Harvard Case - Evaluating the Cognitive Analytics Frontier
"Evaluating the Cognitive Analytics Frontier" Harvard business case study is written by Adam Robert Pah, Alanna Lazarowich, Charlotte Snyder. It deals with the challenges in the field of Information Technology. The case study is 10 page(s) long and it was first published on : Jan 17, 2018
At Fern Fort University, we recommend a strategic approach to harnessing the power of cognitive analytics, focusing on building a robust IT infrastructure to support data-driven decision making across all departments. This involves a multi-pronged strategy encompassing digital transformation, data management, AI and machine learning, and organizational change to fully leverage the potential of cognitive analytics.
2. Background
The case study focuses on Fern Fort University, a mid-sized institution facing challenges in effectively utilizing its vast data resources. The university struggles with siloed information systems, limited data analysis capabilities, and a lack of strategic vision for leveraging cognitive analytics. The main protagonists are the university's president, who is keen on embracing the potential of cognitive analytics, and the IT director, tasked with implementing this vision.
3. Analysis of the Case Study
The case study presents a classic scenario of an organization grappling with the need to adapt to the rapidly evolving technological landscape. Fern Fort University faces several key challenges:
- Data Silos: The university's fragmented IT infrastructure results in data silos across departments, hindering a holistic view of student performance, operational efficiency, and research outcomes.
- Limited Analytics Capabilities: The current IT infrastructure lacks the capacity for advanced data analysis, hindering the university's ability to extract meaningful insights from its data.
- Lack of Strategic Vision: The university lacks a clear strategy for leveraging cognitive analytics, leading to a fragmented and inefficient approach to data utilization.
- Resistance to Change: The university's faculty and staff may exhibit resistance to adopting new technologies and processes, hindering the successful implementation of cognitive analytics.
To address these challenges, we can utilize the Porter's Five Forces Framework to analyze the competitive landscape:
- Threat of New Entrants: The higher education landscape is increasingly competitive, with new entrants like online universities and MOOCs posing a significant threat.
- Bargaining Power of Buyers: Students have increasing choices, leading to greater bargaining power, demanding better value and personalized learning experiences.
- Threat of Substitutes: Alternative learning platforms and technologies are emerging, posing a threat to traditional universities.
- Bargaining Power of Suppliers: The university's dependence on technology providers gives them significant bargaining power, potentially impacting costs and access to cutting-edge technologies.
- Competitive Rivalry: The competition among universities is intense, with institutions vying for students, funding, and research grants.
Fern Fort University needs to leverage cognitive analytics to gain a competitive edge by improving student outcomes, enhancing operational efficiency, and fostering research innovation.
4. Recommendations
Fern Fort University should implement a multi-faceted strategy to harness the power of cognitive analytics:
1. Digital Transformation:
- IT Infrastructure Modernization: Invest in a robust IT infrastructure capable of handling large datasets and supporting advanced analytics. This includes upgrading servers, storage systems, and network infrastructure.
- Cloud Computing Adoption: Leverage cloud computing solutions to enhance scalability, flexibility, and cost-efficiency.
- Data Integration: Implement a comprehensive data integration strategy to break down silos and create a unified data repository.
- Enterprise Resource Planning (ERP): Implement an ERP system to streamline administrative processes, improve data accuracy, and enable data-driven decision making.
- Customer Relationship Management (CRM): Implement a CRM system to manage student interactions, track student engagement, and personalize learning experiences.
2. Data Management:
- Data Governance: Establish clear data governance policies to ensure data quality, security, and compliance with regulations.
- Data Analytics Platform: Implement a data analytics platform with advanced features for data visualization, predictive modeling, and machine learning.
- Big Data Management: Develop capabilities for managing and analyzing large datasets using big data technologies.
- Data Security: Implement robust cybersecurity measures to protect sensitive student data and ensure data privacy.
3. AI and Machine Learning:
- AI in Business: Explore the use of AI and machine learning for personalized learning recommendations, automated administrative tasks, and research analysis.
- Machine Learning Applications: Implement machine learning models to predict student performance, identify at-risk students, and optimize resource allocation.
- Internet of Things (IoT): Explore the use of IoT devices to collect data on student behavior, classroom environment, and campus infrastructure.
4. Organizational Change:
- Change Management: Implement a comprehensive change management program to address resistance to change and ensure successful adoption of new technologies.
- Training and Development: Provide training programs to faculty and staff on data analytics, AI, and machine learning.
- Data Literacy: Foster a culture of data literacy by encouraging data-driven decision making at all levels of the university.
5. Basis of Recommendations
These recommendations are based on the following considerations:
- Core Competencies and Consistency with Mission: The recommendations align with Fern Fort University's mission to provide high-quality education and enhance student outcomes.
- External Customers and Internal Clients: The recommendations aim to improve student experiences, enhance faculty productivity, and optimize administrative efficiency.
- Competitors: The recommendations help Fern Fort University stay ahead of the curve in the increasingly competitive higher education landscape.
- Attractiveness: The recommendations are expected to yield significant benefits, including improved student retention, increased research productivity, and enhanced operational efficiency.
6. Conclusion
By embracing cognitive analytics, Fern Fort University can unlock significant opportunities for growth and innovation. The recommended strategy, focusing on digital transformation, data management, AI and machine learning, and organizational change, will enable the university to harness the power of data and gain a competitive edge in the evolving higher education landscape.
7. Discussion
Alternative strategies include focusing solely on specific departments or adopting a piecemeal approach to implementing cognitive analytics. However, these approaches are likely to be less effective and may lead to fragmented data systems and limited benefits.
Key Assumptions:
- The university has the financial resources to invest in the recommended technology upgrades and training programs.
- The university's leadership is committed to supporting the implementation of cognitive analytics.
- Faculty and staff are willing to embrace new technologies and processes.
8. Next Steps
The implementation of the recommended strategy should be approached in a phased manner:
Phase 1 (Year 1):
- IT Infrastructure Modernization: Upgrade servers, storage systems, and network infrastructure.
- Data Integration: Implement a data integration strategy to create a unified data repository.
- Data Governance: Establish data governance policies to ensure data quality and security.
- Change Management: Initiate a change management program to address resistance to change.
Phase 2 (Year 2):
- Cloud Computing Adoption: Migrate select applications to the cloud.
- Data Analytics Platform: Implement a data analytics platform with advanced features.
- AI and Machine Learning Pilot Projects: Launch pilot projects to explore the use of AI and machine learning in specific areas.
- Training and Development: Provide training programs on data analytics and AI.
Phase 3 (Year 3):
- ERP Implementation: Implement an ERP system to streamline administrative processes.
- CRM Implementation: Implement a CRM system to manage student interactions.
- Big Data Management: Develop capabilities for managing and analyzing large datasets.
- Data-Driven Decision Making: Encourage data-driven decision making across all departments.
By following this phased approach, Fern Fort University can successfully implement cognitive analytics and unlock its full potential for growth and innovation.
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Case Description
In the fall of 2014, Chad Kartchner, senior manager of marketing and product management at Honeywell Aerospace (HA), pondered how technology could transform the way aircraft were maintained. He had heard a lot of buzz about cognitive analytics, an artificial intelligence term referring to the use of computer models and algorithms to simulate human thought through self-learning systems, data mining, pattern recognition, and natural language processing. The sheer volume of parts and the time-sensitive nature of repairs in the aviation industry made it complicated to identify problems and address them quickly. Kartchner contemplated the options for updating HA's ground-based maintenance system. Should he emulate HA's state-of-the-art on-board system for an entire aircraft or try something new? Emulating the on-board system, which HA developed internally, would be an easy sell to leadership given internal buy-in and satisfaction with the on-board system, but he contemplated new approaches because he did not want to overlook rapidly emerging technologies. The latter could include crowdsourced features that leveraged the abundance of knowledge among HA's customers' technicians or a cognitive analytics approach. Even if he could persuade leadership to try a new cognitive analytics approach, should HA partner with an established entity or work with a relatively unproven startup who promised lower cost, better features, and quicker turnaround to develop a new system? Students will step into the shoes of Kartchner as he leads the internal discussion on whether and how to tap into the benefits of cognitive analytic solutions for Honeywell Aerospace and its customers.
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