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
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.”
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.
The situation was not acceptable for company CTO Tim Leonard, who prides the firm on its ability to innovate and use technology to solve problems,
says Langley. The company decided to use Informatica’s Data Quality software to help normalize and aggregate its databases into a master database.
Every truck has some sort of communications system — whether satellite or wireless — and U.S. Xpress found that a third-party supplier’s
database had more than 970 data elements that could be imported and tracked to get a better understanding of truck movements and logistics.
“It is a zero sum game,” says Ivan Chong, general manager for Informatica’s data quality business unit. “So the savings in trucks is the ROI (return on
Using Informatica, for example, the company’s IT team can profile a legacy database made more than a decade ago and get an understanding of how it
works, says U.S. Xpress’s Langley. It makes no sense to build more complex applications, without first cleaning up its databases, Langley says.
“Initially, they wanted a customer relationship management (CRM) system,” he says. “I told them, we are not even going to start CRM until we have
data quality in place.”
The pressure to cut costs and save money is another reason the company tackled its information problems. During the stark recession, demand for the
transportation industry’s services sank nearly a quarter, compared to its prior peak, he says.
“The transportation industry itself has been very, very tough,” Langley says. “Most people have lost money in the last two years.”
Improving data quality across the company has resulted in a more agile business, a situation that is not specific to the transportation industry, says Rob
Karel, principal analyst with Forrester Research. Every industry could use better data on operations, he says.
“The goal is not to get clean data, because clean data does not get you money,” Karel says. “The goal is to fuel your business processes and decisions
in the best way possible.”
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