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Siebel Data Quality Administration Guide > Enabling and Disabling Data Matching and Data Cleansing > In the Siebel application there are various levels at which you can enable or disable data cleansing and data matching as summarized in Table 9. In some of these views you can also specify the vendor used for data cleansing or data matching (the type). The table also shows who is most likely to set the parameters in each view.Table 9. Levels of Enabling and Disabling Data Matching and Cleansing Screen and View Setting/Parameter Value Parameters Set By Values set at the enterprise level Administration - Server Configuration > Enterprises select Data Quality Enable buttonDisable button Application administrator Administration - Server Configuration > Enterprises > Parameters DeDuplication Data TypeData Cleansing Type Data Matching Vendor NameData Cleansing Vendor Name Values set for Data Quality SettingsNOTE: These settings affect all the servers Administration - Data Quality > Data Quality Settings Enable DataCleansingEnable DeDuplication Yes/No Data administrator Values set at the object manager level Administration - Server Configuration > Servers > Select component Data Quality Manager then click > Parameters tab Data Cleansing Enable FlagData Cleansing TypeDedDuplication Enable FlagDeDuplication Data Type True/FalseData Cleansing Vendor NameTrue/FalseData Matching Vendor Name Data administrator Administration - Server Configuration > Servers > Select object manager of application, for example, Sales Object Manager (ENU) then click Parameters tab Data Cleansing Enable FlagData Cleansing TypeDedDuplication Enable FlagDeDuplication Data Type True/FalseData Cleansing Vendor NameTrue/FalseData Matching Vendor Name Values set at the user level Tools > User Preferences > Data Quality Enable DataCleansingEnable DeDuplication Yes/No Data steward and end usersNOTE: A data steward monitors the quality of incoming and outgoing data for an organization.The values of parameters at the user level override the values at the object manager level. In turn, the values at the in the object manager level override the settings specified at the enterprise level. This allows administrators to enable data matching or cleansing for one application but not another and allows users to disable data matching or cleansing for their own login even if data matching or cleansing is enabled for their application. However, data matching or data cleansing cannot be enabled for a user login if data matching or data cleansing are not enabled at the object manager level.Even if data cleansing and data matching are enabled, cleansing and matching are only triggered for fields that are defined in DataCleansing and DeDuplication user properties for the relevant business components.For more information, see the following topics:Enabling Siebel Data Quality at the Enterprise LevelSpecifying Data Quality SettingsEnabling Siebel Data Quality at the Object Manager LevelEnabling Siebel Data Quality at the User Level

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A later point if the data in the sample has changed. You can apply more knowledge from within the Domain Management activity and from within the Data Matching activity (see below).The knowledge discovery process need not be performed on the same data that data correction is performed on. DQS provides the flexibility to create knowledge from one set of database fields and apply it to a second set of related data that needs to be cleansed. The data steward can create a new knowledge base from scratch, base it on an existing knowledge base, or import a knowledge base from a data file. You can also re-run knowledge discovery on an existing knowledge base. You can maintain multiple knowledge bases on a single [!INCLUDEssDQSServer]. You can also connect multiple instances of an application to the same knowledge base. DQS prevents concurrency conflicts by locking the knowledge base to a user who opens it in a knowledge management session.Case Insensitivity in DQSValues in DQS are case-insensitive. That means that when DQS performs knowledge discovery, domain management, or matching, it does not distinguish values by case. If you add a value in value management that differs from another value only by case, they will be considered the same value, not synonyms. If two values that differ only by case are compared in the matching process, they will be considered an exact match.You can, however, control the case of values that you export in cleansing results. You do so by setting the Format Output to domain property (see Set Domain Properties) and by using the Standardize Output check box when you export cleansing results (see Cleanse Data Using DQS (Internal) Knowledge). Domain ManagementDomain management enables the data steward to interactively change and augment the metadata that is generated by the computer-assisted knowledge discovery activity. Each change that you make is for a knowledge-base domain. In the domain management activity, you can do the following:Create a new domain. The new domain can be linked to or copied from an existing domain.Set domain properties that apply to each term in the domain.Apply domain rules that

Base ManagementDQS uses a knowledge base (KB) to identify incorrect or invalid data. The knowledge base is essentially the ‘gold standard’ against which you want to check the quality of your data. DQS allows you to create your own knowledge base (i.e. your own company specific knowledge) or you can use reference data from third parties via the Azure Marketplace. When you install DQS, a sample knowledge base containing global and US country and city data is included.A knowledge base contains domains. A domain contains the values and metadata associated with a data field.In the sample knowledge base below, you can see that there are several domains, each concerned with the properties, rules and values of fields related to countries and cities. You can see that the highlighted domain will check values for country and can automatically correct any synonyms to the correct value – e.g. change ‘USA’ or ‘US’ to ‘United States’. You can also create your own knowledge base domains. In the example below I’ve created a knowledge base domain to check and correct company names. Once the knowledge for your data has been defined in the domains within a knowledge base, you can now perform some data cleansing by creating a Data Quality Project.Data Quality ProjectsA data quality project is the means of using a knowledge base to improve the quality of our source data by performing data cleansing or data matching activities, and then exporting the resultant data to a SQL Server database or a .csv file.To cleanse data, we need to create a data quality project. The data quality project contains the following steps:user defines the source data (SQL table or Excel file) and the mapping of the columns in the source data to the domainsDQS performs an automated cleansing stepuser reviews the results and