The Dào of Search

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One of the greatest achievements of modern search engines is the ability to see the relationships between many different facets of the data. This is enabled by what’s known as “deep faceting” – knowing exactly how many results there are for each facet of the data.

The authors of DataFlow put it this way: “By giving shape to data, we… provide access and insight to the hidden patterns of meaning.”

In my earlier post, Precise to a Fault, I argued that result counts are most useful when they are used to indicate proportionality between facets. In this post, I’d like to look at some of the methods of presenting search-based information that help yield insight into how the data is related.

The purpose of search is to help people find what they’re looking for as quickly as possible. Search engines attempt to facilitate this by taking the user’s query and responding with results, placing what it thinks are the most relevant results first.

Unfortunately, pagination doesn’t always do the best job of guiding the user through the search results in the most beneficial manner. I’ve noticed three common problems.

Can a number be so precise that it actually hinders users rather than assists them?

Take search results for example. It is common practice to indicate the total number of results found for a given query. Google tells me that there are 221,000 web pages about “rubber duckies,” while a recipe site indicates that there are 113 salads and 7 desserts with “avocado.”

I can think of two good reasons for why showing the number of search results has become a standard. First of all, it provides a sanity check. If “Barack Obamaaa” only returns 3 results, it is an indicator that I’ve made a mistake in my search query. Second, it helps me compare one collection with another. The fact that there are twice as many two bedroom flats as one bedroom flats in Greenwich informs me about the area.