Data observability measures your organization’s ability to monitor the overall health and performance of its data. It relies on data management activities and technologies (enabled through AI/ML) to detect, troubleshoot, and alert people about anomalies or other problems that might occur along the data pipeline.
Suppose an exceptional value (anomaly) appeared in a critical dataset. In that case, you could use a data observability tool to detect that problem, find the source, and alert the necessary parties to resolve it.
Companies with Data observability usually contain the following capabilities:
- Monitoring (usually done through a dashboard with data quality as the primary metric)
- Alerting (to tell the relevant people when problems occur)
- Tracking (Logging and following issues until they reach their solution)
- Automation (adapting to your specific situation to identify problems automatically)
- Comparison (Monitoring over time to notice when anomalies occur and measure values against company standards).
Problems with data will inevitably occur. Data observability capabilities will help your organization be agile and efficient in addressing these issues. By doing so, you can minimize their impact, learn from them, and get better at preventing them in the future.