John Battelle ranking a series of data rich/intensive companies based on
Now, a bit more detail on the data categories […]:
Purchase Data: This is information about who buys what, in essence. But it’s also who almost buys what (abandoned carts), when they buy, in what context, and so on.
Search Data: The original database of intentions - query data, path from query data, “intent” data, and tons more search signals.
Social Data: Social graph, but also identity data. Not to mention how people interact inside their graphs, etc.
Interest Data: This is data that describes what is generally called “the interest graph” - declarations of what people are interested in. It’s related to content, but it’s not just content consumption. It includes active production of interest datapoints - like tweets, status updates, checkins, etc.
Location Data: This is data about where people are, to be sure, but also data about how often we are there, and other correlated data - ie what apps we use in location context, who else is there and when, etc.
Content Data: Content is still a king in our world, and knowing patterns of content consumption is a powerful signal. This is data about who reads/watches/consumes what, when, and in what patterns.
Wildcard Data: This is data that is uncategorized, but could have huge implications. For example, Microsoft knows how people interact with their applications and OS. Microsoft and Google have a ton of language data (phonemes, etc.). Carriers see just about everything that passes across their servers, though their ability to use it might be regulated. Google, Yahoo and Microsoft have tons of email interaction data. And so on….
And the results:
Credit John Battelle
These same categories could be used to financially quantify BigData.
Original title and link: The Data Layer - Visualizing the Big Players in the Internet Economy (NoSQL databases © myNoSQL)