Big Data’s Big Impact on the Food You Eat Every Day

There are at-home precautions you can take to minimize your chances of consuming tainted food. For example, you can ensure all your cooked meats reach an internal temperature of 165 degrees before serving, wash your equipment thoroughly to avoid cross-contamination, and refrigerate key foods to keep them from growing bacteria. But what about the safety of your food before it gets to the grocery store?

Recalls are still commonplace, with outbreaks of E. coli, salmonella, and other hazards still jeopardizing human health. Fortunately, thanks to big data and data analysis, our food supply chain is getting safer.

For starters, the global food market has become incredibly complex. Ingredients are produced around the world, shipped everywhere, and in many cases, go through several phases of processing and transportation before they make it to a finished product. When an outbreak is detected in a given product, this makes it difficult to track exactly which ingredient or which batch was responsible for the outbreak.

Thankfully, companies like FoodLogiQ are making it easier to incorporate traceability into the supply chain. Using a lot-level traceability software like FoodLogiQ Connect allows companies to reasonably identify and track the course of their food products as they make their way to consumers. If contamination is detected at any point, it’s far easier (and more cost effective) to take corrective action only on the bad product.

Traceability is nice, but it tends to be most important after a form of contamination is detected. So, how can we get better at detecting contamination? One way is through the whole genome sequencing (WGS) of known pathogens, which gives us better signature markers of bacteria, parasites, and other harmful biological developments that we can use to detect contaminations proactively. Using this data, researchers can develop better detection technology that should feasibly stop the rollout of contaminated products before they get any further down the processing line.

As an added bonus, better understanding of the makeup of certain pathogens can help scientists discover better ways to eliminate them, either by killing them before they infect people, or treating people after they’ve been infected.

Big data can also help food producers and health organizations better understand the conditions for an outbreak. For example, large sets of data may be able to pinpoint certain weather conditions in certain areas or during certain phases of ingredient development that lead to an increased risk of an outbreak.

This not only helps flag different batches for potential contamination, but also gives food companies and organizations insights that can help them develop safer processes. By examining the data associated with outbreaks of the past, food producers can come up with a better plan for production and distribution moving forward.

Data sets aren’t limited to the production and distribution of food, either. The CDC has actually been mining forms of social data to detect and respond to outbreaks faster. For example, the CDC and health organizations with similar goals can peruse Yelp to find reviews of businesses that contain words like “vomit” and “sick” to detect the possibility of a foodborne illness outbreak. It can also use social listening, a way of monitoring social media posts of users, to gather similar information.

Then, if an outbreak is detected, these organizations can use customer data from the restaurants and stores that may be affected to inform customers about potential health complications they may face—or let them know which of their food products may be contaminated.

Big data still has a long way to go for the food industry, but it’s already making our food safer and our lives healthier. With greater insight into the production, distribution, and safety of our food, we can all shop and eat with a little more confidence.