Data warehouse readiness: For successful data warehousing and business intelligence, is it important to build and maintain a comprehensive metadata repository?

Introduction / Context:
Data warehouses integrate data from many sources. Business Intelligence (BI) depends on consistent definitions, lineage, data quality rules, and repeatable transformations. A comprehensive metadata repository captures these details and becomes the backbone for governance and reliable analytics delivery.

Given Data / Assumptions:

• Multiple, heterogeneous sources feed the warehouse.
• There are evolving business definitions (dimensions, measures, KPIs).
• Teams require data lineage, impact analysis, and stewardship workflows.

Concept / Approach:
A metadata repository stores technical metadata (schemas, ETL jobs, column types), business metadata (glossary, KPI definitions), and operational metadata (job runs, data quality scores). Maintaining it improves trust, accelerates onboarding, supports self-service BI, and reduces regression risk during schema changes. Without it, reports drift, definitions conflict, and auditability suffers.

Step-by-Step Solution:
Catalog sources, datasets, tables, columns, and transformations.Document business definitions and owners for core metrics.Track lineage from source fields to report visuals.Record data quality rules, thresholds, and test results.Expose searchable metadata to analysts and engineers.

Verification / Alternative check:
Perform impact analysis for a source column rename. If you can programmatically list affected ETL jobs, tables, and reports, your repository is functioning; otherwise, you will face blind deployments and broken dashboards.

Why Other Options Are Wrong:
Incorrect: underestimates governance needs.Only for real-time or regulatory cases: routine BI also needs robust metadata.

Common Pitfalls:
Letting metadata fall out-of-date, poor ownership, and failing to integrate lineage with CI/CD so changes trigger review.

Final Answer:
Correct