6 Ways to Improve Your Data Quality (With Automated Checks)

In the world of big data, ensuring data quality and reliability is crucial for making informed business decisions, as poor data can lead to faulty predictions and business risks. Frameworks like Soda provide tools for data quality management, allowing engineers to focus on optimizing data infrastructure rather than constantly fixing data issues. By leveraging SodaCL, an intuitive language for data quality checks, both technical and non-technical users can define expectations for datasets, such as ensuring no duplicates or maintaining dataset freshness. Automated data quality checks can shift the focus from reactive responses to proactive measures, preventing issues before they affect business outcomes, and fostering an environment of trust in data-driven decisions. Additionally, establishing data owners and using health scores for datasets can further enhance data reliability, enabling data engineers to concentrate on building new data products and improving infrastructure. Soda offers free trials to help businesses implement these foundational checks and transform their data management practices to ensure high-quality data is at the core of their operations.