Is Data Mining Fraught with Peril?

This fun piece from ABC news describes the use and abuse of data mining. Often, the abuse surfaces when the analytics professional is overly interested in desired results over natural results. If there is not a clear business understanding and the model evaluation is not circled back to the original premise, error will result.

In fundraising, I see many people wanting unique and interesting factors to be the "key" for predicting giving. Instead, our goals should be identifying prospects, prioritizing prospects, predicting behaviors, segmenting our lists, and so on. In prospect identification, data mining is typically followed by prospect research. This hand verification will catch Type I errors. However, the deployment of models to annual giving does not have a safety net. I recommend testing a model like you might a survey or a direct mail piece to control for this potential of error.

I believe the science of data mining is strong and evolving, but I definitely recommend reading the ABC article to introduce some of the "cons."

That's been a good thing in some ways, because it has helped researchers spot trends in everything from politics to the stock market to long range weather patterns. But it's probably also why you get advertisements for stuff you don't want, and why sometimes it rains when it's supposed to be sunny. According to Austin and his colleagues, data mining is fraught with peril.

Read More