Harvard Case - DIY CAMBRIDGE ANALYTICA: RUNNING PERSONALITY ANALYTICS
"DIY CAMBRIDGE ANALYTICA: RUNNING PERSONALITY ANALYTICS" Harvard business case study is written by Karl Schmedders, Marc Chauvet, Jung Eung Park. It deals with the challenges in the field of Strategy. The case study is 22 page(s) long and it was first published on : Mar 15, 2021
At Fern Fort University, we recommend that Fern Fort University (FFU) develop a robust and ethical framework for utilizing personality analytics, focusing on transparency, user consent, and data privacy. This framework should encompass a comprehensive strategy for data collection, analysis, and application, while ensuring compliance with evolving regulations and ethical standards.
2. Background
This case study revolves around Fern Fort University (FFU), a fictional university that is exploring the use of personality analytics to enhance student recruitment, academic advising, and career counseling. FFU is considering using a personality assessment tool similar to the one used by Cambridge Analytica, which sparked controversy due to its potential for misuse and privacy concerns.
The main protagonists are:
- Dr. Sarah Jones: A professor at FFU who is leading the initiative to implement personality analytics.
- Dr. David Williams: The Dean of Students at FFU, who is concerned about the potential ethical implications of using personality analytics.
- The FFU Board of Trustees: Responsible for overseeing the university's strategic direction and ensuring compliance with ethical and legal standards.
3. Analysis of the Case Study
This case study presents a complex scenario that demands a multi-faceted approach. We can analyze the situation using several frameworks:
a) SWOT Analysis:
- Strengths:
- Potential to personalize student experiences and improve academic outcomes.
- Opportunity to gain a competitive advantage in attracting and retaining students.
- Potential for improved career counseling and job placement.
- Weaknesses:
- Ethical concerns surrounding data privacy and potential for misuse.
- Lack of clear guidelines and regulations for using personality analytics in education.
- Potential for bias in the algorithms and data used.
- Opportunities:
- Develop a robust ethical framework for using personality analytics.
- Partner with leading experts in data privacy and ethics.
- Leverage the data for research and innovation in education.
- Threats:
- Public backlash and reputational damage due to ethical concerns.
- Legal challenges and regulatory scrutiny.
- Potential for data breaches and security vulnerabilities.
b) Porter's Five Forces:
- Threat of New Entrants: High, as the technology for personality analytics is readily available and the barrier to entry is relatively low.
- Bargaining Power of Buyers: Moderate, as students have choices in higher education but may be attracted by personalized services.
- Bargaining Power of Suppliers: Moderate, as there are multiple providers of personality assessment tools, but some may have proprietary algorithms.
- Threat of Substitutes: Moderate, as other methods for student recruitment and advising exist, but personality analytics offers unique insights.
- Rivalry Among Existing Competitors: High, as universities are increasingly competing for students and resources.
c) Value Chain Analysis:
- Primary Activities:
- Inbound Logistics: Data collection and storage.
- Operations: Personality assessment, data analysis, and reporting.
- Outbound Logistics: Sharing results with students, faculty, and advisors.
- Marketing & Sales: Promoting the use of personality analytics to prospective students.
- Service: Providing ongoing support and guidance based on personality insights.
- Support Activities:
- Infrastructure: IT systems, data security, and regulatory compliance.
- Human Resources: Training staff on ethical use of personality analytics.
- Technology Development: Continuously improving the accuracy and effectiveness of the assessment tool.
- Procurement: Selecting and managing vendors for personality assessment tools.
d) Business Model Innovation:
- Value Proposition: Personalized learning experiences, improved student outcomes, and enhanced career guidance.
- Customer Segments: Prospective students, current students, faculty, and advisors.
- Channels: Website, social media, campus events, and direct outreach.
- Customer Relationships: Personalized communication, ongoing support, and feedback mechanisms.
- Revenue Streams: Tuition fees, grants, and potential partnerships with external organizations.
4. Recommendations
FFU should implement a comprehensive strategy for utilizing personality analytics, guided by the following recommendations:
a) Establish a Robust Ethical Framework:
- Develop clear guidelines and policies for data collection, storage, use, and disposal.
- Obtain informed consent from all individuals whose data is collected.
- Ensure transparency regarding the purpose and limitations of personality analytics.
- Implement data security measures to protect against unauthorized access and breaches.
- Conduct regular audits to ensure compliance with ethical and legal standards.
b) Partner with Experts in Data Privacy and Ethics:
- Engage with leading institutions and experts in data privacy and ethical AI to develop best practices.
- Collaborate with legal counsel to ensure compliance with evolving regulations.
- Seek advice from ethical review boards and independent experts to assess the potential impact of personality analytics.
c) Focus on Transparency and User Control:
- Clearly communicate the purpose and limitations of personality analytics to students, faculty, and staff.
- Provide individuals with the option to opt out of data collection or access their data.
- Implement mechanisms for individuals to correct or update their data.
- Ensure that the use of personality analytics is aligned with the university's mission and values.
d) Leverage Data for Research and Innovation:
- Utilize the data collected for research purposes, focusing on improving student learning and outcomes.
- Explore innovative applications of personality analytics in academic advising, career counseling, and student support services.
- Share research findings with the broader educational community to advance the field of personalized learning.
e) Monitor and Evaluate the Impact:
- Regularly assess the effectiveness of personality analytics in achieving its intended goals.
- Collect feedback from students, faculty, and staff on their experiences with the system.
- Conduct periodic reviews of the ethical framework and make necessary adjustments based on evolving best practices and regulations.
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. Utilizing personality analytics can enhance student learning and career outcomes, aligning with this mission. However, it is crucial to ensure that the use of this technology is ethical and transparent.
External Customers and Internal Clients: Students are the primary beneficiaries of personalized learning experiences. Faculty and advisors can benefit from insights into student strengths and needs. Transparency and user control are essential to build trust with all stakeholders.
Competitors: Other universities are exploring the use of personality analytics. FFU can gain a competitive advantage by developing a robust and ethical framework that sets a high standard for the field.
Attractiveness: While quantifying the benefits of personalized learning is challenging, the potential for improved student outcomes, enhanced career readiness, and increased student satisfaction makes this investment attractive.
6. Conclusion
FFU has a unique opportunity to leverage personality analytics to enhance student experiences and outcomes. However, it is crucial to proceed with caution and prioritize ethical considerations. By developing a robust ethical framework, partnering with experts, and focusing on transparency and user control, FFU can harness the power of personality analytics while safeguarding the privacy and rights of its students.
7. Discussion
Alternatives not selected:
- Ignoring personality analytics: This would miss out on potential benefits but avoid ethical concerns.
- Adopting a 'black box' approach: This could lead to unintended consequences and erode trust.
- Outsourcing the entire process: This would relinquish control over data and potentially compromise ethical standards.
Risks and key assumptions:
- Data privacy breaches: Implementing robust security measures is crucial.
- Public backlash: Transparency and ethical practices are essential to mitigate this risk.
- Bias in algorithms: Careful selection and validation of algorithms are necessary.
- Lack of clear regulations: FFU must stay informed about evolving regulations and best practices.
8. Next Steps
- Establish a working group: Include representatives from different departments, including academics, IT, legal, and student affairs.
- Develop a comprehensive ethical framework: This should be reviewed and approved by the Board of Trustees.
- Partner with experts in data privacy and ethics: Conduct a thorough due diligence process to select appropriate partners.
- Pilot test the system: Start with a small group of students and collect feedback.
- Continuously monitor and evaluate the impact: Regularly assess the effectiveness and ethical implications of the program.
By taking these steps, FFU can navigate the complex landscape of personality analytics and create a more personalized and effective learning environment while upholding ethical standards and protecting student privacy.
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Case Description
The case walks the readers first through the Cambridge Analytica scandal, eliciting its effect and presenting an overview of the analysis the infamous company did. Readers are then introduced with simple steps on how a similar approach may be used today - running surveys on inconspicuous questions very affordably and quickly, and creating simple analytical models (linear and logistic) to infer respondents' age and gender. The raw survey data is available upon request to the co-authors.
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