Data Modeling is (Still) Data Governance

FEA01x - edited feature imageThe title of this feature should grab the attention of data modeling people as well as data governance people and, perhaps, ruffle both of their feathers enough to gain their interest. The truth is Data Modeling by itself is not Data Governance – in its entirety. But … Data Modeling is (Still) Data Governance. Let me explain.

Data modeling is still a data discipline. Yes, Even after all these years. Through that discipline we continue to design our organization’s data, reduce redundancy, follow standards, and build business-useful definitions for the data.

Data modeling actually does much more than that. Ask any data modeler of Mr. Hay, Mr. Hoberman, Ms. Lopez, and Mr. Silverston’s stature. They can tell you the value that data modeling brings to the organization much better than I can. Several have written about it on the pages of

Data modeling can be done well, or … less well. Some data models include “cheeseburger” definitions (what is a cheeseburger? – a burger with cheese) and some have well thought out and validated business descriptions of data that make that data production and usage infinitely more valuable. The use of data modeling varies widely from organization to organization.

Some organizations have Enterprise Data Models (EDM) that are built to design the entirety of data for the organization. Let me write that again for emphasis – design the entirety of data for the organization. Developing the EDM is often a monstrous task that requires the involvement of a plethora of business and technical people discussing the detailed data and information needs of the organization. Some people view the enterprise model as the place to start the improvement of data and data quality across an organization. Other people view the EDM as a step towards defining and addressing the overall data needs of the enterprise.  Still, others view the development of an EDM as a big waste of time (no telling for some people’s line of thinking).

Some organizations model data for their internally developed information systems and/or for the data that resides in their data warehouse or business intelligence environment. Often these models are smaller than an EDM and are built for specific purpose – although many organizations choose to reuse components of existing models to create new ones. Again, data modeling is all about data discipline.

Other organization purchase industry data models follow described patterns for producing data models, and otherwise take immediate steps to acquire and place discipline around the design phase of defining, producing, and using data. Data modeling is, or has been in the past, viewed as the basis of data management activities for the organization.

There are many reasons to create a data model. These reasons include following data standards, reducing redundancy, putting business definition to data, and coming to grips with how to define data better or manage the definition of data as an important asset. There is no doubting that data modeling is both an art and a science but that the primary reason to model data is to instill discipline around defining data for the organization.

Industry definition tells us that data modeling is a process used to define and analyze data requirements needed to support the business processes within the information systems in organization. Meanwhile, the process of data modeling involves professional data modelers working closely with business stakeholders, as well as potential users of the data and information systems.

According to Steve Hoberman (another data modeler extraordinaire and presently the author of’s The Book Look column, my book’s publisher and long-time contributor), data modeling is the process of learning about the data, and the data model is the end result of the data modeling process.

So why do I say that Data Modeling is Data Governance?

Data Governance is the execution and enforcement of authority over the management of data. Data modeling can be considered the execution and enforcement of authority over the definition of data. The discipline of data modeling involves the “right” people at the “right” time to define the “right” data for the organization. This is the essence of Data Governance.

Data Stewardship is the formalization of accountability for the management of data. If you subscribe to the idea that everybody is a data steward because of their relationship to the data (a core tenet of the Non-Invasive Data Governance™ approach) then certainly the people providing information and assisting the data modelers must also be data definition stewards. And to think, the people that the data modelers work with have been playing the data steward role way longer then the term “data steward” has been trendy.

Data Modeling is Data Governance – or at least a piece of Data Governance – because it is discipline that is necessary to make certain the design of data is the way it needs to be. Organizations that do not model their data have a more difficult time improving the value they get from their data because their data becomes riddled with inconsistency and misunderstanding. Ask any organization that does not model their data if their data is being governed. The sure answer will be “no.”

That is why I say Data Modeling is (Still) Data Governance. Do you agree that Data Modeling is Data Governance? Respond if you do. Or if you don’t. I hope to hear from you.


This feature was originally published in a similar format several years ago. There was also a Real-World Data Governance webinar by this name with DATAVERSITY. Search for it — I am certain you will find it. From time-to-time republishes older content that is still relevant today. Data modeling is still data governance just as it was back then.


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Robert S. Seiner

Robert S. Seiner

Robert (Bob) S. Seiner is the President and Principal of KIK Consulting & Educational Services and the Publisher Emeritus of The Data Administration Newsletter. Seiner is a thought-leader in the fields of data governance and metadata management. KIK (which stands for “knowledge is king”) offers consulting, mentoring and educational services focused on Non-Invasive Data Governance, data stewardship, data management and metadata management solutions. Seiner is the author of the industry’s top selling book on data governance – Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success (Technics Publications 2014) and the followup book - Non-Invasive Data Governance Strikes Again: Gaining Experience and Perspective (Technics 2023), and has hosted the popular monthly webinar series on data governance called Real-World Data Governance (w Dataversity) since 2012. Seiner holds the position of Adjunct Faculty and Instructor for the Carnegie Mellon University Heinz College Chief Data Officer Executive Education program.

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