As Stephen Few delivered his keynote address at the recent Tableau customer conference in Seattle, he suddenly broke his rhythm to look at someone in the audience. “Is that Howard Dresner?” he wondered, surprised.

It was. Howard is the man who as a Gartner analyst in 1989 revived the term “business intelligence,” and he’s one of the industry’s patriarchs. He holds a seat on the TDWI faculty, and he founded Gartner’s business intelligence event.

In that world, Tableau is still an insurgent. Tableau usually bypasses IT buyers on its way to data analysts, who only want to soak insight from data and then show others the results. Many Tableau users are veterans of miserable, lockstep interfaces procured by those IT buyers and made by IT-facing vendors. That at least partly explains why Stephen Few’s evisceration of the Business Objects interface seemed to delight just about everyone in the audience. Even Howard called it “wildly entertaining.”

Two mornings before, Tableau CEO Christian Chabot had started the fire. In his Monday morning keynote, he made allusions to Apple’s “1984″ ad, then went into Tableau 6 — using FAA data on another variety of Big Brother: the airlines on which most of the crowd had arrived in Seattle. Both keynotes were as much fun to watch as the wild flames of a bonfire or a public execution.

Now and then, though, I had to check the exits. My inner skeptic always questions such fervor. I subscribe to no guru, no preacher, no righteous political philosophy, nor any movement that’s “green,” “red,” or “blue.” When you glimpse the underside, they’re all too ugly to bear.

But, again and again, I’ve seen that this is not any of that. The Tableau crowd is simply a bunch of people who’ve found a good, honest tool that responds the way good tools do. They have fun listening to the keynotes, but most Tableau users themselves are about as fired up as chess enthusiasts or weekend car mechanics. They’ve adopted something that’s logical, responsive, and economical, in which the simple interface encourages experimentation and learning.

One of the only signs of celebration was giddy tweeting. I suppose that was hard to take for some. One BI biggie — perhaps speaking for the Ministry of Truth — grumbled in a tweet from far away that there was “no silver bullet,” but most Tableau users don’t follow him and don’t care. Others, such as Howard Dresner and Microsoft lead for BI strategy Bruno Aziza both showed up to see what it was all about.

The conference was sold out. Total paid attendance was around 700 — more than twice last year, which was significantly higher than the first year. Most user conferences, in fact, declined this year and last. This year’s total was also in the same range as TDWI World Conference in San Diego, held just two weeks earlier, and not too far away from the 1000 or so that Howard Dresner says Gartner often attracts.

At this rate, says CEO Christian Chabot, the conference will be forced out of Seattle next year and possibly longer. The only space that will hold a larger crowd than this year’s would be space mashups within an easy walk of each other, and that’s not available next year. Tableau is already looking at San Francisco and other cities, even Las Vegas.

One user, now back home in Lithuania, pondered the future: Giedre Aleknonyte, a data analyst at a phone carrier, said “You know how people say they’ll ‘Google’ to find out some information, even when they don’t actually use Google? Maybe someday when we want to analyze data we’ll say, ‘I’ll just tableau it.’”

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.

Recent email to me from a passionate practioner of creative analysis tells how traditional BI is bad for genuine data analysis.

As I see it, traditional BI processes are still designed to start with the answers, not the questions: “Oh, we can’t give you access to the raw data. Your tools (old thinking) probably are not designed to handle that job.” What happens, as [Stephen] Few and [Christian] Chabot keep screaming from the rooftops, is that BI practitioners and business units, etc, never get to ask the right questions. They don’t get to practice what Tableau calls the Cycle of Visual Analysis. It is so disrespectful to the human mind, it makes me want to puke.

Stephen Few gave a snappy name to his new book, Now You See It, and a cover that signals a gem — all black with a slice of sunset that highlights the “see.”

Inside, many charts are so beautiful — at least to a visual analysis fan — that they rival the waterfalls and trees of old Sierra Club coffee table books. It’s on paper so thick — and even smoother than Few’s first book, Show Me the Numbers — that you might feel like you’re flipping postcards.

While Show Me the Numbers was about how to present visualized data, he explains, Now You See It: Simple Visualization Techniques
for Quantitative Analysis is about understanding them, which apparently includes how to make them.

Few’s a natural teacher. He reviews, again, the basis of visual analysis and how the brain is better wired for visualization than for row-and-column thinking. In each chapter, he takes the reader through by the hand from the most basic concepts of visual data analysis through complex ones. Toward the end, he points the student to the horizon to imagine a future state of visualization. I especially liked the interesting chapter “Prerequisites for Enlightening Analysis.”

My question after a while, though, was who Few’s “you” is. What kind of student does he imagine? If the student really requires the kind of patient explanation found throughout the book, shouldn’t that student do some other kind of work instead? Besides, isn’t visualized data supposed to be understood easily?

I’m a mere beginner at visual data analysis, comfortable with Excel charts but not fully competent with Tableau. Even so, the out-of-the-box brain function that visualization scholars call “pre-attentive cognition” seems to have guided me well through visualized data so far.

I can’t help but skim past some parts of the book. In the chapter on part-to-whole and ranking analysis, for example, he spends four luxurious pages on what I thought would have been too basic for adults. That includes nearly one whole page on the meaning of trend lines: some go up, some go down, and some stay the same.

Do these basic explanations bore me because I’ve seen at least parts of Few’s basic presentation too many times? Or did I learn enough from Show Me the Numbers, from Edward Tufte’s books and presentations, and from a stint with a Tufte-admiring market research boss?

Or could it be that, like so many other kinds of work, visual analysis is a lot easier to observe than to practice? Would I appreciate the explanations if I were up to my elbows every day in visual analysis? Hard to know.

Too basic for some, but the book could be just right for others. This book could be the bridge from the early adopters to mass adoption. Also, longtime analysts could appreciate the refresher — for that slap-on-your-own-forehead moment when reminded of basics.

Few conducts the book like a classroom, narrating with statements like, “We’ll look at the following techniques and best practices” as if coaxing freshmen through a long, hot afternoon session.

He does best when he illustrates points with a story. The chapter on distribution analysis begins with Stephen Jay Gould — diagnosed with a kind of cancer with a median mortality of only eight months after discovery. Gould thought about that statistic and discovered that he was a prime candidate for longer survival. He went on to live another 20 years, and helped this chapter seem shorter.

But there’s got to be more to visual analysis than the simple charts in this book. I wish Now You See It skewed toward more sophisticated visual analysis, the kind I hear about but have not yet reached.

In fact, the phrase “now you see it” has another half, ignored here: “now you don’t.” The two halves suggest a cycle of knowing and then asking again — what Tableau calls the cycle of visual analysis. The Tableau white paper “Visual Analysis for Everyone: Understanding Data Exploration and Visualization” describes it — visually, of course — on page five. I suspect the subject deserves a book.

Let’s see if Few’s got that one up his sleeve for later.

The shouts from the back of the BI room seem to be getting louder. In various ways, they’re saying let Big BI die.

Former TDWI education director Dave Wells, visual analytics critic Stephen Few, and Tableau Software CEO Christian Chabot are back there. Others, too.

Last spring, Wells proposed a new, people-centric definition of business intelligence. (See my Q&A with him for TDWI.). On November 18, he published “The Changing Face of Business Intelligence.” He predicted that the industry will soon “experience change that will have broad, deep and lasting impact.”

In early January, Tableau CEO Christian Chabot talked about the failure of BI platforms to deliver the BI promise. He seemed to laugh in surprise at interviewer Carl Weinschenk’s comparison of Chabot’s thoughts to Ronald Reagan’s 1987 challenge, “Mr. Gorbachev, tear down this wall.” The comparison was apt.

Now last Thursday, Few sounded like he’d had about enough with BI tools “entrenched in a techno-centric paradigm.” He wrote in his weblog, “It’s time for traditional BI vendors to pass the baton.”

I believe it’s now time for the vendors with real decision support solutions to thank the BI industry for the technical infrastructure that it’s provided, but then set themselves apart as a new industry, different from but complementary to BI. Much as groups of people throughout history have arisen and set themselves apart to fix what cannot be fixed within the reigning power structure, the decision-support solutions that people need will only make their mark on the world by leaving the calcified fortress of BI.

Thomas Jefferson might have put it this way.

When in the course of business it becomes necessary to dissolve the bands which have connected us to failed technology, after a long train of abuses and usurpations, it is our right, it is our duty, to throw off such technology and to adopt new tools for decision making.

Big changes can seem to come suddenly. The Berlin Wall, the Soviet Union, the Tech Bubble of the ’90s, and Lehman Brothers all came down to widespread surprise.

I don’t put much faith in predictions. But these three independent thought leaders are all pointing in about the same direction. I’ll be looking for changes coming that way.

If you haven’t already, ask around: Exactly what is “business intelligence”? Some say it’s all about business decision making, and others seem to think it’s all about tools.

We struggle with definitions, but usually not in public. Perhaps that’s why the recent uproar on the weblog of eminent visualization critic Stephen Few felt like a refreshing breeze.

It all began with Few’s damning review of a product whose promoters tripped and gave it the now-sexy “visualization” label. Oops.

Usually, Few’s readers sit back and enjoy the show. He’s one of the few Bi writers with the courage to call out a stinker. But this time, several people sat up in protest. Comments erupted into a weeks-long discussion-turned-scholarly-fistfight over definitions.

After a few swipes at his “mean-spirited” tone—which I don’t see—and other complaints, they found the deeper issue. Colin White, president of BI Research and a keynote speaker at this year’s TDWI World Conference in Las Vegas, arrived late to the discussion but soon led the charge.

One term they fought over was data visualization. To Few, it’s a business function. He wrote that it’s “the use of visual representations to explore, make sense of and communicate data…”

White disagreed. He prefers a more “pragmatic” definition to accommodate the term’s variety of uses. He wrote, “If data or information is presented to a user in a format that aids decision making, then that contitutes data visualization.”

Though White writes that experts must “use clear definitions and terminology,” he wrote in the next sentence, “However, it is important that we accept that other people may have different definitions, and we need to find common ground.” He went on, “We also have to accept that business users may employ technology and use some terms in a completely different way, and it is important to adjust our positions and explanations accordingly.”

Did he mean that terms mean what the person who uses them says they mean? White leaves that and other things unclear in his careful yet still foggy pronouncements. He doesn’t even state his definitions of business intelligence and data warehousing, even when he condascends to Few that his definition is “outmoded.”

Few politely called White’s definition of data visualization “not useful,” and I agree. No term can be useful that has lost its meaning. As Alice said to Humpty Dumpty in Alice in Wonderland, “The question is whether you can make words mean so many different things.”

Label inflation makes it tougher to find a toehold in the market, to write about techniques and tools, and even to have a conversation. When marketing collateral shouts “data visualization” to the general BI market, who will look up if it could mean bad Powerpoint slides? It hurts the whole industry if worthy products can’t find words that make would-be buyers listen.

Few’s review of Lyza looks to me like a case of mistaken identity. Perhaps the company should never have entered the visualization arena. Also, according to at least one BI expert I respect, it is actually a valuable tool. A bloody nose for nothing.

To Alice’s question about making words mean many things, Humpty Dumpty replied, “The question is which is master. That’s all.”

If everyone’s a master, we have label chaos. Instead, industry leaders, journalists and smart marketers should use words as they’re most widely understood. As a rule, the master should be business, the data train’s final stop.

Also see sascom editor Alison Bolen’s “What we call what we do: a lesson in evolving industry key words.”

The junk food of data visualization has found a home. Pie charts show up everywhere, just like trans fats. Visual analysis expert Stephen Few condemns them, and I’ll bet Tableau Software designers held their noses the day they added pie-chart templates.

Now in Philadelphia near college campuses, you get pie charts with your pepperoni. The Economist, with help from BBDO Worldwide, has sponsored infographed pizza boxes. Ponder “arable and permanent crop land by country” and “mushroom exports to the U.S.” as you chew.

The charts do look tasty. Did you know that the U.S. has 12.3 percent arable and permanent crop land? Percent of what, I’m not sure. Did you know that India has almost as much as the U.S? That’s what the figures say; I couldn’t tell which of the slices was bigger.

At participating pizza joints.

It’s hard to see through the smoke as our financial house burns down, I know. But what I’ve noticed is more interesting: the first signs of rebuilding.

This month, three experts I read—visual analytics expert Stephen Few, Competing on Analytics author Tom Davenport and digital-media economy specialist Umair Haque—all seem to have knit recent blog posts with the same thread: honest value in business and the economy.

“The macro crisis isn’t really a financial crisis, an economic crisis, or a solvency crisis,” writes Haque. “It’s an institutional crisis: the economic institutions of capitalism are in shock.”

Perhaps it’s the shock of having been conned. One of the three levels of transformation he suggests is the return of authentic value.

Authentic, long-run value isn’t created through arbitrage or gamesmanship — what we too often confuse strategy for. Games of off-balance sheet accounting, currency hedging, capital structuring, so-called labor arbitrage — where corporations simply shift to the lowest-cost, or most poorly regulated, sources of manpower — don’t create value. They just shift it around. Corporations who play this game of economic musical chairs are in for a rude awakening – because the music just stopped. And so they must rediscover the simple fact that value creation flows from making economic activities not just profitable in the short- run — but meaningful over the long-run.

Davenport lets some air out of “fluffy” social networking. He asks, “How can we really be producing value if we’re all sitting around blogging and Facebook-friending each other?”

All this fluffiness will be hard to maintain in our next period. He writes that “it wouldn’t be a bad outcome if the current crisis led to a more diligent, industrious economic climate.”

Few takes us down to products. He argues for honest value in business intelligence software. He often confronts badly designed dashboards—adorned with eye candy and other silliness—and urges those who listen to educate customers, not pander to them. The response is too often that, yes, we know, but it sells so there’s nothing we can do. Ah, promiscuity as a business strategy!

Any business that measures its success by current sales revenues or profits without regard for the effectiveness of their products will go for the silly stuff every time. I could argue that this is a poor business model because it’s short-sighted and doomed to fail, eventually resulting in declining revenues, but what’s the point? Businesses built on this model lack the foresight to appreciate the greater intelligence of long-term planning around products and services that effectively address the real needs of people. I believe the root problem that belies such business practices is not strategic short-sightedness or a myopic focus on sales—these are symptoms of a deeper, more fundamental problem. I believe that it’s wrong to build a business on self-interest alone.

Enough with this promiscuity. He adds, “Things that don’t work should not be sold—period. That’s good business.”

If the next president and Congress take Davenport’s advice and nurture big importers, high tech would surely be one of the first picks. How devastating, though, if Indian and Chinese entrepreneurs took advantage of an easy opening and overtook the U.S. even there.

I happened to read Few, Haque and Davenport. But I’ll bet they’re among many others these days arguing for a return to honest value and good business.

If you want to come up with effective metrics, forget brainstorming. Drop the creativity. Done well, this is an analytical exercise, says Stacey Barr, and it should aim at deriving concrete, sensory effects to measure.

She’s “the performance measure specialist,” and she lives in Brisbane, Australia. Stephen Few, the BI industry’s leading critic of performance dashboards, refers to her when he’s asked about performance metrics.

I talked to her yesterday as part of my research for my next BI This Week story.

People in search of metrics jump too soon into measuring, Stacey says. One of the first things she does for clients is to insert one critical step: defining what effect they are looking for—and then describing that effect in “sensory specific language.”

She finds that people’s first attempts use “fluffy, vague language to describe those results.” Prime examples: “quality,” “efficient,” “effective,” “sustainable,” “enhanced.” You’ve heard them all before.

“It’s this habit we’ve gotten into of using words that mean seven different things to three different people,” she says. “We’ve got to use words that are more concrete.”

When people are all sitting in a room talking about goals and results, they have the same images in their heads.

“It makes it much easier to measure,” she says. Concreteness is the key.

I suppose that making metrics is like making a movie. No image or sound can tell what a character thinks, feels or intends to do. He has to show it somehow. This concreteness is also what Jon Franklin calls for in his book Writing for Story: Craft Secrets of Dramatic Nonfiction.

Stacey once worked with a local council (to Californians, a county board of supervisors) to raise public participation in meetings. They had been measuring participation with the number of meetings held. That is, more meetings automatically counted as more participation.

More vague words: what does engagement or participation look like?

She asked them, “If the community were more engaged, what would people be doing that they’re not doing now?” At first they said things like more people showing up, a high proportion speaking up, more ideas proposed, and so on.

Eventually they came up with two measures that worked together.

• The number of “fresh faces,” those who’d never attended before. They had decided to give it a try.
• The number of familiar faces, those who’d come to at least half of all recent meetings. They had decided that showing up was worthwhile.

It works.

Come to think of it, doesn’t the need for concrete, sensory effects sound like storytelling?

That winery of mid-’60s TV fame Italian Swiss Colony and its mascot “that little old winemaker, me” often seems to apply in surprising places.

A few weeks ago at a visualization conference, the business intelligence community’s leader in visualization design, Stephen Few, told the room full of dedicated visualizers to be more useful. Some took exception.

In the gentlemanly discourse going on right now within earshot of this blog, they’ve been discussing definitions. What, for example, is “useful”?

Stephen, of whom I should disclose my long-time admiration, responds to criticism from Mike Danziger that his advice is slightly abrasive. Mike had complained that Stephen dismisses such attempts as the Ambient Orb without trying to understand it.

Mike’s got a point. They’re both right. The Ambient Orb is silly, but it still deserves a place on the visualization spectrum-for silly uses. It has no place in business, but so what?

A serious winemaker doesn’t try to understand jug wine. He just spits. Even so, jug wine has its place.

That mid-20th Century jug wine Italian Swiss Colony didn’t even try to be good. But, according to a California wine-industry insider I talked to in 1980, it did pave the way for good wine in the American marketplace.

That insider explained to me that back when wine was still perceived as elite, Italian Swiss Colony applied marketing muscle to break out into a broad middle class market. It didn’t matter that it wasn’t great, it just got people trying it. Many of them liked it enough to try better and better wine, and today California produces some of the world’s best. “That little old winemaker, me” had started a movement.

The trick will be to provide visualization that doesn’t turn off future users. If too many say, “Ick! That’s visualization? I don’t need it!” it could suffer.

If the Ambient Orb had been my only introduction to visualization, I’m not sure I’d have looked any further. I do look further because I saw better efforts first.