Who is looking after the quality of your analytics?

We talk a lot about using analytics to improve the quality of something else. But when was the last time you thought about the quality of the analytics themselves? For many people, unfortunately, this is “never.” And when we do, invariably it’s either too vague or too narrow. Often, there is an implicit assumption: analytics professionals produce high-quality stuff simply because they are technically capable. It doesn’t matter whether it is predictive modeling, designed experiments, classifications, segmentation, or any other product of statistics, advanced analytics, machine learning, data science, or even AI. The challenge is there are so many things…...