Today in Tech: Cognizant Vs TCS; Nate Silver & data crunching

Cognizant Vs TCSCognizant said its quarterly revenue rose to $1.892 billion, up 5.4% sequentially and 18.2% from the year-ago quarter. Mint reports that in North America, it is within striking distance of overtaking Tata Consultancy Services in terms of revenues - it's short of just $1.6 million in that region in the recent quarter. The number reveals more about Cognizant's over dependence on North America than it does about the speed at which its growing. Cognizant gets four fifths of its revenues from the US, and TCS just a little over a half. And, more importantly, outside US, in both UK and rest of Europe, TCS, with its bigger base, grew faster than Cognizant. In a similar vein, Business Line reports that Cognizant grew faster than TCS in the latest quarter. But then again, TCS revenue this quarter was 50% more than Cognizant's. It's fair to argue that Cognizant has a lot of momentum now. But these numbers don't say anything about it. Nate Silver and the science of predictionEveryone seems to be ecstatic about Nate Silver, who predicted US presidential results with amazing accuracy over at his blog - FiveThirtyEight  - in New York Times.

• With sterling call, Nate Silver defines new wave in polling - Reuters
• Election 2012: Has Nate Silver destroyed punditry? - CS Monitor
• Nate Silver Takes A Victory Lap After Obama Re-election : Huffington Post
• Nate Silver nails it : Salon
• Obama's win a big vindication for Nate Silver, king of the quants: CNet

 His achievement has also turned the attention towards his method: data crunching.

• Nate Silver and data win the election: Newsday
• Nate Silver: How a math geek used big data to predict the US elections: Memeburn
• Is Election Predictor Nate Silver A Witch? Probably. And Quantified Self Data Will Make You One Too: Techcrunch

 It's not just about the data, it's the interplay between the data and the analyst. Nate Silver explains it this way in his introduction to The Signal and the Noise: Why So Many Predictions Fail - but Some Don't (which has become a big hit on Amazon since the election results came out.)

The numbers have no way of speaking for themselves. We speak for them. We imbue them with meaning. Like Caesar, we may construe them in self-serving ways that are detached from their objective reality. Data-driven predictions can succeed—and they can fail. It is when we deny our role in the process that the odds of failure rise. Before we demand more of our data, we need to demand more of ourselves.