How do you give portfolio managers real-time visibility into fund performance while eliminating the manual errors that come from fragmented, siloed data? The solution lies in building a connected, scalable data foundation for financial reporting. At Keyrus, we partnered with one of the world's largest alternative asset managers to deploy a governed semantic model and automated validation framework built on Snowflake and Microsoft Power BI. This data foundation playbook saved controllers and portfolio managers roughly 15 hours a month on manual data entry and validation, while cutting semantic model refresh times by nearly two hours.
In this blog post, we share the exact data engineering approach Keyrus used to turn disconnected fund data into a single, reconciled source of truth. Read on to see how a connected data foundation can strengthen reporting accuracy and give your organization real-time confidence in every number. You can also read the full case study here.
What We Accomplished
~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
Client 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.
Reduced risk of future data loss by clarifying existing data governance processes and documenting new protocols.
Developed and optimized the semantic model and dashboard logic.
Benefits
Controllers and portfolio managers now work from a single, reconciled view of investor-level capital data, replacing the manual Excel trackers that once slowed down every close. This enabled a faster monthly close and gave the team real-time visibility into subscription and redemption activity by investor, feeder, and share class.
The optimized semantic model also strengthened reporting accuracy across the board, offering reliable visibility into investor-level exposure, NAV, and returns, and supporting faster identification of the investors driving fund performance. With regression testing automated and data governance protocols clarified and documented, the firm reduced the risk of future data loss and built a more resilient foundation for the reporting it depends on every day.
Together, these improvements freed controllers and portfolio managers from hours of manual validation work each month, letting them spend more time on analysis and investor engagement rather than reconciling numbers by hand.
Keyrus & BFSI Organizations
Through connected data platforms and AI-driven insights, Keyrus helps banking, financial services, and insurance organizations gain actionable insights, ultimately strengthening regulatory reporting, accelerating fraud and risk detection, and improving real-time visibility into portfolio and investor performance.
Keyrus is an international consulting group that turns data and intelligence into tangible, sustainable performance. Data is the organization's genetic code, and our legitimacy is built on 30 years of data foundations. That foundation is why we're built for the new category now emerging: businesses that run on intelligence, not software, where distributed intelligence becomes the infrastructure of the enterprise. Value is moving into the underlying architecture of organizations, and Keyrus architects the Operating System of this new Intelligent Organization.
If your organization is looking to move from reactive to proactive and start running on intelligence, contact our Banking, Financial Services & Insurance team to learn how we can help.