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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →A data governance policy sets expectations, decision rights, and accountability for how an organization manages data. A procedure turns those expectations into repeatable steps, records, and reviews. Effective policies and procedures are tailored to the organization’s data, business uses, legal obligations, structure, and resources—not copied wholesale from another organization.
Policy vs. procedure: what is the difference?
A policy states what the organization requires and who is accountable. A procedure explains how people carry out that requirement in a particular workflow. DAMA-DMBOK describes procedures as documented methods and steps for completing an activity; the distinction is practical: a policy sets the rule, while the procedure tells staff what to do, when, and where to record it.
| Document | Purpose | Example: data access |
|---|---|---|
| Policy | Establishes the requirement, scope, accountability, and exceptions. | Access to sensitive customer data must be approved by the accountable data owner and periodically reviewed. |
| Procedure | Defines the operational sequence, decision points, evidence, and exception route. | A requester submits a ticket; the owner approves or rejects it; IT provisions approved access; the ticket records the decision; access is reviewed on the organization’s defined schedule. |
These examples illustrate one possible design, not a universal control. Documented procedures are useful only when they match the systems and responsibilities staff actually use.
What should a data governance policy cover?
Keep the policy clear enough to guide decisions and specific enough to assign responsibility. It may address the following, depending on the organization’s needs:
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- Scope and purpose: covered data domains, systems, business uses, and intended outcome.
- Decision rights: who owns data definitions, approves access or sharing, and resolves disputes.
- Classification and handling: how data is categorized and what handling rules apply to each category.
- Quality accountability: who defines fit-for-purpose quality expectations, handles issues, and approves corrections.
- Access, sharing, and use: approved uses, authorization, and the route for exceptions.
- Retention and records: applicable retention decisions and how they connect to records and privacy requirements.
- Evidence and oversight: records, monitoring, review cadence, and escalation for noncompliance or unresolved risks.
State which existing privacy, security, records, or quality policies the document relies on. Distinguish legal or contractual obligations from internal choices. NIST’s Joint Frameworks Data Governance and Management Profile Concept Paper says organizations should tailor policies, processes, and procedures to their context, including sector, jurisdiction, organizational structure, and available resources.
How to create policies and procedures
- Set scope and purpose. Identify the domains, systems, business uses, and decisions covered. Name the accountable owner, intended audience, and relationship to existing policies. Limit the policy to a scope the organization can govern and maintain.
- Map obligations and risks. Identify relevant laws, contracts, business commitments, and risk tolerances. Keep legal requirements separate from optional internal controls. For obligations that apply to personal data, obtain jurisdiction-specific interpretation rather than assuming one rule applies everywhere.
- Write the policy rule. State the requirement and its scope. Specify accountable roles, permitted and prohibited actions, exceptions, required evidence, and escalation paths. Examples of possible rules include access approval, data classification, retention, approved sharing, quality ownership, and correction handling.
- Translate each rule into a procedure. For every workflow, document the trigger, responsible person, sequence, system or record used, decision points, evidence, and exception route. A procedure should be executable by the roles named in it, not merely restate the policy.
- Review, approve, publish, and train. Use the organization’s decision structure to resolve comments and approve the documents. Publish an authoritative version, communicate changes to affected roles, and train people on actions relevant to their work.
- Monitor and improve. Decide what evidence will show whether controls operate as intended. Examples include overdue access reviews, unresolved quality issues, exception volume, failed validation checks, and review dates. Investigate patterns and revise rules when business processes, systems, or obligations change.
Who is responsible for data governance?
There is no required universal org chart. Responsibilities can be combined in a smaller organization, but decision rights should still be explicit. A workable model may assign responsibilities as follows:
Rank #2
- Governance council or executive sponsor: sets priorities, approves policy, and resolves escalated disputes.
- Governance lead: coordinates drafting, documentation, communication, training, and review.
- Data owners: make domain decisions and approve access or permitted uses within their authority.
- Data stewards: maintain definitions and coordinate operational quality practices.
- IT and security teams: implement and monitor technical controls.
- Legal, privacy, and compliance specialists: interpret obligations and review policies involving sensitive or regulated data.
- Business users: follow procedures and report unclear steps or practical problems.
Assigning a role is not enough: specify who can make the decision, who performs the work, and where the decision is recorded.
Design data quality controls for the data’s purpose
Accuracy, completeness, consistency, timeliness, validity, and uniqueness are useful quality dimensions, but no single threshold is right for every dataset. Define what “good enough” means for the data’s intended use and the risk of an error. A field used for a financial decision may require different checks from one used only for broad reporting.
Possible practices include profiling data to find patterns and anomalies, validating entries against rules, standardizing formats, cleansing identified errors, and monitoring quality over time. Assign ownership for definitions, thresholds, issue resolution, and approved corrections. Choose measurable checks that teams can run and review; avoid adopting a metric without deciding what action follows when it fails.
Apply legal requirements within their actual scope
GDPR Article 5 sets principles for processing personal data within the Regulation’s scope: lawfulness, fairness and transparency; purpose limitation; data minimisation; accuracy; storage limitation; integrity and confidentiality; and accountability. Article 5(1)(d), for example, says personal data must be accurate and, where necessary, kept up to date. These provisions are not a blanket rule for all business data or all jurisdictions. Read the GDPR text on EUR-Lex.
For breaches, GDPR Article 33 distinguishes the controller’s and processor’s duties. A controller must notify the competent supervisory authority without undue delay and, where feasible, within 72 hours after becoming aware of a personal data breach, unless the breach is unlikely to result in a risk to people’s rights and freedoms. A processor must notify its controller without undue delay. The 72-hour period is a qualified legal deadline, not a general deadline for every organization or every jurisdiction. See Article 33 of the GDPR.
Do not assume that another privacy law uses the same deadline or conditions. Confirm applicable obligations with qualified legal or privacy specialists for the relevant locations and processing activities.
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Choose controls that fit the organization
Governance policies may address controls around data extraction, transformation, storage, and transfer. Examples discussed in the DZone article include access control, logging, classification, encryption, backup, key management, and monitoring. These are options to evaluate against the organization’s systems, threats, obligations, and capabilities—not universal legal mandates. NIST’s concept paper likewise emphasizes tailoring governance and management to organizational context.
When setting controls, connect each one to an identified risk or obligation, name its owner, and define evidence that it is working. A long list of technical controls without assigned responsibility, exception handling, or monitoring is not an operational procedure.
How to evaluate governance software
A February 4, 2025 DZone article by Sukanya Konatam names several products, but does not substantiate a current ranking, current capabilities, or pricing. Treat the names as leads for independent evaluation, not as a recommendation. Compare products against the operating model and needs you have defined:
- Catalog, glossary, ownership, and stewardship support.
- Lineage and impact analysis.
- Policy workflows, evidence, and exception handling.
- Data-quality rule creation and monitoring.
- Integration with current data platforms and identity systems.
- Deployment, security, and jurisdiction requirements.
- Implementation effort, fit with existing responsibilities, and total cost.
Confirm current features, deployment options, and costs directly with vendors. A product cannot substitute for clear decision rights and procedures that staff can follow.
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