4.2 Measurement Model

To evaluate the measurement model, we focus on assessing the reliability and validity of the constructs and their corresponding items. We consider factor loadings of the items, composite reliability and Cronbach Alpha (α) scores of the constructs. For individual items to be reliable, the factor loading should be at least 0.50. Table 2 presents the summary of the reliability and validity score of the measurement model constructs and items. It is evident from Table 2 that the factor loading for each item is well above the threshold of 0.50. Likewise, for construct reliability, the composite reliability and Cronbach’s alpha scores should be greater than 0.70 (Hair Jr et al., 2016 ).

We used Average Variance Extracted (AVE) to validate if a construct sufficiently explains the variance of its items. The AVE score should be greater than 0.60 (Fornell & Larcker, 1981 ). A discriminant validity test is employed to ensure that unrelated constructs are not observed to be related to one another. For this purpose, we used the discriminant validity test of Fornell and Larcker ( 1981 ). The Cronbach’s alpha (α), composite reliability and AVE scores are meeting the minimum threshold criterion.

Table 3 presents the discriminant validity score based on Fornell & Larcker method (Fornell & Larcker, 1981 ). The method compares the square root of each Average Variance Extracted (AVE) in the diagonal with the correlation coefficients of the other constructs. The square root of AVE should be greater than the corresponding correlation coefficients. As in each column (see Table 3 ), the top value (diagonal value) is the highest among all other values signifying that these constructs are not related.

4.3 Structural Model

In the evaluation of the structural model, we perform statistical assessments regarding the predictability of the underlying model as well as the significance of the relationships between various variables of the model. The underlying model should exhibit no collinearity among the constructs to reflect the absence of bias (Fornell & Larcker, 1981 ; Hair Jr et al., 2016 ), i.e., the p-value of the predicates should be less than 5%. To gauge the predictive accuracy of the model, we consider the R 2 score. Threshold scores of 0.25, 0.50 and 0.75 are used to indicate weak, moderate and substantial predictive accuracy of the model. The resultant structural model is illustrated in Figure 2 .

Validated Research Model

For ensuring that the significance of the results is not by chance, we run a bootstrap of our model by considering 5,000 sub-samples. The analysis of the R 2 values shows that our structural model can explain 45% variance on BA continuance intention, 69% variance on technological compatibility, and 59% variance on perceived usefulness. Analysis of the path coefficients (β) of our structural model reveals that confirmation (β=0.441, p=0.000) significantly affects BA continuance intention. Whereas perceived usefulness (β=0.169, p=0.062) and technological compatibility (β=0.105, p=0.327) do not affect BA continuance intention. We also identified that confirmation affects perceived usefulness (β=0.770, p=0.000) and technological compatibility (β=0.833, p=0.000). The findings of the structural model evaluation are summarized in Table 4 .

5 Discussion

We employed the ECM to explore what software practitioners in software and systems development projects expect from BA as well as how this affects their intentions to continue using the BA technologies adopted by their organization. A few existing studies focused on the implementation of BA technologies, but limited attention is given to its users or context of use (Conboy et al., 2020 ; Dennehy et al., 2020 ; Mikalef et al., 2019 ). Studies reported many factors influencing the successful implementation of BA, such as identifying skills, talents and building BA teams, obtaining the required certifications, involving stakeholders and creating a BA culture (Larson & Chang, 2016 ; Liu et al., 2018 ). However, none have investigated the factors influencing BA continuation intention after BA implementation. In this study, we used ECM as a theoretical foundation to understand the mentioned phenomenon from the perspective of Pakistani software professionals. The results of this study are consistent with existing literature that confirmation influence perceived usefulness and technological compatibility. Our study also shows that confirmation directly influences BA continuous intention. Festinger ( 1957 ) suggests that users experience psychological tension when their experience is disconfirmed during actual use. In the context of BA, confirmation tends to elevate users’ perceived usefulness and it might be adjusted by their extent of confirmation. Surprisingly, BA continuous intention is not affected by perceived usefulness and technological compatibility. Whereas, the literature reported that technological compatibility and perceived usefulness are the key drivers of continuance (Bawack & Ahmad, 2021 ; Ahmad et al., 2021 ; Gupta et al., 2020 ; Grubljesic & Jaklic, 2015 ; Bhattacherjee, 2001 ). However, in this study, we observe that technological compatibility and perceived usefulness have no effect on BA continuance in software and system development projects. One reason could be that generally employees or end-users do not make the strategic decision on choosing the BA technologies. The main challenge for BA project managers and decision-makers are to manage various stakeholders’ expectations. The users would continue their BA use if it is compatible with their needs and expectations. These BA related expectations are reported as technologies robustness and highly responsive to user interactions (Larson & Chang, 2016 ; Viaene & Van den Bunder, 2011 ). This may also imply that the actual BA continuation intention is affected by users' work experience, needs and values.

5.1 Theoretical Implications

Our study offers several implications for the existing literature. Firstly, we empirically investigate factors affecting BA continuance in Pakistan and extends the body of knowledge at an organizational level. It is important to investigate the factors that contribute to the BA continuance in software projects. According to Shah et al. ( 2019 ), there is a link between rising adoption and failure of BA. The existing literature mostly focused on BA adoption and not on its continuance intention (Daradkeh, 2019 ). Secondly, this study identified the important enablers of BA adoption based on ECM. We looked into various factors from previous literature related to ECM and adapted them into the BA context of our study. Our findings show that confirmation influence perceived usefulness and technological compatibility as well as confirmation directly influence BA continuous intention. These results are in line with existing studies, which found that such factors are important for BA (Bawack & Ahmad, 2021 ; Ahmad et al., 2021 ; Chen et al., 2015 ; Jaklic et al., 2018 ; Viaene & Van den Bunder, 2011 ). On the other hand, technological compatibility and perceived usefulness do not influence the BA continuance.

Finally, this study comes under big data research (Abbasi et al., 2016 ; Mandal, 2019 ), in terms of assessing the BA initiatives at the individual and team level in the context of software projects. Our study provides a holistic view of BA continuance in software projects and sheds light on the factors that help to elaborate BA role in the management of software projects (Dennehy et al., 2020 ). Our amended ECM research model highlighted determinants for BA continuance and can be suitable to use in future studies with a similar goal. The confirmation factor provides a better understanding of the motivation of BA continuance in software projects especially in developing countries such as Pakistan.

5.2 Practical Implications

To maximize value similar to other technologies, BA continuance requires pervasive use and understanding of their expectations (Wixom et al., 2013 ). In this study, we reveal factors that influence the software and systems developers’ continuance use of BA in their projects. Our study offers suggestions for the smooth use and adoption of BA in software and system development companies. From a managerial standpoint, the ECM provides useful insights. It is the managers’ responsibility to pay attention to software and systems developers use behaviour to support BA continuance journey. Our study exhibits that perceived usefulness, technological compatibility and continuance intention are directly influenced by confirmation. The software and systems developers are more likely to perceive BA usefulness if their expectations are met. It may be prudent for managers to amplify BA users’ prescriptive and predictive expectations because of its direct impact on BA continuance. For instance, the companies can observe individuals temporal personalities and perceptions to adopt BA tools functions accordingly. In this study, confirmation has a significant contributor to BA continuance. This implies that BA tools should be chosen based on software and systems developers needs and should not be disruptive to the existing way of working. To ensure BA continuance and continue to reap its benefits the managers needs to pay more attention to employees actual use experience and initial expectations. This study also reveals that confirmation influence perceived usefulness. This can be also implied that BA vendors need to carefully analyse the users' expectations and prior experiences. Further, it is important to meet users’ expectations, be compatible with organizational values, and work practices. The individual users' expectations perspective is very important, as BA would affect their work performance. If the BA technology does not meet their expectations and disrupts their existing work, it might lead to undesirable outcomes. By considering users’ expectations the manager could pick the best-fit BA for their organization that is aligned with their values and work practices. The managers need to be aware of the temporal characteristics of business analytics tools as they have a range of complexities. By taking into consideration, the adopted model constructs the software and systems development companies can improve the environment for BA continuance. As a result, software and systems developers would be willing to use the BA in their work and their organizations attain its optimum benefits.

5.3 Limitations and Future Research Directions

In this study, we have several limitations that could be considered as a future research direction. Our study respondents were general software practitioners from Pakistan using BA in their projects. The study participants’ recruitment was through direct contact of emails to different organizations. We intentionally selected the BA practitioners to obtain an appropriate data sample, as they had better understand BA and its use at work. This might lead to a biased perception regarding BA continuation intention. Nevertheless, such a diverse number of participants working in a range of projects and companies would be considered as a positive aspect of our study. We did not have any control over the type of participants in terms of their titles, job description and so on. Different organizational positions may have divergent views and varying knowledge about BA, factors that could affect the reliability of the results to some degree.

The respondents can be considered as individuals who are from different organizations and cannot represent the whole team or organization. Further, various stakeholders had different needs throughout the development project. For instance, a product owner may want to see different data than a software tester. This highlight the need for diverse information and investigation on stakeholder analysis. One BA tool is not a silver bullet and the consequence is that tool should support different views for product owners, managers, software testers etc. A multi-stakeholder experiences and perceptions investigation is essential. In the same vein, organizations and businesses need to understand a range of capabilities that are required for BA initiatives. These capabilities investigation might be circle around for instance people, processes, technology, organization and its culture. The temporal factor will bring more value to the BA research area if future research investigates it from various dimensions such as individual level, team level, and organization level. These dimensions are worthy of future research.

It is also worth noting that this study only focuses on the BA continuance in a single country i.e. Pakistan. Therefore, this study can be considered as BA use and continuance in companies in terms of Pakistani cultural aspects. This implies that Pakistani culture could have an influence on the outcomes of our study and may be considered as a limitation of this study. In future, it would be interesting to investigate such studies in various companies, business domains and countries. It would be worth investigating the BA under the existence of norms about time in organizations to explore differences in temporal norms among companies, workgroup types (such as managers, developers, designers etc.). This can be examined using Schriber's dimensions. A replication of this study in various contexts is also a direction for future investigations that will help to have more generalized results. The validation and modifying the research model presented in this research to include temporal complexities of the BA tools or analytics capabilities can also be an interesting adjustment worthy of pursuit.

The information and communication technology industry demand the current software engineering or information systems curriculum modification with an emphasis on both quantitative skills as well as software skills. In this regard, many universities around the globe offer analytics related courses in software engineering and information systems education. We believed that analysts using various analytics tools could be trained as part of software engineering or information systems curriculum. It will be good to consider the perspectives of graduates and obtain their opinions at different career stages. The result of such a study would help to obtain evidence of adjustment and offer opportunities for improvement of the current curriculums to meet the industrial demands.

6 Conclusion

This study examines Pakistani software professionals’ BA expectations in software and systems development projects as well as their intentions to continue using BA technologies adopted by their respective organizations. In the information research stream, it is established that information systems continuance is dependent on its users' confirmation expectations. However, it is not evaluated in the context of Pakistan software professionals and their BA continuance in software and systems development projects. Our study uses ECM as a theoretical model, which highlighted that confirmation is a decisive factor of BA continuance intentions in software and systems development projects. In contrast to the ECM model, our findings show that perceived usefulness and technological compatibility factors do not contribute towards BA continuance intention in software and systems projects in Pakistan. The project managers need to pay attention that individuals will use BA tools when their actual use experience confirms their initial expectation. Such attention will yield to the maximization of value from BA investments. In summary, this study contributed towards the understanding of BA use in software and systems development projects and is expected to stimulate further research in a similar vein.

Abbasi, A., Sarker, S., Chiang, R.H. (2016). Big data research in information systems: Toward an inclusive research agenda. Journal of the association for information systems 17(2), 3. https://doi.org/10.17705/1jais.00423

Acito, F., & Khatri, V. (2014). Business Analytics: Why now and what next? . https://doi.org/10.1016/j.bushor.2014.06.001

Book   Google Scholar  

Ashraf, A., Ravasan, A. Z., Trkman, P., & Afshari, S. (2019). The role of business analytics capabilities in bolstering firms' agility and performance. International Journal of Information Management, 47 , 1–15. https://doi.org/10.1016/j.ijinfomgt.2018.12.005

Article   Google Scholar  

Awa, H. O., Uko, J. P., & Ukoha, O. (2017). An empirical study of some critical adoption factors of ERP software. International Journal of Human-Computer Interaction, 33 (8), 609–622. https://doi.org/10.1080/10447318.2016.1265828

Aydiner, A. S., Tatoglu, E., Bayraktar, E., Zaim, S., & Delen, D. (2019). Business analytics and firm performance: The mediating role of business process performance. Journal of Business Research, 96 , 228–237. https://doi.org/10.1016/j.jbusres.2018.11.028

Ahmad, M. O., Ahmad, I., & Khan, I. S. (2021). Business Analytics Continuance in Software Development Projects – A Preliminary Analysis. In Conference on e-Business, e-Services and e-Society (pp. 622–628). Springer. https://doi.org/10.1007/978-3-030-85447-8_51

Chapter   Google Scholar  

Ahmad, M. O., Dennehy, D., Conboy, K., & Oivo, M. (2018). Kanban in software engineering: A systematic mapping study. Journal of Systems and Software, 137 , 96–113. https://doi.org/10.1016/j.jss.2017.11.045

Bawack, R. E., & Ahmad, M. O. (2021). Understanding business analytics continuance in agile information system development projects: an expectation-confirmation perspective. Information Technology & People. https://doi.org/10.1108/ITP-10-2020-0681

Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly , 351–370. https://doi.org/10.2307/3250921

Biesialska, K., Franch, X., & Muntes-Mulero, V. (2020). Big data analytics in agile software development: A systematic mapping study (p. 106448). Information and Software Technology . https://doi.org/10.1016/j.infsof.2020.106448

Brown, S.A., Venkatesh, V., Goyal, S. (2014). Expectation confirmation in information systems research. MIS Quarterly , 38(3). https://www.jstor.org/stable/26634990

Cao, L. (2017). Data science: a comprehensive overview. ACM Computing Surveys, 50 (3), 1–42. https://doi.org/10.1145/3076253

Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32 (4), 4–39. https://doi.org/10.1080/07421222.2015.1138364

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, pp. , 1165–1188. https://doi.org/10.2307/41703503

Chiang, R. H., Grover, V., Liang, T. P., & Zhang, D. (2018). Strategic value of big data and business analytics . https://doi.org/10.1080/07421222.2018.1451950

Cheng, Y.-M. (2020). Quality antecedents and performance outcome of cloud-based hospital information system continuance intention. Journal of Enterprise Information Management, 33 (3), 654–683.

Cindi Howson, R.S. (2017). Survey analysis: Why bi and analytics adoption remains low and how to expand its reach. Retrieved May 7, 2021, from https://www.gartner.com/en/documents/3753469/survey-analysis-why-bi-and-analytics-adoption-remains-lo

Conboy, K., Dennehy, D., O'Connor, M. (2020). `big time': An examination of temporal complexity and business value in analytics. Information & Management , 57(1), 103077. https://doi.org/10.1016/j.im.2018.05.010

Daradkeh, M. K. (2019). Determinants of visual analytics adoption in organizations. Information Technology & People. https://doi.org/10.1108/ITP-10-2017-0359

Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly , pp. 319-340. https://doi.org/10.2307/249008

Dennehy, D., Pappas, I., Samuel, F., Katina, M. (2020). Special issue: business analytics for the management of information systems development. Information Technology & People . Retrieved May 7, 2021, from http://www.emeraldpublishing.co.uk/products/journals/call_for_papers.htm?id=8790

Doll, W. J., Hendrickson, A., & Deng, X. (1998). Using Davis's perceived usefulness and ease-of-use instruments for decision making: a confirmatory and multigroup invariance analysis. Decision Sciences, 29 (4), 839–869. https://doi.org/10.1111/j.1540-5915.1998.tb00879.x

Elhoseny, M., Hassan, M.K., Singh, A.K. (2020). Special issue on cognitive big data analytics for business intelligence applications: Towards performance improvement. https://doi.org/10.1016/j.ijinfomgt.2019.08.004

Fornell, C., Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research , 18(1), 39-50. https://doi.org/10.1177/2F002224378101800104

Festinger, L. A. (1957). A Theory of Cognitive Dissonance . Row and Peterson.

Dan, V., Chandana G., Raymond H., Stewart B., Carl W. O., Amy M (2021). Worldwide big data and analytics software forecast, 2019-2023. IDC Market Analysis, US44803719. Retrieved July 17, 2021, https://www.idc.com/getdoc.jsp?containerId=US47402321

Grubljesic, T., & Jaklic, J. (2015). Business intelligence acceptance: The prominence of organizational factors. Information Systems Management, 32 (4), 299–315. https://doi.org/10.1080/10580530.2015.1080000

Gupta, A., Yousaf, A., & Mishra, A. (2020). How pre-adoption expectancies shape post adoption continuance intentions: An extended expectation-confirmation model. International Journal of Information Management., 52 , 102094. https://doi.org/10.1016/j.ijinfomgt.2020.102094

Hair Jr., J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). Pls-sem or CB-sem: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1 (2), 107–123. https://doi.org/10.1504/IJMDA.2017.087624

Hair Jr, J.F., Hult, G.T.M., Ringle, C., Sarstedt, M. (2016). A primer on partial least squares structural equation modelling (PLS-SEM). Sage publications.

Halilovic, S., & Cicic, M. (2013). Antecedents of information systems user behaviour extended expectation-confirmation model. Behaviour & Information Technology, 32 (4), 359–370. https://doi.org/10.1080/0144929X.2011.554575

Huang, Y. M. (2019). Examining students' continued use of desktop services: Perspectives from expectation-confirmation and social influence. Computers in Human Behavior, 96 , 23–31. https://doi.org/10.1016/j.chb.2019.02.010

Jaklic, J., Grubljesic, T., & Popovic, A. (2018). The role of compatibility in predicting business intelligence and analytics use intentions. International Journal of Information Management, 43 , 305–318. https://doi.org/10.1016/j.ijinfomgt.2018.08.017

Larson, D., & Chang, V. (2016). A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 36 (5), 700–710. https://doi.org/10.1016/j.ijinfomgt.2016.04.013

Liu, Y., Han, H., DeBello, J. (2018). The challenges of business analytics: Successes and failures. https://doi.org/10.24251/HICSS.2018.105

Mandal, S. (2019). The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility. Information Technology & People, 32 (2), 297–318. https://doi.org/10.1108/ITP-11-2017-0386

Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98 , 261–276. https://doi.org/10.1016/j.jbusres.2019.01.044

Nam, D., Lee, J., & Lee, H. (2019). Business analytics adoption process: An innovation diffusion perspective. International Journal of Information Management, 49 , 411–423. https://doi.org/10.1016/j.ijinfomgt.2019.07.017

Persaud, A. (2020). Key competencies for big data analytics professions: a multimethod study. Information Technology & People. https://doi.org/10.1108/ITP-06-2019-0290

Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y., Podsakoff, N.P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879. https://psycnet.apa.org/doi/10.1037/0021-9010.88.5.879

Reid, F. J., & Reid, D. J. (2010). The expressive and conversational affordances of mobile messaging. Behaviour & Information Technology, 29 (1), 3–22. https://doi.org/10.1080/01449290701497079

Rogers, E.M. (2010). Diffusion of innovations. Simon and Schuster. Free Press (July 6, 2010). ISBN13: 9781451602470

Shah, S., Gochtovtt, A., Baldini, G. (2019). Importance of project management in business analytics: Academia and real world. In: Aligning Business Strategies and Analytics , pp. 81-94. https://doi.org/10.1007/978-3-319-93299-6_6

Shang, D., & Wu, W. (2017). Understanding mobile shopping consumers' continuance intention. Industrial Management & Data Systems. https://doi.org/10.1108/IMDS-02-2016-0052

Susanto, A., Chang, Y., & Ha, Y. (2016). Determinants of continuance intention to use the smartphone banking services: An extension to the expectation-confirmation model. Industrial Management & Data Systems. https://doi.org/10.1108/IMDS-05-2015-0195

Szajna, B., & Scamell, R. W. (1993). The effects of information system user expectations on their performance and perceptions. MIS Quarterly pp. , 493–516. https://doi.org/10.2307/249589

Tam, C., Santos, D., & Oliveira, T. (2020). Exploring the influential factors of continuance intention to use mobile apps: Extending the expectation confirmation model. Information Systems Frontiers, 22 (1), 243–257. https://doi.org/10.1007/s10796-018-9864-5

Venkatesh, V., Thong, J. Y., Chan, F. K., Hu, P. J. H., & Brown, S. A. (2011). Extending the two-stage information systems continuance model: Incorporating UTAUT predictors and the role of context. Information Systems Journal, 21 (6), 527–555. https://doi.org/10.1111/j.1365-2575.2011.00373.x

Viaene, S., & Van den Bunder, A. (2011). The secrets to managing business analytics projects. MIT Sloan Management Review, 53 (1), 65 Retrieved July 17, 2021, https://sloanreview.mit.edu/article/the-secrets-to-managing-business-analytics-projects/

Google Scholar  

Wang, S., Yeoh, W., Richards, G., Wong, S. F., & Chang, Y. (2019). Harnessing business analytics value through organizational absorptive capacity. Information & Management, 56 (7), 103152. https://doi.org/10.1016/j.im.2019.02.007

Wang, S.M., Huang, Y.K., Wang, C.C. (2020). A model of consumer perception and behavioral intention for ai service. In: Proceedings of the 2020 2nd International Conference on Management Science and Industrial Engineering . pp. 196-201. https://doi.org/10.1145/3396743.3396791

Wixom, B.H., Yen, B., Relich, M. (2013). Maximizing value from business analytics. MIS Quarterly Executive 12(2). Retrieved July 11, 2021, https://aisel.aisnet.org/misqe/vol12/iss2/6

Wu, L., Chiu, M. L., & Chen, K. W. (2020). Defining the determinants of online impulse buying through a shopping process of integrating perceived risk, expectation-confirmation model, and flow theory issues. International Journal of Information Management, 52 , 102099. https://doi.org/10.1016/j.ijinfomgt.2020.102099

Yuan, S., Liu, Y., Yao, R., Liu, J. (2016). An investigation of users' continuance intention towards mobile banking in china. Information Development 32(1), 20-34. https://doi.org/10.1177/2F0266666914522140

Download references

Open access funding provided by Karlstad University.

Author information

Authors and affiliations.

Department of Computer Science, Karlstad University, Karlstad, Sweden

Muhammad Ovais Ahmad

M3S Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland

Department of Computer Science and Information Technology, University of Engineering and Technology, Peshawar, Pakistan

Iftikhar Ahmad

College of Business and Economics, Qatar University, P.O. Box 2713, Doha, Qatar

Nripendra P. Rana

Industrial Engineering and Management, Faculty of Technology, University of Oulu, P.O. Box 4610, 90014, Oulu, Finland

Iqra Sadaf Khan

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Muhammad Ovais Ahmad .

Ethics declarations

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Ahmad, M.O., Ahmad, I., Rana, N.P. et al. An Empirical Investigation on Business Analytics in Software and Systems Development Projects. Inf Syst Front 25 , 917–927 (2023). https://doi.org/10.1007/s10796-022-10253-w

Download citation

Accepted : 24 January 2022

Published : 20 April 2022

Issue Date : April 2023

DOI : https://doi.org/10.1007/s10796-022-10253-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Business Analytics
• Data-Driven Decision
• Information Systems
• Technological Compatibility
• Continuance Intention

Advertisement

• Find a journal
• Publish with us
• Track your research

The Leading Source of Insights On Business Model Strategy & Tech Business Models

Business Analysis: How To Analyze Any Business

Business analysis is a research discipline that helps driving change within an organization by identifying the key elements and processes that drive value. Business analysis can also be used in Identifying new business opportunities or how to take advantage of existing business opportunities to grow your business in the marketplace.

Table of Contents

A quick intro to the Business Analysis Framework

On FourWeekMBA, I’ve looked at hundreds of business models of companies from high-tech industries ( Alphabet’s Google , Amazon , Facebook , Apple , and Microsoft ) to more traditional industries, like luxury empires ( LVMH , Kering Group , Tiffany , Brunello Cucinelli , Prada ) and more.

I’ve analyzed from listed public companies, for which data can be found in financial statements, to small businesses for which data is not publicly available.

As I received this question repeatedly, I thought to show a simple framework to analyze any business.

For the sake of this framework, we’ll leverage business analysis to reverse engineer a business to either help it grow or to gather insights that can help us grow our own company.

Keep in mind that business analysis requires a good amount of creativity.

While a single framework is a good starting point, you will need to use your experience, understanding of the industry, and what is available out there to draw a picture of what you’re looking at.

In short, I think a practical approach to business analysis is that of the artist rather than the scientist.

Thus, while we’ll be using a few data points to understand a business, we want to keep our minds able to connect the dots in several areas to draw a picture that unlocks strategic insights that we can test.

To provide a framework as a starting point to analyze any sort of business, you’ll need to answer a few simple questions, each addressing a key element of the business.

We’ll tackle it by looking at three main competitive advantages a business can create over time:

• What’s the key asset? (core asset)

Market moat :

• Who’s the key stakeholder? (stakeholder profiling)
• What player is competing for the same customer? (context mapping)
• What’s the key touchpoint between the brand and the customer? (core distribution )

Financial moat:

• How does it make money? (revenue generation)
• Where’s the real cash? (cash generation)
• How does the company spend money? (cost structure)

Let’s analyze each of those elements to uncover and draw the picture of any business. We’ll start from the outer layer (the financial moat, to get to the core asset.

Financial moat

In the. financial moat stage we’ll answer:

The purpose of the financial moat is to follow the money to dig deeper into the business and move toward what gives it a real market advantage, and eventually, we’ll look for the business core asset.

How does it make money?

Revenue streams are important as a baseline to understand any business.

Following the money can be very powerful in business as it unlocks a set of questions that will help us drill down into the current picture but also to draw some possible conclusions about future operations and strategy .

For instance, if you look at Google revenue streams it’s interesting to notice a few things right away:

• The company still primarily makes money from advertising
• Google revenue streams are diversified (even though advertising is still the primary revenue stream)
• A very small percentage of Google’s revenues come from other bets

From those simple statements, we can drill further down and look at each revenue stream:

• Advertising revenues: Google makes money by two primary mechanisms: Google Ads and Google AdSense
• Other revenues: that comprises things like in-app revenues, but also hardware devices which Google sells
• Other bets: it comprises investments in other ventures

From this first look, we can depart from looking at other bets and other revenues. Not because those are not important for the future. Quite the opposite, one of the hidden gems of Google’s success in the next ten, twenty years might hide there.

But here we’re not trying to predict the future, which is impossible.

We want to reverse engineer the current business to gather some insights which will help us drive our own strategy now (for instance, if you’re building a business today by gaining organic traffic from Google understanding its logic helps a lot!).

Therefore, we’ll decide to drill down more

Why? We want to uncover where the real cash is.

Where’s the real cash?

When asking “where’s the real cash?” we’re not talking about cash flows, but rather about margins. In short, for companies like Netflix which run cash negative business models , it would be misleading to ask where’s the cash.

Instead, we want to look at the part of the business that has high-profit margins. For instance, if we look at Google’s advertising machine we can notice a few things:

To build a cash cow the company might do the following:

• Give up part of the margins on a line of business to strengthen another more strategic and scalable part of the business ( think of how Google splits revenues with network members thus giving up a good chunk of margins, yet by making its search pages way more valuable for users, and advertisers)
• Build a freemium part of the business which while doesn’t get monetized it helps amplify the brand and to build a valuable core asset monetized asymmetrically (we’ll see what that means)

How does the company spend money?

How the company spends its money informs about how it’s investing back into strengthening its core asset, thus building future growth .

Market moat

At this stage, we’ll ask:

The objective here is to understand what creates a competitive market advantage and point us toward the core asset of the company, which makes the business sustainable in the long-term.

Who’s the key stakeholder?

If you look at a companies’ like Amazon the complexity of the business goes well beyond a regular company.

In short, at this stage, it’s important to highlight the difference between small businesses which are more linear in how they approach customers.

And platform business models that instead have a more complex value chain.

We could make this process harder and harder by finding more business types, and classifying them into B2B, B2C, B2B2C, and more.

Or we can take a more straightforward approach.

Who’s the key user/customer, and what’s the value provided to her?

In Amazon’s case, for instance, the company has multiple products and each of them has a different value proposition .

Therefore, focusing on them all would be a mistake, as we want to go back and reconsider.

Who’s the Amazon repeat customer?

The customer who goes back to the Amazon e-commerce platform to buy over and over again is the key customer and where the company has built its success.

When you do look at the customer from that perspective, you stop assuming that Amazon Prime is another revenue stream . Instead, you understand that besides that, that is a way for Amazon to lock-in loyal customers and make their repeat purchases convenient (Prime Customers won’t pay for delivery).

The same happens if you go back and ask a similar question for a company like Google.

Who’s the person that drives up the value of the most important company’s asset?

If you look at Google’s business model , it’s easy to get fooled:

You might assume that as Google makes money by selling advertising to businesses, it will be the advertiser who pays Google to be the most valuable customer.

Yet, in Google’s case, the most valuable customer is the one who doesn’t pay: its users

That is because Google runs an asymmetric model .

In short, the company won’t monetize directly its users, but it will monetize the core asset which is built on top of the free users’ attention.

Where free users provide valuable data to Google’s algorithms, the company matches its technology with the users’ data and sells part of that as paid adverting.

In short, in an asymmetric model user and customers are not the same.

In a more symmetric model instead, users and customers are the same stakeholders.

The customer wearing the hat of the user provides valuable data to the platform. The company refines that data through proprietary algorithms and as a result, it gives back a valuable service to its customers.

That is how the Netflix business model works.

In those cases when the user is what provides valuable data to the core asset of the company, it’s important to understand that the tech company will prioritize its strategy around the user over time.

What player is competing for the same customer?

Once found the key stakeholder, the person who helps the company build its most valuable asset, we can zoom out a bit and understand the context in which the company operates.

One way to find comparable companies to map out the context is to look for those organizations that match the business and financial profile.

We do that because there is no company operating in a vacuum.

And even when a company that is better suited to help customers get things done might dominate.

In many other circumstances, better distribution strategy , capital moats, and more effective business models can help companies dominate beyond the value provided by their core products.

That’s why context matters.

In Google’s case we’ll look at the other players which are also grabbing the attention of users around the globe:

An attention-based model usually follows an asymmetric monetization strategy . Therefore, given Google’s key stakeholders (its users), and the fact that it’s an attention-based model, we can understand right away what products/platforms in the marketplace are comparable:

• Google (Alphabet)
• YouTube (Alphabet)
• Instagram (Facebook)
• Bing (Microsoft)
• TikTok (ByteDance)

Therefore, in order for Google to keep its competitive advantage is important to keep an eye on these.

* Note : The reason why Amazon is on the list as its website is one of the most important product search engines, intercepting the commercial intents of billions of people in the western world.

What’s the key touchpoint between the brand and the customer?

While disruptive startups built their name and grabbed market shares quickly by breaking down the trade-off between value and cost (at the basis of a blue ocean strategy ) there is another component of the success of any organization which can’t be ignored: distribution .

Distribution is the key touchpoint that makes customers connect with a brand , that enables companies to monetize their core assets and that enables them to keep tight long-term control over their business.

The importance of a distribution strategy can’t be overstated. Distribution isn’t just about delivering a product in the hands of the key customer that is also about:

• Enabling the company to be perceived inline with its pricing strategy and the brand ’s identity
• Building up the habits that enable users/customers to become champion of the product (just like you can’t stop using Google)
• Build competitive moats

Finally, at this stage, we can identify the core asset and put all together.

What’s the key asset?

The key asset is the main property that enables the company to make money in the long run.

For a tech business like Google, which is represented by its search results pages endowed by users’ data and algorithms, makes them extremely valuable to advertisers.

If we think of a smaller business or a non-tech company that can be represented by its premises or its brand .

For instance, a small Boutique hotel’s location is the key asset. For a luxury company, its brand is the most important asset.

The former is physical and easily identifiable.

The latter is instead non-physical and abstract, yet still extremely valuable as it enables companies like Prada, LVMH, Tiffany and other luxury brands to capture high margins.

Therefore depending on the company, the main asset might be the technology, data or brand . Or better yet a mixture of those things.

Putting it all together

As we identified the core asset, market, and financial moat, we can move backward to uncover the whole story.

In a case like Google, the company makes its money primarily by monetizing its search results pages (core asset).

It runs an asymmetric business model where the user and the customer are not the same (stakeholder profiling). Products and platforms like Amazon, Facebook and Twitter also draw the attention of users (context mapping), however, Google has a strong distribution network given for instance by the fact the company can cover the whole users’ journey (core distribution ), and most of its money is spent to maintain its core asset competitive (cost structure), while advertisers provide revenues and cash to the company which makes it financially sustainable (financial moat).

Where do you find the data?

A set of useful resources to find the data you need to analyze several businesses are:

• EDGAR Filings

It’s important to remark that when it comes to data it’s not important how many data points you find. Often it requires a bit of creativity to ponder the right question.

In that case, a single data point can tell you a lot about a business that you can use to assess the company or to drive the strategy for your own business.

FourWeekMBA business analysis framework summarized

To analyze any business, you can ask a few simple questions:

Each of those questions will lead to an understanding of the several blocks that make up internal and external strategic forces that shape the business.

Case study: how to make an everyday free tool your go to BI alternative

While it’s tempting to complex things up when performing business analysis, in reality, there is a simple tool, that you have been using for years, which can help you to perform a good part of your analysis: Google.

As pointed out on the Google blog in 2012:

Search is a lot about discovery—the basic human need to learn and broaden your horizons. But searching still requires a lot of hard work by you, the user. So today I’m really excited to launch the Knowledge Graph, which will help you discover new information quickly and easily. …The Knowledge Graph enables you to search for things, people or places that Google knows about—landmarks, celebrities, cities, sports teams, buildings, geographical features, movies, celestial objects, works of art and more—and instantly get information that’s relevant to your query. This is a critical first step towards building the next generation of search, which taps into the collective intelligence of the web and understands the world a bit more like people do. …the Knowledge Graph can help you make some unexpected discoveries. You might learn a new fact or new connection that prompts a whole new line of inquiry. Do you know where Matt Groening, the creator of the Simpsons (one of my all-time favorite shows), got the idea for Homer, Marge and Lisa’s names? It’s a bit of a surprise:

In 2012, Google started to roll out officially its Knowledge Graph (though its attempt to make the search experience even smarter and more semantic started way back and it escalated when the company acquired MetaWeb).

With that, Google started do develop more and more features related to giving beyond the classic ten blue links we have seen for years.

Those features we see appearing more and more on search results are coming from the massive Google’s semantic database made of billions of data points called Knowledge Graph.

Within the Knowledge Graph, Google combined semantic knowledge, to billions of users’ preferences and data, refined by its powerful algorithms and refined by its human raters.

This massive knowledge base is there to be explored, for free, it only requires you to be aware of it.

Industry analysis and setup

When searching for “Amazon” on Google, at the bottom of the page (from desktop) you will find several suggestions from Google, based on the industries where Amazon operates.

In short, Google is suggesting that Amazon primarily operates as an online retailer, and as such it compares it with other retailers (online and offline). Yet Google’s Knowledge Graph also expands on that and tells you more.

Amazon is also an AI company competing against other AI companies which offers you an interesting insight into the products of the company.

At the same time, Google is suggesting that Amazon is also a key player in the cloud space, thus it offers you some perspectives of how the cloud industry looks like by pointing out some direct competitors (like Microsoft and Oracle) and other companies operating in the cloud space.

Expand the research

From there, you can drill down into each of the carousels you see showing on Google to have a more detailed overview and expand the research. You can stretch it as far as you want, depending on the scope of the analysis.

Discover new data points

As an example, when you drill further down and search for “cloud companies” at the bottom of the search result page, you will find other categories of companies part of the cloud industry.

From PaaS to IaaS models, all were born as part of the cloud industry.

Key takeaway

While it’s easy to look for the ultimate business intelligence tools when performing an analysis, in reality, it makes sense to stop for a second and think about what might be the single data points that can give you insights about a company.

From there, you can use explorative tools, like Google to find out and drill down to draft an analysis that can give you different insights and enable you to reverse engineer many large companies.

Key Highlights

• The framework aims to analyze businesses for growth opportunities and strategic insights.
• It involves three main competitive advantages: Core moat, Market moat, and Financial moat.
• Identifying the key asset that gives the company a competitive advantage.
• Understanding the main value proposition of the business.
• Recognizing the key stakeholders and their value in the business.
• Identifying competing players in the same customer segment.
• Understanding the crucial touchpoints between the brand and customers.
• Analyzing revenue generation methods of the business.
• Identifying where the significant cash flows come from.
• Understanding the cost structure and how the company spends money.
• Distinguishing between users and customers in the business model.
• Highlighting the importance of leveraging data and technology.
• Using Google’s Knowledge Graph for insights and research.
• Expanding analysis by exploring suggested entities and categories.
• Discovering new data points to enhance the analysis.
• Focusing on single data points that offer valuable insights.
• Using explorative tools like Google to gather insights about a company.
• Emphasizing the importance of creative analysis and critical questions.
• The framework provides a structured approach to understanding various aspects of a business.
• It aids in identifying growth opportunities, competitive advantages, and strategic insights.
• The use of Google’s Knowledge Graph enhances research capabilities.

Connected Analysis Frameworks

Failure Mode And Effects Analysis

Agile Business Analysis

Business Valuation

Paired Comparison Analysis

Monte Carlo Analysis

Cost-Benefit Analysis

CATWOE Analysis

VTDF Framework

Pareto Analysis

Comparable Analysis

SWOT Analysis

PESTEL Analysis

Business Analysis

Financial Structure

Financial Modeling

Value Investing

Buffet Indicator

Financial Analysis

Post-Mortem Analysis

Retrospective Analysis

Root Cause Analysis

Blindspot Analysis

Break-even Analysis

Decision Analysis

DESTEP Analysis

STEEP Analysis

STEEPLE Analysis

Activity-Based Management

PMESII-PT Analysis

SPACE Analysis

Lotus Diagram

Functional Decomposition

Multi-Criteria Analysis

Stakeholder Analysis

Strategic Analysis

Related Strategy Concepts:  Go-To-Market Strategy ,  Marketing Strategy ,  Business Models ,  Tech Business Models ,  Jobs-To-Be Done ,  Design Thinking ,  Lean Startup Canvas ,  Value Chain ,  Value Proposition Canvas ,  Balanced Scorecard ,  Business Model Canvas ,  SWOT Analysis ,  Growth Hacking ,  Bundling ,  Unbundling ,  Bootstrapping ,  Venture Capital ,  Porter’s Five Forces ,  Porter’s Generic Strategies ,  Porter’s Five Forces ,  PESTEL Analysis ,  SWOT ,  Porter’s Diamond Model ,  Ansoff ,  Technology Adoption Curve ,  TOWS ,  SOAR ,  Balanced Scorecard ,  OKR ,  Agile Methodology ,  Value Proposition ,  VTDF Framework ,  BCG Matrix ,  GE McKinsey Matrix ,  Kotter’s 8-Step Change Model .

Main Guides:

• Business Models
• Business Strategy
• Marketing Strategy
• Business Model Innovation
• Platform Business Models
• Network Effects In A Nutshell
• Digital Business Models

More Resources

About The Author

Gennaro Cuofano

Discover more from fourweekmba.

Subscribe now to keep reading and get access to the full archive.

Type your email…

Continue reading

• 70+ Business Models
• Airbnb Business Model
• Amazon Business Model
• Apple Business Model
• Google Business Model
• Facebook [Meta] Business Model
• Microsoft Business Model
• Netflix Business Model
• Uber Business Model

How to Write a Business Analysis Report [Examples and Templates]

Table of contents

Business analysis reports are a lot like preparing a delicious meal.

Sometimes, the recipe is simple enough that you only need to use the basic ingredients. Other times, you will have to follow specific instructions to ensure those tasty delicacies turn out just right.

Want to make sure your business report never turns out like a chewy piece of meat? You’ve come to the right place.

Stay tuned until the end of this blog post, and we promise you won’t be hungry… for business knowledge!

What Is a Business Analysis Report?

Why is analytical reporting important, what should be included in a business analysis report, how do you write a business analysis report, business data analysis report examples and templates.

• Improve Business Reporting with Databox

A business analysis report provides information about the current situation of your company. This report is usually created by the management to help in the decision-making process and is usually used by other departments within a company.

Business analysis reports can either focus your research on the effectiveness of an existing business process or a proposed new process. Besides, an effective business analysis report should also assess the results to determine if the process changes had a positive or negative effect on the company’s goals. In fact, according to Databox’s State of business reporting , an overwhelming majority of companies said that reporting improved their performance.

Analytical reports are the bridge that connects your company to an effective, data-driven business intelligence strategy . By leveraging analytical reports , you can make informed decisions about your organization’s most critical issues. You will no longer need to rely on gut instinct or anecdotal evidence when assessing risks, threats, and opportunities. Instead, you will have access to a wealth of reliable data to inform your decisions.

Here are some essential benefits of analytical reporting:

• Improve communication and foster collaboration – The most obvious benefit of business analysis report writing is an improvement in communication between all stakeholders involved in the project. Also, analytical business reports can help you to generate more trust and foster better collaboration among your employees and colleagues. By using data analytics reporting tools , you will be able to monitor your employees’ performance on a day-to-day basis. This will allow you to hold them accountable for their actions and give them greater freedom within the business as they know that their superiors have faith in their decision-making capabilities.
• Increase productivity – Without this level of shared insight, businesses struggle to stay on top of their most important tasks and can become less efficient. An effective analytical business report provides the information needed for more efficient internal processes and helps you find more time for strategic activities such as improving your business strategy or working on long-term goals .
• Innovation – In today’s digital age, the pressure to innovate was never greater. When consumers basically have everything they want at their fingertips, stepping up to the plate with a new and improved product or service has never been more important. With an accessible dashboard in place, you will be able to create data-driven narratives for each of your business’ critical functions. For example, if you are a software company, you can use the insights gained from report analysis done with your dashboard software to tailor your product development efforts to the actual needs of your customers. By doing so, you will be able to develop products that are better tailored to specific customer groups. You can also use the same information for developing new marketing strategies and campaigns.
• Continuous business evolution – When it comes to digital businesses, data is everything. No model lasts forever, so having access to a business dashboard software that allows you to constantly keep tabs on your business’ performance will help you refine it as time goes on. If there are any glitches in your business model, or if something isn’t panning out as expected, the insight offered by a business analysis report can help you improve upon what works while scrapping what doesn’t.

A business analysis report has several components that need to be included to give a thorough description of the topic at hand. The structure and length of business analysis reports can vary depending on the needs of the project or task.

They can be broken down into different sections that include an:

• Executive summary
• Study introduction
• Methodology
• Review of statistics

Reports of this nature may also include case studies or examples in their discussion section.

A report can be written in a formal or informal tone, depending on the audience and purpose of the document. While a formal tone is best for executives , an informal tone is more appropriate for technical audiences . It is also a good idea to use something like an executive summary template to report on the results repeatedly with ease.

A good business analysis report is detailed and provides recommendations in the form of actionable steps. Here we have listed some simple steps that you need to follow to write a good business analysis report. Report writing is a major part of the business analysis process. In this section, you will learn how to write a report for your company:

Preparation

Presentation.

Obtain an overview of what you want to analyze in the business report . For example, if you are writing a business analysis report on how to improve customer service at an insurance company, you will want to look through all the customer service processes to determine where the problems lie. The more prepared you are when starting a project, the easier it will be to get results. Here is what your preparation should look like:

Set your goals

The first step in writing this document is to set your goals . What do you hope to accomplish with this paper? Do you need to assess the company’s finances? Are you looking for ways to make improvements? Or do you have outside investors who want to know if they should buy into the company? Once you know what your goal is, then you can begin setting up your project.

PRO TIP: How Well Are Your Marketing KPIs Performing?

Like most marketers and marketing managers, you want to know how well your efforts are translating into results each month. How much traffic and new contact conversions do you get? How many new contacts do you get from organic sessions? How are your email campaigns performing? How well are your landing pages converting? You might have to scramble to put all of this together in a single report, but now you can have it all at your fingertips in a single Databox dashboard.

Our Marketing Overview Dashboard includes data from Google Analytics 4 and HubSpot Marketing with key performance metrics like:

• Sessions . The number of sessions can tell you how many times people are returning to your website. Obviously, the higher the better.
• New Contacts from Sessions . How well is your campaign driving new contacts and customers?
• Marketing Performance KPIs . Tracking the number of MQLs, SQLs, New Contacts and similar will help you identify how your marketing efforts contribute to sales.
• Email Performance . Measure the success of your email campaigns from HubSpot. Keep an eye on your most important email marketing metrics such as number of sent emails, number of opened emails, open rate, email click-through rate, and more.
• Blog Posts and Landing Pages . How many people have viewed your blog recently? How well are your landing pages performing?

Now you can benefit from the experience of our Google Analytics and HubSpot Marketing experts, who have put together a plug-and-play Databox template that contains all the essential metrics for monitoring your leads. It’s simple to implement and start using as a standalone dashboard or in marketing reports, and best of all, it’s free!

You can easily set it up in just a few clicks – no coding required.

To set up the dashboard, follow these 3 simple steps:

Step 1: Get the template 

Step 2: Connect your HubSpot and Google Analytics 4 accounts with Databox. 

Step 3: Watch your dashboard populate in seconds.

Assess the Company’s Mission

It’s almost impossible to write a business analysis report without access to the company’s mission statement. Even if you don’t plan on using the mission statement as part of your business analysis summary, it can help you understand the company’s culture and goals. Mission statements are typically short and easy to read, but they may not include every area of focus that you want to include in your report.

Thus, it is important to use other sources when possible. For example, if you are writing a business analysis report for a small start-up company that is just beginning to market its product or service, review the company website or talk directly with management to learn what they believe will be most crucial in growing the company from the ground up.

Stakeholder Analysis

Who is your audience? Create the reader’s persona and tailor all information to their perspective. Create a stakeholder map that identifies all the groups, departments, functions, and individuals involved in this project (and any other projects related to this one). Your stakeholder map should include a description of each group’s role.

Review Financial Performance

Review the financing of the business and determine whether there are any potential threats to the company’s ability to meet its future financial obligations. This includes reviewing debt payments and ownership equity compared with other types of financing such as accounts receivable, cash reserves, and working capital. Determine whether there have been any changes in the funding over time, such as an increase in long-term debt or a decrease in owners’ equity.

Apart from reviewing your debt payments and ownership equity with other types of financing, wouldn’t it be great if you could compare your financial performance to companies that are exactly like yours? With Databox, this can be done in less than 3 minutes.

For example, by  joining this benchmark group , you can better understand your gross profit margin performance and see how metrics like income, gross profit, net income, net operating increase, etc compare against businesses like yours.

One piece of data that you would be able to discover is the average gross profit a month for B2B, B2C, SaaS and eCommerce. Knowing that you perform better than the median may help you evaluate your current business strategy and identify the neccessary steps towards improving it.

Instantly and Anonymously Benchmark Your Company’s Performance Against Others Just Like You

If you ever asked yourself:

• How does our marketing stack up against our competitors?
• Are our salespeople as productive as reps from similar companies?
• Are our profit margins as high as our peers?

Databox Benchmark Groups can finally help you answer these questions and discover how your company measures up against similar companies based on your KPIs.

When you join Benchmark Groups, you will:

• Get instant, up-to-date data on how your company stacks up against similar companies based on the metrics most important to you. Explore benchmarks for dozens of metrics, built on anonymized data from thousands of companies and get a full 360° view of your company’s KPIs across sales, marketing, finance, and more.
• Understand where your business excels and where you may be falling behind so you can shift to what will make the biggest impact. Leverage industry insights to set more effective, competitive business strategies. Explore where exactly you have room for growth within your business based on objective market data.
• Keep your clients happy by using data to back up your expertise. Show your clients where you’re helping them overperform against similar companies. Use the data to show prospects where they really are… and the potential of where they could be.
• Get a valuable asset for improving yearly and quarterly planning . Get valuable insights into areas that need more work. Gain more context for strategic planning.

The best part?

• Benchmark Groups are free to access.
• The data is 100% anonymized. No other company will be able to see your performance, and you won’t be able to see the performance of individual companies either.

When it comes to showing you how your performance compares to others, here is what it might look like for the metric Average Session Duration:

And here is an example of an open group you could join:

And this is just a fraction of what you’ll get. With Databox Benchmarks, you will need only one spot to see how all of your teams stack up — marketing, sales, customer service, product development, finance, and more. 

• Choose criteria so that the Benchmark is calculated using only companies like yours
• Narrow the benchmark sample using criteria that describe your company
• Display benchmarks right on your Databox dashboards

Sounds like something you want to try out? Join a Databox Benchmark Group today!

Examine the “Four P’s”

“Four P’s” — product , price , place, and promotion . Here’s how they work:

• Product — What is the product? How does it compare with those of competitors? Is it in a position to gain market share?
• Price — What is the price of the product? Is it what customers perceive as a good value?
• Place — Where will the product be sold? Will existing distribution channels suffice or should new channels be considered?
• Promotion — Are there marketing communications efforts already in place or needed to support the product launch or existing products?

Evaluate the Company Structure

A business analysis report examines the structure of a company, including its management, staff, departments, divisions, and supply chain. It also evaluates how well-managed the company is and how efficient its supply chain is. In order to develop a strong strategy, you need to be able to analyze your business structure.

When writing a business analysis report, it’s important to make sure you structure your work properly. You want to impress your readers with a clear and logical layout, so they will be able to see the strengths of your recommendations for improving certain areas of the business. A badly written report can completely ruin an impression, so follow these steps to ensure you get it right the first time.

A typical business analysis report is formatted as a cover page , an executive summary , information sections, and a summary .

• A cover page contains the title and author of the report, the date, a contact person, and reference numbers.
• The information section is backed up by data from the work you’ve done to support your findings, including charts and tables. Also, includes all the information that will help you make decisions about your project. Experience has shown that the use of reputable study materials, such as  StuDocu  and others, might serve you as a great assistant in your findings and project tasks.
• A summary is a short overview of the main points that you’ve made in the report. It should be written so someone who hasn’t read your entire document can understand exactly what you’re saying. Use it to highlight your main recommendations for how to change your project or organization in order to achieve its goals.
• The last section of a business analysis report is a short list of references that include any websites or documents that you used in your research. Be sure to note if you created or modified any of these documents — it’s important to give credit where credit is due.

The Process of Investigation

Explain the problem – Clearly identify the issue and determine who is affected by it. You should include a detailed description of the problem you are analyzing, as well as an in-depth analysis of its components and effects. If you’re analyzing a small issue on a local scale, make sure that your report reflects this scale. That way, if someone else reads your work who had no idea about its context or scope, they would still be able to understand it.

Explain research methods – There are two ways to do this. Firstly, you can list the methods you’ve used in the report to determine your actions’ success and failure. Secondly, you should add one or two new methods to try instead. Always tell readers how you came up with your answer or what data you used for your report. If you simply tell them that the company needs to improve customer service training then they won’t know what kind of data led you to that conclusion. Also, if there were several ways of addressing a problem, discuss each one and why it might not work or why it may not be appropriate for the company at this time.

Analyze data – Analyzing data is an integral part of any business decision, whether it’s related to the costs of manufacturing a product or predicting consumer behavior. Business analysis reports typically focus on one aspect of an organization and break down that aspect into several parts — all of which must be analyzed in order to come to a conclusion about the original topic.

The Outcome of Each Investigation Stage

The recommendations and actions will usually follow from the business objectives not being met. For example, if one of your goals was to decrease costs then your recommendations would include optimization strategies for cost reduction . If you have more than one suggestion you should make a list of the pros and cons of each one. You can make several recommendations in one report if they are related. In addition, make sure that every recommendation has supporting arguments to back them up.

Report Summary

Every business analysis report should start with a summary. It’s the first thing people see and it needs to capture their attention and interest. The report summary can be created in two ways, depending on the nature of the report:

• If the report is a brief one, that simply gives a summary of the findings, then it can be created as part of the executive summary.
• But if it’s a long report, it could be too wordy to summarise. In this case, you can create a more detailed overview that covers all the main aspects of the project from both an internal and external point of view.

Everything comes down to this section. A presentation is designed to inform, persuade and influence decision-makers to take the next action steps.

Sometimes a slide or two can make them change their mind or open new horizons. These days, digital dashboards are becoming increasingly popular when it comes to presenting data in business reports. Dashboards combine different visualizations into one place, allowing users to get an overview of the information they need at a glance rather than searching through a bunch of documents or spreadsheets trying.

Databox offers dynamic and accessible digital dashboards that will help you to convert raw data into a meaningful story. And the best part is that you can do it with a ‘blink of an eye’ even if you don’t have any coding or designs skills. There is also an option of individual report customization so that you can tailor any dashboard to your own needs.

Pre-made dashboard templates can be extremely useful when creating your own business analysis report. While examples serve as inspiration, templates allow you to create reports quickly and easily without having to spend time (and money) developing the underlying data models.

Databox dashboard templates come with some of the most common pre-built metrics and KPIs different types of businesses track across different departments. In order to create powerful business insights within minutes, all you need to do is download any of our free templates and connect your data source — the metrics will populate automatically.

Business Report Examples and Templates

Databox business dashboard examples are simple and powerful tools for tracking your business KPIs and performance. These dashboards can be used by executive teams and managers as well as by senior management, marketing, sales, customer support, IT, accounting, and other departments. If you are new to this kind of reporting, you may not know how to set up a dashboard or what metrics should be displayed on it. This is where a premade template for business dashboards comes in handy.

For example, this Google Ads Report Template is designed to give you a simple way to keep track of your campaigns’ performance over time, and it’s a great resource for anyone who uses Google’s advertising platform, regardless of whether they’re an SMB, an SME or an enterprise.

KPI Report Examples and Templates

KPIs are the foundation of any business analysis, and they can come in a multitude of forms. While we’ve defined KPIs as metrics or measurements that allow you to assess the effectiveness of a given process, department, or team, there are a number of ways to evaluate your KPIs. Through the use of color-coding, user-friendly graphs and charts, and an intuitive layout, your KPIs should be easy for anyone to understand. A good way to do this is by having a dedicated business analyst on your team who can take on the task of gathering data, analyzing it, and presenting it in a way that will drive actionable insights. However, if you don’t have a dedicated analyst or don’t want to spend money on one, you can still create KPI reporting dashboards using free KPI Databox templates and examples .

For example, this Sales Overview template is a great resource for managers who want to get an overview of their sales team’s performance and KPIs. It’s perfect for getting started with business analysis, as it is relatively easy to understand and put together.

Performance Report Examples and Templates

All businesses, regardless of size or industry, need to know how well they are performing in order to make the best decisions for their company and improve overall ROI. A performance dashboard is a strategic tool used to track key metrics across different departments and provide insight into the health of a business. Databox has a collection of 50+ Performance Dashboard Examples and Templates which are available for free download.

For example, if your business is investing a lot into customer support, we recommend tracking your customer service performance with this Helpscout Mailbox Dashboard which will give you insights into conversations, your team’s productivity, customer happiness score, and more.

Executive Report Examples and Templates

An executive dashboard is a visual representation of the current state of a business. The main purpose of an executive dashboard is to enable business leaders to quickly identify opportunities, identify areas for improvement, pinpoint issues, and make data-informed decisions for driving sales growth, new product launches, and overall business growth. When an executive dashboard is fully developed, as one of these 50+ Databox Free Executive Examples and Templates , it offers a single view of the most important metrics for a business at a glance.

For example, you probably have more than one set of financial data tracked using an executive dashboard software : invoices, revenue reports (for accounting), income statements, to mention a few. If you want to view all this data in one convenient place, or even create a custom report that gives you a better picture of your business’s financial health, this Stripe Dashboard Template is a perfect solution for you.

Metrics Report Examples and Templates

Choosing the right metrics for your business dashboard can be crucial to helping you meet your business objectives, evaluate your performance, and get insights into how your business is operating. Metrics dashboards are used by senior management to measure the performance of their company on a day-to-day basis. They are also used by mid-level managers to determine how their teams are performing against individual goals and objectives. Databox provides 50+ Free Metrics Dashboard Examples and Templates that you can use to create your company’s own dashboards. Each is unique and will depend on your business needs.

For example, if you are looking for ways to track the performance of your DevOps team, and get the latest updates on projects quickly – from commits, and repository status, to top contributors to your software development projects, this GitHub Overview Dashboard is for you.

Small Business Report Examples and Templates

A lot of small business owners don’t realize how important it is to have a proper dashboard in place until they actually use one. A dashboard can help you track and compare different metrics, benchmark your performance against industry averages, evaluate the effectiveness of your marketing and sales strategies, track financials, and much more. So if you’re looking for a tool to help you measure and manage your small business’ performance, try some of these 50+ Free Small Business Dashboard Examples and Templates .

For example, this Quickbooks Dashboard template can help you get a clear understanding of your business’s financial performance, ultimately allowing you to make better-informed decisions that will drive growth and profitability.

Agency Report Examples and Templates

Agency dashboards are not a new concept. They have been around for years and are used by companies all over the world. Agency dashboards can be powerful tools for improving your marketing performance, increasing client loyalty, and landing new clients. There is no single correct way to create an agency dashboard. Everyone has their own goals and objectives, which will ultimately determine which data points you choose to include or track using a client dashboard software , but with these Databox 100+ Free Agency Dashboard Examples and Templates you have plenty of options to start with.

For example, you can use this Harvest Clients Time Report to easily see how much time your employees spend working on projects for a particular client, including billable hours and billable amount split by projects.

Better Business Reporting with Databox

Business analysis is all about finding smart ways to evaluate your organization’s performance and future potential. And that’s where Databox comes in.

Databox can be a helpful tool for business leaders who are required to analyze data, hold frequent meetings, and generate change in their organizations. From improving the quality and accessibility of your reporting to tracking critical performance metrics in one place, and sharing performance metrics with your peers and team members in a cohesive, presentable way, allow Databox to be your personal assistant in these processes, minimize the burdens of reporting and ensure you always stay on top of your metrics game.

Sign up today for free to start streamlining your business reporting process.

Get practical strategies that drive consistent growth

12 Tips for Developing a Successful Data Analytics Strategy

What Is Data Reporting and How to Create Data Reports for Your Business

What Is KPI Reporting? KPI Report Examples, Tips, and Best Practices

Build your first dashboard in 5 minutes or less

Latest from our blog

• Playmaker Spotlight: Tory Ferrall, Director of Revenue Operations March 27, 2024
• New in Databox: Safeguard Your Data With Advanced Security Settings March 18, 2024
• Metrics & KPIs
• vs. Tableau
• vs. Looker Studio
• vs. Klipfolio
• vs. Power BI
• vs. Whatagraph
• vs. AgencyAnalytics
• Product & Engineering
• Inside Databox
• Terms of Service
• Privacy Policy
• Talent Resources
• We're Hiring!
• Help Center
• API Documentation

business analyst Recently Published Documents

Total documents.

• Latest Documents
• Most Cited Documents
• Contributed Authors
• Related Sources
• Related Keywords

ANEC: Artificial Named Entity Classifier based on BI-LSTM for an AI-based Business Analyst

Business users across enterprises today rely on reports and dashboards created by IT organizations to understand the dynamics of their business better and get insights into the data. In many cases, these users are underserved and do not possess the technical skillset to query the data source to get the information they need. There is a need for users to access information in the most natural way possible. AI-based Business Analysts are going to change the future of business analytics and business intelligence by providing a natural language interface between the user and data. This natural language interface can understand ambiguous questions from users, the intent and convert the same into a database query. One of the important elements of an AI-based business analyst is to interpret a natural language question. It also requires identification of key business entities within the question and relationship between them to generate insights. The Artificial Named Entity Classifier (ANEC) helps us take a huge step forward in that direction by not only identifying but also classifying entities with the help of the sequence recognising prowess of BiLSTMs.

Customer Segmentation using RFM Model and K-Means Clustering

Today as the competition among marketing companies, retail stores, banks to attract newer customers and maintain the old ones is in its peak, every company is trying to have the customer segmentation approach in order to have upper hand in competition. So Our project is based on such customer clustering method where we have collected, analyzed, processed and visualized the customer’s data and build a data science model which will help in forming clusters or segments of customers using the k-means clustering algorithm and RFM model (Recency Frequency Monetary) for already existing customers. The input dataset we used is UK’s E-commerce dataset from UCI repository for Machine Learning which is based on customer’s purchasing behavioral. At the very simple the customer clusters would be like super customer, intermediate customers, customers on the verge of churning out based on RFM score .Along with this we also have created a web model where an e-commerce startup or e-commerce business analyst can analyze their own customers based on model we created .So using this it will be easy to target customers accordingly and achieve business strength by maintaining good relationship with the customers .

A mathematical model to evaluate return on investment in higher education

Subject. The article assesses the effectiveness of investments in higher education. Objectives. The aim is to assess the performance of investments in higher education for a Master’s student at the Peter the Great St. Petersburg Polytechnic University, in the field of Economics, Business Analyst Specialty. Methods. The methodology, presented in the study, includes three stages. The first assesses the demand for skills, the second assesses how the supply of skills match the demand, and the third – the effectiveness of investments in higher education, based on the developed mathematical model, scenario analysis, and decision tree. Results. We revealed that for a business analyst, the most important categories of skills are project management, decision-making, organizational competencies, communication, and knowledge of corporate software. The most required skills in these categories are the knowledge of business processes, project documentation, systems thinking, teamwork, communication, and well-bred speech. The analysis of correspondence between the competencies required by employers and those acquired in the training process showed that Master’s graduates meet the demand for the position of a business analyst in the labor market by 69%. Conclusions. The evaluation of the effectiveness of investment in higher education for a Master’s student of the Peter the Great St. Petersburg Polytechnic University, in the field of Economics, Business Analyst Specialty, shows that it is more profitable for a Bachelor graduate to continue studying for a Master's degree, rather than go straight to work.

Business intelligence as a decision support system tool

The relevance of the topic considered in the article is to solve the problems of designing management decision support systems for enterprises based on business analytics technology. The research purpose is to analyze the applied methodologies during the design stage of the enterprise information system, to develop principles for using management decision support systems based on business intelligence. The problem statement is to analyze the technologies available on the market, which deal with business analyst systems, their potential use for decision support systems, and to identify the main stages of business analyst for enterprises. Business intelligence (BI) is information that can be obtained from data contained in the operational systems of a firm, enterprise, corporation, or from external sources. The BI can help the management of a company make the best decision in the chosen sphere of human activity faster, and, consequently, win the competition in the market for goods and services. A decision support system (DSS) which uses business intelligence, is an automated structure designed to assist professionals in making decisions in a complex environment and to objectively analyze a subject area. The decision support system is the result of the integration of management information systems and database management systems (DBMS). The internal development of BI is more cost-effective. The methods used are Structured Analysis and Design Technique and Object-oriented methods. The results of the research: the analysis of the possibilities was conducted and recommendations relating to the use of BI within DSS were given. Competition between BI software in business analysts reduces the cost of products created making them accessible to end-users – producers, traders and corporations.

A Literature Review and Overview of Performance Management: A Guide to the Field

The underlining presupposition and the supposition of performance management as a study field have been controversial or have a non-defined concept ever since the field was introduced to the mainstream economy. The paper covers the concept of performance management as a business analyst, scrum master, archeologist, and leader. The research delves into the founding history of performance management and analyzes critical performance management tools. Our findings show that performance management should be seen, managed, and played as an infinite game while creating incentives for the players who will, in turn, drive productivity in any industry.

PECULIARITIES OF USING BUSINESS ANALYSIS TECHNIQUES IN ADMINISTRATIVE MANAGEMENT DURING THE COVID PANDEMIC-19

The current state of motor transport enterprises, which is characterized by negative dynamics of development in all sectors of the transport sector, is studied. The research of scientific works determined the direction of the article and the object of research was business processes in administrative management. That is, it is impossible not to agree with the authors to solve the crisis of modern enterprises. It should be noted that all of them are solved through the mechanisms of the administrative management system. Therefore, it became necessary to form conceptual features of the use of business analyst in administrative management during the Covid pandemic 19. Modern approaches to administrative management are considered, providing reliable administrative management of the motor transport enterprise. Management of business processes in motor transport enterprises of business provides their constant improvement and optimization therefore the most important tools of process management are approaches and methods of improvement of business processes managed by administrative management systems. The researched approaches are aimed at identifying duplication of functions, bottlenecks, cost centers, quality of individual operations, missing information, the possibility of automation and quality management. The main directions and software products for automation of business processes in the system of administrative management are established. It is proved that the holistic application of approaches and elements of business analyst in the administrative management of the enterprise will lead to great chances of maintaining the competitiveness of motor transport enterprises and ways out of the post-crisis crisis. The measures of administrative management concerning improvement of activity of the motor transport enterprises are offered. Therefore, in order for trucking companies to develop and differ from their competitors in the level of services provided and the level of comfort, in the critical conditions of the COVID-19 pandemic it is necessary to radically change the methods of administrative management, ie reengineer business processes.

Business Analyst Tasks for Requirement Elicitation

Dealing with the challenge of business analyst skills mismatch in the fourth industrial revolution, features of the application of game theory in the economic activity of economic entities.

Today, there are a huge number of different tools that help reduce risks, but the problem is that they rely on classical probability theory, statistics, etc. These methods can be effective, but they do not take into account the interaction of market participants, psychological characteristics. These problems entail an increase in risks and, as a result, a drop in income and other difficulties. Often, to solve such problems, a business analyst turns to such a branch of mathematics as game theory. Game theory refers to a mathematical method that looks for optimal strategies in the course of a game, and a game refers to a situation in which there are two or more participants who are fighting to defend their interests. A special advantage of game theory is to take into account the struggle of interests of each party, this helps to better understand the current situation and find the optimal solution plan for the real processes taking place in the economy of an economic entity.

Development of a BI application. Moving from a business idea to formulation of the problem

Development of BI applications and, in general, Business Intelligence are no longer new concepts for the market. Nevertheless, there is practically no literature of practical significance. This article is aimed at analyzing the author’s practical experience with the generation of conclusions and specific advice for a novice business analyst to use in his work. Inexperienced professionals just starting their careers in BI can face a variety of challenges, especially when dealing with business customers and developers. Therefore, the article pays special attention to the description of research objects and their correct interaction with each other. It also provides a detailed analysis of the initial stages: from the customer’s need to develop an application to setting clear detailed requirements for the contractor. The result of this work was the proposed methodology for step-by-step work and analysis of the difficulties that may be encountered on the way of the “newly minted” business analyst.

Export Citation Format

Share document.

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Business Research: Methods, Types & Examples

Content Index

Business research: Definition

Quantitative research methods, qualitative research methods, advantages of business research, disadvantages of business research, importance of business research.

Business research is a process of acquiring detailed information on all the areas of business and using such information to maximize the sales and profit of the business. Such a study helps companies determine which product/service is most profitable or in demand. In simple words, it can be stated as the acquisition of information or knowledge for professional or commercial purposes to determine opportunities and goals for a business.

Business research can be done for anything and everything. In general, when people speak about business research design , it means asking research questions to know where the money can be spent to increase sales, profits, or market share. Such research is critical to make wise and informed decisions.

LEARN ABOUT: Research Process Steps

For example: A mobile company wants to launch a new model in the market. But they are not aware of what are the dimensions of a mobile that are in most demand. Hence, the company conducts business research using various methods to gather information, and the same is then evaluated, and conclusions are drawn as to what dimensions are most in demand.

This will enable the researcher to make wise decisions to position his phone at the right price in the market and hence acquire a larger market share.

LEARN ABOUT:  Test Market Demand

Business research: Types and methodologies

Business research is a part of the business intelligence process. It is usually conducted to determine whether a company can succeed in a new region, to understand its competitors, or simply select a marketing approach for a product. This research can be carried out using steps in qualitative research methods or quantitative research methods.

Quantitative research methods are research methods that deal with numbers. It is a systematic empirical investigation using statistical, mathematical, or computational techniques . Such methods usually start with data collection and then proceed to statistical analysis using various methods. The following are some of the research methods used to carry out business research.

LEARN ABOUT: Data Management Framework

Survey research

Survey research is one of the most widely used methods to gather data, especially for conducting business research. Surveys involve asking various survey questions to a set of audiences through various types like online polls, online surveys, questionnaires, etc. Nowadays, most of the major corporations use this method to gather data and use it to understand the market and make appropriate business decisions.

Various types of surveys, like cross-sectional studies , which need to collect data from a set of audiences at a given point of time, or longitudinal surveys which are needed to collect data from a set of audiences across various time durations in order to understand changes in the respondents’ behavior are used to conduct survey research. With the advancement in technology, surveys can now be sent online through email or social media .

For example: A company wants to know the NPS score for their website i.e. how satisfied are people who are visiting their website. An increase in traffic to their website or the audience spending more time on a website can result in higher rankings on search engines which will enable the company to get more leads as well as increase its visibility.

Hence, the company can ask people who visit their website a few questions through an online survey to understand their opinions or gain feedback and hence make appropriate changes to the website to increase satisfaction.

Learn More:  Business Survey Template

Correlational research

Correlational research is conducted to understand the relationship between two entities and what impact each one of them has on the other. Using mathematical analysis methods, correlational research enables the researcher to correlate two or more variables .

Such research can help understand patterns, relationships, trends, etc. Manipulation of one variable is possible to get the desired results as well. Generally, a conclusion cannot be drawn only on the basis of correlational research.

For example: Research can be conducted to understand the relationship between colors and gender-based audiences. Using such research and identifying the target audience, a company can choose the production of particular color products to be released in the market. This can enable the company to understand the supply and demand requirements of its products.

Causal-Comparative research

Causal-comparative research is a method based on the comparison. It is used to deduce the cause-effect relationship between variables. Sometimes also known as quasi-experimental research, it involves establishing an independent variable and analyzing the effects on the dependent variable.

In such research, data manipulation is not done; however, changes are observed in the variables or groups under the influence of the same changes. Drawing conclusions through such research is a little tricky as independent and dependent variables will always exist in a group. Hence all other parameters have to be taken into consideration before drawing any inferences from the research.

LEARN ABOUT: Causal Research

For example: Research can be conducted to analyze the effect of good educational facilities in rural areas. Such a study can be done to analyze the changes in the group of people from rural areas when they are provided with good educational facilities and before that.

Another example can be to analyze the effect of having dams and how it will affect the farmers or the production of crops in that area.

LEARN ABOUT: Market research trends

Experimental research

Experimental research is based on trying to prove a theory. Such research may be useful in business research as it can let the product company know some behavioral traits of its consumers, which can lead to more revenue. In this method, an experiment is carried out on a set of audiences to observe and later analyze their behavior when impacted by certain parameters.

LEARN ABOUT: Behavioral Targeting

For example: Experimental research was conducted recently to understand if particular colors have an effect on consumers’ hunger. A set of the audience was then exposed to those particular colors while they were eating, and the subjects were observed. It was seen that certain colors like red or yellow increase hunger.

Hence, such research was a boon to the hospitality industry. You can see many food chains like Mcdonalds, KFC, etc., using such colors in their interiors, brands, as well as packaging.

Another example of inferences drawn from experimental research, which is used widely by most bars/pubs across the world, is that loud music in the workplace or anywhere makes a person drink more in less time. This was proven through experimental research and was a key finding for many business owners across the globe.

Online research / Literature research

Literature research is one of the oldest methods available. It is very economical, and a lot of information can be gathered using such research. Online research or literature research involves gathering information from existing documents and studies, which can be available at Libraries, annual reports, etc.

Nowadays, with the advancement in technology, such research has become even more simple and accessible to everyone. An individual can directly research online for any information that is needed, which will give him in-depth information about the topic or the organization.

Such research is used mostly by marketing and salespeople in the business sector to understand the market or their customers. Such research is carried out using existing information that is available from various sources. However, care has to be taken to validate the sources from where the information is going to be collected.

For example , a salesperson has heard a particular firm is looking for some solution that their company provides. Hence, the salesperson will first search for a decision maker from the company, investigate what department he is from, and understand what the target company is looking for and what they are into.

Using this research, he can cater his solution to be spot on when he pitches it to this client. He can also reach out to the customer directly by finding a means to communicate with him by researching online.’

LEARN ABOUT: 12 Best Tools for Researchers

Qualitative research is a method that has a high importance in business research. Qualitative research involves obtaining data through open-ended conversational means of communication. Such research enables the researcher to not only understand what the audience thinks but also why he thinks it.

In such research, in-depth information can be gathered from the subjects depending on their responses. There are various types of qualitative research methods, such as interviews, focus groups, ethnographic research, content analysis, and case study research, that are widely used.

Such methods are of very high importance in business research as they enable the researcher to understand the consumer. What motivates the consumer to buy and what does not is what will lead to higher sales, and that is the prime objective for any business.

Following are a few methods that are widely used in today’s world by most businesses.

Interviews are somewhat similar to surveys, like sometimes they may have the same types of questions used. The difference is that the respondent can answer these open-ended questions at length, and the direction of the conversation or the questions being asked can be changed depending on the response of the subject.

Such a method usually gives the researcher detailed information about the perspective or opinions of its subject. Carrying out interviews with subject matter experts can also give important information critical to some businesses.

For example: An interview was conducted by a telecom manufacturer with a group of women to understand why they have less number of female customers. After interviewing them, the researcher understood that there were fewer feminine colors in some of the models, and females preferred not to purchase them.

Such information can be critical to a business such as a  telecom manufacturer and hence it can be used to increase its market share by targeting women customers by launching some feminine colors in the market.

Another example would be to interview a subject matter expert in social media marketing. Such an interview can enable a researcher to understand why certain types of social media advertising strategies work for a company and why some of them don’t.

LEARN ABOUT: Qualitative Interview

Focus groups

Focus groups are a set of individuals selected specifically to understand their opinions and behaviors. It is usually a small set of a group that is selected keeping in mind the parameters for their target market audience to discuss a particular product or service. Such a method enables a researcher with a larger sample than the interview or a case study while taking advantage of conversational communication.

Focus group is also one of the best examples of qualitative data in education . Nowadays, focus groups can be sent online surveys as well to collect data and answer why, what, and how questions. Such a method is very crucial to test new concepts or products before they are launched in the market.

For example: Research is conducted with a focus group to understand what dimension of screen size is preferred most by the current target market. Such a method can enable a researcher to dig deeper if the target market focuses more on the screen size, features, or colors of the phone. Using this data, a company can make wise decisions about its product line and secure a higher market share.

Ethnographic research

Ethnographic research is one of the most challenging research but can give extremely precise results. Such research is used quite rarely, as it is time-consuming and can be expensive as well. It involves the researcher adapting to the natural environment and observing its target audience to collect data. Such a method is generally used to understand cultures, challenges, or other things that can occur in that particular setting.

For example: The world-renowned show “Undercover Boss” would be an apt example of how ethnographic research can be used in businesses. In this show, the senior management of a large organization works in his own company as a regular employee to understand what improvements can be made, what is the culture in the organization, and to identify hard-working employees and reward them.

It can be seen that the researcher had to spend a good amount of time in the natural setting of the employees and adapt to their ways and processes. While observing in this setting, the researcher could find out the information he needed firsthand without losing any information or any bias and improve certain things that would impact his business.

LEARN ABOUT:   Workforce Planning Model

Case study research

Case study research is one of the most important in business research. It is also used as marketing collateral by most businesses to land up more clients. Case study research is conducted to assess customer satisfaction and document the challenges that were faced and the solutions that the firm gave them.

These inferences are made to point out the benefits that the customer enjoyed for choosing their specific firm. Such research is widely used in other fields like education, social sciences, and similar. Case studies are provided by businesses to new clients to showcase their capabilities, and hence such research plays a crucial role in the business sector.

For example: A services company has provided a testing solution to one of its clients. A case study research is conducted to find out what were the challenges faced during the project, what was the scope of their work, what objective was to be achieved, and what solutions were given to tackle the challenges.

The study can end with the benefits that the company provided through its solutions, like reduced time to test batches, easy implementation or integration of the system, or even cost reduction. Such a study showcases the capability of the company, and hence it can be stated as empirical evidence of the new prospect.

Website visitor profiling/research

Website intercept surveys or website visitor profiling/research is something new that has come up and is quite helpful in the business sector. It is an innovative approach to collect direct feedback from your website visitors using surveys. In recent times a lot of business generation happens online, and hence it is important to understand the visitors of your website as they are your potential customers.

Collecting feedback is critical to any business, as without understanding a customer, no business can be successful. A company has to keep its customers satisfied and try to make them loyal customers in order to stay on top.

A website intercept survey is an online survey that allows you to target visitors to understand their intent and collect feedback to evaluate the customers’ online experience. Information like visitor intention, behavior path, and satisfaction with the overall website can be collected using this.

Depending on what information a company is looking for, multiple forms of website intercept surveys can be used to gather responses. Some of the popular ones are Pop-ups, also called Modal boxes, and on-page surveys.

For example: A prospective customer is looking for a particular product that a company is selling. Once he is directed to the website, an intercept survey will start noting his intent and path. Once the transaction has been made, a pop-up or an on-page survey is provided to the customer to rate the website.

Such research enables the researcher to put this data to good use and hence understand the customers’ intent and path and improve any parts of the website depending on the responses, which in turn would lead to satisfied customers and hence, higher revenues and market share.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

• Business research helps to identify opportunities and threats.
• It helps identify research problems , and using this information, wise decisions can be made to tackle the issue appropriately.
• It helps to understand customers better and hence can be useful to communicate better with the customers or stakeholders.
• Risks and uncertainties can be minimized by conducting business research in advance.
• Financial outcomes and investments that will be needed can be planned effectively using business research.
• Such research can help track competition in the business sector.
• Business research can enable a company to make wise decisions as to where to spend and how much.
• Business research can enable a company to stay up-to-date with the market and its trends, and appropriate innovations can be made to stay ahead in the game.
• Business research helps to measure reputation management
• Business research can be a high-cost affair
• Most of the time, business research is based on assumptions
• Business research can be time-consuming
• Business research can sometimes give you inaccurate information because of a biased population or a small focus group.
• Business research results can quickly become obsolete because of the fast-changing markets

Business research is one of the most effective ways to understand customers, the market, and competitors. Such research helps companies to understand the demand and supply of the market. Using such research will help businesses reduce costs and create solutions or products that are targeted to the demand in the market and the correct audience.

In-house business research can enable senior management to build an effective team or train or mentor when needed. Business research enables the company to track its competitors and hence can give you the upper hand to stay ahead of them.

Failures can be avoided by conducting such research as it can give the researcher an idea if the time is right to launch its product/solution and also if the audience is right. It will help understand the brand value and measure customer satisfaction which is essential to continuously innovate and meet customer demands.

This will help the company grow its revenue and market share. Business research also helps recruit ideal candidates for various roles in the company. By conducting such research, a company can carry out a SWOT analysis , i.e. understand the strengths, weaknesses, opportunities, and threats. With the help of this information, wise decisions can be made to ensure business success.

LEARN ABOUT:  Market research industry

Business research is the first step that any business owner needs to set up his business to survive or to excel in the market. The main reason why such research is of utmost importance is that it helps businesses to grow in terms of revenue, market share, and brand value.

MORE LIKE THIS

Top 17 UX Research Software for UX Design in 2024

Apr 5, 2024

Healthcare Staff Burnout: What it Is + How To Manage It

Apr 4, 2024

Top 15 Employee Retention Software in 2024

Top 10 Employee Development Software for Talent Growth

Apr 3, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

Business Analytics

Train balanced graduates: That’s what we do.

Get prepared for a high demand, high salary career with a degree in business analytics. You'll be able to work in a variety of industries like healthcare, finance, retail, and technology. Learn how to collect, analyze, and interpret data to help businesses make better decisions. 

What  You’ll  Do       

As a business analytics major at Southeast, you'll develop the skills to mine and visualize data, learn how to responsibly use data and work on your critical thinking skills. You’ll also: 

• Utilize machine learning to learn from data and make predictions.
• Use statistical methods to analyze data and draw conclusions.
• Plan, execute, and track projects.
• Write code to manipulate data and build models.

What Can You Do With a Business Analytics Degree?

Business analytics is a high demand degree with the potention for a high salary. The work is challenging and rewarding and you have a wide variety of industries to choose from as a business analytics professional:

• Data analyst
• Business intelligence analyst
• Data scientist
• Marketing analyst
• Operations research analyst

Business Analytics Degree Map

Explore the courses you'll need to complete your degree.

Outcomes & Careers

Successful Outcomes Rate

Students graduating with degrees from the Harrison College of Business & Computing report being employed or furthering their education six months after graduation.   

Data Scientist

According to the Bureau of Labor Statistics, the median annual salary for a data scientist  is $100,910 .  

Operations Research Analyst

According to the Bureau of Labor Statistics, the median annual salary for an operations research analyst is $131,710 .    

According to the Bureau of Labor Statistics,  the job growth for certain occupations that require a busines analytics degree have a growth rate that is much faster than average, including operations research analysts and data scientists.

What You'll Study

Curriculum checklist, business administration core – 30 hours required  .

A grade of ‘C’ is required in each core course.  

• AC221 Principles of Accounting I (3)  
• AC222 Principles of Accounting II (3)  
• BA101 The Business Universe (3)  
• BA490 Business Policy and Strategy (3)  
• BL255 Business Law (3)  
• FI361 Financial Management (3)  
• MG301 Principles of Management (3)  
• MI375 Management Information Systems (3)  
• MK301 Principles of Marketing (3)  

Choose 3 hours:*  

• AC540 International Perspectives of Accounting (3)  
• BA560 Topics in International Business (3)  
• BL560 International Business Law (3)  
• EC580 International Economics (3)  
• FI540 International Finance (3)  
• MG560 International Management (3)  
• MK560 International Marketing (3)  

*International Course taken to meet Business Administration Core requirement may not be counted on a major.  

Support Courses – 27 Hours Required

Some courses may fulfill General Education requirements. A grade of ‘C’ or better is required in each support course.  

• BA252 Business Communication (2)  
• BA452 Professionalism (1)  
• EC215 Principles of Microeconomics (3)  
• EC225 Principles of Macroeconomics (3)  
• EN140 Rhetoric and Critical Thinking (3)  
• MA116 Precalculus A OR MA123 Mathematical Reasoning and Modeling (3)
• MI101 Intro to Computer Applications (3)  
• QM257 Business Analytics I (3)  
• QM258 Business Analytics II (3)  
• SC105 Fundamentals of Oral Communication (3)  

NOTE: All 100 and 200 level core and support courses are prerequisite to all 300 level business core and business major courses.  

Business Analytics Major - 26 Hours – No Minor Required

Required courses:  .

• CS101 Introduction to Computer Science (3)  
• CS155 Computer Science I (4)  
• CS265 Computer Science II (4)  
• CS453 Machine Learning (3)  
• CS505 Data Mining (3)  

Choose 3 hours:  

• AC330 Accounting Analytics and Information Systems (3)  
• EC351 Applied Economic Models (3)  
• EC490 Business Forecasting (3)  
• ER561 Business Planning for New Ventures (3)  
• FA315 Retail Buying (3)  
• MG416 Acquiring Talent (3)  
• MG436 Compensating Talent (3)  
• MK345 Intro to Business Research (3)  
• QM358 Production/Operations Management (3)  

Choose 6 hours:  

• AC555 Forensic Accounting Analytics (3)  
• CS433 Data Analytics (3)  
• HA540 Healthcare Informatics (3)  
• HA545 Healthcare Database Systems (3)  
• MA530 Statistical Learning (3)  
• MK345 Introduction to Business Research (3)  
• QM358 Operations Management (3)  
• QM558 Principles of Supply Chain Management (3)  

Additional requirement:  

• MI001 Microsoft Excel Certification (0)  

General Education Requirements

• Social and Behavioral Sciences – 6 hours  
• Constitution Requirement – 3 hours  
• Written Communication – 6 hours  
• Oral Communication – 3 hours  
• Natural Sciences – 7 hours (from two disciplines, one to include a lab)  
• Mathematics – 3 hours  
• Humanities & Fine Arts – 9 hours (from at least two disciplines)  
• Additional requirements – 5 hours (to include UI100 for nativestudents)  
• Civics examination

Sample 4 Year Plan

Freshman year, fall semester (13 hours).

• MA116 or MA123 (3)

Spring Semester (15 hours)

• General Education (3)

Sophomore Year

Fall semester (17 hours).

• General Education

Junior Year

Fall semester (16 hours).

• Economics Elective (3)
• EC490 or EC351 (3)

Senior Year

Fall semester (15 hours).

• International Course (3)

Spring Semester (14 hours)

• Elective (5)

What will it cost?

Get Funding for Your Education

Missouri’s opened the door to a more educated workforce. We want to help you walk through it. If you’re an adult learner and a Missouri resident returning to college, learn about the Fast Track Workforce Incentive Grant. It’s a statewide financial aid program that can be used to pursue a certificate, degree or industry-recognized credential in a high need area in Missouri. This major is eligible for the Fast Track grant.

Getting the Job

Your education is just one piece to launching an extraordinary career. Once you’ve mastered the material, you still have to find the job you want, make the right connections, sell your knowledge and experience—and if all this is giving you anxiety, don’t panic. SEMO’s Career Services office is here to help you with the next step. They’ll provide the expertise and support you need, so you’re landing your dream job in no time.

Become a Redhawk.

Do more than dream about the future. Take the first steps to make it all happen.

Explore Accounting, Economics, & Finance

Accelerated Master’s Degree

Southeast offers an accelerated master’s degree for current students. You can get both undergraduate and graduate credit for some 500 level courses, meaning you can graduate with an MBA sooner.

Additional Resources

• Office of Admissions
• Scholarships
• Student Organizations & Teams
• Department of Accounting, Economics and Finance
• Harrison College of Business & Computing

Related Programs

Accounting  degree : bsba mode of study : on-campus mode of study : main campus.

With an accounting degree, you’ll be prepared to pass the CPA exam and start a career in finance.

Economics (BS) Degree : BS Mode of study : On-Campus Mode of study : Main Campus

With an economics degree, you’re prepared for a career as a market research analyst, economic consultant, financial analyst, or personal financial advisor.

Economics: Financial (BSBA) Degree : BSBA Mode of study : On-Campus Mode of study : Main Campus

With an economics: financial degree, you’re prepared for a career in finance, investment banking, financial operations, and management roles.

Economics: Business (BSBA) Degree : BSBA Mode of study : On-Campus Mode of study : Main Campus

With an economics: business degree, you’re prepared for a career in finance, investment banking, financial operations, and management roles.

Finance Degree : BSBA Mode of study : On-Campus Mode of study : Main Campus

With a finance degree, you’re prepared for a career as a financial manager, analyst, finance director, or residential banker.

Business Analytics Degree : BSBA Mode of study : On-Campus Mode of study : Main Campus

With a business analytics degree, you’re prepared for a career as a data scientist, operations research analyst, and a financial analyst.

Take the Next Step

Audience links.

Resources for you.

• Current Students
• Parents & Families
• Accepted Students
• Faculty & Staff
• Community Partners

Current Students Resources for you

Featured links.

• University Calendar
• Office Directory
• People Directory
• People Search
• University Closings
• Program Finder
• Academic Calendar

Academic Advising

• Course Catalog
• Register for Classes
• Kent Library

Paying for College

• Pay My Bill
• Financial Services
• Student Employment
• Financial Calendar

Student Life

• Apply for Housing
• Residence Hall Association
• Student Transportation
• Dean of Students
• Career Services
• Schedule an Appointment
• Handshake Jobs

Parents & Families Resources for you

• Family/Parents
• Family Weekend
• About Southeast

Your Student

• New Student Programs
• Opening Week
• Residence Life
• Academic Support
• Health and Wellness
• Department of Public Safety

Accepted Students Resources for you

• Camp Redhawk
• Engage in 8
• First Year Experience
• Textbook Rental

Financial Aid

• Student Orgnizations

Counselors Resources for you

• Find My Admissions Counselor
• Counselor Resources
• College Planning
• Early College Programs

Applying to SEMO

• Admissions Requirements
• How to Apply
• Test Optional
• SEMO Scholarships
• Cost and Affordability

Launching Careers

Alumni resources for you.

• Alumni Association
• Update Your Information
• Share Your Story
• Southeast News

Get Connected

• Alumni Events
• Campus Events
• Show SEMO Spirit
• Giving to SEMO
• Get Involved
• Annual Fund

Faculty & Staff Resources for you

• Profile Spotlight Form
• Offices and Services
• Human Resources
• Training and Development

Faculty Resources

• Faculty Senate
• Center for Teaching and Learning

Staff Resources

• Professional Staff Council
• CTS Staff Council
• Work Order/Order Supplies Request
• Central Receiving

Donors Resources for you

• University Foundation

Making an Impact

• Student Stories
• Ways to Give
• Student Emergency Fund

Connecting with SEMO

• Redhawk Athletics

Community Partners Resources for you

• Economic and Workforce Development
• Skill Development
• University Communications

Workforce Development

• Small Business and Technology Development Center
• Southeast Online
• Regional Professional Development Center

Community Service

• Southeast Serves
• Office of Campus Life
• Office of Greek Life

Media Inquiries

• Redhawk Sports Information

Work Trend Index

Research and data on the trends reshaping the world of work

What Can Copilot’s Earliest Users Teach Us About Generative AI at Work?

A first look at the impact on productivity, creativity, and time.

About Work Trend Index

31,000 people. 31 countries. Trillions of productivity signals.

The Work Trend Index conducts global, industry-spanning surveys as well as observational studies to offer unique insights on the trends reshaping work for every employee and leader.

Annual Report · May 9, 2023

Will AI Fix Work?

The pace of work is outpacing our ability to keep up. AI is poised to create a whole new way of working.

Special Report · April 20, 2023

The New Performance Equation in the Age of AI

New research shows that employee engagement matters to the bottom line—especially amid economic uncertainty

Special Report · September 22, 2022

Hybrid Work Is Just Work. Are We Doing It Wrong?

In choppy economic waters, new data points to three urgent pivots for leaders to help employees and organizations thrive

Annual Report · March 16, 2022

Great Expectations: Making Hybrid Work Work

From when to go to the office to why work in the first place, employees have a new “worth it” equation. And there’s no going back.

Special Report · January 12, 2022

Technology Can Help Unlock a New Future for Frontline Workers

New data shows that now is the time to empower the frontline with the right digital tools

Special Report · September 9, 2021

To Thrive in Hybrid Work, Build a Culture of Trust and Flexibility

Microsoft employee survey data shows the importance of embracing different work styles—and the power of simple conversations

Special Report · April 20, 2021

Research Proves Your Brain Needs Breaks

New options help you carve out downtime between meetings

Special Report · March 30, 2021

In Hybrid Work, Managers Keep Teams Connected

Researchers found that feelings of connection among Microsoft’s teams diminished during the pandemic. They also discovered the remedy.

Annual Report · March 22, 2021

The Next Great Disruption Is Hybrid Work—Are We Ready?

Exclusive research and expert insights into a year of work like no other reveal urgent trends leaders should consider as hybrid work unfolds.

Special Report · 2020-09-22

A Checkup on Employee Wellbeing

Explore how the pandemic is impacting wellbeing at work around the world.

Special Report · 2020-07-08

The Knowns and Unknowns of the Future of Work

Learn how a sudden shift to remote work may have lasting effects around the world.

Special Report · 2020-04-09

Remote Work Trend Report: Meetings

See how global meeting habits changed during the world’s largest work-from-home mandate.

The WorkLab Newsletter: Science-based insights on the future of work, direct to your inbox

Discover more from WorkLab

Additional research on the future of work

Privacy Approach

Microsoft takes privacy seriously. We remove all personal and organization-identifying information, such as company name, from the data before analyzing it and creating reports. We never use customer content—such as information within an email, chat, document, or meeting—to produce reports. Our goal is to discover and share broad workplace trends that are anonymized by aggregating the data broadly from those trillions of signals that make up the Microsoft Graph.

• Current Employees
• Duke & Durham
• Human Resources
• Connect With Us
• External Applicants
• Current Duke Employees
• Duke Health Careers

Prospect Research Analyst I, Business Intelligence, Duke Alumni Engagement and Development (AED)

Durham, NC, US, 27710

PROSPECT RESEARCH ANALYST I

Occupational Summary

The Prospect Research Analyst I performs a variety of duties involving the collection, interpretation, analysis, and documentation of data on prospective donors. The Prospect Research Analyst I will submit reports to leadership and gift officers across schools/units/initiatives with strategic recommendations regarding identification, cultivation, solicitation, and stewardship of donors to Duke University. This position reports to the Associate Director of Research in Prospect Development.

Work Performed

Prospect Identification, Research, and Analysis

• Identify and research new prospects for strategic priorities and objectives for annual, planned, major, and principal giving.
• Partner with frontline fundraisers on routine research requests and to identify specific research needs.
• Monitor news sources for information on prospective and existing donors and trends in business, higher education, and philanthropy. Distribute news alerts as appropriate.
• Maintain, update, and query donor records in the university database; provide system-generated reports for analysis and interpretation.
• Respond to routine requests for giving capacity, business details, areas of philanthropic interest, and other information for a variety of individuals and programs across the university. Respond to requests in a timely, thoughtful, and complete manner.
• Focusing on wealth screening data, conduct research to determine donor giving capability and assess donor interest in various programs across the university.
• Discover, record, and interpret evidence of philanthropic interests in organizations and evaluate impact.
• Using hard, soft, and public wealth indicators identify, evaluate, and track highly appreciated assets and estimate donor gift capacity through the analysis of complex individual, corporate, and foundation financial information. 
• Summarize information relevant to the timing of a gift synthesizing large amounts of data to create a concise and confidential research report in a variety of formats.
• Present information in a variety of written formats including emails, reports, summaries, and abstracts.
• Build strategic partnerships with development officers in schools and units across the university.
• Assist in preparing presidential and dean briefings, bios, and related documentation as necessary. Aid in the development and/or coordination of written strategies for briefings and bios.

The intent of this job description is to provide a representative and level of the types of duties and responsibilities that will be required of positions given this title and shall not be construed as a declaration of the total of the specific duties and responsibilities of any particular position. Employees may be directed to perform job-related tasks other than those specifically presented in this description.

Preferred Education

• Bachelor’s degree required with demonstrated skills in research, writing, and editing.

Preferred Experience

• One year of prospect development experience in a non-profit or institution of higher education or related experience.
• Highly developed scanning, skimming, and reading comprehension skills are essential, as well as superior ability to assess the relative value of, synthesize, and summarize relevant information into coherent, well-written documents.
• Ability to take initiative and exercise sound independent judgement.
• Ability to comfortably handle multiple priorities with attention to detail and timeliness.

Preferred Skills

• Well-developed interpersonal skills and excellent written and oral communication skills with an aptitude for critical thinking and problem-solving; ability to work well under pressure and maintain flexibility. Ability to support peers and leaders is essential.
• Ability to learn to navigate unfamiliar information systems and use a variety of computer applications with little guidance.
• Position requires diplomacy and sensitivity related to institutional prioritization of needs.
• Skillful problem solving, curiosity, discretion, and continuous attention to detail
• Excellent analytical skills and ability to interpret and analyze data

Behavioral Competencies

• Self-Management
• Curiosity and Information Seeking
• Planning, Prioritizing, and Multitasking
• Decisiveness and Judgment
• Attention to Detail
• Expressing Ideas Orally and in Writing

Duke is an Affirmative Action/Equal Opportunity Employer committed to providing employment opportunity without regard to an individual's age, color, disability, gender, gender expression, gender identity, genetic information, national origin, race, religion, sex, sexual orientation, or veteran status.

Duke aspires to create a community built on collaboration, innovation, creativity, and belonging. Our collective success depends on the robust exchange of ideas—an exchange that is best when the rich diversity of our perspectives, backgrounds, and experiences flourishes. To achieve this exchange, it is essential that all members of the community feel secure and welcome, that the contributions of all individuals are respected, and that all voices are heard. All members of our community have a responsibility to uphold these values.

Essential Physical Job Functions: Certain jobs at Duke University and Duke University Health System may include essentialjob functions that require specific physical and/or mental abilities. Additional information and provision for requests for reasonable accommodation will be provided by each hiring department.

Nearest Major Market: Durham Nearest Secondary Market: Raleigh

Duke is an Affirmative Action / Equal Opportunity Employer committed to providing employment opportunity without regard to an individual’s age, color, disability, gender, gender expression, gender identity, genetic information, national origin, race, religion, sex, sexual orientation, or veteran status. Read more about Duke’s commitment to affirmative action and nondiscrimination at hr.duke.edu/eeo.

I am looking for…

I need support for…

• Login or other general help
• Paycheck Protection Program

analyst report

ADP Ranked Market Leader for Workforce Management Basics 2024 by Ventana Research, part of ISG

Your email Download now

Ventana Research, part of ISG, named ADP the Overall Leader and an Exemplary Leader for Workforce Management Basics. The basics buyers guide focuses on the foundational elements of Workforce Management such as timekeeping and scheduling.

This research is a distillation of over a year of market and product research efforts by the Ventana team. The reports are structured to provide an overview of areas commonly covered in the RFP process to provide an independent perspective from a market expert during the buying process. ADP is ranked the overall leader. Ventana evaluates vendors on two axes – Product Experience and Customer Experience.

Ventana Noted ADP Strengths:

• In the customer experience axis ADP is the overall leader due to commitment and dedication to customer needs. The #1 in the market ranking for customer experience was driven by scoring the highest in TCO/ROI due to the breadth of resources available to potential customers to identify benefits and create a business case for the product.
• In the product category for suites, ADP is rated a leader for the capability of the product and for the adaptability of the product. ADP is recognized for its ability to promote scalability and performance.

In the competitive landscape of workforce management solutions, ADP stands out for their exceptional customer experience. Recognized by Ventana Research as a leader in both Workforce Management and Payroll, they offer not just a product, but a partnership that promises and delivers substantial value and return on investment.

Matthew Brown, HCM Research Director, Ventana Research, part of iSG

Get the analyst report

Download now

Get the Report

Your privacy is assured.

Related resources

Managing Your Workforce

Join us for a SECURE 2.0 update and receive practical advice and best practices as you continue to navigate this legislation. SECURE 2.0 added many substantial incentives for employers to enhance their retirement plan and several changes that will help employees increase their retirement readiness.

Get pricing specific to your business