Harvard Case - Evaluating Decisions: Correlation or Causation?
"Evaluating Decisions: Correlation or Causation?" Harvard business case study is written by Gaurav Chiplunkar, Stephen E. Maiden. It deals with the challenges in the field of Strategy. The case study is 8 page(s) long and it was first published on : Aug 23, 2023
At Fern Fort University, we recommend that the university adopt a data-driven decision-making framework to enhance its understanding of the impact of its initiatives. This framework should incorporate rigorous statistical analysis to differentiate correlation from causation, enabling more accurate assessments of program effectiveness and resource allocation. This approach will ensure that the university's investments are aligned with its strategic goals and contribute to its long-term success.
2. Background
This case study focuses on Fern Fort University (FFU), a private university facing challenges in identifying the true impact of its various initiatives. The university has been implementing numerous programs aimed at improving student retention, academic performance, and overall student experience. However, the lack of a robust data analysis framework has made it difficult to determine which initiatives are truly driving positive outcomes.
The main protagonist is Dr. Rebecca Smith, the university's Vice President for Academic Affairs, who is tasked with evaluating the effectiveness of these initiatives and making informed decisions regarding resource allocation. Dr. Smith is concerned about the potential for misinterpreting correlation as causation, leading to inefficient resource allocation and ultimately hindering the university's progress.
3. Analysis of the Case Study
To analyze the situation at FFU, we can utilize a combination of frameworks:
- Porter's Five Forces: This framework helps us understand the competitive landscape of the higher education industry. The increasing competition from online universities and the rising cost of education are key challenges for FFU.
- SWOT Analysis: This framework helps identify FFU's internal strengths and weaknesses, as well as external opportunities and threats. FFU's strengths include its experienced faculty and strong alumni network. However, its weaknesses include a lack of data-driven decision-making and a potential for resource misallocation.
- Value Chain Analysis: This framework helps analyze the key activities that create value for students at FFU. By understanding the value chain, FFU can identify areas where initiatives could be implemented to improve student outcomes.
The case study highlights the critical need for FFU to adopt a more rigorous approach to data analysis. This involves:
- Moving beyond simple correlation: While correlation can suggest relationships between variables, it does not necessarily imply causation.
- Implementing statistical analysis: Using statistical methods like regression analysis, A/B testing, and randomized controlled trials can help isolate the true impact of specific initiatives.
- Developing a data-driven decision-making culture: This requires training faculty and staff on data analysis techniques and establishing clear processes for data collection, analysis, and interpretation.
4. Recommendations
FFU should implement the following recommendations to improve its decision-making process:
- Establish a Data Analytics Team: This team should be composed of experts in statistics, data science, and education research. They will be responsible for designing and implementing data collection and analysis strategies.
- Develop a Data-Driven Decision-Making Framework: This framework should outline the process for identifying key performance indicators (KPIs), collecting and analyzing data, and interpreting results to inform strategic decisions.
- Implement a Pilot Program: Start with a pilot program to test the effectiveness of the new data-driven decision-making framework. This program should focus on a specific initiative, such as a new student support program, and collect data on its impact.
- Invest in Technology and Training: FFU should invest in data analytics software and provide training to faculty and staff on data analysis techniques and the use of the new data-driven decision-making framework.
5. Basis of Recommendations
These recommendations are based on the following considerations:
- Core competencies and consistency with mission: FFU's mission is to provide a high-quality education and prepare students for success. By adopting a data-driven decision-making framework, FFU can ensure that its initiatives are aligned with its mission and contribute to its long-term success.
- External customers and internal clients: The recommendations are designed to benefit both students and faculty. Students will benefit from more effective and targeted programs, while faculty will have access to data that can inform their teaching and research.
- Competitors: FFU needs to stay ahead of its competitors in the increasingly competitive higher education landscape. By adopting a data-driven approach, FFU can gain a competitive advantage by making more informed decisions and allocating resources more effectively.
- Attractiveness - quantitative measures: The recommendations are expected to lead to improved student outcomes, increased efficiency, and better resource allocation, all of which contribute to the university's financial sustainability.
6. Conclusion
By adopting a data-driven decision-making framework, Fern Fort University can move beyond simple correlations and identify the true impact of its initiatives. This will enable the university to make more informed decisions, allocate resources effectively, and ultimately achieve its strategic goals.
7. Discussion
Other alternatives not selected include:
- Continuing with the current approach: This would continue to rely on anecdotal evidence and potentially lead to misallocation of resources.
- Hiring external consultants: This could be costly and may not provide long-term solutions.
Key risks and assumptions:
- Data availability and quality: FFU needs to ensure that it has access to reliable and accurate data.
- Resistance to change: Some faculty and staff may be resistant to adopting a new data-driven approach.
- Investment in technology and training: FFU needs to invest in the necessary resources to implement the recommendations.
8. Next Steps
FFU should implement the following steps to ensure the success of the new data-driven decision-making framework:
- Phase 1 (Months 1-3): Establish the Data Analytics Team, develop the data-driven decision-making framework, and select a pilot program.
- Phase 2 (Months 4-6): Implement the pilot program and collect data on its impact.
- Phase 3 (Months 7-9): Analyze the data from the pilot program and make adjustments to the framework as needed.
- Phase 4 (Months 10-12): Roll out the new data-driven decision-making framework across the university and provide training to faculty and staff.
By following these steps, FFU can ensure that its investments are aligned with its strategic goals and contribute to its long-term success.
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Case Description
Managers, policymakers, and leaders are often presented with data and evaluation techniques, then tasked with deciding whether to go ahead with the strategy or policy. This fictional case aims to build a basic framework to understand how one can determine whether a particular business strategy or economic policy "works." It offers an overview of commonly used evaluation strategies and their associated pitfalls, through the lens of Ramesh Sharma, the CEO of a large nongovernmental organization, who has to decide how to evaluate the effectiveness of a training program to female entrepreneurs. The case is used at Darden in a second-year elective on "India in the Global Economy." It is taught in the second week of the course with the aim of introducing students to basic methods for rigorously evaluating their business and policy decisions. While directly pertinent to the course on India, this case builds a general framework on evaluation techniques that can be easily extended to other contexts and decisions. Therefore, it can also be taught in courses in marketing, business strategy, management, operations, policy evaluation, and ethics.
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