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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 in the Enterprise: Beyond the Hype
Examining the practical applications, challenges, and real-world value of implementing knowledge graphs in large organizations.
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Practical Steps for Improving Metadata Quality in Large Organizations
A pragmatic approach to elevating metadata standards without disrupting existing workflows or overwhelming staff.
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Data Quality Automation in 2026: What's Working and What's Hype
Automated data quality tools have improved significantly, but separating genuine capability from marketing claims requires understanding what automation can and can't do.
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Knowledge Graphs vs Relational Databases for Metadata Management: When to Use Which
Organisations storing metadata face a fundamental architectural choice. Knowledge graphs and relational databases both work, but they excel in different situations.
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Data Lineage Tools in 2026: What's Changed and What to Look For
Data lineage has evolved from a compliance checkbox to a core data governance capability. Here's how the tool landscape has shifted and what distinguishes the leading options.
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Master Data Management vs Data Mesh: Competing Philosophies or Complementary Approaches?
MDM and data mesh represent fundamentally different approaches to data governance. Understanding where each works — and where they fail — matters more than picking sides.
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Semantic Search for Enterprise Knowledge Bases: Moving Beyond Keywords
Keyword search in corporate knowledge bases produces poor results because people don't search using the same terms documents use. Semantic search fixes this — but implementation requires careful planning.
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Building Taxonomies for AI Training Data: Why Classification Still Matters
AI models are only as good as their training data, and training data quality depends heavily on how it's classified. Taxonomy design for AI datasets deserves more attention than it gets.
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Data Catalog Tools Compared: The 2026 Landscape
The data catalog market has matured significantly. Here's how the major platforms stack up on features, pricing, and real-world usability.
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Where AI Meets Knowledge Management: What's Real and What's Hype
Large language models are reshaping how organizations manage institutional knowledge. Some applications are genuinely transformative. Others are solutions looking for problems.
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Knowledge Graph Implementation Patterns for Enterprise
Knowledge graphs promise connected data, but implementation approaches vary enormously. Here are the patterns that consistently deliver value in large organizations.
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Metadata Governance Frameworks That Actually Scale
Most metadata governance efforts collapse under their own weight. Here's what separates the frameworks that scale from those that stall.
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Data Governance Frameworks: A Practical Enterprise Guide
Implementing effective data governance requires more than policy documents. Here's what actually works in large organizations.
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Data Quality Dimensions: Building a Practical Measurement Framework
Measuring data quality requires more than checking for nulls. Here's how to build a comprehensive framework that drives improvement.
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Metadata Standards: The Interoperability Challenge Nobody's Solving
Organizations adopt different metadata standards, creating silos that prevent data integration. Why interoperability remains so difficult.