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Consolidated payroll, EMR, and HR platforms from acquired entities into one governed Snowflake warehouse, eliminating siloed analytics and providing a democratized single source of truth.
A templated discovery-to-delivery framework allowed each new acquisition to be fully onboarded in weeks, with consistent data definitions and KPI logic applied from day one.
AI-powered de-duplication and automated data quality checks ensured clean, compliant datasets with zero manual reconciliation, enabling accurate ROI tracking and growth of all entities.
The client pursued an aggressive growth strategy through mergers and acquisitions, absorbing homecare entities over a compressed timeline. Each acquisition arrived with its own distinct technology footprint - separate payroll platforms, disparate electronic medical record (EMR) systems, and independent HR solutions. While this approach accelerated market presence, it produced a deeply fragmented data environment that made unified decision-making nearly impossible. Four interconnected challenges defined the landscape:
With 12 acquired entities each running independent payroll, EMR, and HR stacks, there was no unified view of operational performance. Leadership lacked confidence in reported figures because the same metric - patient hours, caregiver utilization, or billing revenue - could produce different results depending on which system it was pulled from. Reconciliation was manual, slow, and error-prone.
Data sat locked in system-specific silos with no cross-platform visibility. Analysts had to navigate multiple logins, data formats, and export workflows just to assemble a basic operational report. Time-sensitive decisions around staffing, compliance, and billing were routinely delayed because the underlying data was inaccessible or untrustworthy, directly impacting ROI realization from each acquisition.
Each entity defined clinical, scheduling, and compliance KPIs differently. One acquisition tracked caregiver utilization as billed hours against available hours; another used scheduled hours. With 30+ metrics in play across the portfolio, there was no standardized logic for how performance was measured, making cross-entity benchmarking and group-level reporting effectively impossible.
M&A activity frequently produced overlapping client records - the same patient appearing under different IDs across legacy systems, with different demographic or care-history data attached to each instance. Without a systematic method to identify and resolve these duplicates, ROI tracking was distorted, compliance reporting carried risk, and care coordination between entities was compromised.
Inferenz designed and delivered a purpose-built M&A data integration framework that prioritized speed to value, architectural scalability, and uncompromising data integrity. The engagement combined structured discovery with cloud-native engineering to transform a fragmented, 32-system environment into a single, governed, and analytics-ready data warehouse, capable of onboarding future acquisitions without rebuilding from scratch.
Discovery Workshops for Standardized KPI Definitions
Before a single pipeline was built, Inferenz facilitated structured discovery workshops with client stakeholders across clinical, operations, scheduling, compliance, and finance teams. These sessions mapped each entity’s existing metric definitions, identified conflicts and redundancies, and produced a canonical KPI dictionary covering 30+ standardized metrics. By aligning on definitions before engineering began, the team eliminated the most common source of post-integration rework: disagreements on metrics and their implications.
Snowflake-Powered Centralized Architecture
The integration architecture was built on Snowflake, selected for its elastic scalability, native support for semi-structured data, and zero-copy data sharing capabilities. Each of the source systems was mapped to a structured ingestion layer, with source-specific connectors handling format normalization before data reached the warehouse. The architecture was deliberately modular: each acquisition onboarding followed the same framework, allowing new entities to be added without redesigning the underlying platform.
AI-Powered De-Duplication Framework
A custom AI de-duplication engine was developed to address the client’s overlapping patient record problem. The model used a combination of probabilistic matching and deterministic rule logic — evaluating name variants, date of birth, address fields, and care history patterns to identify records that referred to the same individual across different source systems. Matched records were resolved into a single golden profile, with full lineage retained for audit purposes. This was foundational to accurate ROI tracking: a client counted twice in two systems is an acquisition success story counted once too many.
Automated Data Quality Checks at Every Layer
Rather than relying on manual QA cycles, Inferenz embedded automated data quality checks directly into the pipeline architecture. Validation rules covered completeness (required fields populated), conformity (values within expected ranges or code sets), consistency (relational integrity between tables), and freshness (data currency within acceptable thresholds). Failed records were quarantined and routed to a DQ review queue rather than silently propagated downstream. This produced clean, compliant, and auditable datasets from the first load, establishing a quality baseline that persisted as new source systems were onboarded.
Templated framework reduced integration time from months of custom build to a repeatable 8–10-week delivery cycle for each acquired entity.
AI de-duplication and automated quality checks ensured every dataset entering the warehouse met client-approved accuracy and completeness standards from day one.
Thirty-two disparate payroll, EMR, and HR platforms unified into a single Snowflake warehouse, giving every stakeholder access to one authoritative version of operational truth.
Clinical, scheduling, and compliance metrics standardized across all 12 entities, enabling reliable cross-portfolio benchmarking and group-level performance reporting for the first time.
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