Fractal Analytics

The client faced increasing operational risks and high platform maintenance costs as multiple legacy technologies, including HPQ, Informatica, and Synapse, approached end-of-life. The organization needed to migrate more than 8,000 data objects across over 30 source systems to a modern platform without disrupting business reporting, user experience, or data consistency.

Fractal modernized the enterprise data platform by implementing a Databricks-based data foundation supported by AI-assisted migration and reusable accelerators. The solution automated migration of legacy ETL workflows into PySpark pipelines using agent-based code conversion, implemented reusable data reconciliation and testing frameworks, established enterprise DevOps CI/CD pipelines, enforced attribute-based access control, and deployed reusable ingestion and orchestration frameworks to support scalable pipeline management. The migration workflow included automated code conversion, validation, and issue resolution while ensuring uninterrupted downstream reporting.

The client needed to unify fragmented commercial data distributed across multiple markets, retailers, and business functions, including sales, marketing, supply chain, finance, R&D, innovation, and panel data. The fragmented environment resulted in inconsistent KPI definitions and limited the adoption of analytics across commercial decision-making processes.

Fractal designed and implemented a domain-driven commercial data foundation using a governed Databricks Medallion Architecture. The solution unified more than 10 enterprise data sources, standardized KPIs against Nielsen, harmonized product hierarchies, and implemented configurable data ingestion, curated domain models, feature-store-based KPI metadata, Unity Catalog access control, feature versioning, KPI glossary, and domain ownership. The platform also profiled more than 350 critical data elements across six data quality dimensions and delivered consumption-ready data products supporting dashboards and commercial analytics.