case studies for business analyst

August 20, 2019 From the BA Life of Glenn Hughes, MBA, CBAP Narrated by Jodie Kane << back to the scenarios list -->

Building Customer Centric Teamwork

Implementing a cots package product in a regulated environment, meet glenn hughes, mba, cbap senior business systems analyst consultant in the pharmaceutical industry.

ABC Pharma’s Challenge

Prior to commencing the COTS implementation project, ABC Pharma utilized an RFP process to select a COTS package that will support the needs of their scientists in the R&D and clinical business areas. The scientists need to have thorough documentation and precise content generated through the course of their work. The package will enable upload, storage, management of digital assets – and include record retention rules, management of security and authority levels, and integration of data with other applications. It will also capture the required regulatory audit trails with transparency, compliance and reportable audit trails. These scientists are dealing with human trials. ABC Pharma must do this right.

Solution Selected

Best Product was the chosen COTS package as the best fit for ABC Pharma’s requirements. Their practical implementation approach and cost-effective plan to address the high priority configuration needs uncovered through the gap assessment had won the attention of the business leadership team. The scientists and auditors were looking forward to a more streamlined process that would reduce some of the existing costs and risks associated with managing their digital assets. Leaders were cautiously thrilled with the prospect of better reporting and transparency to help improve regulatory compliance. The technology team was happy to be replacing an unstructured process that had more band-aids than they cared to let anyone know about.

Glenn was pleased at the spirited energy level from both the business and technology folks during the first project kick-off meeting with the vendor. High level build and release plans and architecture diagrams for the implementation of the new COTS package were reviewed. Everyone celebrated with hopeful confidence.

The standard RFP process had provided a solid foundation of the business requirements. Glenn had worked with the business team to create  Personas of the primary stakeholders who would be using the new tool, the scientists and the auditors. The personas really helped to visualize the unique needs and challenges of each customer. Combined with the Journey Map and content strategy, these visuals gave clear insights and exposed an understanding of some requirements that were unique to ABC Pharma.

Kick-off Preparation

Before the kick-off meeting, Glenn had created a presentation that provided an overview of the solution scope for the MVP release. He had started his Stakeholder Map, adding the stakeholders and technology team members that were listed on the project plan. Even though the PM had a stakeholder list, Glenn likes to keep his own list. Understanding the characteristics of each person helps him to plan the best ways to work with the team. After the kick-off meeting, he updated the Stakeholder Map with some changes and additions. He also added some information to clarify roles and accountabilities, and communication preferences. He wants to keep the stakeholder map current because he knows how important it is to effectively build relationships and engage appropriately with the whole project team.

Risk - Streamlining UAT Issues

In the early stages of the implementation process, Glenn had attended his local IIBA Chapter monthly meeting. The speaker had talked a lot about “thinking and being agile” regardless of the SDLC. She challenged BA’s in any industry or SDLC framework to always seek better ways of working with people through empathy and understanding, and to have a full toolkit of techniques ready for use. During the meeting, Glenn thought about the challenges of the regulatory requirements artifacts he had to deliver and the risks of finding issues during UAT in ABC Pharma’s primarily waterfall SDLC.

While necessary for audit and compliance and despite the effort to write them, the lengthy requirements artifact documents posed challenges for business requirements sign-off, developer configurations and the QA teams test cases. They lacked the dynamics of the visualization and models. And typically, by the time issues were identified in UAT, it was too late to affect change without cost of time and/or money. Glenn had an idea to modify their process by demonstrating development work in progress on a regular bi-weekly cycle. The stakeholders would need to agree to and understand that the functionality was in progress, not ready for testing but at a point where things could be changed, within reason, and not delay delivery or add cost. The chance of catching any big gotcha’s early on decreased the risk of failure.

Agreeing on Functional Strategy - Giving Stakeholders More Visibility

After the kick-off meeting, Glenn had scheduled a meeting with the core project team. He had included both business, technology and vendor team members, but only the “pigs” as they would say in Scrum, the ones who have skin in the game. “I have an idea that I’d like to talk about with all of you. I think it may help us, but only if we all work together and agree to open honest communication” was his opening statement as he posted this diagram on the screen:

He explained a process where they would work in iterations or sprints, at fixed time periods and scope. The business stakeholders on the core team would be involved throughout the process, not just at the end, so they had more visibility into the progress and increased opportunity to influence the final product. There would be some additional time investment on the front-end of the work effort, however, he was certain that it would pay off by the reduction of risk in the formal UAT testing phase and increase the stakeholder satisfaction level. Glenn asked everyone to take a few minutes to think about his proposal and jot down their questions, ideas and concerns. After a few minutes, he opened the space for safe dialogue inviting everyone to speak up.

“What about the regulatory documents?” asked Darcy, the audit stakeholder.

“I will continue to work on those as we progress. I believe the final documents will be a better product using this process.” Glenn replied. “And when you review the documents for sign-off, you will have a better understanding of what you are agreeing to”. Darcy smiled. “I like it, count me in”.

Sergey, the lead developer from the vendor spoke up. “We’re going to need data mapping with transformation rules from the source data fields to the target data fields. We also need to know which reports use the target data fields”.

“Yes”, Glenn responded, “those are over 75% completed. I’ll schedule time with you to review the artifacts and ensure that everything you need is included”.

Sergey nodded with approval and then added, “To be honest, I’m a little nervous about demonstrating our progress without the completed functionality. People will need to understand that it may not always look like a lot was accomplished. It’s kind of like constructing a building where the foundation and work under the covers isn’t always pretty, but I’m willing to try it too. Maybe we can find a way to demonstrate the foundation progress.

Glenn looked towards the business SME’s assigned to the project. Dr. Lee spoke up. “If I’m understanding this correctly, this may eliminate the blackhole feeling we have during development and the frustration we have in UAT when something doesn’t look like what we expected?”.

Glenn smiled. “Yes, that’s the plan” he replied. “We’ll work out a process to evaluate any changes you request during the demos and you’ll know right where we are”.

It appeared that everyone was onboard, however, Glenn wanted to get a deeper sense of where people really stood on the idea. Knowing their commitment to it would help him with future planning. He moved to the next slide and said, “Let’s seal it with a Fist to Five vote”.

Glenn breathed a sigh of relief. He didn’t get all 5’s, but he hadn’t expected to. If he had, he would have known that everyone hadn’t bought in to the safety and trust of their teamwork yet. With the 11 core team members, an overall average of 3.9 in the Fist to Five with no one under a 3 was a good sign. He had commitment. After the second demo, he planned to do another Fist to Five to keep his finger on the pulse of the team.

Culture Enables Strategy

“We have a few more minutes so let’s cover one more thing” Glenn said as he looked across the team and pulled up one more slide. “We have agreed on a functional strategy” and he typed in the bullet points as he spoke, “Code deploy to vendor sandbox bi-weekly on Thursday morning. Vendor review with BA after deployment. Bi-weekly demos on Thursday’s at 1:30. Feedback will be documented. Any change requests will be prioritized by the business and evaluated by the technology team.” He then asked, “Does that sound reasonable?”. Everyone expressed agreement.

Glenn continued, “The culture is how we commit to working together, our core values. This is what will make the difference. I have two that I’d like to start with” typing as he spoke, “Periodic retrospection to improve. Honest, candid communication.”

“Ok, I have one” piped up Dr. Lee, “Ask questions to understand”.

“Don’t take it personally” Darcy said.

“I have one to add too” Sergey chimed in, “Remember that we share the same goal”.

“Great start! These Working Agreements help us to solidify our team culture.” Glenn said as he began the closing of the meeting. He reiterated the next steps and closed the meeting 3 minutes ahead of schedule. People tend to walk away from meetings that end early or at least on time in a better state of mind. This slide will be one of the first at the start of each demo as a reminder and to set the tone for the team’s exchange during the demo meetings.

This story demonstrates a great start to the project and the teamwork. It doesn’t always start this well, but it’s possible. For the most part, people like to work together successfully. Remembering to integrate an element of fun into the process contributes to a healthy team culture too. Glenn has facilitated improvements to team engagement and demonstrated leadership in his role as the business analyst.

In Scenario 2, we’ll see what happens next…

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Business Analysis Case Study: Unlocking Growth Potential for a Company 

Have you ever wondered what are the necessary steps for conducting a Business Analyst Case Study? This blog will take you through the steps for conducting it.

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Table of Contents  

1) An overview of the Business Analysis Case Study 

2) Step 1: Understanding the company and its objectives 

3) Step 2: Gathering relevant data 

4) Step 3: Conducting SWOT analysis 

5) Step 4: Identifying key issues and prioritising 

6) Step 5: Analysing the root causes 

7) Step 6: Proposing solutions and developing an action plan 

8) Step 7: Monitoring and evaluation 

9) Conclusion 

An overview of the Business Analysis Case Study  

To kickstart our analysis, we will gain a deep understanding of the company's background, industry, and specific objectives. By examining the hypothetical company's objectives and aligning our analysis with its goals, we can lay the groundwork for a focused and targeted approach. This Business Analysis Case Study will demonstrate how the analysis process is pivotal in driving growth and overcoming obstacles that hinder success. 

Moving forward, we will navigate through various steps involved in the case study, including gathering relevant data, conducting a SWOT analysis, identifying key issues, analysing root causes, proposing solutions, and developing an action plan. By following this step-by-step approach, we can address the core challenges and devise actionable strategies that align with the company's objectives. 

The primary focus of this Business Analysis Case Study is to highlight the significance of Business Analysis in identifying key issues, evaluating potential growth opportunities, and developing effective solutions. Through a comprehensive examination of the hypothetical company's strengths, weaknesses, opportunities, and threats, we will gain valuable insights that drive informed decision-making. 

By the end of this Business Analysis Case Study, we aim to provide a holistic view of the analysis process, its benefits, and the transformative impact it can have on unlocking growth potential. Through real-world examples and practical solutions, we will showcase the power of Business Analysis in driving success and propelling companies towards achieving their goals. So, let's dive into the fascinating journey of this Business Analysis Case Study and explore the path to unlocking growth potential for our hypothetical company. 

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Step 1: Understanding the company and its objectives  

In this initial step, we need to gain a thorough understanding of the hypothetical company's background, industry, and specific objectives. Our hypothetical company, TechSolutions Ltd., is a software development firm aiming to expand its customer base and increase revenue by 20% within the next year. 

TechSolutions Ltd. operates in the dynamic software solutions market, catering to various industries such as finance, healthcare, and manufacturing. The company's primary objective is to leverage its technical expertise and establish itself as a leading provider of innovative software solutions. This objective sets the foundation for our analysis, enabling us to align our efforts with the company's goals. 

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Step 2: Gathering relevant data  

To conduct a comprehensive analysis, we need to gather relevant data pertaining to the company's operations, market trends, competitors, customer preferences, and financial performance. This data serves as a valuable resource to gain insights into the company's current position and identify growth opportunities. 

For our case study, TechSolutions Ltd. collects data on various aspects, including customer satisfaction levels, market penetration rates, and financial metrics such as revenue, costs, and profitability. Additionally, industry reports, market research, and competitor analysis provide insights into market trends, emerging technologies, and the competitive landscape. This data-driven approach ensures that our analysis is well-informed and grounded in reality. 

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Step 3: Conducting SWOT analysis  

A SWOT analysis is a powerful tool to assess the company's internal strengths and weaknesses, as well as external opportunities and threats. By conducting a thorough SWOT analysis, we can gain valuable insights into the company's strategic position and identify factors that impact its growth potential. 

Step 4: Identifying key issues and prioritising  

In the case of TechSolutions Ltd., the analysis reveals two primary issues: an outdated technology infrastructure and limited marketing efforts. These issues are prioritised as they directly impact the company's ability to meet its growth objectives. By addressing these key issues, TechSolutions Ltd. can position itself for sustainable growth. 

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Step 5: Analysing the root causes  

To develop effective solutions, we must analyse the root causes behind the identified issues. This involves a detailed examination of internal processes, conducting interviews with key stakeholders, and exploring market dynamics. By identifying the underlying factors contributing to the issues, we can tailor our solutions to address them at their core. 

In the case of TechSolutions Ltd., the analysis reveals that the outdated technology infrastructure is primarily due to budget constraints and a lack of awareness about the latest software solutions. Limited marketing efforts arise from a shortage of skilled personnel and inadequate allocation of resources. 

Understanding these root causes provides valuable insights for developing targeted and impactful solutions. 

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Step 6: Proposing solutions and developing an action plan  

For TechSolutions Ltd., the following solutions are proposed: 

a) Allocate a portion of the budget for technology upgrades and training: TechSolutions Ltd. should allocate a dedicated portion of its budget to upgrade its technology infrastructure and invest in training its employees on the latest software tools and technologies. This will ensure that the company remains competitive and can deliver cutting-edge solutions to its customers. 

b) Hire a dedicated marketing team and allocate resources for targeted campaigns: To overcome the limited marketing efforts, TechSolutions Ltd. should invest in building a skilled and dedicated marketing team. This team will focus on developing comprehensive marketing strategies, leveraging digital platforms, and conducting targeted campaigns to reach potential customers effectively. 

c) Strengthen partnerships with industry influencers: Collaborating with industry influencers can significantly enhance TechSolutions Ltd.'s brand visibility and credibility. By identifying key industry influencers and forming strategic partnerships, the company can tap into their existing networks and gain access to a wider customer base. 

d) Implement a customer feedback system: To enhance product quality and meet customer expectations, TechSolutions Ltd. should establish a robust customer feedback system. This system will enable the company to gather valuable insights, identify areas for improvement, and promptly address any customer concerns or suggestions. Regular feedback loops will foster customer loyalty and drive business growth. 

The proposed solutions are outlined in a detailed action plan, specifying the timeline, responsible individuals, and measurable milestones for each solution. Regular progress updates and performance evaluations ensure that the solutions are effectively implemented and deliver the desired outcomes. 

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Step 7: Monitoring and evaluation  

Conclusion  

In this detailed Business Analysis Case Study, we explored the challenges faced by a hypothetical company, TechSolutions Ltd., and proposed comprehensive solutions to unlock its growth potential. By following a systematic analysis process, which includes understanding the company's objectives, conducting a SWOT analysis, identifying key issues, analysing root causes, proposing solutions, and monitoring progress, businesses can effectively address their challenges and drive success. 

Business Analysis plays a vital role in identifying areas for improvement and implementing strategic initiatives. By leveraging data-driven insights and taking proactive measures, companies can navigate competitive landscapes, overcome obstacles, and achieve their growth objectives. With careful analysis and targeted solutions, TechSolutions Ltd. is poised to unlock its growth potential and establish itself as a leading software development firm in the industry. By implementing the proposed solutions and continuously monitoring their progress, the company will be well-positioned for long-term success and sustainable growth. 

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10 Business Analytics Case Studies [2024]

In today’s data-driven world, the strategic application of business analytics stands as a cornerstone for enterprise success across various industries. From retail giants optimizing inventory through predictive algorithms to healthcare systems enhancing patient care with personalized treatments, the transformative power of business analytics is undeniable. This compilation of ten business analytics case studies showcases how leading companies leverage data to drive decision-making, streamline operations, and deliver unprecedented value to customers. Each case study reveals unique insights into the practical challenges and innovative solutions that define cutting-edge business strategy, offering a window into the profound impact of data analytics in shaping global business landscapes.

Related: Business Analytics Vs. Data Analytics

Case Study 1: Walmart’s Inventory Management

Predictive Analytics for Inventory Efficiency

Walmart employs sophisticated predictive analytics to manage and optimize inventory across its extensive network of stores globally. This system uses historical sales data, weather predictions, and trending consumer behavior to forecast demand accurately. Walmart’s approach allows for dynamic adjustment of stock levels, ensuring that each store has just the right amount of inventory. This reduces the cost associated with excess inventory and minimizes instances of stockouts, thereby enhancing customer satisfaction.

Real-Time Data Integration for Strategic Decisions

The integration of real-time data from various sources, including point-of-sale systems, online transactions, and external market dynamics, enables Walmart to respond swiftly to changing market conditions. This commitment to security helps reduce risks and strengthens consumer confidence and trust in the brand, which is essential for retaining customers and ensuring satisfaction in the competitive financial services market. By leveraging this data, Walmart can launch targeted promotions and adjust pricing strategically to maximize sales and profitability, showcasing the power of real-time analytics in retail operations.

Case Study 2: UnitedHealth Group’s Predictive Analytics in Healthcare

Enhancing Patient Outcomes with Predictive Models

UnitedHealth Group utilizes predictive analytics to improve patient care within its network significantly. The healthcare provider can identify patients at risk of developing chronic diseases or those likely to experience rehospitalization by analyzing extensive datasets that include patient medical histories, treatment outcomes, and lifestyle choices. This proactive approach allows for early intervention through customized care plans, which enhances patient outcomes and optimizes resource allocation within the healthcare system.

Data-Driven Healthcare Management

UnitedHealth’s analytics capabilities extend to managing healthcare costs and improving service delivery. They can better manage staffing and resource needs by leveraging data to predict patient admission rates and peak times for different treatments. Furthermore, predictive analytics aids in developing new health services and programs that target the specific requirements of their patient population, leading to more efficient healthcare delivery and reduced operational costs. This strategic use of data ensures that patients receive the right care at the right time, enhancing overall patient satisfaction and loyalty.

Case Study 3: American Express Fraud Detection

Machine Learning for Advanced Fraud Prevention

American Express harnesses machine learning algorithms to enhance its fraud detection capabilities. By analyzing patterns in transaction data across millions of accounts, these algorithms can detect unusual behavior that may indicate fraud. Real-time processing of transactions allows American Express to quickly flag suspicious activities and prevent unauthorized transactions, protecting both the consumer and the institution from potential losses.

Building Consumer Trust Through Robust Security Measures

Advanced analytics helps American Express refine its customer verification processes and risk assessments. By continuously updating and training its models on new fraud tactics and scenarios, American Express stays ahead of fraudsters, ensuring robust security measures are in place. This robust emphasis on security reduces risks and enhances consumer confidence and trust in the organization, which is essential for maintaining client loyalty and satisfaction in the competitive financial services market.

Case Study 4: Zara’s Supply Chain Optimization

Responsive Supply Chain to Meet Fast Fashion Demands

Zara utilizes advanced analytics to create a highly responsive supply chain that keeps pace with the fast-changing fashion industry. Zara can quickly adjust production plans and inventory distribution by analyzing real-time sales data and customer feedback. This agility ensures that popular items are swiftly restocked and production of less popular items is curtailed, minimizing waste and maximizing profitability.

Streamlined Operations for Market Responsiveness

Zara’s analytics-driven approach extends to logistics and distribution strategies. Data analytics helps Zara optimize shipping routes and warehouse operations, reducing lead times from design to store shelves. This streamlined process meets consumer demand more efficiently and strengthens Zara’s position in the market by enabling rapid response to the latest fashion trends. This capability is a key differentiator in the competitive fast fashion market, where speed and responsiveness are critical to success.

Related: How to use Business Analytics to Improve Customer Retention?

Case Study 5: Netflix’s Recommendation Engine

Enhancing User Experience Through Personalized Recommendations

Netflix’s advanced machine learning algorithms are the powerhouse behind its highly acclaimed recommendation engine. This system delves deep into individual viewing histories, preferences, and interactive behaviors, such as pausing or rewinding, to customize content suggestions for each user. By tailoring viewing experiences to personal tastes, Netflix significantly enhances user engagement and satisfaction. This personalization makes it easier for subscribers to discover content that resonates with them, increasing their time on the platform and fostering a deeper connection to the Netflix brand.

Data-Driven Insights for Content Strategy

Beyond simply personalizing user experiences, Netflix employs a strategic content development and acquisition approach. Utilizing comprehensive data analytics, Netflix identifies trends and preferences in viewer behavior, such as popular genres or series, to inform its decisions on what new content to create or purchase. This systematic use of viewer data ensures that Netflix’s content library continuously evolves to match the preferences of its audience, maximizing viewer satisfaction and engagement. Moreover, this data-driven strategy enables Netflix to allocate its budget more effectively, investing in projects more likely to succeed and appeal to its user base, optimizing its return on investment.

Through these sophisticated analytics and machine learning applications, Netflix retains its position as a leader in the streaming industry. It sets the standard for media companies leveraging data to revolutionize user experience and drive business success.

Case Study 6: Coca-Cola’s Marketing Optimization

Leveraging Big Data for Targeted Marketing

Coca-Cola effectively utilizes big data analytics to refine its global marketing strategies. Coca-Cola gains deep insights into consumer behavior and preferences by analyzing diverse data sources, including social media interactions, point-of-sale transactions, and extensive market research. This valuable information enables the company to craft marketing campaigns tailored to various demographics and geographic regions. As a result, Coca-Cola enhances its advertisements’ relevance and appeal, significantly boosting its promotional activities’ effectiveness. This targeted approach increases consumer engagement and strengthens brand loyalty and market presence.

Optimizing Marketing Spend and ROI

Beyond enhancing customer engagement, Coca-Cola applies analytics to optimize its marketing expenditures. By meticulously analyzing the performance of different marketing channels and campaigns, Coca-Cola identifies which initiatives yield the highest return on investment. This strategic use of analytics allows the company to allocate its budget more effectively, concentrating resources on the most profitable activities. This efficiency not only reduces wasted expenditure but also maximizes the impact of each marketing dollar. Consequently, Coca-Cola maintains its competitive edge in the fiercely contested beverage industry, continually adapting to changing market dynamics and consumer trends.

Through these strategic big data applications, Coca-Cola sustains and amplifies its leadership in the global beverage market. The company’s adept use of analytics to drive marketing decisions exemplifies how traditional businesses can leverage modern technology to stay ahead in an evolving industry landscape, ensuring continued growth and success.

Case Study 7: Barclays’ Risk Management

Advanced Analytics for Credit Risk Assessment

Barclays uses predictive analytics to enhance its risk management practices, particularly in assessing credit and loan applications. By analyzing a comprehensive set of data, including applicants’ financial histories, transaction behaviors, and economic trends, Barclays can accurately predict the risk associated with each loan. This reduces the likelihood of defaults, protecting the bank’s assets and financial health.

Strategic Decision-Making to Minimize Financial Risks

The insights gained from analytics also aid Barclays in making strategic decisions about product offerings and market expansions. By understanding risk profiles across different demographics and regions, Barclays can tailor its financial products to meet the needs of its customers while managing risk effectively. This careful balance of risk and opportunity is crucial for sustainable growth in the competitive banking sector.

Related: Implementing Business Analytics in Healthcare

Case Study 8: Starbucks’ Strategic Use of Data for Expansion and Localization

Data-Driven Site Selection for Maximum Market Penetration

Starbucks uses advanced geographic information systems (GIS) and analytics to strategically pinpoint the optimal locations for new stores. By evaluating extensive demographic data, performance metrics of existing stores, and competitive landscapes, Starbucks is able to identify sites with the maximum success potential. This systematic approach helps maintain dense market coverage and ensures customer convenience, vital for driving consistent growth. The precision in site selection allows Starbucks to expand its global footprint strategically, optimizing market penetration and maximizing investment returns.

Enhancing Local Market Strategies Through Analytics

Beyond the strategic site selection, Starbucks extensively uses data analytics to tailor each store to its local context. This involves adapting store layouts, product offerings, and marketing strategies to match local consumer preferences and cultural nuances. By deeply analyzing customer behavior data and feedback within specific locales, Starbucks fine-tunes its offerings to resonate more strongly with local tastes and preferences. This localization strategy not only improves the customer experience but also increases customer loyalty and enhances the strength of the Starbucks brand in diverse markets.

These strategic data analytics applications underscore Starbucks’ ability to consistently align its business practices with customer expectations across various regions. By leveraging data-driven insights for macro decisions on new store locations and micro-level adjustments to store-specific offerings, Starbucks ensures its brand remains relevant and preferred worldwide. This comprehensive approach to using data solidifies Starbucks’ position as a leader in the global coffeehouse market, renowned for its forward-thinking and customer-centric business model.

Case Study 9: Nike’s Supply Chain Management

Dynamic Supply Chain Optimization Using Predictive Analytics

Nike employs advanced analytics to manage its global supply chain, ensuring efficient operation and timely delivery of products. Nike’s predictive models optimize manufacturing workflows and inventory distribution by analyzing data from production, distribution, and retail channels. This agile approach enables Nike to quickly adapt to shifting market demands and trends, ensuring that popular products are readily accessible while keeping surplus inventory to a minimum.

Sustainability Integration in Operations

Nike also leverages analytics to enhance the sustainability of its operations. Using data to monitor and optimize energy use, waste production, and material sourcing, Nike aims to reduce its environmental footprint while maintaining production efficiency. This focus on sustainable supply chain practices helps Nike meet its corporate responsibility goals and appeals to increasingly eco-conscious consumers.

Case Study 10: Google’s Data-Driven Decision Making

Harnessing Big Data for Strategic Insights

Google expertly leverages big data to inform its decision-making across its vast services. By analyzing extensive data collected from user interactions, market trends, and technological developments, Google identifies key opportunities for innovation and enhancements. This robust data analysis supports Google’s ability to maintain a leadership position in the tech industry, continually evolving its products to meet the dynamic needs of users globally. Insights derived from big data guide the development of cutting-edge technologies and refine existing services, ensuring Google sustains a competitive advantage.

Enhancing User Experience Through Personalization

Google utilizes advanced analytics to personalize the user experience across all its platforms comprehensively. By understanding detailed user preferences, behaviors, and engagement patterns, Google tailors its services to improve relevance and usability. This dedication to personalization is showcased in customized search results, targeted advertising, and tailored app recommendations to boost user satisfaction and engagement. Based on deep data insights, these adjustments ensure that Google’s services are intuitive and responsive, integral to users’ daily digital interactions.

Optimizing Marketing and Operations with Predictive Analytics 

Beyond product refinement, Google applies its data-driven approach to optimize marketing strategies and operational efficiencies. Using predictive analytics, Google forecasts future trends and user behaviors, enabling proactive responses to market demands. This strategic foresight enhances overall user experiences and drives operational efficiency, minimizing waste and maximizing the effectiveness of its initiatives. By consistently integrating data-driven insights into its operations, Google meets current market needs and shapes future trends, reinforcing its dominance in the global technology landscape. This strategic use of big data is crucial to Google’s enduring success and expansive influence in the digital world.

Related: Role of Business Analytics in Digital Transformation

The diverse business analytics applications illustrated in these ten case studies underscore their vital role in modern business strategy. Through the intelligent analysis of data, companies not only solve complex problems but also gain competitive advantages, driving growth and innovation. From improving customer satisfaction to optimizing logistical operations and managing risk, the case studies highlight how data-driven decisions are integral to achieving business objectives. As companies maneuver through the complexities of the digital era, the strategic use of analytics will continue to be a crucial factor in driving success, converting challenges into opportunities, and leading the way toward a smarter, more efficient future.

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Business Analyst Case Study | Free Case Study Template

Business analyst case studies blog describes an actual business analyst case study. This provides real-world exposure to new business analysts.

In this blog, we will be discussing what is business analysis case study, why develop them, when to develop them and how to develop them. We will provide a real business case analysis case study for better understanding.

Let’s start with understanding what is business analysis before we go to analyst case studies.

Topics Below

What is a business analysis case study 

Why prepare business analysis case study 

When to prepare business analysis case study

How to prepare business analysis case study

Example Business Analysis Case Studies

What is Business Analysis Case Study?

Before we try to understand, Business Analysis Case Study, let's understand the term case study and business analysis.

As per Wikipedia, a case study is:

"A case study is an in-depth, detailed examination of a particular case (or cases) within a real-world context."

For example, case studies in medicine may focus on an individual patient or ailment; case studies in business might cover a particular firm's strategy or a broader market; similarly, case studies in politics can range from a narrow happening over time like the operations of a specific political campaign, to an enormous undertaking like, world war, or more often the policy analysis of real-world problems affecting multiple stakeholders.

So, we can define Business Analysis Case Study as

"A Business Analysis case study is an in-depth, detailed examination of a particular business analysis initiative."

What is Business Analysis?

The BABOK guide defines Business Analysis as the “Practice of enabling change in an enterprise by defining needs and recommending solutions that deliver value to stakeholders”. Business Analysis helps in finding and implementing changes needed to address key business needs, which are essentially problems and opportunities in front of the organization.

Business analysis can be performed at multiple levels, such as at:

• The enterprise level, analyzing the complete business, and understanding which aspects of the business require changes.
• The organization level, analyzing a part of the business, and understanding which aspects of the organization require changes.
• The process level, analyzing a specific process, understanding which aspects of the process require changes.
• The product level, analyzing a specific product, and understanding which aspects of the product require changes.  

Why Develop Business Analyst Case Study

Business analysis case studies can be useful for multiple purposes. One of the purpose can be to document business analysis project experiences which can be used in future by other business analysts.

This also can be used to showcase an organizations capabilities in the area of business analysis. For example, as Adaptive is a business analysis consulting organization, it develops multiple business analysis case studies which show cases the work done by Adaptive business analysts for the client. You can read one such case study for a manufacturing client .

When To Develop Business Analyst Case Study

Business analysis case studies are typically prepared after a project or initiative is completed. It is good to give a little time gap before we develop the case study because the impact of a change may take a little while after the change is implemented.

Most professionals prepare business analysis case studies for projects which are successful. But it is also important to remember that not all changes are going to be successful. There are definitely failures in an organizations project history.

It is also important to document the failure case studies because the failures can teach us about what not to do in future so that risks of failures are minimized.

How To Develop A Business Analyst Case Study

Document business problem / opportunity.

In this section of the business analyst case studies, we discuss the actual problem of the business case analysis example.

ABC Technologies has grown rapidly from being a tiny organization with less than 5 projects to one running 200 projects at the same time. The number of customer escalations has gone up significantly. Profitability is getting eroded over a period of time. Significant management time is spent in fire-fighting than improving the business.

Top management estimated a loss of 10% profitability due to poor management of projects which is estimated at about 10 Million USD per annum.

Document Problem / Opportunity Analysis

For our above business problem, we captured the following analysis details.

Discussions with key stakeholders revealed the following challenges in front of ABCT management:

• There is very little visibility of project performances to top management
• Non-standard project reporting by various projects makes it harder for top management to assess the correct health of the project
• Practically there is no practice of identifying risks and mitigating them
• Project practices are largely non-standardized. Few project managers do run their projects quite well because of their personal abilities, but most struggle to do so.
• Due to rapid growth, management has no option but to assign project management responsibilities to staff with little or no project management experience.

Document Identified Solutions 

Based on root cause analysis, management decided to initiate a project to standardize management reporting. This required the organization to implement a project management system. The organization initially short-listed 10 project management tools. After comparing the business needs, tools, their costs, management decided to go with a specific tool.

Document Implementation Plan

The purchased tool lacked integration into the organizations existing systems. The vendor and organization’s IT team developed a project plan to integrate the new system with the existing systems.

Document Performance Improvements 

After a year, the effectiveness of the project was assessed. Projects showed remarkable improvement wrt reduced customer escalations, better on-time billing, and better risk management. The system also allowed the organization to bid for larger contracts as the prospective customers demanded such a system from their suppliers. The application was further enhanced to cater to the needs of other businesses in the enterprise as they were different legal entities, and their policies were different.

Document lessons learnt

Some of the key lessons learnt during this business analysis initiative were:

1. Stakeholder buy-in in extremely important to the success of the project

2. It is always better to go with iterative approach achieve smaller milestones and then go for larger milestones

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Business Analyst Case Study

A business analyst case stud y is used to give near-world exposure to a business analyst. So, in this post, we will be discussing what is business analysis, what is business analysis, and what are the requirements and strategies of an analyst. Plus, a business case analysis example for better understanding. Let’s start with understanding what is business analysis before we go to analyst case studies.

What is business analysis?

Business Analysis is a search for identifying the business needs, threats, and problems and finding and implementing the solutions and changes which are required for the business.

It has three different roles which define the discipline

• Analysing the whole business, and its elements to identify any process or elements and identifying the spots which require changes.
• To find every possible solution for any business problem and to implement the most suited solution.
• And, therefore, to evaluate the new process of working.

Who is a Business Analyst?

A business analyst also known as BA analyses the business process, systems, documentation, business model, and technologies to identify the problems and to guide the business towards a better process, structure, product, and technology.

In business analysis, there are many more roles than just business analysis like business systems, systems, processes, product analysis, data scientist etc.

And to understand, what is business analyst, now understand the business analyst roles.  

Business Analyst Role

Before we understand the business analyst case study, let’s understand the business analyst’s role in an organization. To get a better understanding of the job and their roles and responsibilities.

Understand Business Requirements 

The very first thing of an analyst is to understand the needs and requirements of the business and what requirements the business is lacking.

Finding Solutions

The business analyst’s role is to find the solutions for problems which are gathered in the business process, requirements systems, technologies etc.

Project Implementation

A business analyst not only has to create a solution plan plus they have to design and implement the solution in an organization. 

Requirements For Function

It is important to analyze what is required to complete the project. As a result, to understand the business analyst’s case study an analyst identifies the requirements needed and fulfils those requirements.

Another business analyst’s role is to test their processes, solutions, and techniques before implementing and making them perfect for the organization. 

Decision Making & Problem-Solving

It is one of the roles which is spread all of their jobs because of making a decision and solving problems. For every problem in business, a business analyst is to find and implement the solution. 

Maintenance of System and Operations

A  business analyst also says that they have to provide maintenance, system validation reports, and deactivation plans. Plus, the analyst is also involved in evaluating the replacement or deactivation is needed.

Moreover, for a better understanding of the business analyst role and these business analyst case studies, here are the business analysis requirements and business analysis techniques. Therefore, it explains how a business analyst works.  

Business analysis requirements

Business analysis requirements are divided into different categories. It is a piece of documentation which includes their needs, things which need updations changes etc.

So business analysis requirements are classified into:

STAKEHOLDER REQUIREMENTS

Firstly, it’s important to understand who are the stakeholders , to understand a business analyst case study the related stakeholders play an important role in understanding their needs and requirements and understanding how business decisions will impact them.

Documenting and fulfilling the stakeholder’s requirements fulfils their requirements and later they fulfil the business requirements.

BUSINESS REQUIREMENTS

Secondly, to create a systematic business plan which includes all the requirements, a working map of the business, and a structure of responsibilities of each person.

SOLUTION REQUIREMENTS

Solution requirements are said to the process or quality improvement i.e. changes that are made in the business process or in quality that will fulfil the stakeholder’s requirements. Such a problem will be discussed later in the business analyst case study. As a result, solution requirements in business analysis requirements are classified into:

• Functional Requirments
• Non Functional Requirments

TRANSITION REQUIREMENTS

These requirements refer to the changes that which business wanted in its process. Therefore, in simple terms, it is a process of a transaction from the current state to the target state.

A transition can be about any process or domain which might be misunderstood, so it’s important to document before starting work on the project. 

business analysis techniques

Business analysis techniques are some of the ways through which business analysts use to determine the environment of the business. These techniques are used later in the business analyst case studies.

Also, these techniques determine which business decisions can be most effective and from which decisions the firm has to face consequences

Here are the 4 most common business analysis techniques:

MOST refers to Mission, Objectives, and Strategies. It helps in evaluating the internal analysis of the mission statement. Furthermore, it formulates strategies to tackle hurdles in achieving organisational objectives

It helps in analysing the external environment of the organization. PESTLE stands for:

• Political: changes in political parties in their ideology, and their policy can affect business decisions.
• Economical: the economic conditions, economic growth and other economic factors.
• Social: environment of social society and analysing how the business will be impacted by society culture  
• Technology: latest technology, and upcoming changes to keep business decisions accurate.
• Legal: Law, rules, and regulations which are related to the business environment.
• Environmental: analysing how the business decision will impact the environment.

In a business analyst case study, a business is divided into four parts. An organization can make four different decisions for each segment. Also, SWOT analysis has four different segments:

• Opportunities

Organization analysis of each aspect of business and each business aspect goes to one of these segments.

So, the organization knows which segments need improvements and what are their USPs   

MoSCoW stands for Must or Should, Could or Would. This technique requires analyses of every requirement and marks its level of prioritization.

Afterwards, requirements with the highest prioritization get priority attention.

To understand an analyst job, a business analyst case study will give a real example. So, here is the problem followed by the solution of how a business analysis example will solve the problem:

In the problem section of the business analyst case study, we discuss the actual problem of the business case analysis example. Furthermore, it is a problem for the consumer goods companies (food industry) that are targeting to expand their business. Therefore, here is the problem for business analysis example:

The target for a business analyst is to find the insights of quality measurement systems’ best practices which are required to create better products and the tools and the process which will be required to do so.

Solution 

The solution for these business analyst case studies is divided into subparts. Moreover, the process for finding quality improvement is to find the benchmarking, creating tools, continuous feedback and finalization.

Information gathering

The very first step of any business problem is to gather information as possible related to that business analysis example. However, gather all the background information related to background i:e information related to the department, and the history of the problem in the organization.

Afterwards, it’s important to understand the various elements which can affect the business analysis strategy. Two models for information gathering:

• PESTEL Analysis: This method analyzes the external environment of the business. The impacts of different environments on your business or your business decisions .
• Porter’s Five Force Model : In the analysis of the business environment or impact on business decisions by evaluating Industry competitors, new entrants, substitutes, buyers and suppliers.

  Identify Related Stakeholders

As we are moving further in our business analyst case study, an analyst needs to identify all the stakeholders who are associated with the decision. It’s important to understand how different groups can be affected by the decision.

So it’s, important to make a decision which suits each group of the business. Different groups in business are:

• Shareholders
• Competitors

Discover Business Objectives

As the business case study examples say after the background information and understanding of the stake behind the decision. Also, it’s important to understand that the decision will reflect the company’s objective. Moreover, every business case analysis example shows that the decision of the business reflects the business objectives, vision and mission.

Analysis & Benchmarking

Moving further in the business analyst case study and according to our problem of improving product quality improvement.

Analysing the recent process of setting up benchmarks. To create high-quality food products, here is the process:

• Firstly measure the old process and benchmarks
• Compare the organization’s benchmark with competitors’ benchmarks and standards.
• Research for standards and benchmarks needed for improving the quality.
• In-depth interviews and a survey frame the conduction by the production head, researchers, and experts, to identify small sports to improve.

Tool Creation

After all the findings and research work , the next step in the business analysis example is to create tools and fill the loopholes in the existing process to create a more suitable method.

Note: The process of tool creation and mapping is theoretical.

Afterwards, a final document which includes the findings, and research. Plus, the most suitable process will get on documents.

Requirements for new process added to the document.

As the name suggests in this business analyst case study the designed plan gets trial runs. The goal is to achieve the perfect quality of food. Moreover, it creates more than one process in theory with different variations.

Finalization

After continuous trials and feedback, it is essential to determine the best alternative in the next step of the business analyst case study. As a result, the organization select the best alternative which is most suited and effective. Calculation of process effectiveness:

• Quality of product

Evaluate Value Added By Project

In the final stage of our business analyst case study, it is important to determine how effective and how the process of improving quality added to the profit levels of the business.

So, it was one of the business analyst case studies to explain real-world working and their requirements and strategies.

What is a case study for a business analyst?

Business case studies, either involve an ongoing issue or a company’s success, and analysts have communicative tools to determine the right decisions for business. Plus they demonstrate higher value & competence.

How do you write a case study for a business analyst?

Steps to writing a case study analysis

• Step 1: Investigate the Company’s History and Growth
• Step 2: Identify Strengths and Weaknesses
• Step 3: Examine the External Environment.
• Step 4: Analyze Your Findings.
• Step 5: Identify Corporate-Level Strategy.
• Step 6: Identify Business-Level Strategy.
• Step 7: Analyze Implementations.

What Does a Business Analyst Do?

Business analysts go by many other job titles, including:

• Business Architect
• Business Intelligence Analyst
• Business Systems Analyst
• Data Scientist
• Enterprise Analyst
• Management Consultant
• Process Analyst
• Product Manager
• Product Owner
• Requirements Engineer
• Systems Analyst

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4 Examples of Business Analytics in Action

• 15 Jan 2019

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

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

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

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What Is Business Analytics?

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

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

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

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

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

Business Analytics vs. Data Science

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

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

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

Business Analytics Examples

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

1. Improving Productivity and Collaboration at Microsoft

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

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

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

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

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

2. Enhancing Customer Support at Uber

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

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

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

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

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

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

Related : How to Analyze a Dataset: 6 Steps

3. Forecasting Orders and Recipes at Blue Apron

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

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

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

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

Related : 5 Business Analytics Skills for Professionals

4. Targeting Consumers at PepsiCo

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

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

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

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

Developing a Data Mindset

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

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

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

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

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What the Case Study Method Really Teaches

• Nitin Nohria

Seven meta-skills that stick even if the cases fade from memory.

It’s been 100 years since Harvard Business School began using the case study method. Beyond teaching specific subject matter, the case study method excels in instilling meta-skills in students. This article explains the importance of seven such skills: preparation, discernment, bias recognition, judgement, collaboration, curiosity, and self-confidence.

During my decade as dean of Harvard Business School, I spent hundreds of hours talking with our alumni. To enliven these conversations, I relied on a favorite question: “What was the most important thing you learned from your time in our MBA program?”

• Nitin Nohria is the George F. Baker Jr. and Distinguished Service University Professor. He served as the 10th dean of Harvard Business School, from 2010 to 2020.

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Top 40 Most Popular Case Studies of 2021

Two cases about Hertz claimed top spots in 2021's Top 40 Most Popular Case Studies

Two cases on the uses of debt and equity at Hertz claimed top spots in the CRDT’s (Case Research and Development Team) 2021 top 40 review of cases.

Hertz (A) took the top spot. The case details the financial structure of the rental car company through the end of 2019. Hertz (B), which ranked third in CRDT’s list, describes the company’s struggles during the early part of the COVID pandemic and its eventual need to enter Chapter 11 bankruptcy. 

The success of the Hertz cases was unprecedented for the top 40 list. Usually, cases take a number of years to gain popularity, but the Hertz cases claimed top spots in their first year of release. Hertz (A) also became the first ‘cooked’ case to top the annual review, as all of the other winners had been web-based ‘raw’ cases.

Besides introducing students to the complicated financing required to maintain an enormous fleet of cars, the Hertz cases also expanded the diversity of case protagonists. Kathyrn Marinello was the CEO of Hertz during this period and the CFO, Jamere Jackson is black.

Sandwiched between the two Hertz cases, Coffee 2016, a perennial best seller, finished second. “Glory, Glory, Man United!” a case about an English football team’s IPO made a surprise move to number four.  Cases on search fund boards, the future of malls,  Norway’s Sovereign Wealth fund, Prodigy Finance, the Mayo Clinic, and Cadbury rounded out the top ten.

Other year-end data for 2021 showed:

• Online “raw” case usage remained steady as compared to 2020 with over 35K users from 170 countries and all 50 U.S. states interacting with 196 cases.
• Fifty four percent of raw case users came from outside the U.S..
• The Yale School of Management (SOM) case study directory pages received over 160K page views from 177 countries with approximately a third originating in India followed by the U.S. and the Philippines.
• Twenty-six of the cases in the list are raw cases.
• A third of the cases feature a woman protagonist.
• Orders for Yale SOM case studies increased by almost 50% compared to 2020.
• The top 40 cases were supervised by 19 different Yale SOM faculty members, several supervising multiple cases.

CRDT compiled the Top 40 list by combining data from its case store, Google Analytics, and other measures of interest and adoption.

All of this year’s Top 40 cases are available for purchase from the Yale Management Media store .

And the Top 40 cases studies of 2021 are:

1.   Hertz Global Holdings (A): Uses of Debt and Equity

2.   Coffee 2016

3.   Hertz Global Holdings (B): Uses of Debt and Equity 2020

4.   Glory, Glory Man United!

5.   Search Fund Company Boards: How CEOs Can Build Boards to Help Them Thrive

6.   The Future of Malls: Was Decline Inevitable?

7.   Strategy for Norway's Pension Fund Global

8.   Prodigy Finance

9.   Design at Mayo

10. Cadbury

11. City Hospital Emergency Room

13. Volkswagen

14. Marina Bay Sands

15. Shake Shack IPO

16. Mastercard

17. Netflix

18. Ant Financial

19. AXA: Creating the New CR Metrics

20. IBM Corporate Service Corps

21. Business Leadership in South Africa's 1994 Reforms

22. Alternative Meat Industry

23. Children's Premier

24. Khalil Tawil and Umi (A)

25. Palm Oil 2016

26. Teach For All: Designing a Global Network

27. What's Next? Search Fund Entrepreneurs Reflect on Life After Exit

28. Searching for a Search Fund Structure: A Student Takes a Tour of Various Options

30. Project Sammaan

31. Commonfund ESG

32. Polaroid

33. Connecticut Green Bank 2018: After the Raid

34. FieldFresh Foods

35. The Alibaba Group

36. 360 State Street: Real Options

37. Herman Miller

38. AgBiome

39. Nathan Cummings Foundation

40. Toyota 2010

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• Writing Case Studies
• Citing Business Sources

Once you have identified a case study that you wish to analyze, the sources listed below can help you analyze the case materials.

• Cengage Learning - Case Studies Explains how to effectively analyze cases and write a case study analysis. Provides a checklist and explanation of areas to consider, suggested research tools, and tips on financial analysis.
• Guide to case analysis From the publisher McGraw Hill. Includes sections on objectives of case analysis, preparing a case for class discussion, preparing a written case analysis and the Ten Commandments of Case Analysis.
• How to Analyze a Case Study From the Simmons University Writing Center.
• Writing a Case Analysis From the University of New South Wales Business School.
• Writing a Case Study Analysis From The University of Arizona, Global Campus, Writing Center.

• << Previous: Open Access Cases
• Next: Case Interviews >>
• Last Updated: Jun 7, 2024 12:40 PM
• URL: https://library.bu.edu/business-case-studies

Case studies

Find case studies.

• Use the keywords "case study" as part of your search on your topic to find case study articles
• Check textbooks in your discipline for case studies relating to chapter topics
• Filter your database search results by item type "case study" 
• Henry Stewart Talks - Business & Management Collection Video lectures and case studies from leading experts from commerce, industry and academia. Select your subject area and then filter by case studies.
• WARC (Advertising and marketing knowledge) Advertising, marketing and communications, including business case studies. Select "Case Finder" and use the categories to browse case studies.
• MarketLine Advantage Company and industry reports, data and analysis. Select options from the browse by or search by case studies

Analysing case studies

Vanessa Van Der Ham 2014, Analysing case studies,  Massey University .

• Writing a case analysis Learn how to write a case analysis with this brief guide from UNSW Business School
• How to analyse a case study This guide from Pearson Education further demonstrates the steps in analysing a case study
• Business resources for students: Case studies This guide to case study analysis from Cengage Learning goes into detail on how to analyse a case

Library resources

Case study skills.

A series of tutorials discussing text-analysis and problem analysis tools to understand, interpret and validate understanding of different case situations.

Sage Research Methods Online

Explore methods and concepts to design research projects, understand particular methods or identify a new method, conduct research, and write up findings, with links over 100,000 pages of SAGE's book, journal and reference content.

Business Research : Enjoy Creating, Developing and Writing Your Business Project.

• << Previous: Statistics

Business Case Studies

• Getting Started
• Case Analysis
• Finding Case Studies in the Library

Free Case Studies

• Buying Cases
• Writing Case Studies
• Case Competitions
• Case Interviews
• Case Method (Teaching)

Many academic and business institutions develop and publish case studies. Some of these organizations provide free access to their case studies:

• Acadia Institute of Case Studies Focuses on entrepreneurship and small business operations.
• Business Case Studies by Company
• Business Ethics Case Analyses
• Canadian Centre for Occupational Health & Safety: Workplace Health Case Studies
• Case Centre Available for a fee.
• Daniels Fund Ethics Initiative Case Studies
• Give to Get Marketing. Marketing and Advertising Case Studies
• HR Open Source Case Studies
• MarketingSherpa Choose "Case Studies" as the content type in the filters.
• MaRS Search for "case study" in the top right search box.
• MERLOT Business Cases
• MIT LearningEdge Case Studies Free case studies by MIT Sloan School of Management.
• Penske. Logistics Case Studies
• Society of Human Resources Management.
• Open Case Studies Project by UBC The Open Case Studies project at UBC brings together faculty and students from different disciplines to write, edit, and learn with case studies that are free and open.
• World's Best Case Studies Short video case studies covering topics including consumer goods, services, and technology.
• << Previous: Finding Case Studies in the Library
• Next: Buying Cases >>
• Last Updated: Jan 17, 2024 11:02 AM
• URL: https://guides.library.ubc.ca/businesscases

Table of Contents

How to write a business case study , business case studies: examples, why case studies are important to business analysts, best business case studies to understand modern management techniques.

A case study is, broadly speaking , an “intensive, systematic investigation of a single individual, group, community, or some other unit” that’s used across numerous fields and subjects. 

Specifically, business case studies examine a challenge or situation involving a single player, which can be an individual or organization. Though the purpose may shift depending on the specific audience (such as customers, business students, decision-makers), they ascribe an analytical framework to a business-related situation, whether past or ongoing. 

Most importantly, business case studies are compelling and insightful narratives. They convey a firsthand account of real-world problem solving, whether successful or not, with implications for modern management. Regardless of the purpose, case studies are a crucial medium for communicating a business analysts’ skills to colleagues, potential employers, clients, and others.   

Step 1: Gather the Facts 

Effective business case studies convey information in a direct, structured manner. Before diving into writing, gather the main facts, data, and resources. 

Consider the “Who-What-When-Where-Why” of the situation you wish to illustrate. Business case studies should focus on a narrow problem and outcome (or potential outcomes) for one player, contextualized effectively.

Step 2: Outline 

After compiling information, the next step is to outline your case study. Planning will ensure that you’re prioritizing the most important details throughout the writing process. There’s no single formula, but the following structure by Express Writers can get you started: 

• Challenge: What was the complication, obstacle, or problem that needed to be solved? Offer context from within the company and, if appropriate, broader trends in modern management.
• Solution: What solutions did you explore? How did you choose your approach? Consider incorporating an effective business analysis technique to build a case.   
• Outcome: What were the results and what effect did they have? Use data to support what you claim the situation to be.

Step 3: Write the Case Study

As you write, consider three elements for each section: details, purpose, and style. Details are selected so that the reader has clear, concise information without feeling burdened. Facts are structurally introduced and explained so that readers can follow a clear timeline. 

Ensure that you’re writing purposefully through each section. All information should be included for a reason as fluff is an immediate red flag that can be considered unprofessional and distracting. Moreover, write with the audience in mind: Are you convincing leaders to make a certain decision, or are you persuading clients that your company has a proven track-record? The purpose will inform the work’s style and tone. Stylistic elements include writing in first or third person, emphasizing keywords and certain action verbs, and wielding persuasive material (such as quotes and graphics).

Step 4: Format, Proof, and Publish

The final phase is to format your case study. Consider the readability of your document. Break up blocks of text with quotes that crystallize compelling points (e.g., “The business saw a 90% reduction in waste.”). 

Ensure that titles, headings, sub-headings, and body text are distinct in size, color, and font without being distracting. The final look should be clean and professional. If your company has a communications team, you can inquire about in-house branding guidelines. 

Proofread your work multiple times before you publish or share business case studies. Copyediting errors are the quickest way to lose the readers’ trust. Ask an outside editor, writer, or colleague to proofread your work.

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Here's what learners are saying regarding our programs:

I was keenly looking for a change in my domain from business consultancy to IT(Business Analytics). This Post Graduate Program in Business Analysis course helped me achieve the same. I am proficient in business analysis now and am looking for job profiles that suit my skill set.

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My experience with Simplilearn has been great till now. They have good materials to start with, and a wide range of courses. I have signed up for two courses with Simplilearn over the past 6 months, Data Scientist and Agile and Scrum. My experience with both is good. One unique feature I liked about Simplilearn is that they give pre-requisites that you should complete, before a live class, so that you go there fully prepared. Secondly, there support staff is superb. I believe there are two teams, to cater to the Indian and US time zones. Simplilearn gives you the most methodical and easy way to up-skill yourself. Also, when you compare the data analytics courses across the market that offer web-based tutorials, Simplilearn, scores over the rest in my opinion. Great job, Simplilearn!

Reading business case studies as you plan and write your own will mold your writing for the better. Leaders in modern management offer models for engaged, effective writing. 

One popular case study published by the Yale School of Management is Coffee 2016 . The report skillfully narrates the complexities of coffee’s global supply chain through the struggles of Andrea Illy, a CEO of the premium coffee company named after his family. The study is an excellent example of highlighting broader industry challenges while staying focused on the decision-making of a single individual or company. 

Another example of foregrounding a key player comes from MIT Sloan School of Management, titled Turnaround and Transformation: Leadership and Risk at Boston’s Institute of Contemporary Arts . The authors show how Jillian Medvedow, a newly-hired director of the art institution, “was able to rebuild (literally and figuratively) a powerless organization.” The case study is powerful because it contextualizes the organization’s history in the Boston area while tracing her unique leadership.

Other studies focus on high-stakes decisions and their results, such as Netflix Goes to Bollywood , another piece by MIT. The authors analyze Netflix’s risks and strategy as it entered the Indian market. The study focuses on corporate culture and financial decision-making, relying on data and examples from Netflix.

Some famous business case studies document a company’s failures and theorize what could have been done differently, such as “ The Rise and Fall of Nokia .” Once a leader in telecommunications, Nokia sold its Device and Services business to Microsoft. The research focuses on Nokia’s missteps and imagines what the company could have done differently, thus demonstrating how case studies can also theorize alternative outcomes.

Lastly, another classic case focuses on how Starbucks undermined its own brand value through rapid, superficial growth. Professor John Quelch’s piece offers a model for concise, self-assured writing that communicates business analysis with confidence. 

Mastering case studies is crucial for business analysts , as their job description is to improve a company’s efficiency and performance by applying their analytical skills to company problems. Business analysts are researchers, problem-solvers, and architects of better management systems, all of which are aimed at improving a company’s bottom line.

In modern management, case studies are a relevant and powerful tool for demonstrating best practices and creating buy-in for new strategies. Business case studies, whether they highlight company successes or ongoing issues, are communicative tools for analysts to influence an organization’s decision-making processes, as well as demonstrate their value and competence.

Gain expertise in the latest Business analytics tools and techniques with the  Business Analyst Master's Program . Enroll now!

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Tackle complex business problems with our certification course today. Use in-demand tools, attend masterclasses from IBM and Purdue University, and solve case studies from Harvard Business Publishing.

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Business and leadership.

The Modern Day Business Analyst Role: AI Tools and Techniques for Success

Data Science & Business Analytics

Program Overview: How Purdue University Can Help You Succeed in Business Analysis

Program Overview: The Reasons to Get Certified in Business Analysis in 2023

Recommended Reads

The Complete Business Intelligence Tool Guide for the Modern Analyst

5 Case Study Mistakes to Avoid at All Costs

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7 Content Marketing Case Studies that Created Real Impact in 2024

A Complete Guide on the Types of Statistical Studies

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47 case interview examples (from McKinsey, BCG, Bain, etc.)

One of the best ways to prepare for   case interviews  at firms like McKinsey, BCG, or Bain, is by studying case interview examples. 

There are a lot of free sample cases out there, but it's really hard to know where to start. So in this article, we have listed all the best free case examples available, in one place.

The below list of resources includes interactive case interview samples provided by consulting firms, video case interview demonstrations, case books, and materials developed by the team here at IGotAnOffer. Let's continue to the list.

• McKinsey examples
• BCG examples
• Bain examples
• Deloitte examples
• Other firms' examples
• Case books from consulting clubs
• Case interview preparation

Click here to practise 1-on-1 with MBB ex-interviewers

1. mckinsey case interview examples.

• Beautify case interview (McKinsey website)
• Diconsa case interview (McKinsey website)
• Electro-light case interview (McKinsey website)
• GlobaPharm case interview (McKinsey website)
• National Education case interview (McKinsey website)
• Talbot Trucks case interview (McKinsey website)
• Shops Corporation case interview (McKinsey website)
• Conservation Forever case interview (McKinsey website)
• McKinsey case interview guide (by IGotAnOffer)
• McKinsey live case interview extract (by IGotAnOffer) - See below

2. BCG case interview examples

• Foods Inc and GenCo case samples  (BCG website)
• Chateau Boomerang written case interview  (BCG website)
• BCG case interview guide (by IGotAnOffer)
• Written cases guide (by IGotAnOffer)
• BCG live case interview with notes (by IGotAnOffer)
• BCG mock case interview with ex-BCG associate director - Public sector case (by IGotAnOffer)
• BCG mock case interview: Revenue problem case (by IGotAnOffer) - See below

3. Bain case interview examples

• CoffeeCo practice case (Bain website)
• FashionCo practice case (Bain website)
• Associate Consultant mock interview video (Bain website)
• Consultant mock interview video (Bain website)
• Written case interview tips (Bain website)
• Bain case interview guide   (by IGotAnOffer)
• Digital transformation case with ex-Bain consultant
• Bain case mock interview with ex-Bain manager (below)

4. Deloitte case interview examples

• Engagement Strategy practice case (Deloitte website)
• Recreation Unlimited practice case (Deloitte website)
• Strategic Vision practice case (Deloitte website)
• Retail Strategy practice case  (Deloitte website)
• Finance Strategy practice case  (Deloitte website)
• Talent Management practice case (Deloitte website)
• Enterprise Resource Management practice case (Deloitte website)
• Footloose written case  (by Deloitte)
• Deloitte case interview guide (by IGotAnOffer)

5. Accenture case interview examples

• Case interview workbook (by Accenture)
• Accenture case interview guide (by IGotAnOffer)

6. OC&C case interview examples

• Leisure Club case example (by OC&C)
• Imported Spirits case example (by OC&C)

7. Oliver Wyman case interview examples

• Wumbleworld case sample (Oliver Wyman website)
• Aqualine case sample (Oliver Wyman website)
• Oliver Wyman case interview guide (by IGotAnOffer)

8. A.T. Kearney case interview examples

• Promotion planning case question (A.T. Kearney website)
• Consulting case book and examples (by A.T. Kearney)
• AT Kearney case interview guide (by IGotAnOffer)

9. Strategy& / PWC case interview examples

• Presentation overview with sample questions (by Strategy& / PWC)
• Strategy& / PWC case interview guide (by IGotAnOffer)

10. L.E.K. Consulting case interview examples

• Case interview example video walkthrough   (L.E.K. website)
• Market sizing case example video walkthrough  (L.E.K. website)

11. Roland Berger case interview examples

• Transit oriented development case webinar part 1  (Roland Berger website)
• Transit oriented development case webinar part 2   (Roland Berger website)
• 3D printed hip implants case webinar part 1   (Roland Berger website)
• 3D printed hip implants case webinar part 2   (Roland Berger website)
• Roland Berger case interview guide   (by IGotAnOffer)

12. Capital One case interview examples

• Case interview example video walkthrough  (Capital One website)
• Capital One case interview guide (by IGotAnOffer)

12. EY Parthenon case interview examples

• Candidate-led case example with feedback (by IGotAnOffer)

14. Consulting clubs case interview examples

• Berkeley case book (2006)
• Columbia case book (2006)
• Darden case book (2012)
• Darden case book (2018)
• Duke case book (2010)
• Duke case book (2014)
• ESADE case book (2011)
• Goizueta case book (2006)
• Illinois case book (2015)
• LBS case book (2006)
• MIT case book (2001)
• Notre Dame case book (2017)
• Ross case book (2010)
• Wharton case book (2010)

Practice with experts

Using case interview examples is a key part of your interview preparation, but it isn’t enough.

At some point you’ll want to practise with friends or family who can give some useful feedback. However, if you really want the best possible preparation for your case interview, you'll also want to work with ex-consultants who have experience running interviews at McKinsey, Bain, BCG, etc.

If you know anyone who fits that description, fantastic! But for most of us, it's tough to find the right connections to make this happen. And it might also be difficult to practice multiple hours with that person unless you know them really well.

Here's the good news. We've already made the connections for you. We’ve created a coaching service where you can do mock case interviews 1-on-1 with ex-interviewers from MBB firms . Start scheduling sessions today!

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Harvard Business Analytics Program

Designed for aspiring and established leaders in any industry, HBAP equips participants with the machine learning and data analysis tools they need to incorporate innovative tech into their business strategy, at the top levels of their organization.

Associated Schools

Harvard Faculty of Arts & Sciences

Harvard Business School

Harvard School of Engineering and Applied Sciences

What you'll learn.

Capabilities in technical, analytical, and operational areas to advance your firm’s position in the global market.

Core data analysis and management skills.

How to keep up with a business world that is constantly impacted by machine learning and artificial intelligence.

An in-depth understanding of modern technologies and practices in next-generation analytics, such as blockchain, digital strategy, and AI/ML.

How to interpret your findings and use them to uncover valuable business insights.

How to apply these concepts to your organization, with a mind toward maximizing efficiencies and outcomes.

Course description

Unlike many other offerings, the Harvard Business Analytics Program features a blended format with live online and in-person components.

The Harvard Business Analytics Program consists of six core courses, two seminars, and two in-person immersions. The rigorous curriculum delivers an authentic Harvard experience and consists of entirely new courses that are frequently updated to adapt to industry changes and emerging technologies. The program can be completed in as little as nine months.

Participants who are accepted into the upcoming cohort can apply for an income-based scholarship of up to 30%. To secure the scholarship offer, participants who haven't already made an enrollment deposit must do so immediately, as scholarships are awarded on a first-come, first-served basis to a limited number of applicants.

Course Outline

Competing in the Age of AI

Artificial intelligence (AI) is revolutionizing the way today's businesses compete and operate. By putting AI and data at the center of their capabilities, companies are redefining how they create, capture, and share value—and are achieving impressive growth as a result. This course will delve into new AI-based business models and operational approaches. Through global case studies on market leaders and innovative startups in diverse industries, the course will explore a range of capabilities that can help your business succeed in today's data-driven environments. Along with learning how to unlock new growth potential, you will deepen your understanding of ethical challenges that come with leveraging massive amounts of data and sophisticated analytics. You will further examine the ethics of AI through real-world case studies that have widespread debate about issues such as academic freedom, corporate responsibility, and bias in AI systems. Recent developments in generative AI are discussed including how large language models are developed and how they can be used by individuals and businesses. Course faculty demonstrate conversing and performing data analytics with ChatGPT.

Programming and Data Systems

Combining technical guidance, analysis of real-world cases, and hands-on coding assignments each week, this initiative enables individuals to navigate technological choices effectively, even without a technical background. Covering areas such as artificial intelligence, cloud computing, networking, privacy, scalability, and security, the program places a special focus on web and mobile technologies. Participants will learn the basics of coding with SQL and Python, and be introduced to fundamental concepts in decision trees, neural networks, LLMs, other types of AI models, and generative AI in order to understand what AI can do for their organization. 

Leadership, Innovation, and Change

This course places a strong emphasis on the leader’s role in enhancing the execution of their existing strategy to outperform competitors, alongside their involvement in driving strategic innovation. We employ the congruence model that links strategy to execution through alignment of culture, people, tasks, structure, and executive leadership. Because ambidexterity requires leaders that can deal with punctuated change and paradoxical strategies, our program concludes with what we know about ambidextrous leadership and leading large system change.

Operations and Supply Chain Management

This program will focus on overseeing product availability, particularly within the dynamic landscape of swift product expansion, brief product life cycles, and interconnected networks of suppliers and customers on a global scale. The key topics examined include inventory management, distribution economics, demand forecasting, supplier management, and the potential benefits of using AI and machine learning to work with data.   

Foundations of Quantitative Analysis

This program serves as an initiation into employing statistical methodologies for tackling business challenges. Key elements of the curriculum include methods for describing and summarizing data, the fundamentals of probability, the basics of study design and data collection, and statistical inference. Data analyses, simulation, and design issues are implemented in the statistical computing package R run within the RStudio interface.

Leadership and People Analytics

Participants will develop practical skills to analyze data in a manner that compliments the frameworks and intuitions typically employed to steer their managerial decisions on people issues. Anchored in data, this program will equip participants with an analytic approach for assessing the diverse factors impacting individual, team, and organizational performance, ultimately resulting in more impactful interventions and initiatives.

Data-Driven Marketing

The once qualitative and instinct-driven aspect of business functions (think “Mad Men”) has evolved into a data-driven profession that relies on quantitative insights on how best to optimize ad creation and placement and influence consumer purchase behavior. This course focuses on the benefits that AI and machine learning bring to marketing including enhanced personalization and customization, as well as pricing optimization and automation. Participants will also learn how AI affects change management and algorithmic bias. This course will examine the ways in which marketing has changed and the new skills and capabilities needed to succeed in this function. 

Data Science Pipeline and Critical Thinking

This program will take a holistic approach to helping participants understand the key factors involved in the data science pipeline, from data collection to analysis to prediction and insight. The curriculum will expand on the application of AI in data science by looking at the role of machine learning. Topics such as large language models, supervised learning, unsupervised learning, Bayes’ Theorem, and deep learning will be explored throughout the program. Projects will give participants hands-on experience developing and running a data science pipeline to ensure that the correct business predictions are being made.

In addition to the online learning components of the program, you will have the opportunity to attend two 2.5-day, on-site and in-person immersions held at the Harvard Business School campus in Boston.

During these experiences, you'll get to engage in direct interactions with your classmates, establish connections with faculty members, and connect with influential figures in the industry during evening events. You’ll also have the chance to tour the Harvard campus and participate in hands-on guided learning exercises. Throughout your experience, you’ll gain an in-depth understanding of deep learning and neural networks, and use the HBS case method to formulate solutions to real-world business scenarios as a way to understand relevant challenges in the industry. Recent topics include fairness in algorithms, data privacy, leading transformational change, and the evolving landscape of AI.

Instructors

Karim Lakhani

Sunil Gupta

Janice Hammond

Hanspeter Pfister

Joseph Blitzstein

Mark glickman.

V. Narayanan

Kevin rader, michael i. parzen.

Marco Iansiti

Henry h. leitner, margo seltzer.

Ayelet Israeli

David Parkes

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Using Python for Research

Take your introductory knowledge of Python programming to the next level and learn how to use Python 3 for your research.

CS50: Introduction to Computer Science

An introduction to the intellectual enterprises of computer science and the art of programming.

Join our list to learn more

Top 20 Analytics Case Studies in 2024

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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

In this article, our research covers:

How to measure analytics success?

What are some analytics case studies.

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

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

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

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

*Vendors have not shared the client name

For more on analytics

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

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

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

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

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

AIMultiple.com Traffic Analytics, Ranking & Audience , Similarweb. Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics , Business Insider. Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are , Washington Post. Data management barriers to AI success , Deloitte. Empowering AI Leadership: AI C-Suite Toolkit , World Economic Forum. Science, Research and Innovation Performance of the EU , European Commission. Public-sector digitization: The trillion-dollar challenge , McKinsey & Company. Hypatos gets $11.8M for a deep learning approach to document processing , TechCrunch. We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million , Business Insider.

To stay up-to-date on B2B tech & accelerate your enterprise:

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14 case studies of manufacturing analytics in 2024, what is data virtualization benefits, case studies & top tools [2024], iot analytics: benefits, challenges, use cases & vendors [2024].

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Strategy Execution for Public Leadership

Implement a public leadership approach for long-term success..

Join Harvard Kennedy School faculty and former Pentagon Chief of Staff Eric Rosenbach to learn how to develop strategies for public leadership success.

What You'll Learn

How do you execute a strategic initiative that has a long-lasting impact? Do you know how to effectively communicate your strategic approach, gathering input and support from key stakeholders? How will you anticipate and respond to scrutiny from shareholders, news media, and the public?

Leaders in government, non-profit, and education organizations face unique challenges when it comes to making strategic, business-oriented decisions. If not thoroughly planned and executed, these decisions have real consequences with high-stakes outcomes. What can we learn from world leaders and experts who have faced these monumental decisions? How can you build a high performing team to successfully execute strategic ideas?

In Strategy Execution for Public Leadership, former United States Pentagon Chief of Staff and Assistant Secretary of Defense for Homeland Defense and Global Security Eric Rosenbach will lead you through real-life public sector challenges, showing you how a purpose-driven strategy execution framework can lead to long-term adoption and success.

This government and nonprofit leadership training online course offers the opportunity to study decisions of key public leaders, like Former United States Secretary of Defense Ash Carter, so you can better understand how to develop strategies that not only align with your organizational goals, but also gain insight into the challenges and scrutiny that come along with making public decisions. Too often, public strategies fail because leaders don’t think about execution, which is made up of leadership and management tools, as well as a team who anticipates all scenarios prior to putting your plan into place. 

Through global case studies and protagonist examples, you will explore topics that set you on a path to strengthen your public leadership skills, including leadership and team development, resource and budget planning, talent recruitment and retention, strategic communications and crisis management, and risk planning and mitigation tactics.

By the end of this leadership development course, you will not only have the tools to create a sound public strategy, but also know how to test and optimize your strategy, increase competitive advantage, and sustain long-term impact and success.

Strategy is not one-size-fits-all. Implement a public leadership approach for long-term success.

The course will be delivered via  HBS Online’s course platform  and immerse learners in real-world examples from experts at industry-leading organizations. By the end of the course, participants will be able to:

• Enhance your strategic thinking by understanding the core elements of public leadership, management, strategy, and execution
• Develop the skills to institute strategic models that align with your goals and your stakeholders
• Prepare to lead your organization to long-term success through a shared understanding of strategic direction
• Create a culture of consistency, both internally and externally, that considers core objectives, resources, and environmental factors  
• Know how to approach strategy both effectively and efficiently, incorporating feedback cycles and creating public value
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• Learn how to strengthen your leadership skills by understanding the styles and traits of an effective leader
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• Learn communications strategies that align with your audience and purpose and compare the uses and roles of different communication channels
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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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