US-based investment management and research firm serving institutional clients including pension funds, foundations, central banks, and endowments.
The client wanted to build a modular, scalable architecture for an efficient database to minimize batch processing time. The client wanted to build a database that could be used to create exhaustive dashboards to monitor the overall performance of the business.
After understanding the complexities and challenges, SG Analytics' data scientists applied the following three-step approach to transforming the client’s legacy system into an intelligent, automated database:
Our team developed a data warehouse combining all the master data tables and transaction tables from multiple databases and sources.
The team then implemented data standardization and historical record maintenance practices by identifying data entry points, choosing data standards, and defining the normalization matrix.
Finally, out team implemented a data cleansing process by detecting and correcting corrupt or inaccurate records. The team also removed inconsistencies based on the client’s standard taxonomy.
Provided a single, unified view of market, client, and public data through real time data integration.
Helped the client develop data governance capabilities to check data quality and validate business rules.
Automated extraction logic to access data across various systems.