Industry 4.0 proposes the hyperconnectivity of the entire value chain of organizations with the goal to be more productive, but above all more efficient. Improving our processes and products, adapting more flexibly and quickly to changes in supply and demand, expanding our market through networks..., are just some of the benefits that can bring us to adapt to this new paradigm shift and that some are already beginning to see.

Among the various elements associated with Industry 4.0, the scientific analysisdiscipline and management of data is being defined as one of the pillars for the success of this reconversion. The interconnection of all the physical products and facilities of a factory, but also their interaction with all the agents in its value chain (customers, suppliers...) will generate a huge amount of data that will subsequently have to be processed.

It is clear that technology is advancing at breakneck speed and what until recently seemed like a dream, is now possible. Today it is possible to have communications for systems in real time, today it is possible to support a large number of connected devices at the same time and in the same place, today we have a new generation of people much more sensitive and closer to the digital world and therefore with a greater predisposition to the use of this technology in its work.

However, these changes and technological advances also present us with new challenges. Challenges that are still pending from industry 3.0. It is true that the momentum of the Industry 4.0 movement has encouraged the automation of many of the processes that were not yet automated, but this automation is predefined by what was already being done. But are our organizations ready to face a greater Degree of automation and interconnection between processes? And above all, are our organizations prepared to face the management of their processes from a lot of data that they have never seen before? Are the people working in them prepared to face the new environments of work? Are they prepared to understand so many data that will be arriving and from which they will have to propose improvements on their own work space? Or to understand both suppliers and customers?

The data analysis and the management based on information will become a strategic element for the development of all areas of all businesses. Therefore, the democratization of data presents us with a new challenge: understanding, thinking and reasoning based on data. If this is so, we need to start by understanding what data is, how it is measured, what information it provides, what happens when I have a lot of data: are they all the same, are they all worthwhile, can we keep just a few, what visualization possibilities exist, will we see them all the same, will it influence our decision making, etc., and then delve into other issues such as what techniques are available and which are the best for each status and goal we are trying to achieve. And not the other way around. Because the scientific discipline of data analysis, is precisely that, a scientific discipline . And as such, it requires study, knowledge and understanding to ensure its correct use and exploitation.

Organizations will be ready to make the leap to Industry 4.0 and embark on a journey into the new world when they unite and relate their experience and knowledge with the new one that is presented to them in the form of data. When they better understand how and why their processes work and from this new knowledge automate them to accumulate experiences that allow them to predict new behaviors. Otherwise, in a few years' time, we may find ourselves with "the sixth container", the one that collects the large amount of data that we have collected and have not known how to use, because in reality it was useless.