A trusted data foundation for every analytics and AI decision you make
Commercial, clinical, and operational data only creates value when it is integrated, modeled, and governed. We design and build the modern cloud platforms that turn fragmented life-science data into governed assets your analytics and AI can build on, today and as you scale.
An AI-ready lakehouse
Governed, vectorized data with clean interfaces agents and RAG can query.
Representative D2Strategy build, shown with synthetic data.
Most analytics and AI initiatives stall not because the questions are hard, but because the underlying data is fragmented, inconsistent, and difficult to trust. In pharma and life sciences that data arrives from dozens of sources: IQVIA syndicated sales and prescriber data, specialty pharmacy and hub feeds, payer and medical claims, CRM systems such as Veeva and Salesforce, MDM and affiliation hierarchies, clinical and safety systems, and a long tail of vendor extracts and spreadsheets. We bring order to that landscape: we assess your current data management against leading-practice standards, then design and build a cloud platform that ingests, transforms, models, and governs your data so every downstream report, dashboard, segmentation, and model rests on a single, defensible source of truth.
What we deliver
Cloud Warehouse & Lakehouse
Scalable, cost-efficient platforms on Snowflake, Databricks, and Microsoft Fabric using a medallion (bronze/silver/gold) pattern that serves both BI and machine learning from one governed store.
ELT Pipelines & Integration
Reliable, automated pipelines that land raw data and move it through tested transformation layers, with monitoring and alerting so failures surface before the business ever sees them.
Data Modeling & Architecture
Dimensional Kimball marts for fast, intuitive reporting and Data Vault 2.0 where auditability and changing sources demand it, often combined in a pragmatic hybrid design.
Governance, Quality & Lineage
Embedded quality tests, a searchable catalog, a business glossary, and column-level lineage, wrapped in HIPAA-aware, GxP-conscious controls your auditors can stand behind.
Master Data Management
Golden records and HCP/HCO identity resolution across IQVIA, claims, and CRM, so the same prescriber, account, or product means the same thing everywhere it appears.
AI-Ready Data
Conformed, documented, well-governed data with the metadata and semantics that grounding, RAG, and analytics agents require to produce explainable, trustworthy results.
Learn moreHow we do it
Good platforms start with a deliberate architecture, not a tool purchase. We map your sources, consumers, decisions, and regulatory constraints, then define a target-state design: platform selection, the layered storage model, ingestion patterns, modeling approach, and a governance operating model, plus a phased roadmap that delivers value in early increments rather than through a risky big-bang rebuild. We are platform-agnostic and cost-conscious: we recommend the warehouse, lakehouse, or hybrid that fits your volumes, latency, team skills, and budget. Many life-science enterprises pair a Databricks lakehouse for ingestion and advanced analytics with Snowflake or Fabric as the governed consumption layer, and we build to whichever pattern serves your roadmap. Open table formats such as Delta Lake and Apache Iceberg keep the foundation portable and future-proof.
- Cloud warehouse and lakehouse builds on Snowflake, Databricks, Microsoft Fabric, Azure, and AWS
- ELT pipelines with dbt and managed connectors (Fivetran-style) for IQVIA, claims, specialty/hub, and CRM
- Medallion layering and open table formats (Delta Lake, Iceberg) for unified BI and ML workloads
- Kimball dimensional marts and Data Vault 2.0 integration layers, version-controlled in dbt
- Data quality testing with dbt tests and Great Expectations, plus reconciliation and control totals
- Cataloging, column-level lineage, MDM, and HIPAA-aware / GxP-conscious security and access control
Representative use case
A growing specialty pharma was running commercial analytics off overlapping vendor extracts, with prescriber identity resolved by hand and every new data source taking weeks to onboard. We implemented a Snowflake warehouse with a dbt-managed medallion architecture, consolidated IQVIA, claims, specialty pharmacy, and Veeva CRM into conformed silver entities, and stood up master data management to produce governed HCP and account golden records. New source onboarding went from weeks to days, reconciliation breaks fell sharply once control totals were automated, and the same governed gold layer now feeds dashboards, segmentation, and the team's first RAG-based assistant. One trusted foundation that BI and AI both build on, instead of competing copies of the truth. Drawn from a real engagement. Data and metrics shown are synthetic to protect client confidentiality.
Cloud-first, Modern data stack on Snowflake, Databricks, Microsoft Fabric, and dbt
Warehouses that are ready for AI, not just BI
Every warehouse we build today is engineered for what comes next: governed, lineage-tracked, and documented so natural-language querying, RAG, and analytics agents can sit safely on top. When you want the full AI layer: agents, retrieval, automated BI development, and LLMOps, our AI Consulting service builds it on this same foundation.
Build the foundation once, and build it right
Tell us your sources, your questions, and your timeline. We will design a warehouse that serves BI today and AI next.