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Data Quality Analysis

Accuracy within your data warehouse is very important. Your warehouse should become the single source of information for reporting within your organisation and the users of this information should be able to trust it implicitly. If any errors creep in to the environment then this trust can be undermined. Even worse, once a level of trust has been established, erroneous reports could be generated and acted upon without the error being identified.

Assessing the consistency of the data in your source systems is an important task that should be conducted at an early stage of development. Only by understanding the errors, ommissions and inconsistencies that exist can you assess their significance and develop strageties for handling them.

You will then be in a good position to build robust validation routines that will form part of the data import process. Any data failing the checks must be logged, excluded and understood before the import can be considered complete. This includes missing data – perhaps one of your stores has not submitted data for a day's trading – you need to know this before you act on a report that shows a drop in sales.