Coming to widespread prominence in 2012, reference data management (RDM) has become a key element in master data management (MDM). RDM provides the processes and technologies for recognizing, harmonizing, and sharing relatively static data sets for “reference” by multiple constituencies (people, systems, and other master data domains). RDM, which is generally a “read-only” file, is basically the dictionary or look-up table that everyone in a company uses to make sense of each other’s data. Inconsistent or non-existent RDM can be debilitating for a company because all systems in a company rely on the reference data as the standard. Without it, business intelligence reports can be inaccurate and systems integrations may fail.
RDM helps prevent compliance risks
Such a system provides governance, process, security, and audit control around the mastering of reference data. In addition, RDM systems also manage complex mappings between different reference data representations and different data domains across the enterprise. Most contemporary RDM systems also provide connectivity, typically a service-oriented architecture (SOA) service layer (a.k.a. “microservices”), for sharing of reference data with enterprise applications, analytical/data science, and governance applications.
Custom RDM solutions using legacy software are no longer viable solutions
Prior to the availability of commercial RDM solutions, organizations built custom solutions using legacy software such as spreadsheets, workflow software (business process management or BPM) and other tools. Such systems often lacked change management, audit controls, and granular security/permissions. As a result, these legacy solutions have increasingly become compliance risks.
Because reference data is used to drive key business processes and application logic, errors in reference data can have a major negative and multiplicative business impact.
Read the full Reference Data Management Field Report from MDM Institute to learn:
- Background on reference data and reference data management,
- Compares and contrasts solution approaches for RDM, and, finally
- Criteria for RDM solution selection.