Making with Data by Samuel Huron, Till Nagel, Lora Oehlberg, and Wesley Willett is a fascinating, one-of-a-kind exploration into physical representations of data. It’s very accessible, providing definitions of terms and assuming that readers may have little prior knowledge of techniques. The book is an inspiring and engaging catalog of possibilities in making data physical.
Physical representations of data connect with the human aspects of each data point, make data concrete and tangible, encourage interaction and reflection, and create more accessible and engaging experiences. Representations of data in physical form have been around for tens of thousands of years. Now, increased access to data and technological tools to create physical representations are providing people with more options and inspiration for creating physical data objects.
Making with Data “offers a snapshot of contemporary data-driven making—examining the stories behind the projects and unpacking the stories, tools, and decisions behind the design” (p. 22). The book is split into five sections based on the type of physicality, and each section provides a series of examples with the creators themselves explaining the project motivation and inspiration, their practices and processes, the materials and tools used, and a reflection.
I particularly enjoyed the materials and tools section of each piece. It was neat to see the variety of tools—including clay, Excel, computer-aided design software, a pipe bender, laser cutters, and more—used across projects and to gain insight into exactly how each piece was created. The practices and processes section was also really interesting to read through to see how each piece developed.
The fives types of data physicality
The first section of the book explores projects in Handcraft, where objects created by hand are encoded with data. My favorite in this section was “Snow Water Equivalent” by Adrien Segal, a cabinet that is also a data sculpture of the snowpack at Ebbetts Pass from 1980 to 2010. I particularly liked this quote from Adrien’s reflection that shows the power of physical data representations: “Artifacts can be a creative expression that captures a particular time and place in the physical realm. They have the potential to raise awareness, bring about dialogue, tell a story, or change perceptions—they can be vessels embedded with layers of information, from which knowledge can be derived.” (p. 49)
The second section examines Participation, where people participate by providing data or being part of the creation process. My favorite in this section was “Let’s Play with Data” by Jose Duarte, an interactive data visualization kit, which includes stencils, shapes of different colors, tape, markers, and a board along with instructions. In the practices and processes explanation, Jose states, “It’s not just about visualizing information but making information visible” (p. 143). The participatory process of the projects in this section makes the information more visible.
The third section covers Digital Production, where digital fabrication is used to create physical representations of data. My favorite in this section was “Wage Islands” by Ekene Ijeoma, a topographic map of New York City based on housing costs that can be moved in and out of water based on wages. In his reflection, Ekene states “I wanted to bridge the gap between facts and feelings in a way that felt familiar and intuitive” (p. 209). This is one of the powers of making with data—encoding information in objects that are familiar and intuitive.
The fourth section provides examples of Actuation, where physical objects change based on changes in data. My favorite in this section was “Loop” by Kim Sauvé and Steven Houben, a moving series of rings that show daily steps for seven days in comparison to a goal. Based on interviews and user testing, they learned that the visualization “had to blend into the environment and be aesthetically pleasing” because it would be part of the home decor and that “it would be beneficial to create an abstract representation that could be ‘read’ by the owner, but provides privacy when observed by others” (p. 270). Each piece in the book included some sort of iteration on the design, and many included a user feedback component. At the end of the book, the authors reflect on the iterative process involved in making physical objects with data, and note that while we see the finished products and a more linear process, experimentation and messiness are key parts of the development.
The fifth and final section explores Environment, where pieces show information about the environment, are part of the environment itself, or use the environment as a setting. My favorite in this section was “Perpetual Plastic” by Liina Klauss, Moritz Stefaner, and Skye Morét, a data sculpture made of plastic retrieved from beaches that shows the fate of plastic created between 1850 and 2015. In the reflection, the creators state, “We deliberately chose to create a “data visceralization” from physical, known objects—not only because they were colorful and relatable, but also because they allowed us to encode data variables into a tangible, situated form, at human scale” (p. 330). Physical representations of data allow for a larger scale and also a more tangible creation than digital ones.
An area for improvement
The only critique I have of the book is that there could have been more diversity in the creators featured. Nearly 90% of the creators are based in North America or Europe and almost three-quarters are male. I also noticed that most of the creators in the book are white.
I reached out to the authors about this critique, and they shared the following (plus a way to get involved and share your own physical representations of data!):
We agree that our book could highlight more data physicalizations from more diverse authors, especially in regard to gender and geography. This is something that was brought up and that we tried to address (at least in some way!) as we were recruiting chapter authors. In part, this might be due to the fact that much of the research in this space, as well as our own professional networks, comes from computer science and engineering, which have long standing and recognized diversity issues.
In addition, we aimed for diversity in various areas, such as the medium, data type, and theme of the data objects, as well as the background (academia or practice), the discipline (artist or scientist), or the seniority of the authors. We wanted to show a wide range of data physicalizations and strived to balance all these aspects. We did make an effort during the process to try to pull in more diverse voices, and the final book includes several chapters from folks who bring a broader range of global perspectives. But still, the distribution of authors in the book isn’t one we’re really happy with.
We are planning to invite more creators to use the template from the book to document their works. Just this Monday [March 13], we released the chapter template under an open license. We are hoping that it will help us to connect with more diverse creators and feature them on our webpage in the future.
Thank you so much to Till, Lora, Sam, and Wes for sharing this response, and be sure to check out the chapter template to share your own work making with data.
Final thoughts
I absolutely loved Making with Data. It’s a beautiful book that was very enjoyable and inspiring to read, and I’m sure I’ll refer back to it for ideas. The variety and creativity of the pieces presented, along with the depth of explanation shared by each of the creators was truly fascinating. And I loved that the book ends with a message of encouragement to the reader to go out and experiment with data, share the process, and embrace the messiness. After reading this book, I certainly want to go make things with data! I highly recommend getting a copy of Making with Data and checking out the accompanying website or on Amazon.
Correction: An earlier version of this article misnamed the project in the fifth section of the book. It is “Perpetual Plastic,” not “Perceptual Plastic.”
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Jenn Schilling is the founder of Schilling Data Studio, a data visualization training and consulting agency. She has a decade of experience applying data science and data visualization in a variety of industries, including supply chain, market research, and higher education. Jenn loves telling compelling stories with data and teaching others how to create impactful visualizations.
