The Power of Influence over Prediction

When Epictetus wrote of the need to make the best use of what is within our power and take the rest as it happens, he may well have foreshadowed a dilemma familiar to modern C Suite professionals.

With Big Data and Technology combining to produce ever more powerful decision making models, is there any meaningful scope left for input from managers?

According to a recent McKinsey Insight the answer is an emphatic yes – but with qualification: it is all about the ability to influence outcome.

The growing popularity of technically sophisticated, computationally intensive statistical approaches has an unfortunate side effect: a shut up and calculate the numbers ethos, rather than one that promotes critical thinking

“Most executives today would probably admit that they are overwhelmed by the volume and complexity of the decisions they face and are grateful when models may relieve some of the burden. But they need to be careful. Decision models are often so impressive that it’s easy to be seduced by them and to overlook the need to use them wisely.

As University of Calgary associate professor Jeremy Fox observed, the growing popularity of “technically sophisticated, computationally intensive statistical approaches” has an unfortunate side effect: a “shut up and calculate the numbers” ethos, rather than one that promotes critical thinking and stimulates ideas about what the numbers actually mean.

Before leaders and their teams apply models, they should step back and consider their ability to influence the outcome. When it is high, the answer isn’t to ignore the data and fly blind, but to establish priorities for tipping the scales through strength and confidence…”.

Coming from one of the major global proponents of “Big Data” this thought piece is a timely reminder of the ongoing value and efficacy of effective leadership in a decision making process. It is worth remembering that all models are abstractions, regardless of whether it is built top-down (fitting real measurements – empirical model) or bottom-up (based on beliefs and knowledge of the interactions – theoretical).

The role of the model may well be to predict or influence outcomes – the essence of leadership contribution and strategy is choosing what not to do (with thanks to Michael Porter).