Alteryx Day 74 – Data Quality Part II Explained #alteryx #alteryxtutorial #youtubevideo

Welcome to Day 74 of the 100 Days of Alteryx Learning Series!
In today’s video, we continue with Data Quality in Alteryx, focusing on advanced validation techniques, rule-based cleansing, exception handling, and automated quality monitoring.

Building on Part I, this session helps you move from identifying issues to creating robust data quality frameworks for enterprise workflows.

What You Will Learn Today
📋 Advanced Data Quality Techniques

• Rule-based data validation
• Data standardization methods
• Exception and anomaly handling
• Creating reusable quality checks

🛠 Data Validation & Cleansing

• Validating business rules
• Detecting outliers and anomalies
• Handling duplicate and inconsistent data
• Creating automated quality controls

📊 Data Quality Monitoring

• Measuring quality KPIs
• Tracking quality issues over time
• Building exception reports
• Creating audit-ready workflows

Hands-On Activities

✅ Apply validation rules to datasets
✅ Build automated exception handling
✅ Detect and remove duplicates
✅ Create quality scorecards
✅ Generate data quality summaries

Real-World Use Cases

• Enterprise data governance
• Customer and product master data validation
• Data warehouse quality assurance
• ETL pipeline monitoring
• Compliance and reporting controls

Best Practices

• Centralize business validation rules
• Build reusable quality components
• Automate exception reporting
• Monitor quality continuously
• Document transformation and validation logic

By the End of This Video, You Will Be Able To:

• Implement advanced data quality checks
• Build automated validation workflows
• Handle data exceptions effectively
• Create scalable data quality frameworks

Tools Covered

• Data Cleansing Tool
• Formula Tool
• Filter Tool
• Summarize Tool
• Unique Tool
• Browse Tool

Perfect For

Data analysts, BI developers, data engineers, and Alteryx users building reliable and production-ready data pipelines.

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