The Stack Overflow Podcast Solving the data doom loop
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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.
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The Data Doom Loop
- The data doom loop describes a cycle of increasing spending on data systems with no improvement in data value.
- This leads to complexity, inefficiency, and wasted resources, evidenced by rising data management costs and stagnant data maturity.
Data Silos and Complexity
- Specialization in databases, like the separation of production and analytics systems, contributes to data complexity.
- Generative AI, with its need for vector databases and quality data, further exacerbates the data doom loop.
Data Duplication and Trust
- Data consumers often orient around specific technology teams, leading to data duplication and inconsistent data flows.
- This lack of organization and trust breakdown wastes resources and undermines data reliability.