Introduction: why “data governance strategy” is suddenly a product problem
If you’re building AI features (or even just piloting them), you’ve probably discovered a frustrating truth: the hardest part isn’t the model — it’s the data. Not because you don’t have data, but because it’s fragmented, undocumented, inconsistent, and politically “owned” by ten different teams.
That’s exactly why a data governance strategy shouldn’t live only in risk, security, or compliance. It’s a product capability — the operating system that determines whether your organisation can repeatedly turn messy information into reliable AI outcomes.
And here’s the shift that changes everything: stop treating data as exhaust (a by-product of operations), and start treating it as a product — with users, SLAs, documentation, quality metrics, and an iteration roadmap. This mindset shows up explicitly in modern approaches like data mesh, which frames “data as a product” as a core principle.
In this article, I’ll unpack the product-thinking shifts that make governance practical, scalable, and genuinely value-generating.
1) Reframe governance: from “control” to “enablement”
A lot of governance programmes fail because they’re experienced as blockers: more forms, more approvals, more “no”. But the best governance functions behave like strong platform/product teams: they create clarity, reduce friction, and make the right thing the easy thing.
A useful definition from DAMA describes data governance as the exercise of authority and control over data assets (planning, monitoring, enforcement).
Product-thinking interpretation: authority and control should produce predictability — consistent definitions, trustworthy datasets, and auditable use of data.
What this looks like in practice
Clear decision rights (who can change a metric definition; who approves sensitive-data access)
Standard “golden paths” (templates for datasets, contracts, access requests)
Governance-as-code where possible (automated checks, policy enforcement in pipelines)
2) Define your “data users” (because data without users is just storage)
If you don’t know who your data is for, you’ll default to either:
a grassroots approach (each team hacks its own datasets), or
a big-bang centralisation (a warehouse nobody loves).
Treat data like a product by starting with a user/problem statement:
Who needs this dataset?
What decisions does it support?
What happens if it’s wrong or late?
What does “good” look like (freshness, completeness, latency, explainability)?
This is also where the FAIR principles become a practical product checklist: Findable, Accessible, Interoperable, Reusable.
3) Build “data products”, not “datasets”
A dataset is a file/table. A data product is a reusable capability with:
a defined purpose and audience
documented semantics (metrics definitions, lineage, caveats)
quality and freshness guarantees
secure, governed access
an owner accountable for outcomes
If you want an organising model, data mesh is a strong reference point: it treats data as a product and pushes ownership closer to domains, supported by a self-serve platform and federated governance.
Minimum viable data product (MVDP)
A stable interface (API/table contract)
Data dictionary + examples
Quality checks (completeness, validity, duplication)
Sensitivity classification + access policy
Observability (usage, failures, drift)
4) Prioritise the six pillars of data readiness (with product trade-offs)
Your notes already nail the reality: AI readiness is multi-dimensional — quality, understanding, structure, governance, impact, fairness/bias. The product-thinking twist is to treat each pillar as a trade-off surface, not a binary checklist.
For example:
A marketing personalisation model may tolerate small missingness but not identity mismatch.
A fraud model may prioritise timeliness and lineage over perfect completeness.
A GenAI assistant may prioritise documentation, provenance, and safe access patterns.
A practical move: create a “readiness scorecard” per use case, not one maturity score for the entire enterprise.
5) Make metadata the feature (not the admin overhead)
Most teams underinvest in metadata until they’re forced to answer:
“Where did this number come from?”
“Can we use this for training?”
“Who accessed PII last month?”
“Why did the model performance drop?”
Metadata is what makes data reusable — and reusability is what turns data into advantage. The FAIR framing is helpful here because it explicitly focuses on machine-readability, not just human documentation.
Productise metadata
Treat the catalogue as a product with UX
Make documentation part of “Definition of Done”
Automate lineage capture where possible
Create “trusted tiers” (bronze/silver/gold) with visible criteria
6) Shift left on risk: governance that ships at the speed of teams
AI raises the stakes because data can leak, drift, or amplify bias at scale. But slowing teams down isn’t a strategy. The answer is shift-left governance:
classify data early
enforce access policies automatically
bake privacy-by-design into pipelines
continuously monitor quality and usage
Done well, governance becomes a delivery accelerator — teams spend less time arguing about definitions and more time building value.
7) Competitive advantage isn’t “having data” — it’s having compoundable data
Every competitor can buy similar models. Many can copy product features. What’s harder to copy is:
proprietary signals
high-quality, well-labelled feedback loops
a governed data operating model that creates compounding improvement
That’s the real moat: your ability to turn operations into learning — reliably, safely, and repeatedly.
This is also why “data as a product” matters: it creates the conditions for reuse, feedback, and iteration — the same mechanics that make great digital product organisations outperform over time.
Conclusion: governance is the product capability that makes AI scalable
A data governance strategy shouldn’t be a slide deck or a compliance exercise. In a product-led organisation, it’s an enabling platform: it makes data discoverable, trusted, secure, and reusable — so AI teams can build faster, and leaders can make decisions with confidence.
If you take one thing away: treat data governance like product management. Define users, ship minimum viable data products, measure adoption and quality, and iterate. That’s how governance stops being overhead — and becomes competitive advantage.
FAQs
1. What’s the difference between data governance and data management?
Governance defines decision rights, policies, accountability, and controls; management is the execution (pipelines, quality processes, platforms, operations). Governance sets the rules of the game; management plays it.
2. How do I start a data-as-a-product approach without a full data mesh transformation?
Pick 1–2 high-value use cases, identify the datasets they depend on, and create minimum viable data products: owners, contracts, documentation, quality checks, and governed access. Scale out once you prove reuse.
3. Do I need a data catalogue before I can do governance?
You need some catalogue capability early — even a lightweight one — because discoverability and ownership are foundational. You can mature tooling over time, but don’t delay assigning owners and documenting definitions.
4. How do FAIR principles apply in commercial organisations?
FAIR is a helpful lens for making data reusable: findable and well-described (metadata), accessible with the right controls, interoperable via standard definitions, and reusable through clear contracts and documentation.
5. What’s the biggest failure mode you see with data governance programmes?
Optimising for control over outcomes: lots of approvals, little enablement. The fix is to treat governance as a product capability — measure friction, adoption, and time-to-data, not just policy coverage.
