The Stack Overflow Podcast
Solving the data doom loop
Feb 14, 2025
Kenneth Stott, Field CTO of Hasura and a data management expert, dives into the complexities of the data doom loop. He shares insights about the paradox where increased investment leads to inefficiencies in data management. Stott emphasizes the need for a 'super graph' to streamline data accessibility and quality amid rising AI use. He also discusses the evolving landscape of data storage and the benefits of specialist databases, all while stressing the importance of proper data documentation for enhancing AI accuracy.
29:54
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Quick takeaways
- Organizations are trapped in a data doom loop, continually investing in new technologies without seeing improvements in data value or maturity.
- Microservices can create data silos that hinder sharing and trust, making it essential to align organizational structure with effective data management practices.
Deep dives
Understanding the Data Doom Loop
The data doom loop describes a cycle where organizations continuously invest in more complex data systems while failing to derive expected value from their data. As companies recognize that their data isn't meeting needs, they tend to add more technology and tools without adequate rationalization, leading to increased inefficiency. Studies indicate that data management spending has risen approximately 10% annually over the last three years, without a corresponding improvement in data maturity. This disconnect raises questions about the effectiveness of current data strategies and highlights the urgency for organizations to address the underlying issues of their data systems.
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