Data Observability For Data Stewards

Continuously monitor the Data Quality of the data

Are your tables healthy and of good quality today? As a Data Steward, can you answer this question instantly?

DQO.ai Data Observability lets you define Data Quality rules for all the tables that you are responsible for. The quality rules will be monitored every day or after each data refresh. Monitor Data Quality KPIs to answer questions about the quality of your data.

One place for Data Quality rules

One place for Data Quality rules

Define all Data Quality checks in one place. Easily apply similar Data Quality rules for similar columns by a copy-paste of the definition.


Data Quality checks are defined in YAML files, one file per table. All rules are easy to edit with popular text editor that also support auto suggestions for possible quality checks.

  • Define your Data Quality requirements in code
  • Profile your data sources and detect data quality issues
  • Detect data integrity issues like missing dimension rows

Data Quality under control

Data Quality under control

Observe and detect Data Quality issues before users will ask you what happened with your tables.

DQO.ai Data Observability will monitor defined Data Quality checks on the tables every day or following a custom schedule. Issues with data availability or validity will be detected before they affect downstream data consumers.

  • Data Quality checks are executed frequently
  • All Data Quality rules promised to data customers are ensured
  • Stale tables (not refreshed tables) are detected by timeliness (latency) tests

Track multiple Data Quality dimensions

Track multiple Data Quality dimensions

Analyze and measure the Data Quality on multiple Data Quality dimensions to detect different types of issues.

All built-in Data Quality checks are clearly divided into multiple dimensions commonly used in the data quality area. Monitor validity, consistency, completeness, timeliness, reasonableness, availability, accuracy, reliability, accessibility and integrity of your data separately.

  • Multiple Data Quality dimensions clearly separated
  • Data Quality monitored from many angles
  • Define custom Data Quality rules to monitor business relevant metrics

Data source documentation

Data source documentation

Treat your Data Quality check definition as a documentation of Data Quality metrics that are ensured for your tables.

The Data Quality definition files are simple and self-descriptive. All Data Quality check names and alert thresholds are written as easy to understand names. You can just share the Data quality definition file (YAML) without revealing the database credentials.

  • Data Quality rules become our documentation
  • The Data Quality rules documentation is always valid because the Data Quality checks are validated daily
  • Your data customers like data engineers, BI developers or data scientists can see which columns are not null, which columns are unique, what are the valid data ranges or data formats

Data Quality issues notifications

Data Quality issues notifications

Get notified when the quality of your tables is not met. Also get notified if your upstream sources behave inconsistently and will affect your tables.

DQO.ai Data Quality checks may be executed anywhere. Simply define a way to get notified when high severity alerts were raised.

  • Build the Data Quality check step into your data processing pipeline, DQO.ai can be called from any pipeline as a command line tool
  • Define alerts at multiple severity level: low, medium, high
  • Define the dependency to upstream tables in the data lineage that could affect the quality of your tables

Ground Truth Checks

Ground Truth Checks

Compare the quality of the data in the database with other trusted data sources to detect differences from the real world.

DQO.ai can compare data aggregated by business metrics across data sources. Pick an aggregation function (count, sum, average) and a list of data dimensions for two related tables and run the comparison (an accuracy data quality check). You can also upload reference data to the DQO.ai Data Quality database to compare it with your tables.

  • Compare data with real world, grouped by business dimensions (like a country or state code), maybe you are missing data from one state
  • Compare data between related databases
  • Set up a custom Python script to pull reference data from external data sources for comparison

No one can understand your data like we do!