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sketch then create

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Summary

This article explores the importance of sketching in data visualization. It explains how sketching can be used to generate ideas, provide clarity, and test different approaches to visualizing data. It also outlines the process of sketching a particular dataset, and how the same dataset can be visualized in multiple ways. Ultimately, it encourages the reader to practice sketching data in order to gain a better understanding of how to effectively present information.

Q&As

What was the advice John Singer Sargent gave to artists?
John Singer Sargent advised artists to "sketch everything and keep your curiosity fresh."

How is data visualization similar to traditional art?
Data visualization is similar to traditional art in that it is a form of creative expression.

What was the goal of the data visualization for the glucose observations?
The goal of the data visualization for the glucose observations was to provide the data in a digestible format, which would be part of a regularly updated report.

What were the pros and cons of using a box plot for the data visualization?
The pros of using a box plot for the data visualization were that it is designed to show distributions well, and the dataset consists of distribution measures. The cons were that there were not enough observations for most of the days, and it is not a well-known chart type.

What benefits are there to sketching data before creating a visualization?
The benefits of sketching data before creating a visualization are that it allows the creator to experiment with novel approaches, get feedback from others early in the process, and be intentional about every element on the chart.

AI Comments

👍 This article is a great demonstration of how data visualization can help enhance understanding of complex information. It provides a thoughtful and thorough overview of the process of sketching data and how it can be used to gain clarity and insight.

👎 This article may be too long and complex for most readers, as it is quite technical and detailed in its explanation of the data visualization process.

AI Discussion

Me: It's about the importance of sketching before you create a data visualization. The author encourages practitioners to grab a whiteboard or tablet and draw a handful of charts that could work for their dataset. They discuss the pros and cons of different visualization types and the importance of considering factors like how familiar the chart type is to the audience and how easy it is to create in a graphing tool.

Friend: That's really interesting. I hadn't thought of sketching before creating a data visualization. What are the implications of this?

Me: Well, it encourages data visualization practitioners to be more creative and thoughtful in their approach. By sketching out ideas first, they can test different visualization types and experiment with novel approaches. This allows them to gain clarity and feedback from others early in the process, and also helps them to avoid clutter and unnecessary elements. Ultimately, it can help to create more effective and engaging visualizations.

Action items

Practice sketching data with the related exercise provided in the article.
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Technical terms

Sketch: A quick drawing or diagram made to explore ideas or test a hypothesis.
Data Visualization: The process of representing data in a visual format, such as a chart or graph.
Whiteboard: A large, white, erasable board used for writing or drawing.
Table: A structured arrangement of data in rows and columns.
Box Plot: A chart that displays the distribution of a dataset by showing the median, quartiles, and range of the data.
Jitter Plot: A type of box plot that shows all individual points while still giving a sense of the distribution.
Dot Plot: A chart that uses dots to represent data points.
Line Chart: A chart that uses a line to connect data points.
Combination Chart: A chart that combines two or more chart types, such as a line chart and a data table.