Messy Analytics

The conclusion of a three part series on practical aspects of using analytical methods by Frank Buytendijk.   I had missed the first two parts, but now plan to go back and read them.  " ... First, when you do statistical analysis, resist the temptation to remove the outliers. Improbable scores or data are usually filtered out of the dataset because it is noise "messing up" the model. However, the outliers might actually represent the most interesting bits. They could be the early warning signal for a black swan coming or could represent new business opportunities that others – following best practices –neatly filter out. If the model is your lens, you won't see any change coming. You won't get any weird new ideas. What you see is what you've always seen. All the model does is confirm your hypothesis. Outliers deserve extra attention.  ... "