Problem Statement
With operations spanning major markets worldwide, the client faced mounting pressure from data scattered across 150+ systems. Finance, marketing, HR, and supply chain teams were working in silos. Cross-functional visibility was poor, AI adoption was stalling, and making informed decisions meant navigating a maze of disconnected data sources. The existing architecture simply couldn't scale or support the organization's ambitions for data-driven decision-making.
Key Challenges
- Massive Data Fragmentation: 150+ source systems operating independently, creating data silos that prevented a unified view of business operations.
- Governance Gaps: No standardized framework for PII compliance, GDPR enforcement, or data quality controls across markets.
- Slow Analytics Delivery: Business teams waited days for insights due to manual processes and legacy infrastructure bottlenecks.
- Limited AI Readiness: Existing data quality and structure couldn't support machine learning initiatives or advanced analytics use cases.
- Multi-Vendor Complexity: Coordinating migration across multiple technology vendors while maintaining business continuity.
Solution Framework
We architected and executed migration to a Databricks-based harmonized mesh platform using a three-pillar approach: engineering automation, governance frameworks, and organizational enablement by:
- Automated data ingestion frameworks handling diverse sources across finance, marketing, HR, and supply chain
- Multi-tier quality system transforming raw data into business-ready insights through Bronze-Silver-Gold-Platinum layers
- Governance framework with PII controls, GDPR compliance, and role-based access
- ML-ready infrastructure supporting AI initiatives like custom GPT for R&D documents
- Self-service analytics through semantic layer, reducing IT dependency
- Change management programs ensuring adoption across business teams
Any Challenges ?
Our Industry Experts can solve your problem.
