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24/06/2026 7:30 pm
TL;DR: Visibility, not more manual review, becomes the limiting factor when data products span many systems, as Collibra says its updated data quality tab can roll up scores across derived relations, branches, and business assets so leaders see a unified health signal instead of fragmented column-level checks.
NHIMG editorial — what this means for NHI practitioners
By the numbers:
- 92% of data leaders admit that high-quality data products are their top priority.
Questions worth separating out
Q: How should governance teams roll up technical data quality into business-facing trust signals?
A: Governance teams should define the aggregation path first, then map every technical source that contributes to a business asset or data product.
Q: Why do data quality programmes fail when assets span multiple schemas and tables?
A: They fail because linear reporting cannot represent branching dependencies.
Q: How do teams know if a data quality score is actually trustworthy?
A: They should verify three things: the lineage behind the score is complete, the refresh cadence matches the asset's lifecycle, and the aggregation rules include all material branches.
Practitioner guidance
- Map aggregation paths before operational use Document which schemas, tables, branches, and derived relations contribute to each business-facing score, then test whether the roll-up matches the asset graph you actually govern.
- Set refresh cadence by asset volatility Use monthly, weekly, or daily recalculation only where the underlying data product changes at that speed, and avoid applying one cadence across every asset class.
- Validate branch coverage in lineage reviews Check that multi-source data products include every contributing branch before exposing scores to business stakeholders or certification workflows.
What's in the full announcement
Collibra's full post covers the operational detail this post intentionally leaves for the source:
- How the metadata graph resolves complex multi-hop aggregation paths across technical and business assets
- Configuration detail for score frequency, dimension assignment, and derived relation handling
- Examples of how the quality tab feeds marketplace filtering and control tower visualisation
- The rollout context behind the enhanced quality tab and the roles expected to use it
👉 Read Collibra's analysis of data quality tab rollups and business asset health →
Data quality tab rollups: what they mean for governance teams?
Explore further
25/06/2026 4:48 am
Data quality fails at enterprise scale when visibility stops at the technical layer. Collibra's post is really about the gap between measurement and trust: teams can record quality metrics, but they cannot reliably translate those metrics into a business-level view of asset health. That is the same pattern identity teams see when technical controls do not surface into governance decisions. The practitioner conclusion is that visibility architecture matters as much as the metric itself.
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.
- Organisations maintain an average of 6 distinct secrets manager instances, creating fragmentation that undermines centralised control, according to The State of Secrets in AppSec.
A question worth separating out:
Q: What should organisations do before certifying a data product based on quality scores?
A: They should review the upstream technical inputs, confirm the roll-up logic covers every contributing relation, and assess whether the business asset depends on any blind spots. Certification should reflect both score and scope, not just a single numeric threshold.
👉 Read our full editorial: Data quality tab rollups expose the health gap in business assets
