Summary
- Highlights 2026 BARC research on trusted data and AI success.
- Shows how data products provide reliable inputs for AI systems.
- Connects data quality with stronger model reliability and confidence.
- Emphasizes trusted data as a foundation for effective AI.
Chapters
There is a question that every data and AI leader eventually confronts. We have the models, we have the infrastructure, so why isn't the AI reliable? The answer, almost always, is the data that feeds it.
In our 2026 global research conducted with BARC, we asked organizations why they adopted data products. 60% cited a single primary reason. They need data that AI systems could actually trust.
Not better algorithms, not more compute. Trustworthy data. The organizations that acted on this are seeing results.
41% already report measurably improved AI model reliability after adopting data products. 45% report greater confidence in the business decisions those models support. And 38% say they have reduced the time their teams spend resolving data quality issues.
Data quality problems are not primarily technical. They are organizational. When data has no clear owner, when schemas are undocumented, when there is no agreed definition of what a customer or a revenue event means, AI systems inherit that ambiguity.
The 2026 BARC and Actian research includes practical guidance on how leading organizations are solving data quality at scale before it reaches the model. Download the full report at actian.com/research.
