Don’t panic. In a new book called Keeping Up with the Quants: Your Guide to Understanding + Using Analytics, Thomas H. Davenport and co-author Jinho Kim set out to advise executives on how to make sensible use of big data, including which questions to ask and how to tell whether the quant jocks really understand the business problem they’re purporting to solve.

Davenport, a visiting professor at Harvard Business School, a research fellow at the MIT Center for Digital Business, and the author of two previous books about quantitative analysis, quotes eminent British statistician George Box: “All [mathematical] models are wrong, but some are useful.” Even more useful is seasoned managers’ intuition. “Few executives are skilled at both analytics and intuition,” Davenport writes. “The goal, then, is to make analytical decisions while preserving the role of the executive’s gut.”

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Karl Kempf, for one, agrees. Kempf is a senior scientist who heads a decision engineering group at Intel (INTC), and whose nicknames around the company are UberQuant and Chief Mathematician. Even so, Kempf believes that good quantitative decisions “are not about the math. They’re about the relationships.” Notes Davenport, “If someone referred to as the Chief Mathematician declares that it’s not about the math, we should pay attention.”

Keeping Up with the Quants goes into fascinating detail about how Intel and other successful companies — including Verizon Wireless (VZ), TD Bank Group (TD), and Merck (MRK) — help managers and data scientists understand each other well enough to collaborate effectively. In Intel’s case, Kempf sends the “math people” charged with solving a problem on a kind of junior year abroad among non-math types, to listen, learn, and pick up some general business knowledge.

“At most, the analyst can be trained, as a new hire would be, to participate in the business process,” Davenport writes. “Kempf judges the low bar for success as when the math person thinks he or she understands the business problem. The high bar is when the business person thinks the math person understands the business problem.”

For their part, executives may need to brush up on their algebra. “The business person doesn’t have to understand, for example, hyperbolic partial differential equations,” Davenport writes. (Well, there’s a relief.) “But at a minimum there has to be a diagram on the white board setting out such questions as, ‘Since A and X are related, if A goes up, in what direction does X go?’” He adds, “As with any other type of model, a few concrete examples — historical or made up — are extremely useful.” So are visual aids like pie charts and bar graphs, a favorite tool of Patrick Moore, who heads the commercial analytics group at Merck.

One of the book’s most practical features is a series of checklists spelling out exactly what managers should expect from quant jocks and vice versa. A sample tip: “As a business decision maker, you should politely push back if you don’t understand something and ask for a different or better explanation.” That might seem obvious, but many non-math types are too intimidated to press for clarity.

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“We’ve seen a number of organizations in which quantitative people seemed to delight in making ‘normal’ businesspeople feel stupid,” Davenport notes. “They would say things like, ‘Surely you know what regression analysis is?’ or ‘I’m sorry, a chi-square test is just too elementary for me to have to explain.’”

If you’re getting that kind of guff, Davenport contends, it’s probably your own fault. Most data analysts are “wonderful people to work with,” he writes, but attitude problems sometimes pop up “in organizations that somehow hired quantitative analysts but ignore them when important decisions come along. Quants, like most people, respect others when they are respected.” Enough said.