While there’s no argument that data quality influences the success of your algorithm, the definition of “high quality” is often ambiguous. It can also be challenging to obtain quality datasets at an affordable cost and at scale. Ultimately, high-quality data is defined as data free of errors. We like to think of it as anything that could mislead your learning algorithm. Here are a few questions to ask when determining how to ensure data quality for your AI and ML algorithms.