Industrial data are considered a keypoint for relaunching European industrial production. The Data Governance Act and European Data Act proposals have introduced the concept of industrial data as tools for optimizing business processes.

Data sharing is still not widespread today, especially in SMEs, which face obstacles in interpreting and adapting to the regulatory framework of reference. The complexity of the regulatory framework hinders data sharing, becoming a barrier for companies, preventing them from entering the data market or forcing them to refrain from collecting, sharing or exchanging data.

For these reasons, the European Union has made data sharing one of the keystones of recent legislative measures, since an effective legal framework for the exchange of data stimulates competition and innovation. The recent European legislative proposals therefore aim to create a legal framework that can promote the sharing and use of data, in particular those generated in industrial settings. The proposed Data Governance Act aims to create a reliable and economically sustainable environment for sharing B2B and G2B data.

Industrial data therefore represents a valuable asset for those companies able to make the most of them. Some characteristics commonly attributed to industrial data are: the inclusion in the macro category of Big Data (as they represent large amounts of data, produced quickly and in real time), heterogeneity, ownership, their organization in a structured way and the generation of data in an industrial context.

We tend to think that data with these characteristics are limited to non-personal data, generated automatically and without human intervention in business processes, however, it is unlikely that personal data will not be collected even in closed industrial environments.

In fact, the GDPR provides a very broad definition of data as personal (“any information relating to an identified or identifiable natural person”), so in all cases where re-identification is possible, it will be necessary to adopt anonymization techniques and balance the maintenance of the economic value of the data with the privacy of the interested parties.

Although all anonymization or pseudo-anonymization techniques improve data protection and privacy, their reliability has been questioned several times. Furthermore, in the future, they may become even less effective with the development of artificial intelligence systems or new data analysis techniques, which is why some experts recommend using a combination of techniques to make personal data truly anonymous. An undoubtedly innovative technique in this area is the generation of synthetic data, that is, artificially generated data through generative machine learning models, which, starting from an original dataset, generates an artificial dataset that maintains its statistical properties.

Additional obstacles to data sharing are technical, regulatory and internal (related to the internal policy of the individual company). In principle, every company should be able to decide how to manage the data it generates. In an industrial context, this is best achieved through fair contracts that take into account the interest of all parties involved, the protection of know-how and the confidentiality of data.

Stamplast, for example, is an Italian SME that produces molds for plastic materials for third parties. In particular, the company follows four different phases in the mold production process: the analysis of the best technical solution, the executive design of the mold, the construction of the mold and the testing of the plastic molds. In these activities, Stamplast collects a large amount of data, from the monitoring of the machinery used to the know-how concerning the specific mold in production. In the concrete case, these data must be protected through the drafting of clear and transparent contracts, as the mold represents valuable information for the customer, who is interested in protecting their complex of technical knowledge and experience, and enhanced through system integration tools, storage and secure data sharing, both within the company itself and with the outside world, such as through the creation of sectoral data lakes.

In addition, to promote competition and collaboration between industries, it is necessary to introduce formats and models based on freely accessible standards in which to guarantee interoperability between devices and different activities for the contemporary industry, which is undergoing a process of profound technological transformation, as well as the introduction of artificial intelligence and IoT systems.

The fourth industrial revolution has been defined as “data-driven” and involves all companies, large and small, that operate within the European Union. Consequently, industrial data represent the first tool to allow Italian SMEs, which own them, to innovate and be competitive in the European market; it will therefore be essential to know how to seize the opportunities offered by the new legislative package presented by the Commission and take advantage of investments to accompany the company in the digital transition.

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