Data quality scorecards have become very popular and many organizations are starting to build them. What they have found is that the path to a meaningful and useful DQ Scorecard is riddled with traps and obstacles.
This online training course gives comprehensive treatment to the processes and practical challenges of data quality scorecarding.
It starts with a few real, live use cases that showcase what a scorecard can do for a company when done right. Systematic treatment of various DQ scorecard challenges is given. Then the course proceeds to the ins and outs of the successful DQ scorecard, from the underlying data model to the effective processes that need to be set up in order to produce the scorecard efficiently. Multiple examples to illustrate every important point are provided in the class.
You will learn:
- The methodology behind data quality metrics calculations
- The best way to organize data quality related metadata collected during typical data quality projects
- Effective data visualization techniques to depict data quality measurements
- Typical pitfalls that accompany data quality scorecard implementation and how to avoid them
- How to achieve scorecard adoption and usage by the business users
This course is geared towards:
- Data quality practitioners
- Data stewards and data governance practitioners
- IT analysts, business analysts, and everyone else involved in data quality management
- Developers tasked with DQ Scorecard creation