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Accessible Analytics - Complex Charts, Large Datasets, and Node Diagrams

Andi Snow-Weaver, IBM and Brian Cragun, IBM

Our world is becoming increasingly intelligent, interconnected, and instrumented, resulting in massive amounts of data being collected. This data is a treasure trove of information that can be mined to improve service, increase sales, determine risk, or make operations more efficient.

Analysis of such large amounts of data, often called analytics, is increasingly desired by governments and businesses alike. The Wikipedia entry on "analytics" explains:

  • A simple definition of analytics is "the science of analysis."

A practical definition, however, would be that analytics is the process of obtaining an optimal or realistic decision based on existing data.

Yet getting useful information from such large amounts of data is a daunting task. IBM understands that analytics often relies on real-time visual renderings that allow users to quickly spot trends and gain insights. The authors explore the accessibility issues of these types of complex visualizations and provide best practices being developed by IBM Research and product teams throughout IBM.

As is often the case with accessibility, best practices for making complex visualizations accessible have applicability in mainstream solutions as well. As more and more business applications move to mobile platforms, similar accessibility issues will be experienced by all users due to limited screen size, the hardware platform itself, viewing the information outside in the bright sun, and of course the current input mechanisms which simply do not adequately address how different users utilize virtual keyboards or gesture technology.

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