erwin Data Quality

Reliable, trusted data demands organizational visibility of data quality insights and well-integrated, augmented data quality tools to ensure data quality remains high. erwin Data Quality ensures organizations have a better view and understanding of data quality, as well as the capabilities to improve it through automated data profiling and data quality scoring, consumer-friendly data quality discovery, and cross-platform data observability and alert triage. Ensure your data is trusted and AI-ready with erwin.

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AI demands reliable data and trusted models

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Build data trust and ensure data is AI-ready

erwin Data Quality, part of erwin Data Intelligence, delivers integrated, automated data quality visibility and the augmented data quality tools to support data and AI governance, fuel data trust and keep the quality of critical data sources reliable. Based on the DQLabs augmented data quality platform, recognized within the Gartner 2024 Magic Quadrant for Augmented Data Quality Solutions, erwin Data Quality leverages metadata stored within erwin Data Catalog to automate data profiling and data quality assessment producing data quality scores shared widely throughout erwin Data Intelligence. Cross-platform data observability, alert triage and self-learning platform capabilities combined with consumer-friendly data quality discovery and quality issue management enable data quality engineers, governance teams and business stakeholders to efficiently work together to ensure high-quality data pipelines.

Choose intelligence-integrated data quality automation

Boost data intelligence and manage the entire data quality lifecycle with erwin Data Quality. Combine data intelligence, automation and an integrated, modern augmented data quality platform with accessible data quality tools to automate the data profiling, quality scoring, ongoing quality monitoring, issue response and data remediation of your most critical data sources. Ensure reliable data for AI use, improve data quality, reduce operational costs and risks, and extend data quality visibility across your enterprise to guide decision-making surrounding data use and to fuel data trust.

Metadata-driven data quality assessment

Initiate the quality assessment of a data source at the environment, table or column level from the metadata within erwin Data Catalog.

Automated data discovery and profiling

Use AI/ML-enabled discovery capabilities within erwin Data Quality to detect data patterns and auto-create business rules for data quality assessment. Auto-profile based on business rules and auto-generate data quality scores.

Data quality visibility for all

Data quality scores appear not only in erwin Data Quality, but throughout erwin Data Intelligence – alongside data catalog metadata, within data lineage, impact analysis and mind map visualizations and can be used as a weighted component inside the automated data value scoring within erwin Data Marketplace.

Cross-platform data observability

Observe data quality across your organization and deploy continuous monitoring on key data sources and critical datasets supporting AI use to alert you if data quality drifts beyond acceptable thresholds so you can take action. Leverage the platform’s self-learning capabilities to evolve data quality measures based on alert response.

Consumer-friendly data quality discovery

Explore data quality through discovery capabilities similar to online shopping sites today. View assets, tables, views, attributes, reports and more filtering by data quality scores, alerts, domains, applications and other criteria.

Data behavioral analysis

Leverage behavioral analysis through data observability capabilities to track data trends over a previous time and forecast future data trends for business operational use.

Data remediation tools

Leverage data remediation tools such as reference or ML-based curation and parsing to intelligently clean and enrich bad data. Integrate with third-party cleansing and enrichment tools and generate coding scripts for use in ETL and data pipeline management solutions.

Data remediation collaboration

Triage issues arising from data observability alerts using built-in issue management capabilities. Extend alert communication and issue collaboration beyond erwin with integration to email, Slack, Teams, JIRA and ServiceNow.

Data quality dashboards

Drill into detailed data quality status, profile assessments, correlations and platform usage through customizable analytics dashboards with erwin Data Quality. Additionally, view a data quality overview within the erwin Data Catalog dashboard.

Easy data source connectivity

Choose from an out-of-the-box library of data source connectors to industry standard data sources including Amazon Redshift, Databricks, Google BigQuery, Microsoft Azure Synapse, SQL Server, Oracle, Snowflake and more.

erwin Data Quality Tour

erwin Data Intelligence delivers the organizational visibility and data quality tools and automation to understand and improve data quality. Take a look:

Data Quality Visibility

Raise the visibility and understanding of source data quality through dedicated dashboards for data quality stakeholders and integrated data quality scoring throughout erwin Data Intelligence. See data quality scores within the data catalog, in data lineage and mind maps, and when conducting impact analysis. Data quality scores can also be leveraged as one component within automated data value scoring in erwin Data Marketplace.

Data Profiling and Analysis

Leverage data catalog metadata to start a quality assessment of a new data source. Then use AI/ML-enabled auto-discovery and profiling inside erwin Data Quality to detect data patterns and automatically generate data quality scoring. Shared throughout erwin Data Intelligence, understandable data quality scores guide data usage and data quality advancement efforts for IT, data governance teams and business users.

Consumer-Friendly Data Quality Discovery

Explore data quality with search and filter capabilities similar to ones you would find in online consumer shopping websites. View assets, tables, views, attributes, reports and more filtering by data quality scores, alerts, domains, applications, etc. to quickly zero-in on the information needed.

Cross-Platform Data Observability

Ensure reliable data with cross-platform data observability that continuously monitors key data sources and critical datasets supporting AI use. Out-of-the-box quality measures and auto-deployment during profiling, combine with no-code advanced anomaly detection, to alert you if data drifts beyond acceptable thresholds. So, you can quickly triage alerts and act on issues accordingly. Self-learning platform capabilities evolve quality measures based on your alert response for efficient future monitoring.

Data Remediation

Leverage data remediation tools including reference or ML-based curation and parsing rules to intelligently clean and enrich bad data. Integrate with additional third-party cleansing and enrichment solutions as needed. Generate coding scripts for use in ETL and other data pipeline management tools to speed issue resolution.

Data Quality Collaboration

Raise data quality literacy through scoring, visualizations and dashboards. Use conversational tools and built-in issue tracking to support collaborative efforts to improve data quality. Send alert notifications and extend issue workflows through email, Microsoft Teams, Slack, JIRA and ServiceNow. Keep stakeholders engaged and working together in data quality initiatives.

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The Value of Integrated Data Quality

High-quality data is critical for businesses when it comes to improving business outcomes, streamlining operational costs, and reducing overall risk. Businesses are looking for deepened integrated data quality and visibility capabilities so that IT, data governance teams and business users across the board are able to ensure appropriate data usage and build data trust.

Stewart Bond IDC Research Director for Data Integration and Data Intelligence Software

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