Leading in a Data-Driven World: Being Fearless and Fierce With Data

In a new class, MBA students learn how data can help them make business decisions and that leaders do not need to be number crunchers.

The instructors emphasize becoming fierce interrogators of data, bringing key questions to the discussion such as: ‘How do these numbers compare to historical data, or to our competitor’s numbers?’
The instructors emphasize becoming fierce interrogators of data, bringing key questions to the discussion such as: ‘How do these numbers compare to historical data, or to our competitor’s numbers?’
Data & Business Analytics, Leadership, The Workplace
Laurie B. Davis

Data underlies most business decisions today, and no leader can operate a successful 21st century business without the insights that data can provide, says Oded Netzer, the Arthur J. Samberg Professor of Business at Columbia Business School.

Netzer and adjunct professors of business Chris Frank, vice president at American Express, and Paul Magnone, head of global strategic alliances at Google, are bridging theory and practice in their course, Leading in a Data-Driven World. The three instructors aim to demystify the notion that using data to solve business problems requires leaders to be number crunchers.

“You don’t need to have been top of class in math or an Excel whiz in order to be able to use data to make decisions,” says Netzer.

Quantitative Intuition (QI), a trademarked set of tools for making smarter business decisions, is the core of the course. QI has three pillars: ask precise questions, look at data in the context of your company, and synthesize your insights to drive action. The course operates on the assumption that the data is in hand and leaders need to decide how to use it to help them make a business decision, not necessarily viewing it through the lens of statistical analysis.

The QI techniques can help direct leaders to purposeful and actionable ways of using data. Precise questioning of data encourages dialogue within a group and can be as simple as asking the data analyst, “What surprised you about the data?” Other questions can focus on what Netzer, Frank, and Magnone call IWIKs, or “I wish I knew” statements.

Leaders need to look at the data from the perspective of their own business acumen and company knowledge. The instructors emphasize becoming fierce interrogators of data, bringing key questions to the discussion such as: “How do these numbers compare to historical data, or to our competitor’s numbers?”

Finally, leaders must synthesize the information and avoid a summary of facts. To best synthesize data, they need to apply their own judgment to advance discussion toward a decision.

Netzer says that although QI is described as a rapid-response tool, that doesn’t mean decisions need to be implemented quickly. “Because we live in a data-rich environment, sometimes a decision is prolonged, and not for the right reason — because there is always an opportunity to see more data,” he explains.

Netzer says the instructors want to dismiss the “certainty myth,” which implies if you keep looking at more data, you’ll be able to reach the nirvana of making certain decisions. “We will never get there,” he says. “There will always be uncertainty in making business decisions.” QI releases leaders from the restraints of expecting perfect data. Asking, “‘When do we think this is good enough for us to go and act on it?’ That’s what speeds up the process,” Netzer says.

The professors all use the QI tools in their business roles, including Netzer, an Amazon Scholar who splits his time between the company and Columbia Business School. Frank and Magnone have variously worked at American Express, Google, Microsoft, IBM and Deloitte, among other companies. Their common observation both on the front lines of business and teaching was that people often fear working with data, which led them to help launch the course.

Research reported in a May 2011 article published by McKinsey & Co. pointed to another reason they want to teach leaders how to use their data: The article noted, “By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.”

Netzer points out that the push for more STEM education may address the first gap, but a lack of managers with the skills to use big data may require more courses like Leading in a Data-Driven World and others now taught at Columbia Business School and elsewhere.

Students say the class gave them tools that they found intuitive but have never used before. They also tell Netzer that their work with data has become much more purposeful, efficient, and actionable.

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