Digital Data & Big Data

[Autom. eng. transl.] When asking myself how to define myself compared to "digital natives", I investigated the origin of the term. This is an expression coined by Marc Prensky in 2001 to indicate students born after 1985 (the turning point in the mass diffusion of computers and graphic interaction systems with them) in an article dedicated to the need to develop adequate learning paths education for the new digital generation. The article provoked a series of criticisms, especially for underlining that being a digital native does not automatically mean having digital skills (Prensky himself later proposed the concepts of wisdom, rather than digital stupidity). Those like me who were born before 1990 would instead be a Digital Immigrant, that is, finding themselves using technologies that did not exist at birth or were not yet so widespread. Native or immigrant, we cannot now avoid dealing with digital data and big data, their grandeur, their applications, and their critical issues. In the agri-food sector there is a lot of potential, from primary production, to marketing, to food safety, to quality control, to process control and beyond. It is easy to understand how purchase and consumption data can be exploited to analyze market trends or, vice versa, the possibility of exploiting digital channels to inform and educate towards increasingly correct eating habits and choices. But also the potential of being able to reach a large number of people in a short time to communicate serious food alerts. Or again, being able to develop rapid quality control systems for raw materials, semi-finished products, finished products, simulate, predict and control production processes. All for the purpose of optimizing the processes of the food system with reduction of losses and waste, reduction of the hygienic health risk of food, improvement of product quality. But is it really new? Science has always strived to find models and relationships, empirical or mechanistic, that allow us to understand, describe and, therefore, predict, control and optimize processes. Think about the history of microbiology and the goals gradually achieved in the food sector. The discovery of the role of pathogens, of the mechanisms of cell death and growth, their dependence on process and environmental parameters, their modeling for the purposes of calculating and controlling adequate heat treatments or storage periods, or for a quantitative risk assessment of consumer exposure. However, the models still need to be improved to take due account of the variability and uncertainty of the data and possible predictions. So what has changed with the digital age? The need to have trained people who know the phenomena underlying the processes, who know what they want to predict and control and why, who know how to evaluate which experimental data are needed and how to obtain it, has certainly not changed. However, the capabilities for collecting and processing data and the availability of data have changed as they have increased and improved. Specialists in the agri-food sector must now understand the potential of digital & big data in their sector, they must know how to search for and communicate with IT and statistical specialists who they can ask to apply advanced machine learning and artificial intelligence systems to their needs. They must realize that along with the potential, new risks for food safety emerge, linked for example to possible cyber attacks on company systems. The interdisciplinarity that has always characterized food production is further increasing. The team of people dedicated to food safety management is no longer the same, new skills are needed alongside the traditional ones