Tableau Public’s score so far reads like one of those old McDonald’s marquees: 4.5 million people have visited data visualizations hosted by the site, says Tableau Software VP of marketing Elissa Fink.

More than 30,000 visualizations — “vizes” — have been published. The most popular of all, says Elissa, have been the ones about homes, personal budgets, and leisure. One of her own favorites is a local real estate blog, Seattle Bubble. “I wish I could have seen blogger Tim Ellis’s data in Tableau Public before I bought my house.”

Another favorite of Tableau staff, who are said to have a healthy contingent of foodies among them, is about cows and their milk. Vizzer Kate Golden at Wisconsin Watch charted the number of cows over the last 80 years in Wisconsin with the gallons they produced. Dairy farmers have 47 percent fewer cows today than at the peak in 1944 and ’45, and they squeeze three times more milk out of the cows they do have. In a YouTube-like moment, vizzer Carpe Diem responded. He mashed in milk prices. They’ve fallen, though it’s unclear how much; the viz fails to note whether the prices are adjusted for inflation.

The visit count keeps accelerating. Past growth feeds more growth. The big names that have joined in help, such as USA Today, The Seattle Times, and CNN Money. There are also influential bloggers like Mish’s Economic Blog and Infectious Greed also pull in visits. But the highest growth rate is among sports bloggers, such as pistonpowered.com and school bloggers like Gothamschools.org.

The “beef” — as in “where’s the beef?” — is whether Tableau Public really is becoming the YouTube of data? It seems to be on the way there.

The crucial factor that distinguishes the YouTube from the NotYouTube is the network effect. The genuine YouTube is the default, the unquestioned center stage. An also-ran may have faster servers, nicer staff, and more permissive rules, but it’s still not YouTube. With volume like this, Tableau Public is well on its way to becoming a true YouTube.

In the meantime, there seems to be another reason for satisfaction at Tableau Software. Elissa asserts “plenty of evidence” that new, purchased licenses for Tableau Desktop and Tableau Server are coming in that started with awareness of Tableau Public.

Wipe away that tear you have shed for BI marketing. Take heart in this: The golden oldies — those tired verses like “faster, better decisions” — have never come closer to receding into the support roles where they belong. A new strategy has been proving itself able to hook even onlookers who swore they really didn’t give a damn.

In the most recent example, a simple Tableau Public-hosted chart on Wired caught me looking. And thinking. It hooked me with a bar chart that compared rates that iPad users pay for downloading data.

Did I say that I really don’t care what iPad users pay per gigabyte?

I looked. There’s the U.S., I thought when I saw it, losing again. I’m used to that by now. But who’s losing worse? Belgium! Why Belgium? I thought of reasons, but none seemed to explain why a gigabyte there was more expensive than a gigabyte in Italy, another country I know a little bit about. Better price supports for waffles than for cannoli? My local Belgian thinks she knows the reason: “Taxes!,” she says. But I told her that that explains nothing at all, and that conversation continues even today.

Sure, it’s all a lot of fun. It’s powerful, too. The simple chart that can hook you on the fly — by making you notice, making you scratch your head, making you try out one angle after another in the quiet of your moment’s pause — can hook customers with modest budgets and legions of casual users to excite.

When some people look at dashboards, they want to see patterns but not reasons. “They don’t want to read the fine print,” said one attendee in Lyndsay Wise’s dashboards seminar at Enterprise Data World in San Francisco yesterday. That’s what the man learned in one data-quality project for a human resources department.

He was frank enough to call drill-down “the fine print” — the suggestion that the “why?” is just noise. He slipped out before I could find out more.

Had his complacent users been victims of abusive parents or bad teachers? I’ve worked with such users. I trust them, I like them, and most businesses couldn’t do without them. But I’m curious about their incuriousness, as some of them might wonder about me.

There’s too much data, we know that. Tom Davenport ponders the overwhelmingness of it all today. The Economist reported on it last month, and Neil Raden wrote about it 15 years ago. The casual users feel it more and more.

For the overwhelmed, there’s the palliative dashboard. It works the way Mozart does for who can’t tell Mozart from Schmozart: knowing it’s Mozart makes them feel good. The palliative dashboard can be contrary to every best practice we know of and still succeed.

One person in the audience told about a pre-dashboard-era CEO who prided himself on having no high school degree. He wanted yesterday’s sales figures on his desk at 8 a.m. every morning. What decisions did he make based on that data? None! It just made him feel good, someone discovered later. Even without his reading glasses on, the patterns on the paper must have looked nice against the wood grain on the desk.

Attention dashboard makers: mind the furniture.

Dear Datadoodle: My title is “strategist analyst,” but I’ve become just “the data geek.” As soon as I get into the fine points of my data, they roll their eyes. In meetings, they make little jokes to each other, or they just stare out the window. Please help. I’ve got loads of great data but managers have no time for me anymore. The Data Geek

The Data Geek has lots of company, says Christine Muser, longtime data analyst, founder of CyCom Solutions, and writer of CyCom’s weblog Pharma-BI. She’s seen data analysts stumble over this problem. She had to deal with it herself.

“I love data,” she says, “so in my younger days I used to just barrel right ahead into the details.” Too often, though, she’d see only glazed looks in her audience.

It was even worse for one man she once worked with. He actually became useless as a strategist. He spent so much time pulling together data, manipulating it in Excel, and poring over results that he kept managers waiting for results. After a while, those who relied on him got tired of waiting — and tired of his overly detailed explanations about dimensions, data sources, and methods.

Now, Christine analyzes more than the data. “It pays to know your audience. If you know they’re very familiar with the underlying data, you can give more details,” she says. If they’re not familiar with the data, “You can say, ‘here’s what we’ve observed’ and ‘here’s the impact,’ then be quiet and see if you get puzzled looks.”

The essential lesson she learned: “Understanding what is meaningful is a really big deal.” A strategist understands what’s significant, and doesn’t bother with the small stuff.

For example, say you’re analyzing a pharmaceuticals market. Your company makes a drug that treats only one symptom, while some competing companies make drugs that treat that symptom plus several others. That difference makes a straight comparison — pill for pill or dollar for dollar — useless. So you have to adjust the data to allow for that difference. But managers usually don’t want to hear how you did it, only that you did take care of it.

A strategist also knows when to ignore buzzwords. A manager who liked to stay current once asked Christine, “Can you do neural networks?” Perhaps, but dare go into what that would take and you risk running out your clock.

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.”

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.

Former TDWI education director Dave Wells keeps running into users whose BI reports might as well be printed. These users simply accept the data as presented and don’t ask questions. That’s nothing new, of course. The difference is that Dave has a way to deal with it.

I caught part of his session today at TDWI World Conference in Chicago: “Understanding Cause and Effect: An Introduction to Systems Thinking.”

For the incurious, Dave prescribes causal-loop diagrams. When he starts drawing, and people can visualize a complex system — especially when they work inside it — they quickly get involved with the analysis. Once involved, they can’t avoid asking questions.

Take the case of the healthcare insurer, for example. His simple lines and arrows demonstrate how badly thought out incentives for data entry clerks actually increases the rate of bad data entering the system.

Available systems-diagramming tools, however, just aren’t good enough yet to do all he needs to do, he says. He showed one, MapSys, that comes closest.

He’s going to go looking. Over the next six to nine months, he’s going to be “that pain-in-the-ass guy from BI” attending every systems-thinking conference he can.

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.

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.