Client Context
A media company moving historical analytics to a cloud platform relied on legacy warehouse, cloud lakehouse, ETL jobs, semantic models, and BI tools to support content, subscription, advertising, and audience analytics. The operating model handled multi-terabyte history and hundreds of reports across North America and Europe, using facts, dimensions, history, metrics, and report definitions. Historical data and slowly changing dimensions were difficult to reconcile. Report continuity had to be maintained during phased migration.
Challenges
- Historical data and slowly changing dimensions were difficult to reconcile.
- Report continuity had to be maintained during phased migration.
- Migration of facts, dimensions, history, metrics, and report definitions from legacy warehouse, cloud lakehouse, ETL jobs, semantic models, and BI tools involved schema, format, ownership, and retention differences.
- Manual sampling could not provide confidence at multi-terabyte history and hundreds of reports or identify transformation-level loss.
- Historical records, attachments, relationships, and audit fields required different validation strategies.
Solutions Implemented
- Automated history, dimension, aggregate, semantic, and dashboard parity validation.
- Ran dual-platform comparison and domain-by-domain cutover.
- Created a migration validation strategy covering profiling, mapping, mock runs, reconciliation, and cutover.
- Automated record, field, relationship, attachment, balance, and historical integrity checks.
- Validated transformations, defaults, deduplication, exception handling, and rejected-record remediation.
- Produced migration dashboards, signed reconciliation reports, defect traceability, and rollback evidence.
Value Delivered
- Reduced the primary testing or operational effort by approximately [50%], subject to validation against approved engagement data.
- Improved confidence in analytics migration.
- Reduced manual reconciliation across migration waves.
- Enabled faster defect isolation between extraction, transformation, and loading stages.
Impact Highlights
- Achieved an estimated [40%] improvement in cycle time, coverage, or processing consistency; replace with the approved client metric.
- Reduced relevant defects, failures, or rework by an illustrative [25%]; confirm before publication.
- Reduced reporting disruption and manual reconciliation.
- Created repeatable controls for subsequent migration domains.
