Imagine the following scene. In a room, representatives of different areas of a company observe a screen with two alternatives to decide. Alternative A is backed by a vast collection of data, while Alternative B is just that, an alternative without any grip. Who raises his hand to give an opinion is Hippo, an English acronym that can be used for both hippopotamus and highest paid person’s opinion (opinion of the highest paid person). Can you imagine what alternative he chose? Exactly, B, the same one that the company ended up implementing.
Organizations have at their disposal an unprecedented amount of information and we are undergoing a technological transformation that allows us to have new tools for efficient processing, such as artificial intelligence, analytics advanced or the machine learning, to name some of the best known. Unfortunately, we cannot affirm that decision-making has evolved in tandem, while intuition continues to play a predominant role. Striking a balance between intuition and analysis is not always easy, and on many (too many) occasions, executives prefer to turn to their intuition rather than dare to explore the exercise of intelligent decisions.
The truth is that we rely too much on intuitions, even without knowing when they are wrong. Suppose we can trust intuitions that are based on real experience, because through it we develop knowledge. Even in this case, it is now known that experience alone is not a strong enough support. In fact, studies confirm that experience increases the confidence with which people hold their ideas, but not necessarily the accuracy of those ideas. Accuracy requires a particular type of expertise, one that arises in the context of regular feedback and is testable.
For our decisions to be less influenced by intuition, it is necessary to support them by effective processes, adequate methodologies and the correct information. The approach is based on the identification of the most relevant decisions. Those that will impact the business metrics that we need to leverage are prioritized, and then collect and analyze the information that can optimize them. In cases where you work with recurring decisions, a data history can be generated to be able to respond to our hypotheses. The more frequent the decision and the more information we have, the easier it will be to work on its predictive value. More and more data will be available in the future. The challenge is knowing what to do with them and using them to better make the decisions that matter. But also, we need people who make complex decisions as a result of a methodologically sound exercise. People who can raise their hands and argue better than Hippo.
Ernesto Weissmann es ‘managing partner’ en Tandem.
Eddie is an Australian news reporter with over 9 years in the industry and has published on Forbes and tech crunch.