Algorithmic forecasting in a digital world

Forecasting processes that involve people working symbiotically with data-fueled, predictive algorithms

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’s just as true in business, where the ability to predict what ahead continues to separate performance leaders from everyone else. That’s what forecasting is all about, yet it’s 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’s another way. Organizations are shifting to forecasting processes that involve people working symbiotically with data-fueled, predictive algorithms. It’s all made possible by new technologies—advanced analytics platforms, in-memory computing, and AI tools, including machine learning.

Keep reading below and download the sixth report in our Crunch time series, Forecasting in a digital world, for the full story.

So what exactly is algorithmic forecasting

The basics

Algorithmic forecasting uses statistical models to describe what’s likely to happen in the future. It’s 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’s where machine learning comes into play. Over time, forecasting accuracy improves as algorithms “learn” from previous cycles.

Models are also more valuable when they’re 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 magic

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. It’s this symbiotic relationship that makes algorithmic forecasting effective—especially when humans are organized to support and share their findings across the enterprise.

The ripple effect

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 gets 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’ve 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’ll 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 isn’t 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 Center of Excellence—can help improve collaboration and coordination while providing efficiencies in data storage, tool configuration, and knowledge sharing. And once the organization develops the forecasting muscle to solve one problem, the capability can quickly be extended and applied in other areas.

It's crunch time for finance

Explore other reports and guides in our Finance in a Digital World^TM “Crunch time” series, and read case studies about digital transformation in the finance function. Whatever your interest, one thing is clear: From cloud computing and robotics to analytics, cognitive technologies, and blockchain, a new class of digital disruptors is transforming how the work of finance gets done.