Analytics scale and governance for complex life-science enterprises

Large life-science organizations rarely have a data shortage. The challenge is turning fragmented commercial, medical, market-access, clinical, and operations data into consistent answers across brands, regions, vendors, and business units. Enterprise reporting loses credibility the moment every brand and analyst defines the same metric differently, and that problem compounds the instant AI agents start generating decisions from inconsistent data, making governance everyone's problem at once. We help enterprises simplify the stack, certify the metrics, and deliver analytics and AI that people trust, without grinding the business to a halt to get there.

How we do it

We meet enterprise standards and work inside enterprise controls. Engagements typically begin with an assessment of architecture, data quality, reporting adoption, governance, and AI readiness, producing a prioritized modernization roadmap rather than a rip-and-replace mandate. We standardize the business layer in a governed semantic and metrics model, certify the KPIs that brands and functions share, and publish data products that dashboards, self-service BI, and AI all consume from one foundation. We modernize incrementally, sequencing the highest-value migrations first and protecting critical cycles, across Snowflake, Databricks, and Microsoft Fabric on Azure or AWS, with dbt-managed transformations, cataloging and column-level lineage, and HIPAA-aware, GxP-conscious security throughout. Where AI is in scope, we operationalize RAG and agents with the evaluation, monitoring, and human review that regulated environments require.

• Enterprise platform modernization on Snowflake, Databricks, and Microsoft Fabric (Azure/AWS)
• Certified semantic and metrics layer that standardizes KPIs across brands, regions, and vendors
• Governance at scale: catalog, business glossary, column-level lineage, and stewardship operating model
• dbt-managed transformations, data products, and reusable validated datasets for BI and ML
• Advanced analytics, RAG, and agents moved to production with validation, evaluation, and review
• Flexible delivery teams that augment or lead, respecting security, compliance, and change control

Representative use case

A large pharma was running the same commercial metrics three different ways across brand teams, regional affiliates, and an offshore vendor, and an early AI assistant was returning answers no one could reconcile. We assessed the estate, validated a shared metrics layer over a modernized Snowflake and Databricks foundation, and migrated priority marts incrementally so weekly cycle reporting never went dark. With one governed definition of demand, share, and access, executive reviews stopped relitigating the numbers, self-service adoption rose, and the team's RAG assistant, grounded in the governed layer, finally produced answers leaders could act on. Within two quarterly cycles, governance at scale had made both BI and AI trustworthy across the enterprise. Drawn from a real engagement. Data and metrics shown are synthetic to protect client confidentiality.

100+, platform-certified data and analytics experts across the modern stack