Master data management definition
Master data management (MDM) is a set of disciplines, processes, and technologies used to manage an organization’s master data. Master data is data about business entities or objects (customers, suppliers, employees, products, cost centers, etc.) around which business is conducted. It is used to provide context to transactional data and is typically scattered around the business in various spreadsheets, applications, and even physical media.
Gartner defines MDM as “a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency, and accountability of the enterprise’s official, shared master data assets.”
MDM seeks to create a single version of truth across all copies of master data to ensure data values are aligned. In doing so, MDM provides an “enterprise-wide infrastructure to standardize, integrate, and establish an authoritative source for data from disparate sources of information that have similar and/or duplicate attributes to support business operations and decisions,” according to professional services firm Earley Information Science.
Once established, MDM ensures the consistency and quality of a company’s data assets, including product data, asset data, and customer data, by making this data available to end users and other applications. Organizations pursue MDM for a variety of reasons; among the most popular are to create internal/operational efficiencies (69%), to improve business process outcomes (59%), and to improve business process agility (54%), according to Gartner’s Jan. 2021 MDM Magic Quadrant.
Master data vs. reference data
Reference data can be considered a subset of master data. Both master data and reference data provide context for business transactions, but master data is concerned with business entities, whereas reference data is about classification and categorization. Reference data rarely changes. Examples include postal codes, transaction codes, financial hierarchies, state or country codes, customer segments, and so on.
According to enterprise data company TIBCO, “Where master data represents key parts of the business, including customer data and data related to business activities and transactions, reference data represents a set of permissible data to be used from the master data for classification.”
Dan Power, president of Hub Solution Designs, a global management and technology consulting firm that specializes in MDM and data governance, says there are five essential components to an MDM program.
Culture: MDM crosses many boundaries in an organization, inevitably leading to political issues. According to Power, a successful MDM initiative requires a savvy leader who can:
- Drive the project
- Keep senior management engaged and supportive
- Allow the business to “own” the initiative but keep IT involved as a supporter and facilitator
- Address the inevitable cultural and political issues
- Balance the need to secure funding (and quick wins) with maintaining the longer-term architectural integrity
- Process: Someone must redesign the organization’s business processes over time to recognize the ROI of an MDM hub. Power recommends starting with a manageable set of business processes; those related to CRM are good candidates, for example, because an MDM hub containing customer data will, of necessity, be tightly integrated with your CRM system.
Technology: MDM is a technology-enabled discipline. Key technology components include an MDM hub, to bring together all source system data; data integration, to get source system data into the MDM hub; and a data quality tool. These three technologies provide a baseline, Power says. Technology for managing reference data, metadata management, business rules, policies, etc., are other possibilities.
Information: For MDM to give you a complete picture of your business entities, you will likely need a combination of internal and external data. Power recommends giving careful thought to the reporting and analysis you want to do on your prospects and customers. You will need to think through all the attributes necessary to support those analytics. This may require external data for attributes such as industry codes, revenue, age, corporate hierarchies, financial risk, etc.
Data governance: A data governance program is the foundation of a successful MDM program. Having data governance in place can streamline MDM by resolving issues such as data ownership and rules for data validation and enrichment.
Master data management tools
There are many solutions available to support MDM programs. Some of the most popular include the following:
Ataccama ONE: This data management platform supports both master and reference data management. It mainly serves midsize to large financial services organizations, focusing on data governance, data quality, metadata management, and MDM.
IBM InfoSphere Master Data Management: Available on-prem or as a fully managed cloud offering, InfoSphere MDM focuses on multiple domain master data use cases. IBM has aligned its MDM strategy with Watson-enabled augmented data management and relationship-driven insights.
Informatica Multidomain MDM: This offering from leading MDM vendor Informatica focuses on multiple domain master data use cases, cloud-native technologies, and AI-driven MDM.
Profisee Platform: Profisee focuses on multidomain MDM with deep integration with Microsoft Azure, though it can be deployed on-prem, in the cloud, or via a hybrid model. It has a modeling engine that enables users to model master data as it exists.
Riversand Platform: This cloud-native solution is offered with a “partner first” strategy. It has many customers in the retail, consumer packaged goods, and food-related sectors with strong growth in transport and services.
SAP Master Data Governance: SAP’s offering can be deployed on-prem or in the cloud and supports all master data domains and implementation styles. Its roadmap centers on evolving master data integrations and cloud-native technologies.
Semarchy xDM: Semarchy uses machine learning for stewardship and advanced matching, survivorship, and classification. It boasts a user-friendly UI. Its operations are centered in Europe, the Middle East, and Africa (EMEA).
Tibco EBX: This solution manages workflow, data quality, and role-specific applications. It’s designed for self-service. In January 2021, Tibco acquired data management and analytics solutions provider Information Builders.
- Business process analyst: $51K-$106K
- Business process manager: $67K-$138K
- Data management analyst: $45K-$91K
- Data management consultant: $45K-$118K
- Data management specialist: $39K-$103K
- Data steward: $40K-$83K
- Master data analyst: $50K-$77K