Practices for turning operational data into indicators, dashboards, and experiments that guide continuous learning and better decisions.
Practical design patterns, a three-panel template, audience-specific narrative scripts, examples, and a publish checklist to turn dashboards into decision-focused learning tools that link metrics to hypotheses, ownership, experiments, and measurable next actions.
Practical, repeatable analysis recipes for common business learning questions — churn drivers, funnel leaks, feature impact, cohort lifetime comparisons, attribution diagnostics, and holdout/experiment patterns. Each recipe includes required inputs, a stepwise procedure, standard SQL pseudocode, validation checks, common confounders, and suggested visualizations to make results reproducible and comparable across teams.
A practical, example-driven primer on when to use lightweight knowledge graphs versus taxonomies, common entity models for organizational knowledge, tagging and relationship patterns, governance and upkeep practices, and simple query patterns that power discovery and recommendations.
A pragmatic, lightweight guide for designing entity models, canonical identifiers, relationship patterns, tagging-to-entity mapping, ingestion checks, and sample queries that make organizational knowledge discoverable, reusable, and agent-ready. Emphasizes incremental rollout, maintenance ownership, and governance that prevents over‑engineering and sprawl.
A practical catalog of dashboard patterns for teams that want dashboards to drive learning and action. Includes four reusable patterns (health card, cohort explorer, experiment results page, root-cause funnel) with purpose, data needs, layout advice, sample visuals, review cadence, ownership, storytelling templates, a checklist for production-quality dashboards, and practical next steps for turning patterns into reusable interactive templates.
A concise, decision-oriented catalog of common analysis recipes (cohort, anomaly, causal, regression, funnel, time series, segmentation, diagnostic) with when-to-use guidance, required data, quick implementation checklists, validation steps, interpretation tips, and common pitfalls.
A practical, reusable template and short guide that helps dashboard authors turn data into a concise narrative that clarifies decisions, surfaces hypotheses, and drives measurable next-step experiments.
A practical, minimal starter for organizational knowledge graphs: recommended core entities, example relations and cardinality, tagging and identifier patterns, sample SPARQL-like queries for common use cases, rollout strategy, and governance checklist to avoid common failure modes.