Data Governance Platform Selector

1. Main problem to solve

Start from the pain, not the vendor list.

Conflicting KPI and metric definitions Nobody knows where data and reports are No lineage - fear of table changes Access control and compliance (GDPR) Data quality - errors, duplicates, gaps AI governance - context and policies for models/agents

2. Dominant data stack

Native cloud governance lowers integration cost.

Microsoft - Azure, Fabric, Power BI, M365 AWS - S3, Redshift, Glue, Athena Google Cloud - BigQuery, Looker, Vertex Databricks - lakehouse as core Multi-cloud / mixed No dominant cloud

3. Organization scale

Enterprise platforms require DG process and team.

Up to 100 people 100-1000 people 1000+ people

4. DG platform budget

Open source = lower license, higher maintenance cost.

Low - open source or pay-as-you-go Medium - SaaS catalog / cloud-native High - enterprise with implementation Not sure - show options

5. Industry / regulatory context

Regulated industries require audit and policies - GDPR, DORA, AI Act.

General / no special requirements Regulated (finance, health, insurance; GDPR, DORA, AI Act) Public sector E-commerce and marketing / analytics

6. Deployment model

Self-host requires a platform engineering team.

SaaS / vendor cloud only Hybrid cloud + on-prem Self-host / open source OK

7. Priority capability

Match the tool to your most important function.

Business glossary and definitions Data catalog / discovery End-to-end lineage Data quality and monitoring Access governance Context and policies for AI

Data Governance Platform Selector

DG platform selector

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