What is Reference Data Management?
Reference Data Management (RDM) is the process of ingesting, storing, and maintaining static financial data such as securities terms and conditions, legal entity identifiers (LEIs), maturity dates, and customer data. An RDM solution allows you to do all this, while also interlinking that data to other relevant data types and associating it with hierarchies and classifications. This data is made available to relevant people and departments such as trading and risk management via integrated downstream systems from one single source, ensuring accuracy, completeness, and availability.
Why is it important?
Reference data management is important because it ensures smooth trades, transactions, analysis and reporting. It enables portfolio management, minimizes trade breaks, supports risk reporting and regulatory submissions, such as MiFID II compliance. Good RDM ensures accurate, complete, and available static data for critical trading and investment activities. Without this it is difficult to achieve operational efficiency because of the time, effort, errors, and associated costs of addressing data gaps, exception management, reconciliations, delayed reporting, and poor decision making based on substandard reference data.
What is reference data management in investment banking?
Reference data management in investment banking, by definition, is the same as above, but in practicality also ensures that the completeness and accuracy of every desk’s book of positions is not lacking due to wrong, missing or unclear securities static. Based on this, risk, finance, and quantitative analysts/strategists can confidently work with each trading book. In order for this to happen, the RDM or data operations team needs to ensure that securities and counterparty static data is fit for purpose.
What is the difference between enterprise data management and reference data management?
The fundamental difference between enterprise data management and reference data management is simply the scope of the data being maintained. Enterprise data management encompasses dynamic data, such as prices, positions, and performance data, as well as the static data mentioned above, while RDM only maintains static data. Rules and workflows differ as well; for example, only data changes might need to be maintained for RDM, whereas whole data sets will change daily for the other data types, e.g. prices, that enterprise data management maintains.
What should I look for?
What you’ll need is a solution that provides you with robust coverage of the data types, attributes and definitions, which can be achieved with an industry standard data model. You’ll also need integration capabilities with down and upstream systems, both internal (such as a data warehouse) and external (such as data vendors). Finally, the solution will need to be accessible to non-IT personnel, with the ability to easily construct meaningful, actionable extracts of the data and UI workflow screens.