#advacned analytics

I have always had this hypothesis, based on my empirical observations and discussions with senior thought leaders, that Finance Organizations have been slow adopters of advanced financial analytical techniques - e.g., statistical techniques for cash forecasts or data mining / regressions for bad debt predictions.  

Came across this blog on Proformative by Rob Kugel of Ventana Research that validates my hypothesis. In a survey conducted with Financial Analytics users, 58% (or almost 3 in 5) say that “significant or major changes are required” in their analytical capabilities / processes / technologies. 

Four main reasons seem to be driving this inertia - (i) too slow to implement; (ii) isn’t adaptable to change; (iii) there aren’t enough skilled people to do this work; and (iv) data used in it is inaccessible or too difficult to integrate.  Most organizations have rightly focused resources on the data challenges. However, as data access and integration has matured with Business Intelligence, Master Data Management and Data Warehouses platforms, the focus should essentially shift to -

hiring the right financial analytics geeks (typically FP&A users with data science skills),

equip them with nimble data discovery & visualization / OLAP / predictive analytics style financial analytical tooling such as Tableau / IBM Cognos TM1 / Oracle Essbase / SAS / R etc., that help in modeling and analysis (versus mere data access and integration); and

help them manage their own modeling environments, co-exiting in a flexible ecosystem that grows in an extendable and governed way. 

The survey clearly points to 71% of Financial Analytics users relying on spreadsheets, but 67% find that these very spreadsheets have been cause of the problem - neither finance analytics users have had an opportunity to upgrade their skills, use the right tools or build in a flexible modeling environment.