The need for rapid and smooth decision-making has grown in all sectors – everyone needs data that has been analysed in a reliable and intelligent way, and they need it in real time, not tomorrow or next week.
Predictive analytics and automatic optimisation have now allowed many, but far from all, to improve the efficiency of various business processes. A fast-growing area of application is now financial management and budgeting.
Analysed and processed data/information provides managers with a deep insight into their company’s actual situation and also a glimpse of the future and proposals for measures. Within follow-up and planning of business, advanced data analysis makes it possible to go from traditional models to more automated analyses that utilise far more data of different types than has been the case up to now.
New types of analytics tools mean it is now possible to collect and utilise even larger and more diverse data masses. Advanced, predictive analytics finds patterns and relationships in places where, until now, it has been almost impossible to find them. These patterns allow us to predict the future in a surprisingly reliable way.
When finance professionals have access to reliable information that is based on both historical data and future scenarios, it means that they no longer need to spend time on traditional, and often very time-consuming, routine tasks and they can instead focus on more important matters: analysing and planning various business options.
The old, goal-based budgeting routines can therefore be consigned to history. The traditional type of budgeting can be automated. Self-learning algorithms can generate “routine budgets”, i.e. predict the future based on existing historic data. This means predictive analytics is the number one tool in financial management. It offers considerable opportunities for more efficient and improved budgeting, especially with growing data quantities.
After budgeting has been automated, the next phase is to produce possible scenarios. For example, how would strategy-based reallocation of resources to a certain area affect the company’s outlook? Or would it be possible to generate added value for the scenarios and subsequently for decision-making by collecting real-time production data for the predictive model? Analytics methods can be used to examine and compare the relationships between financial and non-financial data. This can be used to model cause and effect relationships and to support strategy-based budgeting and decision-making. And this is the point where the finance professionals can finally take over.
Predictive analytics produces the most successful budgets in situations where business operations continue unchanged.
Predictive analytics models can be used to produce budget suggestions based on different scenarios and to quickly produce predictions of the way in which different decisions influence the company’s result.
Automatic optimization can help find the budget scenario that best supports the company’s strategy.