Project information

  • Project type: Data driven web portal
  • Industry: Financial Services / Private Equity
  • Technologies: Snowflake, ETL, Azure Analysis Services, OLAP Cube, DAX, Excel, Power BI, Tableau
  • Current state: Fully operational, maintained and supported by us
  • Result: Portfolio managers can now generate complex reports almost instantly and build new analysis directly in Excel without relying on the legacy reporting system.Portfolio managers can now generate complex reports almost instantly and build new analysis directly in Excel without relying on the legacy reporting system.

Portfolio Management Analytics Platform

Snowflake data warehouse, ETL, and cube-based reporting platform
Financial Services | Snowflake | Azure Analysis Services | Data Analytics
Overview

We developed a modern analytics platform for a long-standing client in the portfolio management domain. The goal of the project was to provide portfolio management teams with faster, more flexible, and more scalable access to business data for reporting and analysis.

The client needed a solution that would allow end users to work more independently with data, including the ability to add or remove fields, upload data more efficiently, and build reports in tools such as Excel, Power BI, and Tableau.

Solution

To support these requirements, we designed and implemented a solution centered around a Snowflake Enterprise Data Warehouse. Raw data from the legacy portfolio management platform was synchronized daily into the warehouse, and we developed ETL processes to populate a newly structured analytical database built with fact and dimension tables.

On top of this data model, we created an OLAP cube in Azure Analysis Services and defined a growing set of DAX expressions to support reporting and analysis. This made it possible for end users to recreate and extend legacy reports directly in Excel while also enabling broader self-service analytics capabilities.

Challenges

One of the main challenges in the project was rethinking complex reporting logic that had previously been implemented through SQL Server stored procedures in the legacy system. We had to redesign this logic within a modern star schema and cube-based model using DAX calculations and analytical structures instead of traditional relational reporting techniques.

The project also required replicating finance-related formula logic from the legacy system in a new analytical architecture that would remain flexible, scalable, and maintainable over time.

Result

The new platform delivered a major improvement in performance and usability. The cube-based reporting infrastructure provided results to end users almost instantly, while similar reports in the legacy system were significantly slower. By the end of Phase I, the client was already using the solution to recreate most of the existing reports in Excel, while also gaining the ability to build new report variations and more detailed analysis.

Today the project is in production, and our work continues with further enhancements and performance improvements.