Search

Emerging online research platforms are bringing new efficiencies to the design research process. But the resulting data is large in scope and dense in nature. And the analytic tools and approaches design teams have come to rely on were not designed to manage this scale of inquiry. As design problems expand in complexity and require more inputs, generating big qualitative data sets is likely to become the new norm. This paper proposes two ways to manage this new condition: (1) adding visual coding techniques to textual coding to counteract "data sameness" and "data sprawl," and (2) developing new tools to support fast meta explorations of data sets.

Globalisation, volatile markets, legal changes and technical progress have an immense impact on business environments in most industries. More and more IT is deployed to manage the complexity. As a result, companies and organisations have to handle growing volumes of data which have become a valuable asset. The ability to benefit from this asset is increasingly essential for business success. Therefore, fast storage, reliable data access, intelligent information retrieval, and new decision-making mechanisms are required. Business Intelligence (BI) and Performance Management (PM) offer solutions to these challenges.

Technological advances have led to increasingly more data becoming available, a phenomenon known as Big Data. The volume of Big Data is to the order of zettabytes, offering the promise of valuable insights with visualisation the key to unlocking these insights, however the size and variety of Big Data poses significant challenges. The fundamental principles behind tried-and-tested methods for visualising data are still as relevant as ever, although the emphasis necessarily shifts to why visualisation is being attempted. This chapter outlines the use of graph semiotics to build data visualisations for exploration and decision-making and the formulation of elementary, intermediate- and overall-level analytical questions. The public scanner database Dominick's Finer Foods, consisting of approximately 98 million observations, is used as a demonstrative case study. Common Big Data analytic tools (SAS, R and Python) are used to produce visualisations and exemplars of student work are presented, based on the outlined visualisation approach.