I changed the noun “numbers” to “data”, and I began to realize how much the facts speak for themselves and reveal the identity of people and companies. Even objects are defined by their characteristics, which, in a simplistic definition, are just data.
It is nothing new that today companies, from the most diverse sectors, identify themselves as “data driven”, that is, companies that make their strategic decisions excluding or reducing the feeling of managers and adhering to what the data show about their reality. And do you know why? Because feeling is permeated by emotions, and these are not always the best way to make decisions, especially when it comes to business. But does the data arguably dispense with executive convictions?
First, it is necessary to understand what feeling is and how it is formed in the minds of managers. According to Alden M. Hayashi of the Harvard Business Review, this term can be understood as intuition, professional judgment, instinct, hunch, beliefs adopted based on a mixture of knowledge about facts related to the subject at hand, but which have already occurred; in previous experiences and/or even in physical sensations, such as “butterflies in the stomach” or “the racing of the heart”. According to his article “When to Trust Your Gut” - translated as “When to trust your gut” - emotions and feelings are essential for choosing which decision(s) to make, as our mind receives and processes a lot of data. continuously unconsciously.
The result of this processing can become conscious through a business choice, for example, which we do not always manage or know how to explain the reasons that led us to take it, but which, in short, are the result of our brain's hours of work with based on facts experienced at other times. Even so, the decision-making factor is the result of a processing of these data that is permeated by emotions.
That's why the events reported in facts or data are the subsidy to detect patterns that can be repeated in negotiations, but what to do with this awareness of the perceived patterns can still be something permeated by emotions. Given the conscious or unconscious importance of information, the importance of data analysis is indisputable, whether in Business Intelligence – the study of what has already happened – or in Business Analitycs – the study or prediction of what may happen, based on patterns.
The truth is that many organizations have the aforementioned understanding, but do not analyze 2 (two) crucial points: their level of data maturity and whether their managers have emotional intelligence to deal with awareness of the facts. Yes, these aspects are of substantial importance, since having the data and understanding what they say does not necessarily guarantee the best decision-making for the company. Just think about the current scenario of imports of fertilizers to Brazil: the data reveals our percentage of dependence on countries like Russia, they point to the prominence of scarcity, perhaps unprecedented, but which decision to take? Keep importing? Thinking about in-house production? Would it be economically viable? What would it take for this to happen? In the compilation of many variables and driven by emotions, we will know the scenes of the next chapters.
Ricardo Cappra, from the Cappra Institute, helps us think about the first point that I consider crucial for decision-making in companies: data maturity. In 2020, Cappra focused on a study with more than 500 professionals from Brazilian companies with the objective of measuring their Analytical Maturity Index (IMA), trying to understand how much the “data” resource was used in the businesses of these organizations; the efficiency of the processes of transforming data into knowledge and what impacts this use of the facts compiled with a view to business generated.
Cappra concluded that the IMA of these organizations pointed out the point use of data in decision making, that is, there are some of their processes that are analytical and that result in indicators based on facts. Such companies understand that they need to improve their data solutions, but they have not yet reached the maturity level of the “Data Company”, in which the definition is autonomous and the facts themselves would point to the ideal decision-making.
