Level: Beginner to Intermediate
Prerequisite: None
Metadata today plays a pivotal role in modern data architectures. Historically, metadata was seen as a passive resource for data warehousing. It has evolved into a foundational element for effective data operations, analytics, and governance. It is the index to data assets and the reference that says what is used where, how, by whom. Without it there is no inventory, no traceability, no automation, and no compliance.
Understanding metadata's role in the analytics ecosystem is crucial. Metadata is not just about finding data; it’s also about managing data, ensuring its accuracy, and automating data operations. With a focus on overlooked but critical areas like operational and usage metadata, this course will cover how metadata enables self-service analytics, facilitates machine learning workflows, and supports automated data operations.
This course is organized around the lifecycle of metadata—from collection to active use—and the importance of aligning practices with the needs of different roles. We’ll explore the specific metadata needs of analysts, engineers, and operators, and the tools and techniques required to support them. We’ll also explore common challenges—like inaccurate metadata, failed catalog projects, and disconnected tools—and show how to avoid these pitfalls by focusing on business use and tailoring practices to support roles.
Whether you're considering a "build vs. buy" decision for metadata tools or simply looking to refine your current practices, this course will equip you with the insights needed to make informed decisions and drive your metadata management initiatives forward. Topics covered include:
- Introduction to Metadata Categories: Descriptive, technical, structural, operational, and other classes of metadata, and how these are applied by different users and business functions
- The Lifecycle of Metadata: From capture, storage, and integration to active use and maintenance, and approaches to ensure metadata consistency, accuracy, and utility
- Challenges in Metadata Management: Common mistakes and how to avoid them; why catalog projects often fail and how to overcome these challenges
- Metadata in the Analytics Ecosystem: How metadata supports discovery, use, and governance; the role of metadata in self-service analytics and machine learning
- Aligning Metadata with Business Needs: Tailoring metadata management practices to support specific business goals; aligning tools, standards, and processes with organizational needs
- Choosing the Right Metadata Tools and Technologies: Overview of available metadata tools, technologies, and standards; "build vs. buy" decisions and technology evaluation
You Will Learn
- How to build a plan for metadata management and what you need to implement to support it
- How to focus on the most important aspects of metadata in your organization and avoid wasted effort
- The categories of metadata that cover the full range of users and applications
- How to map the roles in your environment to metadata classes and related technologies
- How to relate discovery, analytics, data curation, and operations to supporting metadata
- How and why catalog projects fail
- What problems metadata standards were intended to address so you can apply them appropriately
Geared To
- Data management professionals
- Architects
- Data engineers
- Data and analytics leaders