Unlock Data Integrity with ENCODE-IT's Informatica Data Quality Course

Informatica Data Quality (IDQ) is a premier solution that helps organizations ensure the accuracy,
consistency, and reliability of their data. As businesses increasingly rely on data for decision-making,
maintaining high-quality data is crucial. ENCODE-IT’s Informatica Data Quality course will enable
you to gain hands-on expertise in data profiling, cleansing, matching, and standardization. With in-
depth knowledge of best practices for managing data quality across various business processes, this
course will make you a key player in any data-driven organization. Whether you're starting a career
in data management or looking to upgrade your skills, this course is designed to provide you with
the practical knowledge needed to tackle real-world data quality challenges.

Salary Scale in India:
Professionals with expertise in Informatica Data Quality are highly sought after by companies across
industries. As more organizations are realizing the value of clean and reliable data, the demand for
data quality professionals is growing. The salary scale in India for Informatica Data Quality roles
varies depending on experience:
 Entry-Level Data Quality Analysts: ₹4 Lakhs to ₹7 Lakhs per annum
 Mid-Level Data Quality Consultants: ₹8 Lakhs to ₹12 Lakhs per annum
 Senior Data Quality Engineers: ₹12 Lakhs to ₹20 Lakhs+ per annum
Data quality professionals are integral to organizations' success, as high-quality data drives informed
decision-making.

ENCODE-IT’s Placement Assistance and Certification:
Upon completion of the Informatica Data Quality course, you will receive an ENCODE-IT
Certification, a valuable credential that demonstrates your proficiency in data quality management.
Our Placement Assistance team is dedicated to helping you connect with top employers seeking
professionals skilled in data quality and governance. Whether you are entering the job market or
seeking a career upgrade, ENCODE-IT will guide you in achieving your professional goals.

Course Curriculum

1. Introduction to Data Quality and Informatica IDQ
 What is Data Quality? Key Concepts and Benefits
 Understanding the Data Quality Life Cycle
 Introduction to Informatica Data Quality (IDQ) and its Architecture
 Overview of Key IDQ Components: Data Integration, Data Profiling, Data Cleansing, and Data
Monitoring

 Importance of Data Governance and Compliance in Data Quality
 Exploring Data Quality Frameworks and Best Practices

2. Informatica Data Quality Overview
 Installing and Configuring Informatica Data Quality
 Overview of Informatica Developer and Analyst Tools
 Connecting Data Sources: Databases, Flat Files, Cloud Applications, etc.
 Introduction to IDQ Workflows: Creating, Managing, and Running Data Quality Jobs
 Exploring IDQ Repositories and Metadata Management
 Data Profiling and Data Quality Dashboards

3. Data Profiling with Informatica IDQ
 Understanding Data Profiling: Purpose and Benefits
 Using Data Profiling to Discover Data Patterns and Quality Issues
 Profiling Different Data Types: Text, Numbers, Dates, and Geospatial Data
 Generating Data Quality Reports and Summary Insights
 Using Data Profiling to Identify Data Inconsistencies, Duplicates, and Missing Values
 Advanced Profiling Techniques for Complex Data Sources

4. Data Cleansing in Informatica IDQ
 What is Data Cleansing? Techniques and Tools for Cleansing Data
 Implementing Standardization Rules: Address Standardization, Name Formatting, etc.
 Handling Missing Data and Null Values
 Implementing Deduplication: Identifying and Removing Duplicate Records
 Data Transformation for Cleansing Data: Using Mapping Designer for Cleansing Rules
 Automating Data Cleansing and Data Enrichment with IDQ

5. Data Matching and Deduplication
 Introduction to Data Matching: Definition and Applications
 Configuring Data Matching Rules in Informatica IDQ
 Understanding Matching Algorithms: Exact, Fuzzy, and Probabilistic Matching
 Data Deduplication: Techniques for Identifying and Removing Duplicate Data
 Implementing Matching and Cleansing on Large Datasets
 Leveraging Data Matching for Master Data Management (MDM)

6. Data Enrichment in Informatica IDQ

 What is Data Enrichment and Why is it Important?
 Using External Data Sources for Enrichment
 Enriching Data with Address Validation and Geocoding
 Incorporating Third-Party Data into Existing Datasets
 Standardizing and Enriching Customer Data Using Informatica IDQ
 Integrating Data Enrichment into ETL Processes

7. Implementing Data Governance with Informatica IDQ
 Introduction to Data Governance Concepts
 Creating and Managing Data Governance Policies in IDQ
 Ensuring Data Quality Compliance with Regulatory Standards (GDPR, HIPAA, etc.)
 Data Stewardship: Roles and Responsibilities in Data Governance
 Defining Data Quality Metrics and KPIs
 Auditing Data Quality and Tracking Changes in Data Sets

8. Data Quality Reporting and Monitoring
 Introduction to Data Quality Dashboards and Reporting Tools
 Building Custom Reports for Data Quality Monitoring
 Tracking Data Quality Trends and Identifying Key Issues
 Setting up Alerts and Notifications for Data Quality Violations
 Monitoring Data Quality in Real-Time with Informatica IDQ
 Using Reports for Data Quality Compliance and Decision Making

9. Data Quality Integration with Other Informatica Products
 Integrating Informatica Data Quality with Informatica PowerCenter
 Data Quality Integration with Cloud Data Integration Tools
 Enhancing ETL Processes with Data Quality and Cleansing Rules
 Using Informatica Data Quality with Master Data Management (MDM) Solutions
 Integrating Data Quality into Business Intelligence Workflows

10. Advanced Topics in Informatica Data Quality
 Customizing Data Quality Rules and Workflows
 Leveraging Data Quality for Big Data Environments (Hadoop, Spark, etc.)
 Implementing Real-Time Data Quality Monitoring in Streaming Data Environments
 Best Practices for Scaling Data Quality Solutions for Large Enterprises

 Managing Complex Data Quality Scenarios in Multi-Source Environments
 Automating Data Quality Monitoring and Reporting

11. Informatica Data Quality Project Implementation
 Real-World Project: Building a Data Quality Framework from Scratch
 Data Profiling, Cleansing, Matching, and Enrichment for a Business Use Case
 Implementing Data Governance Policies for an Enterprise Organization
 Hands-On Project: Identifying and Resolving Data Quality Issues in a Sample Data Warehouse
 End-to-End Data Quality Implementation: Integrating IDQ with Existing Systems

12. Final Project and Certification Exam
 Final Project: Applying Data Quality Techniques on a Real-World Scenario
 Project Evaluation: Demonstrating Your Data Quality Skills in Action
 Final Exam: Comprehensive Evaluation of Informatica Data Quality Concepts and Tools
 Certification of Completion from ENCODE-IT and Job Placement Assistance

Key Features of the Course
 Tools & Platforms: Informatica Data Quality, Informatica PowerCenter, Informatica Cloud
Data Integration, and more
 Real-World Projects: Gain hands-on experience by working on real-world data quality
projects.
 Certification & Placement Support: Receive an ENCODE-IT certification upon completion,
along with job placement assistance.
 Expert Instructors: Learn from experienced professionals who are experts in data quality,
data governance, and Informatica IDQ.
 Career Advancement: Enhance your career prospects by learning data quality practices that
are highly demanded across industries.