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

Monitor data quality of tables used in the dashboard. Let your dashboards always show the right numbers.

Monitor data quality of tables used in the dashboard. Let your dashboards always show the right numbers.

Data Quality of Source Data

Data quality monitoring of source data

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

DQO data quality specification files are simple YAML files with a predefined set of standard quality checks. Include the checks that are relevant to your dashboard, run them, and effectively profile your data sources as valid data sources for analytics.

  • Define data quality rules for source data
  • Profile the source tables with a predefined set of standard quality checks
  • Rerun quality checks if there are doubts about the quality of the 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 platform monitor the quality of your source data. Correctness checks detect problems at the data format level. Consistency checks detect inconsistent behavior or large changes (changes in averages) for key metrics. Monitor missing data with completeness checks.

  • Continuously monitor all key categories of data quality
  • Detect issues that are beyond the control of correctness (ranges, formats), such as spikes in certain metrics that are not realistic and are likely due to human error
  • Find out why the dashboard is not showing data for certain departments or business units by monitoring completeness in the appropriate dimension (state, department, business units, 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 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

Always Valid Dashboards

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