What's New In Informatica Data Quality 9.0.1

Transcription

1 What's New In Informatica Data Quality

2 Abstract When you upgrade Informatica Data Quality to version 9.0.1, you will find multiple new features and enhancements. The new features include a new Decision transformation and a path to migrate Data Quality repository objects to the Model repository. Enhancements include simplified steps to export and deploy reference data and increased effectiveness in profile and scorecard analysis. Supported Versions Informatica Data Quality Table of Contents Transformations Profiles and Scorecards Export and Deployment... 4 Parameterization Informatica Data Quality to Repository Migration Transformations Address Validator Transformation You can configure the Address Validator to generate Coding Accuracy Support System (CASS) reports for United States addresses and Software Evaluation and Recognition Program (SERP) reports for Canadian addresses. These reports verify that the address validation operations performed on the source address data meet the standards of the USPS and Canada Post. United States and Canadian addresses qualify for postal discount when they are CASS and SERP certified. You can configure the Address Validator to use parallel processing on a multi-processor machine to increase the speed of address validation. You can configure the transformation to create up to eight instances of itself when it runs in a mapping. Note: You cannot use the Address Validator in a rule when it is configured with multiple execution instances. Decision Transformation Decision is a new transformation in version The Decision transformation allows you to apply function logic to data. It provides a series of predefined functions that you can use to create conditional statements. You can define multiple outputs in a statement, and you can define multiple statements in a transformation object. The Decision transformation is comparable to the Rule Based Analyzer in Informatica Data Quality and earlier versions. If you migrate an object containing a Rule Based Analyzer from the Informatica Data Quality repository to the Model repository, the Rule Based Analyzer functionality migrates to a Decision transformation. Note: The Decision transformation does not support all Rule Based Analyzer functionality. If you migrate objects from the repository to the Model repository, review the migration report files for information about the status of the migrated objects. 2

3 Labeler Transformation You can create custom regular expressions to extend the types of token, or string pattern, that the Labeler transformation searches for. You can configure the Labeler transformation to identify Unicode characters. Match Transformation Effective in version 9.0.1, the Match transformation can write results in non-clustered formats. Previously, the Match transformation grouped duplicate records in clusters and wrote the cluster information to output ports. Informatica Data Quality can display duplicate records in clusters or as a list of record pairs that exceed the match threshold. You can view a list of all records that match at least one other record, or you can view each record with the record that most closely matches it. Multi-Strategy Transformations A strategy is a set of operations that you configure to run on data entering a data quality transformation. Effective in version 9.0.1, you can define multiple strategies in a single transformation object. Previously, you defined a single strategy in a transformation. For example, you can configure a Parser transformation to parse multiple types of information from data on multiple input ports. You do not need to create a Parser transformation for each parsing strategy. You can configure multiple strategies in the following transformations: Case Converter Decision Key Generator Labeler Match Merge Parser Standardizer Configure multiple strategies in a transformation to organize common types of data operation in a single object and to simplify the mapping layout. Profiles and Scorecards Profiles You may want to profile a data source multiple times to track data quality improvements. Effective in version 9.0.1, you can add or omit rules from a profile each time you run it to evaluate the data under different conditions. You can maintain the previous results in the Profiling Warehouse or you can clear the results with each run. When you clear the results each time you run a profile, you reduce the disk space used by the profile. When you create a profile that includes multiple rules, you save time creating different profiles for different scenarios. You also simplify your rule management by maintaining multiple rules in a single profile object. You can profile multilingual data and view profile results based on the locale settings in the browser. The Analyst tool changes the Datetime, Numeric, and Decimal datatypes based on the browser locale. You can sort on multilingual data. The Analyst tool displays the sort order based on the browser locale. 3

4 Scorecards You can create groups of related scores to view scorecard data for a particular business concept. When you add a profile column to a scorecard, you can choose to add it to a group. You can add a score to a group within the scorecard. You can view the total number of valid and not valid records and the refresh date for each score in the scorecard. Drilldown You can drill down on columns in the data source that you did not include in the profile. You can drill down on live data for a profile column and a scorecard column. Export and Deployment Exporting Reference Tables Effective in version 9.0.1, you can export reference table metadata and reference data values in a single operation through the Developer tool. Previously, you exported reference table metadata and reference data values in separate operations using the Developer tool and the command line. Importing Reference Tables Effective in version 9.0.1, you can import metadata to any supported database. Previously, you imported reference table metadata to the same database type as the source database. For example, if you exported metadata from an Oracle database in Informatica Data Quality 9.0, you could only import the metadata to another Oracle database. Note: You cannot import files exported from a 9.0 Model repository to a Model repository. If you want to export 9.0 objects for use with Informatica products, first upgrade the repository to Setting the Target Directory You can control the target directory for exported reference data with the INFA_CONTENT environment variable. Use the environment variable to specify an alternative directory to the default directory. Parameterization You can define parameter files that identify objects and resources that you use in a mapping, so that you can change the object used by one or more mappings without editing the mappings in the Developer tool. You can parameterize the following objects and resources: Database connection names Flat file locations and file names Reference tables You can specify parameters in mappings, mapplets, and reusable transformations. Informatica Data Quality to Repository Migration Informatica Data Quality provides batch files that you can use to export the contents of an Informatica Data Quality repository to a Model repository. The batch process exports the repository contents to the file system in XML format and converts the files to a format that the Model repository can read. The process also exports reference data from Informatica Data Quality

5 The batch process writes the reference data to the Model repository and staging area and retains the links between the reference data and the dependent objects from the repository. You can import the converted repository objects to the Model repository through the Developer tool. Author David Handy Documentation Manager, Data Quality 5