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Data Quality Platform Built For Scale
Automate and refine data quality controls with advanced machine learning, minimizing manual effort while delivering data confidence.
Seamless integration with your data stack
enterprise Data quality challenges
Managing enterprise data quality at scale is challenging
Data quality is reactive
Data Quality is often driven by end users spotting bad data points. We need to be proactive about our data quality.
Teams aren't collaborating
Subject matter experts know their data the best – but rely on data teams to deliver high quality data. Business teams and data teams need to collaborate and co-own data quality.
Lack of measurement
We can’t improve what we don’t measure. We need a comprehensive way of establishing a baseline for and measuring impact of data quality across the data ecosystem.
Qualytics for enterprise data teams
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Centralized Repository of all data quality rules – technical data quality to complex business logic – in one place
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Scalable Runtime Environment that scales to enterprise-grade scale, drives downstream remediation workflows and measures impact of DQ
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AI-generated Rules are suggested by Qualytics’ ML engine by analyzing historic data’s shapes and patterns, reducing the effort to manage DQ at enterprise grade by 90%.
Data teams after qualytics
How your team benefits
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Automated rules reduce the effort of writing rules for expectations and calibration by over 90%.
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Easy downstream remediation workflow initiation and Enrichment capabilities remove the need to replicate work to hunt for anomalies, significantly reducing the time to resolution and elevating explainability & root cause analysis
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Centralized rule repository for all technical and business data quality rules gives a single pane of glass for all expectations and calibration for fit.
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Highly usable interface superserves business users to be involved in DQ, while the fully fledged API superserves data engineers to automate workflows within their existing data tooling.
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