4 ways to keep control of your AI data

The use of data dropout to screen out unwanted data is just one of several ways that organizations can control their data--and how much they want of it--for their artificial intelligence. It is a way to assure that the data you're using is relevant for the business problem you want your AI to address. Data scientists use data dropout in AI to eliminate upfront all data that is deemed to be extraneous to a particular AI process. For instance, if all you care about are the demographics for the state of Indiana, you can exclude the data that comes in from other states that is irrelevant to your study. The processing time for data is reduced, and the time to market for AI results is expedited and the quality and value of the data that you input into your AI application is improved.