A resource for understanding context-aware analytics, semantic layers, metrics governance, and AI-powered analytics.
Context-aware analytics incorporates the semantic meaning and business definitions of data - understanding what metrics mean, how they should be calculated, and why they matter. This approach is essential for AI analytics tools that need reliable grounding to avoid hallucinations.
Conversational analytics enables users to query data using natural language instead of SQL or complex BI tools. Learn how it works, its limitations, and what makes conversational BI trustworthy.
7 min readData modeling for AI analytics requires semantic clarity, explicit relationships, and governed definitions that AI systems can understand. Learn how to structure data models that enable trustworthy AI-powered analytics.
7 min readMetrics governance is the practice of establishing ownership, standards, and processes for business metrics. Certified metrics are officially approved, validated calculations that the organization trusts for decision-making.
7 min readOne version of the truth (OVOT) is the principle that each metric should have a single, authoritative definition used consistently across all teams and tools. Learn why it matters and how to achieve it.
7 min readThis site provides educational content on analytics concepts. Content is written for practitioners who work with data, analytics tools, and AI systems. Learn more