The time is ripe for analytics applications in the finance area. Thanks to more data and automated routines, decisions can be made on a more informed basis, and more quickly, which gains competitive advantages. Analytics projects may not be a guaranteed success, and always carry the risk of failure, but they do always deliver insights. Gaining early experience with new methods and learning from this will ensure a head start.
Business analytics are the "showcase topic" of digitization. For thought leader Thomas H. Davenport, "Analytical Competitors" are a new class of company that achieve specific competitive advantages from this approach. Many initiatives of this type exist already. Since financial data plays an important role in most analytics applications, CFO departments should where possible also become involved in projects where they are not the primary party affected. After some initial hesitation, most CFOs are now much more active in this area. Some are even spearheading the digitization efforts in their companies. Marcus Kuhnert, CFO of Merck, started discussing possible applications for advanced and big data analytics way back at the start of 2016.
TARGETED ANALYSIS OF COMPLEX ISSUES
From Horváth & Partners' point of view, there are four particularly useful areas of application in the CFO department: The analysis of specific issues in individual projects that take different approaches, the automation of forecasts with the help of predictive analytics, the development of a risk radar based on semantic analytics, and rule-based evaluation of possible options using prescriptive analytics for decision support.
The first area to apply business analytics extends the traditional understanding of reporting to include complex issues. This adds a powerful analytical reporting system to the increasingly automated standard reporting systems that support the finance function in its role as business partner. This involves the CFO department collaborating with operational managers to analyze targeted aspects of potential improvements, and creating a bespoke analytical framework each time as required. Initially, hypotheses are formulated and the data relating to them are identified. These are used as the basis to enable data modeling, visualization and validation. Using the insights gained, measures can be identified that would lead on the one hand to savings, and on the other hand to increased revenues.
AUTOMATED FORECASTING, SEMANTIC SCREENING
Automating forecasts is the most common area in which business analytics is applied. Predictive analytics enables more efficient forecasting processes and better quality results than traditional statistical methods. On the one hand, this is because this approach allows significantly more internal and external data to be included. On the other hand, it is able to select the most appropriate analytical approaches from the thousands available, and "learn" the models using large data sets. There are now a large number of successful applications in a variety of sectors. They have shown that the quality of delivery depends strongly on the quality and quantity of data and the algorithms applied.
In the third area, business analytics is used as a risk radar – like Horváth & Partners' own Global Risk Radar – acting as an intelligent "fishing net". Semantic analysis is used here to identify good quality information from unstructured mass data, to process it intelligently and gain new insights from it. A database with over 100 million text documents is used as a centralized source of information. Using a risk radar of this type, corporate performance management can take a more proactive approach. This means that the finance department can act as a business partner, providing information to management that would have remained hidden without the big data semantic analysis screening, or would only be discovered at a later date.
FINANCE DEPARTMENTS WHO WANT TO BE
SIMULATION OF FUTURE BUSINESS SCENARIOS
The fourth application area, using prescriptive analytics, is still only rarely found in the finance area. Prescriptive analytics aims to determine the optimum solution to an issue by applying rules. Michael Kappes, Partner and Head of the Planning Business Segment at Horváth & Partners, is convinced that powerful simulation models will in future form part of every company's corporate performance management: "Finance departments who want to be accepted as business partners also need to be able to model complex scenarios flexibly and efficiently. The simulation models required for this are increasingly based on analytical optimization logic."
An example of this is the "Business Simulation" run by SBB Cargo, the freight transport subsidiary of Swiss Federal Railways. The application enables the company to make informed decisions by simulating business issues using a variety of parameters, and evaluating them in monetary terms. Using this, SBB Cargo has increased the return on its freight business, meaning that the investment in prescriptive analytics was rapidly recouped.