Modern business intelligence systems create various advantages for companies by extracting information from extensive data volumes. They enable the automation of processes, rendering them faster and less prone to errors. At the same time, performance and traceability for many important controlling tasks increase sustainably. For example, forecasts can be created more accurately and rapidly, or simulations and scenarios can be planned over the long term with a higher degree of detail. In this way, the controller enables the management with a more forward-looking and proactive business management.
Change of the Classic Controller Image
In the past, the classic controller profile was that of an expert on a company’s numbers. The growing complexity in company structures then caused a shift in that profile toward a business partner. Digitization and the growing significance of the targeted use of company data are now requiring controllers to also develop data science skills. The fact is: The ability to extract information out of large data volumes – and more importantly, to understand it – will be a competitive advantage for companies.
Task and Knowledge of the Controller as Data Scientist
In the role of the data scientist, the controller would process a company’s large data volumes in such a way that the management can effectively use the information. However, this new spectrum of duties does not involve programming activities. Rather, a controller will discuss potential solutions with IT experts and verify those solutions with respect to feasibility and efficiency.
Specifically, a data scientist has knowledge in the following areas:
- Use of databases
- Pattern recognition between data sets
In addition to this, vital knowledge is needed of standard programming languages and methods for the storage, scaling, and implementation of big data technologies.
Only then can efficient collaboration with IT experts be guaranteed. This need for additional expertise is too often neglected in university curricula.
Controller and management should work together to define a process that structures innovations.
How Companies Benefit from a Controller as Data Scientist
Yet this kind of expertise represents the first step towards companies being able to leverage the information available in their data. The controller can also assess whether the company meets the necessary technical conditions. In principle, they should work with management to define a process for structuring upgrades. Initial pilot projects can help to estimate the cost-benefit analysis of upgrades.
After all, big data projects still tend to be associated with high project costs.
The Future of the Controller as Data Scientist
Data science’s influence will change controller work in such a way that a previously retrospective reporting is then supplemented by statistical forecasts. To date, operational but also strategic decisions are based on key figures. Examples include EBIT (earnings before interest and taxes) and cash flow.
Systems for data analysis offer the opportunity to generate indicators that are not only more up-to-date, but can also make statements better tailored to the specific situation. These indicators will not be purely financial in nature.
One example would be the indicator of engagement speed. Social network operators use this to measure the speed with which webpage visitors interact with the content there. Financial indicators will not lose their importance, but the trend is towards sharing financial and non-financial figures alike.
Certainly, key figures will in the future be tailored more specifically to issues.
Become a Business Partner of Your Management as a Controller
Want more information on how the LucaNet software can support you as a controller? Then take a look at our solution pages.