Nowadays a lot of concerns in business environment are focused around data. There are several drivers for that, such as regulations (e.g. GDPR), the growing amount of data, new technologies in data processing and analysis etc.

As most forecasting methods require data, a forecaster analyzes the availability of data from both external and internal sources. The availability of external data is improving rapidly. With the explosion of Internet websites, potential sources of valuable data are becoming limitless. With unstructured data, the need for data mining tools has become a necessity for exploring potential sources of data for consumer analyses and predictive modelling purposes.

As an FP&A professional, you deal with data. Your data travels a long way from its source through applications to your reporting system.The data you deal with is the tip of the iceberg. The major part of data processing is the part that is hidden beneath the water. This article describes the four key actions that can help FP&A to become data-driven.

Nowadays, everyone is talking about being ‘data-driven’. What lies beneath this idea, is the wish to make the decision-making process easier and more effective. But in general, it means delivering the required data of acceptable quality to the relevant decision makers when and where they need it.

What are the FP&A concerns about being ‘data-driven’?

How far can you go with improving your FP&A practices? This article reveals relationship between three important  factors for any FP&A frameworks:  the quality of data, business planning and analytcal tools and techniques. 

Business users want the power of analytics – but analytics can only be as good as the data being analysed. A survey by TDWI has revealed best practices to improve data preparation, finding 76% of businesses hope to increase data-driven decision making and 37% are currently unequipped to do so.