Data Observability for Business Intelligence

Let your dashboards always show valid numbers

How often the dashboard does not work or is not showing all data?

Observe the Data Quality of tables used in the dashboard. Track possible issues by watching data quality issues in the source tables.

Data Quality of Source Data

Data Quality of Source Data

Validate the Data Quality of the source data before you decide to use the tables in the dashboards.

DQO.ai Data Quality specification files are simple YAML files with a predefined set of standard quality checks. Enable those checks that are relevant for your dashboard, run the checks and effectively profile our data sources as a valid source of data for analytics.

  • Define Data Quality rules for source data
  • Profile the source tables with a predefined set of standard quality checks
  • Rerun the quality checks when you have doubts about the Data Quality of your source data

Data Quality Monitoring

Data Quality Monitoring

Monitor the quality of source data before data formats or missing data will affect your dashboards.

Let the DQO.ai Data Observability platform monitor the quality of your source data. Validity checks will detect issues at the data format level. Consistency checks will detect inconsistent behavior or big changes (changes of averages) for the key measures. Monitor missing data with completeness checks.

  • Continuously monitor all key Data Quality measures
  • Detect issues that are beyond validity checks (ranges, formats) like rapid increases in some measures that are not realistic and are probably caused by a human error
  • Find out why your dashboard is not showing data for some departments or business units by monitoring completeness at a relevant dimension (state, department, BU, etc.)

Root Cause Analysis

Root Cause Analysis

Identity the root cause for your Data Quality issues even if it originates from an early stage of the Data Warehouse.

Table dependencies defined in DQO.ai will let you follow the data lineage up to the upstream data source that has unresolved data quality issues or generates many consistency warnings.

  • Find out which invalid table caused issues on your dashboard
  • Learn where your data comes from by following data lineage
  • Know when the source data was fixed and you can refresh the dashboard

Dashboards Always Valid

Dashboards Always Valid

Monitor the quality of those dashboards that are the most important.

Define Data Quality checks for the tables that you use directly in the dashboards. Run queries that retrieve the data that you show in the dashboard.

  • Verify that the queries for your dashboards will return valid data
  • Detect missing data that will make your dashboard incomplete
  • Make sure that the most important dashboards are always reliable

No one can understand your data like we do!