CIOs Have to Learn the New Math of Analytics

CIOs Have to Learn the New Math of Analytics

CIOs Have to Learn the New Math of Analytics
We’re used to algorithms recommending books, movies, music and websites. Algorithms also trade stocks and predict crime, identify diabetics and monitor sleep apnea, find dates (and babysitters), calculate routes and assess your driving, and even build other algorithms. These math equations, which can reach thousands of pages of code and routinely crunch hundreds of variables, may someday run our lives. Companies increasingly use them to run the digital business and gain competitive advantage.

Unleashing an algorithm can lead to new customers and revenue, but it can also bring encounters with ethical and legal trouble. Already, consumer advocates and regulators are training their sights on the dark side of the algorithm revolution, such as creepy over-personalization and the potential for illegal price discrimination.

As CEOs look to chief digital officers and data scientists to conquer the next frontier, CIOs have sometimes been on the sidelines, whether by choice or default. But as business leaders, CIOs may now have to elbow into meetings where Ph.D.s, corporate lawyers and other colleagues are talking about the data-driven future. CIOs need to join those conversations to help steer company strategy, certainly, but also to contribute to decisions about what data to pour into an algorithm and what to keep out, and how to monitor what the algorithm does.

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That includes devising a defensible policy for handling the information produced, says Frank Pasquale, a professor of law at the University of Maryland. “Algorithmic accountability” will become part of the IT leader’s job, he says.

That realization can hurt. Athena Capital Research, a high-frequency stock trader, used a proprietary algorithm called Gravy to slip in big buy and sell orders milliseconds before the NASDAQ exchange closed for the day in order to push stock prices higher or lower, to Athena’s advantage. The Securities and Exchange Commission viewed that as illegal manipulation and last year called out Athena’s CTO for helping other managers plot the most effective use of Gravy during at least six months in 2009. Athena settled the case for $1 million.

No one says CIOs must delve into Ph.D.-level math. But a working knowledge of basic concepts behind algorithms can help avoid bad results and bad press. “Algorithms allow us to get rid of biases we thought were there in human decision-making,” says Michael Luca, an assistant professor at Harvard Business School. “But pitfalls are equally important to think about.”

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Algorithms can be used to make operations more efficient, answer “what if” questions and make new products and services possible. At United Parcel Service, the 1,000-page Orion algorithm does all of that. In 2003, UPS started building Orion (for On-Road Integrated Optimization and Navigation) to optimize delivery routes. You might have six errands to do on a given day. A UPS driver has about 120. The company wanted to save time and fuel by having drivers follow the most efficient routes possible while still making deliveries on time, says Jack Levis, director of process management. Levis oversees Orion and the team of 700 engineers, mathematicians and others who support it.

Cutting just one mile per driver per day saves $50 million per year, Levis says, and Orion has so far saved seven to eight miles per driver per day. UPS is on track to save $300 million to $400 million per year in gas and other costs by 2017.

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The most important thing any manager can do when embarking on an algorithm project is to “work backwards,” Levis says. That is, define carefully what business decisions the company struggles with, then identify what knowledge would help–what information you’d need to teach you the knowledge you lack. Then identify the raw data that–when combined and teased apart and interpreted–would provide that information.

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