Data Governance Strategy: From Policy on Paper to Practice in the Business
Data governance fails when it is treated as a compliance project run inside IT. It succeeds when it is a business operating discipline owned by the people who consume and produce the data. A data governance strategy defines who owns what, how quality and access are managed, and how those choices get adopted across finance, operations, and the data team. This page covers how to build one that lasts.
Qlik Solution Partner of the Year, ’13, ’17, ’19
A data governance strategy defines the policies, roles, and operating model that make data trustworthy and usable. The components:
- data domains and ownership,
- stewardship roles,
- data quality standards and measurement,
- access and classification policy,
- metadata and catalog approach, and
- the operating cadence that keeps it alive.
What is a data governance strategy?
A data governance strategy is the plan that makes data trustworthy, discoverable, and appropriately accessible. It defines the domains of data the business cares about, the people accountable for them, the standards those people are held to, and the operating model that keeps the system functioning.
The word strategy matters. A policy document is not a strategy. A tool deployment is not a strategy. The strategy is the reasoning that turns governance from a set of restrictions into a set of capabilities the business benefits from: faster onboarding, fewer data quality fires, defensible regulatory posture, and analytics teams who spend less time apologizing for the data and more time using it.
Governance is most often weakest in two areas: ownership clarity and operating cadence. A strategy that names owners and defines how governance gets done on a recurring basis solves the majority of practical governance problems most companies face.
- Governance strategy is owned by the business, not just IT.
- Six components: domains, stewardship, quality, access, metadata, operating cadence.
- Strongest when grounded in specific business outcomes (regulatory, onboarding speed, analytics trust).
- Catalog and lineage tooling (Collibra and similar) supports governance; it does not replace it.
- Reviewed quarterly; refreshed annually.
- Most failures are people and process problems, not tooling problems.
Core components of a data governance strategy
Data domains.
Group your data by business meaning, not source system. Customer, product, finance, supply chain, employee. Each domain has a single accountable executive (the domain owner) and one or more stewards.
Stewardship roles.
Stewards are the practitioners who maintain definitions, resolve quality issues, approve access requests, and represent the domain in cross-functional decisions. Stewardship is a named responsibility with allocated time, not an extra duty quietly assigned to a senior analyst.
Data quality standards.
Quality is measured per domain against the standards that matter for the use cases the data supports. We define quality dimensions (completeness, accuracy, timeliness, consistency, validity), set thresholds appropriate to the use case, and monitor them.
Access and classification policy.
Data is classified (public, internal, sensitive, regulated) and access is granted accordingly. The policy is written once and enforced consistently. Access reviews run on a defined cadence.
Metadata and catalog approach.
A data catalog (Collibra and similar platforms) is the place definitions, ownership, lineage, and quality scores live. The strategy decides what gets cataloged, who maintains it, and how it integrates with the warehouse and BI environments.
Operating cadence.
Governance only works if it has rhythm. A data governance council meets on a regular cadence. Steward forums run more frequently. Quality reports are reviewed, access reviews completed, exceptions handled. The cadence is the system.
Who owns data governance
A working governance model has four roles. An executive sponsor (often the CFO, CIO, COO, or CDO) provides air cover and resolves cross-domain conflicts. Domain owners are accountable business executives with authority over their domain. Stewards execute the day-to-day work. A central governance function (a team, sometimes a single program owner) curates standards, runs the council, and maintains the catalog.
The most common failure mode is a central governance team without empowered domain owners. The center cannot govern data it does not own. The second most common is empowered domain owners with no central function, which produces inconsistent practice across the business. Both are required.
The operating model also defines escalation. When a quality issue affects two domains, who decides. When a new data product crosses domains, who approves. Naming the escalation path in advance prevents the political stalemates that drain governance programs.
How catalog and governance tools fit
Tooling is an enabler. Collibra and similar platforms (Alation, Atlan, the native catalog capabilities inside Snowflake and Databricks) provide the catalog, lineage, glossary, and workflow surface the governance program runs on. They do not produce governance on their own.
The decision sequence we recommend is operating model first, policy second, tooling third. Companies that buy the tool first and assume the model will follow consistently report low adoption two years later. Companies that define ownership and cadence first, then select tooling to match, report sustained adoption.
We are vendor-neutral and fluent across the major catalog platforms. Selection depends on existing stack, integration requirements with the warehouse (Snowflake, Databricks), BI tooling (Qlik, Power BI, Tableau), and the maturity of the internal team.
How DI Squared builds a data governance strategy
We apply our Discover, Map, Navigate, Adjust framework to governance specifically.
Discover.
We assess current governance maturity: existing policies, owner identification, quality practices, catalog state, and pain points in the business. We interview business owners and stewards, not just IT.
Map.
We document the target governance operating model: domains, roles, policies, quality standards, catalog approach, and operating cadence. The output is a governance charter the executive team adopts.
Navigate.
We help stand up the first domains, train stewards, configure the catalog (often Collibra), and run the first governance council cycles so the model is real before we step back.
Adjust.
Governance evolves with the business. We help refresh standards, retire policies that no longer earn their cost, and expand coverage to new domains as the program matures.
Data Governance Strategy, answered.
Q: How is a data governance strategy different from a data governance policy?
A: A policy is a set of rules. A strategy is the plan for how those rules get adopted, enforced, measured, and evolved over time. Most companies have policies and no strategy, which is why governance feels like paperwork rather than practice.
Q: How long does it take to stand up data governance?
A: A functioning operating model with one or two domains live can be established in three to six months. Expanding coverage across the enterprise takes longer, typically 12 to 24 months depending on scope and existing maturity.
Q: Do we need a catalog platform like Collibra to do governance?
A: Eventually, yes. A spreadsheet-based catalog works for a small environment, but at enterprise scale the catalog, lineage, and workflow capabilities of a dedicated platform are required. Start with the operating model, then select tooling to match.
Q: Who should own data governance, IT or the business?
A: Both. Executive sponsorship usually sits with a CDO, CIO, or CFO. Domain owners are business executives. A central governance team can sit inside IT, inside a CDO office, or independently. The business owns the data; IT often runs the tooling.
Q: How do we get business buy-in for governance?
A: By framing governance against business outcomes the executives already care about: regulatory exposure, onboarding speed, analytics trust, M&A integration time. Governance is a means, not an end. Lead with the outcome.
Q: What is the relationship between governance and data quality?
A: Data quality is one of the components governance is responsible for. Governance defines who owns quality per domain, what standards to hold it to, how to measure it, and how to escalate when it slips. Quality without governance is firefighting.
Have policies on paper but no governance in practice?
We have helped 200+ companies turn governance from a compliance burden into a capability the business uses. Tell us where the gap is between what is written and what is happening.