Healthcare · Pricing Operations
Turning pricing spreadsheets into a governed data pipeline.
Fortune 10 healthcare enterprise
An Azure-native ETL pipeline that extracts, validates, and loads client pricing distribution forms into a governed SQL database — processing 580+ standardized PDFs in its first three months.
Healthcare · Pricing Operations
Challenge
What was the challenge?
Client pricing arrived as Excel workbooks and PDF distribution forms that analysts manually transcribed into downstream systems. In pricing data, a transcription slip isn't cosmetic — it flows straight into how clients are billed.
The manual process was also slow and opaque: there was no systematic validation step, no audit trail of what was loaded when, and no single source of truth the organization could point to for client pricing.
Approach
How did we approach it?
We built the pipeline on the client's existing Azure estate — Azure Data Factory for orchestration and ingestion, Databricks for validation and transformation, Azure SQL as the governed destination — so the solution slotted into their platform rather than adding a new one to operate.
Validation was designed as a gate, not a report: data that fails business-rule checks is surfaced for correction and never silently loaded. Delivery ran on an agile framework with iterative releases, anchored by a source-of-truth project that established pricing data governance for the organization.
Delivered
What did we deliver?
- An end-to-end pipeline on Azure Data Factory and Databricks that extracts data from pricing distribution forms (xlsx and PDF), validates it against business rules, transforms it, and loads it into an Azure SQL database.
- Production deployment of standardized-PDF processing — 580+ documents processed in the first three months.
- A validation gate that blocks bad data at the door, keeping the database audit-ready at all times.
- The source-of-truth project delivered on an agile framework, establishing where pricing data governance lives.
Mechanism
How does it work under the hood?
- 01Azure Data Factory pipelines ingest pricing distribution forms from their arrival points.
- 02Databricks notebooks run extraction and business-rule validation, then transform validated records into the target schema.
- 03Clean data loads into Azure SQL Server; validation failures are routed for human correction instead of being loaded with caveats.
- 04Iterative agile releases let the pipeline absorb new form variants without re-architecture.
Outcome
What was the outcome?
In its first three months of production the pipeline processed 580+ standardized PDFs, significantly improving the efficiency of pricing operations and replacing per-document manual transcription with a governed, auditable flow.
The organization gained something it didn't have before: a single validated destination for client pricing data that downstream systems can trust.
Learnings
Key learnings
Build on the platform the client already runs — using their existing Azure estate meant governance came almost free and operations had nothing new to learn.
Validation should be a gate, not a report: refusing to load failing data keeps the destination trustworthy in a way after-the-fact checks never do.
Iterative agile releases let a pipeline absorb new document variants continuously instead of demanding re-architecture.
Related stories
Healthcare · Pharmacy Benefits
Digitizing complex pharmacy contracts with generative AI
An end-to-end GenAI pipeline that reads dense pricing contracts — tables, legal language, and billing rules — and turns them into audit-ready structured data, unlocking roughly $64M in annual savings.
Healthcare · Data Operations
Modernizing enterprise data pipelines with Spark and Airflow
A ground-up modernization of legacy data integration onto Spark and Airflow — smarter scheduling, event-driven triggers, and stronger validation — delivering roughly $5M in annual cost reduction.
Work with us