The Definitive Guide to Data Quality

Put simply, data quality is the ranking of certain data according to accuracy, completeness (all columns have values), and timeliness. When you are working with large amounts of data, the data is usually acquired and processed in an automated way. When thinking about data quality, it is good to discuss:AccuracyWhether the data captured was actually correct. For example, an error in data entry causing multiple zeros to be entered ahead of a decimal point, is an accuracy issue. Duplicate data is also an example of inaccurate data.CompletenessWhether all records captured were complete—i.e., there are no columns with missing information. If you are managing customer records, for example, make sure you capture or otherwise reconcile a complete customer details record (e.g., name/address/phone number). Missing fields will cause issues if you are looking for customer records in a specific zip code, for example.TimelinessTransactional data is affected by timeliness.