Haul away the hardware, peel off the software, rinse off the mystique and you see what the people who manage data really are: They’re librarians. That’s the role IT workers should model themselves on.

I’m not talking about technology. I don’t care what tools anyone uses. Whether we’re talking about bound paper known as “books” or bits magically transmitted over “wi-fi,” I don’t care. It doesn’t matter.

I know, the comparison may seem harsh. Librarians are said to shuffle silently among musty old books that no one ever reads. Or, as my friend Karen Schneider puts it, they’re “some misguided brontosaurus snuffling in the antediluvian biblioforest.”

She’s director of the Cushing Library at Holy Names University, just across the bay from San Francisco. She’s one of the actual librarians who resist a trend among some in her profession. They want to run libraries like traditional information technology departments. They’ve been seduced by the old mystique — which in the business world has worn thin.

You know the complaints: IT guards its data like gold bullion instead of serving it to those who can create value with it. It tries to shop its way out of problems. Only the initiated may enter.

Why anyone would want to emulate that, I don’t know. Yet apparently, from what she wrote last week in her blog Free Range LIbrarian, this trend has legs among some who manage libraries.

That trend seems idiotic when you realize what a well run library is all about. Substituting just a few words, you can see a philosophy for IT in the one she describes for librarians:

In the end, what matters, and what we are about, are the ancient truths of librarianship: organizing, managing, making available, preserving, and celebrating the word [data] in all of its manifestations; helping our users build skill sets the fundamentals of which (if not the ephemeral details) will last a lifetime [a fiscal year]; and celebrating and defending the right to read [analyze], however that word is interpreted. This is what we do. This is who we are. This makes us librarians.

Librarians and IT workers, that is. Does technology really make anything new? I say that, fundamentally, nothing is new but the tools.

One of the business intelligence industry’s best explainers, Wayne Eckerson, is leaving TDWI for TechTarget. For the everyday BI expert, it’s good gossip. — But for those still on the learning curve, it’s a cue to switch channels.

“It’s true,” he told me this morning about the rumors, “I’m departing the big TDWI in the sky for other pastures.” This week’s rumors were only the latest; I heard the first wave back in early 2009 that he was looking to jump the fence.

The new pasture, as of November 15, will be as director of research at TechTarget with BeyeNETWORK co-founder Ron Powell. On the side, Wayne will also run a consulting firm, BI Leader Consulting.

For him, it’s a leap. He’s been associated with TDWI since 1995, employed there since 1998. He leaves his marks everywhere: director of TDWI Research, creator of the Executive Summit, for a while director of education. See the long history in his blog post.

For us, it’s a moment to pause in appreciation. Few others I know of can explain basic BI concepts as well. Some of his long roots are in newspapering, and they showed its value in his book, Performance Dashboards: Measuring, Monitoring, and Managing Your Business (Wiley; 2005). It was the first book I ever read on business intelligence and a valuable primer. Without it, I couldn’t have penetrated most of the books, blogs, and articles I found later.

His jump happens to coincide, by the way, with the release of his book’s second edition — still on sale, at least as of Thursday afternoon, at the TDWI Bookstore.

When you come face to face with unfamiliar data, how do you proceed? How do you avoid sending you and your shiny “speed of thought” tool slamming into a dead end? Dan Murray’s got a routine — and he’s also got certain music and right-brained books to go along.

Dan’s first rule: “Don’t pre-think.” It’s the hardest thing for people to learn, he says. “If you go into [data analysis] thinking you know where you’re going, you easily miss the granule of gold.”

He’s the chief operating officer and heavy-hitting data analyst at InterWorks, Inc., an Atlanta-area business consultancy. What seems to me like an unending stream of mid-size businesses from all different industries has kept him running days, nights, and weekends to make sense of each one’s data and unravel old data knots.

From an airport somewhere in the South, he explains, “You have to think like a writer thinks. You don’t know where the story’s going to go.” Screenwriters and novelists often say in interviews that their characters veered off in directions the writer hadn’t anticipated.

He’s been analyzing data ever since spreadsheets first became available in the early ’80s. “I was a huge spreadsheet guy.” Now his tool of choice is Tableau.

The routine goes something like this.

First, get the big picture. Grasp the general outline. How many records do you have? What’s the highest and lowest? For example, if you’re looking at a company’s sales, how many sales, units sold, and so on?

Look for what pops out. Trends often make themselves obvious right away.

Find groups. Build a bar chart to see how it all breaks down. If you’re looking at sales, make groups of products, divisions, for example.

Lay out timelines. Build time series to see any long term trends. Start simply with years, then break it into more detail.

Make maps. If the data contains locations, throw it on a map and see what clusters appear.

Go on tangents. Try making some measures into dimensions. For example, if you have a million invoices, with a range of up to a million dollars, where do most invoices fall? Try cycling through every type of chart. Remember, the cost of any view is just one click.

Look into outliers. Outliers may be just bad data, or they may be interesting. A good place to find them is in scatterplots. “Most of my interesting discoveries are in scatterplots,” says Dan. Seemingly unrelated numbers sometimes have some kind of interesting correlation.

Combine. Put all the charts done so far into one dashboard. Filter all the views based on [things I highlight]. There you can see it all at once. Brains don’t remember more than one or two things at one time, but here you see it all together.

Repeat. Good tools make false steps easy to back out of.

Keep an open mind. He plays music, often the piano music of Frank Kimbrough, such as”The Spins.” He emails, “The lyrical and circular notions of this song reflect how I do analysis. He circles, he dives, he riffs, and then he comes back and does it again in a slightly different way.”

Present and persuade. Jazz, right-brain thinking, motivation, surprise, discovery — it all results in discoveries that must be communicated persuasively for any value to result. Dan recommends the two books by Dan and Chip Heath, Made to Stick and Switch.

Three hours of analysis will show you plenty. “You’ll know just as much as the insiders know.”

Do you have a routine for analyzing unfamiliar data? I’d especially like to hear from users of many different tools, from the most advanced to pencil-and-paper. Please introduce yourself here.

Some of the books Stephen Few reviews may at first glance to have little to do with data analysis. On second glance, though, they have everything to do with it. He often goes into the essence of thinking, insight, and decision making — core knowledge for BI practitioners.

See his latest, posted yesterday afternoon, on Gary Klein’s Streetlights and Shadows.