The Data Jungle: Interface Design for Innovative Forecasts

Having already touched on the troublesome nature of valuing precision over a greater sense of trend direction, part of what makes a forecast tool more useful is an emphasis on user experience. Creating an effective process or tool is often more about the design than it is about the function.

"Apple has become an industry juggernaut on the back of a simple design."

A perfect example of this is the products designed and manufactured by Apple: While often more limited in customizability and depth than their competition in the market, Apple has become an industry juggernaut on the backs of a simple design and an intuitive user interface. When you buy an Apple product, you likely aren't thinking, "This is the most powerful, most complicated tool possible." You're thinking, "This has everything I need and is simple to use."

The Power of Simplicity

Often the most critical design consideration in almost any product is simplicity: You want a tool or app to do everything you need it to, without feeling cluttered or overcomplicated by extraneous functionality or data noise. When it comes to a forecasting tool, making a complex process easy to navigate is critical to having a successful forecast interface.

The increased sophistication of analysis and predictive tools make it easy to cram lots of data into a user interface. With the click of the a button, you can generate endless tables, graphs and all manner of colorful items. But when it comes to effective design - where you can fully visualize and, more importantly, understand and utilize the relevant data - less is more.

The Granular Paradox

Good forecasts engage field teams with a light touch that allows them to build forecasts at a granular level. This ability to construct a highly accurate forecast by taking into account granular elements of data - as well as being able to easily modify the process as it goes along - is something of a paradox. Traditional forecasting often takes on an element of "set it and forget it," letting you enter data and generate a forecast that remains untouched until the forecast period ends. How can you be forecasting if you're always updating?

Solving the granular paradox requires thinking about forecasting as a work process: What does the user need to do, what information does he/she need to do it, what's the simplest way and how do we provide instant feedback as they create it? In this way, you can create forecasts that reject the binary outcome of "right or wrong" and "accurate or inaccurate." 

Interface in Action

One of our clients updates a forecast monthly for over 24,000 SKUs sold across 8,000 customers by over 100 account executives. Accurate forecasts means that each of these SKU/customer combinations needs to be updated for each forecast period. We developed a process whereby volumes are statistically projected, and only the most important ones are highlighted for review.

In another client, where an Excel model required update in every cell for every line, we developed a model that allowed the user to focus only on those items that changed, and the system pushed those changes through to the details. These changes allow for a very fast update process, and let executives focus on the outcome of the forecast, not the minutiae of the process.

"Forecasting should never been a process of waiting to be proven right or wrong."

A Structured Process, Not a Discovery

Forecasting should never be a process of waiting to be proven right or wrong. In the fast-paced, ever-changing modern market, most enterprises can't afford to sit back and wait, fingers crossed that the outcome they prepared and budgeted for happens to occur. Instead, approach forecasting as a structured process, not an analytical discovery model. It needs to be flowed and scripted so that it can be effectively, efficiently and accurately executed for the ultimate good of your enterprise.