2. Topic
■ What is "high quality data"?
■ What are data quality expectations?
you, people or businesses you know have
■ Business issues and data quality
How to you deal with it?
■ What happens when you ignore it?
4. Dimensions
■ completeness – data provided
■ accuracy – reflecting real world
■ credibility – regarded as true
■ timeliness – up-to-date
■ consistency – matching facts across datasets
■ integrity – valid references between datasets
... and there are more
5. Fallacies
■ “good data are error-free and valid”
■ “improving quality means cleansing”
■ “it is IT problem”
■ “it can be fixed”