The New York Times magazine has just published a fascinating article about Google, discussing whether Google has become an aggressive monopolist in the area of search, and if so, whether or not it needs to be broken up under anti-trust law. The article, which is well worth a read, cites case after case where Google ostensibly derailed other companies that had seemingly developed better search tools than Google.
Better search tools than Google? Is that even possible? That’s where I take some slight exception to the article. Possibly in order to make this topic more accessible to a mass audience, it labels all these competitive search providers as “vertical search” companies.
Those of us with some history in the business remember back to around 2006 when “vertical search” was a thing, a thing that has long since faded. At the time, the concept of vertical search was a full-text search engine, much like Google, but one that was focused on a single vertical market or specific topic area. The thinking was that if publishers curated the content that was being indexed, the search results would be stronger, more accurate, more contextual and more precise. A prime example at the time was the word “pumps.” As a search term, it’s a tough one – the user could be looking for a device that moves fluids … or shoes. A vertical search engine, which would be oriented towards either equipment or fashion, would reliably return more relevant responses. Vertical search failed as a business not because it was a bad idea, but because (and let’s be honest here) most of the publishers rushing to get into it were too lazy to do the up-front curation work. And without quality, up-front curation, vertical search quickly becomes just plain bad search.
Vertical search as used in the article really refers to vertical databases. The difference is important because the article also states that parametric search is hard. That statement is simply more proof that vertical search as used in this article means databases. Parametric search is not hard: collecting and normalizing data so it can be searched parametrically is hard. Put another way, searching a database is easy, providing there is a database to search.
Google never wanted to do the work of building databases. It sometimes bought them (example: a $700 million acquisition of airline data company ITA) or “borrowed” them (pulled results from third-party databases into its own search results, effectively depriving the database owner of much of its traffic – think Yelp). What Google did instead was devote unfathomable resources to develop software code to try to make unstructured data as searchable as structured data. While it made some impressive strides in this area, overall Google failed.
With this context, you can clearly see why data products are so important and valuable. Data collection is hard. Data normalization is hard. But there’s still no substitute for it, something Google has learned the hard way. It may be disheartening to see survey after survey where we learn that users turn to Google first for information. But this is the result of habituation, not superior results. For those who need to search precisely, and for those who really depend on the information they get back from a search, data products win almost every time … provided that users can find them. Read this article and judge for yourself just how evil Google may be…