Musings on Data, Analytics and Guessing

Feb 29, 2012   //   by Karen Lopez   //   Blog, Data, Data Modeling  //  2 Comments



It is a capital mistake to theorize before one has data.
-Sir Arthur Conan Doyle

Experts often possess more data than judgment.
Colin Powell

Sir Doyle and General Powell seem to have conflicting points of view about data, but I’m not sure they do. I love my data and yet data alone won’t solve many problems.  I have to figure out which data to use, how current the data needs to be, and how to use that data with other data and my own experiences and biases to get to a decision that’s right for right now.  

We in the data profession pretty much spend our days trying to get quality data to the right people as quickly as we can. We provide analytic services in hopes that management can turn that data into good decisions. We add biases, we filter out biases, we support a lot of guessing.  None that makes Sir Doyle or Gen. Powell wrong.

What if we provide data and analytics to organizations, but mostly management just makes guesses? I recently sat in a meeting where we were asked to keep adjusting the data rules until the analytics would show exactly what values the end users wanted to see.  Of course this is a fine balance: end users need to set the requirements around how data should be processed to produce the analytical solutions, but at some point we data pros can’t get sucked into using decision systems to justify bad decisions.

I’ll leave you with a quote from Mr. Heinlein:

To get anywhere, or even live a long time, a man has to guess, and guess right, over and over again, without enough data for a logical answer.
Robert A. Heinlein

That might be how a man should live, but organizations need to ensure they are working with good data and great analytics.  At some point your competitors will stop flying by the seat of their pants via SWAGs.  And they will most likely be making better decisions than your organization.  If your analytics are there only to make end users feel better about their guesses, you’re doing it wrong.


  • From my work, today

    PM: Why was your SWAG inaccurate?
    Me: What does the acronym stand for?
    PM: It stands for…oh…never mind.

    • That’s wonderful.

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