Do Finance Functions Really Need To Spend More On Analytics Tech?

With the prevalence of analytics technology, is it a necessity for finance departments?


A recent survey of FP&A practitioners by the Association for Finance Professionals (AFP) has emphasised the need for greater investment in analytics technology, with Jim Kaitz, president and chief executive officer (CEO) of the AFP concluding, ‘Greater investment in technology liberates FP&A staff to do what they were hired to do, and what their organizations need them to do; namely, conduct robust analysis and forecasting to better inform their company’s strategic decisions.’

His summary was echoed by a headline in our sister publication CFO Magazine reporting the research, which read, ‘Disappointed with FP&A? Ramp Up Tech Spending.’ The common theme, it appears, is clear: Spend more on technology, or else.

The AFP survey, which questioned 255 leaders in the field, found that in companies where investment in technology accounted for less than 10% of total FP&A spending, an average of 384 FTE days per year - and a median of 60 FTE days - was spent collecting and manipulating budget data. When technology was between 10% and 20% of FP&A spending, the mean number more than halved to 154, while the median also dropped by half to 30 days, and then halved again in companies spending between 20% to 49% of their FP&A budget on tech, down to a mere 62 days on average collecting and analysing data, and a median of only 14 days.

The logic is sound. If you have the best equipment, you’re going to be more efficient, and the best equipment costs money. A farmer with a tractor, for example, is going to be far more effective than one with a horse, the latest predictive analytics tool is going to be more effective than a crystal ball, and so forth. However, it is also important to invest in the right technology. A farmer with a nuclear arsenal, four iPhone 7s, and a T-Shirt gun, is not going to plow their field any better than their neighbor would with their horse. And a company that has invested millions on new analytics technology that’s wrong for their business is not going to be able to make better decisions than a counterpart who has invested far less on the right thing.

Finance leaders have to ask themselves what the technology will add to their business? Is the technology worth it? How many steps your new technology might eliminate from current processes? Even more importantly, they have to factor in all the costs around optimizing their use of the technology. Data analytics software requires spending not just on the systems, but the training around usage and understanding, as well as manpower spent developing the functions and processes, requisite to exploiting their data. The ability to store data also needs to be in place, which may require further investment. Senior Analyst Krishna Roy from 451 Research has noted that considerations like the ability to store and aggregate massive amounts of data and, ‘…the ready availability of economically priced processing power because machine-learning algorithms need a great deal of horsepower.’

With the hype around predictive analytics, machine learning, and other analytics products so high, it is understandable there is a rush by many in the finance function to buy the latest flash toy. And the results, if the right toy is found, speak for themselves in improving analysis. However, while it may be a hoary old cliche, it is true that fools rush in, and it is better to focus investment than bet the house. The risk of having to tear out a new system after you have just made a massive investment in it because there’s something better out there is too great. 

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