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23/06/2026 7:20 pm
TL;DR: Data teams are treating data as a reusable product with named owners, consumer definitions, quality SLAs and lifecycle management because traditional pipeline-first models keep producing distrust and rework, according to Collibra. The governance shift matters because trust becomes an asset property, not a downstream validation exercise.
NHIMG editorial — based on content published by Collibra: Data as a product: How leading organizations are rethinking their data strategy
Questions worth separating out
Q: How should organisations govern data products so business teams trust them?
A: Start by assigning a named owner to each important dataset, then define the consumers, quality standards and lifecycle rules that apply to it.
Q: Why does data-as-a-product reduce shadow spreadsheets and rework?
A: Because it makes the official asset easier to find, evaluate and use than a locally rebuilt version.
Q: When is a data marketplace necessary instead of a simple catalogue?
A: A marketplace becomes necessary when teams need both discovery and operational access, not just metadata search.
Practitioner guidance
- Assign accountable owners to critical data products Map each high-value dataset to a named owner who is responsible for definition, quality, availability and consumer communication.
- Define data contracts for recurring business-critical assets Set explicit expectations for freshness, completeness, schema stability and quality thresholds, then monitor for contract violations as operational events.
- Make discovery and certification mandatory before reuse Require teams to locate data through the approved catalogue or marketplace, review provenance and quality status, and confirm access through the governed workflow before building downstream dependencies.
What's in the full article
Collibra's full article covers the operational detail this post intentionally leaves for the source:
- Examples of how data product ownership is assigned across domain teams and governance functions
- The practical role of data marketplace discovery, access and certification in day-to-day usage
- How quality monitoring and lineage are embedded into the data product lifecycle
- The vendor's view of how Collibra's platform supports registration, governance and access workflows
👉 Read Collibra's analysis of data as a product and governance →
Data as a product: what it changes for governance teams?
Explore further
25/06/2026 3:17 am
Data-as-a-product is really an accountability model, not a tooling model. The article is right to reject the idea that more pipelines automatically create better decisions. What breaks in the old approach is ownership: data exists, but nobody is responsible for its definition, quality or consumer experience. That same failure mode appears anywhere governance is split from the asset itself, so practitioners should treat ownership as the starting control, not an administrative label.
A few things that frame the scale:
- 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.
- 43% of security professionals are concerned about AI systems learning and reproducing sensitive information patterns from codebases, according to The State of Secrets in AppSec.
A question worth separating out:
Q: What should data teams measure to know data-as-a-product is working?
A: Measure whether consumers can find trusted data faster, whether quality issues are routed to a named owner and whether deprecated assets are being retired on schedule. Those signals show whether governance is changing behaviour, not just producing documentation.
👉 Read our full editorial: Data as a product redefines trust, ownership and governance
