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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 Quality in 2026: Where the Discipline Actually Is
Data quality programs in 2026 have matured. Here's what enterprise data quality discipline actually looks like, and where most teams still fall short.
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Master Data Management in 2026: Still Relevant or Quietly Dying?
Is master data management still relevant in 2026, or has the data mesh era killed the MDM discipline? An honest look at where MDM actually fits now.
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Knowledge Graph Enterprise Adoption in 2026: Beyond the Hype Cycle
Knowledge graphs have moved through hype, disappointment, and into a more sustainable adoption phase. Where they're actually working in 2026.
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Metadata Management Tooling in 2026: Where the Market Has Settled
After several years of consolidation, the metadata management tooling market in 2026 has clearer leaders. What's working and what's still ahead.
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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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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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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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Why Data Quality Is the Biggest Bottleneck for AI Projects
Most AI projects don't fail because of bad models. They fail because of bad data. Data quality governance isn't glamorous, but it determines outcomes.
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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.