Data Governance

In depth

Data governance sets rules for how data is defined, created, accessed, shared, and retired. It aligns business stakeholders and data teams around a shared playbook so decisions come from trusted, consistent information.

Core elements typically include:

• People and roles: Data owners, stewards, and consumers with clear responsibilities.
• Standards and definitions: A business glossary, metric definitions, naming rules, and conventions.
• Quality management: Rules for completeness, accuracy, and timeliness, plus monitoring and remediation.
• Security and privacy: Access control, data classification, consent and retention policies.
• Lineage and transparency: Where data originates, how it was transformed, and who changed it.
• Lifecycle and risk: Processes for change management, incident response, and archival or deletion.

Good governance is right-sized. It focuses first on high-impact data such as revenue, pipeline, active users, or churn and grows with your data maturity.

Common misconceptions

• Data governance is only about security. Security is one piece. Definitions, quality, lineage, and ownership matter just as much.
• Data governance slows everything down. The right level of structure removes guesswork and helps you move faster.
• Data governance is a one-time project. Governance is an ongoing practice that adapts as your business and data evolve.

Pro tip

Start with a small, critical set of metrics and assign clear owners. Publish definitions, set access rules, and certify what’s trusted. Expand only after usage and feedback show the need.

Why Data Governance matters

• Trust: Leaders and teams work from the same numbers with shared definitions.
• Speed: Fewer debates, faster decisions, less rework.
• Risk reduction: Controlled access, auditability, and compliance support.
• Reuse: Standardized metrics and datasets reduce duplication and ad‑hoc one‑offs.
• Scale: As more people use more data, governance ensures self-serve analysis remains safe and secure.

Data Governance - In practice

Here’s how a growing company might apply data governance day to day:

1. Define the top 10 business metrics, each with an owner and a plain‑language definition.
2. Set role‑based access for sensitive data and limit edit rights to stewards.
3. Classify data by sensitivity and retention window, then document refresh schedules.
4. Track data quality with simple checks such as null rates and last refresh time.
5. Publish a metric catalog so teams can discover and reuse trusted assets.
6. Review changes using a lightweight approval process before updates go live.

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Data Governance and PowerMetrics

PowerMetrics supports governed self‑serve analytics for SMBs.

• Access control: PowerMetrics enforces access controls for all assets. Users are assigned roles that govern what they can view, create, edit, or share.
• Metric definitions: Create clear, reusable metric definitions and document context so everyone speaks the same language.
• Certification and tagging: Mark trusted metrics and apply tags to group related assets for better organization and faster discovery.
• Quality signals: Use stored history, refresh schedules, and error signals to spot issues early.
• APIs and auditability: Integrate with your stack and keep changes traceable.

Tip: Start with a small set of high‑value metrics, certify them, and use roles to separate creators from viewers. Expand your catalog in PowerMetrics as adoption grows.