Are Big Data assets? Yes, actually no.

Ciro Gaglione

Sky Italia

Luigi Troiano

University of Sannio




Image courtesy of Markus Spiske

How to valuate a company or a project that mainly relies on data processing, as many nowadays in the rise of Data Science? As digital transformation is expanding, also the way of running business is radically changing. As a consequence, estimating the value of modern enterprises  include those resources that are immaterial but essential for operations. Among those, data. So the question is: should data be considered central to the valuation of the (immaterial) capital for business? In other terms, can data be considered in terms of capital asset? Although we all agree on evidence of the key role played by data in defining new services and products, we argue that they are mainly commodities, not very different from other raw materials used by manufacturing industry. In digital companies, algorithms take the place of machinery, thus they should the real assets to consider as capital.

The question is crucial, and the answer is not trivial. Indeed, implications of what is the nature of assets in the digital industry may affect the way companies are valued and projects budgeted. In addition, a better understanding of where the value relies has an impact on the definition of liabilities and on the assessment/management of risk.


Data as assets


A major contribution to this perspective was first given by Moody and Walsh in their paper titled "Measuring The Value Of Information: An Asset Valuation Approach" [1]. As the title reveals, their thesis is that "information satisfies the definition of an asset much better than employees or customers, which are also commonly referred to in the literature as assets." The claim is supported by characteristics generally attributed to an assets in literature [2,3]:

  1. They have the potential of providing future services or economic benefits
  2. They are controlled by the organisation that owns them
  3. They are obtained by internal development or external acquisition

This argumentation is used by several authors and practitioners to support the opinion that data (Big Data in particular) are a new capital asset that should be used to value a company and to measure the ROI. However, in our opinion, this idea is wrongly supported by two misconceptions.


The first is about the difference between information and data. The two concepts are strongly related and often used interchangeably, but they have a different meaning.  On one side the significance of data is given as information, on the other information is represented by data. In economic terms, we should agree on the fact that information is value, thus immaterial by definition, and data is simply a materialisation of that value.  In analogy, a barrel of oil is the materialisation of energy, that is the real value associated to the barrel. The tendency to make confusion between the two appears in the paper of Moody and Walsh, as in many others, wherever they talk about information (value), but in fact as asset they refer to data (materialisation).


Second, the accounting practice recognises commodities as a specific class of assets. This is the case of raw material used for industrial production. Instead machinery is part of capital assets. In the common language, the term "asset" generally refers to this second higher quality class, relegating lesser quality goods to the general class of commodities. Indeed, capital assets are durable, while commodities are consumable.


Assets and commodities [4] provides the following definition of asset:

  1. Something valuable that an entity owns, benefits from, or has use of, in generating income.
  2. Accounting: Something that an entity has acquired or purchased, and that has money value (its cost, book value, market value, or residual value). An asset can be (1) something physical, such as cash, machinery, inventory, land and building, (2) an enforceable claim against others, such as accounts receivable, (3) right, such as copyright, patent, trademark, or (4) an assumption, such as goodwill. Assets shown on their owner's balance sheet are usually classified according to the ease with which they can be converted into cash.

And, a commodity is defined as [5]:


A reasonably interchangeable good or material, bought and sold freely as an article of commerce. Commodities include agricultural products, fuels, and metals and are traded in bulk on a commodity exchange or spot market.


According to definitions above, we believe that commodity is appropriate to describe data because of the following reasons:

  • Data themselves are not valuable; decision made over information conveyed by them produce a real value
  • Data are largely interchangeable, as different sources can be selected and aggregated to produce similar information
  • As other commodities, the cost of data acquired from the market depends on availability and quantity

Some of you could argue that data are durable, in contrast commodities are consumable. However, as also pointed out by Moody and Walsh, we should accept the fact that data is perishable. The reuse of data tends to reduce the information content, similarly to the mineral oil (still a commodity) that becomes exhausted by mechanical usage.


Others could observe that some data requires intensive processing and intellectual work to be produced. Indeed, the information they bring is really valuable. This situation is not different from mining activity to extract gold and other precious metals, that are generally accepted as commodities. The media industry, television among them, heavily relies on valuable contents that are recorded on multiple media. In the first place, the transformation had as effect to move from analog to digital media, but the value still relies in the content by means of pertaining rights, obligations and liabilities. 


If we agree that information is the value and data only a means to bring it, it appears clear that algorithms designed to process data and make use of information should be the assets to take into account as capital, similarly to what happens with machinery used for transforming raw materials into products in the manufacturing industry. Once, we decide to account algorithms as capital assets, it remains the problem of assessing their value.




An emerging discipline known as Infonomics [6], promises to offer an answer to this question.  The word "infonomics" is a fusion of the terms "information" and "economics.", and it has been coined by Doug Laney in the late 90s, just before he introduced the term Big Data in 2001.  The intent of this discipline is provide a methodological framework for studying the relationship between information and economics in order to identify principles and practices useful to the valuation, handling and monetization of digital assets.


As briefly described by Nicole Laskowski [7], valuation of digital assets can be addressed by different methods, belonging to two main groups:

  • Non Financial
  1. Intrinsic value of information
  2. Business value of information
  3. Performance value of information
  • Financial
  1. Cost value of information
  2. Economic value of information
  3. Market value of information

Without going into the details, the application of these approaches reflects the inherent idea that value relies on data as means of transmitting information. Instead, moving the focus from data to algorithms as capital assets also means to find and develop new models and methods able to obtain a valuation of the latter.


In conclusion, Algos should be capital, Big Data are commodities


The question if Big Data are capital assets is not trivial, with impacts on enterprise valuations and liabilities [8]. We believe that looking at data as commodities offers a different perspective, moving the focus on algorithms as means to generate value by processing information conveyed by data. This is not only of interest for accountants and insurers, but at large for everybody that is involved in developing the business, making it operational and managing the associated risks. After, the next big challenge is about how to value algorithms.


  1. Daniel Moody and Peter Walsh, "Measuring The Value Of Information: An Asset Valuation Approach," Proc. of 7th European Conference on Information Systems - ECIS ’99, Copenhagen (Denmark), June 24-26, 1999
  2. Godfrey, J., Hodgson, A., Holmes, S. and Kam, V., Financial Accounting Theory, 3rd Edition, John Wiley and Sons, New York, 1997
  3. Henderson, S. and Peirson, G., Issues in Financial Accounting, 6th Edition, Longman Cheshire, Melbourne, Australia, 1998
  4. asset. Retrieved January 28, 2017, from
  5. commodity. Retrieved January 28, 2017, from
  6. Infonomics.
  7. Nicole Laskowski, "Six ways to measure the value of your information asset," SearchCIO, May 2014
  8. Bernard Marr, "Big Data: How A Big Business Asset Turns Into A Huge Liability,"