Data quality is one of the main challenges in data warehousing and data engineering. If you let inaccurate or inconsistent data get into your system, you risk driving insights that don’t bring any value to your analytics and machine learning projects. In this episode of The Data Standard, host Catherine Tao sits down with data engineer and technology leader Nivi Arunachalam to discuss data quality. Data warehouses are expensive to build, so you can’t afford to base decisions on poor-quality data. If you let it get in, you lose credibility and risk data downtime. To prevent it, you need to ensure data quality at the source. You need to understand the entire data flow, from procurement to the moment it enters the warehouse. That’s how you ensure data consistency and accuracy. Information biases are quite common when relying too much on the people side. Relying on robust tech solutions when collecting, handling, and processing data helps to eliminate any information bias. Tune in with Catherine Tao to listen to this episode, as Nivi Aruanachalam goes deeper into data quality and ways to identify and mitigate potential issues.