How Big Data is Changing Food Consumption

Remember the old saying, “you are what you eat?” Big data is changing that proposition to “you analyze what you eat.” From food safety to trends in restaurant dishes, data analysis is changing the way we manufacture food, track our diets and serve our dishes.

Food Safety

Last week’s chocolate-covered marshmallow Easter eggs may not have contained salmonella after all, but they do reflect a larger trend. From listeria-imbued melons to E.Coli-tainted beef, food scares in America are an all-too-common event. More than 3,000 people die annually as a result of ingesting contaminated food.

Big data is poised to help the issue of contamination. Big data already plays a role in many steps of the food-creation process. Farmers are using it to work around droughts and analyze the quality of their soils. Dairy farmers use big data to automatically check the health of their cows. Restaurants collaborate with the Centers for Disease Control and Prevention to track troublesome ingredients down the supply chain, as well as to ensure the flavor, texture and color of their food is consistent.

Perfecting What You Eat

Infectious diseases aren’t the only area where food is turning into a new frontier for big data. A host of consumer apps help you decide where to dine, how to stick to your diet and what to cook. Gojee, for example, uses big data to generate recipes based on the ingredients you have in your pantry. Fooducate lets you scan the barcode of a product and tells you what’s good about its ingredients, and what’s bad. Others apps can tell you what to eat, where to get it most cheaply, how to burn it off and more.

On the enterprise side, FoodGenius analyzes national and regional dining trends for restaurants. It can tell you, for example, how common it is to serve French fries in a gourmet restaurant in New York, or how much, on average, foie gras costs in American eateries. That way, restaurants can gauge whether their dishes stand out and cost the right amount.

The Secret Sauce to Successful Data Analysis

One reason that these apps are becoming popular, both on the enterprise and consumer side, is that they address a topic we all care about—such as food and health —and they’re easy to use. Combining need fulfillment with ease of use is an ideal scenario for big data. It should be something we can use on a daily basis, accessing analysis easily and frequently to optimize our lives, whether personal or at work.

This principle should be applied across the board for big data analysis. No platform should be so complex that you don’t want to use it. Whether you’re analyzing the food in your pantry or the overhead of a new project, big data should be an intuitive, even fun-to-use partner in the process. Because if you don’t use big data, no matter how much of it you have, it ultimately won’t help you.

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