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Data quality dashboards

DQO has multiple built-in data quality dashboards for displaying data quality KPI. Our dashboards use Looker Studio (formerly Google Data Studio) business intelligence environment. We chose Looker Studio because there is no per-user license fee, which allows granting access to all parties that might be involved in the issue resolution process.

All data quality results are synchronized to a private data quality data warehouse in the Google Cloud. Data quality projects implemented with DQO receive a complementary Looker Studio instance connected to a data quality data warehouse. DQO customers can ask the vendor to access a custom Looker Studio data source, providing direct access to the data quality lakehouse.

Groups of dashboards

Data quality dashboards are divided into multiple groups, depending on the audience and purpose of these dashboards.

  • Governance dashboards. These dashboards show high-level data quality KPIs, aggregated on a macro scale that should be shared at a corporate level. With governance dashboards senior management is able to review key data metrics per connection, data quality dimensions, check category and data streams. The governance dashboards allows filtering data by time period and previously defined data streams which can represent location, business unit, vendor, supplier, or subsidiary.

  • Operational dashboards. Operational dashboards helps data engineers and data owners to identify areas (tables or data pipelines) in a data warehouse or data lake with the highest number of data quality issues that should be addressed.

  • Data Quality Issue Details dashboards. This type of data quality dashboard show detailed information about the issues at the table level. In DQO there are two groups of dashboards in this category: Issue details and Details per category. Issue details group focuses on issues grouped by quality dimensions, check types, check categories or tables. Details per category groups issues by volume, timeliness and completeness. The detailed dashboards are useful for data engineers and data owners to better understand data dynamics during the investigation phase when the data quality issue is being diagnosed and later to confirm whether it has been resolved.

  • Data Source States dashboards. This group of dashboards contains a summaries on most incomplete columns, table freshness, table ingestion delay, the biggest tables measured by recurring checks anf table row count for time partitions.

  • DQ Statistics dashboards. This group of dashboards contains the summaries of daily tests per check, data stream and table, as well as checks no longer in use.

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