Data Quality: Data Quality Management for Developers (DQDQMD)(10.1.1)

Gain the skills and knowledge necessary to implement and automate a data quality assurance process with the Informatica Data Quality platform. In addition to learn how to cleanse, standardize, and enhance data, students will test and troubleshoot their Data Quality solutions. This course is applicable for version 10.1.1.
 
Objectives
After successfully completing this course, students should be able to:
  • Describe the overall Data Quality Management Process.
  • Illustrate the Data Quality Architecture.
  • Differentiate between the Analyst and Developer Roles and Tools.
  • Navigate the Developer Tool and collaborate on projects with team members.
  • Perform Column, Rule, Multi object, Comparative and Mid-Stream Profiling.
  • Manage Reference Tables.
  • Develop standardization, cleansing and parsing Mappings and Mapplets.
  • Identify duplicate records using Classic Data Matching.
  • Create and execute Workflows to populate user inboxes with Exception and Duplicate record tasks.
  • Describe the deployment options that are available when executing Mappings outside of Informatica Developer.
  • Troubleshoot issues that may appear during development.
 
Target Audience
Developers
 
Prerequisites
None
 
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Agenda
Module 1: Course Introduction
  • Discuss course objectives.
  • Walk through the course agenda.
Module 2: Data Quality Process Overview
  • Describe the overall Data Quality Management Process Cycle.
  • Identify dimensions of Data Quality including Completeness, Conformity, Consistency, Accuracy, Duplication, and Integrity.
  • List and describe the Data Quality Processes including Profiling, Standardization, Matching and Consolidation.
  • Differentiate between the Developer and Analyst roles and tools.
  • Describe the Data Quality Architecture.
Module 3: Data Quality Projects and Solutions
  • Examples of customer Data Quality use cases.
  • The types of projects that benefit from cleansed and standardized data.
  • Describe where Data Quality fits in a typical DI/DQ project.
  • The differences between Reporting, Gating and Cleansing projects.
  • Example solutions architecture for typical projects involving Data Quality.
Module 4: Project Collaboration and Reference Table Management
  • Work in the Developer GUI.
  • Review projects created by the analyst including data objects, profiles, rules, scorecards, comments and tags.
  • Reference tables and where they fit into the Data Quality process.
  • Create reference tables by importing a flat file and connecting to an Oracle table.
  • Lab: Review a project created by an analyst user in the Analyst tool.
  • Lab: Build Reference Tables.
Module 5: Working in the Developer Tool
  • List and describe some of the tasks that will be performed in the Developer tool.
  • Work with physical and logical data objects, create a connection to a table, import a flat file and create a logical data object.
  • Explore Developer transformations.
  • Recognize the difference between mappings and mapplets.
  • Describe content sets and their uses.
  • Apply Developer tips and tricks.
  • Lab: Create a project and assign permissions.
  • Lab: Create a connection to an Oracle table and import a flat file.
  • Lab: Build a logical data object.
Module 6: Profiling, Mapplets and Rules
  • Apply knowledge gained to perform Column Profiling and interpret the results.
  • Create and validate mapplet/rule that will be used in a scorecard.
  • Use profiling techniques to debug and speed up mapping/mapplet development.
  • Using Informatica Analyst, update the scorecard with the rule created.
  • Lab: Create a rule to measure the accuracy of data in a field.
  • Lab: Using Informatica Analyst, apply the rule to a scorecard and review the results.
Module 7: Standardizing, Cleansing and Enhancing Data
  • Define what it is to standardize, cleanse and enhance data.
  • Create a mapping to cleanse, standardize and enhance data.
  • Design and develop standardization mapplets.
  • Describe and configure a range of standardization transformations.
  • Lab: Build a Standardization mapping and mapplets using standardization transformations.
Module 8: Parsing Data
  • What it is to parse data.
  • The parsing process and what is involved.
  • The various different parsing techniques and when to use them.
  • Configure key parsing transformations.
  • Lab: Perform parsing using a variety of parsing transformations and strategies.
  • Lab: Complete the standardization mapping.
Module 9: Grouping and Matching Data
  • What it is to match data.
  • What is DQ matching process and explain the different stages of matching.
  • Why grouping is a necessary precursor to matching and the effect it has on matching.
  • Perform grouping using a variety of methods.
  • Review and explain the results of grouping, refining the grouping strategy if necessary.
  • Differentiate between match algorithms and use the most appropriate one for each data type.
  • Lab: Build and fine tune a grouping and matching mapping.
Module 10: Manual Exception and Consolidation Management
  • Identify when exception and duplicate record management is necessary in a project.
  • Exception Management Process.
  • Build mappings to populate the appropriate tables with exception and duplicate record tasks.
  • Lab: Build a mapping that can be used to identify exception data.
  • Lab: Build a mapping that can be used to identify duplicate data.
Module 11: Building, Managing and Deploying Workflows
  • Define workflows and workflow tasks.
  • Identify Human tasks and steps.
  • Create workflows to identify exception and duplicate records.
  • Deploy and execute workflows.
  • Verify tasks have been created in Informatica Analyst.
  • Lab: Build a workflow to populate the Analyst Inbox with Exception Tasks.
  • Lab: Build a workflow to populate the Analyst Inbox with Duplicate Record Tasks.
Module 12: Deploying: Executing Mappings outside of the Developer tool
  • The various deployment options that are available.
  • Create and deploy mappings as applications.
  • Schedule mappings, profiles and a scorecard to run using Informatica Scheduler.
  • Lab: Schedule Mappings to run using Informatica Scheduler.
Module 13: Importing and Exporting Project Objects
  • Identify when to export/import projects.
  • Use Basic and Advanced Import options.
  • Export a project.
  • Lab: Import a project using the Basic method.
  • Lab: Import a project using the Advanced Method.
  • Lab: Export a project.
Module 14: Troubleshooting
  • Provide examples of errors encountered in the Developer and troubleshoot these errors.
  • Identify common mapping and transformation configuration issues.
  • Common workflow configuration errors.
  • Tips for working with the Developer tool.
  • Lab: Optional. Troubleshoot mapping configuration issues.