The Cross-Disciplinary Challenges Of Visualizing Data
Contributed by Morgan Fritz on 25 Mar 2014
Data are growing intensely and pose a substantial data visualization challenge. Every day we generate massive amounts of data in the form of photos we take, electronic messages we send, and queries we make to Internet browsers. Numbers of shared data have more than doubled during the past five years (Internet World Stats). The complexity of collecting and analyzing big data in meaningful ways challenges and changes the fundamental of research, impacting research methodology itself and our approach to tool design. From medicine to sociology, analysis of quantifiable data about life on Earth has allowed researchers to gain new insight making us better understand genetic and molecular underpinnings of disease. Analyzing big data encourages new research questions, triggers new data interactions, and motivates new research technologies and methods. In particular, we experience an enormous increase in development of visual technologies, tools and methods for exploring and analyzing data. Making sense of data visually is fundamental to most research processes. We depend on visual patterns and guidance in everything we see. When reviewing an article in a journal, we first explore graphics and visual representations before reading the actual paper. Visual representations and analytical tools have the potential to augment our reasoning capacities by facilitating perceptual inference, discover patterns, and expand our working memory.