Everywhere you turn, organizations are talking about valuing their data as an asset.
They understand that data is the lifeblood of their enterprise, important to them in their ability to manage their finances, execute customer service, and improve their operations. Yet, what it means to manage data as an asset is not well documented and most organizations don’t account for their data assets as they do for their other assets.
Formal data governance has evolved to better manage data, and it would be much aided if data were more formally valued as an asset, so that the resources applied to manage it could be more closely aligned with the value of data to the enterprise.
Formal asset valuation is something that has evolved over a lot of time, and it continues to evolve. The concept of financial accounting dates back some 7,000 years.
More recent concepts, such as valuing inventory and depreciating assets, did not manifest themselves in accounting practices until the mid-nineteenth century during the industrial revolution. Even more recently, the importance of intangible assets is continuing to become increasingly significant. This class of assets includes intellectual property, like patents and copyrights, and also data.
Organizations value their assets for many reasons that include consistent financial reporting, mergers and acquisitions, customer service, capital budgeting, and taxes. Today, tangible assets like buildings, machinery, land, and inventory, are valued based on their cost, their current market value, or their revenue potential.
The basis of how asset value is assigned is rooted in things such as lenders’ desires to know the value of collateral against which they lend money, and governments’ tax assessments, which use the value of assets to generate tax revenue. Recently, an increasing number of valuations are also based on revenue potential. This can be seen, for example, with stock valuations of start-up companies and with intangible assets, such as intellectual property.
There has been a growing tendency to assign value to intangible assets, like patents, copyrights, trademarks, and brands, as part of the organizational makeup.
In one study, Dr. Margaret Blair, at the time of the Brookings Institute, examined the degree to which value has been tied to organizational intellectual property between 1978 and 1998. Dr. Blair reports that over time intangible assets increasingly determined corporate value.
In studying all nonfinancial, publicly-traded firms in the Compustat database, Dr. Blair showed that in 1978, 80% of firms’ value was associated with their tangible assets and 20% with their intangible assets. By 1988, the makeup had shifted to 45% tangible assets and 55% intangible assets. By 1998, only 30% of the value of firms studied was attributable to their tangible assets, while 70% was associated with the value of their intangibles.
It’s not a stretch to say data should be included as part of organizational intellectual property. In fact, the IRS, in its guidelines for valuing intangible assets in its manual, lists “technical data” as one type of intangible asset.
According to the IRS, the value of an intangible asset can be determined in the same way as for tangible assets: using a cost basis, gauging its value in the marketplace, or based on revenue potential of the asset in question.
The rules allow auditors to apply any combination of these approaches, provided the auditor has sufficient documentation to justify the valuation approach. These same approaches could be applied to data valuation. However, at the time of this writing most organizations do not estimate the value of their data in this way.
There is no agreement on data valuation as of yet. Some attempts have been made on the subject. One example is a paper authored by Daniel Moody and Peter Walsh. The paper – “Measuring the Value of Information: An Asset Valuation Approach” -- looked at different approaches that had, in part, been previously researched to value information, including the different accounting valuation models based on cost, market value, and revenue potential.
Another valuation approach the authors examined, termed ”Communications Theory,” attempted to measure the value of information based on the amount of information communicated. This, they correctly concluded, leaves out the value of the content and is not a useful approach to data valuation. The report concluded that the best cost approximation of data is based on future cash flow. This prediction is reflected in today’s industry trend.
The report had another interesting conclusion on why data may not be formally accounted for as an asset on corporate books. The authors concluded that it is financially advantageous for companies to treat the cost of information as an expense rather than an asset.
The reason for this is simple. By treating data as an expense companies can avoid showing data on their balance sheet and the associated tax implications. However, the paper also determined that, aside from the financial valuation of data, there are practical reasons for valuing data, including greater accountability, measuring IT effectiveness and helping to justify the cost of information systems.
A more indirect approach to valuing data as an asset is also exemplified in a book by Tony Fisher on how and why to treat data as an asset. The book – The Data Asset—How Smart Companies Govern Their Data for Business Success -- does not state outright how data should be valued as an asset, but makes the case that data quality and data governance directly benefit the bottom line.
Mr. Fisher builds the case that organizations’ effective use of data to mitigate risk, increase revenue and control cost is a key differentiator between successful organizations and less successful ones. The book defines risk mitigation as effectively addressing compliance and regulatory issues. Definitions for increased revenue or contained cost are the same as traditional asset valuation methods. It uses many examples to highlight these concepts.
For example, it uses Walmart’s instant feedback up and down the supply-chain as an example for increased revenue. Similarly, the book uses a manufacturing plant’s ability to adjust its machinery to new demands, based on good quality and well managed data, as an example of effective cost control. However, the book does not specify how revenue gained or cost incurred due to data should be tracked as an asset.
Similarly, in a recent book by Peter Aiken – Monetizing Data Management -- the author presents numerous examples of how, when data management is effectively applied, organizational risks and costs are reduced. In addition, the book highlights many case studies that result in improved national security and saving of lives through good data management. While it is essentially impossible to attribute a monetary value to these examples, it is intuitive that data must have significant value if it helps with national security and saving lives.
More and more people and institutions are giving the matter thought. Douglas Laney, an analyst and author with Gartner, for example, has introduced the concept of “Infonomics” in an effort to centralize discussion on valuing data as an asset. One of the concepts he suggested early on is for organizations to keep an internal balance sheet to track the value of their data assets.
John Ladley, in his book Making Enterprise Information Management (EIM) Work for Business, also makes a strong case for managing data as an asset. According to Ladley: “Until data, information, and content are managed as other assets are managed, neither information nor data nor content has a chance to fulfill its potential within organizations.”
Ladley goes on to draft a set of “Generally Accepted Information Principles” creating the foundation for an industry-wide approach to data valuation similar to the Financial Accounting Standards Board’s (FASB) Generally Accepted Accounting Principles (GAAP).
Consideration on how to value intangibles, of which data is a growing part, has again more recently, been given by the accounting and financial industry. While FASB’s guidelines as of late 2014 leave organizations freedom not to explicitly value intangibles unless they are capable of being sold or independently licensed, in early 2016 at FASB a small team of researchers started reexamining updating its rules to value intangible assets.
Clearly organizations recognize that data is an asset. They employ increasing resources, both internal as well as outside experts, to help them manage data at considerable expense. Organizations understand intuitively that incurring data management costs, expensive as they are, still saves them money, reduces their exposure to risk, and may significantly increase their revenue.
In addition, non-monetary benefits, such as the saving of lives or even improving the organization’s perception, present value that is difficult to quantify.
It is not a given that data will be valued as an asset in the organization’s financial statements or continue to be treated as an expense. As data management professionals, our experience reflects that the more an organization values its data as an asset, the more likely it will dedicate the required resources required to govern its data.
Data governance requires stakeholders at every level–executive, managerial and operational. Too often data governance suffers due to lack of involvement at the executive level in particular.
Data governance, while on the executives’ mind, is difficult for executives to quantify. This inability to quantify data governance trickles down to the managerial level, and often data stewards are left to manage data informally, when they have time, and to a degree that may or may not align with the enterprise value of the data.
Data valuation as a true asset will help executives justify resources needed to properly govern data. This will have a net positive affect on data governance, and also on other data management disciplines and help organizations more conscientiously steward their data assets.
(About the author: Mike Fleckenstein has over 30 years’ experience leading data management initiatives. His range of experience includes running a data solutions company, guiding enterprise-level data programs and projects, and installing and building data management solutions. Currently at MITRE, Mr. Fleckenstein supports strategic, data-specific efforts for federal clients. Mr. Fleckenstein is a frequent speaker and published author. He can be contacted at firstname.lastname@example.org. Note: The author's affiliation with The MITRE Corporation is for identification purposes only, and is not intended to convey or imply MITRE's concurrence with, or support for, the positions, opinions or viewpoints expressed in the article.)
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