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.

How do you approach unfamiliar data? An investment banker I talked to last week — one I know from a client’s whitepaper — rejects the “don’t think” method, advocated in my earlier post about Dan Murray. Instead, he thinks first, on paper.

“My approach is driven by having a bunch of gray hair,” says Michael Princi, managing director of ThoughtStorm Strategic Capital, a boutique investment bank and advisory firm in northern New Jersey. “I want to use my business acumen to tease out what might be the underlying issues.”

Experience counts The naive mind is prone to bad mistakes, he says. Take, for example, the 22-year-old analyst in India he employed who spotted this correlation: a firm’s revenue correlated with the number of Bobs in the workforce.

Hypothesize on paper “I first think through my hypothesis on paper,” he says. “‘It gives you a starting point.” If the model is wrong, as it often is, he just tries another one.

Test and repeat If the actual numbers are somewhat close to his expectations, he knows he’s on the right track. It’s the traditional consulting confirm-or-deny method. When the data does confirm his hypothesis, he’s able to run through the data again and again in iterations.

Test and repeat If the actual numbers are somewhat close to his expectations, he knows he’s on the right track. “It’s the traditional consulting confirm-or-deny method. It’s the quickest way I know. When the data does confirm his hypothesis, he’s able to run through the data again and again in iterations.

Does Michael Princi really analyze data differently from Dan Murray? They’ve never met, but Michael guesses not. He says of Dan, “I think he’s probably mapping it out intuitively.”

Do you have a routine for analyzing unfamiliar data? Please introduce yourself here.

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.

I’m sorry to tell you serious types out there, but visual analysis is often a game — in fact, one of the best games in town with Tableau Software’s visual analysis tool. Now Tableau Public is going to bring it to the masses.

In the same way that YouTube spawned a surge of new filmmakers, Tableau Public — free, running the same engine as its desktop sibling, and embedable — will bring on a new generation of data players and spectators.

I was a spectator at a data visualization conference one afternoon two years ago. Tableau Software director of visual analysis Jock Mackinlay had finished his presentation and another person had started his. Yet someone at the control board forgot to flip a switch, and Jock’s live screen remained on one of the room’s big screens. Jock assumed his screen had been hidden, and he kept playing with the data. I don’t have to tell you who seemed to have the audience’s attention until someone pointed out the problem.

The mere visual distraction was minor. Even without narration, I got caught up in the apparent drama as he tried one look at the data after another.

Not long after that, I wondered aloud to someone at Tableau about data hobbyists. I imagined people who foraged for data to analyze then publicize it to start conversations, collaboration, or duels. Data would be their raw material of choice just as scrap metal is to some sculptors or overheard conversations is to some fiction writers.

There was no such community visible then. But I realized this week that I know one now: Dan Murray, a skilled, dedicated Tableau user. He jokes that he’s a “freak” because he analyzes data from the federal budget and posts his often provocative analyses. He’s already been answered by at least one who disagrees with him.

In beta and since its February 11 launch, Tableau Public has hosted a flurry of visualizations, including these: a map of top venture capital firms investments by U.S. region; a chart showing how long it takes to build a technology empire; a history of earthquakes in Haiti; a neighborhood breakdown of housing supply in Seattle; trends in U.S. high school graduation; and studies of deprivation and marginalization in education. In most cases, spectators can become players by selecting subsets of the data to find answers to their own questions.

With popularity comes some misuse. Many of the charts will break rules, such as what happens in another kind of game, YouTube. A New York film editor I know complains that many YouTube-acculturated film editors have neglected basic editing principles. She writes that they rely so much on special effects that they “can’t put two shots together and have them work as an unembellished edit.” On Tableau Public, there will be pie charts, chart junk, and even baselines that do not start at zero. We’ll survive it.

But what’s all this got to do with the very serious practice of business intelligence?

Like monks must have done when printing presses began producing books for the masses, many priests of business intelligence will stand aside, arms folded in the aspe chapel. But I predict that before long even they will appreciate a wider, deeper pool of analytical talent ripening for training and employment.

I suspect that the new bunch will have been sharpened by the give and take of public exposition. They’ll also learn from playing in a huge community the way artists and craftspeople of all kinds improve their skills when they bump into peers every day.

This is a new clue for the future of BI. It can’t help but improve data analysis in business. So let the games begin.

How do you show the value of visual analysis to business people? Dan Murray can show it in demos, but he keeps looking for the “magic dust” that explains in a snap.

He sees visual analysis as a key part of low-cost business intelligence at small- and medium-sized organizations — and he’s set out with evangelical zeal to provide as many of these firms as he can with BI.

A new tactic he’s trying involves a simple chart he carries in his pocket. It’s got just two unmarked axes, horizontal and vertical — and a “blip,” a spike on the time axis. He shows it to people and asks, “What’s the first thing that comes to your mind?”

Everyone sees the blip in their own context. In a bar a block down from a hotel in Atlanta, two guys said, “Patient’s dead.” They were both surgeons. In Dallas, a man said it showed the start of a recession. He was an economist.

Dan hears many answers, but everyone’s first question would be the same: Why? The ability to quickly answer that question, and the many that come along later, is one main difference between visual analysis and simple charts.

He’s surprised at how few people in the many talks he gives around the country know what visual analysis can do. Even among a group of database pros he spoke to recently, who were otherwise full of BI knowledge, few understood.

The first thing everyone sees in a demo of Tableau, the acrobatic visual analysis tool, is how easy users can create reports — and that frightens IT workers who create them for business users. One database pro said after a demo, “About 10 minutes in, I thought my job had disappeared.”

When users roll their own, Dan says, everybody wins. IT has better things to do than write reports. For users, fast reports and real visual analysis means the end of pre-configured questions.

In true visual analysis, each new view shows new “blips,” and each blip prompts new questions: why?, how?, what?, or who? True visual analysis is fast — and it has to be because you make up the questions as you go along.

Dan and I both wonder why visual analysis hasn’t caught on like wildfire. Where has the BI industry missed?

Price is always part of it. To companies used to Excel, Tableau and Spotfire could seem steep. But compared with most tools sold under the BI label, they’re are a bargain.

But mostly, he thinks, it’s that it takes a demonstration to understand the value. “People can’t know what they need until they see it in context,” he says.

Ah, the paradox. That difficulty in explaining the value of visual analysis actually helps explain the value. You don’t have to know what you’re looking at in the beginning. “You just evolve as you work with it,” Dan says. “I often start with a notion but often get a result completely different from what I thought I would.”

Still, I think he’s got something with that “blip” technique.

Dan Murray expects to take another step this week in his thrilling rebellion, spreading the word on high value, low cost BI.

Though it’s a rebellion and may burn with Che Guevara-type zeal, Dan’s methods actually lean way over toward Darwinian evolution. Revolution is expensive and risky, he writes, while evolution is intelligent and incremental. He also likes to point out that Che died brutally at 39 and Darwin died at 73 in bed with family around him.

First, people have to learn the basics, that tough work to create a data warehouse. His rebellion does it with Tableau, spreadsheets and a little guidance.

He’s a busy guy. This week, he’s also speaking at the Tableau Customer Conference in Seattle. He is also COO of Interworks, Inc.

I’ll post the address when I get it.

Dan Murray’s taking on Big BI — and in just under two weeks at the Tableau Customer Conference in Seattle, he’s going to explain his four steps to rebellion — that is, “a high value, low cost BI reporting system.”

Dan devised the system when the company he worked for — which had revenue of about $70 million — couldn’t afford solutions from Big BI vendors. Bids ranged from $130,000 to $580,000.

Dan built his own with Tableau and Excel. The final cost, he writes, was $40,000 — $8000 for Tableau Desktop Pro and the rest for the database and ETL logic.

“I’m on a mission to spread this around the country,” he writes in email. “I consider to be every bit as big a revolution in data as the spreadsheet was to accountants in 1982. It’s thrilling.”