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SmartData Collective > Big Data > Data Mining > Finding, Locating, Discovering
Data Mining

Finding, Locating, Discovering

Daniel Tunkelang
Daniel Tunkelang
6 Min Read
Finding, Locating, Discovering
Illustration generated with FLUX.2 [klein 4B] via Cloudflare Workers AI.
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Thanks to Tony Hollingsworth for alerting me to a post by Alex Campbell entitled “Stark realisation: I no longer depend on Google to find stuff.” The title is provocative link bait, but the take-away is very down to earth: Google is primarily useful for locating information than for discovering it.

Library scientists make a distinction between known-item and exploratory search. The former is about locating information: as an information seeker, you know the information exists, and you can even characterize it unambiguously; but the challenge is to convert that description into a location that allows you to retrieve the information. The latter is about discovery: you don’t know that the information you seek exists, and you may be sure of how to characterize what you are looking for–or even know what exactly you want until you’ve learned something about what is available.

These are extreme points on the information seeking spectrum, and most real-world tasks are in the middle, or combine subtasks of both types. For example, in physical libraries (yes, I’m that old!), I remember finding a book in the stacks and then browsing the nearby books in the hopes of serendipitous discovery. These days, I’d be more likely to scan its bibliography–or to look at the books and articles citing it. A known item can be an excellent entry point for exploration. Conversely, exploration can lead you to discover the existence of information that you then simply need to retrieve.

In common use, words like searching and finding cover this entire spectrum of information seeking activity. This breadth of meaning causes a lot of confusion. I’ve blogged about this before: “What is (Not) Search?”:

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At the very least, I propose that we distinguish “search” as a problem from “search” as a solution. By the former, I mean the problem of information seeking, which is traditionally the domain of library and information scientists. By the latter, I mean the approach most commonly associated with information retrieval, in which a user enters a query into the system (typically as free text) and the system returns a set of objects that match the query, perhaps with different degrees of relevancy.

Back to Campbell’s article. His main points:

  • Social networks have dramatically expanded our network of contacts.
  • Search engine optimization (SEO) experts have killed their own game.
  • The flow of information has changed: information now comes to us, rather than us having to go out and find it.

I like the spirit of the post, but I think he overstates his case. SEO isn’t all bad – in fact, it’s probably a key factor in Google’s effectiveness. And, while social networks enable social search in theory, and information does come to us, we are experiencing filter failure (Clay Shirky’s term) in a big way.

My conclusion: I agree with him about Google’s limitations – Google is primarily a locating tool, not a discovery tool. Unfortunately, I’m not persuaded that social networks and our theoretical ability to construct an ideal in-flow of information have actually delivered on the promise of more efficient information access. But I’m optimistic that we’ll eventually get there.

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