Data quality testing is the process of validating that key characteristics of a dataset match what is anticipated prior to its consumption.
According to Gartner, bad data costs organizations on average an estimated $12.9 million per year. In fact, Monte Carlo’s own research found that data engineers spend as much as 40% of their workday firefighting bad data.