Manual vs Automated, Observability vs Testing: Choosing a Data Quality Approach

Data quality encompasses various approaches, such as manual checks, automation, data observability, and data quality testing, each catering to different organizational needs and maturity levels. Manual data quality involves translating business rules into code, relying heavily on human effort, while automated data quality uses AI to convert requirements into executable checks, reducing the need for manual coding but potentially lacking human judgment. Data observability, originating from software engineering, focuses on the reliability of data over time by monitoring metrics and logs but is primarily reactive. In contrast, data quality testing aims to prevent issues by validating data before it enters production environments, although it can be resource-intensive. The decision on which approach to use depends on the specific needs, resources, and scale of an organization, with many opting for a combination of methods to ensure reliable and trustworthy data.