A large U.S.-based home care provider was undergoing rapid expansion through mergers and acquisitions, onboarding 12 separate agencies across 190+ locations. This growth introduced significant data challenges: 67,000+ patients, 58,000+ caregivers, and 72 million service hours annually were managed in fragmented systems—multiple payroll, EMR, and HR platforms—leading to data silos, inconsistent KPIs (30+ clinical, scheduling, compliance), and a lack of unified reporting. Decision-making was delayed, and ROI tracking was unreliable.

Objectives

Unify disparate systems: Create a single source of truth across payroll, EMR, and HR datasets.
Speedy integration: Rapidly assimilate each newly acquired entity.
KPI consistency: Harmonize metrics definitions and enable tracking across the enterprise.
Data accuracy: Ensure high-quality, automated, end-to-end data processes.
Scalability: Build a framework to easily roll out for future acquisitions.

Solution Architecture

Discovery Workshops
- Collaborated with business teams to define and standardize key metrics across departments.
AI-Powered De-Duplication
- Employed intelligent matching to reconcile overlapping client records, ensuring dependable ROI calculations.
Snowflake-Based Data Warehouse
- Consolidated 32 siloed systems into a single, scalable central repository.
- Enabled fast onboarding of new acquisition data.
Automated Data Quality Framework
- Integrated validation checks to maintain data consistency, compliance, and accuracy from day one.

Tech Stack: Snowflake | Python | Azure Data Factory | Azure Functions | Power BI

Key Outcomes

Speed: New acquisition data integrated in just 8–10 weeks each—significantly faster than industry benchmarks.
Accuracy: Achieved 100% data accuracy through automated checks and AI de-duplication.
Consolidation: Streamlined data from 32 systems into one warehouse, enabling unified analytics and reporting.
Democratization: With Power BI and a centralized warehouse, stakeholders now have self-service access to clean, trusted data.

Business Impact

Streamlined decision-making with real-time, cross-system visibility
Reliable ROI tracking on mergers and acquisitions
Standardized performance metrics across clinical, operational, and compliance functions
Foundation laid for future M&A activity with minimal data friction

This project highlights how an AI-driven, cloud-based data integration framework can unify siloed systems, boost data trust, speed up onboarding, and deliver critical insights at scale.

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