What’s it worth?
During a recent business-value consulting engagement with a global telecom company, the CEO told me he believed that the investments he was making in enterprise information assets were generating huge value. The only problem was he could see no evidence of it other than the retained revenue and revenue growth reported by his marketing team. Shortly after, the director of BI and analytics confided that, in his opinion, the advanced analytics team was a lot more valuable than the company’s information assets, and wanted to know if there a way to quantify the team’s worth.
And then, on an entirely different occasion, the CCO of a multi-national, mobile-service provider asked if it was possible to quantify the value of their data and analytics.
For both of the above engagements, business-value audits demonstrated data and analytics ROI in excess of IRR 330 percent; value which was not reflected in the enterprise performance reporting or the balance sheet. Surprisingly, many commercial and non-profit enterprises find themselves in the same boat.
So, why focus on value?
Data, BI, and analytics, have always been seen as necessary infrastructure and foundational investments. Therefore, they’ve been regarded as part of the cost of doing business – in other words, overheads. And while classified as overheads, the very investments that could help an organisation keep its head above water are at most risk from being cut.
Now, companies are realising that data and information provide a ‘fingerprint’ for customer product usage. They understand that customer interaction can be tracked and analysed across all channels, a benefit which represents a significant competitive advantage for the enterprise. And this realisation has shifted the economic emphasis onto ‘value’.
That said, it’s difficult for organisations to measure the value of data and analytics due to a wide range of factors, including shortfalls in generally-accepted accounting principles and practices.
How can analytic value just vanish into thin air?
The financial benefits resulting from data and analytics can be quantified as revenue growth, cost reduction, margin improvement, cost avoidance, revenue recovery, and cash flow improvement. These classifications eventually show-up in the bottom-line, appearing in the enterprise balance sheet as general reserves, and/or the net profit & loss account. In essence, the assets minus liabilities in the balance sheet are balanced by shareholder funds, general reserves, and net profit & loss.
However, the value generated by data and analytics is neither directly attributed to the top- nor the middle-line and, therefore, sinks to the bottom-line and disappears without trace.
The problem of value attribution
Consider the two examples below and see for yourself where the value of data and analytics went and, within your own organisation, to whom the value would be attributed.
In a deregulated energy and utilities market, the price of the highest bid required to meet demand, dictates the 30-minute clearing price for the wholesale energy market. Energy retailers have recognised that, as volatile spot prices change every 30 minutes, quarterly meter readings do not allow them to forecast usage and demand, potentially creating an unprofitable outcome. Fortunately, granular Interval Meter usage data together with corresponding weather data enables them to develop accurate predictive modelling. This helps them stay competitive and profitable in a free market.
Here’s another example from other industries where although, publically, companies don’t admit they’re losing revenue due, internal audits show that fraudulent activities are costing them up to eight percent of their annual revenue. For an organisation with a billion dollar annual revenue the loss is at least $30 million. Even if only ten percent of this is recovered, that’s at least $3 million top- and bottom-line improvement after a comparatively small investment in analytics (applied to the data the organisation already owns) of, say, $100,000.
Anti-money laundering, SIM Box, and IDD Premium Rate numbers, are known areas of fraudulent activity that require advanced analytics to detect ever-evolving patterns and allow preventive action to be taken in time.
Are data and analytics corporate assets?
The treatment of data as a balance sheet asset is also wrought with difficulties. Although data and information continue to provide growing current and future economic value to the enterprise, they are never captured as either tangible or intangible assets. Instead, the hardware and software that make-up the bulk of the initial investment end-up as tangible assets, with a book value that bears purchase cost and is subject to depreciation. Data scientists and analysts are never treated as assets. They appear as costs in the HR system that roll into profit & loss statements.
Data and analytics projects are either capitalised - appearing as tangible assets and depreciated - or written-off as expenses, destined to appear in the profit & loss account.
Crude oil, gold mine, or data lake
This notion of big data as a mineral or crude oil suggests that data is a natural resource waiting to be exploited. And that this natural resource has a known up-front value which reduces in value over time (depleting asset).
But data has no pre-set value. And I think data and analytics is an investable growth asset that propagates in volume and variety, in line with competitive pressures. Which means it can be enriched to differentiate the business and drive greater business value.
For me, big data is the cumulative effect of small increments of data thrown up during innovative responses to competition – whether that’s while winning a share of the customer wallet or competing for scarce funds in not-for-profit sectors. Critically, business users need to be able to get hold of, optimise, and analyse big data, which means configuring and monitoring data pipelines in and through the data lake so they have constant access to high-quality data.
Gaps in GAAP
Accounting principles (e.g. GAAP / IFRS) are no help either – not with value recognition nor the valuation of data and analytics (e.g. difficulties of assigning value of human talent in the books, and shortcomings of valuing enterprise data as an asset in either tangible or intangible assets). The simple fact of the matter is that the balance sheet provides no direct visibility of data and analytics assets.
Recognising the value of data and analytics
Now we’ve addressed why the value of data and analytics are not directly evident in an enterprise’s financial performance reporting, keep an eye out for my next blog, ‘How to make the value of data and analytics visible to the CFO’.