AUTOMATED DATA QUALITY
Deliver Accurate, Trusted Insights with Qlik's Automated Data Quality Platform
Improve accuracy and reliability with comprehensive data quality automation. Detect, correct, and monitor data issues at scale for confident decision-making across analytics and AI.
Power every decision with reliable, high-quality data
Ensure data accuracy and consistency with automated quality controls that continuously profile, validate, and cleanse data across your enterprise, eliminating errors before they impact decisions.
Accelerate data validation across pipelines and systems
Apply automated quality checks throughout data workflows, catching and correcting issues in real time before data reaches analytics platforms, reports, or AI models.
Automate data cleansing and error detection at scale
Leverage intelligent rules engines and machine learning to identify anomalies, standardize formats, and correct errors automatically across millions of records without manual intervention.
Support confident decisions with consistent, governed data
Establish enterprise-wide quality standards that ensure every team works with trustworthy data, enabling confident decisions based on accurate, validated information.
How does Qlik's automated data quality solution work?
Step 1 - Connect to multiple data sources for profiling
Step 2 - Automatically detect and correct data anomalies
Step 3 - Validate and standardize data
Step 4 - Monitor quality metrics and governance rules continuously
Why choose Qlik for automated data quality?
Complete visibility and control over data quality
Track data quality issues from origin through resolution with automated lineage that shows exactly where problems occur, enabling rapid diagnosis and preventing future issues.
AI-powered quality controls that adapt and learn
Leverage machine learning that automatically identifies data patterns, suggests quality rules, detects anomalies, and adapts validation logic as your data evolves.
Quality enforcement everywhere data flows
Apply consistent quality controls across ETL pipelines, databases, cloud platforms, and analytics tools with unified rules that follow data throughout its lifecycle.
Collaboration tools for technical and business users
Enable data stewards to define business quality rules while providing engineers with powerful automation and integration capabilities through intuitive, role-appropriate interfaces.
Trusted for mission-critical data quality
Join organizations that process billions of records daily with Qlik's quality platform, ensuring accuracy for financial reporting, regulatory compliance, and business intelligence.
Key capabilities of Qlik's automated data quality platform
AI-driven data profiling and cleansing
Automatically analyze data distributions, identify patterns, and detect quality issues with intelligent profiling that understands your data's structure and semantics.
Anomaly detection and correction
Identify outliers, duplicates, and inconsistencies using statistical analysis and machine learning, with automated correction that applies standardization rules and business logic.
Metadata management and data lineage visualization
Track data quality metrics through visual lineage maps that show transformations, validations, and quality checks applied at each step of data movement.
Role-based governance and audit trails
Implement fine-grained access controls for quality rules and maintain comprehensive audit logs that document all quality issues, corrections, and approval workflows.
Integration with ETL, BI, and data catalog tools
Embed quality checks within existing data pipelines, connect with business intelligence platforms, and sync quality metrics with data catalogs for unified visibility.
Enterprise-grade security and compliance
Protect sensitive data during quality processing with encryption, data masking, and compliance controls that meet regulatory requirements for data handling.
Trusted by leading enterprises worldwide
What our customers say
We needed to consolidate data in one place, from heterogeneous sources, updated in almost real-time. That’s what Qlik enables for us.
Connect to 500+ data sources with Qlik’s analytics integrations
SAP
Adobe
IBM
AWS
MySQL
Jira
Azure
MS SQL
Apache
Mongo DB
SAP
Adobe
IBM
AWS
MySQL
Jira
Azure
MS SQL
Apache
Mongo DB
SAP
Adobe
IBM
AWS
MySQL
Jira
Azure
MS SQL
Apache
Mongo DB
SAP
Adobe
IBM
AWS
MySQL
Jira
Azure
MS SQL
Apache
Mongo DB
SAP
Adobe
IBM
AWS
MySQL
Jira
Azure
MS SQL
Apache
Mongo DB
Oracle
Salesforce
Workday
Apache Iceberg
CircleCI
Zendesk
Snowflake
Databricks
OpenAI
Intuit
Oracle
Salesforce
Workday
Apache Iceberg
CircleCI
Zendesk
Snowflake
Databricks
OpenAI
Intuit
Oracle
Salesforce
Workday
Apache Iceberg
CircleCI
Zendesk
Snowflake
Databricks
OpenAI
Intuit
Oracle
Salesforce
Workday
Apache Iceberg
CircleCI
Zendesk
Snowflake
Databricks
OpenAI
Intuit
Oracle
Salesforce
Workday
Apache Iceberg
CircleCI
Zendesk
Snowflake
Databricks
OpenAI
Intuit
Resources
Automated data quality FAQs
Automation continuously monitors all data at scale using predefined rules and machine learning, catching issues immediately and applying corrections automatically rather than relying on periodic manual sampling.
Yes, data stewards can define business quality rules through intuitive interfaces that translate requirements into technical validation logic, while IT maintains control over technical implementation and integration.
The platform can automatically correct standard issues, quarantine problematic records for review, generate alerts for data stewards, and provide detailed error reports with suggested remediation actions.
Machine learning continuously adapts to changing data patterns, automatically updating quality rules and alerting you to significant shifts that may require human review or policy adjustments.