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Data Governance Methodology

Data governance is becoming more and more common in today’s world. In this post, we will look at one commonly used process of implementing data governance. The steps are explained below.

Scope & Initiation

The first step in setting up a data governance system is to determine the scope of data governance. By scope, it is meant how deep and wide the program will be. In other words, you have to determine what will be governed and how thoroughly it will be governed.

It may surprise some that not all data is governed by data governance. For each organization, it will be different but generally, all organizations have data that is excluded from data governance. For example, some organizations will include emails under data governance while others will not. It depends on the situation and there is no single rule.


In addition, it is important to determine how thorough the governance will be. An example of this would be the tolerance for data quality issues. There are times were some data errors are permissible as long as they do not exceed a certain threshold but this also depends on the context


At the assessment stage, the purpose is to determine an organization’s ability to govern data and be governed by policies. Generally, there are three ways of assessing this and they are measuring the capacity to change, the culture of data use, and the ability to collaborate.

The capacity to change is self-explanatory and is a measure of an organization’s ability to accept new policies such as data governance policies. The data use culture is looking at how an organization uses data at that moment. Lastly, collaboration looks at how well people within the organization can work together. Collaboration is critical because data governance generally affects the entire organization and people from multiple departments must work together.


The vision is where terms are defined and steps going forward are set. For example, the organization needs to define what data governance is for them. In addition, requirements for doing data governance are also developed.

Vision setting is a theoretical experience and this is often boring for the more practical action-oriented individuals. However, setting the vision sets the tone for the rest of the project. Therefore, this must be planned and developed.

Align & Business Value

Aligning and business value is for determining the financial value of incorporating data governance into an organization and also refining how things will be measured. For profit-seeking organizations business value is critical. Most projects need to make or at least save money in this setting. For non-profit organizations, the motivation might be to increase efficiency or the ability to better serve stakeholders.

It’s not enough to talk about savings. Evidence must be provided for determining actual savings. This is where metrics come into play. There must be ways to measure the value of a data governance project. Again, how to do this will vary from place to place but it needs to be addressed.

Functional Design

Functional design is focused on the actual process of doing data governance. What will be done must be determined as well as established roles that support this process as well. Principles are often developed at this step and principles are similar to goals in terms of what is expected from implementing data governance. Following principles, the next thing that is developed are standards which are similar objectives in education in which you have some sort of measurable action.

Best practices often encourage data governance to be embedded within existing roles and responsibilities. In other words, setting up another department within an organization and calling it data governance is generally not considered the best way to make this happen.

Governing Framework Design

Once the plan has been developed it is time to find the people who will implement it. governing framework involves assigning processes to people and setting up the various roles associated with data governance. Generally. a lot of the aspects of data governance are being done at an organization but in a disjointed unaware way. Therefore, the main benefit here is not so much to give out more work but rather to make it clear who is already doing what and make sure they are aware of it.

Road Map

The road map step involves data governance going live. This is the point where data governance is integrated into the existing organization. Other things that are done at this step are designing metrics and reporting requirements. In other words, how good or bad does performance have to be on a standard and how will this be reported?

Change management is also addressed here and involves dealing with resistance and making sure that the scope and or goals of the project do not change. There are times when a project will wander from its original purpose which can be frustrating for people.

Rollout and Sustain

Roll out and sustain involves executing the plan and checking its effectiveness. Essentially, this step involves monitoring the data governance implementation and making corrections as necessary.


Data governance is a critical part of most organizations today. However, it can be tricky to figure out how to make this a part of an organization. The information above provides an example of how this could be done.

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