Data Observability For BI Developers

Detect Data Quality issues before the business sees invalid numbers on dashboards

How often the business asks you why the dashboard is missing important information or the numbers don't make sense?

Data Observability can help. Observe the data sources that you use in the dashboards and get warned about potential issues before more dashboards are affected.

Data Quality of the Data Model

Data Quality of the Data Model

Analyze the Data Quality of tables that you will use for a dashboard. Verify that the data meets your needs.

DQO.ai has a library of most important data quality checks. Enable our data quality rules like null checks, duplicate detection, invalid values detection or data integrity 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

No more missing data

No more missing data

Detect data quality issues that will affect the numbers on the dashboards before the business raises an incident.

DQO.ai is a data observability framework. You define your data quality requirements and DQO.ai will continuously monitor the data to detect  when your Data Quality requirements are not met. DQO.ai will raise an alert when the data does not make sense. For example: the sum of a column that you use as a measure has rapidly changed.

  • Make sure that the Data Quality requirements are always satisfied
  • Get an alert when the dashboard shows invalid values
  • Get a warning when the source data changes inconsistently and your dashboards would show very different numbers

Protection from the Data Warehouse downtime

Protection from the Data Warehouse downtime

Get warned when issues in the initial stages of a Data Warehouse or a Data Lake (ingestion, staging, etc.) will corrupt the data mart tables that your dashboard is using before the data mart or your dashboard is refreshed.

Define the data lineage across all tables in the data pipeline. Get warned when upstream tables are affected by data quality issues that will later affect the tables that you are using in your dashboards.

  • Learn about all Data Quality issues that could later affect your dashboards
  • Build a data lineage dependency tree to know how small changes will affect your dashboards
  • Stop the dashboard refresh if the next refresh will corrupt the dashboard with invalid data

Dashboards always working

Dashboards always working

Detect data schema changes that will make your dashboard unusable because the type of a column or a format of a column has changed in a way that will affect the dashboard.

DQO.ai will capture the column schema of the tables that are monitored. The table and column schema will be compared every day to detect changes. Define the expected format of text columns (codes) and valid data ranges (numeric columns). DQO.ai will detect if all the rows meets your requirements.

  • Define the requirements for data and field formats in one place
  • Ensure that the rules for valid data ranges and field formats are always met
  • Detect column schema changes that would make the dashboard fail to refresh

No one can understand your data like we do!