Coordinator: Isabel Meirelles, Associate Professor, graphic design, Northeastern University, U.S.
Rikke Schmidt Kjærgaard
, Assistant Professor, Interdisciplinary Nanoscience Center, iNANO, Faculty of Science and Technology, Aarhus University, Denmark
Miriah Meyer, Assistant Professor, computer science, University of Utah, U.S.
Bang Wong, Creative Director, Broad Institute of MIT and Harvard, U.S.

The amount of data currently available to industry and academia is immense, and has affected how we approach our work in many areas, from advertising agencies to pharmaceutical companies, from social to natural sciences. Called Big Data with a reason these huge data sets are powerful assets for gaining insight into all sorts of phenomena. With the omnipresent access to large amounts of data, mostly unstructured, computational techniques have become integral to data analysis.

However, given our cognitive constraints in understanding patterns from numerical data alone, new methods have been devised to explore and understand datasets, and ultimately communicate findings. Among those new methods, visual analytic tools have played a crucial role in the study of big data. Visualizations are ubiquitous and critically important to generating new knowledge in several fields today. The process of devising visualizations is not trivial; it can be time intensive requiring a methodical approaches from practitioners in many disciplines. What is needed is a ‘common language’ and shared skill sets that transcend conventional professional boundaries from computer science to graphic design. On one hand, the team needs to be able to interpret the underlying structure of a dataset in a very abstract, algorithmic way, as well as understand the process of mapping data attributes to specific visual encoding channels —skills that are natural extensions of basic computer science principles. On the other hand, practitioners need to be able to distill the tasks and define the best perspective into the data that once encoded as visual representations will capture the essence of the dataset —skills that relate to fundamental concepts found in design. Surrounding all of these skills is the need for practitioners to work in multidisciplinary environments and communicate with domain experts in order to extract knowledge about specific application areas — critical analysis, communication, and social skills are highly important.

In our personal experience, each of us had a subset of these required skills and had to learn the others so that we could have meaningful interactions with each other. Given the amazing opportunity that has opened up to all fields of knowledge provided by access to huge amounts of data, the crucial questions are: How do we gain insight? How do we define the appropriate methods to explore, analyze, and communicate information? How do we go about teaching the upcoming generation of visualization practitioners and data scientists all of these skills? We hope for more effective, structured and scalable way to do this, rather than the serendipitous trajectories that we went by. We see several major challenges ahead, from education of the future generation to supporting mechanisms to those already working in this space. For example, can we define a common knowledge base and think differently about teaching computer science and design principles with the goal of visual analysis in mind? How to bring these common set of skills to the cross-disciplinary teams of current practitioners?

The white paper will examine the current state of affairs and articulate the challenges posed by big data and the urgent need for tools to examine and generate new insights. Our goal is to devise a set of recommendations and establish best practices to explore and develop visualization solutions to Big Data.