As companies strive to become data-driven, and with the recent explosion of AI technology demanding ever-increasing amounts of training data, the quality of that data is becoming more important. And there’s a great deal of time and money invested in data pipelines and other technical aspects of data quality such as data consistency, validity, timeliness, and audibility.
But there’s one aspect of data quality that’s equally, if not more, important, and is often overlooked in favor of problems that can be solved by technology—that of completeness, or bias.
The best way to address this issue is to have as diverse a data team as possible in terms of gender, ethnicity, age, national background, education, business expertise, and more.
