Predictive analytics has significantly improved the efficiency of various business processes including financial performance management, which is one of the fastest growing applications. Regardless of the sector, the need to make rapid decisions has increased together with the challenges relating to decision-making. Real-time, reliable, and analysed data is needed.
The aim of financial management is to clarify the current state, and above all the outlook of the company’s business operations based on data analyses and processing. Analytics can be used to shift, in monitoring and planning, from the traditional monitoring and predictive models that are based on financial data to comprehensive datasets and analyses that are based on automation.
New analytics tools facilitate the collection and utilization of increasingly large and diverse data masses. Advanced, predictive analytics finds relationships between complex variables and predicts the future based on them. As information expands from historical data to future outlook, finance professionals are freed from routine tasks and are able to focus on more important matters: analysing and planning various business options.
No more budgeting routines
Old goal-based budgeting routines are no longer needed. Traditional budgeting can be automated. An algorithm can generate a “routine budget”, i.e. predict the future based on existing historic data. Therefore, predictive analytics is the number one tool in financial management. It offers considerable opportunities for more efficient and improved budgeting, especially where data quantities are growing. Analytics enable the shift to predictive budgeting, which is based on automation and facilitates quick reaction to market changes, for example.
Scenarios based on analytic models
After budget automation, 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 including the real-time data of new production in the predictive model generate added value to the scenario and subsequently to decision-making if? 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. This provides an excellent starting point for finance professionals.
Make use of analytics
- Analytics methods can be used to examine and compare the relationships between financial and non-financial data, which supports decision-making.
- Predictive analytics produces the most successful budgets in situations where the operations continue unchanged.
- Predictive models can be used to produce alternative budgets based on different solutions and to produce predictions of the effects of different decisions.
- Optimization can help find the budget option that best supports the strategy from among the scenarios produced.