As if to renounce one more convention of business, a Berkeley-area businessperson I know couldn’t find data, so he went out and got some of his own. He had to evaluate market areas.

For three days, he stood by an ironing board with a map on top in front of the Cheese Board in Berkeley’s Gourmet Ghetto. Each customer coming in or out got handed two stick-on dots, red to mark home and blue to mark work. His map quickly revealed patterns and commute routes.

It was easy. “Everybody’s curious,” said Terry Baird, who had made a career of cooperative/collective food retailing. He had sold advertising for 10 years, and later found himself appointed to unravel the bankruptcy of a once-huge food distributor, an experience he calls “my MBA.” He approached it all without training, only logic and nerve, such as with the ironing board. “It’s like running a three-card monte. A group gathers around you.”

In 1996, he was part of a newly formed collective inspired by the Cheese Board. The new collective trained there as they decided on the new store’s location.

Terry’s question: would any of the proposed locations for a new store overlap another store’s market area?

In three days, he had the answer. On weekdays, none overlapped. But on Saturdays, all did; that’s when people drove in from all over the Bay Area.

The best software might be the kind made without starch.

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.

Wolf Blitzer calls for heads to roll after the Christmas Day attack. But Jill Dychè is a data pro, and she’d rather let the heads think.

“Who should get fired?” is the same conversation as after screwups in corporations, writes Dychè, principal at Baseline Consulting.

Instead, the government should be addressing process issues. Indeed, the real conversation should be how to move forward. These questions should be asked now: “How should we bring identifying data together? What are the key sources? How should integration, access, and usage policies be formulated? What would a sustainable process look like?” Those questions aren’t “who” questions, they’re “how” questions, and they should be front-and-center in the national security conversation.

Read the full blog post.

The bad guys do it. Malware and terrorists distribute themselves, hide, and wait for a call from Mother. Now one company is doing the same with backup storage. As the bad guys have known for a long time, the strategy is cheap and reliable.

“It’s disruptive as hell” for the data storage industry, says up-to-now Tableau vice president of business development and “disruption junkie” Kevin Brown. He’s leaving in-the-pink Tableau for for VC-funded Symform. Giving the news to Tableau CEO Christian Chabot was painful, he says. “I had the siren song in my head, and I just had to chase it.”

It sounds like he’s jumping onto another good product.

As he described the choices for data backup at small- and medium-sized businesses, I glanced at my removable 1TB hard drive an arm’s reach from the keyboard. It’s the center of my backup system. I could take it with me at night, but I usually don’t. A megabyte or two of crucial data does get backed up every hour over FTP, but cost and speed limit me there. After a major earthquake, fire, or burglary, I might be out cold. I’m probably typical.

Instead, I could trade storage with other nodes — indirectly, arranged automatically at a central point. I keep bits of others’ data, and they all keep bits of mine. Each of us uses as much storage as we contribute. Instead of putting my own data on that external drive across the desk from me, I put someone else’s there.

Symform encrypts each file and breaks it up into 64 fragments. They’re dispersed randomly into the cloud. An algorithm known as Reed-Solomon ensures error-free transmission.

But what if one of my fragments goes to, say, Kabul and the computer gets blown up? No problem, he says. The control center monitors the fragments constantly. When one goes missing, the system regenerates it and moves it somewhere else on the globe.

“By an order of magnitude, we’re more secure,” he says.

After a disaster, retrieving the backed up data would go faster, he says, because you’ll be pulling on 96 locations at once instead of just one over one throttled pipe.

They’re charging $30 for unlimited storage. That is, up to the limits of your “contribution folder,” the space you make available in your office. As the sign said at a buffet in Reno, “Take all you want, but eat all you take.”

This is disruptive for the storage industry, perhaps, but not for Kevin’s lunch and golf habits. The Symform office is just a “four iron” away from Tableau headquarters.

Restless minds will want to know what Asian manufacture of furniture, clothes, electronics and other goods has to do with business intelligence.

A globe-trotting industrial engineer who’d rather not be named has been telling me about different perceptions of quality among nationalities. He works on contract to American companies to ensure that product quality lives up to agreements.

When Americans buys new stuff, they assume it’ll come out of the box without dings, dents, scrapes or other flaws. Seams will be tight, electrical joints will be well soldered, paint on the fender will match the hood, wood veneer will be smooth.

According to the engineer, Chinese and Indian manufacturing and warehousing staff he’s worked with see it differently. When he flags a wooden cabinet, for example, with a deep gouge on the corner, the Chinese warehouse manager shrugs. “He’ll say, ‘That’s just because it was moved around the warehouse.’ It’s nothing to him.” Same with a chair with one shade of fabric on the armrest and another on the seat.

What about data? If that’s their cultural bias about dings in furniture, how do they feel about dings in data? Is carelessly handled data as easy to detect?

A friend of Datadoodle recalls this story: He stood before a room full of IT people who worked on mainframes and asked them, “‘How many of you feel responsibility for the quality of the data?’”

Not one raised a hand. He said to them, “OK, now you’re the CIO and I’m a salesman. And I say, ‘You know how those guys are always telling you that you can’t get your data off that old clunky thing? Well, they just want to keep their jobs. You need to get your data off that old-fashioned thing.’”

One day the mainframe is gone in favor of a $10 million replacement. Three years later, the company’s still got transaction glitches. He says to the IT people, “One morning you’ll pick up the paper and you’ll read about it, and you’ll say, ‘If only they still had that old mainframe!’”

Look closely at business intelligence and you see the world. Larissa T. Moss writes today in TDWI Flashpoint about the “the new debate” over enterprise data warehouses vs. personal data warehouses.

One side believes in one-for-all. The other side believes that by taking care of Me first, I can take care of You.

Where have you heard this before? That’s easy: look at the front page of a newspaper. It’s at the core of so many public debates going on now. The environment: should land be protected or should anyone be allowed to run over anything with an SUV? Taxation: should the poor pay less in taxes, or should rich people pay less so they can provide jobs?

Your heart rate might have risen as you read that last paragraph–and so heart rates rise when people think about what happens with their data. Do they get to harbor it in a “personal data warehouse”? Or should they trust the enterprise to give them what they need?

In his 2000 campaign, George W. Bush proclaimed, “It’s your money!” Yeah, it’s your data, too. Even now, six years after the first tax cuts, we’re still debating the wisdom of “it’s your money.” And a decade from now we might still be debating the wisdom of “it’s your data.”

It’ll be decided, I think, by the quality of the people on each side.

Back in the late ’90s at NetWorld+Interop, they debated another hot issue: whether DSL or cable internet would dominate in the long run.

After many insightful comments, one guy said what I still think was the most insightful of all: what would matter most is which side employed smarter people. DSL had the edge so far, he said, because the phone companies had expertise in networking. Cable would lag because its roots were with the guys who used to pave driveways.

The verdict on that one is still not in. So don’t hold your breath over enterprise data warehouses vs the “personal data warehouses.”