Data quality monitoring for data governance

Monitor data quality through unified data quality metrics

Do you monitor data quality across all databases according to the same process?

Define a standard set of data quality metrics that are consistently monitored across all data warehouses and data lakes.

Unified Data Quality

Unified Data Quality

Monitor the quality of all your databases in one place

Connect all data sources to DQO and monitor the same quality measures. Monitor popular data quality dimensions such as validity, availability, reliability, timeliness, uniqueness, reasonableness, completeness and accuracy.

  • Analyze data quality across the enterprise
  • Detect data quality issues at multiple dimensions
  • Compare data quality metrics across different databases

Agreed Data Quality Rules

Agreed Data Quality Rules

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

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

  • Measure all your databases with the same rules, regardless of the type of database or Data Lake you use
  • 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 the most advanced scenarios

Data Quality Documentation

Data Quality Documentation

Document data quality rules for your databases as easy-to-understand, shareable YAML files

Define data quality checks as DQO 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 data quality specification files as data quality documentation
  • Store data quality specification files in a code repository along with your data science and data pipeline code
  • Track changes in 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 dimensions of data quality

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 data quality KPIs.

  • 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 monitoring in their data pipelines

DQO is a second generation data quality monitoring platform that was designed after enabling thousands of data quality checks. DQO has been redesigned to meet the requirements of both data engineering and data science teams. Data quality monitoring should be so simple that the benefits overcome any initial learning challenges.

  • Data quality controls that are easy to understand for data quality novices
  • Edit data quality controls with full autosuggestion support in popular code editors
  • Ability to make large changes to data quality rules, such as renaming a table or column

Database cross checks

Database cross checks

Analyze Data Quality across different databases by comparing aggregated data

Define accuracy checks (comparison with real data) and semi-accuracy checks (comparison with another dataset that should be equal). Data accuracy checks can take data from disparate sources. DQOs can compare aggregate data for selected dimensions.

  • Detect mismatches on different data granularity
  • Detect missing partitions and groups of data in different databases, you may not have data for one state in another copy of the database
  • Continuously monitor differences

REACH A 100% DATA QUALITY SCORE