Master Data Management (MDM) vs. Reference Data Management (RDM)
In the realm of data management, both master data management (MDM) and reference data management (RDM) play pivotal roles. Understanding the nuances and differences between these two complementary disciplines is crucial for any organization aiming to optimize its data strategy.
Making an informed decision between managing reference data and/or implementing MDM can significantly impact how your organization manages and utilizes data. In this article, we will outline the benefits and uses of each system, provide a detailed comparison and address frequently asked questions.
Let’s get started!
Differences Between MDM and Reference Data Management
Before digging into the subtle differences in how these types of data are managed, let’s define the root terms master data vs. reference data:
What is Master Data?
Master data is the core, non-transactional data used across your enterprise, including customers, products, supplier locations and chart of accounts.
Master data management, then, comprises the technology, tools and processes that ensure master data is coordinated across the enterprise. MDM provides a unified master data service that provides accurate, consistent and complete master data across the enterprise and to business partners.
What is Reference Data?
Reference data is a special type of master data used to categorize other data or to relate data to information beyond the boundaries of the enterprise. Common examples of reference data include postal/zip codes, state/country abbreviations, cost centers or internal organizational information.
Reference data management, then, involves ensuring that reference data is accurately and consistently implemented across departments. Reference data is often stored in a data catalog or MDM hub where it provides useful context around critical data and ensures it can be accurately categorized, rolled up and analyzed for operational use, enterprise analytics and more.
Depending on your data management maturity level and readiness, you may consider implementing reference data management first before considering a full multidomain MDM program. Alternatively, you may seek to maximize the value of your implementation efforts and include RDM as part of your MDM program. If you’re interested in learning whether to leverage RDM and MDM together, jump to the end of this post.
Differences Between Master Data Management vs. Reference Data Management
Now that we understand the core definitions for each type of enterprise data, let’s explore several key differences between master data management vs. reference data management:
| Aspect | Master Data Management (MDM) | Reference Data Management (RDM) |
|---|---|---|
| Definition | Management of key business entities (customers, products) | Management of standardized codes and classifications |
| Primary Focus | Ensuring uniformity, accuracy and consistency of master data | Ensuring consistency and standardization of reference data |
| Data Examples | Customer profiles, product information, supplier details | Reference data sets often include country codes, currency codes, product categories |
| Scope | Broad, encompassing various business entities | Narrow, focusing on specific codes and classifications to be made available in database or data catalog |
| Volatility | Relatively lower, as master data is defined as the core, slow-moving attributes of business entities. For example, a customer’s name or social security number would be considered master data while their order history or even address may change over time. | Not usually volatile, as codes and classifications change infrequently. For example, a company may begin doing business in Puerto Rico and need to determine for billing, financial reporting and marketing purposes whether to classify it as a U.S. state or a territory in its databases. However, the codes themselves would rarely, if ever, change. |
| Usage | Utilized across multiple systems and departments; by definition the most broadly shared data in an organization | Also widely shared, as it is used to categorize and standardize both master and transactional data |
| Objective | Create a single source of truth for key business entities. For example, are John Smith and John Smythe the same person or two unique individuals? | Ensure consistent use of standard codes across the organization. For example, did a purchase order originating from the state “Ala” take place in Alaska or Alabama? |
| Integration | Integrates with CRM, ERP and other enterprise systems | Supports various systems by providing standardized reference data in a lookup table or system |
| Benefits | Improved data quality, better decision-making, improved compliance | Consistent financial or business reporting, compliance with reporting standards, improved operational efficiency |
| Examples of Management Tools | Profisee MDM, Informatica MDM, SAP Master Data Governance | Profisee MDM (for reference data), Collibra, IBM InfoSphere |
| Implementation Complexity | Generally more complex due to broader scope | Generally less complex due to a narrower focus |
| Stakeholders | Data stewards, business analysts, IT departments | Data stewards, regulatory compliance teams, business units |
Benefits of Master Data Management
MDM provides a robust framework for managing master data, leading to significant advantages such as improved data quality, enhanced decision-making, operational efficiency, customer satisfaction and regulatory compliance.
Improved data quality
One of the most immediate benefits of MDM is the enhancement of data quality.
MDM ensures that all master data is accurate, complete and consistent across the organization. By creating a single, authoritative source of truth for key business entities — such as customers, products and suppliers — MDM eliminates data discrepancies and redundancies.
While improved data quality is not an inherent business benefit and is not the ultimate output of an MDM implementation, it does lead to more reliable data that can be trusted for decision-making, reporting and analytics to support a specific use case.
A rule to remember is that high-quality data is only critical when it is crucial for maintaining operational efficiency and achieving a specific business goal, so it’s critical to align your MDM strategy with a core business strategy.
For example, a line-of-business (LOB) leader may have a strategic initiative to reduce customer churn by 10% this fiscal year and believes a targeted outreach program is critical to achieving that goal. An IT or data leader within the organization could then suggest mastering customer data to empower better segmentation to power the campaign and ultimately reach a business goal.
Enhanced decision-making
MDM empowers organizations with reliable and timely data, which is essential for making informed business decisions.
When data is consistent and accurate, decision-makers can trust the information they use to develop strategies, identify opportunities and respond to market changes. MDM also facilitates better data integration across various business systems, providing a comprehensive view of the business and enabling more insightful analysis.
Improving decision-making is often a use case for an analytical MDM deployment while our next section on operational efficiency is often achieved in an operational MDM implementation. See our article, “Master Data Management (MDM) Implementation Styles, Explained” for a full breakdown of implementation styles and how they vary in complexity and end use cases.
Operational efficiency
MDM streamlines operations by reducing the time and effort required to manage and reconcile data across different systems.
With a unified approach to data management, organizations can significantly reduce or eliminate the inefficiencies caused by duplicate and inconsistent data.
This not only reduces operational costs but also accelerates processes, allowing employees to focus on more value-added activities.
Enhanced customer satisfaction
Customer data is a critical component of MDM, and having accurate, up-to-date customer information is essential for delivering excellent customer service. MDM enables organizations to provide a consistent and personalized customer experience by ensuring that all customer interactions are based on accurate and complete data.
This leads to increased customer satisfaction and loyalty. By having a holistic view of customer data, businesses can better understand customer needs, preferences, and behaviors, allowing for more targeted marketing and improved customer engagement.
While transactional systems like support ticketing tools or customer satisfaction platforms may often include well-defined XML messages, this will still fail to truly enhance customer satisfaction if the same customer is stored in five databases with three different addresses and four different phone numbers.
For all these reasons, maintaining a high-quality, consistent set of master data for your organization is rapidly becoming a necessity for enhancing customer satisfaction.
Regulatory compliance
While the previous benefits in this list affect the top-line or a company’s ability to generate more revenue or value, MDM also plays a critical role in reducing risk through ensuring that an organization’s data meets regulatory standards and compliance requirements.
By maintaining high data quality and consistency, MDM helps organizations comply with regulations related to data privacy, security and reporting.
Note that regulatory compliance is a benefit of both MDM and RDM, but the key distinction lies in the differences in master and reference data.
For example, MDM can help organizations remain compliant with consumer privacy regulations like GDPR as well as financial mandates like Know Your Customer (KYC) because MDM enforces data quality rules at the row or entity level of a database. Once you know that John Smith has opted out of marketing communications, for example, MDM gives you the actionable insights needed to enforce this policy across different source systems.
Benefits of Reference Data Management
While not as comprehensive or far-reaching as MDM, reference data management alone can provide several core benefits.
Consistently describe master & transactional data and accuracy
One of the foremost benefits of Reference Data Management (RDM) is the ability to ensure consistency and accuracy across the organization. As stated above, reference data includes standard codes and classifications such as country codes, currency codes and product categories and is used universally across various systems and departments.
By ensuring this is consistent and accurate across data types, systems, data silos and lines of business, RDM helps eliminate discrepancies and promotes uniformity. This consistency is crucial for accurate data analysis, reporting and operational processes.
Data standardization
Managing reference data facilitates the standardization of data across the organization. By using universally recognized codes and classifications, RDM helps align different data sets and systems, enabling seamless integration and communication.
Data standardization is particularly important in global organizations where different regions might use varying codes and classifications. RDM ensures that data is harmonized, making it easier to consolidate and analyze data from different sources. This standardization also supports interoperability between different systems, enhancing overall data quality and reliability.
Regulatory compliance
While MDM enforces data quality at the entity level, RDM helps organizations accurately report on the attributes that describe entities. RDM, then, supports regulatory compliance by reporting accurate figures on sales revenue by state, account code, tax body and more.
RDM also supports audit processes by providing a clear and consistent record of reference data usage and management, which is often required for compliance reporting.
Streamlined data integration
Reference Data Management plays a crucial role in data integration efforts. By standardizing reference data, organizations can more easily integrate data from different systems and sources. This is why organizations often manage their reference data as part of an MDM implementation to simplify their integration efforts and generate value early in the process.
This is essential for creating a unified view of the business and enabling comprehensive data analysis. Enhanced data integration supports various business intelligence and analytics initiatives, providing a holistic view of organizational performance. With consistent reference data, organizations can break down data silos and foster a more connected and data-driven environment.
Common Misconceptions About MDM and Reference Data Management
- MDM and RDM are the same: While they are related, MDM and RDM serve different purposes. MDM manages core business entities, whereas RDM manages the code and classifications that describe and provide context around those entities.
- RDM is less important: Reference data is crucial for consistency and compliance, making RDM as critical as MDM.
- MDM only manages customer data: While customer master data management is a common use case, MDM encompasses a wide range of data entities or domains — not just customer data.
Do I Need Both MDM and Reference Data Management?
MDM and reference data management both provide a strong foundation and help put your business on the path to data-driven decision-making. However, smaller organizations with less complex business requirements may see considerable benefits with reference data management alone.
But the synergy between MDM and RDM is what makes them indispensable to any data governance strategy. While MDM provides a comprehensive view of core business entities, RDM ensures that the data supporting these entities is standardized and consistent.
This dual approach not only enhances data quality and reliability but also streamlines data-related processes, reduces operational costs, and supports compliance efforts. For instance, in financial reporting, MDM ensures that account-level data is accurate and up-to-date, while RDM ensures that the codes and classifications used in financial reports are consistent and standardized.
Ultimately, implementing both MDM and RDM allows organizations to unlock the full potential of their data. By ensuring that both master data and reference data are well-managed, businesses can achieve greater operational efficiency, make better-informed decisions and respond with greater agility to market changes.
Manage Reference and Master Data with Profisee MDM
As a leading MDM vendor, Profisee offers comprehensive solutions that integrate both master and reference data management, empowering organizations to achieve robust data governance and drive business success.
In fact, our Application Edition of the Profisee Platform is specifically geared toward businesses that need to see value quickly from a smaller program while retaining the option to expand to full MDM later in their journey.
Whether you are looking to improve customer experiences, streamline operations or ensure regulatory compliance, leveraging both MDM and RDM is key to achieving these goals.
If you’re curious about how Profisee can help improve the quality of your enterprise data to support real business uses cases, request a demo today to see why leading enterprises choose Profisee MDM for their master and reference data management needs.
Frequently Asked Questions About RDM and MDM
Master data refers to key business entities like customers, products, locations and suppliers. Reference data consists of codes and classifications used to categorize master data and can include state abbreviations, cost centers and more.
Master data management (MDM) is a discipline that encompasses the technology, tools and processes to ensure master data is coordinated across the enterprise. MDM provides a unified master data service that provides accurate, consistent and complete master data across the enterprise and to business partners. MDM includes product data management, customer master data management and more.
While it’s possible, integrating both MDM and RDM ensures more comprehensive data governance and consistency. Reference data is often mastered as a domain in MDM implementations, acting as a central reference table for other data domains to refer.
Examples of reference data include state/country abbreviations, postal/zip codes, internal company codes (departments, sales territories, product hierarchies etc.), currency codes and language information.
Reference data is important because it ensures consistency and accuracy across different systems and processes by standardizing codes and classifications such as country codes and product categories. This standardization is crucial for reliable reporting, analytics and regulatory compliance, as it provides the necessary context for interpreting master and transactional data.
Reference Data Management (RDM) ensures consistency and accuracy across systems by standardizing codes and classifications, leading to improved data quality. It supports regulatory compliance by maintaining accurate data and enhances operational efficiency by streamlining data integration and reducing errors. Effective RDM facilitates better data analysis and decision-making by providing a reliable foundation for interpreting master data.
However, implementing RDM requires upfront planning and organizational resources to implement properly. And even mature RDM implementations require ongoing management and maintenance. Organizations need to consider how to most effectively integrate RDM with existing systems, and there can be resistance from leaders and employees to adopting new processes. Ensuring continuous data accuracy and managing updates requires forward-looking planning and effective change management strategies.
Master data management (MDM) improves data quality and consistency by creating a single source of truth for key business entities, enhancing decision-making and operational efficiency. It supports regulatory compliance by ensuring accurate and reliable data and enables better customer experiences through comprehensive and up-to-date customer profiles. MDM also facilitates data integration across various systems, providing a unified view of the organization.
However, implementing MDM requires dedicated planning and implementation efforts, often involving financial costs and investments in technology and personnel training. Organizations might face challenges in integrating MDM with existing systems and processes, and there can be resistance to change from employees accustomed to previous, more decentralized data management practices.
