Concepts and Practices to Ensure Data Quality

Poor data quality can lead to lost revenue, reduced operational efficiency, and bad business decisions. To maintain high-quality data, businesses should identify the most important dimensions, track metrics against those dimensions, and establish SLAs on those metrics. Two guidelines for meaningful data metrics are measuring what matters and making metrics actionable. Practices that can improve data quality include preventing issues that can be prevented, investing in data infrastructure tooling, checking metrics deeply and often, aligning data quality with stakeholder impact, and arming the data team with data quality tools.