It’s no secret that some people hear about Tableau’s passionate users and wonder what all the fuss is about. Back in June, in fact, one skeptical industry analyst tweeted to a Tableau fan, “Pal, you seem to have had a bit too much Tableau Kool-Aid.”

Tableau users I know just shrug. People who say things like that find passion for data suspicious, and there’s nothing you can do for them.

Then Tableau itself invited a delegation of industry analysts, most of them from the traditional end of BI, to its annual conference at the Encore hotel in Las Vegas. The company hopes for their blessing to make that leap across the chasm from early adoption to early majority.

My big question: Would the industry “influencers” and Tableau’s influential users play nice together?

I hang out with both groups, the doubters and the devoted. I do periodic retreats to TDWI and other events. I’ve also been an observer of Tableau since 2008 when I blogged that “Tableau is the new Apple.” I have no stake in Tableau’s success except that I think it’s a strong part of BI’s dream fulfilled, a bearer of fruit.

Experts can quibble over its limitations all they want to, but they must acknowledge one thing: It excites users. Few other tools do.

I spotted trouble on the first morning. In the opening keynote, CEO Christian Chabot had invoked one of his favorite themes: how Tableau would “change this tired, paternalistic BI order.” As usual, he got applause. To illustrate an anecdote about dairies, he pulled out a bottle of milk and poured himself a glass. Things were going well.

But about then, an industry expert tweeted from somewhere in the audience. He hinted at a suspicion of Kool-Aid: “It’s just a visualization tool with publishing capabilities.”

He might as well have asked what these 1400 or so nut cases were doing there, packed into that ballroom? Why, going by numbers from Tableau CMO Elissa Fink, did Experian send 17 people, Apple 19, and eBay 35? Did they come for the gambling, the shows, and a sweet sip of delusion?

Two special meetings with Tableau founders and the delegation of experts went better. As we sipped water from the Encore’s handsome tumblers, Chabot and fellow founders Chris Stolte and Pat Hanrahan talked about business plans and technology. Most of the influencers asked about the technology. We’ll have to watch their blogs for reactions.

Eventually, we left technology for more interesting, big picture questions. Neil Raden, of Constellation Research, asked how the company would grow and still satisfy the new demands of the broad new audience? Other technology vendors have stumbled on this. I asked a similar question: If they do as everyone expects and offer an IPO, how would their passion and vision endure under the new pressures?

The gist of both answers: They said they’re not doing this for the money, and they’ll continue to be driven by the same passion for a great tool, and that they’ll be guided by the same integrity. Cynics will scoff, but I believe them.

Meanwhile, out on “the street,” influential Tableau users expressed harsh opinions of the BI regulars.

One man with long experience in business intelligence and data warehousing, whose employer prohibits public statements, called the general class of BI experts “process junkies.” He said, “They don’t understand that I have this data and I want to understand what it tells me. It doesn’t fit.”

Similarly blunt: “I don’t care what these supposed experts think,” said Dan Murray, a longtime Tableau user and chief operating officer of InterWorks Inc, a fast-growing technology consultancy. The company is listed in the Inc. 5000, and it attributes much of its growth to database development and Tableau visualization.

“The BI people are back where we were a long time ago,” said Murray. “We’re past that.” To him, the people who really matter in data analysis now are the ones with passion for data analysis. He said, “Those are the superstars.”

Just who the superstars are marks the line between those who’ve had the “Kool-Aid” and the BI regulars. Most of the usual experts seem to live in the backend, where database administrators and other geeks rule. Back there, the game is all about process and data hygiene. The experts love to talk about all that, and only a few actually analyze data.

Up where the data analysts work, it’s all about analyzing data. They take seriously all the factors that the mainstream BI world does — such as data quality and data governance — but always with the end in mind, not as ends in themselves.

Ask them what they like about Tableau and their answers come down to one point: the thing gets out of the way and let them work almost as fast as they can think. It does so far better than any other data tool they’ve known. They feel that the tool is designed with them in mind — not for any purchaser, not for any security goon, and for not any consultant’s ego.

They are passionate. I had gone to dinner with a half dozen Tableau users when one wondered aloud about the Las Vegas airport’s on-time record. Someone had his laptop along, loaded with FAA data from an earlier analysis. We found seats in a bar near the casino and looked at the data. I don’t know of many others for whom data analysis beats ESPN.

We ordered beers.

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.

I hate to be so timely, but I saw a Steve Jobs quote today that I like.

When you first start off trying to solve a problem, the first solutions you come up with are very complex, and most people stop there. But if you keep going, and live with the problem and peel more layers of the onion off, you can often times arrive at some very elegant and simple solutions. Most people just don’t put in the time or energy to get there.

He’s talking about design, but it goes for so many other things we do in the analytics industry, such as visualization, business analysis, public speaking, and writing.

So often, I struggle through someone’s blog post, webinar, or viz only to wish I hadn’t. The payoff was too small. Had the author given it more thought, it might have become something or at least my effort would have seemed worth it.

Howard Dresner is a celebrity in the business intelligence industry, but most people at last year’s Tableau conference didn’t even recognize him when he showed up there.

Who needs BI? Tableau Software liked to think it had left BI behind. BI people, after all, were the control freaks who denied access to data. They sneered at Tableau’s “pretty pictures.” They cared more about data hygiene than data analysis.

But there he was. Stephen Few spotted him in the audience a few minutes into his keynote and paused to wonder if it was really him. Tableau vice president of marketing Elissa Fink welcomed him. I and some others said hello. Mostly he wandered alone.

But he’s coming back this year — to speak. He’ll be among 10 on the “experts track” at the Tableau Customer Conference in Las Vegas. Others include BI veteran Claudia Imhoff, Cindi Howson of “BI Scorecard,” and Performance Dashboards author Wayne Eckerson.

They’re all worth listening to. But the one most Tableau people would feel at home with is Paul Kedrosky. Unlike the others, he’s not from the BI world at all. He’s an “investor, speaker, writer, media guy, and entrepreneur,” according to his blog’s “about” page. But I know him as the man who counts ladders.

At last fall’s Defrag conference in Boulder, he told about using the California Highway Patrol’s count of fallen ladders on freeways as a leading economic indicator. Who says data must come from conventional sources? He’s serious and creative, a mix Tableau people appreciate.

He’s written that we live in a “golden age of data visualization,” but I’ve found no elaboration. I’ll be listening for that.

As for the other nine “experts,” the first thing I’ll look for is the size of their audiences.

There’s nothing like a Tableau release. No one but Tableau users tweet so exuberantly, not even a flock of birds at dawn. What’s going on here?

This week Tableau Software released version 6.1, on paper just a single decimal point up from last fall’s Six. We got more iPad readiness, improved maps, and other handy improvements. But to read Twitter, this was a much bigger deal than that.

No, I don’t work for Tableau. I just know it when I see something going on. People love this tool. I’ve seen few other products of any type loved so much — perhaps not since backyard mechanics tinkered with Volkswagen bugs, or Mac people discovered icons, or designers found Adobe Illustrator.

I read one cynic this summer sneer about “Kool-Aid,” as if to be so enthused is to have entered a death pact with zombies.

I don’t know what flavor Kool-Aid such sneering BI zombies are hooked on, but it’s their loss. What’s going on must be too simple for their tortured minds: Users love tools that respond. Not that learning them is always easy. But once learned, good tools prove capable, consistent, and simple.

Imagine how much fun the customer conferences are. They’re one of the few business events where, instead of talking about solving problems, people actually do solve them.

Most people shopping for business intelligence tools think they’re sophisticated, observes a sales person who often represents a large vendor in TDWI exhibit halls. Most of these people are deluded.

He may sound harsh, but he observes all this not bitterly but with good humor.

“They buy [BI] like anything else they buy,” he explains. “They put out a bunch of RFPs and go through this whole process and stuff, and eventually they buy the one that’s maroon.”

Over years, he has identified the top three reasons people buy, and the underlying motivation:

1. Career. The chosen tool decides the career of those who will learn it, massage it, and become co-dependent with it. Greed guides the choice; a good choice may ensure steady, lucrative employment for decades to come.

2. The boss’s choice. The boss says, “Buy that one.” Buyers fear defying the boss and like the boss’s power to give status. The blessed few may seem sexy, at least to themselves.

3. Default. They buy one brand and only that brand. They say, “We’re an Oracle shop” or “We’re an IBM shop,” and for them that’s reason enough. These irk this sales person.

The brand loyalists are the ones who seem to irk him the most, even though his brand often benefits from their foolishness.

Even the brand’s high cost can’t shake their trust. “A lot of people assume that more money means more quality and less risk. No, it doesn’t!” he says.

“They buy this big, fancy thing that no one uses.” Instead, many would be better off with a less expensive brand that delivers almost all of the name brand’s features. The left over funds should be spent on training.

The reason many shy away from training? They’d have to admit that there’s ramp-up time. “It’s kind of the Sarah Palin view: ‘Do we really need sophistication?’”

I don’t mind long lines at trade show buffets. All else being equal, the length roughly equals attendance, which roughly indicates industry fizz. For an indicator, though, the analytics/business intelligence industry can do better than that.

How do we take the pulse? What if those who promote the technology, advise the clients, and build the systems actually measured their collective progress with a number? It would be an ongoing, forever-updating, simple benchmark. It would be the industry’s Dow Jones Industrial Average.

Four or five years ago, when the industry seemed simpler, I envisioned a number like this. Why not?

First hurdle: the lack of complete public data. We have it for a few vendors, such as IBM and Oracle, but not for the industry’s many startups. For example, how does Tableau’s ascent figure in, and how do we measure it?

Could we count the number of BI-related hash tags in Twitter? Maybe, but what does the number indicate? Is it success or marketing people out of work? Tweets from Tableau, for example, have been way down for six months or more. Here, down means up. They must have something cooking.

Slightly more credible than buffet lines are the annual surveys by industry analysts. Business intelligence as a priority is up — or was it down? — this year from last, they say. But what did survey respondents mean by “business intelligence”?

Traditional deciders say “BI” to signify plumbing — metaphorically, the path from groundwater to kitchen sink. Did you ever see a plumber’s dream house? He’s installed no drywall; he wants the pipes exposed to admire the workmanship and efficiency: No leaks, good flow. Cooking, bathing, and washing the cat all come later if there’s time.

The non-plumbers make use of the water. The “kitchen sink” is where all that nice, clean data pays off. So the ideal point of measurement would be where data-based evidence reaches decision makers’ minds — say, just before it commingles with the tarot cards.

At that point, how strong is the influence of data analysis? How good is the analysis? How good and complete is the data?

If we could find a proxy for that pulse, we just might have found the Dow of BI.

I’ll ask around among industry mavens, especially the the cooks.

When, when will someone write a novel about business intelligence? I can see it now. Amazon will try to sell me From Here to Analysis, Data in the Afternoon, and Lolita, DBA.

But titles are easy. Writing the novel might be tough, since the author would have to run on sheer imagination. There’s no apparent intrigue. The BI crowd plays it safe. Forget sex. The BI crowd works too hard. Forget guns. The BI crowd shoots only bullet points.

In fact, the best strategy might be Alfred Hitchcock’s favorite: the macguffin. It’s a plot element, often ephemeral, that drives the main characters to do what they do regardless of the macguffin’s value. As the plot moves along, the macguffin fades into the background.

In BI, the most common mcguffin is technology — while the real issue is effective use of data in business decisions.

Remember “Psycho”? The woman steals money from her boss and flees. Her boss is sure to follow. We’re looking off in that direction when out of nowhere comes the shower scene. Holy crap! We never saw that one coming!

Hitchcock’s aim was fear, but McGuffins have other uses, too. In business intelligence, the macguffin creates comfort. In the BI plot, the shower scene is instead endless talk about technology, data quality, data this and data that. All the time, lurking in the culture, unexamined and feared, is classic business dysfunction. But leadership can’t fix it, so they go shopping.

What’s a novelist to do? Use it all. Study up on the technology for a week and interview people on the front lines. Then drape BI technology over a standard plot set in an enterprise.

Business people buy BI tools, and so they’ll buy the BI thriller.

What do you get if appliances keep getting smarter, faster, and roomier inside? Choice A: abolition of IT, because who needs geeks? Choice B: license to be sloppy.

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.

Why should the smart people have all the fun with year-end predictions? You can issue your own! At this time of year, even hopeless nitwits can seem smart.

Once you set up a blog — any free service will do — all you have to do is throw together your trends. Keep these easy-to-use techniques in mind.

• Re-use last year’s trends. Does anyone really believe that 2010′s trends sat down in December for a cosmo and never stood up again? You can safely predict that this year’s trends will be next year’s, too.

• Search in Google for your industry’s name and “trends.” Take notes, rewrite a little bit and, boom, you’re an expert.

• Water the evergreens. For 2009, someone predicted, “Data interpretation will become a significant challenge for new BI users.” Will become? Can you imagine fewer business people having trouble interpreting data no matter what year it is?

• Follow in the draft of top vendors. Competition cyclists know that the easiest place to ride is just inches behind another rider. See where Oracle, IBM say they’re going and point in that direction. If a gang of marketing departments push an idea, it’s guaranteed to find at least a few new customers.

• Quantifying is risky but, done cleverly, it adds credibility. Just make sure your numbers can’t be verified. One clever expert sees 15 chiefs of analytics being hired in 2011. Bingo! The mere presence of a number, any number, gives the feel of certainty. Even if someone wanted to count, how would they do it?

• It’s good to be vague, but better to be incomprehensible. Suppose your crystal ball shows video becoming a big deal in 2011 (as if it weren’t already). Don’t just write “video,” as one hapless analyst did. Instead, pile on enough mumbo jumbo to let readers feel smart for having understood anything at all. Those who’ve tried to read 50 or 100 words will tweet about your “great” predictions.

• Aim for the horizon. Don’t let yourself be bound by others’ definition of “year.” If your vision fails to come true in 2011, you’re just that much further ahead of your time.

Above all, you must enter to win. After the first weeks of January, normal standards set in. If you feel like a fraud, remember that last week’s predictions are like last night’s eggnog. All people remember is the party, and all your readers will remember is your name.