Hadoop Comparing DBMS vs. RDBMS: Key differences

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 from different IT systems. One of the core disciplines in the overall data management process, MDM helps improve data quality 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 computing in system architectures 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.

Importance of MDM

Business operations depend on transaction processing systems, and BI and analytics increasingly drive customer engagement efforts, supply chain management (SCM) and other business processes. But many companies don't have a clear single view of their customers. A common reason is that 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. The same kind of issues can also apply to product data and other types of information.

Master data management programs provide that single view by consolidating data from multiple source systems into a standard format. 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 and data analysts a complete picture of individual customers without having to piece together different 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 (SSOT) -- 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:

  • A registry architecture, which creates a unified index of master data for analytical uses without changing any of the data in individual source systems. Regarded as the most lightweight MDM architecture, this style uses data cleansing and matching tools to identify duplicate data entries in different systems and cross-reference them in the registry.
  • A consolidation approach, in which sets of master data are pulled from various source systems and consolidated in the MDM hub. That creates a centralized repository of consistent master data, also primarily for use in BI, analytics and enterprise reporting. But operational systems continue to use their own master data for transaction processing.
  • A coexistence style, which likewise creates a consolidated set of master data in the MDM hub. In this case, though, changes to the master data in individual source systems are updated in the hub and can then be propagated to other systems so they all use the same data. That offers a balance between system-level management and centralized governance of master data.
  • A transaction architecture, also known as a centralized This approach moves all management and updating of master data to the MDM hub, which publishes data changes to each source system. It's the most intrusive style of MDM from an organizational standpoint because of the shift to full centralization, but it provides the highest level of enterprise control.

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; it creates unified views of data from different systems virtually, without requiring any physical data movement.

Benefits of MDM

One of the primary business benefits that MDM provides is increased data consistency, both for operational and analytical uses. A uniform set of master data on customers and other entities can help reduce operational errors and optimize business processes -- for example, by ensuring that customer service representatives see all of the data on individual customers and that the shipping department has the correct addresses for deliveries. It can also boost the accuracy of BI and analytics applications, hopefully resulting in better strategic planning and business decision-making.

MDM initiatives can also aid efforts to comply with regulatory mandates, such as the Sarbanes-Oxley Act (SOX) and the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. New data privacy and protection laws -- most notably, the European Union's General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) -- have become another driver for master data management, which can help companies identify all of the personal data they collect about people.

In addition, MDM dovetails with data governance programs, which create standards, policies and procedures on data usage overall in organizations. MDM can help improve the data quality metrics that typically are used to demonstrate the business value of data governance efforts. Also, MDM systems can be configured to give federated views of master data to data stewards, the workers charged with overseeing data sets and making sure that end users adhere to data governance policies.

MDM best practices

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.

While MDM is aided by technology, it's as much an organizational -- or people -- process as it is a technical one. As a result, it's important to involve business executives and users in MDM programs, especially if master data will be managed centrally and updated in operational systems by an MDM hub. The various data stakeholders in an organization should have a say in decisions on how master data should be structured and policies for implementing changes to it in systems.

Schema describes MDM initiatives
Successful MDM initiatives start with project scoping between business process owners and MDM teams. Integration planning may show the business process incorporates data producers, data consumers or both.

Connecting MDM's expected benefits on the use of data assets to corporate strategies and business goals is generally a must to get management buy-in for a program, which is needed both to secure funding for the work and to overcome potential resistance internally. Also, business units and analytics teams should get training on the MDM process and the purposes behind it before a program starts.

MDM also must be addressed as an ongoing initiative rather than a one-off project -- frequent updates to master data records are commonly needed. Some organizations have created MDM centers of excellence (CoEs) to establish and then manage their programs in an effort to avoid roadblocks on the efforts to incorporate common sets of master data into business systems.

Challenges of MDM

The potential benefits of master data management increase as the number and diversity of systems and applications in an organization expand. For this reason, MDM is more likely to be of value to large enterprises than small and medium-sized businesses (SMBs). However, the complexity of enterprise MDM programs has limited their adoption even in large companies.

One of the biggest hurdles is getting different business units and departments to agree on common master data standards; MDM efforts can lose momentum and get bogged down if users argue about how data is formatted in their separate systems. Another often mentioned obstacle to successful MDM implementations is project scoping. The efforts can become unwieldy if the scope of the planned work gets out of control or if the implementation plan doesn't properly stage the required steps.

When companies merge, MDM can help streamline data integration, reduce incompatibilities and optimize operational efficiency in the newly combined organization, but the challenge of reaching consensus on master data among business units can be even greater after a merger or acquisition. The growing use of big data systems in organizations can also complicate the MDM process by adding new forms of unstructured and semistructured data stored in a variety of platforms.

This was last updated in November 2019

Continue Reading About master data management (MDM)

Dig Deeper on Data management strategies

Business Analytics
Content Management