Data Catalog Requirements Checklist
Short answer: A data catalog requirements checklist should cover discovery, glossary, ownership, lineage, access, quality signals, stewardship workflows, integrations, metadata automation, adoption, security, administration, and evidence needs.
A catalog requirements list should be based on the decisions users must make and the governance workflows the organization must operate. This prevents selection from becoming a demo-led feature comparison.
Discovery Requirements
Users need to find the right data and understand whether it is trusted for their purpose. Search results should expose meaning, owner, quality, lineage, and access context.
- Search by business term, table, dashboard, metric, and domain.
- Certified or recommended assets.
- Owner, steward, and support route.
- Usage, freshness, quality, and sensitivity indicators.
Glossary and Semantics
The catalog should help teams agree on shared meaning. Glossary workflows need ownership, approval, versioning, relationships, and connection to real assets.
- Business terms, metrics, entities, and synonyms.
- Approval and change workflow.
- Term-to-asset and term-to-report mapping.
- Version and deprecation history.
Lineage and Impact
Lineage requirements should reflect real impact questions. Include technical and business lineage, refresh cadence, gaps, and how users interpret change risk.
- Source-to-target lineage for critical paths.
- Column, table, job, model, and BI lineage where needed.
- Impact analysis before pipeline or metric changes.
- Manual override and gap documentation process.
Access and Policy Context
Catalog users need to know whether they may use data and how to request access. Sensitive data context should be visible without exposing protected values.
- Classification and sensitivity labels.
- Purpose and permitted-use metadata.
- Access-request workflow and approvals.
- Policy exceptions with expiry and audit evidence.
Integration Requirements
List the systems that must feed or consume metadata. Requirements should include authentication, refresh cadence, automation, APIs, and operational ownership.
- Warehouses, lakehouses, BI, orchestration, and quality tools.
- Identity and ticketing systems.
- API and metadata export needs.
- Monitoring for connector failures and stale metadata.
Rollout and Adoption
The checklist should define how catalog value will be measured after purchase. Adoption depends on workflow integration, training, stewardship capacity, and visible trust signals.
- Priority domains and data products.
- Active user and search success metrics.
- Stewardship queue and metadata health metrics.
- Training, communications, and product-owner routines.
Primary catalog references
Use current vendor documentation and open metadata references to validate data catalog, glossary, lineage, workflow, and integration requirements before procurement or rollout.
Frequently Asked Questions
What should be in data catalog requirements?
Include discovery, glossary, ownership, lineage, access, quality signals, stewardship workflow, integrations, security, metadata automation, rollout, adoption metrics, and evidence requirements.
Who should define catalog requirements?
Business owners, stewards, analysts, data engineers, governance, security, and platform owners should define requirements together because each group depends on different catalog workflows.
Related DataKrypton Strategy Guides
Implementation guides with current search demand
- Data Quality Framework Guide
Define quality dimensions, ownership, thresholds, and incident routines for trusted analytics and AI.
- Snowflake vs Databricks Comparison
Compare warehouse, lakehouse, governance, streaming, AI, and cost tradeoffs before choosing a cloud data platform.
- Apache Kafka Data Engineering Guide
Plan event-driven pipelines with contracts, schema management, observability, replay, and operational controls.
- Data Catalog Comparison: Alation, Collibra, and Atlan
Evaluate catalog tools by stewardship workflow, lineage, discovery, governance, and adoption needs.
- Master Data Management Guide
Use MDM patterns to improve customer, product, supplier, and reference data used across systems.
- Data Governance for Financial Services
Govern risk, finance, customer, regulatory, lineage, quality, access, and evidence workflows in financial services.
Practical checklists and scorecards
- Data Quality Framework Checklist
A practical checklist for data-quality owners, thresholds, controls, incidents, and leadership review.
- Snowflake vs Databricks Decision Matrix
A workload-fit matrix for analytics, governance, streaming, AI, team skills, and cost decisions.
- Kafka Data Pipeline Readiness Checklist
A readiness checklist for topics, schemas, ownership, retention, monitoring, replay, and contracts.
- Data Catalog Evaluation Scorecard
A catalog scorecard for discovery, glossary workflow, lineage, stewardship, integrations, and adoption.
- MDM Readiness Checklist
A readiness checklist for master-data domains, owners, survivorship, matching, quality, and adoption.