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25/06/2026 10:29 pm
TL;DR: Gartner expects organizations to abandon 60% of AI projects through 2026 when they lack AI-ready data, while poor data quality already costs the average organization $12.9 million annually, underscoring how fragmentation turns detection into delay and undermines trust, according to Collibra. Data governance has become an operational control, not a reporting layer.
NHIMG editorial — what this means for NHI practitioners
By the numbers:
- Gartner predicts that through 2026, organizations will abandon 60% of AI projects that aren't supported by AI-ready data.
- Poor data quality already costs the average organization $12.9 million every year.
Questions worth separating out
Q: How should teams govern AI-ready data when quality signals are fragmented across tools?
A: Teams should treat fragmentation as a governance defect, not just an operational inconvenience.
Q: Why does poor data quality create so much risk for AI and compliance programmes?
A: Poor data quality is risky because AI systems scale the input they receive, including errors and missing context.
Q: What signals indicate that data governance is not working in practice?
A: The clearest signals are repeated manual investigations, alerts that stall without action, and unresolved issues that reappear in finance, audit, or model performance reviews.
Practitioner guidance
- Map every quality alert to an owner and policy Require anomalies to resolve with lineage, policy reference, and business owner before they enter the remediation queue.
- Preserve issue history as evidence Archive break records and resolution steps in a system that can support audit, compliance, and model-risk review without reconstruction.
- Align AI data trust to governed catalog entries Tie the datasets used for training and scoring to catalog metadata, policy status, and downstream consumer visibility.
What's in the full announcement
Collibra's full article covers the operational detail this post intentionally leaves for the source:
- The specific workflow for connecting quality alerts to lineage, business terms, and policy context.
- The product-level explanation of how archived break records support audits and evidence requests.
- The operational design behind remediation routing and governed handoffs across teams.
- The platform mechanics for scaling rule templates and issue management across domains.
👉 Read Collibra's analysis of AI-ready data quality and observability →
Bad data and AI-ready data governance: what teams need to know?
Explore further
25/06/2026 11:02 pm
Data quality fragmentation is now an identity governance problem in disguise. The article describes a familiar enterprise failure mode: control signals exist, but they do not travel with the asset, owner, or policy context required to act. That same pattern weakens NHI, IAM, and AI governance when teams split monitoring, lineage, and accountability across different systems. The practical conclusion is that governance without context becomes theatre.
A few things that frame the scale:
- The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities, 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.
A question worth separating out:
Q: Who should be accountable when AI consumes bad data and produces bad outcomes?
A: Accountability should sit with the governance chain that allowed untrusted data to remain consumable without clear controls. That usually means the data owner, the operational steward, and the risk function all have defined responsibilities. If no one can explain why the data was trusted, the control model is incomplete.
👉 Read our full editorial: Bad data scales wrong answers in AI-ready data governance
