The Dangers of Bling Data Visualizations

Here is a great article by Michael O’Connell and Eric Novik (originally published on information management) that discusses how poor visualization techniques lead to loss of data, insight, and usability. Given the volume of information that’s pouring into the enterprise from so many disparate sources, knowledge workers need to be able to visualize information in order to analyze it and extrapolate insights effectively. When business users can visualize information, they’re… 

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Why We Remember Some Visualizations and Forget Others

Memorable visualizations

This a great article by Tiffany Trader (originally published on HPC Wire) that addresses our concern that data visualization is being reduced to BI chart simplicity because of the limitations of BI tools and a lack of imagination and skill by visualization designers:  Researchers from Harvard and MIT have teamed up to address an important question: what makes a data visualization memorable? The conventional opinion is that it’s easy to… 

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Finally—some clear thinking about proper uses of data visualization.

At AVS, we frequently struggle with the steady stream of unqualified “expert” opinions about best practices in data visualization that litter the blogosphere and misinform those new to the practice area.  How refreshing to come across the excellent post by Anna Li (originally posted at http://www.poynter.org/uncategorized/224413/why-rainbow-colors-arent-always-the-best-options-for-data-visualizations/) that underscores the importance of color selection and strategy in application design.  The OpenViz API makes each point discussed by Anna a fundamental capability… 

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Is SMB ready for Big Data? Probably not…

How small data can also be big data

Mark van Rijmenam, founder of BigDataStartups.com nails it in this Smart Data Collective piece (via http://smartdatacollective.com/bigdatastartups/142421/how-small-data-can-also-be-big-data) How Small Data Can Also Be Big Data Gartner’s definition of big data dates back from 2001, when Doug Laneydefined big data as increasing volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources). Since then the term big data has been redefined many times by many… 

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