Understanding data quality

Data quality is about which data is good for business. Data quality can be different based on source systems, reliability of incoming data, data entry, and so on. Data quality is important because bad data would cause bad business. Bad data quality is one of the barriers of Business Intelligence systems. In other words, one of the building blocks of a robust Business Intelligence system is ensuring the data quality is high.

Data quality issues can be divided into the following categories:

• Uniqueness

• Validity

• Accuracy

• Standardization

• Completeness

Uniqueness is about multiple copies of the same data, such as Bill Gates and Bill Geates. In this sample, both names seem to be the same. Validity is about different kinds of validation for data, for example, range validity of age is something between 0 and 150 (if there is someone who will live that long). Accuracy is about the correctness of data, for example, the wrong opening date for a store will be considered bad data and...