What’s the future of BI? Last fall, one sharp source of mine answered, “Two words: Tableau and QlikView. You didn’t hear it here.”

Those are startling words coming from that source, a well-regarded BI consultant known for big-name clients and their big deployments.

At about the same time, a column of mine appeared in Information Management titled “Don’t call it BI” — in which I mentioned Tableau and a few smaller tools. A reader emailed, “You should also become familiar with QlikView.”

My many Tableau-using friends say QlikView is hardly worth a look. Poor visualization! Control panels! Scripting! “It’s so — yesterday,” one emails.

It’s “yesterday” to some yet it’s the future to others. It’s time for a look.

Both Tableau and QlikView promise the same magic: Listen to one pitch and you might think that you’re listening to the other. Each sets itself up against traditional, big-iron BI. Each claims to empower business users by giving them all the data and control they need for free discovery. Each is easy to use. Go inside each tent, though, and you see how different they are.

Metaphorically speaking, Tableau is West Coast. It’s built for discovery by the individual. Just show up and ride on the breeze, the demos seem to say, free as a seed fairy on a meadow. The inevitable mistakes of discovery are quickly undone and forgotten. Create the most dazzling visualizations — “vizzes” — thanks to built-in best practices that nudge you toward beauty and punch.

One of the most attractive aspects is users’ effervescence. They seem to be riding on the wind and solving business problems all at once. Their rapture sweeps me away every time I’m near it.

If Tableau is West Coast, QlikView is East Coast. Its community is bigger, the third-party add-ons are more plentiful, support seems more available, and overall workflow feels more structured. It too is built for discovery, but it’s discovery rooted in community. The “associative experience” reveals relevant data, and you can create your own views and in quick succession ask any questions, anticipated or not. But unless you’re working alone, someone else probably defined the data and its structure for you. This is QlikView’s counterpart to Tableau’s meadow, though it’s more like a manicured garden than Tableau’s unfenced field of daisies.

QlikView’s boundaries may be more apparent than Tableau’s, but I suspect that there’s at least as much power there. I just haven’t yet been able to judge it for myself well enough.

The trouble for me is that I’ve used it alone, as if stuck in a remote cabin. Though even Thoreau might have liked the “associative experience,” QlikView really comes alive only when you link to others.

As in Tableau, any QlikView user can create or modify a workspace, a document linked to one or more sets of data with any number of displays. Unlike Tableau, QlikView isn’t so finicky about data; for one thing, linking to Excel spreadsheets is easier.

I can’t speak with assurance just yet on the differences between QlikView and Tableau Server — more on that later — though I think I see a QlikView edge there.

One other advantage for QlikView is clear: built-in collaboration. True, Tableau workbooks can be passed around in a variety of ways forever. But as with our atomized life on the West Coast, such a community would be for me, the hypothetical manager of a group, too loose for comfort.

Tableau users will shudder, as if about to be extradited back to Maine. “Great, central authority all over again,” they would say. Yet when I imagine myself managing a group, I would feel disabled without a tight, integrated social structure.

“It’s the soft stuff that matters,” TechTarget research director Wayne Eckerson likes to say. Such stuff is what interests me more than anything: Who are these people and how did they choose what they did?

Have most Qlik or Tableau users chosen their tool the way most of us choose spouses, religion, and politics — guided by our relationships? How many software shoppers qualified their candidates with lists of requirements and features and followed through based on evidence? Did they do what a veteran sales person at a large BI vendor sees?: “They gather requirements, they issue RFPs, they visit trade shows, they talk to vendors, and ultimately they pick one because they like its color.”

I think it’s usually about “color,” color being the cover story for something most people can’t quite describe. For now, though, I’m happy to say that at least my first question has been answered: Yes, QlikView belonged on that list in “Don’t call it BI.”

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A marketing manager I know stopped me in mid sentence. He didn’t want me to call his business intelligence product a “tool.”

Why? “It sounds small,” he said. But it is small, I pointed out. It’s smaller than many others in its space. It’s downloaded in under a minute and unpacks itself on a desktop in a few minutes.

But he waved that rationale away as if it were a fly, and I should have known. Marketing people, like the parents of gladiators, prefer their progeny to be perceived as big. Bigness casts dark shadows over competitors and conceals weakness. Industry insiders give big competitors good odds.

Users, though, have more immediate, personal concerns. They want something that feels good, works consistently, and adapts easily. This describes a “tool,” a label that should be taken as a compliment, not an insult.

To understand the value of good tools, read what farmer and essayist Wendell Berry writes about them. Over the years, he’s thought about them often, such as in his 1970s essay on the Marugg grass scythe.

It is the most satisfying hand tool that I have ever used. In tough grass it cuts a little less uniformly than the power scythe. In all other ways, in my opinion, it is a better tool because, it is light, it handles gracefully and comfortably even on steep ground, it is far less dangerous, it is quiet and makes no fumes, it is much more adaptable. In rank growth one narrows the cut and shortens the stroke. It always starts — provided the user will start. Aside from reasonable skill and care in use, there are no maintenance problems. It requires no fuel or oil. It runs on breakfast. Its cheaper to buy than most weed eaters and is cheaper to use than any other power mower. And best of all its good exercise.

I’d bet that everyone dreams, at least secretly, of software that matches the Marugg. Sadly, though, people with other agendas usually make the final decision — people whose careers depend on buying not tools but “solutions.” My friend the marketing manager has to appeal to those who write the checks. But I don’t care. I’ll keep saying “tool.”

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Hang out for a few days at an industry event and you’re doused in trends, hearsay, and preoccupations. It permeates your mind. Acquired phrases come out of your mouth, the hotel food starts tasting good, and at night your dreams may tell you more than you want to know about hot topics.

Sometimes, I just have to get away. The easiest way at the recent TDWI conference in Las Vegas was down the long hallway toward the casino. Among the rooms named for murdered emperors, the “Justin II” was wide open with a noisy crowd of TDWI-badged guys inside.

I stood in the doorway to listen. One of them saw me and asked, “Have you seen it?” Another one asked, “Tell us! What’s it like? How big?”

When I asked what they meant, a third one said impatiently, “Where have you been? Big data! It’s here, and it’s coming, and it’s really big!” Someone near him added, “It’s big. That’s for sure.”

“Velocity, volume, variety,” a fourth man recited. “Unstructured data!” said another one. And another, “Predictive analytics!”

Behind the crowd was a bartender shaking a cocktail mixer. “Hey, guys. I’d like you to try a new drink. We’re very excited. We call it One Two Three Hadoop!” The crowd turned to him, and each one took a glass from the long row of samples.

“Mmm,” they all said as they sipped and sniffed. One asked, “Will this handle it?” The bartender nodded solemnly.

Suddenly, the room quieted. A man in a hurry was forcing his way toward the bar. “Hadoop! Double!,” he told the bartender. The man swallowed the drink, wiped his mouth, and said to the crowd, “Anyone here ever worked with Hadoop?” A man raised his hand, and the man looked him up and down as if considering him for day labor. “You’ll do. Come!”

“Just like that?” the Hadoop worker said. Another man in the crowd pushed forward and said to the man in a hurry, “Let me pose a question, sir! How do you define ‘big data’?”

The man in a hurry just looked at him, annoyed.

“Yes!” said a man in the back. “How will you know it when you see it?”

But the man in a hurry was already half way out the door as his new helper scurried to catch up.

A tall, skinny man next to me said, “That fella’s in too much of a hurry. It’s sure no way to get to fact-based decision making.” A fat man on my other side said, “Or unstructured data!” From behind, I heard, “Or predictive analytics!”

I leaned over to the tall, skinny man and tried to say just loud enough for him alone to hear, “Am I missing something? All this sounds like the same old BI, just bigger, more variety, and all that.”

The man turned and looked at me, his eyes wide and his mouth open and ready to say the words he hoped his mind would deliver soon. Finally, his mouth widened into a grin, and he laughed. Then I heard laughter from all around me. They were doing what crowds do, which is whatever someone else does first.

The bartender held up another bottle. “You guys need alternative solutions to enhance your big data readiness,” he said. “Take a lick of this very smooth 15-year-old MapReduce.” The crowd pivoted back toward the bar.

“This one is the best yet,” one man wheezed, his eyes watering as he resisted a cough. A man near him said, “Yep, this is a big drink. That’s what we need. Big data’s going to be big.” Another added, “Terabytes big!” Another, “Petabytes big!” And a third man, “Zettabytes big!”

A second bartender had appeared. He said, “We’re very excited about this new premium product. We call it NoBS NoSQL.” He poured a new row.

One man gulped his drink and reached for another. “Just how big do you suppose big data is?” he said. A short man next to him took a drink and pronounced confidently, “I’d say about this big,” and spread his arms wide.

A third and fourth bartender had just appeared, and I had to get out of there.

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I’ve rarely heard resistance to BI expressed quite so well.

A woman at an oil company said to Metrics Insights founder Marius Moscovici, “If someone wants to know something, they pick up a phone.”

The hell with easy information delivery. Make them ask for it.

I might understand what Marius witnessed if the woman were faced with any of the heavy hitters in business analytics. These things come encrusted with big promises, but big promises come with big upheaval. Data gets disturbed that some people wish would stay buried. They want to say, “Everything’s fine here. Go away.”

You can imagine why. They might fear that data quality isn’t up to snuff, or that someone’s got to govern all that stuff whether it’s “big data” or little data, or they could simply fear anything not invented back when they had nothing to lose.

It’s harder to understand when the information has already been refined and put in a box — and simply needs delivery to a doorstep.

The simple tool from Metrics Insights — the company and the tool go by the same name for now — seems to follow in big tools’ wake and fill in where they won’t. Users drag and drop tiles to assemble other tools’ output onto one screen. It’s simple self-service BI for easy focus, collaboration, and mobility.

It’s making headway with four customers that include Barnes and Noble. Looks to me like a good tool that’s just trying to bridge that last yard.

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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.

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

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Visualized data seems as natural as eating and sleeping, doesn’t it? Yet the first economic time-series wasn’t plotted until 1786, according to our patriarch of viz Edward Tufte in his 1983 book Visual Display of Quantitative Information.

What took so long? I suppose humanity really did suffer from lack of an Excel chart wizard. People had been making maps for centuries, but apparently no one had made the leap from maps to abstract quantities like time and money. That wasn’t so easy to do, after all. You can’t just scratch numbers in your clay, you have to think about it first.

It all comes back to what’s probably already a cliche: simple is hard.

Still, what took so long?

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