Building a Connected, Scalable Data Foundation for Financial Reporting

~15

Hours/month saved on manual data entry and validation for controllers and portfolio managers

Cut Hours Down to Minutes

Of manual data validation through regression testing automation

~2

Hour reduction in semantic model daily refresh times through SQL and DAX optimization

Background

Our client is one of the world's largest and most influential alternative asset managers. Founded in 1990 and headquartered in America, the firm has evolved from a specialist in distressed debt into a diversified global powerhouse. With over $800 billion in assets, this client frequently pursues the latest in cutting-edge technology to continually be a leader in how asset management firms operate.

Challenge

The firm’s growth strategy relied on expanding across asset classes and investment structures, including Equity, Credit Fund Look-Through, and Third-Party Fund investments. However, this expansion outpaced their data infrastructure, and dependence on manual and fragmented data processes limited real-time portfolio analysis, constrained reporting, and increased operational risk. This created a lack of standardized, connected data across the fund ecosystem, making it difficult for controllers and portfolio managers to track investor activity efficiently and for stakeholders to trust the accuracy and timeliness of reporting. Additionally, Sigma processes overwrote a week’s worth of manual overlays, which resulted in a hefty manual effort to recover and backfill the data.

Approach

Semantic Model Development: Built the semantic model governing the fund’s dashboards, optimizing its performance and supporting updates. Regression Testing: Automated regression testing of the semantic model. Investor Capital Tracking: Automated monthly investor capital tracking. Technical Documentation: Authored technical documentation for the fund's data models and dashboards. Data Governance: Clarified and documented data governance processes and supported data recovery efforts. Data Validation: Validated dashboard metrics to ensure reporting accuracy.

Key results

Gave controllers and portfolio managers a single, reconciled view of investor-level capital data — replacing manual Excel trackers — enabling faster monthly close and real-time visibility into subscription and redemption activity by investor, feeder, and share class.

Developed and optimized the semantic model and dashboard logic.

Reduced risk of future data loss by clarifying existing data governance processes and documenting new protocols.

Benefits

Enabled faster monthly close and real-time visibility into subscription and redemption activity by investor, feeder, and share class. Additionally, helped offer reliable visibility into investor-level exposure, NAV, and returns, supporting faster identification of investors driving fund performance.