Framing Big Data, part 2 of 2

Let us say you have Google or even Facebook – you have a billion users. It is almost impossible for Google and Facebook to understand whom their customers are. Intuition or even just like talking to some customers is going to give you an extremely biased view. You are going to basically base your Kaiser Fungentire description of one billion people based on interactions with what a hundred people? Having the data and the numbers are really important. Like we said previously, completely trusting the numbers and the analysis is also very foolish. You need human intelligence to interpret these numbers. It is really an interplay of the numbers and your interpretation because ultimately, even though the numbers will never give us cause of information – they can never really tell you with certainty that A causes B, it would tell you that A is related to B. It is human interaction that is needed that kind of tie these things all together into a credible story. Forget the notion that you will find one story that is correct, and everything else is wrong. All we are trying to look for is a story that is our best story, given our constraints of what we can and what we cannot.

…says Kaiser Fung, author of a new book, Numbersense a previous book, Numbers Rule Your World: The Hidden Influence of Probabilities and Statistics on Everything You Do and the popular blog, Junk Charts.

Kaiser Fung is a professional statistician with over a decade of experience applying statistical methods to marketing and advertising businesses. He holds an MBA from Harvard Business School, in addition to degrees from Princeton and Cambridge Universities. He is Vice President of Business Intelligence and Analytics at Vimeo, a high-quality video hosting platform for creative people. He previously worked at Sirius XM Radio, American Express, [X+1], Exodus Communications, and Sonus Networks. He is also an adjunct professor at New York University teaching practical statistics.

This is the second of two podcasts with Kaiser. The first one posted last week, Framing Big Data, part 1 of 2.

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