Reference Data Management
Reference Data Management (RDM) is the practice of managing the code lists, mappings, hierarchies, and classifications that underpin virtually every reporting and analytics initiative, and every data integration and migration project.
So what is Reference Data Management?
Nearly every analytics platform, integration project, data migration, regulatory report, and business application depends on reference data management. Yet in most organizations this data is maintained in spreadsheets, embedded within application code, or scattered across individual projects, making it one of the largest unmanaged risks in the enterprise data estate.
Reference Data Management (RDM) is the practice of managing the code lists, mappings, hierarchies, and classifications that underpin virtually every reporting and analytics initiative, and every data integration and migration project. It becomes necessary as its own distinct data architecture component in the spaces between operational systems (i.e. data integration, data migration and data warehouses etc.).
RDM encompasses several distinct disciplines:
- Reference Data list management: In its simplest form, RDM involves maintaining a list of codes as an authoritative set of valid values.
- Mapping: Relating one code set to another. This is the crosswalk described in the integration and migration use cases, e.g., CRM opportunity statuses mapped to ERP order statuses, legacy position codes to new position codes.
- Categorization: Assigning each member of a code set to one of a discrete, business-defined set of categories.
- Augmentation: Enriching a code set with additional attributes not found in a source system. What distinguishes augmentation from categorization is typically augmentation involves continuous values rather than discrete categories.
- Hierarchy management: Defining the multi-level structures that roll up detail levels to summary levels. These take two forms: natural hierarchies, where every branch passes through the same fixed levels (Store → Region → Operating Area), and parent-child hierarchies of arbitrary depth, as found in organizational structures and the chart of accounts.
- Banding: Defining how a continuous value maps onto discrete categories through a set of contiguous ranges. For example, price values of $0-$100 classified as low, $100-$1000 as medium, above $1000 as high.
In practice, it determines things like:
- How organizational structures roll up and aggregate in a report.
- How applications exchange codes with one another in data integration.
- How migrations translate legacy values to target systems.
- How enterprise data is classified into regulatory categories for regulatory reporting.
Effective RDM should involve a clear division of responsibilities. Business stewards own the data in the lists, mappings, and hierarchies, while IT/Data practitioners manage table creation, synchronization, security, and deployment. A robust platform like TitanRDM provides a centralized end-user interface, enhanced developer productivity, data integrity, validation, and governance, and facilitates data integration through bidirectional synchronization with your data platform and via APIs.
Learn more
Deep dive into Reference data management (RDM). Learn about the challenges facing businesses, the typical uses cases, capabilities and collaboration requirements of a good RDM system, Governance and implementation options, and the future of AI and automation for RDM. Read the white paper: Reference Data Management - The Missing Layer in Enterprise Data Architecture.