When the project scope shifts, the key is controlling schema changes and maintaining clear data contracts. We immediately review upstream/downstream impacts, update ETL pipelines, and rerun data validation tests. Versioning datasets helps isolate changes, and documentation keeps everyone aligned. Consistency isn’t about freezing, it’s about adapting deliberately without breaking trust in the data.

You’re 80% through your project, and then the scope changes. New data sources. New rules. New deliverables. Here’s what’s worked for me: 1) Define Your Data Contracts Early: Specify each dataset's content, use version control for schema changes, and keep communication clear. 2) Set Up Automated Validation Pipelines: Use automated checks to quickly catch data issues like nulls or schema mismatches. 3) Implement Robust Data Lineage Tracking: Map data flow end-to-end with tools like dbt or DataHub to maintain clarity and trust. 4) Version Your Data Logic: Track changes in business logic to avoid confusion and preserve historical consistency. 5) Keep Stakeholders In The Loop: Proactively share scope changes to avoid downstream surprises.

Centralise all project-critical data in one authoritative, version-controlled location (like a well-governed database, data lake, or collaboration tool). This ensures changes propagate from one reliable point.

As the schema changed, I should immediately review the impacts and update the ETL pipelines, keeping stakeholders in the loop.

To ensure data consistency after an unexpected project scope change, I would: 1.Re-align data requirements based on updated business goals and priorities 2.Collaborate closely with stakeholders to confirm new expectations and use cases. 3.Update data governance artifacts to reflect changes in rules, definitions, and lineage. 4.Conduct incremental testing to validate data integrity and gather continuous feedback. This approach ensures the product continues to deliver trusted, business-aligned data despite shifting scope.