In-Depth

Six Myths about Data Analysts

The growing importance of data analysts in business has brought along a few mistaken beliefs about them.

• By Ted Cuzzillo
• 06/01/2011

Let's clear up one thing about data analysts. The good ones -- the ones who know how to use all the data so painstakingly governed, transformed, normalized, cubed, and talked about -- are anything but anti-social geeks.

"You should be a little bit of a ham," says Interworks COO Dan Murray, who's always got an eye out for new talent. The best data analysts love the data and love presenting it. Even better, he says, "A really great data analyst should get people excited."

This was one of six themes that emerged when I asked a handful of data analysts what people don't understand about them and their work. They answered with surprising passion.

Myth #1: Data analysts are geeks. Fact: Analysts are good communicators.

One analyst recalls being discovered within the global manufacturer he works for. (I can't name him or the company.) He's good at translating data that buyers for Wal-Mart understand, he said. "Folks don't expect that." Then he heard what someone said who'd seen his presentation: "'Wow, it's good to know we have people like that.'"

A manager who recruits analysts said, "The really great data analyst can get people excited." That's often done by being excited themselves. "I liked being the first one to figure things out," he said. "I knew I was the first to get the data, so I was the first to know the story. It's fun discovering stuff."

Another analyst said that people don't understand that analysts "care deeply about the journey and the destination."

Myth #2: Analysis is all about insight. Fact: It's all about impact.

Insight means nothing if nothing changes. At Zynga, the game producer, "Analytics is about impact," says vice president of analytics Ken Rudin. "In our company, if you have brilliant insight and you did great research and no one changes, you get zero credit."

Impact can mean working the organization to get the right tools. Interworks COO Dan Murray tells of one analyst he worked with who "had the stamina" to persist after the CIO initially rejected the tool he felt he needed. "It's OK to break the rules if everything comes out OK."

Myth #3: Data analysis is easy. Fact: Data analysis takes time to learn.

Marketing demos look so easy, leading you to believe that a tool makes the job a cinch. Demonstrators with years of practice and painstakingly formed data emphasize their tool's ease of use. No doubt you've heard: "Getting to the answer is as simple as drag-and-drop ... and the-tool is smart enough to know what I want!"

Why not hire a beginner? "It takes a lot of skills to show nuances and make it engaging and something that people come back to. It's just as much an art as a science," says Tor Stahl, mobile analytics product manager at mFoundry. People also don't realize what it takes to pull data together and solve problems. Juniors are prone to get stuck on side issues and lose track.

Myth #4: Statistics is the most important skill. Fact: Business smarts are more important.

Ken Rudin at Zynga looks for analysts who "know what questions to ask." He might present an applicant with a problem his staff is puzzling through and ask what approaches the application would recommend. A good answer is, "It flattened out compared to what?" A not-so-good answer might involve degrees of standard deviation.

Myth #5: Analysts work at the "speed of thought." Fact: Thought is often a slow, non-linear process.

Questions take time to answer. Analyzing data can be like "chasing the cheese through a maze," says one analyst. "Then you reach a dead end, and you go back and start again. Each project is different. It's a puzzle."

Even when the answer is found, figuring out how to display it right takes time.

Myth #6: Analysts are a rare breed. Fact: We're all data analysts.

It's true that experienced analysts are hard to find right now -- but we're all becoming data analysts as the everyday flood of data rises. The clearest evidence, where amateurs display work alongside professionals, is in tools such as Tableau Public and IBM's Many Eyes.

On such sites, torrents of new amateurs are filtering into trickles of power users and pros. What about those who don't progress? One particularly passionate and optimistic analyst wrote, "I hope that the collective wisdom of these exercises have significant impact in the light and life of the world and for the pure good."

I hope so, but I'd settle for more realistic perceptions.

Ted Cuzzillo is a journalist and industry analyst focused on analysts' tools and needs as well as the environments in which they work. You can contact him directly at [email protected]. If you're a data analyst, he'd appreciate your participation in his survey; you'll receive a free preview of his report when it's complete.