I have been asked many times is data a business asset?. Why is it that an intangible asset like data is not in the company’s balance sheet - a statement of the assets, liabilities, and capital of a business at a particular point in time? Technically, an intangible asset is a non-physical asset that has a multi-period useful life. Examples of intangible business assets are patents, copyrights, customer lists, trademarks, brand names, logo, and data. While data in the recent years has provided competitive advantage to many companies, five key reasons make it challenging for data to find a place in the balance sheet.
- Costing. Any asset listed on a company's balance sheet should have an identifiable fair market value (FMV). While many companies talk about data as a monetizable asset, they struggle to put a $ figure both on the cost of data management in the data lifecycle (from origination to consumption) and the benefits that data brings to the organization. An equipment or a building if assessed by two independent assessors would pretty much land on the same $ figure. But calculating the costs and benefits pertaining to data is challenging due to complex factors associated in the data lifecycle.
- Depreciation. When tangible assets age, they lose their value i.e. depreciate. But when data assets age, they can lose or gain value. For example, when master data (which represent business entities such as customers and products data) age, it gains its relevance and value as these data objects are usually shared in the enterprise. But when transactional data (which represent business events such as invoices and deliveries) age, it loses its relevance and its value. In addition, if the data is incorrect and its useful life is just a few days or weeks, then data cannot be properly depreciated or amortized. This requires specific depreciation methods and regulations which we lack today.
- Context. Assets listed on a company's balance sheet are usually utilized in a similar manner at all times. For example, equipment would be used by two different companies in almost the same manner. The same cannot be said of data as data is largely contextual. For example, if an oil and gas company with 1 million historical purchase orders gets acquired by another oil and gas company, the value of these data records for the acquirer is very marginal who would be bringing their own procurement, polices, procedures, and their own data (on suppliers and goods).
- Capture. As per IFRS and GAAP accounting principles, any asset listed on a company's balance sheet should be an acquired or captured asset. For example, even though an intangible asset such as Apple’s logo carries a huge name recognition value, it does not appear on Apple’s balance sheet because the logo was developed internally by Apple and was not acquired.
- Compliance. Data can very quickly transform itself from an asset to a huge liability if it has poor security and privacy compliance. While tangible assets like machinery or buildings can also become a liability for a company, the rate of change in asset to liability conversion in an intangible asset like data is significantly higher compared to a tangible asset. Cambridge analytics, a company that thrived on data, filed for insolvency and closed operations in May 2018 within 2 months of the Facebook data breach issue.
Basically, an intangible asset like data brings subjectively into asset valuation; and businesses loathe unpredictability and vagueness. However, data can potentially find a place in the balance sheet if we can assign the $ value to the data assets. In fact, AT&T placed customer lists (a master data element), an intangible asset, in its balance sheet in 2011 for $ 2.7 billion. In this backdrop, the Data Monetization domain has significantly matured in the last few years. The key is assigning the $ value to the data asset which is the first step in data’s journey towards finding a place in the balance sheet.
Dr. Prashanth H Southekal is the Managing Principal of DBP-Institute , a data monetization firm which monetizes business data for insights, compliance, and customer service/operations. He brings over 20 years of Data and Information Management experience consulting/working for companies such as SAP AG, Shell, Apple, P&G, and General Electric in North America, Asia and Europe. Dr. Southekal has published two books on Information Management including the most recent Data for Business Performance.