How has big data affected the traditional analytic workflow?

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The pursuit of business analytics or other analytics processes varies a great deal, and should be assessed on a case-by-case basis. However, there are some general ways that using big data sets has changed how professionals approach analytics projects.

Probably the most important way that big data has affected analytics is in the way that data stores are analyzed. Before big data, data stores were usually analyzed on a linear, one-by-one basis. Before computers, this was done by hand. Then Excel spreadsheets and other tools allowed more efficient linear analysis of analytics. For instance, a spreadsheet would tabulate different customers and their purchase histories, and users would build reports on average purchases, going line by line and taking each record into account. This was the prevailing method of doing analytics until big data came on the scene.

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In the big data world, the analysis is usually done through extensive algorithms and patterns sorting. It's generally not done by hand because it would take too long and require too many resources. Also, the structural tools that come along with big data mean that analytics doesn't have to be done by hand. There is an emerging use of something called heuristics or probability work that allows for much more effective analytics based on pattern recognition and other strategies that supersedes the process of traditional statistical analysis.

To that end, modern businesses are quickly investing in all sorts of hardware and software tools to use these more sophisticated data mining methods. Big data has vastly affected the ways that we analyze nearly anything, from a science project to a business process. Simply put, the software tools handle the data and sort it with automation and something approaching artificial intelligence.

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