With the proliferation of tools to collect and analyze data that can inform problem solving and decision making, the use of big data and data analytics has become ubiquitous throughout many industries. While the food industry may be slower to adopt big data and data analysis than some other industries, such as healthcare, it’s catching up as food scientists and other experts recognize its potential as a powerful tool to address large, complex problems in the food industry.
Food safety is one such problem. Affecting every step along the food supply chain, food safety relies on a company’s ability to gather reliable data in a timely manner and then act on that information as needed. From food traceability to digital pest management to better detection of foodborne illness breakouts to reductions in food spoilage, big data and data analytics are being employed to advance food safety at the local and global levels.
“Big data can be used at all steps of the food value chain to improve food safety,” says John Donaghy, PhD, head of food safety at Nestle in Switzerland. On the farm or at primary processing steps, he cites several data types that can be collected to improve food safety, such as water analytical test data, hygiene status of workers, and certification status of farms/processors. At the consumption/public health end of the food chain, he cites the use of big data and data analytics for communicating recalls to consumers and for source tracking foods that cause foodborne illness outbreaks. Between these end points, he indicates numerous areas during manufacturing where data can be collected, e.g., microbiological verification testing, process control data, and environmental monitoring data. “Data relevant to food safety and quality can be collected at so many steps throughout [the] food chain; even real-time monitoring of temperature during logistics and transport in the supply chain can be incorporated into dynamic risk management,” he adds.
For food manufacturers and processors, from small to large businesses, the potential impact of big data and data analytics to improve food safety can be enormous. A 2022 report by the Global Food Safety Initiative (GFSI) Science and Technology Advisory Group (STAG) describes the potential impact on business, as well as what businesses should be thinking about when considering the use of big data and data analytics in their own organization (see Tables 1 and 2, below).
A final key question for businesses, according the GFSI report, is: “Do businesses understand the strategic impact of big data on their operations and do they have the appropriate talent strategy for these changes?”
Several food safety experts offer their views on the value of big data and data analytics for food manufacturers and processors that may help businesses better answer these questions.
Collecting Big Data: Internal and External Sources
Suzy Sawyer, food safety, quality and regulatory digital and analytics leader at Cargill underscored the growing role that data plays for food companies to ensure safe, quality products. “What we’ve discovered at Cargill is that the vast amount of data collected from internal and external sources can be used to help identify potential food safety improvements, analyze, and manage quality control, and mitigate food supply chain risks,” she says.
She cited a number of internal sources of data collection, including data gathered manually (plant floor quality and safety checks and observations), as well as sources from digitized technologies such as sensors (inline processing from machines/processes), data loggers (sensors capturing characteristics such as temperature and humidity), and instrumentation (near infrared detection instruments).
External data sources include technologies designed to exchange data collection to improve food safety, such as regulatory notifications or alters, food-related media, weather, and commodity prices.