While the potential of analytics to bolster the bottom line for business has been buoyed by the publicity of the growing volume and variety of data flooding many corporate networks, just about any industry can benefit from analyzing data sources already in place.

That is the assertion of a new essay from Deloitte University Press that aims to shed light on the potential of data sources to bolster a firm’s operations – aside from the hoopla around big data.

The essay acknowledges the potential of big data, but warns that myths and misconceptions surrounding the notion can lead to expensive errors.

“One major misconception is that big data is necessary for analytics to provide big value,” the article notes. “Not only is this false, it obscures the fact that the economic value of analytics projects often has as much to do with the psychology of de-biasing decisions and the sociology of corporate culture change as with the volumes and varieties of data involved.”

Instead of waiting to master the nuances of big data, the authors advise companies to pursue near-term applications of analytics that involved readily available data sources.

For example, Deloitte notes that it recently worked with University of Toronto researchers to examine the value of using predictive analytics in the college admissions process.

The data used contained millions of records in a structured format, not technically “big data” because it was not large enough or so varied in format that traditional data analysis techniques could not be used to exploit it.

The consulting firm worked with the university to build a model using high school transcript information to predict academic success at the university.

“Based on an analysis of the model’s predictive accuracy, we estimate that the university can use the model to boost the number of high-achieving students admitted between five and ten percent,” the essay notes. “The major point is that the university has the means to improve key admissions decisions using a transparent, interpretable model constructed from an uncontroversial data source using common sense, standard statistical methodology, and a dash of inspired creativity.”

Because it can be costly to gather and analyze big data, Deloitte points out that the data volume, variety and velocity that characterizes big data is only one of many things to consider before starting an analysis project.

The paramount issue is gathering the right data that carries the most useful information for the problem at hand,” the article notes. “In the context of predicting or analyzing human behavior the relevant aspect is the behavioral content of emerging data sources. Anyone who has worked with large volumes of behavioral data knows that past behavior often does predict future behavior, and often in surprising ways.”

For example, personal credit information is likely to predict who may default on a loan or who is most likely to have a car accident. Human resources is another example where behavioral data can be used in data analysis to help operations.

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