Comparing Approaches to Data Governance

art01x-image-edThree approaches to implement data governance include 1) the Command and Control approach, 2) the Traditional approach, and 3) the Non-Invasive approach. This article compares and contrasts the approaches and quickly summarizes each approach. The approaches are broken down by the primary components of a data governance program spelled out in my recent articles that address a framework for Non-Invasive Data Governance™ [part 1, part 2].

The original comparison of the approaches can also be found in my September 2016 installment of my Real-World Data Governance webinar series with Dataversity – Successful Data Governance Models and Frameworks. The diagram I used in that webinar is shown in figure 1 below.


Figure 1.


  • In a Command and Control approach to Data Governance: Roles are assigned to people, often as something new. Immediately, data governance is perceived as something that is over and above the existing levels of responsibility and thoughts turn to how much time it will take and how data governance competes with completing their job function.
  • In a Traditional approach to Data Governance: People are identified into roles based on seniority and ownership of systems and data resources. Responsibilities are often spelled out in policy requiring governance of formal charters and designation of people into the specific roles called out by the data governance program.
  • In a Non-Invasive approach to Data Governance – People are recognized into roles based on their existing relationship to the data. People who define data are assisted during the task of defining new data. People who produce data understand the impact of the data they produce. Data users are formally educated, made aware of and expected to follow all rules associated with using data. Being recognized for something brings with it a positive connotation and positive expectations.


  • In a Command and Control approach to Data Governance: All processes are new and governed. Data governance is all about taking control of processes and redefining all processes specifically to govern the data. Often times people are told that data governance is the reason we are following the process and spells out penalties for not following process.
  • In a Traditional approach to Data Governance: There is a single process for how to govern data. Often times the process is labeled “The Data Governance Process.” The process is applied to every activity and the process is recognized as being the main dimension of the program. By calling processes Data Governance Processes, the discipline is singled out as the reason for having delays.
  • In a Non-Invasive approach to Data Governance: Governance is applied to existing and new processes. Processes are not given the label of being a data governance process; they are called by their original name – “request for access,” “issue resolution,” “project methodology,” and so on. When new processes are defined, they too are governed from the beginning.


  • In a Command and Control approach to Data Governance: Data governance is communicated with a tone of “you will do this.” Data governance is new to the people and the organization and people are told exactly what to do in an authoritative manner. This is not always bad – and in fact some organizations require strong top down direction and the demand for improved behavior.
  • In a Traditional approach to Data Governance: Data governance is communicated as something that you should do. Often times, data governance is spelled out in policy and a directive is given for a specific group of people to take primary responsibility for governing data across the organization.
  • In a Non-Invasive approach to Data Governance: Data governance is communicated as something we already do but can do better. Since people are recognized for their relationship to the data, most responsibilities are conveyed through the formalization of activities that people already have associated with how they define, produce, and use data.


  • In a Command and Control approach to Data Governance: Organizations measure the value of data governance through return on investment (ROI). In other words, the expectation is that data governance will bring in money directly from the results of data governance through improved capabilities, or save the organization money directly due to the governance of data. These results are often difficult to demonstrate.
  • In a Traditional approach to Data Governance: The quality of the data is measured as the way to determine the effectiveness of data governance. Typically, organizations benchmark the quality of data definition, production, and usage, and put metrics in place to measure the improvement of costs associated with each of these activities.
  • In a Non-Invasive approach to Data Governance: The effectiveness of data governance is measured through the value demonstrated from existing and new information-based resources. Return on investment is typically measured from improved operational efficiency and effectiveness of analytical capabilities brought forth from other investments in information technology.


  • In a Command and Control approach to Data Governance: Tools are purchased early, raising the expectation level of the organization. With this approach, the tool becomes the focus of the program and people are expected to learn the tool and integrate the tool into daily routine and process. Data Governance tools are often selected without a complete understanding of tool capabilities and requirements.
  • In a Traditional approach to Data Governance: Existing tools are leveraged before new tools are acquired to drive data governance. Organizations following this approach look first to the tools that they have in place and focus on specific activities such as improving data definition through modeling, data production through improved integration capabilities, and data usage through improvement of data protection capabilities.
  • In a Non-Invasive approach to Data Governance: Existing tools and industry-proven templates and models are leveraged to define requirements for future tool needs. The non-invasive approach calls for developing tools internally and leveraging existing industry templates that address specific governance needs as a means of flushing out detailed tool purchase requirements.

In this article I have quickly compared three of the most-effective, but very different approaches to implementing data governance programs in organizations with fairly to severely complex data environments. There are upsides and downsides to each of the approaches which I shared in the webinar.

The quick paragraphs above call out the most significant differences in the approaches when compared using the primary components of the Non-Invasive Data Governance framework shared in earlier this year.


submit to reddit

About Robert S. Seiner

Robert S. (Bob) Seiner is the publisher of The Data Administration Newsletter ( – and has been since it was introduced in 1997 – providing valuable content for people that work in Information & Data Management and related fields. is known for its timely and relevant articles, columns and features from thought-leaders and practitioners. Seiner and were recognized by DAMA International for significant and demonstrable contributions to Information and Data Resource Management industries. Seiner is the President and Principal of KIK Consulting & Educational Services, a data and information management consultancy that he started in 2002, providing practical and cost-effective solutions in the disciplines of data governance, data stewardship, metadata management and data strategy. Seiner is a recognized industry thought-leader, has consulted with and educated many prominent organizations nationally and globally, and is known for his unique approach to implementing data governance. His book “Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success” was published in late 2014. Seiner speaks often at the industry’s leading conferences and provides a monthly webinar series titled “Real-World Data Governance” with DATAVERSITY.

  • Richord1

    To establish governance, the first thing that needs to be
    understood is what is being governed. Most approaches to data governance fail
    or stall. Rather than data governance, organizations should adopt Metadata Governance.

    Data governance focuses on the aftermath of poorly designed data, ill-conceived
    business processes which results in a reactive form of data management.

    What most organizations are lacking is Data Literacy. It’s like trying to govern books when no one knows how to read. Or governing manufacturing when you have little knowledge about the product. Before the roles and responsibilities, before the processes, tools and metrics it is necessary for the people to understand the data. Most importantly we should not spend time governing data but governing metadata.

    Governing metadata requires an understanding of the nature of the data. Its origins, its semantics, how it is used. Data Literacy helps to understand the subjectively of metadata and the numerous and ever changing interpretations of metadata.

    The first step in any data governance initiative should be to learn about metadata. Create a comprehensive business glossary and data dictionary for the data. This best practice alone will result in a deep understanding of the metadata and will expose many of the most challenging issues an organization will face when attempting to govern data. What will be discovered are ambiguous definitions, lack of naming standards, a lack of semantic understanding of the data and discovery of the various interpretations of metadata.

    Based on this discovery a realizable and sustainable data governance program can be then be developed. Like the sage advice “you can’t manage what you don’t measure”, with data governance the advice should be; “you can’t govern that which you don’t understand”.

  • Marcelo Malheiros

    Data Governance needs to well defined before any action is taken as well as that people must be very well educated in this area to take the lead or part in such endeavor.

    I believe that Data Governance is quite a multi-dimensional discipline and there is no bullet proof approach to be succeeded. The three proposed scenarios are very well defined but they are more for illustration than for practical use. There are multiple combinations that are quite possible to be implemented as well as multiple variations that have not been described in the three scenarios.

    The reality is that decision on any strategy, including deploying Data governance, should be driven by enterprise culture, budget, available resources and senior management / business expectations (or pain).

    My Golden Rules and DG:

    – Manage it as a Program and not a sequel of tasks / projects
    – Plan BIG and execute small
    – Find a strong political support (a champion)
    – Find a strong technical leadership (the architect)
    – DG must suit the enterprise and not the other way around: cultural shifts never happen over night!

  • Frank Harland

    In addition to both Marcelo and Richord, with whom I agree, it seems like the three methods are related to maturity levels of the organisation. Would you say so?
    Furthermore, what can keep us from mixing the different dimensions of the methods where that is appropriate? Which dimensions, Roles, Process etc. are specifically characteristic to a method?

We use technologies such as cookies to understand how you use our site and to provide a better user experience. This includes personalizing content, using analytics and improving site operations. We may share your information about your use of our site with third parties in accordance with our Privacy Policy. You can change your cookie settings as described here at any time, but parts of our site may not function correctly without them. By continuing to use our site, you agree that we can save cookies on your device, unless you have disabled cookies.
I Accept