Data Management vs. Data Governance: Improving Organizational Data Strategy

By   /  December 12, 2017  /  No Comments

Data Management vs. Data GovernanceSuccessful companies capitalize on their organizational data assets through effective understanding of how to best leverage the similar, but notably different, practices and concepts of Data Management vs. Data Governance.

According to Dr. Peter Aiken, Facebook has an estimated worth of more than five times that of United Airlines, $200 billion vs. $34 billion. The difference? Basic Data Strategy goals that utilize Data Management along with Data Governance (a central element within the greater practice of Data Management) lead to higher revenue and business value.

Dr. Aiken, the Founding Director of Data Blueprint, understands unlocking business value from data. Data Blueprint is a Data Management consulting firm putting firms on the right path to leverage data for competitive advantage and operational efficiency.

Dr. Aiken said that most organizations do not treat data as an organizational asset.  In a recent DATAVERSITY® interview he noted that businesses “don’t know what they are doing with their data nor what they have. How can they expect to use data as a strategic asset?” Dr. Aiken added that this lack of understanding leads to continued frustration within organizations trying to deal with their data issues. Certainly, not knowing how to leverage data as an asset and, hence, how to integrate Data Management and Data Governance practices effectively affect the extent that businesses can meet their objectives.

What is Data Management?

According to Dr. Aiken, Data Management describes how data as an asset is operationalized and used to support and organizational strategy. It may be tempting for a manager think the company has done and is doing Data Management for years and knows the ins and outs of it. He responded:

“Data Management has gone from a singular discipline to a more complex discipline, affecting how we conceptualize the problem of Data Management and our understanding of data.”

In the earliest days of computing, Data Management meant tracking simple punch cards used to input values. Today computing has become ubiquitous. Consumers have phones, apps, tablets, and numerous ways to enter information. As a result, Data Management has become a more intricate, requiring redefinition of what is a data asset.

According to Dr. Aiken, data assets share three characteristics. He noted that data is the only asset that organizations have that is:

  • Non-Depletable: Data assets can be reused.
  • Non-Degradable: Data assets, when maintained properly, retain their value and do not wear-out.
  • Durable: Data assets have lineage, they can generate flows of goods and services over time.

Furthermore, he iterated that, “Because of these three characteristics, data as a category is deserving of its own strategy and requires governance”. Not only do managers need to know how to operate with data (Data Management), they need to know what needs to happen right now to the data (Data Governance).

What is Data Governance?

Data Governance is a part of the overall practice of Data Management, which includes numerous other concepts and practices as well.

He stated:

“Would you want your only asset that you have in your organization that is not depletable, not degradable over time and a durable asset to not be governed? Data Governance is a way of us getting better with how we as an organization do.”

In other words, Data Governance is best defined as managing data with guidance (Data Strategy & The Enterprise Data Executive). An organization looking to define or better hone in on a Data Strategy needs Data Governance to make their data assets more supportive of its business goals.

According to Dr. Aiken:

“Most organizations have no idea what data they have, they have no idea how good their people are at using data, and therefore they have no idea how their organization is using data to support their strategies.”

Data Governance addresses this big gulf by telling management what needs to be done, he said. This does not happen by painting broad strokes or pre-specifying what the Data Governance will look like in the next ten years or trying to do a big plan. He asks organizations to:

“[Remember that]in time, we should have some sort of plan around data assets, since they are non-depletable, non-degrading, and durable. Data Governance really has to be a process whereby you start to get people aware that data is something that they need to govern and then how the governing of that data can lead to improved business outcomes.”

Disentangling Data Management vs. Data Governance

Dr. Aiken advised that the “Data Governance is going to tell Data Management what [it] should be doing now to immediately support the organization.” Then Data Management has the needed understanding to how to continue, provide, and add to existing business practices around data.

He gave the example of a health care system that needs to have a single doctor’s list. According to Dr. Aiken, in this example, Data Governance comes in with the guidance “it is important for this particular hospital chain to know when someone walks in do they have full privileges. He continued. “The organization decides to spend 12 weeks working on a list of people who have full access at our facilities.”

At this point, he said that many people assume they need to purchase some big Master Data Management tool to solve all the facets of Data Governance. He counseled:

“Before spending ten million dollars on a global solution or a good consulting company, we need to… [pick three aspects of Data Governance] and use Data Management to do them.”

He declared, “Most of the organizations, when they put governance in place also need to put something in place that will help with quality around that and maybe learning about Master Data Management.” Here, he advised, find someone, in the company, with good with SQL technology to build a prototype Master Data Management solution. Then play with the Data Management solution for a year.

In the case of the health care provider, it may not get the exact list, but it will reveal the kinds of questions you need to ask a vendor. According to Dr. Aiken, implementing this Data Governance directive would prepare the company to have a good discussion with the vendors. There an organization could figure out, quietly on its own, instead of paying $300 an hour to an army of consultants. For example, the discussion of how to handle cases of physicians working for its hospital and for different health care providers need to take place. He said, that this conversation “would never have come up in the preliminary discussion but only because you played with a Master Data prototype for a year.” In this example, he illustrated that the organization did:

“Data Governance [to] try to get a handle on the physicians that are associated with the hospital system. By working in that space for a bit Governance told them not to buy a 30 million-dollar solution the first time. Buy it as a 2nd or 3rd approach.”

Best Data Management and Data Governance Practices

While each company works within different contexts and data assets, Dr. Aiken provided three general principles.

  • Doing Data Management and Data Governance is not Solely an IT Project:

“IT does a wonderful job of Data Management, but they are not the ones that can assign the value. They Can’t prioritize. They are not familiar enough with what is happening in the business world, typically. They may achieve terrific technical value success [while] not achieving the business big picture.”

The busiest business people need to be involved too.

  • Get Some Outside Perspective: He mentioned thatIt is very unlikely an organization will have a very large pool of Data Management and Data Governance processes.” He said that unfortunately businesses typically approach this problem by just hiring someone themselves. “Given that two people don’t know that they don’t know that they are not qualified it may make sense to share hiring panels, between agencies if possible.”
  • Take Reactive and Proactive Approaches to Data Management vs. Data Governance: Aiken likened this to the “fire station” model of Data Governance. He stated:

“The Governance team is going to be somewhat divided between solving very specific things (putting out fires literally) and on the other side of it eliminating these seven deadly data sins (education and prevention). [Organizations] need to balance the time between proactive and reactive approaches.”

Once organizations recognize data as something inherently valuable, they can begin to take steps developing a return on their data assets investment. As he said, using a combination of Data Management practices and Data Governance tools effectively makes the best use of technology, people, policy, and processes towards achieving business outcomes.

 

Photo Credit: Sergey Nivens/Shutterstock.com

About the author

Michelle Knight enjoys putting her information specialist background to use by writing technical articles on enhancing Data Quality, lending to useful information. Michelle has written articles on W3C validator for SiteProNews, SEO competitive analysis for the SLA (Special Libraries Association), Search Engine alternatives to Google, for the Business Information Alert, and Introductions on the Semantic Web, HTML 5, and Agile, Seabourne INC LLC, through AboutUs.com. She has worked as a software tester, a researcher, and a librarian. She has over five years of experience, contracting as a quality assurance engineer at a variety of organizations including Intel, Cigna, and Umpqua Bank. During that time Michelle used HTML, XML, and SQL to verify software behavior through databases Michelle graduated, from Simmons College, with a Masters in Library and Information with an Outstanding Information Science Student Award from the ASIST (The American Society for Information Science and Technology) and has a Bachelor of Arts in Psychology from Smith College. Michelle has a talent for digging into data, a natural eye for detail, and an abounding curiosity about finding and using data effectively.

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