Summary
- Shows how data observability continuously monitors data quality.
- Helps detect issues before they impact AI systems.
- Combines observability, data products, and data contracts.
- Builds a trusted foundation for scaling AI initiatives.
Chapters
Over this series, we have examined why AI leaders build data products, how adoption has accelerated, and what it takes to deploy autonomous AI at scale. There is one more question the most mature organizations are now asking. We built the data product, but can we trust it right now in real time?
In the 2026 BARC and Actian research, we asked organizations what their top data priority was for the next 12 months. 43% named data observability, ahead of scaling data products, ahead of introducing data contracts, ahead of everything else.
Observability means continuous monitoring of data pipelines, detecting drift, catching anomalies, and alerting teams before bad data reaches an AI system. The most mature organizations in our research are doing something more ambitious. They are applying data product principles not just to their datasets, but to their AI models themselves.
They define ownership of AI models. They document their inputs and outputs as schemas. They set quality thresholds.
They monitor for drift and degradation. In short, they treat AI models as products with the same rigor they apply to data.
The BARC research closes with three recommendations for data leaders. First, treat data observability as infrastructure, not an afterthought. Second, extend data product discipline to every AI asset your organization produces.
Third, use data contracts to create the trust layer that autonomous AI requires.
The organizations that will lead in AI over the next three years are not waiting for better models. They are building the data foundation right now.
See where your organization stands. Download the full report at actian.com/research.
