At the end of theory you'll find lots and lots of data

In its efforts to create a web-based "word processor for data," Swivel, as I've mentioned before, is on a mission to improve our collective numerical literacy, or our numeracy.

As missions go I think it's pretty cool.

For example, posting only two rules, it recently completed a business edition of its hosted software, permitting companies to manipulate and share their data in new ways. The rules? If you make your uploaded available to the public, Swivel is free. If want to keep it private and secure, you can pay. It actively shares tips for hacking its graphs. Journalists are making increasing of public data to make stories more compelling.

But if you like, as I definitely do, the conceptual bottom line, Swivel is simply taking advantage of what Wired recently called an end to theory. Raising the public's statistical literacy is one smart way to explore a new frontier. Wired:

The Petabyte Age is different because more is different. Kilobytes were stored on floppy disks. Megabytes were stored on hard disks. Terabytes were stored in disk arrays. Petabytes are stored in the cloud. As we moved along that progression, we went from the folder analogy to the file cabinet analogy to the library analogy to — well, at petabytes we ran out of organizational analogies.

At the petabyte scale, information is not a matter of simple three- and four-dimensional taxonomy and order but of dimensionally agnostic statistics. It calls for an entirely different approach, one that requires us to lose the tether of data as something that can be visualized in its totality. It forces us to view data mathematically first and establish a context for it later. For instance, Google conquered the advertising world with nothing more than applied mathematics. It didn't pretend to know anything about the culture and conventions of advertising — it just assumed that better data, with better analytical tools, would win the day. And Google was right....

Speaking at the O'Reilly Emerging Technology Conference this past March, Peter Norvig, Google's research director, offered an update to [statistician] George Box's maxim [that 'all models are wrong, but some are useful']: 'All models are wrong, and increasingly you can succeed without them.'