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5 Lessons From Facebook On Analytics Success
Ken Rudin, Facebook's head of analytics, shares five lessons the social networking giant has learned to improve the accessibility, understanding and use of business insights.
November 20, 2013
3 Min Read
Business analytics teams can't just generate insights and leave it up to the business to put them to use, says Ken Rudin, head of analytics at Facebook. The goal should be to make an impact on the business, and that means taking ownership of the challenge of turning insights into action.
So what do you do to make sure the analytics team's efforts to detect data correlations, spot customer behavior, and uncover possible improvements and efficiencies don't go to waste? In a recent interview with InformationWeek, Rudin shared five suggestions for driving analytics adoption:
1. Make it understandable. It's one thing to give salespeople lists of prospects. It's another thing to explain that you have algorithms that analyze buying patterns and purchase history to spot ripe prospects for particular upselling or cross-selling offers. It's even better if you provide an interface that lets users play with buying variables and explore prospect data on their own.
"If you don't understand what's behind the analysis, it's more likely you'll fall back on intuition," says Rudin.
[ For more analytics insight from Ken Rudin, read Facebook Exec: Databases, Hadoop Belong Together. ]
2. Make it visual. "The more friction you add, the fewer people will use the data, and the harder it will be to make an impact. We're using a variety of visualization technologies so we can move away from dumping table extracts into Excel."
3. Make it discoverable. Establish one place where people go to find analyses and certified data organized by topic. It can be as simple as a portal with a set of pointers, but it must be easily accessible and comprehensive.
4. Use a hybrid organizational model. Facebook's data analysts collaborate with one another and get strategic direction from Rudin in a centralized way, but they are embedded within business units, so the operational work is decentralized. This approach ensures a level of centralized control, so data analysts can learn from their peers and don't duplicate efforts. At the same time, day-to-day work is in tune with business needs and benefits from the diversity of having data analysts working alongside business unit leaders, project leaders, designers, and engineers. The roles within your team may differ, but the idea is to guarantee a balance of perspectives.
5. Train employees. Facebook holds intensive, two-week-long Data Camp training sessions for analysts and line-of-business employees. One goal is to train people on a variety of data analysis tools. More important, however, are sessions designed to spark thinking about how to solve the real-world business problems submitted by Facebook's business units.
"We have a running collection of problems that business units are working on, and we ask the Data Camp participants -- analysts, project managers, designers, engineers, people from finance -- to think through the problem." In some cases, Data Camp work has contributed to solving real-world problems.
The training session your organization uses may not be as long as two weeks, but Rudin says it's important to make it a full-time commitment without forcing employees to keep up with email and their usual responsibilities. "We give them a lot of work, and they're expected to come up with answers and insights that really lead to an impact on the business."
As for the imperative to make an impact, Facebook encourages teams to look beyond asking questions; you have to think through what you would do differently if you could answer the question. "If the team doesn't know what it might do differently, I'm going to choose a different problem."
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About the Author(s)
Executive Editor, Enterprise Apps
Doug Henschen is Executive Editor of InformationWeek, where he covers the intersection of enterprise applications with information management, business intelligence, big data and analytics. He previously served as editor in chief of Intelligent Enterprise, editor in chief of Transform Magazine, and Executive Editor at DM News. He has covered IT and data-driven marketing for more than 15 years.
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