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 one librarian puts it, they're "some misguided brontosaurus snuffling in the antediluvian biblioforest."

The director of the Cushing Library at Holy Names University, just across the bay from San Francisco, is 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 Karen Schneider wrote recently 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.

You guessed it: sloppy and proud. Out of the loud, unruly marketplace we have now for analytics appliances will come new, bold categories to satisfy every need. One will satisfy what is now the widespread obsession with data quality. So boring now, appliances in this niche will make data quality problems seem quaint with a "smart" data maid that follows us around.

At least that's what I came away with from a phone call with Kim Stanick, marketing consulting to BI vendors. She said, "We're comparing all these things on the same playing field, and maybe different playing fields are evolving." She said analysts should get busy and define these new fields.

With a data maid picking up after us, what happens to IT? Well, it's sure not going away, says one sales manager and longtime IT watcher. (Like so many people with corporate jobs, he won't let me go anywhere near identifying him.)

"They're going to go kicking and screaming up to the next higher level of function," he says. Up there on that new, higher level, the IT team will be smaller. It will also be more strategic, more collaborative, and have a better understanding of the business.

But what happens on the business side? Will those who were chronically unable to explain their needs now find new talent for expression? Did they take a class? No, the new software comes embedded with the requisite knowledge.

Will sloppiness ensue? It did once before, back when the typist pool receded as managers adopted PCs. Grammar and punctuation — those quirky little standards that reduce noise and amplify signal — gradually fell out of style.

"Oh, but we've got spell checkers for that!," the do-it-yourselfers protest. But those things still can't say to a confused writer, "Uh, Mr. Tryingtoohard, what are you trying to say?"

The same fate may lie ahead for data quality.

We still have to account for a third group: business intelligence consultants. In this story, they all moved on to other niches. Even today, that's what some have already done — and more on that in a future post.

He "over-thought," says the one whose cube such a guy walked into. Theresa Doyon has been routinely navigating datasets in the 50 to 300 million-unit range for 10 years. She's good at all-terrain tools like SAS and KXEN. She could have produced the report he wanted. Instead, she asked him, "Why?"

They talked for an hour. What he actually needed, she discovered, was more of a descriptive report — something that gives a picture of a current situation. All he wanted to do was figure out how to allocate resources.

This is where things usually go wrong. "We've got all these great tools," she says, "but I don't see us using them as well as we could." We could write this off as a communication problem, but she believes it's much more than that.

It's something like a man who walks into a kitchen with a carton of eggs and tells the cook to fry them all — when actually he's just hungry and happened to have found eggs. Something else might suit him better. Bacon?

Business people complain that they get reports, not insights. But they're not sure how to ask for the data. Meanwhile, analysts complain that they're asked for a fire hose of analysis and that it's always due yesterday. But they deliver. Asked for a report, they produce a report. Asked for a model, they produce a model.

"But that's not what the business people really want," says Theresa. 'What they really want is to answer some sort of business question, like how's my marketing doing? What can I do differently?"

"If you had a strategy and you did bite-size tests and learned as you go," she says, "you could start to use the analytics that would really drive the insight." We can really improve the way things are done. That's the missing link.

At least some organizations have found that link. A home furnishings retailer she worked with recently had her work closely with marketing people as their questions and her analysis evolved.

The retailer had been suffering as ever more of its customers feared they'd soon lose their homes. The living room sets that looked so good just months before seemed to lose their appeal. Theresa's assignment was to, in effect, come up with a silver lining.

"It was a very big and ambiguous question," she recalls. She worked with the client on a series of projects over nearly two years as the sour economy evolved. She estimated opportunities, customer targets, and gave the marketing people she worked with critical guidance on the launch of new programs. She recalls, "It came out quite well."

See her LinkedIn page here.

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.