What is data governance software?
Data governance software supports the people and processes that make business data trustworthy, usable, and appropriately protected. It connects definitions, owners, quality rules, access decisions, and issue resolution. Governance is not a single dashboard or policy document; it is a repeatable operating model across systems.
Capabilities to evaluate
Ownership and definitions
Identify authoritative datasets and assign business and technical stewards. Maintain clear definitions for important terms such as customer, active order, or revenue. Record where a definition applies and who can approve changes.
Quality and issue management
Set measurable checks for completeness, validity, consistency, and freshness. Route failed checks to the right steward with context, severity, and a due date. Track root cause and resolution so the same issue does not repeatedly return.
Access and change control
Document who may view or change sensitive data and why. Use role-based approval paths and an audit trail. Link access decisions to classification, purpose, and retention requirements rather than relying on informal requests.
Implementation plan
- Start with a small number of high-value data domains and business outcomes.
- Assign accountable owners and agree on definitions and quality thresholds.
- Map the source, transformation, and consuming systems for critical fields.
- Configure issue intake, stewardship review, approval, and remediation workflows.
- Test access requests, definition changes, failed checks, and owner handoffs.
- Measure quality trends, issue aging, ownership coverage, and access review completion.
How INFORMAT can support governance workflows
INFORMAT's low-code platform includes connected data tables, forms, views, workflows, role-based permissions, and integrations. Those components can be used to build stewardship processes and issue tracking around an organization's existing data landscape. Automatic lineage discovery, cataloging, and specialized privacy controls must be evaluated separately for the target architecture.
FAQ
Is data governance the same as data quality?
No. Quality is one outcome of governance. Governance also covers ownership, definitions, access, lifecycle, and decision rights.
Who should own a data issue?
A business steward should own the meaning and impact, while technical teams investigate system causes. Define the handoff clearly.
Should governance begin with every dataset?
No. Start with a critical domain where poor data has a measurable business cost, then expand the model after it works.