Executive Development Programme in Automating Data Quality Checks

This course is designed for senior managers and data professionals aiming to enhance their organization's data quality through automation. Participants will learn to implement advanced data quality checks, integrate these checks into existing processes, and leverage automation tools to streamline data management.

Graduates will gain proficiency in selecting appropriate automation tools, creating efficient data validation workflows, and improving decision-making through accurate and reliable data. They will also understand the business value of automated data quality checks and how to measure their return on investment.

1. 1. Introduction to Data Quality: Learners will understand the importance of data quality and explore foundational concepts such as data accuracy, completeness, consistency, and timeliness. They will gain basic skills in identifying and assessing data quality issues.
2. 2. Data Quality Assessment Techniques: This module covers various techniques for assessing data quality, including manual and automated methods. Learners will learn how to use tools and scripts to evaluate data quality metrics.
3. 3. Data Profiling and Analysis: In this module, learners will delve into data profiling, a process for discovering and analyzing data characteristics. They will learn how to use data profiling tools to identify data anomalies and trends.
4. 4. Data Quality Rules and Validation: This module focuses on creating and implementing data quality rules and validation processes. Learners will gain practical skills in defining and enforcing rules to maintain data integrity.
5. 5. Automating Data Quality Checks with Python: Learners will explore the use of Python for automating data quality checks. They will learn to write scripts for data validation and profiling, and use Python libraries like pandas and NumPy.
6. 6. Advanced Data Quality Tools and Platforms: This module introduces advanced data quality tools and platforms, such as Trifacta, Talend, and Informatica. Learners will learn how to use these tools to manage and enhance data quality.
7. 7. Data Quality in Real-World Applications: In this module, learners will apply data quality concepts and techniques to real-world scenarios. They will work on case studies and projects that involve improving data quality in various business contexts.
8. 8. Data Quality Governance and Compliance: This module covers the principles of data quality governance and compliance. Learners will understand how to establish data quality policies and ensure adherence to regulatory requirements.
9. 9. Best Practices for Data Quality Management: In this module, learners will learn best practices for managing data quality, including change management, data stewardship, and ongoing monitoring. They will gain skills in developing and maintaining a data quality management framework.
10. 10. Future Trends in Data Quality Management: This final module explores emerging trends and technologies in data quality management, such as machine learning and artificial intelligence. Learners will gain insights into how these technologies are shaping the field and how they can be applied to improve data quality.