See my latest column in BI This Week (TDWI), “6 Conditions for the Rise of Business Analysts.”

As they rise, analysts may end up ruining the neighborhood for both IT and business people.

What’s the hardest part of training a new data analyst? Resetting the trainee’s mindset.

“They start out with the idea that there’s a right answer,” says Joe Mako.

Joe’s leaving his job — where about one year ago he began analyzing data — to go work for the producer of Lyza. Lyzasoft CEO Scott Davis sees him as a “prototype” of a kind of creative, resourceful analyst that Lyza was designed for. Joe will engage with other analysts to evangelize Lyza and to help new users ease into the flow.

Joe, 29 and a veteran of two Army tours in Iraq, started out on the help desk. He answered calls from within the company, an ISP. Many callers couldn’t or wouldn’t analyze their own data, so Joe did it for them. His boss also enlisted his help — and now won’t dare go without a backup.

The first people he’ll help get into the flow are the two women who’re replacing him, and he’s got to do before he starts at Lyzasoft on November 9. They’re some of only a few in the his group who applied. Most others refused the “boring” work with “ugly” data.

New users, he says, want to know, “Where’s my wizard?” There is none. “But that’s why I enjoy these tools.” He uses Lyza and Tableau primarily. “They stay out of my way. They enable me. It’s just me and the data. … That’s what’s neat. But [new users] don’t know where to start.”

“I’m handed crazy files without any structure,” he says. The first thing new users have to know is that, no matter how ugly the data may be, it really can be cleaned up. He demonstrated to his new trainees, he says, and “they were blown away.” After that, he started showing them how they can clean up data on their own.

He explained basic steps and functions. Then he showed them how to combine tools, such as how to use two functions in sequence. And deeper still.

“It takes time playing to figure out where you need to get to,” he says. “You have to just go and play. If one thing doesn’t work, you try something else.”

“I always thought that data was exact,” he says. “If not, it was garbage and I’d throw it out.” But he later learned that there’s usually only a portion that’s garbage — that somewhere within the crazy mess there’s a story. “Even if every data point is wrong, there still might be some trend you can see. If there’s a bunch of ugly data, how do you figure what he story is?” It takes a willingness to figure it out, to untangle it, to find out what’s in there.

That’s a skill, not a talent, he says. “I’ve watched [his two replacements] get it closer and closer, learning to merge other data in, to reshape it and finally produce the output.”

Closer and closer. Business will trudge ahead, training a Joe here and a Joe there until people don’t complain anymore about boring work with ugly data. Someday, many more people will welcome the chance to do this work.

Most of those versatile researchers of the data-driven world — the business analysts, creative analysts, or even cowboy analysts — probably run on a different schedule from their managers. Paul Graham’s latest essay compares “manager’s schedule” and “maker’s schedule.”

I’m no analyst, just a writer. But the more analysts I meet, the more I find that analysts and journalists share a surprising number of characteristics. One of them, I think, is the tendency to run on “maker’s schedule,” as explained by Graham:

When you’re operating on the maker’s schedule, meetings are a disaster. A single meeting can blow a whole afternoon, by breaking it into two pieces each too small to do anything hard in. Plus you have to remember to go to the meeting.

Meetings are interruptions. They mess up any breadcrumb trail of thoughts that has not yet been laid down in permanent memory. That’s why I’m writing this at 11:39 at night, when I can let a ghost of an idea hang unattended without fear of new email or a phone call dissolving it. Graham writes about once keeping a dinner-to-3 a.m. workday.

Now he wears a VC hat, and he can’t avoid meetings. So he schedules “office hours” at the end of the day.

He hopes that pointing out the two kinds of schedule will make it easier for the “makers.” Their schedule tends to offend managers.

I hope this insight spreads.

What do you think? Do analysts you know work this way? Post a comment.