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SQL Server Integration Services (SSIS) includes a DQS Transformation to correct data from a  connected data source. The DQS Transformation Output column is populated with one of five  status as listed below. You can use a Conditional Split Transformation to separate  out the output records based on its status. You can apply different business logic to each  stream or insert them to a table of file destination for manual processing. To clean data using the SSIS DQS task follow these steps: Open a new SSIS package in Visual Studio Create a new Data Flow Task Add a Source Component to extract the data to be cleansed Add a DQS Cleansing transformation and connect it to the Source Component Output Edit the DQS Cleansing transformation and set the Data Quality connection manager and Data Quality Knowledge Base to use for the cleansing activity Click on the Mapping tab and map the Input Columns to the corresponding Knowledge Base Domain, then click OK Add a Conditional Split tr

The DQS Client allows you to run a Data Quality Project to perform a Data Cleansing Activity.  During the Data Cleansing Activity, DQS detects new, invalid, and correct values based on existing  valid values, Synonyms and Term-Based Relations. The interactive data cleansing allows  you to accept or reject suggestions and corrections that the DQS cleansing activity identified.  Once all records have been processed, you can export the data cleansing results data and  cleansing info. To cleanse data using DQS knowledge follow the steps below. Open the Data Quality Services client and connect to the DQS instance Click on New Data Quality Project button from the Data Quality Projects section Type a name and description for the Data Quality Project Select Suppliers from the Use Knowledge Base drop-down list Click on Cleansing under the Select Activity list, then click Next Select Excel File from the Data Source drop-down list Browse and select the SuppliersDomain.xlsx file Se

A matching knowledge can be useful when building and maintaining unique, unduplicated lists as Customers, Products, and Suppliers. You can use a DQS knowledge base to maintain  unduplicated lists of Suppliers for example, by building a DQS knowledge base and setting up  a DQS Matching Policy. During a matching activity, DQS evaluates the matching rules setup  in the Matching Policy against the records being processed. It then provides a matching score  for each record. If the matching score is greater than the minimum matching score configured  then the two records are considered matches. Create a matching policy Follow the steps below to create a matching policy. Open the Data Quality Services client and connect to the DQS instance Click on Open Knowledge Base button from the Knowledge Base Management section Select Suppliers from the Knowledge Base list Click on Matching Policy under the Select Activity list, then click Next Select Excel file from the Data Source dro

The Data Quality knowledge base can be populated using interactive or computer-assisted  processes. During the installation process of SQL Server 2016 Data Quality Services, you can  choose to install the Data Quality Client to interactively create and maintain a DQS knowledge  base. The DQS Client can be used to create data domains and add domain values manually or  by importing them from an Excel spreadsheet or a data cleansing project. In addition to interactively maintaining data through the DQS Client, you can maintain data  by running a computer-assisted activity known as Knowledge Discovery. The Knowledge Discovery  activity analyzes a sample of data that is used for data quality criteria. The algorithms  built into DQS look for data inconsistencies and syntax errors and then propose changes to the  data. You can then approve or reject the proposed changes or apply corrections manually. To perform a Knowledge Discovery activity for the SuppliersName in the Suppliers Kno

A knowledge base consists of domains. Each domain represents the data in a data field. Each  value in a data field or domain is known as a domain value. DQS provides the ability to validate,  cleanse, match and deduplicate values from any dataset against domain values in the DQS  Knowledge Base. Domains are created by performing a domain management activity. To create a DQS domain  for valid supplier names in the Suppliers Knowledge Base, follow these steps: Open the Data Quality Services client and connect to the DQS instance Click on Open Knowledge Base button from the Knowledge Base Management section Select Suppliers from the Knowledge Base list Click on Domain Management from the Select Activity list, then click Next Click on the Create A Domain icon Type the following information on the Create Domain window: Domain Name: SupplierName Description: List of valid Supplier Names Select String from the Data Type drop-down list Check the Use Leading Values option C

Data Quality Services can be used for a wide variety of use cases to ensure that the data is suited  for business usage. Data can be cleansed manually or computer-assisted by establishing matching  and cleansing rules or by leveraging cloud-based services of reference data providers. Incorrect or bad data is often a symptom of weak or lack of data validation rules during data  entry. Other forms of inconsistent data can arise from data integration processes and data corruption.  Bad, incorrect and inconsistent data create inefficiencies in the business processes and  can lead to loss of revenue and increased operational costs. Bad data is often amplified during  data analysis, reporting, data mining and data warehousing. The main use cases for a DQS Knowledge Base involves the need for business users, information  workers or IT professionals to create, maintain and execute data quality operations  resulting from data incompleteness, inconsistency, inaccuracy, invalidity, dupli

Install DQS A Data Quality Knowledge Base (DQKB), is a repository of knowledge and metadata that describes  and defines the data domains and rules to cleanse and match data. The DQKB is used in  an organization to identify and correct invalid or incorrect data to ensure that data is consistent,  and trustworthy. A DQKB stores domain values that have been validated along with terms,  spelling errors, business rules, and reference data that can be used to perform data quality  cleansing and matching actions on the data. A Data Quality Services knowledge base can be created as a new and empty knowledge  base or by importing an existing DQS knowledge base from a DQS (.dqs) file. In our example,  we will be creating a Suppliers DQS Knowledge base. To create a new and empty DQS knowledge  base, follow these steps: Open the Data Quality Services client and connect to the DQS instance Click on New Knowledge Base button from the Knowledge Base Management section Type the following

The installation of DQS is a two-step process which consists of running the SQL Server 2016  Setup wizard and a command prompt based application called the Data Quality Server Installer.  To start the installation process, locate the SQL Server 2016 installation media and run  Setup.exe. This will open the SQL Server 2016 Installation Center. Click on Installation menu option  and then click on New SQL Server Stand-Alone Installation Or Add Features To An Existing  Installation to launch the SQL Server 2016 Setup wizard. Click Next. On the next window,  select the installation type option Perform A New Installation of SQL Server 2016, if SQL Server  2016 Database Engine has not been previously installed. In this case, you will also need to  install SQL Server 2016 Database Engine as it is required for Data Quality Services. If there is already an installation of SQL Server 2016 Database Engine and you would like to  add DQS to the existing instance, select Add Features To An Ex