Data quality in a data warehouse is critical (this sounds so obvious and axiomatic), more so than in an operational system. Strategic decisions made on the basis of information from the data warehouse are likely to be more far-reaching in scope and consequences. Let us list some reasons why data quality is critical. Please examine the following observations. Improved data quality:
boosts confidence in decision making,
enables better customer service,
increases opportunity to add better value to the services,
reduces risk from disastrous decisions,
reduces costs, especially of marketing campaigns,
enhances strategic decision making,
improves productivity by streamlining processes, and
avoids compounding effects of data contamination.
As an IT professional, you have heard of data accuracy quite often. Accuracy is associated with a data element. Consider an entity such as customer. The customer entity has attributes such as customer name, customer address, customer state, customer lifestyle, and so on. Each occurrence of the customer entity refers to a single customer. Data accuracy, as it relates to the attributes of the customer entity, means that the values of the attributes of a single occurrence accurately describes the particular customer. The value of the customer name for a single occurrence of the customer entity is actually the name of that customer. Data quality implies data accuracy, but it is much more than ...