Data quality services sql server 2016
Data Quality Services by MicrosoftSQL Server Data Quality Services (DQS) is a knowledge-driven data quality product. DQS enables you to build a knowledge base and use it to perform a variety of critical data quality tasks, including correction, enrichment, standardization, and deduplication of your data. DQS enables you to perform data cleansing by using cloudbased reference data services provided by reference data providers. DQS also provides you with profiling that is integrated into its data-quality tasks, enabling you to analyze
the integrity of your data
Introduction to Data Quality Services
The data-quality solution provided by Data Quality Services DQS enables a data steward or IT professional to maintain the quality of their data and ensure that the data is suited for its business usage. DQS is a knowledge-driven solution that provides both computer-assisted and interactive ways to manage the integrity and quality of your data sources. DQS enables you to discover, build, and manage knowledge about your data. You can then use that knowledge to perform data cleansing, matching, and profiling. You can also leverage the cloud-based services of reference data providers in a DQS data-quality project. 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.
The data-quality solution provided by Data Quality Services (DQS) enables a data steward or IT professional to maintain the quality of their data.
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Answering that Need with DQS
For data to be usefully analyzed, it must be consistent, accurate, and trustworthy. When incoming data is non-uniform, duplicated records are created and the data starts losing its value. Feodor Georgiev provides a thorough walkthrough on setting up DQS and creating the rules it uses to function as a first step towards data cleansing. The amount of data that we generate and work with daily keeps increasing in volume. We are responsible for keeping this data clean so we can get the most value out of its analysis. For example, we can imagine how hard it would be to perform aggregations on sales per vendor if the vendor is identified by name, and most of the vendors are entered with several different spellings in their name, depending on the data source. These flaws can easily be caused either by incorrect data entry, system failure, data corruption, or when Master Data Services are not used within the enterprise.