According to Gartner, poor data quality could cost organizations up to $12.9 million annually. It has been predicted that by this year, 70% of organizations will track data quality metrics to reduce operational risks and costs. In fact, At Tiger Analytics, we’ve seen our customers embarking on their data observability journey by focusing on data quality checks retrofitted to their pre-existing data pipelines. We’ve helped our customers leverage their homegrown data quality framework and integrate it with their existing data engineering pipelines to improve the quality of data. Here’s how we did it.