There is a growing realization that data is one of the most valuable enterprise assets. A year ago, Economist published an article pointing out that “data, not oil, is today’s most valuable resource“. A couple of months ago, Forbes joined the conversation asserting that “data was not the new oil“. Acknowledging the immense value and potential of data, Forbes pointed out that it was an entirely new commodity. Unlike oil, data is digital and thus, it needs to be managed differently.
One of the key themes and drivers of data governance is the need to manage data as an asset.
As everyone will agree, valuable assets need appropriate care and management. This is still a new discipline, but its importance is rapidly increasing. As discussed in the keynote panel at this week’s Data Governance and Information Quality Conference in San Diego, many CDOs (Chief Data Officers) today are assuming profit and loss responsibilities.
The term data asset is used in data governance to describe any data element or a data structure that has value to an organization and enables it to perform its functions. A data asset may be a database or a dataset. It may also be a structure (e.g., a table or a view) within a larger data asset, down to a specific column or field.
What do we need to know and do in order to manage data assets?
To better understand this topic, let’s consider a definition of the word “asset”. What do we mean exactly when we use the term “asset”?
Virtual assets such as data or intellectual capital tend to be less understood than physical assets (buildings, equipment, etc.). Oxford Advanced Learner’s Dictionary describes an asset as a valuable or useful quality, skill or person; or something of value that could be used or sold to pay off debts. This definition helps in establishing that an asset could be described as any entity that has value, creates and maintains that value through its use, and has the ability to add value through its future use.
“Use” is the key component of an asset’s value. Do we know how our data is being used?
It is typical for an enterprise to gather information about its data in the metadata management systems and repositories. Much of the information about data assets is collected directly from the data sources. Known as technical metadata. this information can tell us the number of tables in a database, the number of columns in a table or the datatype of a column, whether its values are unique, the min and max range of values, and so on. While such information can be important,
technical metadata does not tell us the meaning of the data or how a data asset is being used and by whom.
Or what security and regulatory requirements it must satisfy. Or ny other key information that identifies in what way the data asset delivers value to an organization. In other words, what actually makes data an asset. Further, information about data assets is too commonly locked in the proprietary, silo-ed metadata management systes,
The use of a data asset can only be described by connecting it to other relevant enterprise assets such as activities, processes, functions, business applications, technical infrastructure and policies.
And, of course, the user communities that perform activities during the course of which they create and use data. As an economic entity, an asset’s use and its value may be different in different stages of its lifecycle such as design, development, active use and retirement.
Management of a data asset, therefore, requires capturing its connections to all other relevant enterprise resources.
Through the “cross-asset” connections described here, an enterprise can understand the economic benefits of a data asset and implement strategies for preserving and gaining its value throughout the lifecycle stages.
TopBraid EDG is the only data governance solution purposely built for the connected enterprise.
To learn more about how it can help organizations to realize, preserve and increase the value of their enterprise data assets, take a look at our new white paper about building bridges between data assets and their business context.