Course Overview
Data Quality Management is essential for ensuring that an organization's data is accurate, consistent, and reliable. This course is designed to provide participants with a comprehensive understanding of the principles and practices necessary to maintain and improve data quality across various systems and processes. The course covers data governance, data cleansing, data profiling, and data quality assessment techniques. By the end of the course, participants will be equipped with the tools and knowledge to implement effective data quality management strategies in their organizations.
Course Duration
5 Days
Who Should Attend
- Data Analysts and Data Scientists
- Database Administrators
- IT Professionals involved in data management
- Business Analysts and Managers
- Quality Assurance Professionals
- Anyone responsible for maintaining or improving data quality in their organization
Course Objectives
By the end of this course, participants will be able to:
- Understand the fundamental concepts of data quality and its importance to business operations.
- Learn to identify common data quality issues and their sources.
- Gain practical skills in data profiling, cleansing, and validation.
- Develop strategies for implementing effective data governance frameworks.
- Master tools and techniques for continuous data quality assessment and improvement.
Course Outline:
Module 1: Introduction to Data Quality Management
- Understanding Data Quality: Definitions and Importance
- Key Dimensions of Data Quality: Accuracy, Completeness, Consistency, Timeliness, and Uniqueness
- The Impact of Poor Data Quality on Business Operations
- Introduction to Data Quality Frameworks and Standards
Module 2: Data Governance and Data Quality
- Defining Data Governance and its Role in Data Quality
- Establishing Data Ownership and Accountability
- Data Stewardship: Roles and Responsibilities
- Creating and Implementing Data Governance Policies and Procedures
Module 3: Data Profiling and Data Quality Assessment
- Introduction to Data Profiling Techniques
- Identifying Data Quality Issues through Data Profiling
- Data Quality Assessment Methods and Tools
- Interpreting Data Quality Metrics and Reports
Module 4: Data Cleansing and Validation Techniques
- Techniques for Cleaning and Standardizing Data
- Data Deduplication and Matching
- Ensuring Data Integrity through Validation Rules
- Automating Data Cleansing Processes
Module 5: Continuous Data Quality Improvement
- Implementing Data Quality Monitoring and Auditing
- Root Cause Analysis for Data Quality Issues
- Building a Data Quality Improvement Plan
- Best Practices for Sustaining Data Quality in Dynamic Environments
