Can Data Be a Balance Sheet ‘Asset’?

Can Data Be a Balance Sheet ‘Asset’?

The distinct characteristics and dynamics of data call for new approaches to express its value. Companies are now ready to put data on the balance sheet

Oil, soil, sunlight, oxygen, and even carbon dioxide; data has had several metaphors applied to it to underscore its criticality in today’s world and, at times, the negativity it generates – depending on the situation. In 1975, tangible assets comprised up to 83% of a company’s valuation; but today up to 90% lies in intangible assets – data, intellectual property, brand, reputation, and trust. Doubtlessly, it is the most valuable asset that organisations can own today. And hence, data fits the International Financial Reporting Standards (IFRS) framework which defines an asset as follows:

“An asset is a resource controlled by the enterprise as a result of past events and from which future economic benefits are expected to flow to the enterprise.”

Seven of the top eight companies in the world by market valuation are data companies – Microsoft, Apple, Amazon, Alphabet, Facebook, Alibaba, and Tencent. Acquisitions of data asset-heavy companies are increasing; for example, Microsoft acquired LinkedIn for $26 billion, Facebook’s acquisition of WhatsApp totalled $22 billion, and Google has acquired Fitbit. Yet, ‘data-as-an-asset’ is yet to find a place in the balance sheets of companies as they struggle to find the right way to measure it.

90% of corporates will declare data as an asset

Analyst firm Gartner reports that even the most info-savvy organisations are yet to list their data in the asset column of their balance sheets. It’s not that they don’t value data — they just have no accounting models for measuring that value. Consulting and IT services company Accenture, however, predicts 90% of corporate strategies will explicitly mention data as a critical enterprise asset by next year. The International Monetary Fund (IMF) says that a transparent mechanism for the valuation of data is urgently required. We could declare data a tradable asset through such an exchange mechanism, using market forces to price it for specific uses based on demand – IMF argues.

How to assign a $ value to data?

Basically, an intangible asset like data brings subjectivity into asset valuation; and businesses loathe unpredictability and vagueness. However, data can potentially find a place in the balance sheet if we can assign a dollar value to the data assets. In fact, AT&T did place customer lists (a master data element)in its 2011balance sheet as an intangible asset for $ 2.7 billion. In this backdrop, the Data Monetization domain has significantly matured in the last few years. The key is assigning a $ value to data assets, and that is the first step in data’s journey towards finding a place in the balance sheet.

As organisations grapple with the idea of assigning a dollar value to data, Gartner is trying to push forth its own formula for addressing this challenge. Gartner analyst Doug Laney even wrote a book last year on the subject he named ‘Infonomics’, after the field of inquiry he spearheaded on the topic. His basic idea: shoehorn information into the intangible asset class in standard accounting, thus representing information alongside other intangible assets like Intellectual Property rights and licenses.

Archaic accounting – a stumbling bloc

Sounds promising to be sure – but the devil is in the details. It seems that either representing information as an asset class is simply too hard, or perhaps the advantages of the status quo outweigh any logical reason to change how we account for information. Laney reports that although data meets the formal, established criteria of a balance-sheet asset, archaic accounting practices disallow the capitalisation of information assets on financial statements. As a result of this omission, many organisations are neglecting their data – and missing out on huge opportunities to drive revenue from it.

Consulting firms are not far behind in capitalising on this new idea of treating data as an asset in corporate balance sheets. Deloitte differentiates the “relief from royalty” method, which estimates how much a company would be willing to pay to acquire data it did not own from a third party. The Bennett Institute has identified “stock market” valuations that measure the advantage gained by companies that invest in acquiring data and developing data capabilities.

Cost, income & market approach

There are three approaches being discussed about how to put the dollar figure to data – Cost, Income, and Market – and each of those comes with its respective challenges. The “cost” approach involves identifying the total costs to generate, collect, store, and replace the data, as well as costs if lost – thus determining the profit margin and calculating the value of the subject data. Although useful for data owners and processors to conceptualise the value of their data, it is limited to capturing economic value created from data or return on investment. Nor does this model capture liability costs, such as those pertaining to privacy and security.

The “income” approach measures the impact data has on a company’s bottom line by estimating incremental revenue, costs, and capital, and the impact on future cash flows that companies can derive from the data. However, it is difficult to differentiate between value added by the underlying data itself versus value-added more broadly by other dimensions of product/service performance or experience. This subjectivity makes it harder to predict the potential future value created.

The “market” approach measures the current value of data based on what others pay for it or comparable assets in an active marketplace. However, it does not capture the value of data that businesses choose not to trade for competitive advantage reasons. Without a critical mass of buyers and sellers, data markets will not settle on a price that adequately reflects data’s economic value. They also fail to capture the option value of data and are limited in meeting the demand for data even if it is extremely valuable in tackling societal challenges.

Required a new mindset

The distinct characteristics and dynamics of data – contextual, relational, and cumulative – call for new approaches to articulating its value. This requires a mindset shift – businesses should value data based on cases that go beyond the transactional monetization of data and also take into account the broader context, future opportunities to collaborate and innovate, and value created for its ecosystem stakeholders. Assessing data against the key value and cost drivers, in the context of different use cases and with attention to shared value for stakeholders, will encourage companies to think about the future value data can help generate – beyond the existing data lakes they sit on– and open them up to collaboration opportunities.

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