Big data: How a trucking firm drove out big errors

By Rob Lemos, CIO |  IT Management, Analytics, big data

During a team meeting at trucking company U.S. Xpress addressing how to cut costs in response to the economic slowdown, one executive lamented that, if only he had data on truck idling times, he could save significant costs on fuel.

Dale Langley, CIO for the Chattanooga, Tenn., company, took the voiced frustration as a challenge. Langley's IT team had embarked on a comprehensive information management strategy soon after Langley joined the company in 2009. The infrastructure implemented as part of that strategy paid off: It took the IT team less than six weeks to create an application to track the amount of time that trucks were idling, using up costly fuel without going anywhere.

The intelligence on its business allowed U.S. Xpress, the third largest privately-owned trucker in the United States, to save about $6 million a year across its fleet of 8,000 tractors and 22,000 trailers.

"It is one of those things where, if you don't measure something, you don't manage it," Langley says. "So as soon as we began to measure (idling times), we started to have an impact."

Big Databases, Hidden Data

The capability to mine its massive and disparate databases for such information is relatively new for U.S. Xpress, a conglomerate that includes a handful of companies such as Arnold Transportation, Smith Transport, and Total Transportation. The corporation's primary data center consists of 200 servers and a storage area network in its corporate building with an outsourced, hot disaster recovery site about 15 miles away.

Before the corporation embarked on its information management strategy, each firm had its own information system and network. The companies had 130 different applications with not data integration, stewardship for the data, and no master database. The balkanized IT environment led to data chaos: Delving for answers to business questions took weeks and months, says Langley. Without a master database, the company had customers showing up in multiple systems and assets -- such as trucks, trailers, drivers and orders -- in different databases.

The company's employees had somehow found 178 different ways to spell Wal-Mart, making even a simple client search difficult.

"Until you get that kind of thing cleaned up, your data is not worth anything," he says.

Informatica Tools Cut Through Mess


Originally published on CIO |  Click here to read the original story.
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