In the ever-evolving technology landscape, data analytics and data strategy continues to play a larger role in economics and business models. Director of the Center for Applied Artificial Intelligence at the University of St. Thomas, Dr. Manjeet Rege, co-hosts the “All Things Data” podcast with adjunct professor and Innovation Fellow Dan Yarmoluk. The podcast provides insight into the significance of data science as it relates to business models, business economics, and delivery systems. Through informative conversation with leading data scientists, business model experts, technologists, and futurists, Rege and Yarmoluk discuss how to utilize, harness, and deploy data science, data-driven strategies, and enable digital transformations.
Rege and Yarmoluk spoke with Sarah Medrano about Cargill’s sustainability initiative to solve key challenges around food waste with data and predictive analytics. Medrano is a digital product leader at Cargill Business Studios. While Cargill has deep roots in traditional agriculture industry and less in digital, the Digital Business Studio is allowing Cargill to meet more of the need for speed and agility, striving for faster innovation, operating more like start-ups. Here are some highlights from their conversation.
Q. Please tell us about the ReSKUed initiative … the context of the problem and the opportunity ReSKued represents.
A. Our goal at ReSKUed is rooted in our mission to have zero waste to landfill. The work we’re doing is to better understand all the causes that lead perfectly good food to end up in landfills instead of in the hands of the people who need it. From a data perspective, it paints an inefficient marketplace. On one side of the marketplace, we have food distributors with large amounts of food waste due to several causes; on the other side of the marketplace, we have consumers, 10-15% of U.S. families, experiencing ongoing, regular food insecurity. It’s such an interesting problem, where we have food going to landfill while we have so many people who need it and want it, and that’s really at the core of what we want to solve.
Q. Could you explain more about how this is really a data problem?
A. Waste prediction is probably the most important piece of this. Today we find a lot of product destined to landfill because it’s close to its expiration date, and we’re trying to intercept and find its next best home. The data shows us trends and order patterns for certain items, and if we can predict food that’s going to waste and intercept it earlier before it gets too close to its expiration date, food distributors can make more margin on it. Therefore, we like to use data to think about all the ways we can identify these items earlier so they can find a more useful life.
Listen to their conversation here:
