The ability for businesses to accurately predict risk and develop insights has traditionally involved manual drudgery, spreadsheets, and been confined mainly to the finance department.
With the advent of new technologies such as predictive analytics, in-memory computing, and artificial intelligence (AI), smart Chief Finance Officers (CFOs) are harnessing their power to automate the process, free up human capacity, and get deeper, more accurate insights.
The success of any business, from small start-up to large enterprise, depends on how accurately they can predict future performance, as well as recognise and respond to warning signals.
Deloitte recently launched a report titled Forecasting in a digital world, the sixth in its Crunch Time series for CFOs, which delves into the advantages of algorithmic forecasting and why it will change and challenge the way businesses look at and consume data.
There is a shift away from having people gather, compile and manipulate data, to handing over the menial work to the machines – which employ data-fuelled, predictive algorithms to sift through historical data and use statistical models to describe what is likely to happen in the future.
It is a process that relies on warehouses of historical company and market data, statistical algorithms chosen by experienced data scientists, and modern computing capabilities that make collecting, storing, and analysing data fast and affordable.
Algorithmic forecasting is a well-oiled machine, with more than 80 percent of the work happening automatically. Every piece of financial data a decision maker could want is available on their device and all they need to do is ask—literally.
How it change the workforce
While it seems like the machines are taking over, humans are not left entirely out of the process. The success of algorithmic forecasting depends on collaboration with the machines and among people from different teams, including finance, data analytics, and business.
The business finance talent model should evolve to keep up with changes in how work gets done and that will likely require a different mix of people than what organisations have in place today.
However, once they hit their stride, these teams can move across the range of forecasting needs, embedding capabilities in the business and driving integration. These teams are integral to establishing an algorithmic solution that can work for the business, bring insights to life within the organisation, and support continued business ownership of the outcomes.
How it changes the workplace
The new teams required for algorithmic forecasting to succeed and the pulling of human resources from other departments will need the workplace to evolve into a more collaborative space, banishing outdated silos.
Forecasting is not limited to finance but all functions, from marketing to supply chain to human resources – basically all functions that need to predict the future to drive important decisions.
While CFOs may not lead function-specific forecasting, they should help shape these forecasting initiatives since finance will inevitably use the outputs they generate.
A shared forecasting infrastructure — even a physical Centre of Excellence (CoE)—can help improve collaboration and coordination while providing efficiencies in data storage, tool configuration, and knowledge sharing.
The beauty of algorithmic forecasting is that once the work is done to solve one specific problem, the same process and capability can be extended and applied in other areas.
Algorithmic forecasting doesn’t create anything out of thin air and it doesn’t deliver 100% precision. However, it is an effective way for getting more value from planning, budgeting, and forecasting efforts.
A commitment to algorithmic forecasting is both cultural and statistical. Making it happen involves people working with technology – neither is enough on its own. Every company will make its own unique journey from its current approach to planning and forecasting to an improved approach.