I am currently a data scientist & am also a certified lean six sigma black belt. I specialize in the Big Data Finance, EPM, BI & process improvement fields where this convergence of skills has provided me the ability to understand the interactions between people, process and technology/ tools.
I would like to address the need to transform traditional EPM processes by leveraging more machine learning to help reduce forecast error and eliminate unnecessary budgeting and planning rework and cycle time using a 3 step ML approach:
1st, determine which business drivers are statistically meaningful to the forecast (correlation) , eliminating those that are not.
2nd, cluster those correlated drivers by significance to determine those that cause the most variability to the forecast (causation).
3rd, use the output of 1 and 2 as inputs to the forecast, and apply ML in order to generate a statistically accurate forward looking forecast.
Objection handling, in my experience, focuses on the cost, time and the sensitive change management aspect- how I have handled these, for example, is as such :
- Cost: all of these models can be built using free tools like R and Python data science libraries, so there is minimal to no technology/tool capEx/opEx investment.
- Time: most college grads with either a business, science or computer engineering degree will have undoubtedly worked with R and/or Python (and more) while earning their degree. This reduces the ramp time to get folks acclimated and up to speed. To fill the remaining skill set gap, they can use the vast libraries of work already provided by the R / Python initiatives or the many other data science communities available online for free as a starting point, which also minimizes the time due to unnecessary cycles and rework trying to define drivers based on gut feel only.
- Change: this is the bigger objection that has to be handled according to the business culture and openness to change. Best means of handling this is to simply show them. Proof is in the proverbial pudding so creating a variance analysis of the ML forecast, the human forecast and the actuals will speak volumes, and bonus points if the correlation and clustering analysis also surfaced previously unknown nuggets of information richness.
Even without the finding the golden nugget ticket, the CFO will certainly take notice of a more accurate forecast and appreciate the time and frustration savings from a less consuming budget and planning cycle.