I highlight specific examples of data visualization that successfully condense large amounts of information into intuitive, visually obvious charts, focusing on clarity and effective communication techniques for complex datasets.
I discovered an impressive periodic table of visualization methods that organizes 100 different graphs and diagrams into six functional groups. It covers everything from metro maps and treemaps to strategic metaphors and data-driven scatterplots.
LLMs can generate, interpret, and even invent visualizations. The analyst's role shifts toward asking better questions, testing novelty, and judging whether a visual is useful.
I found that AI now outperforms my 20 years of data visualization experience in creative ideation. While Gemini and Claude suggest innovative xenographics and animations, my value has shifted to selecting the right chart based on audience and taste.
New reasoning models can often deliver the final analytical artifact directly, so asking for output instead of code is now a viable workflow for some data tasks.
I share Peter Norvig's talk on managing massive datasets, where he explains why having vast amounts of data can be more effective for problem-solving than complex algorithms, a core principle in modern machine learning.