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A colleague once wrote an great article about data. He discussed that in a world where all cars are either red of white, survey data would declare all cars pink.

While the survey data would be useful at making it clear that there were NO green, blue or Yellow cars, it would also be completely wrong.

I love predictive analysis. I also think that every decision should be based on data. Lastly, I think that the experience, insight and general human-ness of the people involved are the best way to evaluate and interpret data.

So, let's move toward MUCH better predictive modeling and MUCH MUCH better analysis and understanding.

Living in the world of informatics, I agree with all above. More and better information can be produced from the rich and robust streams of data now available. Modeling predicts what will occur with precise reliability statistics... which simply means that we can measure the amount of predictive error or standard deviation from the norm. None of that, however, implies that the norm is best or always appropriate, or that the outlier practices might not be superior in a particular case. Or that we should actually be interested in what is being modeled.

Knowledge can be improved with better information, but it still requires thought for proper application. The human brain that informs "the gut" is still the most versatile analytical machine known, because it can connect gaps in data and perceive patterns better than any alternative system.

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