Moving Data Platforms to Knowledge Platforms - From Data Architecture to Enterprise AI

Overview

Discover how knowledge platforms and graphs revolutionize enterprise AI by transforming traditional data architectures into dynamic, context-rich knowledge systems for enhanced data utilization.

Syllabus

• Introduction to Data and Knowledge Platforms
Overview of Data Platforms
Introduction to Knowledge Platforms
The Role of Knowledge Graphs in Enterprise AI
• Traditional Data Architectures
Components and Functions of Data Architectures
Limitations of Traditional Data Infrastructures
• Transitioning from Data to Knowledge
Understanding Context and Semantics in Data
Data Integration and Interoperability
Introducing Metadata and Ontologies
• Building Knowledge Graphs
Basics of Graph Theory and Networks
Core Technologies: RDF, OWL, SPARQL
Design and Implementation Strategies
• Leveraging Knowledge Platforms in Enterprise AI
Enhancing Data Discovery and Utilization
Enabling Advanced Analytics and Machine Learning
• Use Cases and Applications
Industry-specific Applications (Finance, Healthcare, etc.)
Case Studies of Successful Knowledge Platform Implementations
• Tools and Technologies
Overview of Current Platforms and Software
Comparative Analysis of Popular Knowledge Graph Tools
• Challenges and Best Practices
Addressing Scalability and Performance
Ensuring Data Quality and Consistency
Strategies for Change Management in Organizations
• Future Trends in Knowledge Platforms and AI
Emerging Technologies and Innovations
The Evolving Role of AI in Knowledge Systems
• Course Wrap-up and Recap
Key Takeaways
Future Learning Pathways and Resources

---

Subjects

Data Science