Prediction is a difficult art. There are some questions that include random variables that simply can’t be predicted. What will the spot price of oil be at close on June 20, 2017? You may be a fabulous forecaster who takes into account historical trends, published production figures, geopolitical risks etc etc and be more informed than anyone on the planet on the topic, but the likelihood of precisely hitting the spot price at close is very very low. This means that although you may be, on average, more accurate than a less capable forecaster, you will nevertheless more than likely to get the final answer wrong. Is this just as useless as someone who is just guessing?
So, are perfect forecasts really the golden standard we need to aim for? Or instead like the metaphorical “running away from the bear meme”, don’t we just need to be better than “the other guy” to get competitive advantage? The answer is Yes, you just need to be better at predicting than your competitors. You don’t need to achieve the impossible…i.e. perfect accuracy of your predictions. Many people simply abandon any effort to get better at prediction once they realise that perfection is unattainable. This is a big mistake…getting better at prediction is both worthwhile and eminently doable.
There is no advantage in predicting things that are perfectly predictable and no-one can predict the totally unpredictable. The competitive advantage lives in the middle. Being better than everyone else at forecasting hard to predict things gives you an edge even though you are unlikely ever to get the answer perfectly right.
In fact, as the diagram above shows there is no competitive advantage in either “totally unpredictable” or “fully predictable” events. No-one is going to get rich predicting the time of the next lunar eclipse anymore. Equations and data exist that make forecasting eclipse events to the second quite mundane. Similarly no-one can predict the next meteor strike (yet), so we are all as inaccurate as each other and no better than pure guesswork regarding when and where the next one will strike. But in between these two extremes there’s plenty of money to be made.
In the above chart the actuals are the orange dots and the blue line is a typical forecast. The typical forecast (blue line) even got the answer perfectly right in period 5, hitting the actual number of 33 precisely. But the Superforecast (orange line) is almost twice as accurate as the typical forecast and yet never got the precise answer correct in any one period. A decision maker armed with the Superforecast is going to be in a much better position than someone armed with the Typical forecast.
So the key is to be as accurate as possible and more accurate than your competitors when it comes to predicting market demand, geopolitical outcomes, crop yields, productivity yields etc etc. Although still unable to predict perfectly accurately, being better than everyone else yields significant competitive advantage when deciding whether to invest your capital, divest that business, acquire that supplier etc etc. So how do you get better at predicting the future…Well that’s where a combination of Big Data and Superforecasting come in.
Big Data is the opportunistic use of the data both internal to your organisation and available from 3rd parties which can use modern data crunching technology to make better predictions about what is likely to happen. Superforecasting is the practical application of techniques borne from cognitive science (commonly misnamed as Behavioural Economics) that overcome human’s natural cognitive biases and lack of statistical/probabilistic thinking to improve forecasting across any expertise domain. Between the two, any organisation can significantly improve its forecasting capability and reap the benefits of clearing away more of the mists of time than their competitors.
The key is not giving up simply because perfect prediction is impossible.
Do you know what activities in your organisation would seriously improve their performance based on efforts to improve their predictive accuracy and then significantly impact the bottom line?