Monday, September 18, 2017

Data Quality Services in SQL Server

We know that data is the business asset for every organisation which is audited and protected. So, data quality and data cleansing have always been the biggest challenges to any enterprise that deals with data. In SQL Server, Data Quality Services (DQS) is a data quality solution to maintain the quality of data which ensure that the data is suited for business usage.
Simply, Data Quality Services is a knowledge-driven data quality product which enables us to build a knowledge base and use it to perform a variety of critical data quality tasks, including correction, enrichment, standardization, monitoring, and de-duplication of our data. Apart from this, it enables to discover, build, and manage knowledge about data. 
High-quality data is critical to the efficiency of businesses. 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 data, resolving problems caused by bad data in business intelligence or data warehouse workloads.
DQS Features - Data Quality Services provides the following features to resolve data quality issues-
  1. Data Cleansing: In this activity, DQS modify, remove, or standardized of data that is incorrect or incomplete, using both computer-assisted and interactive processes.
  2. Matching: In this activity, DQS identify of duplicates records in a rules-based process that enable to determine what constitutes a match and perform de-duplication.
  3. Reference Data Services: This will verify the quality of data using the services of a reference data provider. We can use reference data services from Windows Azure Marketplace DataMarket to easily cleanse, validate, match, and enrich data.
  4. Profiling: This enables to analyze 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. We can create a data quality solution in which profiling is just as important as knowledge management, matching, or data cleansing.
  5. Monitoring: Monitoring activity provides the ability to verify that data quality solution is doing what it was designed to do.
  6. Knowledge Base: Data Quality Services is a knowledge-driven solution that analyzes data based upon knowledge that builds with DQS. This enables to create data quality processes that continually enhances the knowledge about data and in so doing, continually improves the quality of data.

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