Harvard Case - Data-Analytics-Based Decision-Making at Teach for India
"Data-Analytics-Based Decision-Making at Teach for India" Harvard business case study is written by Milind Sohoni, Vijaya Sunder M, Sandeep Chitla, Arun Rout, Aditya Mallya. It deals with the challenges in the field of Operations Management. The case study is 9 page(s) long and it was first published on : Jul 3, 2022
At Fern Fort University, we recommend Teach for India (TFI) implement a comprehensive data-driven strategy to enhance program effectiveness, optimize resource allocation, and drive sustainable impact. This strategy should leverage technology and analytics to improve program design, student outcomes, and organizational efficiency, ultimately strengthening TFI's ability to empower underprivileged children across India.
2. Background
Teach for India is a non-profit organization committed to addressing educational inequity in India. TFI recruits and trains high-potential individuals to become 'Fellows' who teach in under-resourced schools for two years. The organization faces challenges in effectively measuring program impact, optimizing resource allocation, and scaling its operations. This case study explores how TFI can leverage data analytics to overcome these challenges and achieve greater impact.
The main protagonists in this case study are:
- Shaheen Mistri: CEO of Teach for India, who is actively seeking ways to enhance the organization's effectiveness and impact.
- The TFI team: Dedicated professionals who are committed to improving the lives of underprivileged children through education.
- The Fellows: Highly motivated individuals who dedicate two years of their lives to teaching in under-resourced schools.
- The students: The beneficiaries of TFI's programs, who are striving to overcome educational challenges and achieve their full potential.
3. Analysis of the Case Study
TFI's current approach to data analysis is fragmented and lacks a centralized system. This limits their ability to gain comprehensive insights into program effectiveness and student outcomes. To address this, we propose a framework based on the following key pillars:
1. Data Collection and Integration:
- Establish a centralized data warehouse: Consolidate data from various sources, including student performance, teacher feedback, program activities, and external benchmarks.
- Standardize data collection: Develop consistent data collection protocols across all programs and locations to ensure data quality and comparability.
- Leverage technology: Implement data management systems and tools to streamline data collection, storage, and analysis.
2. Data Analysis and Insights:
- Develop robust analytical models: Use statistical techniques, machine learning algorithms, and predictive analytics to identify patterns, trends, and key drivers of student outcomes.
- Conduct longitudinal studies: Track student progress over time to assess the long-term impact of TFI's programs.
- Develop dashboards and visualizations: Present data insights in an accessible and actionable format for stakeholders.
3. Actionable Insights and Decision-Making:
- Program Design and Improvement: Use data to identify areas for program improvement, tailor curriculum to specific student needs, and develop innovative teaching methodologies.
- Resource Allocation: Optimize resource allocation based on data-driven insights into program effectiveness and student needs.
- Impact Measurement: Develop robust impact evaluation frameworks to demonstrate the effectiveness of TFI's programs and track progress towards achieving organizational goals.
4. Organizational Culture and Capacity Building:
- Promote a data-driven culture: Train staff and Fellows in data literacy and analytical skills to empower them to make informed decisions.
- Establish a dedicated data analytics team: Hire skilled professionals to support data collection, analysis, and reporting.
- Foster collaboration: Encourage cross-functional collaboration between program teams, data analysts, and external partners.
4. Recommendations
1. Implement a Data-Driven Strategy: TFI should develop a comprehensive data-driven strategy that aligns with its mission and goals. This strategy should outline data collection protocols, analytical techniques, and action plans for leveraging data insights to improve program effectiveness and impact.
2. Invest in Technology and Analytics: TFI should invest in robust data management systems, analytical tools, and skilled personnel to support its data-driven strategy. This includes implementing a centralized data warehouse, adopting data visualization software, and hiring data analysts.
3. Develop a Data-Driven Culture: TFI should foster a data-driven culture by training staff and Fellows in data literacy, analytical skills, and the importance of using data to inform decision-making.
4. Establish a Data Governance Framework: TFI should establish a clear data governance framework to ensure data quality, security, and ethical use. This framework should define roles and responsibilities for data management, access control, and data privacy.
5. Partner with External Experts: TFI should seek partnerships with universities, research institutions, and technology companies to leverage their expertise in data analytics and program evaluation.
5. Basis of Recommendations
These recommendations are based on the following considerations:
- Core competencies and consistency with mission: Leveraging data analytics aligns with TFI's mission to empower underprivileged children through education. It enables the organization to measure impact, optimize resource allocation, and continuously improve program effectiveness.
- External customers and internal clients: Data-driven insights will benefit TFI's external stakeholders, including donors, partners, and government agencies, by providing evidence of program effectiveness. It will also empower internal clients, including staff, Fellows, and students, to make informed decisions and achieve better outcomes.
- Competitors: Many other education-focused non-profits are adopting data-driven approaches. By embracing data analytics, TFI can stay competitive and demonstrate its commitment to evidence-based practices.
- Attractiveness: The potential benefits of a data-driven strategy are significant, including improved program effectiveness, enhanced resource allocation, and increased donor confidence.
6. Conclusion
By embracing data analytics, Teach for India can transform its operations, enhance program effectiveness, and achieve greater impact in addressing educational inequity in India. This will require a strategic approach that involves data collection, analysis, action planning, and organizational change. By leveraging technology, fostering a data-driven culture, and collaborating with external experts, TFI can unlock the power of data to create a more equitable and impactful future for underprivileged children.
7. Discussion
Alternatives not selected:
- Continuing with the current approach: This option would limit TFI's ability to measure impact, optimize resources, and improve program effectiveness.
- Partnering with a data analytics firm: While this option could provide expertise, it might be costly and could limit TFI's control over data and insights.
Risks and key assumptions:
- Data quality and security: TFI must ensure data quality, security, and privacy to maintain trust and integrity.
- Resistance to change: Some staff and Fellows may resist adopting a data-driven approach.
- Resource constraints: Implementing a comprehensive data-driven strategy requires significant investment in technology, personnel, and training.
8. Next Steps
Timeline:
- Month 1-3: Develop a data-driven strategy and secure funding.
- Month 4-6: Implement a centralized data warehouse and data management system.
- Month 7-9: Train staff and Fellows in data literacy and analytical skills.
- Month 10-12: Develop and deploy analytical models to gain insights into program effectiveness and student outcomes.
Key milestones:
- Data warehouse implementation: A fully functional data warehouse should be operational within six months.
- Staff training: All staff and Fellows should receive data literacy training within one year.
- Analytical models deployment: At least two analytical models should be deployed within one year to support decision-making.
By taking these steps, Teach for India can leverage data analytics to create a more effective, efficient, and impactful organization that empowers underprivileged children across India.
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Case Description
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.
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