… accounting, for the value of data.

Accounting is a discipline that uses data, and lots of it to make sense of and steer businesses. And experience in combination with hindsight (or rear views) from data is the only way that is available when managers and accountants use this information to solve operational challenges. But, when there is little or no precedence, this can be just too precarious.

In situations such as this, there is as a need to look ahead accurately and reliably, to anticipate customer behaviour when competitors are transforming prices, product combinations and offers/deals at the snap of the fingers, the payoff/risk to managing data astutely is exponentially amplified. When looking ahead, and not at the rear (history), to drive ahead of competition a clear and trusted forward view is vital. And to not ‘look’ forward, some suggest is irresponsible and downright dangerous.

Analytics combines our understanding ofmathematics, statistics and people behaviours…

… with current data or information to deliver just this — accurate and consistent predictions of future occurrences of individual customer behaviour, specific machine maintenance limits, rates & extent of spread of disease, voter intentions and much more.

Evidence of increasing returns/loss from the new role of information and insights from data has taken a dramatic turn, and become an essential component of everyday business. What has been opaque until now, is the use of insights from data and its applications that form the very foundation of economic growth and profitability. Traditional and entrenched ways of accounting are due for disruption as competing theories that have stressed the role of capital stock and natural resource are no longer warranted as they continue to be taught at tertiary institutions and perpetuated by convention & rules. And yes, I am proposing changes in the way we account for data.

Assets are to be used for the benefit of the business. But how does your organisation treat its data assets?

Data is an intangible asset. And, intangible assets are long lived assets used in the production of goods and services. They lack physical properties and represent legal rights or competitive advantages (a bundle of rights) developed or acquired by an owner. In order to have value, intangible assets need to generate some measurable amount of economic benefit to the owner, such as incremental revenues or earnings (pricing, volume, and better delivery, among others), cost savings (process economies and marketing cost savings), and increased market share or visibility. Owners exploit intangibles either in their own business (direct use) or through a license fee or royalty (indirect use). The International Glossary of Business Valuation Terms (IGBVT) is a glossary of business valuation terms that defines intangible assets as “non-physical assets such as franchises, trademarks, patents, copyrights, goodwill, equities, mineral rights, securities and contracts (as distinguished from physical assets) that grant rights and privileges, and have value for the owner.”…

Just as assets are identified & classsed for valuation, then depreciated — data can and should be treated the same. Data also needs maintenance, replacement/destruction like many other asset classes.

Data can and should be monetised for benefit by means of its use (utility) and in its raw form (sold and re-sold).

Asset Register (identification & inventory) — assets are identifiable and inventorised or catalogued. Assets are identifiable as all physicals asset are audited and accounted for. But, more often than not, data assets are neglected. Just last week, both a CEO and CIO that I worked with were surprised that they had over 650 data sources within the organisation when it was generally thought that there was no more than perhaps 50–60 sources of data: or 10x more than initially anticipated. If some of the most successful organisations I work with do not know what data elements exist, let alone how many or how valuable these assets are; how is data to be monetised?

Asset Classes — assets are often grouped in meaningful ways, likely also to allow for depreciation rules to be applied. For example, furniture, electronic equipment and buildings are different because of the way in which physical assets are classified. Company divisions, office locations, and projects are other ways in which physical assets can be classified.

Data is classified under domain (customer, product, employee, organization); use (orders, billing, research); location (data store, data center, computer), where and to whom it is delivered (report, query, end user) amongst others. As with physical assets, classification gives us many ways to assess groupings and individual data assets and their use.

Money & Risk (Valuation) — valuing a physical asset is vital for a number of reasons. Apart from understanding the money value, a critical reason is the managing of risk and reliability of the business. Insurance and security assets require, as well as the investment in maintenance to be made are not insignificant for many businesses.

Data is traditionally never valued (in a valuation sense). The value of data may not necessarily be in money (dollars), but is linked as like the jugular for business processes — what would be the impact of losing customer names and addresses or billing data? What would be the impact if we lost the ability to plan supply chain and manage inventory from the loss of product, pricing and channel data?

Depreciation — physical assets depreciate. Depreciation is accounted for as an expense item on an income statement as the value of physical assets are affected by time. The apportionment of asset value over time reflects a need that assets need to be replaced at the end of a (its) useful life. Similarly, the value of data is affected by time, and have a predetermined ‘shelf-life’. How much time needs to elapse for a customer record before the accuracy of the customer data deteriorates and is unusable? Or time passed since the last order for a part from a supplier for confidence in its price? This illustrates a loss of value over time and is, in fact, a depreciation of data assets.

Asset Hygiene & Maintenance — as physical assets require maintenance over their useful life, conditions evaluated, risks of failure assessed, and maintenance decisions made; data assets require the same. Data asset maintenance consists of data audits for accuracy, currency, completeness and cleanliness using ongoing hygiene and quality governance programs that extends to all data in the business. It is a common practice to apply data hygiene/quality & governance practices to data warehouses and master data but this is just as important for all data sources and application data as well. This makes data consistent and correct wherever it is stored and is a counterpart to standardising on operations, customer experience or that simple modular office furniture for the business.

Replacement or Destruction — physical assets have a concept of ‘end of useful life’. At this point a decision is made to replace or destroy it, depending upon whether that particular physical asset is still needed for business operations.

Business data, however, is never destroyed. Data in the form of digital communications, accounts, legal agreements and the like are often stored or archived and often forgotten because already negligible storage costs that continue to decline.

But to keep data from depreciating in value, many financial services and retail businesses are replacing and supplementing their data with more up-to-date information from both internal and external sources, often generating more data and more value in the process. So for data assets, the important thing to do is to evaluate the opportunities that will arise from ensuring that data assets are up to date. Do data assets ever need to be destroyed? When products get obsolete or are no longer supported, business units are sold off, or customers move away or die, other business activities make data elements worthless, it is time to destroy data. This comes down to managing data assets closely to maximise their value to the business. And the relatively new area of data governance becomes ever so important. Unlike physical assets, where these decisions are made near or at the end of each asset’s physical life, data assets are more dynamic and these decisions should be evaluated regularly.

And finally, the use of analytics puts the data asset we already have into working mode, and to deliver the advantage all successful business have and the others crave for. This is being responsible for the use of existing assets that have already proven to deliver more profit, more efficiency and more growth; rather than simply squander it away.