Scaling Healthcare Data Processes for Improved Efficiency

Our client, a healthcare analytics SaaS company, faced growing challenges with their data processes as they onboarded more customers. 

Handling sensitive healthcare data required extensive processes like cleaning, transformation, and validation. The company needed a scalable solution to improve efficiency while maintaining the highest data quality standards.

The Solution:

Data Governance Framework
To standardize data and ensure consistency, we implemented a robust Data Governance framework, which included:

• Data Catalogue & Lineage:
  • Created a comprehensive data dictionary and ERDs for standardized documentation.
  • Linked data sources with source-code repositories for easy reference.
• Metadata Standardization & Ownership:
  • Established processes for consistent metadata creation.
  • Defined clear data ownership to enhance collaboration and accountability.

Data Processing: Optimizing ETL and Automating Quality Tests

To support efficient and scalable operations, we improved their ETL processes and implemented automated data quality tests, focusing on:

• Reusable ETL Scripts:
  • Reduced processing times and sped up new pipeline development.
  • Enhanced scalability with modular, maintainable scripts.
• Automated Data Quality Testing:
  • Deployed granular-level alerts for quicker issue detection.
  • Automated testing pipelines, eliminating manual post-project checks.

Results: By implementing a structured data governance framework and automating data quality checks, we improved our client's operational efficiency and set the stage for long-term success.