Dataquality checks you need to keep in mind while validating your data, ensuring it is reliable, and can be trusted before reporting or performing analysis:
- Data Completeness - check whether the data has all required data fields and check missing values.
- Data Consistency - make sure data is uniform across different sources
- Data Accuracy - compare the correctness of the data with already known or expected values.
- Data Timelines - check whether the data is up-to-date within expected timelines.
- Data Relevance - is the data relevant to the business or its requirements and does it meet its purpose.
- Data Integrity - is the data logically consistent and adhering to the defined business rules in place. #data #dataengineering #dataanalytics #dataintegrity #datascience #dataengineers #datascientists #dataanalysts
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