Data quality

It is important that the stored data is useful, error-free, and meant for its intended purpose. High-quality data gives actionable insights, whereas poor-quality data leads to poor analysis, and hence, to poor decisions. Errors in the data in these industries can break regulations, leading to legal complications. The following factors can help to evaluate data quality:

• Completeness: Are there values missing in the data set?
• Validity: The data matches the rule set. 
• Uniqueness: The data has minimal redundancies. 
• Consistency: The data is consistent across various data stores. 
• Timeliness: The data represents reality from a required point in time. 
• Accuracy: The degree to which the result of a particular measurement, calculation, or specification conforms to the correct value.