My former boss and colleague Andrew Pace recently commented on the nature of the network and how he was rebuffed by a colleague for overlooking the fact people that make up the network and this is the most sigificant piece of a network. I would like to respectfully disagree with his post. Andrew used to boast that he is 100% right 50% of the time and in this case I believe he was right during the initial part of his musings on this topic.

What is the significance of the network in the 21st century? What we understand as the network is a contemporary realization, or maybe the automated reality, of the old adage that the total is greater than the sum of its parts. And quite frankly this realization was made possible by the amazing things that computers are doing with data.

Page Rank is arguably the shot heard throughout the Web. With their Page Rank algorithm Google was able to solve a problem that was plaguing relevancy in Internet search results: we’re all a bunch of dirty rotten liars. Back in the Yahoo/Alta Vista early days of search engines people were figuring out ways to game the system by lying through their metadata. In order to have their crappy cover band’s web page show up when a user searches for the Rolling Stones the cover band simply needed to put ‘rolling stones’ into its metadata.

Page Rank came along and solved the problem by saying, ok, we will let the network sort out the relevancy and if the network can prove that your website is a good one, you will be rewarded in search results rankings. This is the significance of the network. For better or for worse, the network can prove whether or not the data byproduct of the people is in fact worth what those people claim it is worth.

As Ian Ayers points out in his book Super Crunchers, the world is now using data to make better predictions than traditional experts. What is more, the statistical models being used by doctors, corporations, governments and non-profits are able to leverage the network effects of large data sets to verify how well those predictions are performing and improve those predictions instantly as new data becomes available.

I believe that my issue here is all sematics and I may simply be quibbling over something petty. However, I am splitting hairs over this point because this is a troubling area for libraries in my view. If we get caught up in the mushy people narrative over one of the most significant cultural shifts that is occurring right now, we will miss the point and consequently we will miss the opportunities to maintain the cultural relevancy of libraries in the future. The danger, in my opinion, is similar to the paralogism that because I know the structure of a MARC record I understand how it is stored in a modern RDBMS.

It is imperitive that we know how Lucene/Solr works so that we can make better resource discovery systems. It is similarly imperitive that we understand how to get in the super crunching game. As Andrew and his colleague Lorcan Dempsey have noted on numerous occassions, we need to do much more with our data, because it’s the network effect, stupid.

(For the record, I do not intend to call either Andrew or his colleagues stupid, I am just leveraging a theme that he and I have been riffing on for a couple of years.)

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