As I post more to our blog you’ll notice a common theme…efficiency. Efficiency of time, effort, materials - you name it and I’ll try to figure out how to it do better, faster and cheaper. And yes, it can be as annoying as it sounds to those around me. And yes I realize that taking time to write this post could be seen as an inefficient use of otherwise productive work time, but even robots need some down time - see Futurama, Bender Bending Rodríguez.

This post was inspired by some time I spent flipping through Super Crunchers at a local bookstore. The first line sums it up nicely:

“Recommendations make life a lot easier. Want to know what movie to rent? The traditional way was to ask a friend or to see whether reviewers gave it a thumbs-up. Nowadays people are looking for Internet guidance drawn from the behavior of the masses.”

Now, don’t immediately raise the social community banner on me. Ian Ayres, the author of Super Crunchers, is more inline with the Moneyball school of thought and looks to good old-fashioned number crunching to generate those helpful recommendations.

The most obvious examples of this are the collaborative filtering algorithms on Amazon that serve you recommended items. And there’s Google, learning all about you (maybe too much) from your search habits. One of my favorites is Farecast.com, which uses historical price information and other market indicators to determine if the current price for an airline ticket is a good buy or if the price is trending down, and you should wait. I try to be as efficient as possible with my spending , i.e. cheap, so a site like Farecast let’s me rest easy when I purchase an airline ticket.

To me there are benefits to this line of thinking beyond getting consumer-purchasing recommendations. I can envision an online experience that is tailored to my interests, not just on a specific site, but across all online content. News sites that show me recommended stories based on my previous reading habits so I don’t have to dig through content that is of no interest to me. Or having a search engine that understands context based questions rather then just matching keywords…dreams of the semantic web? Maybe, but as we become more networked and connected as a society the pool of information will be even more overwhelming.

So while Amazon learns more as you interact on their site, what is it learning about all those others sites you browse? Nothing. Amazon has no access to your shopping habits off their site.

For me this idea of decentralized and disparate data aggregation and management is one of the major questions moving forward. Every time we use the Internet we leave huge amounts of fractured data in our wake.

How can that data be synthesized and analyzed to make the Internet smarter and my time online more efficient?

Andy Sackmann, Client Services