Data Quality Services
Incorrect data can result from user entry errors, corruption in transmission or storage, mismatched data dictionary definitions, and other data quality and process issues. Aggregating data from different sources that use different data standards can result in inconsistent data, as can applying an arbitrary rule or overwriting historical data. Incorrect data affects the ability of a business to perform its business functions and to provide services to its customers, resulting in a loss of credibility and revenue, customer dissatisfaction, and compliance issues. Automated systems often do not work with incorrect data, and bad data wastes the time and energy of people performing manual processes. Incorrect data can wreak havoc with data analysis, reporting, data mining, and warehousing.
High-quality data is critical to the efficiency of businesses and institutions. An organization of any size can use DQS to improve the information value of its data, making the data more suitable for its intended use. A data quality solution can make data more reliable, accessible, and reusable. It can improve the completeness, accuracy, conformity, and consistency of your data, resolving problems caused by bad data in business intelligence or data warehouse workloads, as well as in operational OLTP systems.
DQS enables a business user, information worker, or IT professional who is neither a database expert nor a programmer to create, maintain, and execute their organization’s data quality operations with minimal setup or preparation time.
DQS provides the following features to resolve data quality issues.
Data Cleansing: the modification, removal, or enrichment of data that is incorrect or incomplete, using both computer-assisted and interactive processes. For more information, see Data Cleansing.
Matching: the identification of semantic duplicates in a rules-based process that enables you to determine what constitutes a match and perform de-duplication. For more information, see Data Matching.
Reference Data Services: verification of the quality of your data using the services of a reference data provider. You can use reference data services from Windows Azure Marketplace DataMarket to easily cleanse, validate, match, and enrich data. For more information, see Reference Data Services in DQS.
Profiling: the analysis of a data source to provide insight into the quality of the data at every stage in the knowledge discovery, domain management, matching, and data cleansing processes. Profiling is a powerful tool in a DQS data quality solution. You can create a data quality solution in which profiling is just as important as knowledge management, matching, or data cleansing. For more information, see Data Profiling and Notifications in DQS.
Monitoring: the tracking and determination of the state of data quality activities. Monitoring enables you to verify that your data quality solution is doing what it was designed to do. For more information, see DQS Administration.
Knowledge Base: Data Quality Services is a knowledge-driven solution that analyzes data based upon knowledge that you build with DQS. This enables you to create data quality processes that continually enhances the knowledge about your data and in so doing, continually improves the quality of your data.