Data Quality Rules
The Data Quality Rules module reveals what data quality rules are, why they are used and what benefits they can bring to an organisation.
Explanations of some of the more complex functions used within rules are provided, along with industry specific examples.
The module describes how data quality rules can be used for quantification of data quality errors, comparison across multiple data sets and enhancing reporting options.
Learn what data quality rules are.
Learn why data quality rules are useful.
Learn what value and volume quantification is.
Learn how data quality rules are used to refine scope, compare across multiple data sets, and enhance reporting options.
Learn how data quality rules differ in complexity when using options such as;
- Regular expressions
- Domain tables
- Joining data
Who is Suitable?
Knowledge of Data Profiling would be useful but is not necessary mandatory.
As this is a data assessment and control module the content is especially useful for data profilers and data analysts but would also be a useful for a wide spectrum of data roles; including but not exclusive to data owners, data modellers, data architects and data lineage analysts.
- What are data filters?
- What are DQ Rules?
- What is the difference between the value and volume of data quality errors?
- What are cross system comparisons?
- Why do DQ rules require significant investment from business experts?
- Who writes data quality rules?
- What is the difference between data profiling and data quality rules?
- What are data quality targets and thresholds?
An Interactive Presentation
10+ Interactive Questions
3 Final Practice Tests – 30 questions over 30 minutes
Final Examination – 60 questions over 30 minutes