Informatica Data Quality Analyst - Beginner's Guide

Name: Informatica Data Quality Analyst - Beginner's Guide
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Informatica Data Quality (IDQ) Analyst Course

Created bySid Inf

Last updated 6/2019

English

What you'll learn

Perform data profiling and root cause analysis to identify data quality issues opportunities and provide recommendations to work with business and IT for remediation.
Work with business stakeholders to gather requirements, conduct data profiling and track metrics.
Proactively identify, prioritize and re-mediate data quality issues.
Work with cross-functional areas: IT, Data Architecture, Data Integration, Business Intelligence, Analytics, and business stakeholders.
Participate as a member of key cross-functional working groups related to data quality, and the company’s Data Governance Working Group representing the data domain.
Partners with development and business resources in all phases of Data Quality Implementation.
Perform analysis and provide recommendations for modification or creation of business/technical rules based on data results.
Excellent analytical ability to understand business issues associated with data management, architecture and governance, business process, in terms of data and related standards.
Create and monitor data quality scorecards and data integrity efforts to works with data owners/stewards to resolve data quality issues.

Course content

10 sections • 54 lectures • 5h 11m total length

Introduction and Prerequisites to Informatica Analyst Course7:23
Introduction and Prerequisites to Informatica Analyst Course
Good to Have Knowledge3:49
Let's see what are some of the prerequisites (good to have, but not mandatory) knowledge for a better foundation and understanding of the overall tool and the examples.
Different Products of Informatica Corporation4:38
One of the frequently asked questions by many who are new to the whole Data Warehouse and Informatica world is:
Doesn't Informatica mean the ETL tool?
Yes, it is. But it is called as Informatica Power Center. There are multiple other tools and products which Informatica Corporation has which helps the Enterprises with their data management.
Informatica Architecture Review4:50
A quick overview on how the Informatica Architecture is weaved into have the Informatica Data Quality services and the communication setup between Informatica Power Center and Informatica Data Quality.
Informatica PowerCenter (IPC) Vs Informatica Data Quality (IDQ)6:21
Another common question is what does Informatica Data Quality do which Informatica Power Center can not.

Projects that require Data Quality3:28
Let's see what are the different kinds of projects in which Informatica Data Quality tool can be used. Note, that this is not limited only to this.
Why implement Data Quality?4:22
Let's understand the reasons for implementing the Data Quality.
Benefits of improving Data Quality5:13
Let's understand the different benfits of improving the Data Quality in an enterprise.
Single Stage Project Architecture3:02
Where will be the Data Quality exist in a single stage Data Warehouse environment.
Multi Stage Project Architecture3:23
Let's see where will Data Quality exist in a Multi stage Data Warehouse environment.

Data Quality Project Roles9:18
The image shows the different roles which are usually connected to a Data Quality implementation. In this lecture, let's talk about the IDQ specific roles which are 'Analyst' and 'Developer'.
Analyst Vs Developer Tasks5:14
What are the different task's done by Analyst and Developers?
Analyst Vs Developer Tool2:52
What are the differences between Analyst and Developer tools?

Common Object Terminology4:57
Mapping : Reads data from Sources, applies transformation logic and writes transformed data to outputs
Mapplet : Group of reusable functions
Rule : A Mapplet built by the Analyst
Profile : Analysis of Data for a data set
Transformation: An object the generates, modifies or passes data
Reference Table: Used to validate, parse, standardize and enhance data.
New Terms which are often used6:15
Let's talk about the new terms which are often used in Data Quality
Standardization (Developer)4:08
What is Standardization?

Why Profile the data?2:47
How to create the Profile and verify the results?11:20
Multiple executions of Profiles and results verification8:42
Comments and Tags1:18
Advantages of doing the Column Profiling5:02
Profile Collaboration between Developer and Analyst4:12
What is Profile Collaboration?2:08
Project & Folder Naming Conventions / Standards4:40
Rule Creation and adding the existing rules to Profile, result comparision10:03
How does Multi File Profiling work?8:50

Flat File Import for Reference Tables5:17
How to Create a Reference table from a Profile and add data?6:03
In this lecture, we will see the steps to add the data from a profile to a Reference table. We also have an option to create the Reference table if that does not exist.
What is a Managed Reference Table?5:05
Let's learn a about the Managed Reference Table.
What is an Unmanaged Reference Table?5:14

Introduction to the Analyst Tool14:29
There is not much difference between the Analyst version 9.6 to 10x as we have discussed earlier. But as most of the clients still use 9.6 we are covering these as well.
Analyst Tool Layout3:23
Analyst tool layout for version 9.6
Different Menu Options6:49
Analyst tool different menu options for version 9.6
Introduction the the Analyst, Developer and the Services14:51
Basic intro to Analyst Developer and Services
Library Workspace4:57
Library Workspace overview
Connection Workspace4:30
Connection Workspace overview
Job Status Workspace7:35
Job Status Workspace overview
Project Workspace3:07
Project Workspace overview
The Global Search option3:42
Let's talk about the global search option.
Object Locking Mechanism3:28
Object Locking Mechanism overview
How to import a file using the Analyst?10:43
Steps to import file in the Analyst tool

Requirements

Mandatory: Data Quality Fundamentals (Refer to the existing course on Udemy by Inf Sid)
Good to know: Data Warehouse Concepts - Beginner to Advanced (Refer to the existing course on Udemy by Inf Sid)
Good to know: Informatica Power Center - Beginner to Advanced (Refer to the existing course on Udemy by Inf Sid)
Good to know: Cloud Data Warehouse Concepts (Refer to the existing course on Udemy by Inf Sid)
Good to know: Informatica Cloud - Data Integration (Refer to the existing course on Udemy by Inf Sid)

Description

Informatica Analyst or IDQ Analyst is a web-based application client that analysts can use to analyze, cleanse, standardize, profile, and score data in an enterprise.

Business analysts and developers use Informatica Analyst for data-driven collaboration. You can perform column and rule profiling, scorecarding, and bad record and duplicate record management. You can also manage reference data and provide the data to developers in a data quality solution.

Organizations use Informatica Analyst to accomplish the following tasks:

Profile data. Create and run a profile to analyze the structure and content of enterprise data and identify strengths and weaknesses. After you run a profile, you can selectively drill down to see the underlying rows from the profile results. You can also add columns to scorecards and add column values to reference tables.
Create rules in profiles. Create and apply rules within profiles. A rule is reusable business logic that defines conditions applied to data when you run a profile. Use rules to further validate the data in a profile and to measure data quality progress.
Score data. Create scorecards to score the valid values for any column or the output of rules. Scorecards display the value frequency for columns in a profile as scores. Use scorecards to measure and visually represent data quality progress. You can also view trend charts to view the history of scores over time.
Manage reference data. Create and update reference tables for use by analysts and developers to use in data quality standardization and validation rules. Create, edit, and import data quality dictionary files as reference tables. Create reference tables to establish relationships between source data and valid and standard values. Developers use reference tables in standardization and lookup transformations in Informatica Developer.
Manage bad records and duplicate records. Fix bad records and consolidate duplicate records.

Who this course is for:

Business Analysts
Data Analysts
Data Scientists
Data Analytics Professionals
Data Warehouse Professionals
Big Data Professionals
ETL Architects
ETL Developers
ETL Testing/QA Professionals

Informatica Data Quality Analyst - Beginner's Guide

What you'll learn

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Course content

Introduction5 lectures • 27min

Data Quality5 lectures • 19min

Analyst Vs Data Quality3 lectures • 17min

What is involved?3 lectures • 15min

Informatica Analyst Version 10.0.07 lectures • 42min

Data Profiling10 lectures • 59min

Mapping Specification5 lectures • 31min

Reference Table4 lectures • 22min

Informatica Analyst Version 9.6 (Limited Features) VM based11 lectures • 1hr 18min

Bonus Section1 lecture • 2min

Requirements

Description

Who this course is for: