The decision intelligence knowledge base.
Long-form, citation-friendly explainers of the concepts behind the modern AI-decision stack. We wrote these because the answers we found elsewhere were either marketing fluff or research-paper dense. These are the explanations we wish we’d had.
What is Decision Intelligence?
The Gartner-defined category that produces decisions, not insights. Four required elements (action + impact + validation + execution), the six-stage DI loop, how DI emerged from BI, and how to evaluate a DI platform.
What is Augmented Analytics?
Gartner's 2017 category. The four augmented capabilities, where AA stops short of decision-shaped output, the vendor landscape (ThoughtSpot, Tellius, Sisense, Power BI Copilot, Pyramid), and when you need Decision Intelligence on top.
What is Conversational Analytics?
The conversational interface for enterprise data. The full stack — NL understanding, NL-to-SQL, LLM reasoning, multi-agent governance — and why a generic LLM fails this job. Hallucinations, schema-blindness, and the architectural answers.
What is a Decision Flow?
Decision Flows turn recurring questions into one-click decisions. Anatomy (trigger + steps + outputs + actions), how they differ from dashboards and reports, examples across retail, finance, and operations, and how to build one.
How does AI generate SQL from natural language?
NL-to-SQL is harder than it looks. The naive-LLM failure modes (hallucinations, wrong joins, schema-blindness), the schema-aware approach with semantic knowledge graphs, the multi-agent verification loop, and what makes Diwo's SQL Generator Agent different.
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