## Highlights We’ve reached peak infographics. Are you ready for what comes next? ([View Highlight](https://read.readwise.io/read/01jbf0qyfpy99h37aq2t0ancf4)) --- Data visualization pioneers such as William Playfair, John Snow, Florence Nightingale and Charles Joseph Minard were the first to leverage and codify this potential in the 18th and 19th centuries, and modern advocates such as Edward Tufte, Ben Shneiderman, Jeffrey Heer and Alberto Cairo are among those responsible for the renaissance of the field over the last 20 years, supporting the transition of these principles to the world of Big Data. ([View Highlight](https://read.readwise.io/read/01jbf0nsj7gy2pcs8aw9t6ws0s)) --- Data represents real life. It is a snapshot of the world in the same way that a picture catches a small moment in time. Numbers are always placeholders for something else, a way to capture a point of view—but sometimes this can get lost. ([View Highlight](https://read.readwise.io/read/01jbf0rj1jqrd12a1rd7rrhwrk)) --- Since clarity does not need to come all at once, we layered multiple visual narratives over a main construct that served as the jumping-in point for readers to begin and follow their interest. We call this process nonlinear storytelling; people can get happily lost exploring individual elements, minor tales and larger trends within the greater visualization, while being naturally invited to engage with the visual on deeper levels. ([View Highlight](https://read.readwise.io/read/01jbf0txv41j1zmepmea55j5ht)) ---  --- Thoughtful design comes to the rescue again. What I always do when I start a new data project is to move away from the screen and start drawing. I draw with data in my mind, but with no data in my pen: I sketch with data to understand what is contained in the numbers and in their structure, and how to define and organize those quantities in a visual way to create opportunities to gain insight. ([View Highlight](https://read.readwise.io/read/01jbf1acfxqzpx34vc774kphss)) --- Sketching with data—in a way, removing technology from the equation before bringing it back to finalize the design with digital tools—introduces novel ways of thinking, and leads to designs that are uniquely customized for the specific type of data problems we are working with. ([View Highlight](https://read.readwise.io/read/01jbf1anhypsx9mz0a09cjvg38)) ---   --- To expand their data-drawing vocabulary, designers can access hundreds of years of visual information encoding—the evolution of music notation from medieval times to contemporary music, the experimentation with geometric shapes that characterized Avant-Garde artists of the last century. These visual languages, while clearly pursuing different goals, have a lot in common with data visualization: They draw on common perception principles and use simple shapes, select symbols and a definite range of colors to create basic visual compositions that deliver a message and please the eye. ([View Highlight](https://read.readwise.io/read/01jbf1bdeesqw5rv75eksr78p4)) --- How a dataset is collected and the information included—and omitted—directly determines the course of its life. Especially if combined, data can reveal much more than originally intended. As semiologists have theorized for centuries, language is only a part of the communication process—context is equally important. ([View Highlight](https://read.readwise.io/read/01jbf1kzkkqh7616k67pcch50p)) --- This is why we have to reclaim a personal approach to how data is captured, analyzed and displayed, proving that subjectivity and context play a big role in understanding even big events and social changes—especially when data is about people. ([View Highlight](https://read.readwise.io/read/01jbf1m817rtvwpe8re8e54wt7)) --- Data, if properly contextualized, can be an incredibly powerful tool to write more meaningful and intimate narratives. ([View Highlight](https://read.readwise.io/read/01jbf1ma4v5v6gzqn5jqxt6y14)) --- Let’s stop thinking data is perfect. It’s not. Data is [primarily human-made](http://www.printmag.com/print-magazine/identity-design-maps-paula-scher/). “Data-driven” doesn’t mean “unmistakably true,” and it never did. ([View Highlight](https://read.readwise.io/read/01jbf1mrjq79zkjfp1ftavxmqz)) ---