What you'll learn:
- Understand Data Management Solution
- Learn how to improve data quality, governance, and master data management
- Empower your organization with AI-powered data management and automation
- Learn what is data observability and why we need it ?
This course offers a clear and practical introduction to modern data governance and data quality management, specifically designed for beginners to intermediate professionals seeking to strengthen their knowledge of how trusted data is built and maintained across enterprise ecosystems.
By the end of this course, you’ll have a strong foundational understanding of how organizations manage trusted data using structured, repeatable workflows. The concepts are presented in a widely applicable manner — making it a great starting point no matter what platform your organization uses, from emerging solutions to well‑established data management environments.
You’ll explore key topics such as:
Profiling datasets to assess accuracy, completeness, and consistency
Creating and applying rule-based validation logic to ensure data quality
Managing business glossaries, metadata, and classification hierarchies
Tracking and resolving data quality issues through stewardship workflows
Leveraging AI-driven features for anomaly detection and smart suggestions
Aligning governance efforts with compliance, risk, and business policies
Monitoring data assets and understanding how to operationalize governance in real scenarios
Whether you're a data steward, analyst, engineer, or business stakeholder, this course helps you understand how governance and quality work in modern data environments and prepares you for deeper tool-specific learning and project participation.
No prior experience is required — only a curiosity to learn how organizations ensure their data is reliable, usable, compliant, and ready for analytics, AI, automation, and decision-making at scale.
