DQO.ai

A centralized hub for profiling, automating, and monitoring data quality.

Initial stage to evaluate data quality.

Automatically proposes configurations for data quality checks using a rule mining engine to detect common issues.

Enables running data quality checks directly from data pipelines, verifying data contracts, and preventing corrupted data from being loaded.

Identifies data anomalies and schema drifts, grouping them into data quality incidents and allowing assignment to relevant teams.

Measures data quality using key performance indicators that can be proven to business sponsors, verifying SLAs for data domains and vendors.

Simple process to connect various data sources for monitoring.

Configure data quality checks using YAML files within Visual Studio Code, offering code completion and in-place documentation.

Checks for incomplete tables/columns, invalid values, patterns not matching, and applies policies automatically.

Starts with basic statistical analysis for quick insights, then uses a rule mining engine for automated check configuration.

Allows defining unique checks and rules using Jinja2 and Python.

Automatically generates checks based on data characteristics.

Validates these crucial aspects of data.

Creates regular expressions from sample data for complex pattern validation.

Generates SQL queries to validate issues.

Create personalized dashboards using the data quality data warehouse for analysis.

Monitors tables incrementally at a partition level for scalability.

Define and share an inventory of approved data quality checks within the data quality team.

Integrates with DataOps practices by storing data quality definitions in YAML files in Git and running checks from pipelines.

Uses advanced AI to predict and detect anomalies considering seasonality.

Detects changes in data volume, characteristics (min, max, mean, sum), missing/freshness, and schema drifts.

Tracks issues, automatically groups similar issues into incidents, allows viewing/filtering/managing incidents, and automates notifications.

Create multiple notification filters for specific scenarios.

Aggregates all data quality metrics.

Provides a variety of pre-configured dashboards (governance, operational, detailed).

Build dashboards using a custom connector for Looker Studio.

Proves data quality with quantifiable numerical KPIs.

Supports running checks on various databases and data platforms.

Defines data quality checks as SQL query templates.

Query results of existing custom data quality checks and import them into the data quality warehouse for KPI integration.

Caters to Data Scientists, Data Engineers, BI Developers, Data Operations, DevOps, and Data Stewards.

Applicable for Data Ingestion, Data Warehousing, Data Lake, Data Sharing, Business Intelligence, and Data Governance.