Subscribe to DSC Newsletter

Why is Data Missing from the Balance Sheet?

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.

 

  1. 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.
  2. 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.
  3. 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).
  4. 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.
  5. 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.

 

Author

 

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.

Views: 954

Comment

You need to be a member of Data Science Central to add comments!

Join Data Science Central

Comment by DEREK BELYEA on May 22, 2018 at 9:46am

"Until someone fortifies the material value of high-quality data as a consistent source of revenue and growth potential then companies will intentionally omit it from balance sheets."

This is not the problem. Lots of non-data assets show up on balance sheets that are not a "consistent source of revenue and growth potential".  They are there because FASB and IFRS require it and/or allow it.  As a first small step I would like to see better note disclosure of data assets where they are a material component of the financial position of reporting entities.

  

Comment by DEREK BELYEA on May 22, 2018 at 9:28am

FASB and IFRS go to great pains (and give accountants lots of work) to assign values to some assets and some liabilities but completely ignore others.  Yes, investors are not stupid.  They understand the value of these "hidden" assets or else they would not assign an extra half trillion dollars of value to Facebook over its "book value".  

My issue is that too many managers within organizations assign greater importance to assets that have assigned values than those that are hidden.  "What gets measured gets managed." This is a contributing factor to the widespread reluctance to implement appropriate data governace structures.  It is hard to monetize data when data quality has a low priority, metadata is a mess and the asset management lifecycle is an afterthought.

Comment by David Blaszkowsky on May 22, 2018 at 9:09am

There is a big difference between the accounting meaning of "asset" and the popular use of the term.  "_____ is our most important asset" is a routine phrase -- here we insert "data", rather than people or innovation, but that doesn't make it an accounting asset.  But, let's not worry too much ... good data, like good logos (and other intangible intellectual property that don't qualify) are still present in P&L accounting in terms of their revenue and profit generation capability.  they also increase the value of the company, which can be shown on the balance sheet.  

When I was an SEC director I had insights into the FASB and IFRS rule-making processes, and they are complex and lengthy, with lots of demands to take steps to monetize one or another activity.  Maybe some day data will make the grade.  But, until then, we know data is a critical asset, in the business sense, and we should do everything we can to make data reliable -- fit for purpose -- to run our businesses more profitably and for greater market value.  In the end, these are the most important kinds of value, right?

Comment by DEREK BELYEA on May 21, 2018 at 10:51am

Facebook is a company that exists primarily on the rent that advertisers will pay for access to its data.

At the end of 2017 the market value of Facebook was slightly in excess of USD 500 billion and the book value of its equity was roughly USD 75 billion. Few would dispute that the primary value of this company is its data, without which it would most probably not exist.  Yet its data assets appear nowhere in its balance sheet.   

This gap of USD 425 billion represents the value placed by investors on the earning value of its data. For context this amount  exceeds the market value of  the entire US airline industry. It is an amount just too large to explain away as a "subjective" difference. As such it highlights a critical shortcoming of "generally accepted accounting standards".

Comment by Cristian Vava on May 21, 2018 at 2:41am

Indeed it would be rather difficult to include data and processing algorithms on the Balance Sheet. The Financial Accounting was conceived this way to prevent "overly creative" interpretations of reality which would lead to extreme valuations. Ten years ago I was asked to do a financial valuation for processes and procedures to better understand the market value of Southwest Airlines. Delving into the analysis I had the revelation of why the rules seemed so rigid. So data and processing algorithms are not an unique case. Can you put a value on the corporate culture or deny that it has a real financial value?

For these cases we use the Managerial Accounting. Data is an expense and a liability. Think of the cost associated with setting up and running a data center from hardware to electricity, licenses, and salaries to everyone from database developers to attorneys. Then you have the liabilities associated with data collection, storage, usage, breaches, disposal. When you do an Activity Based Costing (ABC) analysis of an even moderate corporation you find that the expenses associated with data touch some of the most unexpected corners. Example: when you visit a doctor the receptionist asks you to sign some forms. So part of the receptionist's salary must be included in the data collection cost.

By itself data can't be on the revenue side since it brings value only in symbiosis with algorithms, software, processes, and procedures. It is just a small part of an entire environment. The same data can bring millions to the right company or be just a collection of random bits to everyone else. The same ABC analysis will show the value of data in the particular context where it is used. Outside of that context most likely the same data remains a bunch of useless bits taking space on a storage media.

A managerial valuation brings more clarity even if you can't take it to the bank. And this is good or bad depending on what you want from it. It is bad because you may want to get investment or a line of credit and by itself your data cannot support any valuation. It is also good because it forces a more comprehensive analysis that takes into account the operation of the entire company or at least division using the data over its entire cycle from generation to disposal. It is also good because a managerial valuation unveils surprising insights about where and how the value is generated from the data and no corporate board would ever accept to let that information go out. This is what makes apparently quasi random bits a profitable business.

Comment by Donald Ray Presnell on May 15, 2018 at 10:00pm

Until someone fortifies the material value of high-quality data as a consistent source of revenue and growth potential then companies will intentionally omit it from balance sheets.

Follow Us

Videos

  • Add Videos
  • View All

Resources

© 2018   Data Science Central ®   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service