This course is for data quality stewards who perform data management tasks, such as data quality improvements, data enrichment and entity resolution.
Learn how to
- create and review data explorations
- create and review data profiles
- create data jobs for data improvement
- establish monitoring aspects for your data.
Who should attend
Data Quality Stewards
There currently are no prerequisites for this course.
This course addresses DataFlux Data Management Studio software.
Introduction to DataFlux Methodolgy and Course Flow
Getting Started with DataFlux Data Management Studio
Working Through the PLAN Phase of the DataFlux Methodology
- creating data collections
- designing data explorations
- creating data profiles
- designing standardization schemes
Working Through the ACT Phase of the DataFlux Methodology
- introduce data jobs
- work with data quality nodes including Standardization, Identification Analysis, Right-Fielding, Parsing and Change Case
- work with data enrichment nodes including Address Verification (US/Canada) and Geocoding (self-study)
- work with entity resolution nodes including Match Codes, Clustering and Surviving Record Identification nodes
- examine multi-input/multi-output data jobs
Working Through the MONITOR Phase of the DataFlux Methodology
- introduce business rules and Business Rules Manager
- use business rules in data profiling
- use business rules via tasks in data monitoring jobs
- establish and view data alerts