I jotted down the following fact from a session yesterday at the Disney Analytics & Optimization Summit: Organizations that invest in analytics perform better in the market.

That's quite an assertion. Tweetable for sure. But it was a late-afternoon presentation and I was hungry for supper. So I forgot about it until SAS CEO Jim Goodnight and SAS Senior Vice President Jim Davis gave their joint keynote this morning: "The High-Performance Organization: How Today's Leading Organizations are Leveraging Technologies & Analytics."

Underlying the long title is a simple truth: numbers matter. And companies who embrace this are game changers.

Like Macy's, a leading US department-store chain. Macy's uses SAS high-performance analytics to analyze 270 million price points each week. It’s a gigantic computational problem that once took 30+ hours of computer crunch time. Today it takes about two hours. It's worth noting that the chain this year had its most profitable second quarter in more than a decade.

Speed was a highlight of the next customer example Goodnight shared. He described how large investment banking firms using SAS are recalculating entire risk portfolios at record speeds, handling hundreds of predictive computations for pricing portfolios in no time. "A bank in Singapore, using high-performance analytics, was able to reduce processing time from 18 hours to 12 minutes," he said.

Goodnight said some may wonder what's the big deal about speed. He answers with a question of his own: "What would you do with the extra time if your code ran in five minutes instead of hours or days? I want to reset how you think about business problems. I invite you to let your mind explode with ideas. Things are possible now that we could never think of before."

That'll tweet.

Next up: research statistician Oliver Schabenberger, who did three live SAS High-Performance Analytics demos – two of which hadn’t been seen outside of SAS. He ran analysis on an enormous financial-services data set that could take 11 to 24 hours (depending on hardware used) to process using non-high-performance methods. With SAS High-Performance Analytics, it took under a minute.

If the gasps and tweets following these demos are any indication, SAS may be onto something. My personal favorite tweet: "#SAS high performance procs and 1 billion records. Any guess how fast?!?! Holy crap 52 seconds! #daos11"

Sampling is so 20th century. There's no good reason, given today's data-handling capabilities (in-database and in-memory analytics, multi-core processors, etc.) to exclude a single byte.

"If you don't analyze all of your data because it might choke your analytic environment, then your problem is not too much data; you have the wrong analytic environment," Schabenberger said. "We don't want the amount or kind of data to limit the analytics you can do."

Davis followed with his trademark humor and riff on what's cool. On his iPad, he showed sample social-media analysis results produced from SAS analysis:

• All those red lines signifying unhappy customers occurred when there was a kink in the company’s supply chain.
• The bulge on the bubble chart? That's a group of Twitter users who love someone's brand.

On a more serious note, Davis got on his soapbox to clarify the difference between what some call analytics (backward-looking summary reports, etc.) and SAS' specialty – business analytics, which lets companies see what's ahead.

He brought up a slide with the names of ten brands likely to disappear in 2012, according to the Wall Street Journal.

This drove home the factoid I should've tweeted the day before, that the most resilient, successful companies invest in analytics. They trust the predictive capabilities of business analytics to adjust quickly to changing market conditions. They thrive in most circumstances because they can see around corners and make smart decisions before their competitors even notice anything is wrong.

"With business analytics," Davis said, "you can say, 'We don't have to be on the list of brands that will be in trouble in the next 18 months.’"