How to mind map a data visualisation

Pete Moore explores how mind mapping can be used as a precursor to effective data visualization, bridging the gap between user requirements and technical data modelling. He demonstrates how structured questioning, brainstorming, and visual thinking improve the clarity and usefulness of dashboards and reports. Through a worked example using U.S. election data, he shows how deeper inquiry leads to more meaningful and actionable visualizations . [00:03:59] Framing the Goals: Engaged Brains and User Experience Pete outlines his objectives: encourage inquiry, focus on user needs, and define usefulness as “look and learn.” [00:05:41] Data vs Information vs Knowledge He distinguishes raw data from information and knowledge, emphasising that visualization enables informed decisions rather than replacing judgment. [00:07:12] The Central Role of the User Requirement Effective visualization starts with dialogue; developers must interrogate requirements rather than assume what users want. [00:09:35] What Users See vs What Data Professionals See Users see charts and reports; data professionals see relationships, models, and multidimensional structures. [00:13:56] The Risk of Confirming Hunches Data is often used to reinforce pre-existing beliefs; better questioning can unlock deeper insights. [00:14:52] Why Mind Maps Fit Data Thinking Mind maps create space for free association and structured brainstorming before formal modelling begins. [00:16:16] Hub-and-Spoke Thinking and Star Schemas Pete draws parallels between mind maps and data warehouse star schemas, highlighting structural similarities. [00:18:38] Worked Example: U.S. Election Results A simple request (“Who won?”) becomes a richer exploration involving electoral college votes, margins, states, and swing analysis. [00:25:29] The Danger of Oversimplified Visualizations Using only the popular vote would misrepresent the election outcome, demonstrating why precise definitions matter. [00:27:18] Adding Context: Margins, Swing, and State-Level Detail By expanding the inquiry, visualization becomes analytical rather than merely descriptive. [00:30:12] From Mind Map to Logical Model The mind map serves as a precursor to a structured logical model, which then translates into a technical data model. [00:32:35] Further Learning and Recommended Resources Pete recommends key books and modelling approaches, including storytelling with data and Sun Modelling, to deepen visualization practice. Featuring: iMindMap