An article in the Wall Street Journal reports, “Surging prices for food staples from coffee to meat to vegetables are driving up the cost of groceries in the U.S., pinching consumers and companies that are still grappling with a sluggish economic recovery.” [“Food Prices Surge as Drought Exacts a High Toll on Crops,” by Tony C. Dreibus, Leslie Josephs, and Julie Jargon, 18 March 2014] As food prices increase, so does the number of people going hungry since they can no longer afford to buy all the food staples they require. This is not just a problem for people in the United States. The agricultural sector, like most other sectors, has been globalized. A drought in America’s breadbasket regions cause ripples in the food supply chain that spread around the world. The article continues:

“Federal forecasters estimate retail food prices will rise as much as 3.5% this year, the biggest annual increase in three years, as drought in parts of the U.S. and other producing regions drives up prices for many agricultural goods. The Bureau of Labor Statistics … reported that food prices gained 0.4% in February from the previous month, the biggest increase since September 2011, as prices rose for meat, poultry, fish, dairy and eggs. Globally, food inflation has been tame, but economists are watching for any signs of tighter supplies of key commodities such as wheat and rice that could push prices higher.”

You might recall that the last time that global food prices rose sharply (2010-2011) there were food riots in some developing countries. Back then, Gary Blumenthal, president and chief executive officer of World Perspectives, a Washington (D.C.) agricultural consultant, proclaimed, “Imperfect weather has collided with perfect food demand.” The result was disastrous. We could be heading into a similar crisis. Dreibus, Josephs, and Jargon report:

“In the U.S., much of the rise in the food cost comes from higher meat and dairy prices, due in part to tight cattle supplies after years of drought in states such as Texas and California and rising milk demand from fast-growing Asian countries. But prices also are higher for fruits, vegetables, sugar and beverages, according to government data. In futures markets, coffee prices have soared so far this year more than 70%, hogs are up 42% on disease concerns and cocoa has climbed 12% on rising demand, particularly from emerging markets. Drought in Brazil, the world’s largest producer of coffee, sugar and oranges, has increased coffee prices, while dry weather in Southeast Asia has boosted prices for cooking oils such as palm oil.”

Food security for a burgeoning global population depends on a stable and affordable source of food. The question is: “Why are we surprised when a new, weather-related threat to the food chain emerges.” Big Data analytics and good risk management can help stabilize the food chain by understanding past and emerging weather patterns, recommending when and what foods should be stored for future use, and so forth. Right now there appears to be a three-year pattern. Back in 2008 food prices jumped 5.5%. Dreibus, Josephs, and Jargon report that was the biggest increase in food prices in almost two decades. Three years later (2011), food prices jumped again by 3.7%. Now, another three years on, food prices are once again on the rise. How high will they go? As noted above, the increase will come close to that experienced in 2011. A graphic that accompanied the Wall Street Journal article shows that in some cases the jumps have been significant.

Dreibus, Josephs, and Jargon go on to note that it’s not just the poor who are closely watching food prices. “The price increases [also] pose a challenge for food makers, restaurants and retailers,” they write, “which must decide how much of the costs they can pass along and still retain customers at a time of intense competition and thin profit margins.” Here, too, Big Data analytics could help take some of the guesswork out of pricing decisions. Using cognitive computing technologies, companies can factor in many different kinds of data (e.g., weather, buying habits, food preferences, fuel costs, and so forth), to discover relationships that might not otherwise be obvious. The objective of such analysis is to discover actionable insights that can help executives make more informed decisions. Mark Gibbs writes, “One of the really hard things about having Big Data is figuring what to do with it. There are obvious questions that can be asked such as ‘what’s the correlation between demographics and purchasing choices’ but when it comes to complex inductive reasoning you need expertise and thus we’ve seen the rise of a new analytics role: Data scientist.” [“Not enough data scientists? Use AI instead,” NetworkWorld, 7 March 2014]

Gibbs was kind enough to feature my company, Enterra Solutions^®, in his article because he wanted to highlight the fact that not all companies can afford to employ a data scientist; and, even if they could, they might have a difficult time finding one. So what’s the solution? Let a cognitive computing system stand in for the data scientist. Gibbs notes that systems like Enterra’s Cognitive Reasoning Platform™ “can ingest huge amounts of data and using natural language processing transform it into knowledge using a generalized ontology to discover the meanings of words in context along with the implicit rules and relationships as used by humans.” He continues:

“Then, when a question is asked in what is more or less natural language, the database of knowledge is accessed by Enterra’s Hypothesis Engine. The Hypothesis Engine is an artificial intelligence system that applies common sense and domain-specific ontologies to further structure the knowledge. Next, using Enterra’s Rules-Based Inference System it can determine an objective and find the facts to support that objective (backward chaining) as well as using facts to determine objectives (forward chaining) as determined by the knowledge found and its significance. Other engines in the system weigh results, formulate database queries, and analyze assets and all of these components pass data back and forth between themselves based on rules and inferences to derive conclusions.”

I would have used the word “insights” rather than “conclusions,” but the point is that technology has advanced enough that we should now be able to analyze appropriate data about some of the world’s largest challenges — like food security — and gain actionable insights. Making the food supply chain more stable through years of abundance and paucity should be one the priorities for all stakeholders in the years ahead. We shouldn’t have to endure years of feast or famine.