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24/06/2026 7:08 pm
TL;DR: Data quality is now the biggest barrier to successful AI projects for 44% of organisations, according to BARC, and Collibra’s 2026 scorecard frames trustworthy data as the precondition for production AI, GenAI features, and governed decision-making. The real issue is not cataloging alone, but whether governance can enforce trust where data is actually consumed.
NHIMG editorial — based on content published by Collibra: Three years a Market Leader. In 2026, Collibra came out on top
Questions worth separating out
Q: How should teams govern AI systems that rely on enterprise data for decisions?
A: Treat the data layer as part of the control plane.
Q: Why do data governance gaps become identity risk for AI programmes?
A: Because AI systems inherit trust from the identities that access and route data into them.
Q: What breaks when governance only documents policy instead of enforcing it?
A: The programme loses the ability to prevent misuse at the point of access.
Practitioner guidance
- Map AI assets into identity governance workflows Classify models, agents, and training datasets alongside human and machine identities so provisioning, review, and offboarding are handled through one governance model.
- Verify policy enforcement at the data layer Confirm that access controls are enforced in Snowflake, Databricks, BigQuery, or equivalent platforms, not only documented in a catalogue or policy register.
- Separate visibility from enforceability Review whether the governance platform only reports policy drift or actually triggers remediation when access violations occur in live systems.
What's in the full article
Collibra's full post covers the vendor's scoring details and platform-specific capabilities this analysis intentionally leaves at a higher level:
- The BARC scoring dimensions and how Collibra ranked across Portfolio Capabilities and Market Execution.
- The vendor’s explanation of its Data & AI Governance and Data Shopping use cases.
- The specific way Collibra describes enforcement in Snowflake, Databricks, and BigQuery.
- The report framing behind its three-year Market Leader result and category positioning.
👉 Read Collibra's analysis of the 2026 BARC data intelligence score →
Data trust and AI governance: what practitioners need to know?
Explore further
25/06/2026 4:17 am
Trustworthy data is becoming an identity governance problem, not just a data management problem. Once AI systems consume governed data directly, access, classification, and accountability all become part of the same control surface. That means the quality of identity decisions now affects the quality of AI decisions, especially where machine identities and human approvals both touch the same data estate. Practitioners should treat data trust as part of identity architecture, not a separate analytics concern.
A few things that frame the scale:
- Organisations maintain an average of 6 distinct secrets manager instances, creating fragmentation that undermines centralised control, according to The State of Secrets in AppSec.
- Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap, according to The State of Secrets in AppSec.
A question worth separating out:
Q: Who should be accountable for governance when models and agents use enterprise data?
A: Accountability should sit with the teams that own both the AI use case and the underlying data controls. That usually means identity, data governance, and platform teams sharing responsibility for provisioning, review, and enforcement. If ownership is unclear, AI risk becomes everybody’s problem and nobody’s control.
👉 Read our full editorial: Data trust is now the control plane for enterprise AI governance
