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master data management (MDM)

By Craig Stedman

What is master data management (MDM)?

Master data management (MDM) is a process that creates a uniform set of data on customers, products, suppliers and other business entities across different IT systems. One of the core disciplines in the overall data management process, MDM helps improve the quality of an organization's data by ensuring that identifiers and other key data elements about those entities are accurate and consistent, enterprise-wide.

When done properly, MDM can also streamline data sharing between different business systems and facilitate data processing in IT environments that contain a variety of platforms and applications. In addition, effective master data management helps make the data used in business intelligence (BI) and analytics applications more trustworthy.

Master data management grew out of previously separate methodologies focused on consolidating data for specific entities -- in particular, customer data integration (CDI) and product information management (PIM). MDM brought them together into a single category with a broader focus, although CDI and PIM are still active subcategories.

Importance of master data management

Business operations depend on transaction processing systems, and BI and analytics increasingly drive marketing campaigns, customer engagement efforts, supply chain management and other business processes. But many companies don't have a single view of their customers. Commonly, that's because customer data differs from one system to another. For example, customer records might not be identical in order entry, shipping and customer service systems due to variations in names, addresses and other attributes.

Similar inconsistencies can also occur in product data and other types of information. Such issues cause business problems if critical data can't be accessed or is missed by end users. Master data management programs help avoid that by consolidating data from multiple source systems into a standard format to provide the needed single view of business entities.

In the case of customer data, MDM harmonizes it to create a unified set of master data for use in all applicable systems. That enables organizations to eliminate duplicate customer records with mismatched data, giving operational workers, business executives, data scientists and other users access to comprehensive customer information without having to manually combine different data entries.

What is master data?

Master data is often called a golden record of information in a data domain, which corresponds to the entity that's the subject of the data being mastered. Data domains vary from industry to industry. For example, common ones for manufacturers include customers, products, suppliers and materials. Banks might focus on customers, accounts and products, the latter meaning financial ones. Patients, equipment and supplies are among the applicable data domains in healthcare organizations. For insurers, they include members, products and claims, plus providers in the case of medical insurers.

Employees, locations and assets are examples of data domains that can be applied across industries as part of master data management initiatives. Another is reference data, which consists of codes for countries and states, currencies, order status entries and other generic values.

Master data doesn't include transactions processed in the various data domains. Instead, it essentially functions as a master file of dates, names, addresses, customer IDs, item numbers, product specifications and other attributes that are used in transaction processing systems and analytics applications. As a result, well-managed master data is also frequently described as a single source of truth -- or, alternatively, a single version of the truth -- about an organization's data, as well as data from external sources that's ingested into corporate systems to augment internal data sets.

MDM architecture

There are two forms of master data management that can be implemented separately or in tandem: analytical MDM, which aims to feed consistent master data to data warehouses and other analytics systems, and operational MDM, which focuses on the master data in core business systems. Both provide a systematic approach to managing master data, typically enabled by the deployment of a centralized MDM hub where the master data is stored and maintained.

However, there are different ways to architect MDM systems, depending on how organizations want to structure their master data management programs and the connections between the MDM hub and source systems. The primary MDM architectural styles that have been identified by data management consultants and MDM software vendors include the following:

In addition to a master data storage repository and software to automate the interactions with source systems, a master data management framework typically includes change management, workflow and collaboration tools. Another available technology option is using data virtualization software to augment MDM hubs, which creates unified views of data from different systems virtually, without requiring any physical data movement.

Benefits of master data management

The following are some of the primary business benefits that MDM provides:

MDM best practices

Best practices for managing MDM programs include the following actions:

Challenges of master data management

Despite the benefits it offers, MDM can be a difficult undertaking. These are some of the common challenges it presents to organizations:

Key steps in the MDM process

MDM initiatives typically are long projects that include various phases and tasks, including the following key steps:

  1. Identify all relevant data sources for a particular domain and the business owners of each data source.
  2. Work with the various business stakeholders to agree on common formats for the master data across all the systems.
  3. Create a master data model that formalizes the structure of the master data records and maps them to the various source systems.
  4. Also with the stakeholders, decide what type of MDM architecture to deploy based on business needs and planned applications.
  5. Deploy any new systems or software tools that are needed to support the MDM process.
  6. Cleanse, consolidate and standardize data to fit the master data model, using data quality management and data transformation techniques.
  7. Match duplicate data records from multiple systems and merge them into single entries as part of the final master data list.
  8. Modify source systems as needed so they can access and use the master data during data processing operations.

Software that can be used to automate master data management tasks is available from various vendors, including MDM specialists and larger providers that offer a full line of data management tools. MDM software typically includes features for data cleansing, data matching and merging, workflow management, data modeling and other functions. In addition, it often incorporates data stewardship and data governance features or is integrated with companion tools that provide them.

Key roles and participants in an MDM initiative

Because of their complexity and their broad impact on business operations, MDM programs should involve a wide range of people in an organization. The level of involvement varies depending on the role: Some data management professionals might work full-time on MDM, while others devote part of their time to it, and business stakeholders usually take part on an occasional, though regular, basis.

These are some of the key positions and participants in the MDM process:

05 May 2023

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