If Your Data Is Bad, Your Machine Learning Tools Are Useless

Poor data quality is enemy number one to the widespread, profitable use of machine learning. While the caustic observation, "garbage-in, garbage-out" has plagued analytics and decision-making for generations, it carries a special warning for machine learning. The quality demands of machine learning are steep, and bad data can rear its ugly head twice -- first in the historical data used to train the predictive model and second in the new data used by that model to make future decisions. To properly train a predictive model, historical data must meet exceptionally broad and high quality standards. First, the data must be right: It must be correct, properly labeled, de-deduped, and so forth.