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Data is an Asset – What Kind of Asset

Data is an Asset - What Kind of Asset

Data is a special type of Circulating Asset, with very distinct and useful properties

Data professionals often talk about the importance of managing data and information as organizational assets, but what does this mean? What is the actual business value of data and information? How can this value be measured? How do we manage data and information as assets?

Data as a Circulating Asset

Data and information are a special type of asset called a circulating asset, as opposed to fixed assets such as buildings or vehicles. Circulating assets morph easily from one form into another (e.g., cash can be used to buy raw materials), and can be used and disposed of without having to obtain permission from, perhaps, a creditor. There are many types of circulating assets, such as Floating Assets (which includes cash and anything else that can easily be converted into cash), Producer Goods (which includes raw materials and fuel used one time in creating goods for market), and finally, Data and Information.

But data and information are very special types of circulating assets, with some very special properties to manage:

  • Data is immutable; that is, it is not consumed as it is used and is therefore always available for additional reuse, up to the end of its useful life.
  • Data is copyable; that is, it can exist in multiple places at the same time and can be used simultaneously by multiple people.
  • Data is indivisible; that is, it must be used within a context that gives it meaning and business value.
  • Data is accumulative; that is, data can be combined with other data and transformed into additional data assets at will.

Another critical difference between data and other assets is that there is a “fitness for purpose” aspect to data that doesn’t exist for other assets. When you spend cash or liquidate stocks, you don’t have to ask whether they are fit for the intended purpose. With data assets, effort must be expended to ensure that their quality, timeliness and relevance qualify them for the purpose for which they are being used. The question “Is this data good enough?” must always be asked and answered. 

Data Asset Value and Data Quality

These special characteristics of data give some insight as to how data and information should be managed to generate value for organizations. For one thing, it means that the value of data assets is directly tied to their sharing and reuse. Data needs to be in motion, not static, being used to create “virtual value streams” of information that link organizations to their customers and other stakeholders in creative and engaging ways.  Most organizations do not get any significant ROI (Return on Investment) from their data because much of their data sits in application-specific databases or Excel spreadsheets and are only used to support one application or business function. Data and information that is not shared and reused across business units does not contribute significant value to an organization! 

It also means that data and information must be managed, to ensure their “fitness” for whatever purpose they are being used for. Organizations must be able to trust the data quality, accuracy, timeliness and business-relevance for information to be “fit for use”. All assets must be managed; the difference with data is that users must know enough about the data they are using (e.g., the source, quality, meaning and timeliness of the data) to decide how to use it, and what to use it for. As mentioned above, data only has value when used within a context that gives it meaning and value.

Since data only has meaning within a particular context, consumers of data and information need metadata to help them decide how to use it. This is an important difference between data and other assets; you don’t need much metadata when deciding whether to spend a $20 bill. But you do need metadata when trying to decide whether a given set of accounting data is fit for, say, a year-end report to shareholders, regulators and auditors, or is only suited to be used for monthly trial balances.

One final point about asset management: Generally speaking, what is being managed is not the asset itself, but rather the behavior of stakeholders regarding that asset. Organizations don’t manage money so much as they manage the behavior of people about spending, and the reporting of their spending. An inventory manager controls when new inventory is ordered, and in what quantities. What data managers administer are the processes by which data assets are acquired, evaluated, enhanced, provisioned, used and (eventually) disposed of, in ways that ensure the maximum amount of value at least cost for the company.


Asset management is rarely about the management of things; it is focused on the management of people and processes.  Data and information must be managed as an asset, and more importantly, as a specific type of asset – circulating asset to ensure its value to an organization or business.


Larry Burns

Larry Burns is a specialist in the field of data and database management, with a career as a data architect, data modeler, database developer, consultant, and teacher.  He has developed an extensive repertoire of tools, techniques, and expertise that enables businesses to reuse their data and derive maximum value from their databases more easily. Larry has an extensive background in application development, and contributed significantly to the success of many major projects.  He has also been involved in teaching, lecturing and writing on various topics of database management and application development, particularly Agile Development.

Larry is the author of the books "Building The Agile Database" (Technics Publications, 2011), "Growing Business Intelligence" (Technics Publications, 2016) and "Data Model Storytelling" (Technics Publications, 2021) and has been an instructor and advisor in the University of Washington's certificate program in Data Resource Management.

Larry holds degrees from the University of Washington (B.S.), Seattle University (M.S.E), and certification as a data management professional.

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