Data Quality Management Mastery
Collibra Data Quality: Strategy, Tools, and Governance, Solve Issues, Improve Trust, Drive Value, Clean Data for Success
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What you'll learn
- Business risks of poor data and the value of data quality across industries
- Core data quality dimensions (accuracy, completeness, timeliness, etc.) with real examples
- The role of data governance, policies, and data stewards in managing quality
- How to define, measure, and track data quality metrics effectively
- Techniques for profiling, assessing, and visualizing data quality
- How to build quality into business processes and enable continuous monitoring
- How to overcome common data quality challenges like siloed systems and lack of ownership
- and more clear knowledge
Description
This is an Unofficial Course.
In today’s data-driven world, organizations rely heavily on accurate, consistent, and timely data to drive decision-making, meet regulatory requirements, and deliver value. This comprehensive course is designed to equip professionals with the knowledge and practical skills needed to understand, assess, manage, and improve data quality across the enterprise.
The course begins by exploring the foundational concepts of data quality, including its definition, importance, and the key characteristics that define high-quality data—such as accuracy, completeness, consistency, timeliness, and uniqueness. Learners will examine the risks associated with poor data quality and the substantial benefits that high-quality data delivers to businesses.
Participants will gain insight into data quality frameworks, including widely recognized standards like DAMA-DMBOK2 and ISO 8000. These frameworks provide structure and best practices for managing data quality effectively and aligning with global standards. You’ll learn how to apply these frameworks to real-world data environments.
A significant focus is placed on data governance and its crucial role in sustaining data quality. You’ll explore the responsibilities of data stewards, the creation of data quality policies, and the importance of accountability and ownership within an organization. The course also covers how to define and track data quality metrics that align with business goals.
Learners will be introduced to techniques for assessing data quality through profiling, scorecards, dashboards, and root cause analysis. These tools and methods help identify data issues and monitor quality over time. You’ll learn how to visualize data quality metrics and communicate insights clearly to stakeholders.
The course offers hands-on strategies for improving data quality, such as data cleansing, standardization, matching, de-duplication, and enrichment. You’ll explore how to validate and enhance your data using internal and external sources, ensuring it meets organizational and regulatory standards.
To ensure sustainability, the course emphasizes embedding data quality practices into daily business processes and establishing continuous monitoring and automated quality controls. It also highlights the importance of fostering a data-literate culture where quality is a shared responsibility.
Finally, the course presents industry-specific use cases and common challenges faced in sectors like financial services, healthcare, and government. You’ll learn how different industries tackle data quality issues, what pitfalls to avoid, and how to overcome barriers such as siloed systems and unclear ownership.
Whether you're a data analyst, data steward, data engineer, or business professional working with data, this course provides a practical and strategic roadmap to mastering data quality.
By the end, you'll be equipped with the tools, frameworks, and insights needed to ensure your organization’s data is trustworthy, accurate, and ready to deliver value.
Thank you
Who this course is for:
- Data professionals such as data analysts, data engineers, and data scientists who want to ensure the quality and reliability of the data they work with
- Data stewards, data quality managers, and governance professionals responsible for maintaining and enforcing data quality standards
- IT and business professionals involved in data projects or digital transformation initiatives
- Project managers and team leads who work with data-driven teams and need to understand the value and impact of high-quality data
- CDMP or DAMA certification candidates looking to strengthen their knowledge of data quality frameworks and best practices
- Anyone working with data who wants to gain a solid foundation in assessing, improving, and sustaining data quality across their organization
- No prior experience in data quality is required—this course covers both fundamental concepts and practical techniques suitable for learners at various levels.
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