On the Absence of Input

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There was a little flurry of press out of SXSW yesterday, covering Foursquare founder Dennis Crowley’s statement that the service has the potential to act like the “Marauder’s Map” of Harry Potter fame: “There is enough data that we should be able to make that Harry Potter map and give it to everyone in the room.” 

With the amount of data Foursquare gets, and the increasing number of data sources available to them, Crowley thinks (and I largely agree) that Foursquare can start to act as a living map of the people and places that matter to us.

But this also reminded me that Crowley had actually made a similar reference nearly four years ago, crediting Kevin Marks for the the proposition that “we need to not be building the Marauder’s Map and instead be building the Weasley Clock.”

I remembered that particular tweet because it kicked off a chain of thought for me that ended up in writing a blog post entitled Magic, Technology, Synthesis. My reading of the statement pretty much comes down to this snippet from the post:

You’ve got one artifact [the Marauder’s Map] that shows you a constantly changing, basically unfiltered stream of what’s happening right now, and another [the Weasley Clock] that reduces a similar complex set of real-time data into a simple form that is immediately accessible and useful in a specific context. The clock offers a reduction—an obvious, almost ridiculous oversimplification—of what is offered by the map, but that reduction is what makes the clock useful. The clock tells you basically, not exactly, what’s going on.

I still think that this “reduced” view offered by the Weasley Clock is potentially more interesting, and more valuable, than the live map approach (though also much more difficult to build), but that’s not what I’m here to write about, exactly.

Part of the point that Crowley was making was that Foursquare has a crapload of data coming in. And it’s being piled on top of several more craploads already accumulated. And that got me to thinking: one of the things that you get when you get a big data set is the gaps, the ellipses. You start to see where things aren’t, as well as where they are.

And that opens up a whole new world. Possibly a creepy and unsettling world, but it’s one that’s worth considering.

If Foursquare sees that a friend and I have been checking in around the same neighborhoods, but not checking in together, why doesn’t it sugest that we meet? Or if I haven’t checked in recently with someone who was once a frequent companion, propose a reunion.

And the model can be extended further. What if Path, or Facebook, noticed that one of my friends hasn’t posted anything to the service in a couple of weeks and called that to my attention? If that person is really a friend, maybe I should check in on them, right? Maybe they’re just busy, but maybe they’re at a point where a text or a phone call would help them.

This introduces some social complexity, of course: maybe I’m not checking in with Alice or Bob because we broke up, and I don’t really want a reminder of that fact. But the fact that these social services have massive data sets that point to what isn’t happening, as well as what is, feels like an area worth exploring.

Services like Timehop mine  your personal history, giving you reminders or where — or who — you were in the past, why aren’t more social services looking more closely at the people who are a part of that past?

At this point I suspect that we have more than enough dots to start filling in some of the gaps between them, sketching out the lines that are personal relationships. If this software really is social, after all, it’s those lines that matter.

  • http://birch.co/ Mark Birch

    Those gaps are pretty large when it comes to the dynamic between two or more people. A single person can be pretty predictable, but once you add that second person, the challenge becomes exponentially harder.

    This was similar to a problem I was working on several years ago to build a much better CRM technology by analyzing relationships via email conversations. We could predict salesperson behavior to a scary degree of accuracy, but it was significantly harder to create any reliable models around their interactions with customers.

  • http://absono.us whitneymcn

    Not surprising, but CRM was in the back of my head as I was thinking about this.

    Seems like you’ve got a little advantage when approaching it from explicitly social software, since (in theory, anyway) you’re starting from two people who have a relationship that isn’t determined by where one of them is in a sales cycle: it might be a bad time to try to close a sale, but if a friend disappears from Path, it’s probably a good time to contact them.

    But in the larger sense, yes: trying to get to the “why” behind human relationships is much, much harder than who/what/where/when, but I think that people building social stuff will need to start thinking more about that end of things.