Big Data Valuation: Assessing the Future Economic Benefit of Data through IP Valuation Approaches

Disclaimer: I am not an IP lawyer, an economist or an accountant. This series of articles on the topic of data valuation captures opinions I developed from taking an online course on IP Valuation. They are not meant to represent the views of my employers, past or present. The purpose of this article concerning technical, legal or professional subject matter is meant to foster more dialogue about the subject and does not constitute legal advice.

This article describes my experience trying to better understand the value of Big Data by seeking inspiration from the way the future economic value of patents is assessed. I did this by taking the IP Business Academy’s IP Valuation 1 course [1] and reflecting on how it could be adapted to value data.

Discussions of Value in Big Data

Big Data has long been characterised by attributes such as increasing Velocity, Variety and Volume. Each of these attributes can be quantified by metrics that are supported a widening array of technologies. Yet its “V” for value remains less described and the means for quantifying it less clear.

When is it worth providing and maintaining a data set? I’ve spent most of my career supporting knowledge infrastructure to create and manage scientific data and I’ve always asked myself this question. It is a question that would be relevant to anyone who has spent time acquiring, generating, cleaning, analysing or developing software to manage data.

Yet it can be very difficult to answer. To know worth is to appreciate costs versus benefits and in research settings both can be difficult to quantify. For example, when I worked in university research labs, the team labour needed to create data management tools was not put on a time sheet and the benefits of creating data sets seemed indirectly related to the apparent influence associated with research publications that depended on them.

Generating and maintaining data sets can create great value, but it can refer to a broad number of contexts such as historical value, social value, scientific value and economic value. When we hear of Big Data holding great value, we should be clear about what kind of value we are describing.

By extension, the term asset — something that has value — can have many contexts as well. When I worked for the MRC National Survey of Health and Development [2], I helped maintain an information asset registry as part of information governance best practice. Data had been collected for decades from study participants. Recent questions and biomedical test data could provide scientific value. The life stories captured on microfiche about participants could provide societal value. The punch cards, old mainframe wheels and hand-typed response forms could provide historical value.

Distinct collections of all of these could have formed information assets, but their value in the registry was often measured in relative terms based on the impact that would happen if their availability, confidentiality or integrity were compromised for the study [3].

In a growing number of Big Data conversations, we hear how data is the most valuable asset an organization may have. Yet accounting practices appear vague about whether data can be an asset reported on a balance sheet. Accounting standards accommodate the reporting of intangible assets such as patents, copyrights, trademark and goodwill but it is not clear whether they accommodate the reporting of data as an asset. According to one CPA Journal article, “Because data is an intangible asset that is not recognised as an asset by modern accounting standards, it is often not managed as an asset” [4].

In his book ‘Infonomics’, Doug Laney provides this insight:

“Public companies are required to inventory, quantify, or assess the value of other assets, but not their information assets. Yet, these are either their primary source of revenue generation, or increasingly and tangibly contribute to their top line. Even intangible assets, such as copyrights, patents and trademarks, are recognised and reported”

I’m not an accountant, an economist or an IP lawyer. However, as a career professional working in knowledge infrastructure, I found this idea very difficult to comprehend. I spent a long time trying to confirm this was true and I recognise that this is a changing area. Some of it remains unclear to me, but it raised an important question:

How could the growing assertion of Big Data holding future economic value be reconciled with an accounting profession that might have trouble recognising it as an asset?

It is this question which ultimately led me to explore data valuation as a topic. Data can provide many kinds of value but I was particularly interested in how its future economic value could be quantified.

The Experiment: Learn Methods for doing IP Valuation and Apply it to Data

After I took Doug Laney’s Coursera course on Infonomics [6], I decided that I had to explore the topic of data valuation. I began to ask where I could take a course on IP Valuation and eventually, I was referred to a course taught through the Munich-based IP Business Academy. The course material[1] appeared to focus mainly on patents and supporting the material in the European Standard DIN 77100: “Patent valuation — General principles for monetary patent valuation” [7].

I assumed that the material could be applied to all forms of Intellectual Property Valuation, but I didn’t take it for granted that it would necessarily apply to data.

Would IP Valuation Necessarily Apply to Data Valuation?

As I would later learn in the course, it is very important in valuation that you are comparing like with like. I wasn’t sure how comparable data was with patents or intellectual property in general:

· Intangibles such as patents are better defined than data.

· IP rights such as patents are protected by well-established international agreements and national laws. Data do not have a comparable body of legal work to support it.

· A patent is a kind of property, but it is not always clear whether data is legally considered ‘property’

· A patent is a kind of IP right, whereas data seems to be in search of IP rights

· Valuation scenarios for data seem to emphasise its role as a supporting good that contributes to other assets rather than as an asset itself

· Accounting standards would recognise a patent as an asset more easily than they would recognise data as an asset

The Term Data is Difficult to Define

You need to define something before you can value it or compare it to other things. Yet there is no universal definition of ‘data’ or ‘information’ [8][6], and they are often not distinguished in legal debates or forms of legislation [9]. Terms such as ‘patent’, ‘copyright’ and ‘trademark’ are given widespread legal context through well-established international agreements such as the Paris Convention and WIPO treaties. No analogous body of legal work exists for data.

Data is Difficult to Classify

Data can be difficult to categorise as well as define. It is not clear whether data can or should be considered a form of property [9] and this has implications for how an investment could be protected. One of the important characteristics of property is that its owners are granted exclusivity rights. As I would learn in the course, “the key to realising economic value from IP is leveraging that exclusivity within a business model activity” [1].

The Awkward Protection Afforded to Data by IP Rights

Extending the problems of classification, whereas patents are a kind of IP right, data is a thing in search of IP rights. There are no IP protections specifically designed for data, and the prospect of creating one is considered problematic by some [9][10]. Combinations of copyright and sui generis database property rights can provide some protection, but they can only protect some aspects of it under certain circumstances [10][11]. Trade secrets provide a way of protecting data, but they require security controls to maintain. They can lose their protection once they are legitimately disclosed [12] and they can lose their value if other organisations happen to publish intellectual material that happens to be similar in nature [13].

The Subservient Role of Data in Supporting Valuation of Other Intangible Assets

Putting aside the challenge of defining data or ensuring exclusive use for its owner, another challenge in data valuation appears to be perceptions of the role data plays in innovation. A patent could be the main focus of valuation and it may support the value of yet other patents. Yet it seems the most common way to value a data set is to determine its contribution to other IP products that have been valuated.

In hypothesis-driven developments, the hypothesis drives the kind of data that should be collected to help prove it. The data sets created could have some potential to help answer other questions, but often the data set will be very specific and designed to be subservient to the initial question. In these activities, it may make sense to value inventions that follow from the development efforts and attribute proportional value to contributing data sets.

In data-driven developments, an intent will drive the selection of data sources to consider in an analysis. However, it may not be clear at the outset which variables or data sources will be relevant to answering a given question. In the world of Big Data, the increasing Volume, Velocity and Variety of data accumulation can make it difficult for any one person to appreciate how variables could relate to one another. It can then appear to take on a life of its own and be capable of raising or answering multiple questions that were not anticipated. If this view is held by its creators, valuation would tend to consider the Big Data as main assets rather than just supporting goods.

The Power of Fascination with Big Data Meets the Power of the Balance Sheet

As Big Data sets grow along multiple “V”s of expansion, the result may eventually overwhelm the human capacity to make rational decisions about how best to harness them. Perhaps it’s the lack of easy immediate comprehension about it all now that makes us tend to place great hope and value on its potential. It is this future art of the possible, alongside emerging fruits of Big Data that have helped motivate me throughout the years I’ve spent supporting scientific knowledge infrastructure.

Yet, the plain view of the accountancy profession provides vital grounding for enthusiasm. As the earlier mentioned CPA Journal indicates: “Data must be processed and converted into something other than what it is to achieve its ultimate value…” [4].

Despite all the reasons I’ve mentioned that would make me cautious in applying IP Valuation approaches to data, I decided to take the course. Being able to estimate the value of data provides a powerful tool for shaping data strategies. It is an important topic to consider regardless of whether it is applied to organisations in public, industrial, commercial or NGO sectors.

There didn’t appear to be any valuation standards specifically for data, and it could turn out that IP Valuation approaches were very applicable. My goal was not to come up with a magic formula but to understand the process for how valuation was done.

The Next Article

In this piece, I wanted to introduce the topic of data valuation and my motivation for pursuing it through a course on IP Valuation. My next article will be: ‘Big Data Valuation: The Potential Benefits of Mock Valuations for Guiding Data Collection’. In it, I will describe the potential benefits for conducting mock valuations to better drive data provisioning.

Articles in the Series

  1. Big Data Valuation: Assessing the Future Economic Benefit of Data through IP Valuation Approaches
  2. Big Data Valuation: The Potential Benefits of Mock Evaluations for Guiding Data Collection
  3. Big Data Valuation: The Value of Synergies
  4. Big Data Valuation: The Importance of Market Protections, Complementary Goods and Data Asset Management
  5. Big Data Valuation: The Impact of Specificity of Complementary Goods on Data Reuse
  6. Big Data Valuation: Cost, Market and Income Approaches
  7. Big Data Valuation: Indicators and Determinants of Value
  8. Big Data Valuations: A Pause in a Journey of Learning


[1] Wurzer, A. (2021). Certified University Course IP Valuation 1. IP Business Academy. Retrieved from:

[2] MRC National Survey of Health and Development. (2021). Archive. Retrieved from:

[3] Watkins, S., Calder, A. (2020). IT Governance: An International Guide to Data Security and ISO 27001/ISO 27002, 141, Kogan Page.

[4] Collins, V. (2019, June) Managing Data as an Asset. The CPA Journal. Retrieved from:

[5] Laney, D. (2017). Infonomics: how to monetize, manage, and measure information as an asset for competitive advantage. Routledge.

[6] Laney, D. (2021) Infonomics 1: Business Information Economics and Data Monetization. (MOOC). Coursera. Retrieved from:

[7] DIN Standards (2011). Patent Valuation — General principles for monetary patent valuation. English translation, DIN 77100:2011–5. Retrieved from:

[8] Borgman, C. L. (2016). Big data, little data, no data: Scholarship in the networked world. MIT press.

[9] Barczewski, M. (2018). Value of information: intellectual property, privacy and big data (№7). Peter Lang Publishing Group.

[10] WIPO (2019, October). Intellectual Property in a data-driven world. WIPO Magazine. Retrieved from:

[10] Introduction to intellectual property rights in data management, Research Data Management Group, Cornell University. Retrieved from:

[11] Debussche, J, Cesar J. (2019, March). Big Data & Issues & Opportunities: Intellectual Property Rights. Retrieved from:

[12] Frequently Asked Questions: Trade Secrets, WIPO. Retrieved from:

[13] Nirwan, P. (December 2017), Trade secrets: the hidden IP right. WIPO Magazine. Retrieved from:



I work in scientific computing and I’m interested in art history, folklore, oral history, legends, biotech, argentine tango and medicinal plants.

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Kevin Garwood

I work in scientific computing and I’m interested in art history, folklore, oral history, legends, biotech, argentine tango and medicinal plants.