Case studies

Scaling Platform Innovation with a Multi-State Health System

data modernization DBT Electronic Health Records Epic Data Fivetran snowflake

Enabled rapid data refreshes and long-term scalability with a metadata-driven ingestion framework designed for Epic Clarity and Caboodle.

Improved trust in insights across 70% of analytics products through automated data quality validation and robust error handling.

Challenge

A large nonprofit health system spanning six states was significantly encumbered by data silos and in dire need to integrate Epic data with other sources. These challenges were already impacting the system’s ability to innovate and make data-driven decisions, while plans to undergo a merger only threatened to worsen these challenges and limit future scalability.

Prior to their engagement with Hakkoda, the system’s manual ingestion processes were both labor-intensive and error-prone. This created bottlenecks in data availability and reduced enterprise-wide trust in analytics outputs. Operational overhead, meanwhile, was a major barrier to timely insights.

Previous attempts to modernize with a major global systems integrator failed to deliver the scalable, efficient data infrastructure needed to support the organization’s growing analytics demands and complex ecosystem. They needed to bring in another data partner they could trust to deliver results.

Solution

Hakkoda partnered with the health system to scale their proprietary data platform by implementing a metadata-driven ingestion framework designed specifically for Epic Clarity and Caboodle data, replacing manual workflows with automated, API-driven processes using Fivetran HVR and dbt.

This modernization dramatically improved scalability and performance, enabling faster, more reliable data refreshes and reducing latency across data pipelines critical to clinical and operational analytics.

Enhanced observability, including robust error handling and automated data quality validation, boosted confidence in data integrity across 70% of the organization’s analytics products, while reducing the operational burden on internal data teams, which were freed up to focus on other mission-critical tasks.