Demystifying Master Data Management

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Thomas Edison may have understood the importance of Master Data Management before the term was formally coined. He said, “The value of an idea lies in the using of it.” While ideas in the early 20th century relied on thoughts and things, ideas in this information age depend on data.

Take reference data: unchanging tags comprising an individual or entity (e.g. name and address). Businesses need this to characterize other data across a suite of business applications or to relate data in a database to information beyond the boundaries of an enterprise, comparable to how blood flows across different organs in a body and can be donated to other people. Reference data offers a good place to start demystifying Master Data Management.

Say a firm does automated account processing for its customers. The organization’s algorithms look up reference rata sets, supplied by external customers, to speedily fill bills or invoices. These need to be paid and routed for customer approval and financial research. Throughout all company processes, the reference data must be consistent and uniform. It needs to be accessible across a wide range of transactions and activities, providing a company’s lifeline. How would a company like this achieve the desired results?

Master Data Management works well as a systematic approach toward addressing business’s requirements using reference data throughout. Good Master Data Management ensures reference data can be used throughout (e.g. from the moment customers import data through automated accounts processing, to the resulting financial reports and payment approvals. MDM reduces transaction costs, integrating systems and simplifying data structures.

What is Master Data Management?

Simply put, Master Data Management takes raw data and transforms it into an information product or products structured to be used by other business processes. This master data can be reference data or data from business transactions or activities. Good MDM controls master data values and identifiers, enabling accurate, timely, and consistent use across systems of business entities.

According to Donna Burbank of Global Data Strategy:

“Master Data Management (MDM) can help build this 360-degree view of key business information to allow you to take full advantage of your organization’s data for better business outcomes.”

While Frank Cerwin of Data Mastery, Inc. says:

“An MDM program is often prescribed as a treatment for defective data that resulted in debilitated business processes. Data defects can be caused by poor quality or from an inappropriate fit to the business purpose that it was applied to. Additionally, like a drug, master data can be abused.”

As a result, as long as they implement carefully, an enterprise can meets its larger business objectives, helping customers fulfill their centralized customer-view applications and specific business purposes.

Master Data Management Components

Good Master Data Management encompasses the following:

  • Understanding the Master Data Context/s

Master data fuels MDM. As Scott Taylor, The Data Whisperer, at MetaMeta Consulting states, businesses “need the end result of [MDM], which is the content itself. That’s what feeds other systems, drives processes across personas, and delivers value.” This means understanding how a company’s business rules dictate specific master data formats and ranges, and knowing how to control these values in a shared context.

For master data to fuel MDM, it must be organized into relevant business schemas. Reference data, imported from multiple customers, needs to be made relevant to work activities, (e.g. automate account processing, from the example above). Humans intervene with this reference data and add new data or transform it into an information product (e.g. adding transactions to invoices, matching bills).

The data transformation throughout the company needs to work within the larger business context, including enhancing the reference data. When customers view the final information (e.g. that bills have been paid), the reference data used throughout the production process needs to be made available. MDM provides the framework needed to move and use raw master data.

Since MDM involves a complete 360-degree business view, all company departments contribute to conception of the master data context. What may be relevant information to one business department may not be to another and may not relate to the master data context. Listing what comprises master data, including reference data, and the systems that generate master data, gives a picture toward integrating master data between other systems, throughout the entire business. But this is only a start. Providing cross-organizational commitment to the master data’s relevancy and guidance to its contextual structure becomes critical. A Data Governance program fills this need.

  • Transforming Raw Data to Good Quality

Taylor says, “Master data provides the truth in data that lets you derive meaning from data.” While understanding the master data and its context helps, it does not guarantee that the incoming raw data will adapt well to that context.

The raw data needs to be cleansed and filtered so that it is fit for data consumption. All authors, users, and administrators need to be able to use the data. Good quality master data comes by implementing data governance processes and regulatory compliance requirements within the understood MDM context. Reference data, as a subset of master data, needs to also conform to Data Quality requirements. Only when internal and external customers can rely on the data for reference, transactions, and activities and actively trust it, can the data be truly master sata.

  • Using a Master Data Hub

Once master data pertinent to a company is contextualized and packaged, it needs to be available and accessible through a hub architecture. Such architecture typically contains technological-enabled solutions assisting business and IT collaboration, as Gartner notes. Its MDM Quadrant reviews these MDM products. Three different kinds of MDM hubs, mentioned by Dan Power, range from a persistent hub comprising all business-critical data from the hub into the source system, to a registry hub containing only the identifying information and key record identifiers, to a hybrid of the two.  

Regardless of hub design, as noted by Forrester, a good MDM system gives physical:

“Context in customer experience—sitting between systems of record and systems of   engagement to translate, manage, and evolve dynamically the full fidelity of customer identity through interactions directly or as viewed through indirect business processes and supporting activities.”

A specific example can be found from Michael Hiskey, CMO of Semarchy, who actively uses an intelligent data hub. He explains that his intelligent data hub works best for an organizational concept, like customers, or partners, or suppliers and how this master data relates to other data elements. Managers like Hiskey can extract master data and integrate it with other business systems to reduce transaction costs through simpler data sharing interactions. That is the beauty of such an organizational schema. While a master data context is more abstract, the hub, the heart of Master Data Management, makes it real.

  • Manage the Data Lifecycle in the Hub

A hub that digitally synchronizes reference, transaction, and activity data, generated by different systems, is only a piece of MDM. This MDM center needs to be maintained throughout the data lifecycle. This requires data curation to preserve, share, and discover master data. These data curation activities need to be guided by Data Governance 2.0 to secure itself a place as a core of MDM. Also, as Peter Aiken emphasizes, organizations need a governance framework to “ensure individuals manage master data in a desirable manner.” Combining data curation with Data Governance ensures that the master data usable at the start remains usable as the business grows and changes over time. After all, the value of master data lies in its employment.

Limitations to MDM

Master Data Management excels at doing business activities. However, as a system, it does not always support these transactions. MDM needs to be concrete. Organizations must communicate well between offices, train new and existing employment, and improve using data across systems.

Metadata Management, as a system, stands firmly for these more abstract tasks. For example, the automated account processing company would find it cumbersome to train document processors to correct account information or to communicate any issues with the data between coworkers, or even across business departments. Companies also need to secure data to comply with regulations. Reference data, like names and addresses themselves, just do not provide insight on these issues. Master data is the raw material that package information, and MDM describes how to do this.

Sendhil Mullainathan, professor of economics at Harvard University, says, “The problem with data is that it says a lot, but it also says nothing.” Use Master Data Management to deal with data and a different system to figure out what the data says.

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