Crunch Time 6: Algorithmic forecasting in a digital world has been added to Bookmarks.
Crunch Time 6: Algorithmic forecasting in a digital world
Improving the forecasting process with predictive analytics
CFOs are in a prime position to challenge the way the enterprise looks at and consumes data. In the area of forecasting, they can champion an innovative, data-driven approach that will help people predict the financial future of their business. By modelling the potential impact of important decisions, they can help generate smarter insights and stronger business outcomes.
Manual forecasting processes? There is another way.
Human fascination with the future is part of our evolutionary heritage. Those who can foresee and navigate risks have always been more likely to survive than those who can’t. That is just as true in business, where the ability to predict what is ahead continues to separate performance leaders from everyone else. That is what forecasting is all about, yet it is surprisingly difficult (and often expensive) to do.
Traditionally, forecasting has been a mostly manual process with people gathering, compiling, and manipulating data, often within spreadsheets. There is another way. Organisations are shifting to forecasting processes that involve people working symbiotically with data-fuelled, predictive algorithms. It has all made possible by new technologies — advanced analytics platforms, in-memory computing, and AI tools, including machine learning.
Keep reading below to learn more and download the PDF at right for the full story.
So what exactly is algorithmic forecasting?
Algorithmic forecasting uses 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 analyzing data fast and affordable.
Beyond the basics
Forecasting models offer more value when they can account for biases, handle events and anomalies in the data, and course-correct on their own. That is where machine learning comes into play. Over time, forecasting accuracy improves as algorithms “learn” from previous cycles.
Models are also more valuable when they are grounded in richer, more granular data. In some cases, that might involve using natural language processing, which can read millions of documents—including articles, social posts, correspondence, and other text—and feed them directly into the algorithms. People can’t do that, not at the speed and scale required.
The real lift from algorithmic forecasting comes when it’s combined with human intelligence. Machines help keep humans honest, and humans evaluate and translate the machine’s conclusions into decisions and actions. This symbiotic relationship makes algorithmic forecasting effective— especially when humans are organised to support and share their findings across the enterprise.
Bottom line: 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 your planning, budgeting, and forecasting efforts. We have seen companies substantially improve annual and quarterly forecast accuracy – with less variance and in a fraction of the time traditional methods require – while building their predictive capabilities in the process.
The ripple effect of algorithmic forecasting
How work changes
With algorithmic forecasting, Finance does more insight-driven work and less manual drudgery. Instead of spending their time grinding through spreadsheets, humans get to bring their expert judgement to the process. Leading finance organizations are already using automation tools to help with manually intensive work like transaction processing. Automating routine forecasting tasks is another area ripe for improvement.
How the workforce changes
Your finance talent model should evolve to keep up with changes in how work is done – and that will likely require a different mix of people than you have in place today. Algorithmic forecasting depends on collaboration among Finance, data analytics, and business teams. Once they hit 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 organization, and support continued business ownership of the outcomes.
Our experience has shown us that some finance professionals are simply better at forecasting than others. They have learned to set aside their biases and look at the bigger picture objectively – and they have a knack for understanding algorithmic models and uncovering flaws that others may miss.
You will need storytellers, too—folks who really understand the business and can translate analytical insights into compelling narratives that trigger appropriate actions.
How decision-making changes
Making choices becomes a more interactive process with advanced forecasting techniques, resulting in smarter, more informed decisions, even on the fly. In-memory computing, predictive analytic software and visualization tools enable management to easily and quickly ask “what if” questions and produce a range of scenarios to help them understand potential impacts on the business.
How the workplace changes
Forecasting is not limited to Finance. Functions from marketing to supply chain to human resources all have needs for predicting 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—can help improve collaboration and coordination while providing efficiencies in data storage, tool configuration, and knowledge sharing. In addition, once the organization develops the forecasting muscle to solve one problem, the capability can quickly be extended and applied in other areas.
Common applications for advanced predictive models
Many of the companies we work with have begun their digital finance journeys by investing in cloud, in-memory computing, and robotic process automation. Others have broadened their ambitions to include advanced analytics, with an emphasis on forecasting. They want to create predictions that enable faster and more confident decision-making. This is where those digital investments can begin to pay off. Traditional approaches to forecasting can take far too long, cost far too much, and generate too little insight about potential future outcomes.
The most common applications for algorithmic forecasting we see today are:
Integrated financial statement forecasting
Working capital forecasting
Indirect cash flow forecasting
Competitive actions and implications
Tax trade-offs and revenue/profit implications
Market or country-level forecasting
Direct cash flow forecasting
Employee retention and attrition modeling
What can you gain? More accurate, customised modelling and improved performance. Learn more by downloading the PDF now.