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Working effectively with large volumes of data presents challenging technical and human factors issues. For example, how can today’s analyst iteratively process, display and explore 10s of millions records in order to identify new trends or patterns. Over the last few years, we have been wrestling with some of the more pragmatic aspects of trying to build and deliver a big data analytic solution that meets the needs of the typical analyst.
In this talk, we will discuss several of the technical and human factor issues with making big data accessible to the data scientist or analyst. Topics covered will include what type of interactive analysis support is required for large datasets, what types of visualizations are needed for big data, what type of scripting or programming support is needed for analysts, how to address the typical analyst task flow in a big data solution and the need for non-linear task flow support. In addition, they will discuss some of the generic ways of dynamically visualizing data and the fundamental principles of good visual design for data.
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