on to Data Quality Assessment & Control
The module explains the typical methods used for data quality assessment such as data profiling, data quality rules data quality dimensions, data quality KPIs and how these are monitored through data quality management information.
The module then describes how methods such as data quality transformations, standardisations and domains are used to help improve data quality.
The module then finishes off by detailing how Data Lineage Testing & Data Audit, Data Quality Issue Management and Data Management Incentivisation are used to control and maintain the quality of the organisations data.
Learn what data quality assessment & control is.
Learn what data profiling is.
Learn what data quality rules are.
Learn what data quality dimensions are.
Learn what data quality KPIs are.
Learn what data quality management information is.
Learn what data transformations & standardisations are.
Learn what data quality domains are.
Learn what data lineage testing & data audit is.
Learn what data quality issue management is.
Learn what data management incentivisation is.
Who is Suitable?
No prior knowledge is required so all Data Practitioners can undertake the course.
As this is a foundations of data management module the content is especially useful poeple who are looking for a steady paced introduction to data management. The module is also very useful for managers who need to know the fundamental information and theories in order to understand data specialists work.
- What is data profiling?
- What are some typical outputs from data profiling?
- What are DQ Rules?
- What are data filters?
- What are data quality targets and thresholds?
- What is a KPI?
- Can you give an example of a volume KPI and a value KPI?
- What is DQMI?
- How are domain tables produced?
- What is Data Management incentivisation?
An Interactive Presentation
10+ Interactive Questions
3 Final Practice Tests – 30 questions over 30 minutes
Final Examination – 60 questions over 30 minutes