This is part one in a two-part series on master data management. If you’re looking for the list of MDM vendors, that blog post can be found here.

Master Data Management: An Overview, Best Practices and Tools 

Master Data Management (MDM) is a discipline for specialized quality improvement defined by the policies and procedures put in place by data governance teams. The ultimate goal is to provide end-users with a single authoritative view of information so they can make better decisions. 

What is master data management?

MDM has both a business and an IT connotation. In business, MDM refers to the discipline of defining and managing an organization’s critical data assets such that there is a single point of reference—“one trusted source of the truth,” so to speak. Mastered data includes reference data as well as analytical data that supports decision-making throughout the organization.

In IT, MDM refers to a tool or set of tools that support the business by collecting data into a master file; removing duplicate data records and standardizing the data; and preserving its quality and integrity in order to maintain an authoritative source of master data that can be used throughout the organization.

Why is master data management needed?

The primary objective of master data management is to ensure that the organization is not making decisions and conducting business transactions using multiple, disparate versions of the same data records. This happens a lot, especially in companies that have grown through acquisitions and mergers, or that operate data systems in walled-off silos. A lack of data integration often leads to data disparity and duplication.

For example, multiple data records pertaining to the same customer is common in large banks that have one IT system to keep track of deposit accounts (e.g., the core banking system), another system for mortgages and loans, another for credit accounts, and yet another for electronic and mobile banking. The disconnect between these separate systems can cause duplicate or conflicting customer outreach or marketing programs when one side of the banking house doesn’t know that “John Doe” with a savings account is the same person as “John J. Doe” with a mortgage. 

A similar situation has happened in the media and communications industries where it’s now common for an umbrella company to own consumer services such as cable and satellite television, mobile phone service, landline service, and Internet connectivity. One customer might have separate records in each line of business, complete with separate bill statements, and no way to see all services across the customer’s accounts. This disconnect impacts the enterprise’s ability to serve the customer in an efficient manner and to gain the insight needed to streamline new and existing programs.

Done right, MDM enables the entire organization to make data-driven decisions that provide operational agility, superior time-to-value, and revenue generation. The insight from master data can improve the business by helping it launch new products and services faster, delivering exceptional customer service and experiences, supporting digital transformation projects, and achieving regulatory compliance.

What is master data?

Master data represents the business objects that contain the most valuable, agreed-upon information shared across an organization. It is commonly referred to as the business-critical data about parties, places, and things.  

What types of data do companies possess?

The six types of data typically found in corporations are: 

  1. Unstructured Data – Data found in email messages, white papers, magazine articles, corporate intranet portals, product specifications, marketing collateral, PDF files and the like.
  2. Transactional Data – Data pertaining to business activities (often related to system transactions, such as sales, deliveries, invoices, trouble tickets, claims and other monetary and non-monetary interactions) that have historical significance or are needed for analysis by other systems. Transactional data are unit level transactions that use master data entities. Unlike master data, transactions are inherently temporal and instantaneous by nature.
  3. Metadata – Data about other data. It may reside in a formal repository or in various other forms, such as XML documents, report definitions, column descriptions in a database, log files, and configuration files.
  4. Hierarchical Data – Data that stores the relationships between other data. It may be stored as part of an accounting system or separately as descriptions of real-world relationships, such as company organizational structures or product lines. Hierarchical data is sometimes considered a super MDM domain because it is critical to understanding and sometimes discovering the relationships between master data.
  5. Reference Data – A special type of master data used to categorize other data or used to relate data to information beyond the boundaries of the enterprise. Reference data can be shared across master or transactional data objects (e.g. countries, currencies, time zones, payment terms, etc.).
  6. Master Data The core data within the enterprise that describes objects around which business is conducted. It typically changes infrequently and can include reference data that is necessary to operate the business. Master data is not transactional in nature, but it does describe transactions. The critical nouns of a business that master data covers generally fall into domains such as Customer, Products, Suppliers, Locations, and Assets. Further categorizations within those domains are called subject areas, sub-domains, or entity types. 

Source: Profisee, https://profisee.com/master-data-management-what-why-how-who/ 

What are best practices for master data management?

As mentioned earlier, master data management has both a business and an IT slant. In 2017, Information Management magazine published a list of MDM best practices developed by data scientist Sanjay Kumar. His focus is on the business aspects of MDM, and rightfully so. MDM is a business issue first, and a technology issue second. Kumar’s best practices include the following:

  1. Establish a business case. The business case should be tool-agnostic and look at the use cases and potential benefit that will be delivered. The business case can then also be used to determine critical capabilities to be delivered by any proposed tool.
  2. Get executive sponsorship. As with any enterprise project, the correct level of sponsorship is key. Sponsorship must go beyond providing budget – key business stakeholders must be involved in the MDM steering committee that will drive decision making and resolve interdepartmental conflicts.
  3. Get business involved. Many of the day to day decisions require business knowledge and sign off. For example, a client may have different telephone numbers in the Sales and Orders systems. Business must be involved in determining which number is most likely to be correct. Without this active involvement, IT runs the risk of losing critical information.
  4. Invest sufficient time in planning and evaluation. A common mistake is to rush the purchase of a tool. A clear understanding of the business case and supporting use cases, an understanding of the current state of master data, and fit to the existing technology stack should all be considered before selecting a tool.
  5. Institute MDM governance and stewardship. MDM governance must consider much more than the process for merging or approving duplicates. MDM governance must consider data mappings, decisions around which systems are most trusted (at an attribute level), data quality standards, match rules, and much more. The MDM Governance team also brings business and IT stakeholders together to ensure sponsorship and business engagement.
  6. Adopt the right technology and architecture. MDM can incorporate elements of batch integration, real-time integration and even integration to “new” data sources such as big data elements. Decisions on how data will be synchronized back to source must be well-governed and tested and may accommodate one or many of these integration strategies.
  7. Define the data quality strategy. A common mistake is to assume that master data management will deliver quality master data. Instead, poorly planned and delivered MDM can actually make data quality worse. Data standardization, enrichment, scrubbing and matching strategies must be in place if MDM is to deliver value. Data quality requirements should also be considered when selecting an MDM tool or platform.
  8. Get the right team. MDM and data quality management are niche competencies and you will probably require specialist help. However, we suggest that you co-source your MDM team – including stakeholders from your business, your IT team, and any vendors or systems integrators. This helps to drive business buy-in and skills transfer, assuming that your team puts in the time.
  9. Adopt a phased approach. MDM is a massive program and can run into years of effort. Break the program down into small steps that deliver incremental value and ensure that your Steering / Governance Committee is actively involved in resolving issues, and in prioritizing each new month’s effort to deliver maximum value based on changing business priorities.
  10. Deliver and communicate incremental value. Each phase, identified in 9 above, should be linked to the value it generates. For example, if telemarketing campaigns are a high priority, but customer contactability is poor, then a focus might be to consolidate alternative telephone numbers for each client and present these as a telesales list. As the next step, one may add email addresses, and so on. The value delivered should be communicated to other stakeholders, who may have their own, similar priorities that will feature in a later phase.

Source: 10 best practices for master data management, Information Management magazine.

In part two of this series, we’ll look at popular MDM tools and provide links to live online reviews of MDM software vendors