Data assessment and data quality assurance
To be methodical with our discussions here, let's look at how data assessment compares or stacks up to data quality (assurance).
Data quality assurance, or often referred to as tidying the data by data scientists, is the process of addressing (perhaps perceived) issues or concerns that had been identified within data. These issues affect the use, quality, and outcome (performance) of a database or data model—data quality, of course, being relative to the proposed purpose of use (of the data, database, or data model).
Categorizing quality
Typically, issues with data quality may be categorized into one of the following areas:
- Accuracy
- Completeness
- Update Status
- Relevance
- Consistency (across sources)
- Reliability
- Appropriateness
- Accessibility
You'll find plenty of data quality categorizing overlap between statistical and non-statistical data. Sometimes, a data quality issue may appear to apply strictly to a particular genre—stat versus non-stat—but after further...
