Data Governance vs. Master Data Management

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“Data Governance is the creation of rules, the execution of those rules, and the adjudication of any violation of the rules,” remarked Frank Cerwin in a recent DATAVERSITY® interview. He likened its structure to the three branches of the government: “The legislative branch makes the laws, the executive branch executes the laws, and the judicial branch adjudicates violations of the laws.” Cerwin is the President and Managing Principal with Data Mastery Inc. and he spoke about the differences and similarities between Data Governance and Master Data Management.

Master Data Management

Master data is a type of data that describes subjects related to the ‘who,’ ‘what,’ and ‘where’ in business transactions communications, and events. The ‘who’ could be a customer or an employee; the ‘what’ could be a product or service, and the ‘where’ could be a store, office, or a virtual location.

Master Data Management (MDM) is a program of operational processes governed and executed on a foundation of people and technology to maintain and deliver master data that is understood, trusted, controlled, and fit of purpose.  Data Governance creates the rules and adjudication for operational processes  of each phase of the master data’s life cycle from  its creation, access, use, and disposal.

Data Governance and Master Data Management

Master Data Management includes processes from the creation of master data thru to its disposal. Data Governance creates the rules and adjudication of the operational processes that are executed within those processes.  Therefore, Data Governance does not sit as a separate process, according to Cerwin. The governance rules can be executed thru something as simple as a dropdown box on a user interface.  For example, a user is given four things to choose from – “there’s no fifth choice” he said. “That’s executing and enforcing the rule.”

Master Data Management requires Data Governance. “You really aren’t managing master data unless you’ve included Data Governance,” he said. The rules created within Data Governance ensure quality and privacy of the master data “because the concepts of MDM and Data Governance are labeled differently, they’re often thought of as mutually exclusive, but they’re not. They’re all intertwined.” Also, keep in mind that governance rules are attached to the data, no matter where it goes.  Rules do not just start and stop with the MDM application:

“Those rules go with the data. If that’s personal information, and it has to be encrypted, then it ought to be encrypted from the system of record and thru all applications and media that use and store it.”

Understanding Master Data

Cerwin finds that both master data and MDM are often misunderstood. One area that is misunderstood is that master data must be shared to be considered as “master data”:

“Some people will start to split hairs and say, ‘Oh, it’s only master data if it’s shared.’ I disagree. Basically, it’s all still master data.”

There is master data that is used only be a single transaction, shared across a group of applications, and shared across the enterprise. “But it’s still master data, and it’s funny how some organizations will argue over what is and what isn’t master data.” This ‘limited’ perspective can ultimately lead to siloing master data attributes and excluding some application-specific master data attributes that have a relationship to enterprise master data attributes which can lead to quality issues for both attributes.  Cerwin states that “master data” was first recognized in the 1970s when mainframe applications were developed.  “We had master files and transaction files; the master files contained master data attributes – that’s where the term ‘master data’ originated.” 

Start with the Life Cycle

Cerwin recommends starting by mapping the events contained within the business subject area of the domain, from the first event to the last event of the life cycle. The mapping provides a clear understanding of the different phases of the business subject and the states that the subject goes thru during its entire life cycle. Next, map in the business areas, and where they engage in the life cycle, he said. Then map in the master data that those business areas originate and use.  “I have found that a master data lifecycle mapping can be eye opening for many business areas; for the first time they may discover how they inter-relate with other business areas.

The Master Data Marketplace

Cerwin suggests looking at Master Data Management as a service that follows a business model – as a marketplace – where data can be ‘purchased,’ including by internal applications and all expenses of operating the MDM program can be recovered. Operating MDM as a business leads to long-term sustainability.  A successful MDM program has no end-date.  “The first MDM program I launched in 1993 is still active today,” said Cerwin.  The marketplace concept also can help subscribers recognize the value of the MDM program. Keep in mind that “master data is not discretionary.” Whether an MDM program and application exists or not, the business processes needs the master data, he said, and one way or the other, they’re going to get the data. The real question is to what degree are you managing your master data without an MDM program and proving that your MDM program can manage the data more effectively, efficiently, and at a lower cost.

Governing Behavior

Data Governance is not really about governing data, he said, because data by itself has no cognitive or decision-making capabilities to follow rules. It takes people – the key component of the People-Process-Technology framework – to create the automation or manually execute the rules.  So, Data Governance is actually about governing people’s behavior.  Cerwin uses a stop light as an example of governance.  

“You’re not governing the street, you’re not governing the car, you’re governing people’s behavior: when they see a red light they stop; when they see a green light they go; when they see a yellow light, they go faster – at least where I live they do.” 

He added that even autonomous self-driving cars require a person to encode the rules into software that control the car – so it’s the software developer enabling governance.

Armed with this realization, your perspective and approach change to focus on the people who create, execute, and adjudicate governance rules.  This results in effective and sustainable governance within the MDM program.

An Evolving Perspective

In the 42 years Cerwin has been in the industry, he’s seen a recent increased focus on data. Some of that change can be attributed to government regulations. Another reason is the desire for businesses to better understand and service their customers.  This includes recognizing who their best customers are and reducing attrition and strengthening their relationships with these customers.  Businesses want to understand what products the customers purchase, what products they may be interested in purchasing, and what store and internet web site they visit.  Basically, the “who, what, and where” within transactions, communications, and events – the master data.  An MDM program should be a critical initiative of all business strategies.  Data Governance ensures the privacy, quality, and fit-for-purpose of the master data within the initiative.

Image used under license from Shutterstock.com



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