Streamlined Data Management for Fintech Efficiency

Every financial institution handles data in various forms, and analysts rely on access to this data to conduct analysis. Data engineers are responsible for designing, building, and maintaining systems that collect, store, and process data. How data is interacted with depends on the role of the person using the tools. Traditionally, manipulating data in a production environment required engineering teams to submit change requests. Support teams would then process these requests and implement the required actions in the production environment on behalf of the requesting team.
The process of coordinating with application support teams, obtaining management approvals, as well as securing sign-offs from database administrators (DBAs) and DevOps teams, can be time-consuming. Even minor database script changes trigger another round of approvals, further complicating data fixes or executing data definition language (DDL) statements.
These inefficiencies in data manipulation can be addressed by developing tailored data management tools. Ensuring data is handled securely, with appropriate authentication and authorization, is critical to the success of financial systems. As the need for secure access to production data and performing Create, Read, Update, Delete (CRUD) operations based on business requirements grows, various processes and tools have been developed to meet these needs. I led the development of DataDoor, a comprehensive data management platform designed to address these challenges.
The launch of the DataDoor platform has significantly modernized data management processes. This innovative solution streamlines User Data Management (UDM) migration and automation, replacing the traditional manual change request workflow with an advanced, rule-based automation system. With DataDoor, users can manage and implement data changes more efficiently, greatly reducing the time and complexity involved. This shift has resulted in significant time and cost savings, with savings in the millions of dollars.
The platform includes several key modules: the Data Setup and Integration module, which connects data sources with both test and production environments; the Data Editor module, which allows users to create and apply dynamic rules; the Business Validation module, which controls access to data editing features; and the User Access Provision module, which integrates security and governance protocols.