Booster Analytics

My last story was about empowering Universities with the data mesh, such that faculty can easily build data products that students and upper administrators can consume. Unfortunately, this is rather complex and very challenging, and your data mesh project can easily turn into a mess.

While the concept of Data Mesh has gained traction in various industries for its promise of decentralizing data ownership and enhancing data accessibility, its application in higher education poses several challenges that must be carefully considered.

1. Complexity and Expertise Requirements

Implementing a Data Mesh architecture requires a high level of technical expertise and understanding of distributed data systems. Many academic institutions may lack the necessary in-house expertise to effectively design, implement, and maintain such a complex system. This gap could lead to increased reliance on external consultants and vendors, driving up costs and potentially compromising the quality and security of the implementation. Additionally, finding the right vendor to help with the project is extremely difficult in Asia. Most vendors actually do not have adequate knowledge of the data mesh concept.

2. Resource Intensiveness

Data Mesh initiatives demand significant resources, including time, money, and personnel. My plan was to host this on GCP, but as a public university, we are not allowed to put the data outside Thailand. So, this put a lot more constraints on the project. Universities often operate under tight budget constraints and must prioritize spending. The costs associated with developing, deploying, and maintaining a Data Mesh architecture might outweigh the perceived benefits, especially when considering other pressing needs within the institution.

3. Cultural and Organizational Resistance

I ran into the “ We have done it this way, and every student is very happy”. Higher education institutions are traditionally siloed and resistant to change. Shifting to a Data Mesh approach requires a cultural transformation that encourages collaboration and data sharing across departments. This change can face substantial resistance from faculty and staff who are accustomed to operating within their own silos. Overcoming this resistance requires strong leadership and change management strategies, which are not always present or effective in academic settings.

4. Data Governance and Security Risks

Decentralizing data ownership increases the risk of inconsistent data governance practices. Ensuring data quality, security, and compliance across multiple departments can be challenging. Inconsistent practices can lead to data breaches, loss of sensitive information, and non-compliance with regulations such as PDPA in Thailand.

5. Integration with Existing Systems

Everyone who has worked with University IT understands how difficult it is to get them moving in a new direction. Universities often have a patchwork of legacy systems and databases that are not designed to work together seamlessly. Integrating these systems into a cohesive Data Mesh architecture is a daunting task that can lead to significant technical hurdles and interoperability issues. The complexity of integration can result in extended timelines, increased costs, and potential disruptions to daily operations.

6. ROI Uncertainty

This is understandable, and you have to plan this wisely and carefully. I did not do my due diligence well on this topic and have suffered quite dramatically. The return on investment (ROI) for a Data Mesh initiative in higher education is not guaranteed, but you can focus on the feedback from students as a part of the size of gain. The benefits of improved data accessibility and enhanced decision-making capabilities must be clearly demonstrated to justify the initial and ongoing investments. Without clear, measurable outcomes, stakeholders may remain skeptical about the value of the initiative.

7. Focus on the Core Mission

You must sell the project as a part of improving an education project. Universities’ core mission revolves around education and research. Diverting substantial resources and focus towards implementing and managing a Data Mesh architecture may detract from these primary goals. Institutions must weigh the benefits of advanced data management against the potential distraction from their core educational mission.

Conclusion

While Data Mesh offers promising advantages in terms of data accessibility and decentralized management, its implementation in higher education is fraught with challenges. The complexity, resource intensiveness, cultural resistance, governance risks, integration issues, ROI uncertainty, and potential diversion from the core mission must all be carefully considered. Universities will thoroughly evaluate whether the benefits of a Data Mesh architecture outweigh the significant hurdles and potential drawbacks in their specific context, so make sure that you cover all these challenges well in your plan.