How to Evaluate Data Observability Tools

The article discusses how to evaluate data observability tools, which are crucial for organizations that rely heavily on data-driven decision making. Data engineering teams often face massive tech debt and ambitious roadmaps, leading to data quality issues being discovered by stakeholders rather than engineers. This erodes trust in the data platform. To address this issue, data observability tools should improve baseline testing and alerting strategies using predictive models and machine learning-based anomaly detection. They should also facilitate test-driven development and provide comprehensive out-of-the-box features. The evaluation of these tools requires broad implementation across all data assets and a trial period of about 30 days to understand their effectiveness fully. Adopting a data observability tool can help diagnose existing quality issues, prevent future ones, and improve overall trust in the data platform.