Cloud Data Quality:
Profiling and Monitoring

onDemand | IDMC Data Quality | Self-Paced | November 2024 Release

Course Overview

Learn the fundamentals of Informatica Cloud Data Quality, including Intelligent Data Management Cloud (IDMC) architecture, the graphical user interface (GUI), and Data Quality assets. The course emphasizes analyzing and monitoring data, featuring activities such as creating and interpreting profiles, building rules to detect anomalies, scorecarding to track data quality, and identifying and exporting exception records.
This course is applicable to November 2024 Release.

Objectives

After successfully completing this course, students should be able to:

Describe Informatica Intelligent Data Management Cloud Architecture
Describe what Cloud Data Quality is and how it can be utilized
Profile data to identify anomalies
Create Dictionaries to hold reference data for verification and standardization routines
Use Rule Specifications to configure business rules
Monitor data quality over time with Scorecards
Identify exception records and download them for manual correction
Explore how to profile alternative source data

Target Audience

Business Analyst
Data Analyst
Data Stewards
Developer
Business User

Prerequisites

None

Agenda

Module 0: Course Introduction

Lab: Creating the Cloud User Account
Lab: Installing the IDMC Secure Agent
Lab: Importing the Training Content

Module 1: Informatica Intelligent Data Management Cloud Overview

Introduction to Informatica Intelligent Data Management Cloud (IDMC)
Informatica Intelligent Data Management Cloud Terminology
Informatica Intelligent Data Management Cloud Architecture
Informatica Intelligent Data Management Cloud Services
Runtime Environments
Connections
The Administrator Service
Lab: Defining a Flat File Connection
Lab: Creating a Training Folder

Module 2: Cloud Data Quality Overview

What is Data Quality?
Discussing the Data Quality Management Process Cycle
Listing and explaining the Dimensions of Data Quality
Describing Data Quality Functions, Inputs, and Outputs
Cloud Data Quality Services and Assets

Module 3: Cloud Data Profiling

Profiling Data
Reviewing Profiling Results and Identifying Anomalies
Profile Features
Lab: Profiling Data
Lab: Profiling Insights

Module 4: Dictionaries

What are Dictionaries and why are they used?
Creating Dictionaries
Lab: Creating a Dictionary to standardize the Company
Lab: Copying and updating an existing Dictionary to standardize data
Lab: Creating a Dictionary to enhance data

Module 5: Rule Specifications

Introduction to Rule Specifications
Building Rule Specifications
Lab: Creating a Rule Specification to Validate the Company Field
Lab: Creating a Rule Specification to Validate Contact Details
Lab: Creating a Rule Specification to Derive a Nameprefix
Lab: Creating a Rule Specification to Cleanse and Standardize the Company

Module 6: Profile Rules Review and Analysis

Application of custom Rules and Assets to profiles
Lab: Applying the Custom Rules to a Profile and Review
Lab: Applying Custom Assets to a Profile and Review

Module 7: Scorecards

Scorecard Overview
Updating a Profile and defining Rule Occurrences
Reviewing Scorecards

Module 8: Exception Management

The Exception Management Process
Configuring an Exception Task
Lab: Defining an Exception Rule Specification to Identify Records with Invalid Companies
Lab: Defining an Exception Rule Specification to Identify Records with Invalid Addresses