Executive Briefings

Which Tool Do You Need to Analyze, Interpret Huge Amounts of Data Collected?

In the last few decades, statisticians and computer scientists have produced a dazzling arsenal of extremely powerful tools to help managers translate data into business decisions.

Having access to a wide array of versatile solutions is not ordinarily considered a problem in the world of business. But the rise of "big data" has also brought along with it the explosion of mathematical models made possible by today's low-cost computing and storage platforms. Ironically, this poses a number of substantial challenges to managers trying to making sense of ever-growing quantities of information.

For example, says, Wharton PhD student Eric Schwartz, managers may be tempted to, as he put it, "flex their data-science muscles" and use a statistical model that is simply too complicated for the task at hand. The result, he notes, might well be that the model produces bad advice.

Alternately, managers may waste time trying to figure out which of several dozen possible models would be the most precise fit for the data set they are using. But the time it takes to play statistical guessing games after the analyses would be better spent running their businesses, Schwartz says.

"Wouldn't it be nice to be able to know, just from looking at the data, how complicated a tool you should use with it?" he asks.

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Keywords: supply chain management IT, supply chain solutions, supply chain systems, interpreting large amounts of enterprise data 

Having access to a wide array of versatile solutions is not ordinarily considered a problem in the world of business. But the rise of "big data" has also brought along with it the explosion of mathematical models made possible by today's low-cost computing and storage platforms. Ironically, this poses a number of substantial challenges to managers trying to making sense of ever-growing quantities of information.

For example, says, Wharton PhD student Eric Schwartz, managers may be tempted to, as he put it, "flex their data-science muscles" and use a statistical model that is simply too complicated for the task at hand. The result, he notes, might well be that the model produces bad advice.

Alternately, managers may waste time trying to figure out which of several dozen possible models would be the most precise fit for the data set they are using. But the time it takes to play statistical guessing games after the analyses would be better spent running their businesses, Schwartz says.

"Wouldn't it be nice to be able to know, just from looking at the data, how complicated a tool you should use with it?" he asks.

Read Full Article


Keywords: supply chain management IT, supply chain solutions, supply chain systems, interpreting large amounts of enterprise data