AI DATA QUALITY
Improve Data Accuracy and Trust with Qlik's Generative AI Data Quality Platform
AI-driven data quality for reliable, analytics-ready insights. Automatically cleanse, validate, and enrich data with AI-driven intelligence.
AI-driven data quality for reliable, analytics-ready insights
Move beyond manual rules. Use Generative AI to proactively detect, understand, and fix data issues at scale.
Automate cleansing and standardization with generative AI
Automatically correct formatting inconsistencies and standardization errors using LLMs that understand semantic context.
Enhance data accuracy with continuous AI validation
Continuously monitor data streams with AI models that adapt to changing data patterns to flag inaccuracies instantly.
Scale quality management
Apply consistent quality rules across your entire data estate without writing complex manual scripts for every source.
How Qlik's generative AI data quality engine works
Step 1 – Ingest and profile data across systems
Step 2 – Use GenAI to detect errors and suggest corrections
Step 3 – Apply automated cleansing and enrichment
Step 4 – Deliver trusted data to AI and analytics platforms
Why choose Qlik for generative AI-driven data quality?
AI you can trust
Oversee AI-driven changes with "human-in-the-loop" validation and strict governance policies.
Quality at enterprise scale
Deploy AI data quality agents across hybrid and multi-cloud environments to drive consistency everywhere.
Simply ask your data
Use natural language prompts to define quality rules, search for anomalies, and generate transformation logic.
Key capabilities of Qlik's GenAI data quality platform
AI-powered anomaly & error detection
Automatically identify outliers, duplicates, and pattern violations that traditional rule-based systems miss.
Automated data standardization and enrichment
Standardize formats (addresses, dates) and enrich records with missing information using external knowledge bases.
Semantic type detection
Use AI to automatically understand if a field contains PII, financial data, or other sensitive categories.
Natural language rule creation
Create complex data quality rules simply by describing them in plain English.
Dynamic remediation
Suggest and apply fixes for dirty data automatically based on learned patterns and user feedback.
Quality health dashboards
Visualize trust scores and quality metrics across all your pipelines.
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
Generative AI data quality FAQs
Traditional tools rely on hard-coded rules. GenAI can understand semantic context, infer intent, and detect complex anomalies that were not explicitly programmed, handling "unknown unknowns" effectively.
Yes, Qlik's platform is built with enterprise security at its core. Data governance policies are enforced, and PII is protected during the AI analysis process.
No, the platform is designed with a low-code/no-code interface and natural language support, making advanced AI capabilities accessible to data stewards and analysts.