Data Testing and Data Quality Management

Overview

This course is designed to provide participants understanding of Data Quality concepts and principals. During the course participants will learn the importance of high quality of data, key differences between testing data and Data Quality, what are dimensions of Data Quality, how to assess Data Quality, and what steps are to be taken to improve it. It also shows how Data Quality affects business and is important on organizational level.

Target Audience

• Data Engineers
• Data Analysts
• Data Product Owners
• Business Managers
• Project Managers

Prerequisites

• Basic understanding of databases and SQL

Content

Day 1:

• Introduction to Data Testing and Data Quality Management
  • Overview of the course objectives and structure
  • Importance of data quality in decision-making and business processes
  • Difference between testing data and data quality
• Understanding Data Quality
  • Definition and dimensions of data quality
  • Common data quality issues and challenges
  • Impact of poor data quality on organizations and decision-making
• Organizational Processes and Data Governance
  • Importance of data governance in ensuring data quality
  • Roles and responsibilities of data stewards and data owners
  • Establishing data governance frameworks and processes
• Data Quality Assessment
  • Techniques for assessing data quality
  • Data quality assessment tools and methodologies
  • Designing and implementing data quality assessment processes
• Data Quality Metrics and Monitoring
  • Defining key data quality metrics and indicators
  • Establishing data quality thresholds and benchmarks
  • Implementing data quality monitoring and reporting processes
• Data Cleansing and Remediation
  • Techniques and approaches for data cleansing
  • Addressing data quality issues and errors
  • Implementing data remediation processes

Day 2:

• Data Quality Improvement Strategies
  • Data quality improvement methodologies
  • Best practices for maintaining data quality over time
• Data Quality Management Tools and Technologies
  • Overview of data quality management tools and technologies
  • Selection criteria for data quality tools
  • Integration of data quality tools into existing data management systems
• Data Quality Governance and Regulatory Compliance
  • Relationship between data quality governance and regulatory compliance
  • Ensuring data privacy and security in data quality management processes
  • Compliance considerations in data quality management
• Case Studies and Practical Applications
  • Real-world examples of data quality management implementations
  • Case studies illustrating the impact of data quality on organizations
  • Practical exercises and assignments to reinforce learning
• Course Review and Conclusion
  • Recap of key concepts
  • Q&A session

For all inquiries regarding education, please contact us at learn@croz.net.

More information:

Data quality with Great Expectations.