If you plug the quoted phrase “the data finds the data” into any of the search engines, the first hit will be one of several essays on Jeff Jonas’ blog. Other evocative phrases that lead to Jeff’s blog include “perpetual analytics”, “sequence neutrality,” and “persistent context,” but while those will soon resonate once you scratch the surface of Jeff’s work, none is as broadly compelling as “the data finds the data.” As sound bites go, that one’s a keeper.
Jeff Jonas is chief scientist for IBM’s Entity Analytic Solutions. His long career in data surveillance, and recent interest in privacy-respecting data surveillance, has drawn a lot of media attention lately. In the mainstream he’s appeared in Newsweek and on NPR. In the techsphere, Tim O’Reilly blogged about Jeff’s visit to PC Forum, Dan Farber interviewed him at the Web 2.0 conference and Phil Windley wrote a detailed review of his keynote at ETech 2007.
Given our shared interests — including surveillance, analytics, security, privacy, and manufactured serendipity — it’s surprising that I only recently became aware of Jeff’s work. Of course, we’ve been working different ends of the same street. He’s focused on finding bad guys: casino fraudsters, terrorists, and others who collaborate secretly. I’ve focused on helping people who collaborate openly do so more effectively. And yet…these really are two sides of the same coin.
Here’s an example of “the data finds the data” in Jeff’s world, from his article in IEEE Security and Privacy entitled Threat and Fraud Intelligence, Las Vegas Style. You have two records that refer to the same person, but you don’t know that they do. Then a third record appears which relates to each of the first two, and which establishes that all three refer to the same person. The first two pieces of data find one another, through the agency of a third piece of data.
Here’s an example of “the data finds the data” in my world. On June 17 I bookmarked this item from Mike Caulfield, who is a local friend, the webmaster at Keene State College, and a forward thinker about Net-enabled education. On June 19 I noticed that Jim Groom — who is a distant acquantance at the University of Mary Washington and another forward thinker on the same topic — had responded to Mike’s post. Ten days later I noticed that Mike had become Jim’s new favorite blogger.
I don’t know whether Jim subscribes to my bookmark feed or not, but if he does, that would be the likely vector for this nice bit of manufactured serendipity. I’d been wanting to introduce Mike at KSC to Jim (and his innovative team) at UMW. It would be delightful to have accomplished that introduction by simply publishing a bookmark.
But even if that weren’t the vector, the point is that given the overlap between Jim’s published work and Mike’s published work, it’s likely that they would sooner or later have discovered one another. In the realm of personal publishing, thanks to syndication and search, data tends to finds data. And when it does, people find each other.
This process of discovery works best, of course, when there’s common data available to the syndication and search engines. When the same things have different URLs or different names, the connections are non-obvious.
For non-obvious connections that don’t want to be found, you need a technology like the one Jeff Jonas sold to IBM. It goes by the name NORA: non-obvious relationship awareness.
For non-obvious connections that do want to be found, though, we can help the process along in a variety of ways. Publishing hyperlinks is one way to expose non-obvious relationships. Publishing key words and phrases is another. So, for example, in reading up on Jeff Jonas’ work, I realized that the privacy-assuring version of NORA, called ANNA, which uses one-way hashes to obscure private information while still enabling matching and discovery, is related to Peter Wayner’s notion of translucent databases (1, 2).
I’m not the first one to make that connection — Noah Campbell noted it last fall — but this item will strengthen it, in a way that may help some data find some other data, and some people find some other people.