Sid Adelman tells me that years ago he let his then-brother-in-law, an insomniac, read a chapter of his book on data warehousing. The guy fell asleep.

Text analytics was one of those things I heard about every so often. Like so many terms in this business, the term comes out of a speaker’s mouth or PR person’s press release only to blow away. There’s no story, no context, nothing to chew on.

Then came a press release at BI This Week with a rare combination: surprise and concreteness. It said text analytics would help with food safety. I’m all for food, but I had no idea what text analytics had to do with it.

I emailed UK-based Linguamatics, publisher of the nifty tool they call I2E. What’s this I hear about food? Product manager Phil Hastings, ready to call it a day in Croatia, called to explain the features to me, barely post-breakfast and not fully verbal. I2E was indeed a powerful little thing, but I still didn’t get the food angle.

It wasn’t until I got William Hayes on the phone that things started making sense. He’s director of library and literature informatics at pharmaceutical research company Biogen Idec. They don’t do food, but close enough.

If you think the Sunday New York Times is enough for one day, consider what the research community has to bear. Hayes says, “If you’ve got 20 million articles to read, where do you start?’

“The research industry works under a tougher knowledge model than terrorist intelligence gathering,” says Hayes. “Our ability to tap that ocean of literature is like dropping a line into the ocean for fish.”

In general, a scientist can read 150 to 200 full text journal articles a year, he explains. A curator can review about 100 abstracts a day “for a few days before you start going nuts.” Text mining is the only way to keep up with the ocean of literature produced each year.

The food industry fries potatoes, but it also has to keep a lookout on research.

TNO information analyst Fred van de Brug told me the acrylamide story: Most people in the food industry missed the first warning. Scientists had published a discovery in 2000 about a possible carcinogen known as acrylamide, which can develop in starch-rich foods like potatoes as they are fried. By the time the warning finally hit the public media in 2002, millions of people became frightened, perhaps unnecessarily. Text mining would have given food processors time to head off a crisis.

I2E is more agile than standard text mining. You can learn to use it in a few hours. Hayes told me, “If you can remember bits of grammar and have some concept of what you’re researching, it’s a piece of cake.”

It’s a story in progress for BI This Week.

In a good example of “show, don’t tell,” Tableau Software’s weblog demonstrates the power of its product with a story: how rich, middle-income and poor voters compare in liberal, conservative and battleground states. The political story is awkward to tell in words, but it’s easy in pictures. Pictures that tell stories is what Tableau’s all about.

Conventional software marketers would have beat the drum like high school cheerleaders: Faster! Better! Bigger! They would have also offered a demo with screenshots flipping by showing data that means nothing to you.

Tableau’s approach is subtle. In “More can be simpler when telling data stories,” Tableau director of visual analysis Jock Mackinlay critiques a chart from Andrew Gelman’s new book, Red State, Blue State, Rich State, Poor State. The obvious story is, of course, the political one. In a chart borrowed from the book, you see that poor voters have similar opinions on social and economic issues whether they’re in a mostly conservative, liberal or battleground state. Rich voters are much different from each other, and middle income voters are the most disparate of all.

I start playing with theories. What is it about poor voters that makes them align like this? What makes the rich voters different from each other? The important thing is not my theories but that I got involved with the story.

Then Jock offers more: He says he can improve the chart. The minute I wonder how he might do it, I’m into the next story. He ponders, “Another way to add more to this data view is to back it with the raw data so that I could add to the story by creating new views. In particular, this view causes me to wonder what would happen if there is a shift in the relative importance of social and economic issues. Are there red and blue states that might turn into battleground states because they contain a large percentage of poor voters?”

What stories does his improved chart tell? Just download and open it in Tableau. And if you don’t yet have Tableau on your desktop, it’s always available for a free tryout. In any case, he just gave you a demo of visual analysis that actually caught your attention.

Other products might not lend themselves to this approach, but many would with a little creativity. I just don’t understand why more software marketers don’t try it.

A friend who’s deep in the financial applications world goes into a lot of sales presentations intending to buy something. But he complains to his wife later, “About 30 seconds into it, I just about couldn’t keep my eyes open.”

Once in a while, though, some salesperson tells a story. Then it sticks. Then he can overlay the story onto the product’s features. Then it’s real. What a relief.

I was half asleep as the TDWI keynote warmed up early last Thursday morning. Bob Paladino is not a bad speaker, but at first the good scrambled eggs and coffee were better. Then he got into the Southwest Airlines story, and I looked up.

The five guys at my table also looked up. They picked up the handouts for the first time and followed along.

The Southwest story is a little old by now. TDWI’s muffins were at least fresh. But Paladino was telling stories, and that won out over the muffins.

It’s all about storytelling.

Does the habitual obscurity make anyone sound smarter? If you’re dumb it might. But if you want to deliver information, suggestions, products, ideas, insights, and god help you a blog, it’s best to be accessible. Stories do it.

Oh, one more thing. The co-keynote speaker followed and had no stories. One by one, several people around me suddenly seemed to remember the muffins, and they stood up.