Modeling in 4 Dimensions

Modeling is a big idea and one that I started noodling on many years ago. In the last couple of weeks, I’ve looked at the cloud computing model, or what it ought to be. Today I am looking at a broader paradigm. One of the great things about new paradigms is that there is no model per se. In a way, a new paradigm is an opportunity to create the model; it’s an incipient model, perhaps.

The most interesting models or modeling I can think of are those that take place in four dimensions. You will recall without much prompting that we live in four dimensions, time plus the three that define space, and from this we get Einstein’s space-time.

People who study the big bang speak of matter, space and time as “condensing” out of the event. That choice of word has always fascinated me.

There might even be more dimensions that we don’t know about or comprehend. How can that be? I don’t know; the only analogy I can make is that my dog lives in the same four (or more) dimensions as I do but he’s only apparently aware of the ever-present now.

Like Riding a Bike

Four-dimensional modeling is not hard to comprehend, but like a dog, we routinely fall back a dimension to deal with reality, especially in business. Let’s use the analogy of riding a bike. The bicycle stands against a wall in four dimensions like everything else, though it can be comprehended in three very easily. But riding the thing is definitely a four-dimensional experience. You can’t ride a bike unless you make a conscious effort to go through space-time balanced on those two wheels.

Learning to ride a bike requires modeling — either training wheels or the expert hand of an adult or perhaps an older sibling who models for you the feeling of keeping your balance. No amount of discussion beforehand is very instructive for a first-time rider because words fail to communicate the feeling in your stomach as you take your first ride.

In business, our models are typically three-dimensional. The two that make the most interesting contrast for me are the accounts receivable (AR) report and the sales forecast. The AR report tells you in a two-dimensional grid about what was done in the one-dimensional past. It gives you an accurate representation of what is owed and what will come in, barring some future four-dimensional miscue.

The sales forecast is much different. We treat it like the AR report but with far different expectations. The forecast is purely four-dimensional, but we insist on treating it like the AR report which, though not perfect, is physics compared to the sociology of the forecast.

An Artifact of the Times

My point, and perhaps it is not a big one, is that good forecasting requires a four-dimensional model, and that can’t be done with a report. You might disagree, and the evidence is on your side, mostly. For decades we’ve used a standard forecast report to predict future revenues, but honestly, it’s been far less than satisfying. We get our forecasts wrong quite a bit. My data invariably shows that sales forecasts rarely have a 90 percent confidence level. It’s a narrow range — 50 percent is as good as a dartboard, so there isn’t a lot of room to work in. We complain about forecast accuracy with the same frequency we complain about the weather, but as Mark Twain wryly observed about the weather, we never do anything about it.

That standard sales forecasting via reports and spreadsheets has worked so well for so long is not a tribute to the method but an artifact of times when we were able to sell standardized products into huge markets. Demand was usually sufficient to backfill one opportunity with another when necessary.

But today’s markets are a bit different. We are doing far more cross- and up-selling than ever. Product lines are expanding, but categories are relatively stagnant. Getting the forecast right has never been more important because margins are smaller and there are fewer deals with which to backfill.

The solution for the sociology of the forecast might be the same as the solution for the weather — a model moderated by computer processing power. Rather than focusing on a single report that amounts to a 3D snapshot in time, we need two things. First, a model must capture past information and integrate it into the present. But the model has to also offer enough predictive value from prior experience to enable us to self-correct and avoid a crash.

A child (or a trained circus animal, for that matter) on a bike can do this, and it should not be terribly difficult for us big-brained adult humans to do so with a forecast. I have been impressed with the advances made by analytics companies in this area in the last few years, though they often discuss everything in terms of analytics. I would rather they approach this in terms of a model or riding a bike, though. Perhaps that would make the idea of using analytics less daunting.