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.

So many of us who feel drawn to visual analysis can’t understand why everyone can’t see the value. “Pretty pictures,” the skeptics mutter. On Eager Eyes, Robert Kosara makes important points that I haven’t seen before.

Toward the end of his post he writes, “We need a new term.” He rejects the aged and indefinite “visualization” and the baggage-laden “visual analytics.” He prefers “visual analysis.”

Whatever we call it, it’s harder to use than it seems.

You have seen the bar and pie charts, but do you actually know what they mean? Do you know how to use them to tease the relevant information out of your data? Can you handle more than two dimensions of data and still find meaningful structures? There is so much more to visual analysis than what Excel offers you.

Good, but then he’s not clear. He writes, “The key problem is that people are much more interested in clicking through interesting pictures than learning about actual analysis work done using visualization.”

Which people? He can’t mean the ones who actually analyze. He must mean the casual users, the data consumers, the armchair analysts — and they will always click through. He writes that those who value visual analysis have to fight the idea that it’s just pretty “or risk the trivialization and marginalization of visualization as an analytic tool.”

You’d think the tide was coming in an threatening a sand castle. But from everything I’ve seen, genuine visual analysis seems to be more and more popular. Even elementary visual analysis works better than the ugly alternatives.

Who are we fighting? The ones who don’t care and never will? No, they’re no more a threat than fast food is a threat to good food. To most people, fast food is good enough — and so are pie charts.

The ones to watch out for are those who sell fast food under the good food banner — the ones who’d propagate sloppy techniques and call it visual analysis. That’ll really spoil our appetite.

For more on “good food,” don’t miss the “Information Visualization Manifesto.”

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.

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.

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.”

Back in February when I heard about Lyza, I thought right away of Tableau. Despite each one’s different strengths in data discovery and analysis, each appeals to the same broad group.

It’s an old group that’s getting new attention: creative analysts, or “cowboy analysts” to some. The like their data raw, not aggregated. They ask questions, forage, synthesize, analyze, and publish.

Joe Mako is one of them. Tomorrow, he’s launching a website for people like himself who use both Tableau and Lyza. Makometrics will publish every Monday morning and sometimes more often.

Joe is a network engineer at a Midwest ISP. He started at the tech support desk, where he saw how much help people needed looking at their data. “They didn’t understand exploring data,” he says. “They just don’t care.” But Joe cared enough to help with data analysis, and pretty soon someone gave him a tag line: “Make it happen with Mako.”

Posts he’s lined up so far:

• He’ll walk through data analysis problems from challenge to resolution. “I’ll be practicing something akin to the cycle of visual analysis.” (See the Tableau video “The Zen of Visual Analysis.”)
• Analysis of strengths and weaknesses of Tableau and Lyza
• Analysis of his “Visualizing Rambo Kills”: how he approached the dataset, and how he created the final result.
• Demonstrate sophisticated techniques in Lyza and Tableau. He’ll go into detail on such things as combining Lyza’s “previous” and “if” functions and the basics of summary functions like “sumcolumn” and “avgcolumn.”

Check it tomorrow (Wednesday, July 1): makometrics.com.

Long before Scott Davis made the self-service ETL tool he calls Lyza, he tried to find out how analysts really work. He remembers in particular the woman in a focus group who said, “I want to stay close to the data.”

He didn’t understand at first. The data was right in front of her, neatly summarized. But she meant all of the data, every little bit of it. She wanted to snap open a zillion-row-long window that she could scroll down to see the figures flip by. (Yes, you can; I saw it yesterday.) She wouldn’t try to read them, she’d only see their shapes. She could say, for example, “Hmm, I see that just two thirds are under 1000.” Davis calls that visualization with browse—as legitimate a use of “visualization” as any I’ve heard of.

He also thought about how people use Excel. In fact, it helps explain’s Excel’s popularity. They have the data, and they have the formulas, and you can reveal either one. If a number shows up that doesn’t look right—say it’s six figures instead of five—you just look at the formula. You say, “Oh, that’s the annual figure. I forgot to divide by twelve.”

Something similar goes on at all levels of analysis: a rapid back and forth from question to answer, back to a rephrased question, and back to an adjusted answer.

Forget the flow charts. Forget the “data train,” a metaphor I admit to having used. Analysis is more like what my labrador does when she knows there’s something good nearby. She sniffs in what looks like a random pattern until you realize she’s narrowing the range.

What drives analysts crazy about working with IT, he says, is that the data’s taken away. The conversation goes like this: the IT guy asks what the analyst wants; the analytst describes her best guess; the IT guy goes away and does it. But that may not be what the analyst really needed, and the anallyst may not realize it until the first data’s tried and proves inadequate or suggests yet another path.

I can relate, because it’s like writing. I do a lot of scribbling and writing over, and I don’t have time to explain it. If I had to tell a typist what to write, I’d write much less.

Visualize the bumper stickers: “free the analysts” but also “free IT.”

Now, Larissa T. Moss has her doubts. Perhaps she’ll sit for a demo. I’d like to hear what she says.

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.”

Among the visarazzi— data visualization’s foot soldiers, scientists and evangelists—”chart junk” is a no-no. If you make a bar chart about trees, for example, don’t for god sakes actually show drawings of trees. That would be silly.

However, the visarazzi probably don’t mean to prohibit chart junk food.

Tableau VP of marketing Elissa Fink has made what we might call a mashup. She presents data with pictures in Tableau Desktop. The fact that mashed potatoes has a high ratio of fiber to fat, for example, is appropriately and artfully augmented with a slim photo.

What a purist would reject as chart junk actually adds to our understanding by reaching us on the level at which we actually know food and intuit its truest qualities. Food is more than data. It is sensation, association and even celebration. The pumpkin pie photo, for example, reminds the analyst of the nourishing fiber that makes itself known in every mouthful. Pumpkin pie, in fact, packs far more fiber than the lowly brussels sprout.

Besides, what harm does that tiny photo do to the chart, especially a chart that appears just once a year?

So go ahead, trust the data. Have that second slice.