Initiate the quality assessment of a data source at the environment, table or column level from the metadata within erwin Data Catalog.
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 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.
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.
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.
Leverage behavioral analysis through data observability capabilities to track data trends over a previous time and forecast future data trends for business operational use.
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.
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.
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.
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.
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 <a href="/products/erwin-data-intelligence/data-marketplace.aspx">erwin
Data Marketplace</a>.
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.
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.
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.
