Introduction

Why is Data Profiling and Quality Assessment Important?

Data Profiling and Quality Assessment help organizations:

• Identify and resolve data inconsistencies, duplicates, and errors
• Ensure data completeness, accuracy, and validity for decision-making
• Comply with industry standards and regulations (GDPR, CCPA, HIPAA, etc.)
• Improve operational efficiency by reducing data-related errors and inefficiencies
• Enable better integration, migration, and transformation of data

A robust Data Profiling and Quality strategy supports:

• Better Data Governance – Ensuring accountability and ownership of data
• Enhanced Data Trustworthiness – Increasing confidence in data-driven decisions
• Optimized Business Processes – Reducing operational risks due to bad data
• Regulatory and Compliance Adherence – Avoiding fines and legal complications

Objectives

By the end of this course, participants will:

• Understand the key principles of Data Profiling and Data Quality Assessment
• Learn how to detect and resolve common data quality issues (e.g., duplicates, inconsistencies, missing values, outliers, and errors)
• Implement data validation and cleansing techniques using modern tools
• Use AI and Machine Learning for anomaly detection and data quality improvements
• Automate data quality monitoring and reporting
• Integrate Data Profiling with Data Governance and Compliance frameworks
• Gain hands-on experience with leading Data Profiling and Quality tools

Who Should Attend?

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

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

Training Agenda

Day 1: Introduction to Data Profiling and Quality Assessment

• Fundamentals of Data Quality & Why It Matters
• Key Dimensions of Data Quality: Accuracy, Completeness, Consistency, Timeliness, Integrity, and Uniqueness
• Introduction to Data Profiling Techniques
• Common Data Quality Issues and Their Business Impact
• Regulatory & Compliance Considerations for Data Quality
• Hands-on Exercise: Exploring data quality issues in sample datasets

Day 2: Data Profiling Techniques & Tools

• Types of Data Profiling: Column Profiling, Structure Discovery, Relationship Analysis, Rule-Based Profiling
• Using SQL for Basic Data Profiling
• Automated Data Profiling with Open-Source & Enterprise Tools (Informatica, Talend, Trifacta, Microsoft Purview)
• Data Anomaly Detection using AI & ML Techniques
• Case Study: Implementing Data Profiling in a Banking Institution
• Hands-on Exercise: Conducting Data Profiling using Python (Pandas & Great Expectations)

Day 3: Data Quality Assessment & Cleansing Techniques

• Measuring Data Quality Metrics
• Handling Missing Data: Imputation vs. Deletion Techniques
• Detecting and Resolving Duplicates & Inconsistencies
• Standardization & Normalization of Data
• Applying Business Rules for Data Cleansing
• Case Study: Data Cleansing in a Healthcare Data System
• Hands-on Exercise: Data Cleaning and Standardization using Talend

Day 4: Automating Data Quality Monitoring & Governance

• Automating Data Quality Checks using Metadata & Business Rules
• Building Data Quality Dashboards for Continuous Monitoring
• Integrating Data Quality into Data Governance Frameworks
• Using AI & ML for Continuous Data Quality Improvement
• Implementing Data Lineage to Track Data Quality Over Time
• Case Study: Automating Data Quality Monitoring in an E-Commerce Company**
• Hands-on Exercise: Implementing Automated Data Quality Reports in Microsoft Purview

Day 5: Advanced Topics & Implementing a Data Quality Strategy

• Scaling Data Quality Solutions for Big Data & Cloud Environments
• Data Quality in Data Warehouses vs. Data Lakes
• Data Quality for Business Intelligence & Analytics
• Creating an Enterprise Data Quality Strategy
• Future Trends in Data Profiling & Quality Assessment (AI-driven Profiling, Data Observability, Augmented Data Management)
• Capstone Project: Designing & Implementing a Data Quality Assessment Framework

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 Profiling & Quality
• 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 Data Profiling & Quality Assessment strategy

Key Benefits

• Gain a deep understanding of Data Profiling and Quality Assessment
• Learn how to detect, analyze, and resolve data quality issues
• Implement data validation, cleansing, and anomaly detection techniques
• Work with leading data quality tools for hands-on experience
• Automate data quality monitoring using AI & ML-based solutions
• Ensure regulatory compliance and better data governance
• Earn a Certificate in Data Profiling & Quality Assessment upon completion