When Big Data is not Big Understanding

Good article from Tim Harford (he of the enjoyable “Undercover Economist” books) in the FT last week called “Big data: are we making a big mistake“. Tim injects some healthy realism into the hype of Big Data without dismissing its importance and potential benefits. The article talks about the four claims often made when talking about Big Data:

  1. Data analysis often produces uncannily accurate results
  2. Make statistical samplying obsolete by capturing all the data
  3. Statistical correlation is all you need – no need to understand causation
  4. Enough data means that scientific or statistical models aren’t needed

Read more at Finextra

When Big Data is not Big Understanding

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