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1/22/2016 4:05:00 PM Write a Letter to the Editor Write a letter to the Editor

Data: Separating the Wheat From the Chaff

By Joe Howard

This story appears in the Jan. 11 print edition of iTECH, a supplement to Transport Topics.

The old adage “separating the wheat from the chaff” technically describes the process of separating grain from the casing in which it’s grown, but over the years, the saying has broadened to describe any process that isolates what’s valuable by winnowing out what’s worthless. For fleets trying to wring value from the massive volume of data they produce each day, separating the wheat from the chaff can be achieved through careful development of analytics programs, which zero in on factors that shape a company’s results by setting aside those that do not.

BEST OF JANUARY iTECH: More stories, columns

“The most relevant information is not always the most obvious,” said Dean Croke, a vice president with Omnitracs Analytics, during the annual joint conference of the Information Technology & Logistics Council and National Accounting & Finance Council of American Trucking Associations. The conference was held during October in conjunction with ATA’s Management Conference & Exhibition in Philadelphia.

Croke said that relevant information is identified through a process called reductive modeling, which filters out irrelevant data and isolates the most prevalent and frequently occurring information.

“The system studies all of [the information] and kicks out the things that do not appear throughout,” Croke said. “It takes three months to get a model; it takes a lot of iterations to narrow it down. You have to start with everything to figure out which things are relevant. A model tells you what matters.”

And filtering through the mountains of data being collected is not a problem — the technological capability is there for fleets to fine-tune analytics to suit their needs, he said.

“We have increasing ability to process the data,” he said. “We can do it in a manner of seconds; the costs of sending data and the cost of storing data all have come down. The key to effective data analytics is selecting the metrics around which to build [an analytics] model.”

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