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Visual Analytics - An Interaction of Sight and Thought - JMP

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Good models don’t hide variables <strong>and</strong><br />

their relationships.<br />

Inputs<br />

?<br />

Outputs<br />

Most <strong>of</strong> the applications that I’ve seen marketed by business intelligence s<strong>of</strong>tware<br />

vendors for predictive analytics allow data to be entered on one end (inputs) <strong>and</strong> then<br />

results (outputs) pop out the other; what goes on in between remains hidden in a black<br />

box. Unfortunately, without seeing what goes on in that black box, our brains aren’t fully<br />

engaged in the process <strong>and</strong> too much is missed.<br />

Predictive analytics are most revealing when they allow us to see how all the variables<br />

that contribute either directly or indirectly to the outcomes that concern us relate to<br />

those outcomes <strong>and</strong> to one another. To underst<strong>and</strong> these relationships, we must see<br />

them; we must watch how changes in one variable directly cause or indirectly influence<br />

changes in the others. For this to happen, predictive models must be displayed visually<br />

in a way that allows: (1) our eyes to see the relationships <strong>and</strong> changes; <strong>and</strong> (2) our<br />

minds to make sense <strong>of</strong> them.

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