4 data quality best practices to help you get to data ROI faster

Data-driven organizations often face challenges with data quality, resulting in a lack of trust when data is incomplete or inaccurate, and recognizing that maintaining high-quality data is an ongoing journey rather than a destination is crucial. To enhance data quality, organizations should implement best practices such as automating data monitoring to proactively detect issues, utilizing tools like Slack for real-time alerts, and employing techniques like assertions, metric monitoring, and anomaly detection. Furthermore, improving the change management process is essential, including version-controlling code, understanding the impacts of data changes through data lineage and data diff tools, and establishing a thorough code review process to ensure changes are well-understood and bugs are minimized. By adopting these strategies, organizations can increase the reliability and value of their data, thus building trust with stakeholders and end-users over time.