You need to be constantly exploring the data in your AI pipeline
Poor data quality is hurting artificial intelligence (AI) and machine learning (ML) initiatives. This problem affects companies of every size from small businesses and startups to giants like Google. Unpacking data quality issues often reveals a very human cause.
More than ever, companies are data-rich, but turning all of that data into value has proven to be challenging. The automation that AI and ML provide has been widely seen as a solution to dealing with the complex nature of real-world data, and companies have rushed to take advantage of it to supercharge their businesses. That rush, however, has led to an epidemic of sloppy upstream data analysis.
