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SGKG is proudly supported by Team400, an Australian AI consultancy helping enterprises navigate practical AI implementation.
Knowledge governance and data standards
Best practices for managing knowledge assets at scale.
Clear, practical guidance for data professionals and governance practitioners. We focus on standards, frameworks, and proven approaches that work.
What we cover
- Data governance frameworks and standards
- Knowledge management systems
- Metadata and information architecture
- Compliance and policy implementation
What you can expect
- Clear explanations of complex standards
- Implementation guidance and templates
- Case examples from real programs
- Best practices from industry leaders
Latest posts
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Data Lineage Tools Comparison 2026: What Actually Works
Data lineage tools promise to track where data comes from, how it's transformed, and where it goes. Most are expensive and underwhelming. Here's what actually delivers value.
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Master Data Management Without an MDM Platform
MDM platforms cost hundreds of thousands. Most organizations don't need them. Here's how to manage master data effectively without buying expensive software.
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Knowledge Graphs for Supply Chain Visibility
Supply chains involve hundreds of entities and relationships. Knowledge graphs provide the semantic structure needed to understand complex, multi-tier supplier networks.
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Metadata Standards for AI Training Datasets
As AI systems consume vast training data, standardized metadata becomes critical for transparency, accountability, and data quality assessment.
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How to Build a Business Glossary That People Actually Use
Most business glossaries become shelfware within months. Here's a practical approach to building one that stays relevant, gets adopted, and delivers measurable governance value.
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Why Most Data Governance Frameworks Fail (And What Actually Works)
Most organisations that implement data governance frameworks abandon them within two years. The problem isn't the framework — it's how it's deployed. Here's what the successful implementations do differently.
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Enterprise Knowledge Graphs: Reality Check
Why most knowledge graph projects fail and what actually works
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Semantic Layers in Data Mesh Architecture
How semantic layers interact with data mesh principles and why this matters
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DAMA-DMBOK vs Practical Data Governance: Bridging the Gap
The DAMA-DMBOK framework is comprehensive but theoretical. Here's how to bridge the gap between what the book says and what actually works in organizations.
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Metadata Management: The Boring Skill That Matters Most
Nobody gets excited about metadata management. But organizations that do it well consistently outperform those that don't on every data initiative.
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Ontology Design: Where Theory Meets Practical Limits
Formal ontologies promise precise knowledge representation. Real-world implementation hits practical limits that theory doesn't fully address.
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Metadata Quality Decay: Why It Happens and How to Prevent It
Metadata quality degrades over time in every system I've worked with. Understanding the mechanisms of decay helps design systems that resist it.
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Graph Database Query Optimization: Patterns That Actually Matter
Most graph database performance problems stem from a few common query antipatterns. Understanding them prevents hours of optimization struggle.
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Metadata Drift: How Knowledge Systems Decay Over Time
Even well-designed knowledge graphs and metadata systems degrade as terminology evolves, contributors change, and organizational priorities shift.
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Five Data Catalog Implementation Mistakes That Doom Projects
Data catalog projects fail for predictable reasons. Here are the mistakes that waste millions and how to avoid them.