What is Reference Data Management?

Welcome to Ataccama’s guide to the fundamentals of reference data management.  This article outlines everything there is to know about what reference data is, how it connects to referential data management, and why it matters for data analysts, governance stewards, and other data professionals.

What is reference data management?

Before we can define reference data management, we must first define what reference data is. Reference data is data that defines the values that are used to classify and characterize other data. This is sometimes known – not always correctly – as master data, golden record, golden copy, or single source of truth.

Reference data management is a crucial component of master data management (MDM) that has evolved into a mature data management discipline in its own right. Effective governance often relies on reference and master data management working together, with MDM handling core business entities like customers or products and RDM providing the standardized codes and categories that describe them.

Think of the codes and descriptions that make up RDM as how businesses identify information about any number of business activities, such as information about financial transactions, locations, forms of measurements, and inventories.

Why is reference data management important? 

Both volumes of data and opportunities to use it effectively grow through rapidly evolving and maturing technologies. That means data increasingly has an operational impact on organizations. Effective reference data management ensures day-to-day operations across businesses, charities, educational institutions, and other establishments continue without disruption. 

With the increasing volumes of transactional data, the importance of reference data management is growing too. Without it , we would have difficulty organizing and sharing common information about millions of products and services. We would even have difficulty determining the correct locations for the delivery of those products and services, especially in the context of enterprise data governance.

Why reference data management matters

As the amount of data grows, reference data management (RDM) has become essential for keeping daily operations running smoothly. It works hand in hand with referential data management, since organizations need both consistent values and well-maintained relationships between records to avoid errors and ensure reliable analytics.

When managed well, it reduces risk, supports compliance, and creates real business value. Here’s how it helps organizations:

• Keeps operations on track by preventing small errors, such as the wrong code on a transaction, from disrupting billing, inventory, or shipping.
• Reduces costly mistakes since accurate reference data minimizes delays, duplication, and inconsistencies across teams and systems.
• Improves analytics and reporting with clean, standardized data that makes trends easier to identify and ensures compliance with industry regulations.
• Strengthens governance through automated controls that improve data quality, availability, and oversight.
• Boosts efficiency by replacing spreadsheets, emails, and manual queries with a centralized system for reference data.
• Enables collaboration by giving teams and partners a single, trusted source of reference data and a shared understanding of codes and categories.
• Increases trust in data so that business leaders can make better decisions with confidence.
• Drives synergy with Master Data Management (MDM), helping deliver a complete 360-degree view of business domains.
• Delivers business value through higher productivity, better customer service, and stronger compliance across the organization.

What are some examples of reference data?

In theory, reference data could apply to just about anything. One of the clearest reference data examples comes from the automotive industry.  Just think about ordering a brand new Jaguar because you’ve been so successful in your data career. 

You’ll be able to customize the type of engine, wheels, seats, warming mechanisms, audio options, and more. Somewhere in their system, Jaguar will store this client reference data in structured tables to categorize and validate each selection during the configuration process. 

For example, here is a table that contains information about different available engines:

Attributes such as Main fuel type and Engine category are reference data examples that help categorize and describe broader product data. 

In this case, the system connects client reference data with product master data to ensure consistent categorization and accurate product configuration. 

Here is an example of the ICD-10 codeset for classifying diseases:

The list continues to row Z98.89.

These codes are also grouped into taxonomies and hierarchies for easier consumption by users.

Given reference data’s utility, it is typically a static, i.e., non-changing dataset that once defined, governs the use of data in an organization until it is changed.

What are some common reference data management problems?

Like any other data, if ungoverned and unmanaged, reference data becomes a liability, rather than an asset.

Here are some examples of what actually happens in organizations:

• Lack of a single source of the truth for reference data, which results in
  • Departments managing reference data in silos and synchronizing them slowly and irregularly
  • Using older versions
• Lack of data governance principles
  • Rules for using external reference data
  • 4-eye principle for approving changes, i.e., business workflow
• Using unsuitable tools: typical Excel → more in a separate section

How does this impact data stewards?

• Manually collecting requirements and reconciling differences throughout departments
• Manually updating reference data in consuming systems
• Involving IT and maintaining SQL queries or other technical solutions

How does this impact business and operations?

• Fines in highly regulated industries such as pharmaceuticals or airlines.
• Inconsistent financial reporting
• Lack of alignment between different lines of business
• Operational mistakes

Here is an example of what can happen when two different sets of codes represent the same concept:

Why you shouldn’t manage reference data in Excel

The typical reason users of data run into problems with reference data is that they have not been managed by a sophisticated tool. Instead, it was built and maintained in programs, such as Excel, which are not fit for purpose.

This is why reference data should never be managed in spreadsheets.

Excel lacks flexibility, validation capabilities, and integration features that modern reference data management tools provide. 

• They provide very limited search functionality.
• They offer very limited ways to validate data or proactively alert users to change.
• They lack automated versioning capabilities and can exist across independent silos.
• Manually merging spreadsheet updates can take hours, and simple tasks, such as adding a column to a codebook, for example, can impact their ability to be exported and shared with other systems and users.

They lack the advanced approval workflow capabilities that are necessary to update critical reference data.

How does reference data management (RDM) work?

RDM provides governance architecture to centralize and manage any reference data. A reference data management framework establishes the structure, workflows, and approval processes that ensure codesets are validated, distributed, and controlled consistently across all systems.

The data itself is stored in a single RDM hub or database repository because most data stewards, architects, and reference data managers believe an RDM solution should be the single and trusted source for the creation and company-wide distribution of reference data.

1. Reference data modeling

Reference data is modeled or structured by domain.

The model may be based on best practices or heavily influenced by external industry standards. HR, manufacturing, finance, and other departments will require different codeset models and approaches.

2. Reference data imports and mapping

Given the stability of codesets, importing reference data from outside the hub should not be a frequent occurrence. New codesets will typically be devised and modeled within the system as part of a deliberative process. Pre-existing codeset data may be copied and modified.

With more complex solution architectures involving legacy systems, centralized reference data authoring might not be possible. 

In this case, reference data is authored and synchronized between several systems, including the RDM solution itself, which holds the single version of the truth.

3. Data quality

Data quality is important for reference data just as it is for any other data. Therefore, it is important to embed data validations into the reference data authoring workflows. This will prevent mistakes from happening.

Additionally, reference data management solutions are able to maintain the referential integrity of data automatically.

4. Workflows and approval

Since reference data has such an organization-wide impact, any changes to it are usually subject to a tightly controlled workflow process. As part of RDM governance, a designated team of data stewards and subject matter experts participates in this process to ensure the 4-eye principle before any changes are published. 

They collaboratively view, comment, create, update, or even delete codesets. Upon approval, they will be published for viewing and disseminated across all relevant business systems.

5. Versioning

Anticipating required updates to reference data should be a priority, as policy changes, new products, parts, location changes, or other new procedures will necessitate ongoing management.

RDM addresses this requirement through versioning. In the example below, the first row, indicating the current, or active part specification QIX-102-A, is set to be automatically ‘retired’ on March 13, 2015. Upon retirement, RDM will simultaneously publish the replacement part specification, QIX-201-C.

6. Sharing trusted reference data

In addition to promoting a centralized governance approach, RDM’s data management mission includes sharing data with all relevant business systems and users.

There are multiple ways governed reference data can be leveraged and made available throughout an organization:

• System/application integration: RDM can directly integrate with business systems to publish data directly to hard drives, the cloud, or FTP used by HR, finance, sales, marketing, and more.
• Data Catalogs: Reference data is a major offering now provided by modern data catalogs. Data catalogs can become a trusted source for reference data, enabling users to search and request code sets for business tasks and transactions.
• Data Warehouses: Companies can also use their data warehouses to store archived or historical reference data for analytical reporting. Data marts are likely to maintain commercially active versions of reference data to support customer-facing activities and applications.

Start managing reference data centrally 

Reference data management captures the “What, Where, When, Who, and How” of organizational data. Well-governed RDM data becomes a reliable asset that supports accurate reporting, stronger compliance, and more effective analytics across the organization.

If you’re looking for more information about technology that can help you implement RDM, or if you’re generally interested in it as part of your data career, take a closer look at our modular reference data management platform, Ataccama One.

It’s the only solution that natively integrates RDM, master data management, data quality, and a data catalog. Get started in no time in our secure, scalable cloud.

Frequently Asked Questions

What is reference data management?

RDM is the process of keeping standardized codes, lists, and values such as country codes, product categories, or industry classifications consistent across systems. It ensures data stays accurate, trustworthy, and well-governed.

What is the difference between reference data management and referential data management?

RDM standardizes codes and values, while referential data management maintains relationships between records. One keeps values consistent, the other preserves links.

How does reference data management relate to master data management?Master data management governs core entities like customers or products. Reference and master data management work together by pairing those entities with standardized codes and categories.