Data visualization has great promise. People who see a demonstration of a data visualization application immediately see its potential to provide insight into their information. Ideas abound as to what types of analysis data visualization could be used to enhance.
Yet, while many pilot applications have been developed, data visualization has yet to achieve mass deployment. Why? What have been the obstructions to providing instant insight into information? There are several factors that enter into the equation. One is the platform issue. As data visualization first appeared in the early 1990s, it required a sophisticated graphics workstation running a UNIX operating system. Sun or Silicon Graphics workstations were implemented on analysts' desks, but were generally not widely deployed throughout the enterprise. The need for specialized hardware and a UNIX architecture turned away some potential data visualization users. Within the last year, however, visual applications have been developed to run on Windows 95 or NT PCs. While at NationsBank we have been doing data visualization applications for two years, it was only recently I could run them on my laptop. The cost of producing applications has been another deterrent to mass deployment. While some ad hoc landscapes can be used to visualize any multidimensional data, the best results usually come from customizing the visual presentation based on information content and meaning. It usually requires four to eight weeks to develop a visual application unique to the data at hand, which is not significant based on traditional operational applications, but is very significant when compared to the cost of developing Excel charts or graphs, for example.
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