Strategic Analytics Quotes

“Excellence in Statistics: Rigor Statisticians are specialists in coming to conclusions beyond your data safely—they are your best protection against fooling yourself in an uncertain world. To them, inferring something sloppily is a greater sin than leaving your mind a blank slate, so expect a good statistician to put the brakes on your exuberance. They care deeply about whether the methods applied are right for the problem and they agonize over which inferences are valid from the information at hand. The result? A perspective that helps leaders make important decisions in a risk-controlled manner. In other words, they use data to minimize the chance that you’ll come to an unwise conclusion. Excellence in Machine Learning: Performance You might be an applied machine-learning/AI engineer if your response to “I bet you couldn’t build a model that passes testing at 99.99999% accuracy” is “Watch me.” With the coding chops to build both prototypes and production systems that work and the stubborn resilience to fail every hour for several years if that’s what it takes, machine-learning specialists know that they won’t find the perfect solution in a textbook. Instead, they’ll be engaged in a marathon of trial and error. Having great intuition for how long it’ll take them to try each new option is a huge plus and is more valuable than an intimate knowledge of how the algorithms work (though it’s nice to have both). Performance means more than clearing a metric—it also means reliable, scalable, and easy-to-maintain models that perform well in production. Engineering excellence is a must. The result? A system that automates a tricky task well enough to pass your statistician’s strict testing bar and deliver the audacious performance a business leader demands. Wide Versus Deep What the previous two roles have in common is that they both provide high-effort solutions to specific problems. If the problems they tackle aren’t worth solving, you end up wasting their time and your money. A frequent lament among business leaders is, “Our data science group is useless.” And the problem usually lies in an absence of analytics expertise. Statisticians and machine-learning engineers are narrow-and-deep workers—the shape of a rabbit hole, incidentally—so it’s really important to point them at problems that deserve the effort. If your experts are carefully solving the wrong problems, your investment in data science will suffer low returns. To ensure that you can make good use of narrow-and-deep experts, you either need to be sure you already have the right problem or you need a wide-and-shallow approach to finding one.”
― Harvard Business Review, Strategic Analytics: The Insights You Need from Harvard Business Review