Data Liberation Analytics
Global Healthcare Organization — Technology & Platforms · 2023
1,000 Analysts. Zero Self-Service.
A global healthcare organization with a petabyte-scale data environment had a critical bottleneck: every data request required engineering involvement. Over 1,000 analysts across clinical, operational, and commercial functions needed data access — but every query went through a central data engineering team, creating a backlog measured in weeks, not days.
The data platform — built on MAPR with Apache Hive and HBase — was powerful but inaccessible to non-technical users. Schema complexity, access control gaps, and the absence of a semantic layer meant analysts couldn't self-serve even simple queries. The engineering team spent the majority of their time fielding ad hoc requests instead of building.
The organization needed to democratize access to its petabyte-scale data estate without compromising governance, security, or data quality standards.
Enable 1,000+ analysts to access petabyte-scale data independently — without compromising governance, lineage, or data quality — and eliminate the engineering bottleneck entirely.
Four Phases.
Total Liberation.
The Results.
The engineering team shifted from a data access bottleneck to a platform capability team — building new capabilities instead of fulfilling repetitive query requests. Analyst autonomy increased from near-zero to full self-service within a governed framework.
Stack.
30,000+ Man-Hours Saved
Data locked away
from your analysts?
Tell us about your data access bottleneck. We'll show you what governed self-service looks like at petabyte scale.