Design and Data: Strategies for Designing Information Products in Team Settings

Abstract

This chapter aims at linking data and information to creative design, focusing on collaborative processes at early phases of the design with data. The chapter aims at providing clarity in a large space around design and data. Thus, it serves as a guide for design team’s approach towards the challenges of data design. Consequently, design is one of the key disciplines involved in data and information visualization (Moere and Purchase 2011). This chapter starts with a short introduction of ideas and concepts in the intersection of data, information, and design. It looks at users and designers as the main stakeholders, and considered the purpose of designed information. Following this introduction, we first focus on design artifacts essential for collaborative data design practices. Secondly, we focus on what it means to integrate data with design and the potential roles of data in the data design process. The chapter outlines a general design process with methods and approaches towards early design challenges. Furthermore, this chapter concludes with an annotated bibliography to guide further reading. Along the chapter runs an example case of a real information product that helps for better understanding. It links the more theoretical elaborations to the application level of a concrete design case.

Further Reading

In the following, a few directions for further reading will be introduced briefly. There is a recent wealth of books on data visualization of which a few are presented here. First, pure data and information visualization is introduced well in books by Edward Tufte, an early advocate of presenting information to a reader effectively. You will find that he is (provokingly) outspoken against any kind of noise that masks data or information in their presentation. Also, he is quite close to design in his approach to understand and questioning needs to visualization from a data practitioner’s point of view. A good starter is Envisioning Information (Tufte 1990), with Visual Explanations: Images and Quantities, and Evidence and Narrative (Tufte 1997) as a follow-up. More theoretical views on visualization are available from Information Visualisation (Spence 2001) and Information Visualization: Perception for Design (Ware 2012).

Second, there are recent books on designing visualizations and interaction with data, for instance, The Functional Art: An introduction to information graphics and visualization (Cairo 2012), Now You See It (Few 2009), or Raw Data – Infographic Designers’ Sketchbooks (Heller and Landers 2014). The latter book does not only present finished designs, but looks behind the scenes and shows ways of working and translating data into masterful visualizations. Thus, a very practical guide is Designing Data Visualizations (Iliinsky and Steele 2011). For an introduction into D3 as the currently most popular toolkit for visualization on the web, Interactive Data Visualization for the Web (Murray 2013) is highly recommended.

Other related disciplines, such as generative art can be inspiring as well. However, Generative Gestaltung (Groß et al. 2009) or Design by Numbers (Maeda 2001) are good starting points. A growing trend entails the use of visualization techniques in journalism and media. They are however, not the focus of this chapter. Unfortunately, little work is published so far for physical visualizations (Fens and Funk 2014). Also, multi-modal information products, which we also target in this chapter and an interesting list of physicalized visualization can be found here: http://dataphys.org/list/.

As for a bit more advanced reading on what is happening currently in the area of data visualization, there are three important conferences on visualization-related topics: IEEE VIS, ACM SIGGRAPH, and Visualized (non-academic) conference. There is also a relevant journal, ACM Transactions on Visualization and Computer Graphics, which publishes articles like Mental Models, Visual Reasoning and Interaction in Information Visualization (Liu and Stasko 2010), which are worth reading. For less academic and more practical data design resources, there is a lively community on Twitter and on different websites. Thus, be sure to check out http://www.datastori.es, www.visualization.org, and http://blog.visual.ly.

Collaboration in data visualization and interfaces mostly refers to the collaborative use of such interfaces and products, and not to their design or development. However, there are a few exceptions looking at what challenges research on collaborative visualization (design) has to tackle still. This is increasingly moving away from the collaborative use of visualization and visual analysis towards collaborative design (Heer et al. 2008; Isenberg et al. 2011).

A whole different area is demarked by literature on rationality, psychology, and statistics. Everyday Irrationality (Dawes 2001) is recommended for getting a general overview of how people experience information and interpret it (often in their favor or naively). To dive into statistics and behavioral psychology, there are again many sources to choose from e.g., Becoming a Behavioral Science Researcher: A Guide to Producing Research That Matters (Kline 2008) or Straight Choices: The Psychology of Decision Making (Newell et al. 2007).