Reference Data Management

Reference Data Management (RDM) is the practice of governing and distributing the code lists, classifications, hierarchies, and cross-system mappings that operational systems run on and that every reporting, analytics, integration, and migration initiative depends on.

To understand Reference Data Management (RDM), you first need to understand what “Reference Data” is. Two competing definitions of the term tend to confuse the subject. This section works through both to arrive at a clear, definitive definition of Reference Data Management.

The narrow definition comes from the Data Management Association’s DMBOK (Data Management Body of Knowledge, the profession’s standard reference text). In this case, Reference Data is the lists of valid values behind the attributes of Master Data entities. It’s discussed as a sub-topic of Master Data Management, which is the management of the core or “master” entities of your business (E.g., Customer, Product, Employee). It is the lists of valid country codes, currency codes, and status codes that sit behind the attributes of these master data entities. Operational systems will generally maintain their own lists of reference data.

The broader definition adds to the narrow definition and includes the mappings, hierarchies, categorizations, bandings, and augmentation of data that determine how data from different systems is translated, grouped, rolled up, and assigned for the purposes of data integration, migration, warehousing, reporting, and analysis.

The difference between the two is ownership. For the narrow definition, an operational system is the system of record. For the broader definition, in most organizations, there is no system of record. It survives only in a sprawl of poorly governed spreadsheets, CSV files, and hard-coded transformation logic.

This is where Reference Data Management earns its place in the data architecture as an independent capability. It becomes the system of record for the reference data that no operational system holds. The mappings, categorizations, hierarchies, and bandings that reporting, integration, and migration depend on.

***Reference Data Management, then, is the practice of authoring, owning, approving, storing, and distributing the code sets, mappings, categorizations, hierarchies, and bandings that every reporting, analytics, integration, and migration initiative depends on.

RDM encompasses several distinct practices:

  • List management. Maintaining an authoritative set of valid values for an attribute of a master data entity, for example, the valid position codes for an Employee.
  • Mapping. Relating one code set to another, for example, mapping a legacy chart of accounts to its replacement during an ERP migration.
  • Categorization. Assigning each member of a code set to one of a discrete, business-defined set of categories, for example grouping cost centers into functional areas for management reporting.
  • Augmentation. Enriching a code set with attributes the business needs but no source system holds, for example, a reporting label, an owner, or a regulatory flag.
  • Hierarchy management. Defining the multi-level structures that roll detail levels up to summary levels, for example, a GL account hierarchy or a product taxonomy.
  • Banding. Defining how a continuous value maps onto discrete categories through a set of contiguous ranges, the continuous-value counterpart to categorization, for example age bands or revenue tiers.

Across all six, RDM adds what spreadsheets cannot: versioning, approval workflow, distribution and a full audit history.

The first practice, list management, is the exception to the ownership rule above, but it fits with RDM in a few circumstances. Where operational systems have no adequate way to maintain a list, or where a common definition is required across several systems, RDM becomes the authoritative source. Separately, in fields like healthcare, banking, and stockbroking, external bodies maintain industry reference data. In these cases, the system of record sits outside the organization altogether, and RDM’s job is to ingest, version, and distribute it to every system that needs it.

Reference Data Management in many organizations is implemented piecemeal as 100s of spreadsheets, CSV files, and hard coded rules. A better option is a dedicated Reference Data Management system like TitanRDM.

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.

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