Introduction

Why Automate Data Cleansing?

Poor data quality leads to:

• Faulty business decisions due to incorrect or missing information
• Inaccurate analytics and reports affecting business insights
• Compliance risks (GDPR, CCPA, HIPAA) due to inconsistent or incorrect data
• Inefficiencies in AI & ML models, leading to biased or poor predictions

By automating data cleansing, organizations can:

✅ Improve accuracy by detecting and fixing duplicates, inconsistencies, and missing values
✅ Reduce manual effort and costs while increasing efficiency
✅ Enable real-time data cleansing for streaming and transactional data
✅ Ensure compliance with regulatory standards
✅ Optimize business intelligence and analytics workflows

Objectives

By the end of this course, participants will be able to:

• Understand the fundamentals of data cleansing automation
• Implement rule-based and AI-powered data cleansing strategies
• Use modern data cleansing tools for automated error detection and correction
• Integrate cleansing workflows into data pipelines (ETL, ELT, and real-time processing)
• Leverage Machine Learning & AI for advanced anomaly detection
• Automate data validation and quality monitoring
• Ensure data governance and regulatory compliance through automated cleansing

Who Should Attend?

This course is ideal for professionals involved in data management, data quality, analytics, and compliance, including:

• Data Engineers & Data Architects
• Data Quality Analysts & Data Stewards
• ETL & Data Integration Specialists
• Business Intelligence (BI) & Analytics Professionals
• Chief Data Officers (CDOs) & IT Managers
• Database Administrators (DBAs)
• Compliance & Regulatory Officers

Training Agenda

Day 1: Introduction to Automated Data Cleansing

• Fundamentals of Data Cleansing & Quality Management
• Common Data Quality Issues: Duplicates, Inconsistencies, Missing Values, Format Errors
• Manual vs. Automated Data Cleansing: Benefits & Challenges
• Overview of Leading Automated Data Cleansing Tools
• Regulatory & Compliance Considerations in Data Quality (GDPR, CCPA, HIPAA, ISO 8000)
• Hands-on Exercise: Identifying Data Quality Issues in a Sample Dataset

Day 2: Rule-Based Automated Data Cleansing

• Building Rule-Based Data Cleansing Workflows
• Standardization & Normalization Techniques
• Deduplication Strategies & Automated Matching Algorithms
• Using Business Rules to Cleanse Data in ETL Pipelines
• Hands-on Exercise: Implementing Automated Deduplication with Talend

Day 3: AI & Machine Learning for Data Cleansing

• How AI & ML Enhance Data Cleansing & Anomaly Detection
• Using ML Models for Data Imputation & Correction
• Outlier Detection & Pattern Recognition with AI
• Automating Cleansing for Unstructured & Semi-Structured Data
• Case Study: AI-Powered Data Cleansing in Financial Services**
• Hands-on Exercise: Using Python & Great Expectations for AI-Based Data Cleansing

Day 4: Integrating Automated Data Cleansing into Data Pipelines

• Automating Cleansing in Batch vs. Real-Time Data Processing
• Integrating Cleansing Tools with ETL/ELT Pipelines (Informatica, Talend, Azure Data Factory)
• Ensuring Data Quality in Data Warehouses & Data Lakes
• Monitoring & Auditing Data Cleansing Workflows for Compliance
• Hands-on Exercise: Building an End-to-End Cleansing Pipeline with Trifacta

Day 5: Implementing Enterprise Data Cleansing Strategies

• Building Scalable & Resilient Data Cleansing Architectures
• Metadata-Driven Cleansing for Automated Governance
• Best Practices for Continuous Data Quality Improvement
• Future Trends: AI-Driven Data Cleansing & Self-Healing Data Pipelines
• Capstone Project: Implementing an Enterprise-Level Automated Data Cleansing Strategy

Methodology

This course blends theory, practical exercises, and real-world case studies to provide a hands-on learning experience.

• Instructor-Led Training – Expert insights on Data Cleansing & Automation
• Hands-On Labs – Applying techniques using real-world datasets & tools
• Case Studies & Industry Examples – Learning from real-world data quality challenges
• Group Discussions & Workshops – Collaborative problem-solving
• Capstone Project – Designing an end-to-end Automated Data Cleansing strategy

Key Benefits

• Gain a deep understanding of Automated Data Cleansing
• Learn how to detect, analyze, and resolve data quality issues using automation
• Work with leading data cleansing tools for hands-on experience
• Implement AI & Machine Learning for anomaly detection & data correction
• Automate cleansing workflows within ETL/ELT pipelines
• Improve data quality governance and compliance
• Earn a Certificate in Automated Data Cleansing Tools upon completion