Data Modeling is a form of Data Governance — or at least a piece of Data Governance — because it takes discipline, which is necessary to make certain the design of data is the way it needs to be. Organizations that do not model their data have more difficulties 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 answer will surely be “no.”

Data modeling is all about data definition but has a much wider impact on the data of your organization. The quality of the data definition has a direct impact on many other aspects of the data lifecycle. Quality data definition impacts how data is produced and directly impacts how the data is or will be used throughout your organization. These statements are the basis for this whitepaper. Let us restate these as facts — the quality of data definition has a direct impact on the quality of the production and usage of the data. If quality data definition is that important, then we better make certain that we execute and enforce authority over how we define data. That means that we must govern the process of how we define data. Again, data modeling is a discipline that must be governed, making data modeling a form of data governance.

The whitepaper describes the three actions of governing data, governing the action of defining/producing/using data, relating data definition/production/usage to data modeling, focus a data governance program on the three actions, data definers/producers/users, applying non-invasive data governance to the three actions, and why data modeling is a form of data governance.