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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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Knowledge Graphs Meeting Generative AI: Where the Architecture Lands
Generative AI has finally given knowledge graph teams a reason to exist outside the data ivory tower. The architectural patterns are stabilising.
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Metadata Quality Metrics That Drive Behaviour, Not Just Reports
Most metadata quality dashboards are read once and ignored. The metrics that actually change behaviour are narrower and harder to negotiate.
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The data catalog adoption gap in mid-2026: why nobody's using the thing you bought
Most enterprise data catalogs sit at single-digit adoption rates a year after launch. Here's what the working ones do differently in 2026.
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Taxonomy versioning: how to change a vocabulary without breaking everything downstream
Versioning a taxonomy is harder than versioning code. Here's how mature data teams handle it in 2026 — and why most still get it wrong.
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Data Contract Implementation in May 2026: Where the Practice Sits
Data contracts have moved from concept to early operational reality. Here's where the practice sits in mid-2026 and what's actually working.
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Data Product Thinking in Mid-2026: Where the Practice Actually Lives
Data product thinking has spread unevenly across organisations. Here's where the practice actually lives in 2026 and what's working.
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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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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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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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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.