The workflow before#
Bridge manages $49.8B in gross assets across more than 30 funds, and its data lived in separate property management, accounting and investor CRM systems joined by point-to-point feeds. Analysts pulled extracts by hand, reconciled them in spreadsheets and rebuilt the same numbers for each investor report. There was no master record for a property, fund or investor, so definitions drifted between teams. Staff spent close to a third of their time gathering data instead of analyzing it.
What we built#
We started with a two-month audit of every source system, data flow and reporting need, then ranked use cases by business value. Investor reporting automation came first, followed by portfolio performance, property-level reporting and risk monitoring.
We built a central pipeline on Azure: source systems land in Azure Data Lake Storage, Databricks cleans and models the data, and Power BI serves executive, investment, operations, investor and risk dashboards from one governed copy.
Profisee master data management now holds the golden record for properties, funds, investors, vendors and the chart of accounts, so a new acquisition maps into the same structures instead of adding another silo.
Before and after#
| Measure | Before | After |
|---|---|---|
| Investor report preparation | Several days of manual pulls and rework | 55% less time, from one governed source |
| Cross-system reconciliation | Manual, in spreadsheets | Automated in the pipeline, about 60% less effort |
| Data quality issues in reports | Frequent | About 60% fewer |
| Master records | None; each system kept its own | One golden record per property, fund and investor |
What's next#
With one governed data layer in place, the next step is Level 2, Connected Intelligence: an assistant that answers portfolio and investor questions directly from the Databricks models, starting with the investor relations team.
