Data-Analytics-Based Decision-Making at Teach for India
The case is designed to be used in courses on Nonprofit Operations Management, Data Analytics, Six Sigma, and Business Process Excellence/Improvement in MBA or Executive MBA programs. It is suitable for teaching students about the common problem of lower rates of volunteerism in nonprofit organizations. Further, the case study helps present the importance and application of inferential statistics (data analytics) to identify the impact of various factors on the problem (effect). The case is set in early 2021 when Shefali Sharma, the Strategy and Learning Manager with Teach For India (TFI), faced a few challenging questions from a professor at the Indian School of Business (ISB) during her presentation at an industry gathering in Hyderabad, India. Sharma was concerned about the low matriculation rate of TFI fellows, despite the rigorous recruitment, selection, and matriculation (RSM) process. A mere 50-60% matriculation rate was not a commensurate return for an investment of INR 6.5 million and the massive effort put into the RSM process. In 2017, Sharma organized focused informative and experiential events to motivate candidates to join the fellowship, but it was not very clear if these events impacted the TFI matriculation rate. After the industry gathering at ISB, Sharma followed up with the professor to seek his guidance in performing data analytics on the matriculation data. Sharma wondered if inferential data analysis could help her understand which demographic factors and events impact the matriculation rate.
- Illustrate the importance of inferential statistics as a decision support system in resolving business problems
- Formulating and solving a hypothesis testing problem for attribute (discrete) data
- Visually depicting the flow of work across different stages of a process