Data Democratization Implementation in Investment Management

This case study outlines the implementation of a data lakehouse architecture by a major diversified financial services firm to achieve data democratization. The firm, managing approximately $75 billion in assets, faced significant challenges with data accessibility due to a closed SQL Server-based infrastructure. Portfolio managers and research teams lacked direct access to raw data, leading to bottlenecks in investment decision-making. The solution involved adopting Dremio as the semantic layer, utilizing Azure Data Lake for storage, and Kubernetes for orchestration. This transition enabled self-service access to data, eliminating delays and allowing for automated reporting and experimentation. The results included improved operational efficiency, reduced time-to-access for critical investment data, and enhanced stability with no unplanned outages over several years. Future initiatives include migrating to Apache Iceberg and exploring AI applications for document search and summarization, demonstrating the firm's commitment to continuous improvement in their data platform.