What it Takes to Succeed with Big Data

by   |   April 16, 2014 5:30 am   |   0 Comments

Thomas Davenport

Thomas Davenport

One key leadership trait for big data seems to be a willingness to sponsor experimental activity with data on a large scale. Big data, at least today, requires some educated faith. ROI is difficult to define in advance—particularly when it involves new products and services or faster decisions. This is especially true in the discovery phase. As Tasso Argyros, cofounder of Aster Data (now Teradata Aster) notes, “It’s rare that there is a budget for discovery.”²

There are some leaders who are willing to venture into big data on faith, however. At LinkedIn, for example, cofounder Reid Hoffman had also been a founder of PayPal, and knew there were substantial opportunities from exploiting online transaction data. It was primarily his decision to begin hiring data scientists into the product engineering organization. He encouraged them not only to try to develop new products and services, but also to contact him directly if their ideas got stuck in the process or the hierarchy.

That’s exactly what Jonathan Goldman, a data scientist at LinkedIn whom Hoffman had helped recruit, did when he had an idea for a new application that became People You May Know (PYMK).³  The application recommends people you may want to network with who have background attributes in common with you. Goldman created an early prototype of PYMK, but had difficulty getting the product engineering organization to incorporate it into the LinkedIn site—or even to try it.

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After Goldman approached Hoffman with his problem, Hoffman allowed him to create a test ad on the LinkedIn site. The click-through rate on those ads was the highest ever seen. Goldman continued to refine how the suggestions were generated, incorporating networking ideas such as triangle closing—the notion that if you know Larry and Sue, there’s a good chance that Larry and Sue know each other. Goldman and his team also got the action required to respond to a suggestion down to one click.

LinkedIn’s top managers quickly made PYMK a standard feature. That’s when things really took off. I’ve already mentioned in chapter 1 that PYMK messages achieved a click-through rate 30 percent higher than the rate obtained by other prompts to get people to return to the site. Millions of people paid repeat visits who would not have done so otherwise. Thanks to this one feature, LinkedIn’s growth trajectory shifted significantly upward; PYMK is credited with bringing in several million new users. It wouldn’t have happened without Goldman’s idea—and Hoffman’s support of it.

It won’t always be necessary for data scientists to go directly to the company’s chairman, but it’s not a bad idea for senior executives to open a direct channel to them in the early days of the big data era. Part of taking an interest in experimentation is eliminating barriers to the implementation of innovative ideas and offerings.

Leaders of big data–intensive organizations also need some degree of patience. A good deal of “mucking around in data” may be necessary before there is any sense of a payoff. It may even be necessary to keep data around for multiple years before its value is known. Jeff Bezos of Amazon is known for saying, “We never throw away data,” simply because it is difficult to know when it may become important for a product or service offering down the road.

Leadership of big data firms may also require some new senior management roles. There are no examples—to my knowledge, anyway—of “Senior Vice Presidents of Big Data,” but there are some roles that include that function. Take, for example, Nora Denzel, who was the senior vice president not only of marketing, but also of big data and social design at Intuit (and big data actually comes first—her official title there was Senior Vice President of Big Data, Social Design and Marketing). There is a logic to combining these roles; at Intuit, big data is used to improve the website, build customer loyalty, and improve customer satisfaction—all marketing objectives.

Intuit has a great track record of developing products, services, and features based on big data. In the tax processing application TurboTax, for example, users are informed about how likely their tax returns are to be audited based on past customer experience. In the accounting package Quickbooks, products that customers buy and list in their financial records are the basis for targeted offers (called Easy Saver) of discounts on those products. Both Quickbooks and Mint, the personal finance site purchased by Intuit, inform business owners how their performance measures and costs relate to other small businesses.⁴

There are also new senior management roles at other firms involving the combination of big data and analytics. The insurance giant AIG, for example, brought in long-term analytics leader Murli Buluswar to be chief science officer—one of a growing number of C-level analytics executives in large firms. Buluswar oversees a variety of analytical projects and groups, involving both big data and small. His staff includes data scientists and conventional quantitative analysts. He commented in an interview: “From the beginning of our science function at AIG, our focus was on both traditional analytics and big data. We make use of structured and unstructured data, open-source and traditional analytics tools. We’re working on traditional insurance analytics issues like pricing optimization, and some exotic big data problems in collaboration with MIT. It was and will continue to be an integrated approach.”⁵

We’re already beginning to see more roles of this type, with a variety of specific titles. One variation is the chief data officer (CDO) role, which is pretty common in large banks. In principle, I think it is a fine idea to combine the responsibility for data management and governance with the application of data—that is, analytics. In practice, however, most of the CDO incumbents seem to spend the great majority of their time on data management and not much on analytics. Most of them don’t have strong analytics backgrounds either.

There are some exceptions, of course. John Carter was the CDO at Equifax, where he led efforts to build the company’s analytical capabilities—while still wrestling with many data management issues as well. And Carter has a PhD in statistics. Now, however, he has a different job at Charles Schwab. He is Senior Vice President of Analytics, Insight, and Loyalty, which should allow more of a focus on what the company should do with big data.

There is another new analytics-intensive role at eBay. Zoher Karu, who led analytics efforts at Sears, will be the new vice president of customer optimization and data. Karu told me that the job was initially described in terms of customer analytics, but he felt that the term “optimization” suggests a stronger focus on achieving results.⁶ Other companies, such as McGraw-Hill, are creating chief digital officers to both advocate for big data and analytics and manage online channels. Bank of America and Wells Fargo also combine those roles, although they don’t employ the title.

Then there are the C-level titles that are purely focused on analytics. FICO, the University of Pittsburgh Medical Center, and the Obama 2012 campaign are three organizations that have named chief analytics officers. If you are really serious about analytics—not just the data management activities required for big data—and you want to employ them in a variety of functions and units around your organization, I recommend the creation of this sort of job and title.

Reprinted by permission of Harvard Business Review Press. Excerpted from Big Data at Work: Dispelling the Myths, Uncovering the Opportunities by Thomas H. Davenport. Copyright 2014. All rights reserved.

A complete list of citations can be found in the book.

Thomas H. Davenport is the President’s Distinguished Professor of IT and Management at Babson College and a research fellow at the MIT Center for Digital Business. He is also co-founder and research director at the International Institute for Analytics and a Senior Advisor to Deloitte Analytics. He is author of the new book Big Data at Work and the best-selling Competing on Analytics.






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