Data Topics

Advertisement

What Is Master Data Management (MDM)? Definition, Components, Benefits, Uses

By Michelle Knight on

Master data management (MDM) is a set of practices and tools that help organizations define, unify, and manage their most important shared data assets. MDM provides a single, trusted view of key business entities like customers, products, and suppliers, ensuring data consistency and accuracy across all systems and departments.

By ensuring master or “golden” records for critical data, MDM eliminates the data silos and inconsistencies that can lead to poor decision-making, operational inefficiencies, failed AI projects, and even compliance risks. Without the single sources of truth that MDM provides through golden records, data quality (DQ) suffers. Advanced analytical results are unclear, and vary across systems and teams.

To counter these disadvantages, MDM synchronizes technical, procedural, and work activities. Foundational elements such as data governance, the execution and enforcement of data activities and roles, and clear business objectives provide the basis.

In addition to these solid fundamentals, MDM relies on appropriate technologies. This kind of data architecture combination makes it easier for AI and people to understand, construct, and maintain MDM processes more quickly and readily.

Master Data Management Defined

MDM definitions highlight the collaboration between IT and business as an important attribute. The Gartner glossary holds IT and business accountable for ensuring master data is accurate, integral, and semantically consistent. Datamation describes MDM as “the overlap of technology and business operations” that support the data governance policies enabling data accessibility.

As MDM provides a reliable structure, this data can be copied at one point of a business interaction and used in other transactions. This capability makes it an authoritative resource.

MDM Technical Components

MDM relies on a good data architecture. It is an infrastructure that identifies how different data entities relate to and interact with each other based on a business context. This organization makes it easier to understand and maintain critical business information. At its core, MDM handles different kinds of technical components.

Master Data

Master data provides information about business entities around where business is conducted. This information rarely changes and provides a golden source.

Different data domains, tied by activities conducted across an organization, underlie master data. These areas include customers, employees, locations, products, inventories, and financial structure.

Business rules dictate master data format and allowable ranges. As a collection, this information comprises critical data elements (CDEs) that have a strong impact on business functioning and growth.

Such CDEs form the master data crucial to a team’s day-to-day operations, decision-making, and benefits from newer technologies. Moreover, different systems and people share this information to proceed with subsequent business transactions.

Reference Data

Reference data categorizes values in the domain to control how other data is described. With the control that reference data provides and its unique identifiers, master data becomes more uniform, easier to define, and has better quality.

Examples of reference data include order status and two-letter USPS state codes. Without reference data, searching and reporting become very cumbersome.

For example, different departments may record an in-progress order status as “open,” “in progress,” “pending,” or “in-progress.” To find out whether a product on order is in progress, people would need to look up all these permutations and more.

Instead, a pull-down list that describes order attributes, including an “in-progress” entry, makes it easier for the business to understand what is happening to the products ordered.

Metadata

Metadata is information about the master data collected: what, where, why, when, and how. This information guides users on what reports to run and the lineage of the master data.

To ensure quality master data, companies need to have good metadata management. Metadata about the master records underpins data governance and regulatory compliance. Through metadata, people and systems can run MDM activities that address data quality issues.

Technology and Tools

MDM uses a combination of tools to ensure the organization’s master data is complete, accurate, and consistent. These include automated algorithms that improve and fix data quality, deliver data sets, integrate master data among systems, coordinate data maintenance activities, and enrich data during creation.

Machine learning (ML), a subfield of AI that helps train machines to make decisions or complete tasks independently, reduces the workloads from governing and administrating the data. Cloud technology makes MDM and its services more accessible, from almost anywhere. Read further about MDM tools and best practices.

MDM Foundational Elements

MDM requires foundational elements to work in tandem with its technical components. These aspects combine people, processes, and platforms to synchronize MDM activities and achieve goals. 

Managing and altering master data can be difficult and time-consuming, as it involves large, complicated data sets. To ensure success, companies implement and iterate on these foundational elements as part of MDM. 

Clear Business Objectives and Data Strategy

Clear business objectives are a must. Many executives understand the need for MDM programs but lack knowledge about how they connect with business value.

Consequently, they need a data strategy – the direction of how data activities, including MDM, support business objectives. Getting this guidance requires talking with the stakeholders across the organization, especially those with direct financial risks. From there, senior managers will determine if an MDM program should be considered and why.

Defined Roles and Responsibilities

Effective MDM requires businesses to clarify who needs what information from master records. Moreover, corporations must connect roles and responsibilities to providing master data information.

These positions include those who supply, consume, and service the master data. Synchronizing data activities performed by these different roles requires much coordination, and a data governance program plays an important role in this alignment.

Data Governance

Data governance (DG) takes a holistic approach to all corporate data. It refers to the policies and frameworks that ensure appropriate data accessibility, security, and usability.

MDM capabilities inform DG about what data elements to define, metadata management priorities, and auditing needs for regulatory compliance. In return, data governance takes responsibility for good data quality to maintain MDM integrity and reliability.

Data Quality

MDM requires a commitment to good data quality according to an organization’s culture and maturity level. While MDM technical components can bring consistency and structure by matching similar records, they cannot service MDM needs alone. 

Data quality guidelines address incompleteness, inconsistencies, and inaccuracies. For example, an AI model retrieves the correct invoice status, even when the same entity is a supplier or customer. The invoice status could be different depending on the entity’s role.

Is MDM the Same as Enterprise Data Management?

The foundational elements and data management components of MDM are similar to those found in enterprise data management (EDM). They bring useful and quality information, helping advanced analysis and AI models succeed.

However, MDM and EDM differ in their approaches. EDM encompasses a range of processes, practices, and activities governing a business’s overall data management. It covers all internal and external data assets, including sales and administration.

For example, EDM services network logs and alerts. This information supports cybersecurity and IT infrastructure maintenance. 

MDM focuses on ensuring that all business units can use and share CDEs. This may not include logging information, as it pertains to IT only. So, MDM takes on an important piece in EDM but does not replace it.

Benefits of Master Data Management 

MDM’s precision and unity across departments provide a variety of benefits. Notably, MDM facilitates the quality data needed for trustworthy analysis and efficient AI training and content-handling.

Most importantly, businesses rely on MDM to meet their objectives: profitability, cost savings increases, and operational efficiencies. These benefits result in improved customer service and faster releases of applications and goods. The “Master Data Management 101” article discusses these benefits in detail.

MDM Use Cases         

The benefits of master data management have led to tangible results across a variety of industries, as demonstrated by specific use cases. Examples include establishing streamlined operations and more accessible insights. See the bulleted list below:

  • A bank uses AI to match and merge names without deterministic rules outlined and audited. MDM cleanses, analyzes, and sorts the bank’s data before integrating generative AI. This gives the AI model better data to learn from and with to work.
  • A company in the oil, gas, and chemical industry must have a material master system for maintenance, repair, and operations (MRO). This repository required an AI to classify  thousands of parts, by specification quickly and thoroughly. MDM led to achieving this goal because AI flagged duplicates and advised on master list consolidation.
  • A retail company needed to improve its data quality and customer experience. With MDM, this seller provided key metric information in near real-time to business executives, identified specific consumer roles for the sales team, and better optimized business resources.
  • A global MedTech company commissioned a project team to create and implement a master data management program. Consequently, this organization improved the consistency with which it captured data points. Also, improved data quality contributed to improved analysis and decision-making.
  • A global leader in paints and coatings faced critical data challenges, resulting from more than 30 strategic acquisitions. To get a better handle on addressing specific customer data challenges and a unified view of these consumers, this company turned to MDM. With a single platform for managing and governing all customer data, the enterprise has a comprehensive system to quickly onboard new entities, reduce data redundancies, and streamline customer management.
  • The Republic of Indonesia wanted to improve its MDM to a more transparent, efficient, and just justice system. The organization used a data management maturity model (MD3M) to assess its capabilities, and received recommendations on strengthening data management elements from data structure to data quality and data protection. These steps brought the Republic of Indonesia closer to its objective.
  • A reputable charity leading reforms relied on fundraising through donations or trading. Fund and donor data management pose challenges with the multiple ways to receive funds. MDM gave this nonprofit trusted, reliable, consistent data about its donors that was synchronized. Consequently, this charity could focus on the right people for appropriate fundraising activities.

MDM and Emerging Technologies 

Master data management has evolved significantly in the 2020s and continues to greatly improve.

  • Augmented data management system simplifies data quality inspections, data cleansing, detecting anomalies in data, and data mining.
  • Modern MDM solutions increasingly leverage the cloud infrastructure, offering scalability, flexibility, and cost efficiency. 
  • The architecture supports some big data benefits, including processing structured and unstructured data. Additionally, MDM capabilities integrate data in near real-time.
  • Machine learning technologies have improved the accuracy and reliability of master data.
  • MDM has enhanced security and privacy.
  • MDM implements data governance and compliance checks within its practices, meeting complex regulations.

With these advances, MDM has greater capabilities to address emerging data-driven use cases.

  • MDM systems provide AI with the data quality it needs, more quickly. This ensures AI generates better outputs.
  • IoT (Internet of Things) projects will benefit from integrated master data management and its cloud capabilities where it can scale the data it receives.
  • As MDM has better security and integrates better with blockchain, this will enhance master security and integrity, ensuring trustful transactions and preventing unauthorized alterations to customer data.

These opportunities promise a competitive advantage to leverage the best of MDM and provide the necessary quality. MDM will continue to remain a core capability of emerging technologies. As data volumes continue to grow and emerging technologies become increasingly reliant on high quality, consistent, and reliable data, master data management will play a crucial role in ensuring the accuracy and integrity of an organization’s critical data assets. 

Leave a Reply