A Global Biotech Company Accelerates Treatment Decisions and Drug Discovery on a Self-Serve Data Platform

The client is a global biotechnology and cancer diagnostics company. Their long-term plan rests on improving existing genetic tests, developing new ones, and monetizing real-world clinico-genomic data for R&D partners. The bottleneck was the data platform underneath every one of those goals.

01 The Challenge

Genomics data lived in legacy tools, and every query waited for an engineer

Annotation workflows were not designed to accomodate the growing volumes of data required for R&D and testing. Clinical and genomic data lived in separate stores that did not query together. Self-serve analytics did not exist – every cohort question went through a data-engineering queue. Third-party software licensing added tens of millions of dollars in annual cost without delivering the throughput the R&D teams needed.

The client wanted a unified platform that let researchers query paired clinico-genomic data in seconds, and let biopharma partners build cohorts without routing every request through a data engineer.

02 The Approach

Three phases. AWS HealthOmics at the core. Retirement of the legacy stack on the other side.

Provectus opened the engagement with a data-and-analytics strategy specific to the client’s mission, then delivered against it in three phases:

• Migrate existing datasets and pipelines to AWS
• Unify, integrate, and harmonize diverse clinico-genomic data
• Build the self-serve products on top: cohort identification, analysis, and analytics

Each phase had a defined output gate. The migration finished before unification started. Unification finished before the self-serve layer went live.

03 The Build

AWS HealthOmics plus a unified schema plus a self-serve UI that eliminated the ticket queue

The platform is built on AWS HealthOmics with Amazon QuickSight on top. Clinical and genomic data land in a unified warehouse that supports collaborative query, analysis, and visualization of annotated genetic variants matched with clinical data.

Annotation workflows accelerated because the foundation underneath them streamlined and scaled the workload. The self-serve UI lets researchers run cohort queries in seconds – the same queries that used to take hours through a data engineer.

The legacy third-party analytics tool was retired. The annual licensing bill – eight figures – went with it.

04 The Results

From hours to seconds. From $10Ms in licensing to zero.

“This platform is a step change in our clinical operations and R&D. The tasks that used to take hours can now be accomplished by researchers in minutes, without any help from IT or engineering.” · Head of R&D

The annotation throughput rose; unit cost on data operations fell. The client can now provide faster, more affordable genetic testing and diagnostic services while supplying partners with richer data for drug discovery and trial design.

The platform is also the revenue vehicle for paired clinico-genomic data the company monetizes to biopharma – a market measured in tens of billions.

05 What’s Next

The success can be replicated by other HCLS organizations sitting on fragmented real-world data

The infrastructure, data platform, and the self-serve system on top are parts of the Evidence Lens blueprint. Other diagnostics and pharma companies with fragmented clinico-genomic data can start from the baseline this engagement tuned.