Consistency measures whether data follows an organization’s rules, constraints, formats, and representations across records and sources. It is one dimension of data quality, alongside accuracy, completeness, and timeliness. section 11.3.2
Practical consistency checks include:
- Standardizing timestamps from different sources to a specific time zone or using the local time consistently. section 11.3.2
Daily forecast validation
A daily forecast dataset can be tested for unique dates, humidity between 0 and 100, nonnegative wind speed, and absence of missing values. Embedding these checks in transformation steps catches problems before downstream users encounter them. section 11.3.2
