Fifteen data sources into one lakehouse, and models that finish
A large member-owned credit union — United States
The challenge
A substantial data footprint was spread across more than fifteen disparate sources, which made personalised member experience impossible and slowed every business decision that depended on analysis.
What was done
Working with a Microsoft partner, data from all sources was centralised into a lakehouse on Microsoft Azure, becoming a single analytics platform for the institution.
- More than fifteen disparate sources unified into one lakehouse.
- Model run time cut by 89%, changing what analysis was practical.
- Personalised member experience built on a complete data picture.
- Business decisions informed by analytics rather than by extract reconciliation.
