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Data Observability

Data observability lets you monitor and track the quality of your data over time using machine learning. By collecting, aggregating, and analyzing signals from multiple sources, it gives teams a comprehensive view of the health of their data so they can quickly detect, diagnose, and resolve problems — and make informed decisions based on accurate data. It ties directly into your data governance, providing a single source of truth for data lineage, quality, and schema changes.

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All data collected by scanners is anonymous and MINEO only uses anonymous statistics to perform data observability.

How data observability works in MINEO?​

Data observability is based on algorithms that track the quality of data over time. The basic workflow of this process consists of the following steps:

  1. Create a data source to your data.
  2. Define a scanner that is a process that periodically scans the data and tracks automatically changes
  3. Manage and track the issues generated.

Use cases of data observability​

  • Real-time data monitoring: Data observability enables organizations to monitor their data in real time, ensuring that data quality is maintained and data anomalies are detected and addressed immediately. This results in reduced data downtime and improved decision making based on accurate data.
  • Root cause analysis: By providing insight into data lineage and the relationships between data sources, data observability tools can aid in root cause analysis and troubleshooting of data pipeline issues.
  • Compliance and auditing: Data observability can be used to track changes made to data and ensure compliance with data governance policies, as well as to support auditing and compliance reporting.
  • Performance Optimization: By providing insight into the volume and completeness of data tables, data observability can help organizations optimize their data pipelines for performance and scalability.
  • Data Governance: Data observability can help implement and enforce data governance policies by providing a single source of truth for all data-related information, including data lineage, quality, and schema changes. This leads to improved data management and increased confidence in the data used for decision making.

See also​

  • Scanners — define the processes that periodically scan your data and track changes automatically.
  • Issues — manage and track the issues generated by your scanners.
  • Data sources — connect the databases that scanners monitor.