I don’t mind long lines at trade show buffets. All else being equal, the length roughly equals attendance, which roughly indicates industry fizz. For an indicator, though, the analytics/business intelligence industry can do better than that.

How do we take the pulse? What if those who promote the technology, advise the clients, and build the systems actually measured their collective progress with a number? It would be an ongoing, forever-updating, simple benchmark. It would be the industry’s Dow Jones Industrial Average.

Four or five years ago, when the industry seemed simpler, I envisioned a number like this. Why not?

First hurdle: the lack of complete public data. We have it for a few vendors, such as IBM and Oracle, but not for the industry’s many startups. For example, how does Tableau’s ascent figure in, and how do we measure it?

Could we count the number of BI-related hash tags in Twitter? Maybe, but what does the number indicate? Is it success or marketing people out of work? Tweets from Tableau, for example, have been way down for six months or more. Here, down means up. They must have something cooking.

Slightly more credible than buffet lines are the annual surveys by industry analysts. Business intelligence as a priority is up — or was it down? — this year from last, they say. But what did survey respondents mean by “business intelligence”?

Traditional deciders say “BI” to signify plumbing — metaphorically, the path from groundwater to kitchen sink. Did you ever see a plumber’s dream house? He’s installed no drywall; he wants the pipes exposed to admire the workmanship and efficiency: No leaks, good flow. Cooking, bathing, and washing the cat all come later if there’s time.

The non-plumbers make use of the water. The “kitchen sink” is where all that nice, clean data pays off. So the ideal point of measurement would be where data-based evidence reaches decision makers’ minds — say, just before it commingles with the tarot cards.

At that point, how strong is the influence of data analysis? How good is the analysis? How good and complete is the data?

If we could find a proxy for that pulse, we just might have found the Dow of BI.

I’ll ask around among industry mavens, especially the the cooks.

Users I interview for case studies tell me good stories. But most come with a poison pill: you can’t write it because it’ll embarrass someone. Just try getting it through the rounds of approval.

The smart marketer’s answer: Make up a composite!

If, as Financial Times columnist Gideon Rachman writes, fiction is the best way to understand Libya under dictator Muammer Qaddafi, then it just might work to understand the drama within organizations.

How much do you really understand from news accounts from Libya? Before the current crisis, most news emphasized Muammer’s weird style of dress, his ranting speeches, and his “voluptuous Ukrainian nurse.” Here at home, case studies emphasize rational and unnamed executives weighing pros and cons and coming up with insight and decisions. The corporate story is all so safe and pure you find yourself hoping the nurse appears.

The average case study’s best hope is to be marked “present” and forgotten. Fiction — once set free with the clear label of “composite” — has freedom to imagine and dramatize. It is best at the subtlety you need for insight.

Obviously, you wouldn’t use nasty stuff. That stains everyone. But use the stuff that’s merely too much for the squeemish.

Here’s my rant: BI is made for stories. Each success is a story, starting with “we didn’t know” or even “we were so screwed up, we couldn’t even…” The story meanders through “we searched and tried.” The stories worth blasting from the roof tops end with the details behind “now we know, and we’re this much more profitable.” Everyone knows and understands composites.

By the way, I think the part about the nurse is true.

What do you get if appliances keep getting smarter, faster, and roomier inside? Choice A: abolition of IT, because who needs geeks? Choice B: license to be sloppy.

You guessed it: sloppy and proud. Out of the loud, unruly marketplace we have now for analytics appliances will come new, bold categories to satisfy every need. One will satisfy what is now the widespread obsession with data quality. So boring now, appliances in this niche will make data quality problems seem quaint with a “smart” data maid that follows us around.

At least that’s what I came away with from a phone call with Kim Stanick, marketing consulting to BI vendors. She said, “We’re comparing all these things on the same playing field, and maybe different playing fields are evolving.” She said analysts should get busy and define these new fields.

With a data maid picking up after us, what happens to IT? Well, it’s sure not going away, says one sales manager and longtime IT watcher. (Like so many people with corporate jobs, he won’t let me go anywhere near identifying him.)

“They’re going to go kicking and screaming up to the next higher level of function,” he says. Up there on that new, higher level, the IT team will be smaller. It will also be more strategic, more collaborative, and have a better understanding of the business.

But what happens on the business side? Will those who were chronically unable to explain their needs now find new talent for expression? Did they take a class? No, the new software comes embedded with the requisite knowledge.

Will sloppiness ensue? It did once before, back when the typist pool receded as managers adopted PCs. Grammar and punctuation — those quirky little standards that reduce noise and amplify signal — gradually fell out of style.

“Oh, but we’ve got spell checkers for that!,” the do-it-yourselfers protest. But those things still can’t say to a confused writer, “Uh, Mr. Tryingtoohard, what are you trying to say?”

The same fate may lie ahead for data quality.

We still have to account for a third group: business intelligence consultants. In this story, they all moved on to other niches. Even today, that’s what some have already done — and more on that in a future post.

A friend of mine runs the library at a small university near me, and she hears pitches all the time for neat technology. I suppose she doesn’t hear much about BI, just library stuff, but let’s not get hung up on the details.

To keep her priorities straight, she keeps a “ruthless focus” on the library’s real needs. She keeps Maslow’s hierarchy of needs in mind, the theory that people satisfy needs in order, from basic needs like breathing all the way up to “self actualization.”

She tells about one upstate New York librarian she heard about back when libraries were first urged to go online.

A consultant went to visit a small library — one of those Barbie Dream libraries that are hot in the summer, cold in the winter, and staffed so minimally that the library worker covering the single desk will excuse herself to change the toilet paper and greet the UPS delivery person.

So the consultant explained to the library director that the online catalog could do this, and it could do that, and it would have all these marvelous functions, and the library would be so much farther ahead, etc. etc.

And the practical old librarian who had been quietly listening tilted her head and replied, “I’d still rather have a flush toilet.”

I hear so much about “solutions” that I think are probably not solutions at all. So I found K.G’s excellent post refreshing. See what you think.

To get a data architect I know worked up, just ask him about how customers end up buying the wrong tools.

How about sales people who push federation tools on those who actually need data warehouses?

“It all sounds extremely sexy,” says my source, who works for a major business intelligence vendor and whom I can’t identify. “You have a lot of people who exaggerate their ability to combine data to provide business solutions. … They don’t prototype, they don’t profile, they don’t actually think about the problem or do testing or even send some high school data analyst out with Excel to put something together that [the customer] might want. They don’t do that.”

Many sales people tout EII because that’s what they have to sell, he says. “The EII tools give you your data, warts and all,” he says. It’ll work fine as a data warehouse substitute “if the data’s pretty clean to start with, if it has a somewhat similar structure, if you can define the data you need, if the data’s relatively common across all the sources, and if there’s not much duplication.”

Even if the salesperson has a more appropriate tool than what the customer asks for, the customer may never hear about it. “‘Fine!,’” thinks the salesperson. “‘If you want to buy a hammer, that’s fine. If you want to buy a wrench, that’s fine. It’s not like I care. It’s just sales to me.’”

Just once, says my source, he’d like to hear one of these questions: “How long does it take for a novice to become OK at this task?” Or, “How long would it take for an expert to become proficient at these two things?” Or, “If I have a failure, what is your tool’s usual process for recovery, and what gives your tool more integrity than others?”

Mark Madsen, meanwhile, has been been thinking about similar problems but from a different perspective. He’s research director at the Third Nature consultancy and a keynote speaker at this month’s TDWI conference in Las Vegas.

One source of problems he sees is vendor marketing. “It’s all about ‘our tool does this’ or ‘has these features,’” he writes in email. “A lot of people don’t think about them that way. They think about them as ‘what this tool is for.’” People end up using an ETL tool for real-time synchronization, for example, or a federation tool in place of a data warehouse.

Even product documentation can lead users down dark paths. “All those docs that say what the features are help when you know what feature you want,” he writes. “When you’re trying to accomplish a task, you’re thinking in a different way.” A common result: convoluted solutions.

“I once did something in an ETL tool,” he writes, “and the product developer said, ‘That’s not how you do that.’ They had built around an improper conception of how users apply it.”

Design schools tell you that every user has a theory of how anything works, he writes, which determines their approach to it. Wrong theories explain why people push on doors that need to be pulled, for example. He says that this insight has made him change his approach to teaching his courses or showing clients.

“I’ve realized that I need to start with the ‘what this thing is for’ and move into what you do with it, and how it works.”

Mark may go into this more in his keynote at this month’s TDWI World Conference in Las Vegas. His long-running “Clues to the Future of Business Intelligence” — perhaps the “Cats” of tech presentations — has been one of the most interesting I’ve seen in any tech industry. I expect “Stop Paving the Cowpath” to be worthwhile.

Here are two tips from LucidEra veterans Ken Rudin and Darren Cunningham about BI in the mid-market: Forget “freemium” — the new term for free service leading to paid service — and be wary of users’ ability to analyze data.

Rudin co-founded the company and in June saw it fold for lack of renewed funding — in spite of what he described as “extremely happy customers” and a rapidly growing base. At the end, Rudin was chief marketing officer and Cunningham was vice president of marketing.

Unlike in sales to enterprises, the mid-market customers LucidEra pitched typically lacked skill in data analysis and had little time to learn.

At first, LucidEra offered a 90-day free trial of its SaaS analytics — the “free” model, which assumes non-paying customers are completely self-service. That failed. Half the prospects said it was great, said Rudin, but the other half balked.

“We asked them, ‘Well, didn’t it meet your needs?’” he recalled. “They’d say, ‘No, we just don’t see any value there.’” His voice rose as he recalled his surprise and exasperation. These customers had been using nothing more than spreadsheets. “It made no sense to me. How could they get no value?”

When he questioned further, he found they’d been doing “essentially nothing interesting” with the service. They had been running the simplest reports, not asking new questions or reaching for new insight in any way.

“We were offering a powerful tool,” he said, “and they were saying they didn’t know what to do with this thing.” He compared it to installing an MRI machine in someone’s living room and expecting the person to diagnose themselves.

“Free” has worked for some BI-related vendors — he mentioned Salesforce.com and Jaspersoft — but never to untrained users who must be convinced of the value.

LucidEra dropped free trials and instead offered the free Pipeline Healthcheck. It was a cookie-cutter approach, said Rudin, to demonstrate the value. He compared it to a routine medical checkup. Any doctor knows if the patient’s blood pressure is too high, as any analyst can tell if salespeople should let go of dead prospects sooner.

Customers liked it. Many came away with pages of notes from the discussion about what to do. For example, LucidEra found a significant opportunity for a cable company in the Northeast.

At first, Pipeline Healthcheck seemed to work. Then usage fell off. When LucidEra called to ask why, customers explained, “When you came out here and told us all that stuff, that was great. But we can’t remember what you did. We just aren’t as good a this as you are, so we can’t use it.”

Several customers asked if they could simply buy the analysis service. They wanted LucidEra to come in once a quarter and do a health check. “Instead of having an MRI machine,” said Rudin, “they just wanted a doctor.”

Cunningham said, “Don’t overestimate people’s ability to interpret data.”

That’s why we have professional data analysts.

Yesterday I asked business analysts at the Tableau conference in Seattle about their work. Here are two quick sketches.

• One of the two arrived at her present employer six years ago to do the company’s first analysis of its website sales. She used several years of accumulated data to show which content was making money and which wasn’t. When she had organized the job into a routine, she handed it off to someone else and moved to the next question: What parts of the marketing was working? Again, she worked it into a routine and gave the task away. Next: what were visitors doing on the site? And now she has begun to answer similar questions for the company’s new site.

Not long ago, she was put into the IT group, among a bunch of guys coding in Java and doing other work she knows little about. She flashes a grimace at the mention.

She worries about her career. “How would I market this?” she asks.

• Another analyst was practically a librarian 15 years ago. People from other departments told him what reports they wanted — for example, SEC filings — and he delivered. Then some people asked for summaries, which made him think about other ways he could add value.

At some point, the value-adding incorporated data analysis, which grew. For years, he was the only analyst, but now he manages four others.

He’s the bridge between the data-generating IT department and the data-craving marketing department. He seems unconcerned about his career.

Complete versions may come next week.

Read a pile of technology marketing and you quickly assume that you alone despise many of the words you keep hearing. They’re words like optimize, leverage, synergy, and utilize. People in this industry don’t really talk like that, do they?

Many don’t, at least not in private, and they don’t tweet like that, either. One tweet trail at Gartner BI Summit complained about exactly this kind of word — these miserable words with all the wild flavor bred out of them like factory-farm tomatoes.

On the list of suggested extinction, Jill Dychè listed optimize and fact-based. Scott Davis listed leverage, co-optition, and dot-bomb. Someone also threw in win-win, synergy, and the lovely utilize.

Mark Madsen cautioned that banning all those words would leave marketing with nothing but proper names and prepositions.

The whole discussion started off when Dychè posted a link to David Silverman’s article in Harvard Business “10 Business Words to Ban.”

Now Dave Wells has followed with a weblog post. He left the TDWI conference in Las Vegas last week with his head “afloat in buzzwords.” So many new terms every quarter, and so much ambiguity. ” Maybe its time that we define our terms and differentiate between similar sounding terms.” He goes on to list a few he’d like to see on the endangered list.

Let the movement flourish.

Almost the same moment I read that for the fourth year in a row executives rate BI the top tech priority in 2009, I hear Tableau Software’s news: last week saw the most downloads of trial software ever. “Not by a little, by a lot,” says marketing and PR VP Elissa Fink.

The poll of Australian executives, by Gartner, also indicated that purchases faced more scrutiny. The press release mentions “CFOs doing final negotiations on pricing and maintenance.” I assume that Australian business is not significantly different from U.S. business.

Tableau, of course, lands like a mint on most CFOs’ pillows. Remember the old Apple ad for the first iMac, which describes the “three steps to installation”: Step one, unpack. Step two, plug in. There was no step three. Tableau needing “maintenance”?

Fink can’t explain the surge. There’s been no recent change in marketing. Does the Gartner poll help explain it? Possibly.

I’m sorry if this sounds like marketing. I happen to like Tableau’s product, not to mention their foodie-friendly user conferences.

Let’s see what other new-wave, light-on-their-feet products might have news next week at the TDWI conference in Las Vegas.

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.”