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.

In this article, I would like to share some insights on why your FP&A analytical transformation might fail and how to avoid this. The article is based on more than 20 years of relevant experience transforming large organizations.

The explosion in computing and data processing power has led to an exponential increase in data available to business. Paradoxically, this has led to business leaders becoming more uncertain about what to do with this data. Hence, business is scrambling to put the appropriate “analytics” capability in place. This generates a lot of friction and tension because business leaders and managers, who have been brought up in a very different world, have to scramble to learn new languages and redress their relationship with data.

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.

Excel is still a popular tool when it comes to preparing financials or analysis. However, we often hear financial professionals complaining about how inadequate Excel can be. So why have we not “rid” ourselves of this seemingly “inadequate” tool. This article explores the pros and cons of using spreadsheets. There are a few best practice tips that may be helpful to the vast majority of spreadsheet users.
 

Digital transformation has created new opportunities. It's possible to generate insight from more sources of data, faster than ever before. But technological advances have also increased competitive pressure. How can FP&A teams adapt to these challenges and redefine their role in modern business? How can FP&A remain relevant in an organisation where everyone is an analyst?