The pattern
Teams proudly announce “we now have data products” only to discover six weeks later that the AI engineers still cannot get the features they need in the right shape, freshness, or quality.
The usual suspects
- Data products built for reporting, not for feature engineering or training.
- No contract or SLA around freshness, schema stability, or completeness.
- Ownership ends at “the pipeline runs” instead of “the consumer can trust it for production AI.”
- Feature drift between training and serving because the product was never versioned properly.
- Overly generic products that try to serve every use case and end up serving none well.
What good data products for AI actually look like
- Purpose-built with the consumer (ML engineer) in mind from day one.
- Explicit contracts: freshness SLAs, schema evolution rules, and quality gates.
- Versioned and time-travel capable by default (Delta Lake + proper partitioning).
- Feature stores or reusable feature groups inside the product so the same logic is used for training and inference.
- Clear ownership and a feedback loop when the product fails downstream AI workloads.
The blunt rule
If your data product cannot be dropped straight into a training pipeline or real-time inference service without extra ETL, it is not a data product for AI. It is just another reporting table with better marketing.
The fix
Design data products with AI consumption patterns first. Treat them as production assets with the same rigour you apply to the models themselves. The payoff is dramatically faster AI delivery and far fewer “data is the problem” conversations.