Data Governance: Approvals, Classifications, Lineage, and Quality

Managing data goes far beyond collecting and modeling it. As your ontologies grow and multiple teams contribute to them, it becomes essential to control who changes what, classify sensitive information, trace the origin of data, and ensure its quality. This is exactly what the new Governance module in Ontologie delivers.

Why data governance matters

In a collaborative ontology project, several situations become problematic without governance:

• A team member modifies a critical entity without prior review.
• Sensitive data (personal information, financial records) is not identified as such.
• There is no way to know where a piece of data came from or what transformations it went through.
• Data entry errors and inconsistencies accumulate undetected.

The Governance module addresses these four needs with features built directly into your workspace.

What's new

Multi-level approvals

Approvals let you define validation workflows before certain changes take effect. You can configure:

• Who must approve: one or more team members, by role or by name.
• Which actions trigger an approval: creating, modifying, or deleting entities, relations, or properties.
• How many approvals are required: single sign-off or multi-level consensus.

When an action subject to approval is triggered, it is placed on hold. The approver receives a notification and can accept or reject the change with a comment. The full decision history is retained.

Practical example: you define that any modification to a "Contract" entity requires approval from the legal lead. A team member renames a contract — the change remains pending until it is validated.

Data classifications

Classifications let you label your entities and properties according to their sensitivity level. Four levels are available by default:

Level	Description	Example
Public	Unrestricted data	Product name, category
Internal	Data reserved for the organization	Internal processes, KPIs
Confidential	Restricted-access data	Customer data, pricing
Restricted	Highly sensitive data	Personal data, financial records

Classifications are visible directly on entities in the Modeler. They serve as the foundation for access policies and compliance reports.

Practical example: you classify all properties containing email addresses as "Confidential." A governance report then lists every entity containing confidential data and verifies that access is properly restricted.

Lineage (data traceability)

Lineage answers one question: where does this data come from, and where does it go?

For each entity or property, you can view:

• The origin: which data source, connector, or manual entry created the data.
• Transformations: which workflows or agents modified the data.
• Downstream dependencies: which dashboards, reports, or workflows consume the data.

Lineage is built automatically from events in your workspace. Every modification, every workflow execution, every Live Data ingestion enriches the traceability graph.

Practical example: a KPI on your dashboard shows an unexpected value. By consulting the lineage of the source data, you discover that a workflow changed the calculation formula the previous week.

Data quality rules

Quality rules let you define constraints on your data and automatically detect anomalies:

• Completeness: verify that required fields are filled in.
• Uniqueness: detect duplicates on a field or combination of fields.
• Format: validate data formats (email, phone number, postal code, etc.).
• Consistency: verify relationships between entities (e.g., a "Contract" must be linked to a "Customer").

Results are displayed in a dedicated dashboard with an overall quality score and per-rule indicators. Entities with errors are flagged directly in the Modeler.

Practical example: you create a rule "Every customer must have a valid email." The system analyzes your entities and flags the 12 customers whose email field is empty or malformed.

Getting started

1. Open your workspace and navigate to Settings > Governance.
2. Enable the modules you need (approvals, classifications, quality).
3. Create your first rule — either an approval rule or a quality rule.
4. Classify your sensitive entities via the properties panel in the Modeler.
5. View the lineage of an entity by clicking the "Lineage" tab in its detail view.

Governance features are available to workspace administrators. Approvers are configured by administrators.

Next steps

• Approvals — Complete guide to approval workflows
• Classifications — Configure sensitivity levels
• Lineage — Understand data traceability
• Quality — Define and track your quality rules
• Support — Need help setting up governance?