The quality of data directly determines the quality of insights you are trying to get. In a world where businesses are increasingly relying on data-driven decision, it is essential for modern organizations to build an effective ETL (Extract, Transform, Load) pipeline to move, process, and prepare data from various sources into a usable format ready for analytics. But the most important thing is that without properly testing the quality of data, these pipelines can quickly become conduits for misinformation, inefficiency, and costly errors.