The supply of (and demand for) food data is accelerating, but the lack of data standards hinders innovation.
Below are several examples of the areas we plan to address.
Transparency in the Food Chain
When foods are bought and sold, companies do not have a common language to communicate the ingredients they're transacting. As foods move through the supply chain, the lowest common denominator of information (e.g., ingredient lists and nutrition info from packaging) is passed along. As a result, the intricacies of the food chain are lost. Creating transparency across the food chain is even harder when food travels across international borders, or if we want to find the origin of trace ingredients.
If all the parties in the food chain use the same identification system for all of the ingredients in their products, it would help buyers and sellers to optimize their businesses.
accelerating digital innovation
Nutrition and wellness are some of the fastest growing areas for high tech innovation. Entrepreneurs are experimenting with new ways to help consumers understand what they're eating and adjust their lifestyle. Startups focus (correctly) on creating engaging user experiences. Nutritional data is necessary for calculations in the background, but startups don't have the resources to create their own nutritional database. If a comprehensive, scientifically sound, easy-to-use data set were available to them, entrepreneurs could accelerate their innovation.
Imagine if every inventor had the world's food data at their fingertips.
Resources for Food-Related Research
Food Science and Nutrition programs explore the relationship between food, manufacturing, distribution programs, and the human body. Researchers gather the most relevant, rich data sets available to develop their studies. A well-organized food "codex", easily accessible to all researchers, could supercharge scientific exploration.
Imagine if the chemical and nutritional data for every food were available to scientists on day 1 of their research projects.
Enabling Big Data Analytics
Big Data looks for patterns in huge volumes of mixed data sets. In the world of food, this might mean tracking all of the meals that have been consumed across an entire population. Mining this data for insights is much easier if data can be compared across contexts. A food data taxonomy can be the "universal key" that helps to make associations and patterns across many different data sources easier to identify. By studying patterns across entire populations, we might be able to identify the sources of chronic disease, or evaluate the holistic impacts of diets on health.
Imagine if the full power of Big Data could be applied to food-related data
Recognition for Local Differences
We know that when it comes to food and nutrition, origin matters. Labels like "organic" offer only a rough signal of how, from a chemical/nutritional perspective, an apple from here is different from an apple from there. Even if producers go through the expense to have their products tested to certify their content, the data systems of the food chain are not equipped to recognize these nuances. Once my apples get mixed into someone else's apple juice, they will simply be tracked as "apple" on the ingredient list, and the juice's nutrition will be derived from default values for "apple, red" in a rudimentary database.
Imagine if we had the sophistication to recognize the differences in the quality of ingredients from different producers. How might that encourage a market for healthier, more sustainable food?