![]() ![]() He describes his work: “I love taking all kinds of information – data, numbers, ideas, knowledge – and making them into images. David McCandless, on the other hand, has popularised artistic visualisations and introduced data as a storytelling category to a wider audience.Business intelligence expert Stephen Few sums up his disdain for the ornamental aspect of infographics: “When visualizations are used primarily for artistic purposes, they are not what we call data visualizations or infographics, which are terms that have been in use for a long time with particular meanings.”.In some cases though, graphical elements such as text and typography are used liberally to blatantly convince the audience what they should be seeing.ĭecoration is the one single design element that divides the schools of thought about infographics: The objective is to improve cognition by utilising graphics to enhance the human visual system’s ability to see patterns and trends. However, they do agree on a common goal – to present and communicate complex information quickly and clearly. Not even the experts agree what exactly constitutes an infographic. If we are not using a data visualisation or data exploration tool to interactively communicate about the data, we may need to frame the story differently, either in terms of presentation slides, message conveying images or using a clever infographic. So although you don’t need to be a graphical designer per se, you still need to know how to apply the correct settings and configurations to apply good design principles. Of course you can still force it to use the wrong graph type, or configure it to produce really bad visualisations. Tableau has a clever wizard-like function, which recommends the appropriate graph types for the data and analysis under consideration. If you use the correct configurations, you can create some really effective data visualisations – where the message that the data conveys clearly stands out, without being cluttered by unnecessary ink and other designed widgets. The developers of these tools have taken great care to follow the recommendations by data visualisation specialists like Stephen Few. Graphic Design in Data Visualisation toolsĭata visualisation tools like Tableau have some essential aspects of graphical design beat practices already built-in. However, the ability to create “self communicating” visual narratives often requires a separate skill - often using separate tools (more about that under infographics below). Even with good quality data and rigorous statistical techniques, if the results of an analysis are poorly presented, they will neither convince the right people nor convey the right message. when it must communicate its message “by itself”.Ī data scientist must have the ability to create visualisations that convey the stories about the insights discovered in the data. ![]() However, to me there is a huge difference when a visualisation is used to augment the verbal explanation of a hypothesis to an audience vs. (R can do static visualisations, and tools like Processing and Flare are use to create rich interactive visualisations). Communicative visualisations are intended to appeal to a wider audience, where the goal is to visually convince them of a hypothesis.(Some products like Tableau and Qlikview focus primarily on exploratory data visualisations.) Their goal is to help find and develop a hypothesis about the data, and their audience is typically rather small. These may consist of scatter plot matrices and histograms, where labels and colours are minimal by default. Exploratory data visualisations (as named by John Tukey) are intended to facilitate the data analyst’s understanding of the data.There are two breeds of data visualisations, which differ in their audience and application: In fact, some tools focus predominantly on data visualisation. ![]() Visualisations are appearing as graphs, maps and other graphical elements in the reports and dashboards produced by all the major BI tools. ![]() With the vast amounts of data now being analysed, visualisations are even more important to make sense of the data and to communicate the message(s) contained therein. It responds more effectively to visual representations than to textual data, especially tabular data. The human brain is highly adapted to discerning visual patterns – sometimes even when there aren’t any. Should there be a graphical designer in the data science team? Should graphic design be taught in the data science curriculum? This post is about an interesting debate around the relevance of graphic design skills in the data scientist’s portfolio. ![]()
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