There’s something that often gets lost in discussions about artificial intelligence and advanced analytics: the importance of the data. Having good, clean data is absolutely essential, but all too often, companies lack trust in that critical resource, which can lead business leaders to make bad decisions or resort to following gut instincts.
No matter how good your analytics are, you’re not getting anywhere if you can’t trust your data. The goal for many companies when constructing predictive analytics is to get the data as clean as possible, a process that studies show can consume up to 80% of a data scientist’s time. But even if the data is 100% true, your model may give the wrong predictions if the data doesn’t accurately reflect the thing you’re trying to predict.
Author: Alex Woodie