Tips3 ways data analytics can fail

5 June, 20193 min

Whether you are using a spreadsheet or advanced data analytics with machine learning or AI – unless you are vigilant, your foraging for insights from data can so easily go wrong.

A big part of the problem is that ultimately, data is always viewed through a human lens. Unfortunately, our brains did not evolve to process complex numerical data and our instincts, biases and desires can significantly alter our understanding of the data being presented to us.

We have no way of knowing if data is right or wrong simply by looking at it. So we have to be very careful when making decisions based on it.

In this short video – 3 causes of insight failure are highlighted. Each compounding the risks that our decision making may be flawed.

  • GIGO – Garbage In Garbage Out
  • People see what they want to see
  • Lies, damned lies and statistics

I also include some hints at ways to avoid these problems.

Credit: SimonF

Data Analytics Part 2 by 2decipher
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