Step by step to more intelligent forecasts
Why a collaborative, prove-it-as-you-go approach is critical to truly harnessing the power of advanced data and analytics.
Just about every business leader today understands the power of advanced data and technologies to deliver valuable new insights.
Artificial intelligence (AI), machine learning, terabytes of data, and predictive algorithms that train themselves to get smarter—these are now firmly established capabilities, not some science fiction vision of the future. They are powering more intelligent forecasting and generating bottom-line value for leading companies today.
But here’s what many business leaders still miss: Intelligent forecasting is much more accessible and executable than most realize. All that data and technology and the self-taught algorithms may sound like a massive enterprise planning initiative. But, by design, successful intelligent forecasting projects are highly iterative, step-by-step efforts that often show results in just a few months, as we outline in our thorough new report, Demystifying intelligent forecasting.
You don’t need to rebuild your entire forecasting platform in one giant project—and in our experience, that rarely works anyway. Instead, plan to go one step at a time, establishing capabilities and demonstrating results forecast-by-forecast, gradually expanding that recipe across the enterprise, and allocating resources and investment as you go.
It just starts with the first step.
One of the biggest barriers we continue to see with the move from traditional forecasting to more advanced predictive forecasting is that it sounds complicated.
From KPMG’s new report, Demystifying intelligent forecasting
Setting up for success
Iteration and steady expansion are key themes of effective intelligent forecasting initiatives, but that doesn’t mean “winging it” right out of the gate. Early alignment on both the short- and long-term goals is critical, with fierce collaboration across all appropriate teams.
Most important, the overall planning should be business-led and technology-enabled, with an invested function like finance or operations (or both) typically leading the way. And that concept of “fierce collaboration” is not simply corporate white noise: One of the most common mistakes that we see on intelligent forecasting initiatives is multiple teams testing multiple new tools without any coordination—often working from different data and technologies.
But a coordinated, step-by-step approach ensures that all are working from the same data, predictive tools, and business rules. Bonus: This iterative approach means the project’s required investment will scale as it proves value.
The roadmap for successful intelligent forecasting initiatives typically includes four foundational phases, as we outline in a related in-depth report, Intelligent forecasting: From proof of concept to connected capabilities. Here’s a quick look at each phase:
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From back office to mission-critical
Another big finding from our report is the dynamic rethink that is under way around the long-standing assumptions that managed services is usually best suited to back-office functions. The 800 executives in our survey—from companies with at least $1.5 billion in revenues in major sectors like retail, financial services, and healthcare—are moving beyond that traditional, functional-only model.
Increasingly, they expect to connect an expanded level of managed services throughout the enterprise, and especially as they battle for the required talent in growth-critical areas like digital innovation, customer experience, and risk management. Indeed, 80 percent of the executives said that the talent from their managed services partners will play an essential role in sustainable business transformation.
With this approach, companies can better link the front, middle, and back offices into a more fully connected enterprise that provides all areas of the business a better view of—and a bigger stake in—the company’s goals. And the executives in our survey expect that connectivity will deliver significant business outcomes. For example, about 7 in 10 anticipate improvements via managed services at scale in each of the following areas:
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Essential components
For some companies, another barrier on intelligent forecasting is wrapping their collective head around the encyclopedia of technology terms. It can all sound, well, hard.
We’ve found that breaking things down into a few key areas can help, and it’s important to note: These are established tools and techniques that many companies are using today in their forecasting platforms. Here’s a closer look at a few key components:
Predictive models
This is intelligent forecasting’s “brain”—the algorithmic models that crunch the data and generate advanced insights. AI and machine learning enable the models to scan large amounts of data quickly—at speeds today that humans simply cannot match. There are hundreds of established predictive models publicly available, but most companies will drill down to just a handful that work best for their forecasting needs.
Data and signals
This is the engine room, powered by both internal and external data. Most companies can start with their existing data, and then can add new data over time, and especially as they are able to measure the increased value and accuracy. Incorporating new external data signals can be especially powerful—for example, using local weather and transit data to sharpen the accuracy of retail store revenue trends.
Visualization and interfaces
These platforms cover how users get their data—from spreadsheets to web dashboards to power-user apps—while ensuring that everyone is working from the same data and forecasts, no matter the output. These are typically integrated right into a company’s EPM system, allowing teams to collaborate and do sophisticated scenario-modeling.
Advance directly to “Go”
Step by step, steady iteration, and ongoing improvements. Done correctly, intelligent forecasting is the “project” that’s never finished. And that’s by design.
The overall business world will continue to change, and your competition—both the “known” of today and the “unknown” of tomorrow—will continue to come after your market share. But intelligent forecasting can help you maintain your edge with forecasts that are designed to continually evolve, improve speed and accuracy, and deliver powerful business insights that generate bottom-line value.
And, ultimately, it’s not just the algorithms that will get smarter over time.
