What Is Master Data Management (MDM)?

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Master data management (MDM) is the creation of a single master record for all data items from across every internal and external data source and application used by an enterprise. The data is thoroughly cleaned to create a single source of truth for the organization, known as a golden record. This golden record ensures accuracy in queries and reports and facilitates confidence in data-driven decision-making across the entire organization. This article looks at the benefits and challenges of master data management and presents common use cases and best practices for organizations seeking to implement it.

How Does Master Data Management Work?

As organizations continue to take in data on an unprecedented scale—and increasingly rely on that data to inform everything from decision-making and operations to customer relationships and business intelligence—their dependence upon that data grows. It needs to be accurate, consistent, and reliable.

Master data management describes the process of cleaning and preparing that data by deduplicating, reconciling, and enriching it before admitting it to a storage repository where it can be used and maintained. The purpose of advance data preparation and cleaning is to assure all users across the enterprise that the data is accurate and can be trusted.

This achieves two important goals:

• ensuring that business decisions and reporting are informed by accurate data
• reducing conflicts by giving everyone across the enterprise access to the same data

An organization’s master data records are referred to as golden records because the data they contain has been fastidiously cleaned, refined, and validated, representing the “best version of data truth.”

Master Data Management Processes

Master data management is a process that involves both people and technology, but it has to begin with organizational buy-in. Preparing and moving data to an MDM repository is tedious and can be expensive, and maintaining a single source of truth for an organization means establishing new ways of working with data to ensure that it remains accurate and consistent.

The first step involves identifying all relevant data sources and the “owners” who will be responsible for them. The data in these sources must all be reviewed. Depending on the size of the organization or how it has worked with data historically, this can be a time-consuming stage.

For example, consider an enterprise that has acquired another company that used entirely different systems. All data items from both sides must be cross-referenced for duplicate record types and reformatted to a uniform format. At the same time, it must be flagged for inaccuracies, anomalies, or incompleteness, and any imperfect records must be eliminated.

This painstaking work is usually performed with the help of data-mapping software, a tool often built-in to master data management systems. The IT team leading the MDM process then creates a structure for the master data records that maps these items to their names in the original source systems. Once the master data records are mapped to all variants in other systems, the next step is for the organization to decide how it wants to maintain and use data going forward.

One approach is to immediately consolidate all data to the uniform names in the MDM repository; another is to let users continue using the original, disparate names in their resident systems and let the master data management system automatically consolidate the data into the uniform data repository. Both approaches are valid and will depend upon the preferred workflow.

Benefits of Master Data Management

MDM benefits organizations in many ways, but here are some of the most common:

• Creates uniform data—every department across the organization uses the same golden record data, ensuring that it is consistent, accurate, and reliable.
• Assists with regulatory compliance—aggregate information from disparate departments and systems can be difficult to gather and can sometimes be in conflict, but standardized MDM data is in a single place and presents a more accurate picture.
• Reduces IT asset costs—eliminating duplicate, incomplete, and extraneous data reduces the amount of needed storage capacity and saves money on storage and processing hardware.
• Improves customer satisfaction—sales and service referencing identical data can lead to better outcomes by giving everyone who interacts with customers a 360-degree view of the customer experience.

An Example of Master Data

Master data is the data at the heart of company operations, and encompasses a wide range of information about people, places, transactions, communications, interactions, and other business operations.

As an example of how it might work, a company might track three types of master data in its MDM repository:

• End data items, such as “customer” or “product” information
• Stored transactional data, such as how much of a product the customer orders and on what dates
• Derived analytical data, such as the average order amount and order frequency for a customer

From an operational standpoint, these three types of master data give the company a way of tracking the customer, the product, and the customer’s product consumption rate, and of predicting analytically when the customer is most likely to reorder and at what product quantity. Having a reliable record of this data can help with everything from budgeting to stocking to predicting sales and provide useful information for marketing campaigns and promotions.

Master Data Management Use Cases

Most organizations can benefit from implementing a master data management strategy, but it’s especially well suited for certain applications.

Mergers and Acquisitions

When one business acquires or merges with another, they need to also merge their data. Often the data is kept in different systems and formats, with different terminology. Master data management can help them identify commonalities and reconcile differences using uniform conventions, providing a more holistic, seamless data record.

Customer Service and Satisfaction

MDM can help create a 360-degree view of customers and the customer experience by unifying information that flows in from sales, service, fulfillment, returns, and even manufacturing and product development. When all of this information is consolidated into an MDM repository, any department can see how the customer has interacted with the organization. This enables employees to improve customer satisfaction and build customer loyalty and revenue potential.

Product Engineering and Manufacturing

Consolidating separate parts catalogs in purchasing, engineering, and manufacturing in an MDM repository can preclude duplicate orders and alert purchasers to issues that may have emerged in other departments. This can prevent mistakes that can happen when engineering product designs don’t sync with manufacturing bills of material. A uniform parts database can also consolidate outside part numbers and references to the same part—for example, a military specification part number that must be mapped to an internal part number for the same part.

Compliance and Regulation

Compliance auditors and regulatory officials are increasingly demanding cross-departmental reports that integrate data from across an entire organization. An MDM strategy that standardizes data from disparate departmental systems can make this hybrid reporting easier, facilitating compliance and avoiding missteps.

Master Data Management Challenges

Despite the clear benefits of master data management, implementation is difficult and can be costly. Here are the biggest challenges enterprises face when tackling MDM.

Organizational Buy-In

It’s easy to commit to an MDM project, but difficult to get everyone to do their part on a daily basis. MDM is not a one-and-done solution—it requires an ongoing commitment to implement in the first place, and to maintain over time.

Complexity

Standardizing data from a diversity of data sources is not easy work. How do you know for sure that one data name in the accounting system means the same thing as another variant in manufacturing, for example? End users most familiar with the systems must make interpretations and agree on a single, uniform term for data item variants.

Data Standards

There are differences in how systems record and format data. Regulatory requirements can make things worse—for example, a company that functions in multiple countries may find that some countries require numerics to be expressed into more than two places to the right of a decimal point while others do not. Meeting reporting requirements might require different data formats to be maintained in different systems, adding to the complexities of MDM.

Unstructured Data

Unlike traditional system-of-records data, unstructured data—photos, videos, emails, and text messages, for example—does not come with data item labels. These objects must be hand-annotated by users, a time- and labor-intense effort.

Timeline

MDM is a data infrastructure project that involves people and systems across an entire organization. It takes time to implement, and results are not always immediately visible. Stakeholders might see the time, effort, and money being spent without being able to readily see what it is or how it will deliver business value.

Trends in Master Data Management

Master data management is not new, but the field is evolving as organizations find themselves more and more dependent on data for all aspects of their work. As MDM grows in popularity, here are the trends shaping the market:

• An exponential growth of Internet of Things (IoT) data that must be consolidated and brought under management alongside other data.
• Vast amounts of unstructured data that must be annotated and linked with core system data.
• More corporate initiatives for customer-centric companies with a 360-degree view of customer data.
• The introduction of more artificial intelligence and machine learning that works on centralized data to uncover market, business, and operational trends.
• The movement to omnichannel sales and service where customer activity can be conducted and holistically integrated via chat, phone, and brick and mortar solutions.

Bottom Line: Going Forward with Master Data Management

Initiating a master data management strategy is a colossal effort, which historically has limited the work to very large enterprises where cross-channel and cross-departmental data integration is essential. Smaller companies might lack the resources to launch major MDM projects, but they  still have the need.

Technology is evolving to keep pace with the demand. Many vendors—enterprise resource planning (ERP) and customer relationship management (CRM) suppliers, for example—are already incorporating MDM tools directly into their systems to put them in reach of smaller businesses.

In all cases, data and data channels are expanding, and MDM systems capable of addressing all types of data and unifying them into single data expressions will be indispensable data management assets for organizations of all sizes. The volume of data is only going to continue growing, and businesses need to plan for how they consistently store and access data now and in the future.

Read The 5 Stages of Data Lifecycle Management to learn more about how organizations collect, interact with, and store the information that fuels their work.