Harmonizing an Organization’s Terminology for Effective Data & AI Practices

Categories: DM Fundamentals, Featured
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About Course

In this course, you will gain a clear understanding of the foundational concepts of data, metadata, information, and knowledge—and how they interrelate in the context of data management.

You’ll explore how data models and lifecycle stages provide structure for transforming raw data into meaningful information and actionable knowledge.

We examine the challenges of defining key terms such as “data asset” and “data product,” highlighting how definitions differ across industry authorities.

You’ll also review a component-based perspective that helps clarify what makes a data product distinct from a data asset. This knowledge supports consistent terminology and alignment in your organization’s data practices.

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What Will You Learn?

  • How to interpret and compare definitions of key data and AI concepts to create clear terminology for your organization
  • how to apply practical techniques for aligning data, metadata, information, and AI terminology across different organizational contexts
  • how to use harmonized terminology to scope data and AI initiatives more accurately and support consistent governance practices

Course Content

Topic 1. Definitions of data, metadata, information, knowledge, and their relationships
This lesson introduces the foundational concepts that shape every data management activity. You will explore the definitions of data, metadata, information, and knowledge as they appear across respected industry sources—and discover why these definitions often differ. The lesson explains how these concepts relate to one another in both technical and business contexts, highlighting where ambiguity and circular reasoning can create challenges. By understanding these relationships, you’ll gain clarity on how meaning is created, how context transforms raw data into information, and how organizations build knowledge that supports decisions and future actions. This foundation prepares you to scope initiatives more accurately and align communication across stakeholders.

  • Lesson 1.1. Definitions of data, metadata, information, and knowledge, and their relationships
    00:00
  • Definition of a model
  • Definition of a data model
  • Data model types
  • Data model levels
  • Order of levels

Topic 2. The concepts of a data model and a data lifecycle
This topic introduces the concepts of a data model and a data lifecycle, two lenses that help us understand how data is structured and how it moves through an organization. Because no standard lifecycle model exists, we rely on the version defined in the O.R.A.N.G.E. Framework to provide a clear and practical foundation for this course.

Topic 3. The relationships between data, metadata, and information in different contexts
This topic examines how the relationships between data, metadata, and information differ when viewed through the lens of a data model and through the stages of the data lifecycle.

Topic 4. The definitions of a data asset and a data product
This topic explores the definitions of a data asset and a data product and explains how these two closely related concepts differ and relate to one another in practice.

Topic 5. An AI system in the data context
This topic introduces a definition of an AI system that shows how all its components align with the same elements that form a data asset or a data product, reinforcing their shared structural foundation.

Topic 6. Taking practical steps
This topic outlines the practical steps an organization can follow to harmonize its data and AI terminology and apply it consistently across its practices.

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Data Crossroads Academy