Data Quality Operations Center

Measure, react and reach a 100% data quality score

DQO is an open-source, DataOps friendly data quality monitoring tool with customizable data quality checks and data quality dashboards

SUPERVISE THE DATA QUALITY PROCESS

Assessing data quality requires measuring the quality of data through KPIs and assigning a score to each table and data source.
When a drop in the data quality score is detected, you have to involve multiple parties in resolving the data incident.
DQO handles the entire process from detection to resolution.

Measure data quality KPIs

Calculate data quality KPIs as a percentage of passed data quality checks with data quality dashboards.

Detect and manage issues

Identify data anomalies and schema drifts.
Group issues and track their resolution.

Compare tables

Calculate data quality KPIs as a percentage of passed data quality checks with data quality dashboards.

Identify root causes

Find out why the data quality has decreased. Is it caused by timeliness, completeness, validity, etc.?

DATA QUALITY MONITORING

Start with a simple statistical analysis to get a quick insight into data. Then, run advanced profiling to choose checks for monitoring.
  • More than 140 built-in table and column data quality checks
  • Measure completeness, timeliness, validity, consistency, reasonableness, and accuracy
  • Create custom checks and rules with Jinja2 and Python
Learn more >

DATA QUALITY MONITORING

Start with a simple statistical analysis to get a quick insight into data. Then, run advanced profiling to choose checks for monitoring.
  • More than 140 built-in table and column data quality checks
  • Measure completeness, timeliness, validity, consistency, reasonableness, and accuracy
  • Create custom checks and rules with Jinja2 and Python
Learn more >

ANOMALY DETECTION

Automatically observe your data to detect potential data issues as soon as they appear and before anyone else is impacted. 

Apply data observability to detect changes in:

  • data volume,
  • data characteristics min, max, mean and sum,
  • missing or not timely data,
  • schema drifts.

MEASURE DATA QUALITY KPIs

Aggregate all data quality metrics in a dedicated data warehouse. Calculate data quality KPIs as a percentage of passed data quality checks. Use hundreds of data quality dashboards.
  • Governance, operational, and detailed dashboards
  • Build dashboards with a custom Looker Studio Connector 
  • Prove the quality of the data with numerical KPIs
Learn more >

MEASURE DATA QUALITY KPIs

Aggregate all data quality metrics in a dedicated data warehouse. Calculate data quality KPIs as a percentage of passed data quality checks. Use hundreds of data quality dashboards.
  • Governance, operational, and detailed dashboards
  • Build dashboards with a custom Looker Studio Connector 
  • Prove the quality of the data with numerical KPIs
Learn more >

MANAGE INCIDENT WORKFLOWS

Keep track of the issues that arise during data quality monitoring. Automatically group similar data quality issues into incidents.
  • View, filter and manage the incidents
  • Assign issues to respective teams
  • Automate incident notifications
Learn more >

Why DQO

CUSTOM DATA QUALITY DASHBOARDS

Create custom data quality dashboards thanks to access to the personal data warehouse.

ANALYZE PARTITIONED DATA

Monitor tables at any scale using incremental data quality checks at a partition level.

CUSTOM RULES

Define a custom inventory of approved data quality checks, shared by the data quality team.

DEVOPS FRIENDLY

Store data quality definitions in Git, and run checks from your data pipelines.

how it works

Connect data sources, start monitoring your data
and measure data quality with KPIs.