What is data governance?
In practice, it comes down to a handful of questions: what does this metric mean, who owns it, who can see this data, and what happens when something needs to change?
Teams often see governance as a brake on speed. Done well, it's the opposite. When definitions are agreed and access is clear, teams can use data confidently without double-checking every number.
What are the parts of data governance?
| Part | What it covers | Example |
|---|---|---|
| Definitions | What each metric and field means | One agreed definition of ROAS |
| Ownership | Who is accountable for each data set or definition | Finance owns revenue, and marketing owns channel taxonomy |
| Access control | Who can see and change what | Role-based access by team, client, or market |
| Quality and monitoring | Checks that data is complete and current | Alerts when a feed stops updating |
| Lineage and change control | How changes are made, reviewed, and traced | Version-controlled definitions with an audit trail |
What is an example of data governance?
Changing a KPI on purpose
Finance asks marketing to report revenue net of returns. Without governance, some dashboards update and some don't, and for months nobody is sure which version a report uses.
With governance, the definition is changed once by its owner, the change is reviewed and recorded, and every report and tool picks it up at the same time.
Data governance vs. data management vs. data security
| Data governance | Data management | Data security | |
|---|---|---|---|
| Main question | What are the rules, and who decides? | How is data stored, moved, and maintained? | How is data protected from misuse? |
| Focus | Policies, definitions, ownership | Pipelines, storage, operations | Access, encryption, threat prevention |
| Relationship | Sets the rules | Carries them out day to day | Enforces protection |
Why does data governance matter for marketing and AI?
Marketing data comes from dozens of platforms, markets, and teams, and AI tools now act on it directly. Without governance, inconsistent definitions spread into every report, and agents can act on data they shouldn't trust or access.
- Trusted numbers. Reports agree because definitions are shared.
- Safe access. People and agents only reach the data they're allowed to.
- Audit readiness. When an auditor asks how a number was produced, the answer is a record, not someone's memory.
- Reliable AI. AI answers stay consistent because the data underneath doesn't drift.
How does Alli do this?
Governance is built into Alli's data foundation: governed ingestion through Alli Datasources, version-controlled definitions and lineage in Alli Semantic Layer, and controlled deployment workflows. Change a definition once, and it applies everywhere in Alli, without a version control problem across spreadsheets.
Above the data, each part of Alli carries controls that fit what it does, all on the same permissions model, such as role-based access and audit trails in Generative Workflows and OAuth-based access for agents through Alli MCP Server.
See how Alli governs dataData governance FAQ
What is the goal of data governance?
To make data trustworthy and usable: agreed definitions, clear ownership, the right access for the right people, and changes that are deliberate and traceable.
Who is responsible for data governance?
Usually a mix: data teams maintain the systems, business owners such as finance and marketing own definitions, and security and legal set access and compliance rules.
What is the difference between data governance and data management?
Governance sets the rules and decides who owns what. Data management is the day-to-day work of storing, moving, and maintaining data according to those rules.
Why does AI need data governance?
AI tools answer and act on the data and permissions they're given. Governance keeps that data consistent and limits what agents can access.
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