Knowledge Graphs and Graph Analytics for Business

Course Category : Data Management

An advanced programme for understanding knowledge graphs and graph analytics, transforming complex data relationships into insights that strengthen decision-making, pattern discovery, and enterprise knowledge management.
Duration: 5 Days
Level: Advanced

Introduction

Modern organisations derive greater value from data when relationships among entities, events, processes, and information assets can be explicitly represented and analysed. Knowledge graphs and graph-based data models provide a powerful framework for capturing these connections and revealing insights that conventional tabular approaches may overlook.
This course examines the architectural and analytical foundations of knowledge graphs, including nodes and relationships, semantic modelling, ontologies, graph analytics algorithms, and business-oriented use cases. It connects graph technologies with fraud detection, recommendation, customer intelligence, enterprise knowledge management, and decision support..

Targeted Audience

Data, Analytics, and Business Intelligence Managers
Data Engineers and Data Architects
AI and Data Science Professionals
Knowledge and Information Management Professionals
Business and Digital Transformation Analysts
Data Governance and Quality Professionals
Data Product and Platform Managers
Technology and Strategy Consultants

Targeted Skills

Understanding Knowledge Graph Architecture and Concepts
Node-and-Relationship Data Modelling
Semantic Modelling, Ontologies, and Entity Linking
Interpretation of Graph and Network Analytics Algorithms
Evaluation of Business Graph Analytics Use Cases
Integration of Distributed Enterprise Knowledge
Network, Pattern, and Relationship Analysis
Enterprise Graph Technology Strategy Development

Expected Outcomes

Explain the fundamental principles of knowledge graphs and graph databases.
Differentiate conventional data models from graph-based approaches.
Design conceptual models representing entities, relationships, and properties in business contexts.
Explain the role of ontologies, semantics, and entity linking in enterprise knowledge organisation.
Interpret major centrality, pathfinding, community detection, and similarity algorithms.
Evaluate graph analytics applications in fraud detection, recommendations, and customer intelligence.
Identify governance, quality, and scalability requirements for graph environments.
Formulate an enterprise roadmap for adopting knowledge graph capabilities.

Training Topics Index

Knowledge graphs and the business value of connected data
Nodes, edges, properties, entities, and semantic relationships
Graph models versus relational and conventional data models
Property Graphs, RDF, and knowledge representation principles
Enterprise use cases and criteria for selecting graph approaches

Identifying business entities, relationships, and properties
Graph schema and conceptual model design
Ontologies, taxonomies, and semantic vocabulary principles
Entity resolution, record linkage, and identity relationships
Knowledge quality, provenance, context, and semantic consistency

Graph analytics foundations and network structure measures
Centrality and influence analysis
Pathfinding, connectivity, and shortest-path concepts
Community detection and network clustering
Similarity, link prediction, and relationship pattern discovery

Customer networks and commercial relationship analysis
Fraud detection and identification of unusual entity relationships
Recommendation systems and affinity analysis
Enterprise knowledge management and connected information discovery
Relationship-driven insights for strategic decision support

Selecting graph architectures according to scale and use case
Integration with enterprise data warehouses, lakes, and platforms
Governance of entities, relationships, metadata, and semantics
Performance, security, scalability, and lifecycle considerations
Developing an enterprise knowledge graph roadmap and business case

Course Features

Updated and Interactive Content
Hypothetical Examples and Case Studies
Pre- and Post-assessments to Measure Impact
Verified Certificate with a QR Verification Code

Knowledge Graphs and Graph Analytics for Business