Reference data management also referred to as RDM, is a technique that many businesses apply to develop hierarchies and classifications. RDM is leveraged across various systems throughout an enterprise or organization. There are several critical components of reference data management that are important to remember. These include business intelligence, analytics, reference data tracking, and much more.

If you want your RDM protocols to work more effectively, it’s important that you understand some of the key components of the method. With the right frameworks and internal standards, you can enhance greater levels of connectivity. It’s also beneficial if you understand the foundations of RDM and how to capably integrate it into your business architecture.

Data Mapping

When you define RDM, it’s often sensible to start with data mapping. In many cases, RDM is used to utilize canonical sets of reference data. Canonical data often includes country codes, languages, currencies, and other pertinent information. In addition to that, any effective reference data management hub needs to be able to manage other types of data. The data may be use-case specific, application-centric, or relegated to a particular industry. Beyond that, it’s prime to incorporate a system that can maintain and manage the relationships between your reference datasets and the developing permutations of that data.


Your RDM platform should facilitate an optimal level of connectivity and accessibility. If you’re working with downstream applications and systems or remote subscribers, it’s important that your data is readily available for access. This interconnectivity also provides for a higher level of convenience amongst consumers. As a result, your consumers should be able to access the reference data when they need it and in a format that is convenient. This is paramount for a robust reference data management solution. Without the enhanced connectivity from point-to-point, it’s less likely that you’ll be able to access and interpret data as efficiently.

Lifecycle Management

Data governance user interfaces, workflow processes, and end-to-end data migration and management are all components of a quality reference data management solution. The platform should also incorporate some form of lifecycle management for all of your enterprise reference data. Otherwise, there may be added technical strain on your IT teams. If you’re looking to improve the uses of your resources, it’s important to invest in a system that allows you to put the delegation of lifecycle management tactics in the hands of the appropriate business users. This reduces workflow burdens and creates more sensible data pathways.

Access Control

As with any digital platform, security is absolutely crucial to brand success. With the prevalence of data breaches, software security holes, and digital vulnerabilities, it’s important that your RDM solution allows for stronger cybersecurity protocols. Access control is a solid first step. You can limit who or what has access to any of the reference data being utilized and view access logs to verify compliance. Role-based security is essential in almost any enterprise, be it defined by user roles, group roles, or departmental access parameters.

Import and Export Functions

Naturally, manual data processes have a way of slowing down business workflows. Due to this, it’s important that you have a system in place that enables you to easily import and export large amounts of data without significant delays or downtime. Inbound or outbound mappings from data definitions should allow for standard flat files, XML, or CSV formats. This provides additional options for enterprise users and allows greater levels of data freedom across your organization. Plus, it streamlines standard workflows.

Reference data management is a beneficial tool for a variety of brands and businesses. It’s a cornerstone of a quality master data management strategy and can greatly enhance a number of your daily processes.