Data quality refers to data attributes such as completeness, validity, consistency, timeliness, accuracy, and relevance. Data warehousing, business intelligence, and CRM (customer relationship management) are some of the many applications that depend on a high degree of data quality.
To measure data quality, perform preliminary data profiling techniques and analyze the relationships between the data. Then design a set of data quality rules based on that analysis and compare the data against those rules. To improve data quality, it is imperative to determine how any "dirty data" enters the system, and to take measures to prevent it from entering the system in the future. Data standardization is one way to maintain conformity to the data quality rules.
Ensuring data quality with processes such as data profiling usually requires intensive processing of large volumes of data, which can hog resources and delay downstream applications. Data warehousing and other applications dependent on high data quality pose the same challenges. DMExpress™ is the fastest, most reliable, and cost efficient enterprise data integration software.* It speeds applications and delivers information to business users when they need it. DMExpress provides a performance solution for optimizing your data quality tools and other critical applications.
* Based on actual comparisons against leading competitors, including Informatica, IBM DataStage, and others. Claims are based on results of specific tests and may vary depending on environment.
