How Open Data Infrastructure preserves choice in the age of AI

Open Data Infrastructure is presented as an enterprise architecture that extends beyond open table formats to include portable governance, compute, business semantics, and AI context, enabling organizations to avoid vendor lock-in while retaining control over their data. At dbt Summit, leaders from Fivetran, dbt Labs, Snowflake, Microsoft, and MotherDuck emphasized that formats such as Apache Iceberg allow multiple engines to access shared data, but consistent, transferable security policies are also necessary to prevent governance from becoming another proprietary barrier. This separation of storage and compute lets organizations select the most suitable engine for transformations, interactive analytics, or AI workloads, encouraging vendors to compete through performance, cost, and innovation rather than switching friction. As AI increasingly relies on enterprise data, the discussion identified semantic models, metrics, and business definitions as another critical portability challenge, with emerging efforts seeking to make this context reusable across BI, analytics, and AI tools. The transition to open infrastructure must be gradual and practical for organizations with established data stacks, allowing them to introduce interoperable formats and tools without replacing existing systems, while preserving flexibility for future technology changes.

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