| name | purview-data-map |
| description | Guidance for the Microsoft Purview Data Map - the foundation that scans and maps data sources across multicloud and on-premises estates to power cataloging and governance. Covers source registration, integration runtimes (managed vs self-hosted), scan rule sets, classifications, collections, and cost control. WHEN: Purview Data Map, scan data sources, register data source, data discovery, integration runtime, map enterprise data, multicloud data scanning, classification scan, collections, glossary. |
| license | MIT |
| metadata | {"author":"Microsoft","version":"0.1.0"} |
Microsoft Purview Data Map
The Data Map is the foundational layer of Microsoft Purview data governance: it discovers, scans,
and maps metadata and classifications from data sources across Azure, multicloud, SaaS, and
on-premises - powering the Unified Catalog, lineage, and downstream protection decisions.
When to use
Building an enterprise-wide inventory of data assets and their classifications as the basis for
governance, security, and data product publishing.
Do not use this skill for in-tenant Microsoft 365 classification (use purview-data-classification)
or for AI prompt visibility (use purview-dspm-ai).
Pick the right integration runtime
| Source location | Use this runtime |
|---|
| Azure (Storage, SQL, Synapse, Fabric, Cosmos) | Managed (Azure-hosted) runtime |
| AWS S3 / RDS, GCP BigQuery, public SaaS | Managed runtime, with credentials in Key Vault |
| On-premises SQL, Oracle, file shares | Self-hosted integration runtime (SHIR) on a domain-joined Windows host |
| Private-endpoint-only Azure sources | SHIR or VNet-integrated managed runtime |
| Power BI tenant | Native connector, no runtime config |
Rule of thumb: managed runtime first; deploy SHIR only when network or private-endpoint reach
requires it - and treat the SHIR host as Tier-0 infrastructure.
Approach
- Plan collections - Design the collection hierarchy (by business domain or geography)
before registering sources; collections drive RBAC and asset organisation.
Verify: a draft collection tree exists and maps to data-owner accountability.
- Register sources - Connect data sources (Azure Storage, SQL, Synapse, Fabric, AWS S3,
databases, Power BI, etc.) into the right collection with appropriate credentials.
Verify: each registered source shows correct subscription/account and target collection.
- Choose an integration runtime - Use the managed runtime for cloud sources; deploy a
self-hosted integration runtime to reach on-premises or private-network sources securely.
Verify: SHIR status is Running and self-update is enabled.
- Configure scans and rule sets - Schedule scans with scan rule sets; apply
classifications (built-in SITs and custom) and lineage extraction where supported. Start
incremental, not full.
Verify: scan history shows successful runs with classified assets counted.
- Curate - Review discovered assets, apply glossary terms, and assign data owners/stewards;
tune custom classifications based on real matches.
Verify: top assets have owners and at least one glossary term.
- Govern access - Use collections to organise assets and scope permissions by domain;
prefer collection-level role assignment over root.
Verify: collection-admin roles assigned to domain owners, not platform team only.
- Operate - Monitor scan failures, classification drift, and cost; right-size scan frequency
per source criticality.
Verify: a weekly scan-health report exists.
Guardrails
- Scope and schedule scans to manage cost and source load; avoid scanning everything at full
depth on day one - sampling first, full second.
- Secure the self-hosted integration runtime host as sensitive infrastructure - it holds
credentials and reaches into production data sources; patch, restrict logon, monitor.
- Validate classification accuracy before relying on it for downstream protection - sample matches
per SIT and tune confidence levels.
- Use Key Vault for credentials; never embed secrets in scan configuration.
- Plan capacity - Data Map is metered by capacity units; oversized scans inflate cost without
governance value.
Common anti-patterns
- Registering every source into the root collection and assigning everyone Data Reader.
- Scheduling weekly full scans on petabyte data lakes - blows out capacity and budget.
- Running SHIR on a workstation or shared jump host.
- Skipping the glossary and ownership step - assets get classified but nobody acts on them.
- Treating Data Map as a one-off load instead of a continuously curated catalogue.
Example prompts
Register and scan data sources in the Purview Data Map.Configure an integration runtime for multicloud data scanning.How do I map enterprise data and run classification scans?Plan data discovery across cloud and on-prem sources.Design a Purview collection hierarchy aligned to business domains.
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