By definition, eating is one of the purest “gut feel” things any of us do. But there’s always been a fair amount of what we now call “analytics” in any human diet, long before computers, databases, and the rest of modern information technology emerged.
Measurement is the heart of analytics, and measurement has always been the essence of food preparation. That’s because cuisines have always been the art of measuring, combining, processing, and consuming specific ingredients in specific proportions according to specific steps.
Every culture — in fact, every human — has criteria (often unstated) for assessing the “too much, too little, just right” dimensions of what they’re putting in their mouths. Depending on the cultural context, these calculated assessments may or may not involve formal units of measure, but they usually aren’t random in nature. That’s why cookbooks exist: Recipes present recommended measurements based on what others have found most palatable.
You may have noticed the recent coverage of IBM “Chef Watson.” It goes to show that measurements and analytics, in the big data sense, can enliven the postmodern palate.
Cooking has always relied on the food chain to deliver the ingredients for sustenance. Analytics in the modern industrial food chain are amazingly sophisticated. They are light-years beyond what our hunter-gatherer forebears practiced in the field. Every process in the “farm to fork” supply chain — cultivation, processing, packaging, distribution, and preparation — is managed and optimized through big data analytics.
We even have analytics on the consumption side, in the form of ingredients labels, online rating services, and other decision-support resources. Indeed, many modern consumers won’t put anything in their grocery carts or go to any eatery unless they’ve checked it out thoroughly in advance. Not to mention that many of us take our consumption cues from the never-ending marketing campaigns targeted at our tastes and pocketbooks.
If you think about it, agriculture has always been focused on “campaigns,” in the sense that structured sets of group activities (such as tilling, planting, irrigating, harvesting) focus on a very specific outcome (such as avoiding starvation for another year). As humans move away from traditional food gathering practices toward repeatable industrial processes, campaigning has become essential to introducing new practices throughout the supply chain. Every innovation in this chain — from cultivation of new crops, dissemination of new farming practices, development of new packaged foods, and popularization of new cuisines — relies on analytics-driven campaigns to overcome traditional practices and to gain widespread adoption.
Overcoming wasteful practices in the food chain is also the stuff of marketing campaigns. For example, the adoption of “precision agriculture” depends on demonstrating to agribusinesses and smaller farmers the value of analytics-intensive practices that rely on big data analytics, embedded environmental sensors, geospatial land-management applications, and more.
Precision agriculture is all about helping cultivators to plan their next annual campaign all the way from sowing to reaping. Farmers can optimize their planting, irrigation, harvesting, and other operational decisions using data gathered from farm-strewn sensors. This data might be measurements of soil, weather, irrigation, fertilizer, and pesticides. In addition, it might include multispectral aerial images of farm fields gathered from satellites, airplanes, and drones.
If we consider the aggregate decisions of all farmers in any growing season, a lot of natural resources might be wasted if the right decisions aren’t made at every stage in the process. Encouraging sustainable practices demands a constant public-education campaign targeted at the various roles that different parties play in the journey from farm to fork.
As this article states, more than 70 percent of the world's fresh water is used in trying to get food from farms to your table. You may shrug off that metric until you realize that a larger percentage of U.S. food crops are grown in regions — California and the Great Plains — still in the throes of a long-running drought. Conserving the limited water resources of these environments is critical to everyone’s survival.
Other areas of food-chain waste can be reduced through analytics embedded in other end-to-end chains. Improved weather modeling can reduce weather-related crop damage. Geospatial analytics can reduce waste of water and fertilizers in the cultivation process. Supply-chain analytics can reduce spoilage in the distribution of food to factories and stores. Predictive demand planning can reduce the risk of food going unpurchased and being discarded before it goes bad.
Consider these analytics-driven practices as the proverbial “cookbook” for sustainable food chain management campaigns in the modern world.