5 criteria of data quality and how to test for them

Data Quality Assurance (Data QA) is pivotal in deriving meaningful insights from data, surpassing concerns about speed, scale, and cost-efficiency. It underpins critical business functions such as business intelligence, machine learning, and enterprise security by ensuring data accuracy, completeness, consistency, uniqueness, and validity. The blog post discusses how poor data quality can lead to faulty analysis and strained stakeholder relationships, while high-quality data fosters better collaboration and decision-making. It introduces five core criteria for assessing data quality and provides SQL test examples for each, using the NYC Taxi dataset as a reference. The article also highlights Tinybird's utility in simplifying data quality tests, offering integration capabilities with tools like Grafana and Apache Airflow. The platform's free Build Plan encourages data engineers to explore these processes further, fostering a community for data practitioners to collaborate and share insights.