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Data Value Management: The Unsung Hero of Data Governance

Data Value ManagementThe enormous enterprises of the 21st century have many years of accumulated data resting across multiple disparate ERP systems, legacy systems, and data environments. As a result, it’s making data management very complicated and the prioritization of data-related work an exceptionally daunting undertaking for Data Governance organizations. In order to elevate this data management dilemma, successful Data Governance organizations must implement a value-based data management process that attempts to objectively quantify the data’s relevancy to the business.

To further validate that the business relevancy of data is still one of the most misunderstood data problems that organizations face, I asked my college-bound, millennial-aged son what came to mind when I say, “What does data value management mean to you?” His response, in a matter of fact tone, was, “Its value of data and how to organize it. It needs to be organized to tell a story on what to do first based on what is the most important, what you should know first, and even what is irrelevant. You should be able to organize data from most important down to unimportant, and yet it still be relevant.” Okay, I guess it is all about data “relevancy” after all. So, based on that profound word of wisdom from a young man a fraction of my age, the challenge that presented itself was, how does one measure relevancy? There has to be a model or set of models that can help large enterprises manage data relevancy.

After some inner reflection on past value management endeavors, and a peak at what Gartner has to say, I can confidently state that if an organization evaluates its data as a whole using the following six value streams, the data’s underlying value to the organization, and relevancy, will be determined. The six value streams are:

  • Cost Value Model: This model assesses the financial impact to a company when data is lost, stolen, or corrupted.
  • Economic Value Model: This model assesses the degree to which data actually contributes to generating income for the company.
  • Internal Value Model: This model expresses the level of privacy and uniqueness of the data, as it relates to the success and enablement of the enterprise.
  • Market Value Model: This model is used to address the perceived monetized value of the data for any given marketplace.
  • Performance Value Model: This model aligns itself with data used to help companies clarify and monitor their business drivers (e.g., technological innovation, analytical reporting, superior products, excellent service, and ongoing customer support).
  • Quality Value Model: This model concerns itself with scoring the quality of data required to ensure data is fit-for-purpose across the enterprise.

Even when determining the overall score across these six data value models, or value streams, there is still a bit of guess work involved in the scoring process. Determining the data’s value or relevancy is not an exact science. However, if you consider some of the definitions implied by the models, you will have a better grasp of how to apply data value management across your Data Governance organization.

Understanding the relevancy of any given data set provides important feedback to the business on what data is extremely important and what data should clearly have the most attention or resources available to manage it through its lifecycle. Once an enterprise begins to focus on speaking about data in terms of value, they tend to see the following characteristics manifest themselves across the enterprise:

  • Improved regulatory adherence and compliance reporting, especially surrounding BCBS 239.
  • Improved data quality processes.
  • Improved data management capabilities.
  • Improved performance reporting across business areas that drive performance.
  • Mindset of performing the right best practice for the right data at the right time for the right expenditure of time and money.
  • Self-sustaining approach to data value management through data value model learnings.

Without the knowledge of the data’s business relevance, the enterprise is most likely investing energy and money on governing the least important data, and is at the risk of losing its competitive edge. So, in order to get an accurate representation of the relevancy or value of the enterprise’s data, implement the value models one or two at a time, and as you learn more about your data assets, adjust accordingly. This will help your Data Governance organization develop a fully functioning data value management process with the capability to determine and track the appropriate value for any given data asset.[/vc_column_text][/vc_column][/vc_row]

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