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When Big Data Goes Bad

November 23, 2012

In episode 6 of the RadioFreeHPC‘s podcast, they discuss a couple of examples of when Big Data goes bad. These are the articles mentioned:

Here are the conclusions I noted down (often with my own bias) from the podcast and from the articles:

  • Be careful with conclusions: One cannot blindly follow any software. People making decisions based on software-generated advice should have at least a basic understanding of what data goes into the software and what are the assumptions made.
  • Be careful with input: Parameters used in the software must be chosen with good reason.
  • Be careful with output: Unexpected results must trigger further investigation. “Computer says no” [1] is no valid answer.
  • Care about additional information/feedback: Do not discard any evidence that may contradict the initial findings without careful consideration.
  • Be careful with decision making: Only enable people to execute the kinds of analysis that they are prepared to execute correctly.
  • Care about visibility: make sure everybody that should see the data has seen it.
  • Care to ‘connect the dots’: need an integrated view over different systems/datasets that influence some decision.
  • Care about usability: “effectiveness of any technology is down to the people that use it.” Systems/recommendations must be understandable and easy to use by the people who make decisions, not system architects or statisticians.
  • Be careful with complex systems: as systems get stacked over legacy systems, even IT personnel loses track of what exists and where. Care about simplifying the stack.

All of these topics and more are being discussed in the context of the EU FP7 BIG project. If you’re interested in these discussions, there are a few discussion lists that you can join to contribute to the conversation. For example, two groups relevant for the discussion above:

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