But in 1988, Edward Tufte’s The Visual Display of Quantitative Information was already five years old.Footage of analysts operating Excel 2.0 to make pie charts c. 1988Now, imagine what it was like to make data visualization 15 years ago..Geospatial options were more advanced, with ArcGIS providing more and more cartographic functionality in its many red toolboxes.The difference in the traditions backing geospatial data visualization and network data visualization: in 2003 it was already version 8.0 of ArcGIS whereas that same year saw the release of the first modern network data visualization tool: Cytoscape.I know how much the data visualization has changed because I spent the last ten years or so making data visualization products in one form or another..Big public-facing work like ORBIS and Kindred Britain as well as less public data visualization for fun or to support research, analysis and exploration..I have long said that we should be more comfortable with critique in data visualization but without context the remarks I make might seem arbitrary and mean-spirited.So I was happy to have this opportunity to give context and express my concern that there has been a convergence of tools and modes but no corresponding reorganization of thought and practice..It seems like we’re still talking about and evaluating data visualization as if it was 1988 or 2003 when the number of people doing data visualization, the capabilities of their tools and the expectations of their audiences has dramatically increased.We continue to split the data visualization community into old categories like analysts using BI tools to create reports, developers using code to make custom data visualization, journalists creating data-driven stories or data scientists leveraging exploratory data analysis..These categories of practice map directly to particular tools and modes that have, as of late, begun to transform.1st WaveAnd so I’d like to offer up that there was, in the modern sense of data visualization, a 1st wave centered on Edward Tufte that emphasized clarity, simplicity and direct 1-to-1 mapping of data points avoiding as much transformation as possible..A sort of chart-as-sentence with clear structures and rules like you might see in The Elements of Style.Wave 1: Clarity2nd WaveThe 2nd wave focused on systematizing the encoding of information necessary for the development of tooling to produce data visualization..The grammar of graphics focuses in a razor-sharp way on encoding data via channels onto geometry..This is a system for encoding graphics from data, where the data attributes correspond and dynamically affect the length, angle, color or position (or any other graphical character) based on the data and changes in the data.Wave 2 was about taking these theoretical systems and producing the tools necessary for any data visualization practitioner to create any graphical expression based on data..That’s why we’ve seen such a proliferation of tools and libraries for data visualization but a concurrent rise in hideous graphics posing as charts.The search for a perfect specification for encoding data attributes through graphical channels is a means to an end..You can make beautiful graphics with R, you can have hierarchical charts in Tableau, you can easily deploy email reports from your custom dashboard.At Netflix, we’re experimenting with analytical notebooks designed not for exploratory data analysis but explanatory data visualization and the collaborative and communication needs required in that mode..Storytelling techniques common to data journalism are top-of-mind to stakeholders who have grown sophisticated in their tastes and expect cued animation and personalized frames of reference.There are more and more of these trends that we need to better understand:Once esoteric chart types, like treemaps and node-link diagrams, are now so accessible that they appear everywhere, and now it takes a really weird chart to be declared a xenografic.Notebooks are being used as dashboards and also as artifacts in the data engineering and transformation process.Data visualization in R has grown nearly as robust and interactive as data visualization in BI tools or custom applications.People are growing more comfortable with stylized data visualization (sketchy but also ISOTYPE).Where are we headed?These factors all contribute to what I think will define a third wave of data visualization where modes like notebooks, dashboards and long-form storytelling converge, as will the tools to create them and the literacy of the audiences they are made for..But criticism is hard — hard to hear and hard to give well.Part of the reason we’re so bad at giving, taking and fostering critique is that data visualization has long been an individualistic pursuit.. More details