Adjusting your business forecast as you go calls for more than just historical records—you need real-time data from internal records and, increasingly, from external feeds. If your business is to sell umbrellas, for example, then the long-range weather forecast is critical. More typically nowadays, a company can respond when it knows how many people on social media are “liking” its advertisements or latest products.

It also requires making data much more widely available across the organization. A special offer posted on the company website could trigger a surge in orders: so traditionally the website manager informs the product manager and the product manager makes enquiries to ensure adequate stocks and so on up and down the chain of command. But there is always a chance that something gets overlooked—maybe the goods are in stock but a forklift driver is out sick.

If instead you have a system that automatically broadcasts an alert to all relevant parties, warning of a likely surge in demand, you cut the risk of human error. Broadcasting an alert should not mean swamping every busy staff member with a flood of irrelevant data. The best solutions today allow each user to tailor their own dashboard to deliver just the type and format of data they need. The alert may be as simple as a flashing light, but a well-designed system will then allow the receiver to “open” the alert and dig deeper into the background data if the alert sounds relevant.

As well as making data more widely available—customized to accredited users—these systems also allow data input. Collecting data from the outermost edges of an organization has never been easy. But an automated system can standardize and store input from any number of such sources. Then an appropriate algorithm could make an order of magnitude prediction of expected performance based on a combination of such figures and on past experience.

In particular, you can generate algorithms based on key performance indicators (KPIs) that can include both internal data—such as the number of customers currently on your website or in your retail outlets—and external data such as the weather, competitors’ activity, inflation and other economic data. These algorithms can actually reduce the data flood by selecting only those KPIs and ignoring lesser factors unless they reach a critical level. Adaptive organizations respond quickly because they respond to important, relevant data.

“You can capture data that’s not always necessarily financial in nature and bring it into reporting or analysis tools and that’s something that is really, really new,” said the CFO of a Santa Barbara phone-call tracking business. “Companies are seeing that there’s a huge benefit to that in terms of predictive analysis… it forces the finance group to evolve and become really much more focused on analytics.” 

The move to continuous forecasting

Continuous forecasting, as enabled by these solutions, does not mean dropping core activities to dig up past data. It means keeping an eye on the road as you navigate today’s business traffic—cultivating a habit of ongoing alertness to opportunities, hazards and real-time performance. And the more people sharing that sense of purpose, the greater the commitment to success.

There is a general feeling that this is the way forward, but that there is much more that can be achieved in terms of continuous forecasting. There are also important implications for accountability and compliance, without the risks of manual revision while traditional spreadsheet data passes up the business hierarchy. Automated reporting not only ensures consistent data for everyone, it also keeps a full record of that data and any revisions that would provide a clear audit trail if needed.

Jon Louvar is the director of product strategy at InsightSoftware.com, specializing in planning functionality. He travels the world overseeing the utilization of Hubble software.

A longer version of this article will appear in an upcoming edition of AFP Exchange.