My hypothesis, based on observations and discussions with industry analysts / consulting practitioners / senior executives, is that Finance Organizations have been slow adopters of advanced analytical techniques - e.g., statistical techniques (say for cash forecasts) or data mining / regressions (probably for bad debt predictions).
Rob Kugel's (Ventana Research) recent blog on Proformative validates my hypothesis. In a survey conducted with Finance users, 58% (about 3 in 5 people) say that “significant or major changes are required” in their analytical capabilities / processes / technologies. Ventana's survey also points to 71% of Financial Analytics users continuing to rely on spreadsheets, when 67% find that these very spreadsheets have been cause of the problem. Per Rob, four main factors seem to be driving this inertia within Finance - (i) slow to implement; (ii) adaptability to change; (iii) non-availability of skilled workers; and (iv) information that is inaccessible or difficult to integrate. .
In a recent blog post, Anders Liu-Lindberg claims that Finance has not adopted analytics with causes spread across IT and Finance departments - former not business savvy and later unable to find talent to analyze data.
While we debate on root causes, at least one thing seems consistent - Finance Organizations are indeed slow adopters of analytical techniques. This is further acerbated when seen in the light of Board of Directors’ expectation from CFOs – with CFOs (as performance data stewards) being increasingly asked to provide analytical insights to help organization / business partners make better business decisions.
My belief on this topic is as follows - in the past, organizations primarily deployed resources on IT oriented initiatives such as Information
- Hiring the right Financial Analytics professionals as power users within Finance (typically
FP&A and Data Science skills) - Equip such power users with nimble data discovery & visualization / predictive analytics / multi-dimensional online analytical processing tools. (e.g., Tableau / IBM Cognos TM1 / Oracle Essbase / SAS / R etc.) by partnering with IT; and
- Create a culture in which power users are able to model business problems, analyze data, derive and share insights in support of business decisions, while co-exiting in a flexible ecosystem that grows in an extendable and governed way.
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