The fourth FP&A Board Connect was dedicated to the subject "How to Use Predictive Analytics and Machine Learning for Better Quality Forecast (Janssen Case Study)".
In the first FP&A Board Connect, Takeshi Murakami, Business Manager to CEO/President at Microsoft Japan, a speaker of the second Tokyo FP&A Board, explains how Microsoft achieved remarkable results by using predictive analytics and machine learning in FP&A.
Many of us have heard about promise of predictive analytics (PA) in machine learning (ML). Over 50% of organisations think that data science and ML are critical for success. At the same time, less than 20% of finance teams are deploying data science today. Why did this happen?
As I walk around various offices or even in social gatherings, I find many conversations about AI, Robotics, Big data. And logically then the discussion quite often rolls into how our life will change due to availability of data, how each of our actions is turning into data, how the future consumer behavior thus can be predicted etc. Thus people quite often discuss predictive analysis (PA) and we hear stories about its use in elections to predict voters' behavior, customer behavior, payment risks, etc.
Scenario analysis, sensitivity analysis and what-if analysis are very similar concepts and are really only slight variations of the same thing. All are very important components of financial modelling – in fact, being able to run sensitivities, scenarios and what-if analysis is often the whole reason the model was built in the first place.
We are entering the era of digital FP&A where human and artificial intelligence work hand in hand in order to achieve better analytical results. The new world of FP&A requires on-demand continuous planning process where various business scenarios can be played almost in real-time. Both driver-based planning and FP&A predictive analytics are essential tools for implementing flexible dynamic planning and forecasting process.