Quick Reference Guides

A practical guide to selecting visualizations, with specific recommendations based on data types and goals.

Explore different categories of AI applications, use cases, and ROI factors to guide informed AI investments.

Discover the seven executive behaviors that truly transform culture—not just through training or dashboards, but through trust, transparency, and action.

This 1-page reference helps you challenge assumptions, clarify logic, and sharpen critical thinking through 6 categories of Socratic questioning—ideal for data-informed decision-making and thoughtful discussion.

Discover which visualization methods communicate data with the most accuracy—and which ones introduce unnecessary cognitive load or confusion. A must-have for every designer of charts, dashboards, and presentations.

A curated set of analytical thinking principles designed to help you improve the clarity, insight, and decision-making impact of your data visualizations.

This guide breaks down the four major categories of AI—from automation to augmentation—and explores how they intersect with human decision roles. A great reference for choosing the right type of AI for your business context.

Designed for business professionals, this guide explains each step of the machine learning process in plain language, highlighting where human input is critical.

This visual guide defines what organizational data literacy really means and outlines the behaviors, barriers, and cultural shifts required to move beyond tools and training toward true decision fluency.

Explore a visual overview of strategic foresight and future-readiness, including systems thinking, scenario planning, and how to navigate uncertainty in data-informed decisions. Great for leadership workshops and team planning.

A quick reference guide to uncovering why many data literacy efforts fail—and how to fix them. Learn to spot surface-level wins, shift toward decision fluency, and build habits that transform data culture.

This guide outlines the non-technical skills—like critical thinking, ethical oversight, and communication that business professionals need to effectively use AI. A must-read for navigating AI-driven work with clarity and confidence.

This guide explains how classifying different types of data improves governance, security, decision-making, and organizational value in a practical, business-friendly way.

Smart AI decisions start by matching the right human skills to the right AI outputs. This guide helps you navigate AI outputs more effectively by starting where real-world interactions begin — with the type of output you’re working with.

An overview of three primary analytics organizational structures—centralized, hybrid, and decentralized—highlighting their characteristics, benefits, and potential drawbacks.

A checklist-based approach to foundational data principles, helping you drive responsible, actionable, and ethical data use.

Helps you spot common flaws in business questions that undermine insight quality, and how to ask better ones.

Discover the everyday actions leaders can take to build data-thinking habits on their teams. These behaviors create the conditions for teams to think critically, ask better questions, and use data more effectively.

Provides a matrix of decision types, data sources, and tools to make better, faster strategic decisions.

A structured framework for categorizing and understanding data types to support better decisions and organizational alignment.

Helps professionals spot key moments to apply data, with guiding questions and practical examples.

A design and accessibility checklist for creating clear, intuitive, and inclusive visualizations.

A quick reference on applying systems thinking mindsets and tools to enhance data-driven decision-making.

Highlights how data, analytics, and AI are reshaping work, roles, and necessary future skills.

A handy two-page reference outlining key steps and strategies for effective prompt engineering with AI systems.

A practical framework to help you think critically when using AI. Learn how to use, evaluate, question, and reflect on AI outputs responsibly.

Best practices for responsibly using generative AI to augment human analysis and critical thinking.

A checklist to follow each of the 8 stages of the data-informed decision-making process.

This guide arms you with critical skills to evaluate ad hoc visualizations commonly encountered in media and blogs.

A practical guide on the impact and effectiveness of visual data.

Covers the general benefits of using data visualizations like speed of processing, retention and memory, etc...

This guide provides an overview of the 9 components of data literacy, listing the competencies for each.

This guide walks users through a classification framework to evaluate decisions by impact, risk, stakeholder involvement, and complexity.

This visual memo outlines why data culture isn't just an IT or L&D project—it's a leadership responsibility. Explore what great leaders do differently to drive data-informed transformation and build trust in the age of AI.

Explore a wide range of data visualization types with this easy-to-scan guide. Whether you're presenting comparisons, trends, parts-to-whole, or relationships, this overview helps you understand the strengths and best uses of each chart style.

Learn a simple, repeatable five-step framework for working smarter with AI outputs. The AI Thinking Loop helps you frame, use, evaluate, question, and decide—building more critical, responsible, and confident AI-assisted decisions.