To understand why this is the case, it is important to break down the terms. When we talk about data culture, we are talking about a corporate culture based on the use of data and information to support all types of decisions that the company makes: who to hire, what benefits and salaries to offer, how to develop products, which customers to accept or not. Having a data culture means using the information collected throughout the company’s life – and other market information that can be acquired – to make better decisions about each of these points.
In the corporate market of much of the world, and especially in Brazil, most companies value the previous experience and opinion of leaders and managers more kuwait whatsapp number code than the data itself. There are dozens of cultural and historical reasons for this favoritism, but the fact is that this appreciation often clashes with what the data shows, resulting in decisions that ignore the trends or paths that the data suggests in favor of what the manager believes to be the right thing to do. This, obviously, is not a data culture.
However, creating a data culture is not just about making all decisions based on what past numbers show us. We also need to look at what is happening in the present, at today’s operational reality, and combine that with past information, adjusting course as necessary. In other words: data can give us an incorrect or incomplete view of reality, and the decision we make based on it can be wrong. Companies need to be able to quickly recognize this type of error and adjust their processes, through constant collection and analysis of information. This is what we are addressing when we talk about “efficiency” in the use of data.
An efficient data culture, therefore, requires a mindset of constant experimentation and evaluation. It is not enough to simply analyze past data, nor to focus solely on collecting operational information in real time. It is necessary to combine both skills in an operation capable of experimenting and adjusting decisions and processes as new data emerges, without wasting time criticizing or looking for someone to blame when an experiment goes wrong. What is behind the experimentation mindset is the understanding that most tests will fail anyway, and that more important than getting things right is knowing how to recognize and correct your mistakes quickly.
We have said many times that the future belongs to companies that know how to work with their data best and that can efficiently transform this data into positive results. To do this, you need to incorporate not only the appreciation of information into your company culture, but also the freedom to make mistakes in the day-to-day running of the company. Without this, you may even have a culture of listening to and analyzing data, but it will hardly be efficient and will hardly generate real value for your company.