Data Observability for Data Governance

Observe Data Quality through unified data quality metrics

Are you monitoring the Data Quality of all databases following the same process?

Define a standard set of data quality metrics that are consistently observed across all your databases and Data Lakes.

Unified Data Quality

Unified Data Quality

Observe the quality of all your databases in one place.

Connect all data sources to DQO.ai and monitor the same quality measures. Detect Data Quality issues from multiple angles by monitoring all popular data quality dimensions like validity, availability, reliability, timeliness, uniqueness, reasonability, completeness, accuracy – to name a few.

  • Analyze Data Quality across the enterprise
  • Detect Data Quality issues at multiple dimensions
  • Compare Data Quality metrics across databases

Agreed Data Quality Rules

Agreed Data Quality Rules

Verify the Data Quality of all databases and Data Lakes with the same set of approved Data Quality rules.

Select a subset of Data Quality checks that should be enabled for all your databases. Define additional custom Data Quality checks or modify DQO.ai built-in Data Quality checks to meet your requirements and policies.

  • Measure all your databases with the same rules, independent of the database or Data Lake technology behind
  • Customize Data Quality rules to meet your unique needs
  • Define custom quality checks as templated SQL queries (Jinja2 compatible), Python code or Java classes for most advanced scenarios

Data Quality Documentation

Data Quality Documentation

Document the Data Quality rules for your databases as easy to understand YAML files that can be shared.


Define Data Quality checks as DQO.ai Data Quality specifications. The specifications are easy to read and clearly show the type of checks and their expected thresholds for different alert severity levels.

  • Use the Data Quality specification files as a Data Quality documentation
  • Store the Data Quality specification files in the code repository along with the data science and data pipeline code
  • Track the changes to the Data Quality requirements by comparing specification files in Git

Compare the same metrics

Compare the same metrics

Ensure that all database teams are monitoring the same Data Quality dimensions and everybody has the same understanding how accuracy differs from consistency or reasonableness.

Introduce the same Data Quality checks across the whole organization. Let every data team use and measure the same Data Quality checks, tracking the same KPIs for dimensions.

  • Use the same Data Quality dimensions across the organization
  • Compare Data Quality KPIs in the same way across databases
  • Improve the overall Data Quality across the whole enterprise

Simple Data Quality

Simple Data Quality

Convince data teams to apply Data Quality and Data Observability steps in their data pipelines because it is so simple to enable Data Quality.

DQO.ai is a second generation Data Observability tool that was designed after enabling thousands of Data quality checks. DQO.ai was redesigned to meet both the requirements of data engineering teams and data science teams. The Data Quality and Data Observability should be simple enough that the benefits overcome any initial learning challenges.

  • Data Quality checks that are easy to understand for newcomers in the Data Quality space
  • Edit Data Quality checks with full autosuggestion support in popular text editors
  • Enable big changes to the Data Quality rules like table or column renames

Database cross checks

Database cross checks

Analyze Data Quality across different databases by comparing aggregated data.

Define accuracy checks (comparison to a real-word data) and semi-accuracy checks (comparison to another dataset that should be equal). Data accuracy checks may pull data from disparate sources sources. DQO.ai will compare aggregated data for selected dimensions.

  • Detect mismatches at different data granularities
  • Detect missing partitions and groups of data across databases, maybe you don’t have data for one state in another copy of the database
  • Continuously monitor the differences

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