A large health payer's tangle of warehouses couldn't support modern analytics. Trexin's 12-week assessment (56 leaders, 4 health plans) designed a future-state platform (data lake, sandboxes, analytic workbenches, and an AI/ML library) with an incremental build roadmap.
A large payer group was good at aggregating the data feeding its transactional systems, but its array of repositories, warehouses, and consuming systems was inefficient and ineffective for modern analytics. The VP of Enterprise Data Solutions and the Chief Enterprise Architect asked Trexin to assess and design a modern data-analytics platform and enterprise analytics capability.
We applied Trexin’s Strategy, Assessment & Roadmap methodology over a 12-week timebox, opening with stakeholder interviews across 56 operational leaders and 4 health plans to align on goals and emerging analytics needs. We translated the selected tactics into a future-state vision: a market scan of analytics-workbench technologies, an analytics-lifecycle process architecture (sourcing → ingestion → validation → integration → wrangling → presentation), a conceptual reference architecture, a plan to align two existing warehouses with the new platform, and an investment roadmap.
Guided by 7 consensus-driven tactics, the design landed on four incrementally buildable components (Data Lake & Ingestion, Sandbox Data Access, Analytic Workbenches, and an AI/ML Algorithm Library) with implementation options (on-prem, cloud, or managed service) and workbench capabilities framed around distinct Business-Analytics and Data-Science personas.
We design the data foundation AI actually depends on, and a roadmap leadership can fund and build in stages.
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