Date posted: 16/08/2017 2 min read

When is big data bad data?

In episode 12 of the Acuity podcast, host Mike Lynch and Acuity publisher Andy McLean speak to author and Ted talk speaker Cathy O’Neil about her book, Weapons of Math Destruction.

In Brief

  • Big data can be problematic and dangerous when used incorrectly.
  • Big data seems to be the catch-all solution to issues around the world, but that is not necessarily the case.
  • Big data will never get even close to 99% accuracy.

In Cathy O’Neil’s book, Weapons of Math Destruction, she talks about how problematic and dangerous big data can be when human bias occurs.

Big data is often bandied about as the silver bullet to countless problems. From helping us make smarter business decisions through to choosing which suburb to live in; big data seems to be the catch-all solution to issues around the world.

Mix it all up with a dash of artificial intelligence and you’ve got a winning formula right?

Not necessarily.

In the instance of political polling, O’Neil explains the process: “They put 25 people in marketing silos, and they often assume that other people outside of these marketing silos will have the same opinions, so that’s a very indirect way of inferring people’s opinions about things. But, that is the way that big data works”.

O’Neil believes that results gleaned from big data will never get even close to 99% accuracy, so the notion that algorithms and analytics are used in places assumed to have high standards is a real fear. 

“Things like hiring people, deciding how long they should go to prison, or deciding whether a given person is good at their job and whether they deserve to get fired… I think everyone deserves to assume a standard of accuracy, but that is not what’s happened.”

O’Neil is an American mathematician, data scientist, Ted Talk speaker and author. She speaks to Acuity about these issues, providing more in depth examples in episode 12 of the Acuity podcast.

To listen to this episode, and others, visit

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