<img height="1" width="1" style="display:none;" alt="" src="https://px.ads.linkedin.com/collect/?pid=8366258&amp;fmt=gif">
Skip to content

Let's talk about your next big project.

Looking for a new career?

View job openings. kpi-check-icon

By submitting, you consent to KPI Partners processing your information in accordance with our Privacy Policy. We take your privacy seriously; opt out of email updates at any time.

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

    kpi-check-white-svg

    Case Study: Modernizing Clinical Trial Forecasting on Databricks | 12x Cost Optimization

    Case Studies

    Re-Architecting Clinical Enrolment Forecasting on Databricks: 12x Platform Cost Optimization  

    At a Glance

    Industry:  Biopharmaceutical Services 
    Location: United States (Global Ops)
    Client:  Leading Global Clinical Research Organization 

    A global biopharmaceutical services organization operating multi-study clinical programs relies on enrolment forecasting to manage execution timelines and mitigate delivery risk. Forecast accuracy and refresh reliability directly impact study governance and operational planning. As study volume increased, the existing ingestion and transformation framework within the SNAP analytics environment struggled to scale efficiently.  

    The organization engaged KPI Partners to modernize its Forecast Data Product within its SNAP analytics platform. KPI redesigned the legacy ingestion and transformation framework using Databricks to improve reliability, enhance scalability, and reduce platform costs while strengthening trust in clinical reporting. 

     

    Schedule A Discovery Call

    Technology

    • Azure Databricks
    • XGBoost
    • spark
    • Microsoft_Azure_KPI Partners
    • ml_flow
    • Microsoft Power BI
    • Databricks Unity Catalog-1
    • KPI_Accelerators_logo-1

    The Challenge

     

    Compute-Heavy Full Loads

    ADF full-load pipelines ran for nearly 10 hours, and incremental loads ran for about 45 minutes, driving high Azure resource consumption and increasing platform costs.

     

    Pipeline Fragmentation

    The team managed six separate ADF pipelines for full and incremental processing, increasing orchestration complexity and maintenance overhead.

     

    Hard Delete Reconciliation

    The team executed weekly full reloads to address source-side hard deletes, adding unnecessary processing time.

     

    Reporting Discrepancies

    Business reports diverged from source data until the team ran a full load to realign datasets.

     

    Workflow Timeouts

    Long-running full loads frequently timed out, forcing production support teams to monitor pipelines continuously and intervene manually.

     

     

    Scaling Clinical Analytics with Incremental-First Architecture | 12x Cost Optimization

    Tags: Case Study, Microsoft Azure, Databricks, XGBoost, MLflow, Apache Spark, Microsoft Power BI, KPI DataBridge Suite, Clinical Trial Forecasting, Data Pipeline Optimization, Structured Streaming, Healthcare Data Modernization, Clinical Data Engineering, Biopharma Analytics Modernization

    Comments

    Comments not added yet!

    Your future starts today. Ready?

    Lets Talk
    kpi-top-up-button