The explosion in computing and data processing power has led to an exponential increase in data available to the 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.

This article will focus on is the modeling of a company as a whole, its consolidated future financial positions, incomes, growth and risks, as opposed to the detailed budgeting of one specific aspect of a company’s business, such as how to increase contract to sales conversion rate. 

As I walk around various offices or even in social gatherings, I find many conversations about artificial intelligence (AI), robotic process automation (RPA), and big data. And logically, then, the discussion quite often rolls into how our life will change due to the availability of data, how each of our actions is turning into data, how future consumer behaviour 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' behaviour, customer behaviour, 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.
 

In this blog I take a broader view of new products and talk about how best to monitor progress post-launch when information is still a little sketchy, volumes are still very low and reporting mechanisms may not yet be fully in place.

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.