Fighting the Curse of Knowledge with Data Visualization

Christopher Penn
Aug 24, 2017 · 3 min read
Fighting the Curse of Knowledge with Data Visualization.png
Fighting the Curse of Knowledge with Data Visualization.png

Answers come from questions.

Better answers come from better questions.

What begets better questions? Better data, and better ways to look at that data. When presented with a massive amount of data, such as a large table of 20 variables, it’s nearly impossible for regular people to glean answers from the data itself.

For example, look at this dataset of search and social media data:

wall of unhelpful data.png
wall of unhelpful data.png

We can’t make much of it, and thus we’ll pay little attention to it — which means few or no questions.

How many reports have you read which are similar — just massive dumps of data with no way to interpret it?

Why We Vomit Data

The reason we vomit data like this is what’s known as the curse of knowledge:

“The curse of knowledge is a cognitive bias that occurs when an individual, communicating with other individuals, unknowingly assumes that the others have the background to understand.”

Chances are, the person preparing the data and doing analysis on it has been staring at it for so long that they’ve started to draw insights from it. For the person preparing the data and stuffing it into a report, they know what the data says.

But they’re the only person who does. Everyone else sees an eye-glazing table and that’s it.

The Cure to the Curse of Knowledge is Data Visualization

Here’s how we cure the curse of knowledge: by preparing excellent data visualization which makes it accessible to everyone, regardless of level of knowledge.

Suppose we take that massive table of data and turn it into a correlation matrix:

commented_correlation_matrix.png
commented_correlation_matrix.png

Instead of a wall of numbers, we now have a starting place to ask better questions:

  • Why are some things darker or lighter?
  • Why do some things have a relationship and others do not?
  • Why do some things we expect to be related have no relationship?

Note that the chart above doesn’t provide answers. It provides refinement for us to ask better questions.

A good visualization should pique our curiosity, spur new questions, and compel us to find answers.



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Originally published at Christopher S. Penn Marketing Blog.

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    Christopher Penn

    Written by

    Cofounder @ TrustInsights.ai, ninja, Marketing Over Coffee cohost, keynote speaker, Buddhist. New Book: https://gumroad.com/l/aiformarketing

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