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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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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.
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Extracting Structured Knowledge from Unstructured Data: The Knowledge Graph Challenge
How organizations are using NLP and machine learning to build knowledge graphs from documents, emails, and unstructured sources.
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Data Governance Challenges for AI Training Datasets
How organisations are applying data governance frameworks to AI training data, addressing quality, lineage, bias, and compliance issues unique to machine learning.
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Ontology vs Taxonomy: Choosing the Right Knowledge Organisation Model
Understanding when to use taxonomies versus ontologies for knowledge management, with practical examples and implementation considerations for 2026.
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Data Lineage Tracking: Theory vs Reality
Why tracking where data comes from and how it transforms is harder than it sounds
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Why Metadata Standards Fail in Practice (And How to Fix That)
Understanding the gap between theoretical metadata frameworks and real-world implementation
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Data Lineage Tracking: Why Enterprise Data Teams Can't Ignore It
Understanding where your data comes from and how it's transformed is essential for compliance, debugging, and trust in analytics.
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Semantic Layer Architecture: Bridging Business and Data
A well-designed semantic layer translates raw data into business concepts that non-technical users can understand and trust.
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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.