Data Governance: which data to expose and which to protect?

The development of Industry 4.0 has allowed the introduction, within factories, of technologies that intend to exploit the power of data, which are presented as information about their production processes, useful for monitoring productivity and reacting promptly to requests from customers and market changes.

Predictive maintenance, resource procurement optimization, supply chain efficiency are just some of the activities within which Big Data operates successfully. This large amount of data, given its critical potential for success within companies, needs to be protected and treated securely. But what are the data to protect, and which ones to share?

The increasingly widespread introduction of devices belonging to the vast world of the Internet of Things has led to the creation of data flows which, if not adequately controlled, do not allow to obtain the advantage they promise. Here is where the role of Data Governance processes fits. Through what can be, in a very simplistic way, defined a data administration activity, it is possible to understand which ones are to be protected from prying eyes and which, instead, to share and exploit to improve the work and results of the entire production process.

Data Governance: what does it mean?

The more data is extracted from industrial processes, the more it will be necessary to organize them to make them useful: only in this way can the factory be defined as a real smart factory. This is why we talk about Data Governance: in other words, managing data is a fundamental activity for improving business processes. Specifically, the Data Governance Framework can be defined as a system of decision-making rights and responsibilities regarding information processes, carried out according to agreed models that describe who can take the actions, with what information and when, if necessary, to intervene according to predetermined methods.

The data to protect with Data Governance

The GDPR is the general regulation on the protection of personal data with which all companies have recently had to deal. The regulation defines that the personal data released by users are to be protected, that is, that they cannot be published or made accessible by third parties without prior consent from the parties concerned. But what is meant by personal data? The GDPR identifies in this category all information that identifies or makes identifiable, directly or indirectly, a natural person, that is, all data “which expresses the physical, genetic, mental, commercial, cultural or social identity of these natural persons”. For example phone, credit card, account information, license plate, customer number or address are all personal data. But there’s more: the term “personal data” must be interpreted as broadly as possible , so you also need to consider less explicit information, such as work time records (which include information on when an employee starts and ends his or her work day) or a user’s IP address. personal, therefore, it will be necessary to prepare a privacy policy to which people can choose to give consent or not.

Data to be shared with Data Governance

Within the production processes, it is difficult to come into contact with personal data. The data provided by the tools, in fact, are not considered as such as they do not refer to people. With the advent of Big Data (large amounts of information about their plants or processes), companies of all sizes are exploring how data analytics can help them make strategic decisions and gain competitive advantage. Because of this, the data will need to be protected from external attacks or prying eyes, but it will be essential to be able to share them within the company. In this sense, the challenges of data governance include:

• process standardization
• data traceability
• data protection from external attacks

Since the amount of data to be analyzed can be really large and these data can refer to multiple departments within the production process, companies must be able to build a collaboration project within company teams, obtaining the consent of managers, up to to involve every employee in a data-driven business culture.

An example of Data Governance in its meaning of data sharing can be done thinking of a production process in the pharmaceutical field. To prepare a certain product, some raw materials are stored inside reactors, which are constantly monitored by level probes. Until a few years ago, these probes were not connected to the network: this means that an employee had, from time to time, to manually go to check the measurement data, and consequently place orders for the supply of stocks. Today, thanks to the IoT, the network and Data Governance, it is possible for the level probes to automatically collect such measurement data and send them — via encrypted protocols — to the control room of the production process.

But that’s not all: this data can be accessed from the corporate cloud, allowing for example the view to the corporate board of directors even if they were on the other side of the world. This is an example of data sharing and management that can significantly improve production processes.

Industry 4.0 solutions outline a future in which technology will take on ever greater importance in terms of process optimization, safety improvement and reduction of operating costs. A future in which specific figures such as data scientists may be needed for the profiling and correct use of such data, but above all a future in which data science will prove to be a critical success factor for one’s business.