Data Platform Modernization for Life Sciences

Migrate from legacy infrastructure to cloud-native, AI-ready data platforms – with GxP-compliant governance, semantic layers, and automated BI migration.

Assess Your Platform Readiness

90%

Processing time improvement and $150K licensing savings via full-stack modernization

$750K

Annual savings and 40% error reduction via unified manufacturing analytics

100+

Reports migrated from Tableau to Power BI with automated conversion

The Problem CDOs and Data Leaders Face

Every life sciences company wants to be AI-ready. Few have the infrastructure to support it. The typical pharma data landscape is a patchwork of Oracle data warehouses, Informatica ETL pipelines, departmental SQL Server databases, and vendor-hosted analytics tools – none designed for modern AI workloads.

This legacy infrastructure creates three problems. First, it cannot support the data volumes and processing speeds modern analytics requires. Second, it lacks the governance and semantic layer needed for trusted AI. Third, it is expensive to maintain. Platform modernization is not optional – it is the prerequisite for every other analytics and AI ambition.

What We Do

We modernize life sciences data platforms with cloud‑native, AI‑ready architectures, automated BI modernization, and GxP‑compliant governance to accelerate trusted insights at scale.

Talk to our Expert

Cloud Data Platform Migration

We migrate from Oracle, Teradata, SQL Server, and other legacy platforms to Snowflake, Databricks, and Microsoft Fabric. Our Flash Migrate accelerator handles assessment, conversion, validation, and cutover – compressing timelines by 20–40%.

Data Integration Modernization

We replace legacy ETL pipelines (Informatica, SSIS, DataStage) with modern integration tools (Fivetran, dbt) that are code-based, version-controlled, and easier to maintain.

BI Migration & Modernization

Our BI Converter automates approximately 70% of Tableau-to-Power BI migration effort, handling calculation logic, data connections, and visual design translation. This reduces a 12–18 month project to 4–6 months.

Governed Semantic Layers

We build semantic layers (Semantic Edge) that provide consistent business definitions, data quality monitoring, and lineage tracking across the enterprise. This is the foundation for trusted AI.

GxP-Compliant Data Governance

Documented data lineage, quality monitoring, role-based access controls, and audit trails – all designed for GxP, HIPAA, and GDPR compliance from the start.

Who It’s For

Built for life sciences CDOs, data platform, engineering, analytics, and governance teams driving cloud‑native, AI‑ready data modernization.

Data Platform Modernization for Life Sciences_who_Infographics

Use Cases

Life sciences organizations modernize data platforms to reduce complexity, improve performance, and enable AI‑driven analytics at scale. These use cases showcase how cloud migration, BI modernization, governed semantic layers, and GxP‑compliant data architectures help teams improve reporting speed, streamline analytics workflows, and support enterprise‑wide data consumption.

Oracle to Snowflake Migration

Full-stack migration from Oracle DWH + Informatica + Tableau to Snowflake + Fivetran + dbt + Power BI, with governance and AI readiness built in.

Tableau to Power BI Migration

Automated BI migration using our BI Converter accelerator, preserving calculation logic and visual design while reducing licensing costs.

Manufacturing Analytics Modernization

Unified delivery process analytics with self-service, commentary/action tracking, and real-time monitoring of batch delivery, quality, and safety.

Data Fabric Architecture Design

Logical connection layers that unify access across clinical, commercial, and operational data without mass migration - the foundation for scalable AI.

Results

90%

Processing time improvement, $150K licensing savings, 100+ reports migrated (Snowflake, dbt Cloud, Fivetran, Power BI)

$75K

Annual savings, 40% error reduction via unified manufacturing analytics (Power BI, Power Apps, SQL Server)

See our case studies

Resources

Explore proven strategies, frameworks, and success stories across cloud migration, BI modernization, and compliant analytics in life sciences.

Blog

The Data Platform Modernization Playbook for Life Sciences: Cloud, Governance, and AI Readiness

Blog

How AI Is Reshaping Medical Affairs: From MSL Reporting to Scientific Intelligence

Blog

Payer Intelligence in the Age of AI: How to Optimize Market Access Before Day One

Case Study

Enhancing Pharma Manufacturing Operations with AI-Driven Predictive Maintenance

How long does a pharma data platform migration take?

Timelines vary by scope, but our Flash Migrate accelerator compresses typical migrations by 20–40%. A full-stack migration (DWH + ETL + BI) for a mid-size analytics environment typically takes 4–8 months.

What is the difference between a data fabric and a data lake?

A data lake stores data in one place. A data fabric creates logical connection layers that provide unified access across multiple systems – without requiring all data to be moved. For pharma, data fabric architectures are often preferred because they respect existing system boundaries and compliance requirements.

Is GxP compliance possible in the cloud?

Yes. All major cloud platforms (AWS, Azure, GCP) support GxP workloads. We design architectures with documented lineage, validated environments, quality monitoring, and audit trails that satisfy GxP requirements.

How does BI Converter work?

BI Converter parses the metadata, logic, and structure of existing Tableau workbooks and translates them to Power BI. It automates approximately 70% of the effort, with expert review handling complex custom visualizations.

Data Platform Modernization for Life Sciences

90%

$750K

100+

The Problem CDOs and Data Leaders Face