Agile Analytics: What It Is, and 10 Best Practices for Using It

 
 
By Chris Preimesberger  |  Posted 2013-07-26 Print this article Print
 
 
 
 
 
 
 
 

Enterprises of all sizes are now realizing they have dormant data in silos that they can put to work for them. For example, this so-called big data, if analyzed correctly, can help project sales spikes, predict raw material needs and help companies understand their customers better, and this is just a sampling of what it can do. However, too often, enterprises find themselves ill-equipped to gather, cleanse and analyze this data, and therefore unable to act upon potential insights or gain competitive advantages. Agile big data analytics focuses not on the data itself but on the insight and action that can ultimately be drawn from nimble business intelligence systems. Rather than beginning with investment and platform building, Agile analytics starts with learning and testing, so that companies can build their models and strategies based on solid answers to their most crucial business questions. The sources for this slide show include big data consultancy ThoughtWorks and eWEEK reporting.

 
 
 
 
 
 
 
 
 
 
 
 
 
 

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