Data Quality Dimensions
The Data Quality Dimensions module describes what a data quality dimension is and how these dimensions are relevant to data quality.
An explanation is given to what data quality dimensions tell you and the implied hierarchy of DQ dimensions.
The module explains each of the different dimensions and key information about that dimension, such as completeness, uniqueness and consistency etc.
Learn what data quality dimensions are.
Understand what data quality dimensions tell you.
Understand the implied hierarchy of data quality dimensions.
Learn the specific dimensions of data quality such as:
Who is Suitable?
Knowledge of Data Quality Rules 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.
- Why is it useful to split data quality into dimensions?
- Can you explain the difference between consistency and persistency?
- Why must accuracy be checked against an external or trusted data?
- What is meant by timely data?
- What are data quality targets and thresholds?
An Interactive Presentation
10+ Interactive Questions
3 Final Practice Tests – 30 question over 30 modules
Final Examination – 60 questions over 30 modules