It's no secret that machine learning methods are highly dependent on the quality of the data they receive as input. If you think of machine learning as a manufacturing process, the higher the quality of the input data, the more likely it is that the final product is of high quality as well. This relationship presents a big challenge to analytics teams when it comes to figuring out the right data for helping to solve business problems. It is necessary for those teams is to prepare all datasets to achieve a machine learning process free of errors. This involves setting up quality standards and fixing data issues like missing values or columns with low statistical variance, as well as selecting the right data types, removing duplicate data, and more.