microsoft-purview-and-azure-data-governance

name	microsoft-purview-and-azure-data-governance
description	Guides agents through Microsoft Purview and Azure-native data governance workflows. Use when designing collections, scans, classifications, lineage, policy boundaries, and governed publishing across ADLS, Synapse, Data Factory, Azure Databricks, Fabric, and Azure analytics estates.

Microsoft Purview And Azure Data Governance

Overview

Use this skill when Microsoft Purview is the governance control plane for Azure data platforms. It helps agents design metadata collections, classification strategy, scan scope, lineage expectations, and governed publish behavior across Microsoft analytics surfaces.

When to Use

• designing Purview collection and ownership structure
• defining scans, classifications, glossary, and lineage expectations
• governing datasets across ADLS, Synapse, Data Factory, Azure Databricks, or Fabric
• improving trusted discovery and certification for shared data products
• aligning Azure-native governance with privacy, security, and release controls

Do not assume Purview is only a catalog tool. It often becomes the platform evidence and policy surface for governed analytics.

Workflow

1. Define governance scope. Clarify:

   • in-scope platforms
   • business domains
   • critical data products
   • stewardship and ownership model

2. Design the metadata operating model. Decide:

   • collections
   • glossary boundaries
   • classifications and sensitivity labels
   • scan cadence and ownership

3. Define trusted publish behavior. Require:

   • certification or endorsement rules
   • lineage completeness expectations
   • ownership visibility
   • ties to regulated-data controls where relevant

4. Align Azure services with governance. Check how Purview works with ADLS, Synapse, Data Factory, Databricks, and Fabric rather than treating each system separately.

5. Validate operational sustainability. Make sure scans, classifications, and lineage remain useful as assets, teams, and environments grow.

Common Rationalizations

Rationalization	Reality
"Scanning everything is the same as governing it."	Governance also needs ownership, trust signals, and useful boundaries for consumers.
"Purview can be added after pipelines are done."	Late governance usually means weak lineage, poor certification, and inconsistent discovery.
"Each Azure service team can manage metadata separately."	Fragmented governance weakens platform-wide trust and policy evidence.

Red Flags

• collections do not map to real ownership or domains
• scan scope is broad but lineage and certification are weak
• classifications are inconsistent across Azure services
• Purview is disconnected from publish or security decisions
• stewardship expectations depend on tribal knowledge

Verification

• Governance scope and stewardship model are explicit
• Collections, scans, and classifications are intentionally designed
• Trusted publish behavior includes lineage and certification expectations
• Azure services align to one governance model
• The model stays sustainable as adoption grows