Financial Services Firm Builds AI-Ready Data Foundation

A global investment management firm with $500 billion in assets under management was drowning in data but starving for insights. Over decades of acquisitions and system implementations, they had accumulated 200+ data systems with no unified view of clients, portfolios, or risk exposures.

Analytics teams were paralyzed. They spent 80% of their time hunting for data across systems, reconciling conflicting numbers, and manually preparing datasets. The remaining 20% wasn't enough to deliver the sophisticated analytics the business demanded.

AI initiatives were stalling before they started. Data scientists would identify promising use cases—client churn prediction, portfolio optimization, risk modeling—only to discover that the data they needed was scattered, inconsistent, or inaccessible.

ELMET conducted a comprehensive data strategy engagement, interviewing stakeholders across investment, operations, risk, and technology. The assessment revealed that the firm had rich data assets—they just couldn't access or trust them.

The strategy defined a vision for becoming a 'data-native' investment firm where data and AI capabilities would be competitive differentiators. A prioritized roadmap balanced quick wins with foundational investments, ensuring the strategy would deliver value while building long-term capabilities.

The implementation centered on a modern data architecture with three layers: a scalable data lakehouse for storage and processing, a data product layer with curated, governed datasets for key business domains, and a self-service analytics platform enabling business users to explore data without IT bottlenecks.

Automated data governance ensured quality and compliance. Data lineage tracked every transformation. Quality monitors flagged issues before they impacted downstream analytics. Access controls ensured sensitive data was protected while enabling broad analytical use.

Within 18 months, the transformation was evident. Analytics teams now spend 80% of their time on analysis instead of data wrangling. Twenty-five new AI use cases have been deployed, from client personalization to alternative data integration. The firm now considers data and AI capabilities a key differentiator in competing for institutional mandates.