Harvard Case - Data Modelling and Management for Big Data
"Data Modelling and Management for Big Data" Harvard business case study is written by Srikumar Krishnamoorthy. It deals with the challenges in the field of Information Technology. The case study is 5 page(s) long and it was first published on : Mar 3, 2016
At Fern Fort University, we recommend a comprehensive strategy to address Fern Fort's data management challenges, focusing on a robust data model, advanced analytics, and a secure, scalable IT infrastructure. This strategy will enable Fern Fort to leverage its vast data assets for improved decision-making, personalized learning experiences, enhanced research capabilities, and ultimately, a more competitive position within the higher education landscape.
2. Background
Fern Fort University, a large, research-intensive institution, faces challenges in managing its growing data volume. The university collects data from diverse sources, including student records, course management systems, research databases, and online interactions. This data presents an opportunity to gain valuable insights into student performance, research trends, and operational efficiency. However, the lack of a centralized data model, limited analytical capabilities, and outdated IT infrastructure hinder Fern Fort's ability to effectively leverage this valuable resource.
The case study focuses on the challenges faced by the university's IT department in managing this data deluge. The department grapples with data silos, inconsistent data quality, and a lack of standardized data governance. This situation hinders the university's ability to make data-driven decisions, personalize learning experiences, and optimize resource allocation.
3. Analysis of the Case Study
This case study can be analyzed through the lens of several frameworks:
- Data Management Framework: This framework highlights the need for a comprehensive data management strategy encompassing data governance, data quality, data security, and data integration.
- Data Analytics Framework: This framework emphasizes the need for advanced analytics capabilities to extract meaningful insights from the vast data sets. This includes tools and techniques for data visualization, predictive modeling, and machine learning.
- IT Infrastructure Framework: This framework evaluates the university's existing IT infrastructure, identifying limitations and recommending upgrades to support the growing data volume and analytical needs.
Key Issues:
- Data Silos: Data is scattered across various systems, hindering data integration and analysis.
- Data Quality: Inconsistent data quality across different sources poses a challenge for accurate analysis.
- Limited Analytical Capabilities: The university lacks the tools and expertise to effectively analyze large datasets.
- Outdated IT Infrastructure: Existing infrastructure struggles to handle the increasing data volume and complex analytical tasks.
- Security Concerns: The growing data volume raises concerns about data security and privacy.
4. Recommendations
Phase 1: Data Model & Governance
- Develop a Centralized Data Model: Establish a comprehensive data model that integrates data from various sources, ensuring data consistency and standardization. This model should adhere to industry best practices and be designed for scalability.
- Implement Data Governance Framework: Implement a robust data governance framework that defines data ownership, data quality standards, and data security protocols. This framework should be aligned with industry regulations and best practices.
- Establish Data Quality Management Program: Develop a data quality management program to ensure data accuracy, completeness, and consistency. This program should include data cleansing, validation, and monitoring processes.
- Invest in Data Management Tools: Implement data management tools for data integration, data warehousing, and data quality management. These tools should be chosen based on their scalability, security, and compatibility with the university's existing IT infrastructure.
Phase 2: Data Analytics & Insights
- Develop Data Analytics Capabilities: Invest in data analytics tools and expertise to enable the university to extract meaningful insights from its data. This includes tools for data visualization, statistical analysis, predictive modeling, and machine learning.
- Establish Data Science Team: Form a dedicated data science team with expertise in data analysis, machine learning, and statistical modeling. This team will be responsible for developing and implementing data-driven solutions.
- Develop Data-Driven Decision-Making Culture: Foster a data-driven decision-making culture across the university by providing training and resources on data analysis and interpretation.
Phase 3: IT Infrastructure & Security
- Upgrade IT Infrastructure: Upgrade the university's IT infrastructure to support the growing data volume and complex analytical tasks. This includes investing in high-performance computing resources, data storage solutions, and network infrastructure.
- Implement Cloud Computing Solutions: Leverage cloud computing solutions for data storage, data processing, and analytics. This will provide scalability, flexibility, and cost-effectiveness.
- Enhance Cybersecurity Measures: Implement robust cybersecurity measures to protect sensitive data from unauthorized access, breaches, and cyberattacks. This includes implementing strong access controls, data encryption, and regular security audits.
5. Basis of Recommendations
These recommendations are based on the following considerations:
- Core Competencies and Consistency with Mission: The recommendations align with the university's mission of providing quality education and research. By leveraging data effectively, the university can enhance student learning, improve research outcomes, and optimize resource allocation.
- External Customers and Internal Clients: The recommendations address the needs of both external customers (students, researchers, and industry partners) and internal clients (faculty, staff, and administrators). By providing access to data-driven insights, the university can better serve its stakeholders.
- Competitors: The recommendations help the university stay competitive in the evolving higher education landscape. By embracing data-driven decision-making and leveraging advanced analytics, the university can differentiate itself from its competitors.
- Attractiveness: The recommendations are financially attractive, as they will enable the university to optimize resource allocation, improve efficiency, and generate new revenue streams.
6. Conclusion
By implementing these recommendations, Fern Fort University can transform its data into a strategic asset. A robust data model, advanced analytics capabilities, and a secure, scalable IT infrastructure will enable the university to make data-driven decisions, personalize learning experiences, enhance research capabilities, and ultimately, achieve its strategic goals.
7. Discussion
Alternatives:
- Outsourcing data management: This option could be considered for specific tasks, but it's important to ensure data security and control.
- Adopting a 'big data' platform: This could be a viable option for large-scale data processing and analysis, but it requires significant investment and expertise.
Risks:
- Data security breaches: The university must implement robust cybersecurity measures to protect sensitive data.
- Data quality issues: Maintaining data quality requires ongoing effort and investment.
- Resistance to change: Implementing a new data management strategy requires buy-in from all stakeholders.
Assumptions:
- The university has the resources and commitment to invest in the recommended solutions.
- The university has the necessary expertise to implement the data management strategy.
- The university can effectively address data security and privacy concerns.
8. Next Steps
- Form a data management task force: This task force will be responsible for developing and implementing the recommended strategy.
- Conduct a feasibility study: This study will assess the technical feasibility and financial viability of the recommendations.
- Develop a pilot project: Implement a pilot project to test the effectiveness of the recommended solutions.
- Communicate the strategy to all stakeholders: Ensure buy-in from all stakeholders and address any concerns.
Timeline:
- Phase 1 (Data Model & Governance): 6-12 months
- Phase 2 (Data Analytics & Insights): 12-18 months
- Phase 3 (IT Infrastructure & Security): 18-24 months
By following these steps, Fern Fort University can successfully transform its data into a strategic asset, driving innovation, enhancing efficiency, and achieving its strategic goals.
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Case Description
CFX Inc, an e-commerce start-up based out of India, has built a large e-marketplace that allows sellers and buyers to transact online. The firm currently has 30,000 sellers and aims to have around 50,000 sellers by FY 2015-16. The company has around 6Mn customers today and anticipates their growth to triple in the next 2 years. In order to provide best shopping experience to their growing customer base, the firm needs to collect, store and analyze different kinds of data (transactional, behavioural, syndicated, and demographic) and improve their customer shopping experience. The company is in the process of identifying and designing suitable data management systems to sustain and manage their business growth. As part of this initiative, they hire a consultant to study their data management requirements, design a data model and offer implementation related recommendations. The management expects the consultant to come out with a concrete set of recommendations in terms of the nature of solution, choice of the database, a data model that suits CFX's requirements, cost-benefit trade-offs involved and implementation considerations.
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