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Pitt Ohio Express tackles data quality without compromising flexible customer service

The word “automation” conjures a treadmill of smooth business processes, like ATM card commercials where everything flies along as people swipe their cards - until someone decides to pay in cash, pulls a five-dollar bill from his wallet and everything crashes into chaos.

In reality, people who deal with customers, manage their eccentricities and the occasional exceptions of daily routines are critical to the ongoing existence of most businesses. When a customer calls, it’s his business you want, not his compliance.

Pitt Ohio Express is a Pittsburgh-based, less than truckload (LTL) freight carrier that provides odd lot truck shipping through the Midwest and Mid-Atlantic states. From 22 terminals, Pitt Ohio accepts between 9,000 and 11,000 invoices daily. Less than one-third of orders come through electronic data interchange; the rest show up in paper handoffs between drivers and customers. About 98 percent of the time, orders are placed and picked up on one day and delivered the next. Necessarily, the emphasis is on operational efficiency rather than data quality.

It’s a poor foundation for a business intelligence (BI) program, but few in the LTL business would be surprised to hear that. “On any given day we’re not entirely sure who’s going to pick up or call us for a pickup,” says Justine Russo, manager, Business Intelligence Competency Center (BICC) at Pitt Ohio Express. “So we have our drivers out in various regions servicing their areas with a set of deliveries and calling into the dispatcher and finding out where freight needs to be picked up.”

It’s something like controlled chaos, and it doesn’t answer well to a clock. Drivers might return to terminals at 5:00, 7:00 or even 9:00 p.m., but the business requires that all invoices be entered into the transactional system by 11:00 p.m. to set up processes for next-day delivery. It’s efficient because Pitt Ohio knows its deadlines and employs data entry clerks that work very quickly.

“It’s a business process problem we don’t necessarily want to solve, because we don’t want to change our operational system and slow down that freight bill entry process,” says Christine Kowalecki, data quality analyst at Pitt Ohio Express. “So we really needed an efficient process on the back end that would clean up the data” in order to support sales, market and trend analysis.

From Paper to Portal

Before Pitt Ohio built its data warehouse, there was no shortage of valuable operational data, but it came delivered in green-bar paper phonebooks not suited for any serious analysis. It became Kowalecki’s problem to manipulate transactional data so it could go into the data warehouse in a consolidated manner and be cleansed for reporting, various cubes and BI systems.

The initial solution was to create a customer master record with both shippers’ and receivers’ names entered. It took time and much manual labor to find the data entry errors behind multiple identities and addresses, but once standardized, it allowed historical information to be consolidated. “We took those addresses, determined which was the correct one and kind of ‘smushed,’ which is our technical term, the other bad addresses into it,” Kowalecki explains. “Now we could call it one address, see all the revenue, see the correct revenue, but you can also see what’s behind it, see the bad data as historical. If a freight bill entry clerk went to create another shortcut with a bad address, it would actually go to the correct one.”

Along with the pain of manual reconciliation, a remaining problem was legacy technology that had run out of flexibility. “As we were consolidating locations, there were business rules built into the old application that could not be changed as our business changed,” states Russo. “We were running into problems with recreating shortcuts, so Christine [Kowalecki] wound up doing a lot of manual work either in Excel or with this slow piece of software.” Pitt Ohio shopped vendors for a new solution and, in the end, chose DataFlux dfPower Studio.

According to Russo, fuzzy matching in dfPower Studio ended most of the manual reconciliation and was able to match 650,000 records to some 10,000 actual, active customers at 30,000 locations. The initial goal was 95 percent consolidation, but Russo says the actual figure was closer to 99 percent. “Sales gets really excited to see a consistent name because then they get associated data on industry, pricing and things like that,” Russo says. If there are occasional disagreements over what sales thinks a customer actually is, Kowalecki is the gatekeeper who determines what ends up in the data warehouse.

Just as important, consolidated information is placed in the hands of those who directly need it through a portal interface called SIM, for sales information management system. “It is a single report view of any customer showing monthly totals, year-over-year of revenues, tonnage, shipments, servicing as well as service levels for that customer’s on-time percentage,” explains Russo. The report can be printed or emailed to the customer as a measure of Pitt Ohio’s service performance.

SIM carves information regionally or by other dimensions but mainly provides a useful and simple view for the nonpower user or analyst. “You type the customer name and get a 12-month and year-to-date (YTD) view,” says Russo. Separately, there are 30-plus online analytical processing (OLAP) cubes for sales managers or analysts to slice and dice to better understand the book of business.


It might seem extravagant for a company with $261 million in revenue in 2007 to invest in a BI competency center. As it turns out, Pitt Ohio’s CFO had attended Gartner, Inc. conferences, took up the challenge and made it his business to support the work being done by Russo, Kowalecki and the IT team.

“The data warehouse had started almost as a Skunk Works project just to see what would happen if we had this data, and then the need for data quality came about quickly, because there was no view of the customer,” explains Russo. “One of the original questions came from our president, who asked, ‘How many customers do we have?’ and nobody could give him an answer. The greenbar reports were already unacceptable to our CFO, and we could see where we were going.”

Pitt Ohio’s BICC reports to the CFO and sits in finance rather than IT. The center is staffed by Kowalecki, a pair of business analysts (who also interface with IT on tools and templates) and Russo, who oversees strategy and works across business and IT lines to get data from applications into a format the analysts can use. IT provides report writing, cube building and manages table and view creation for the data warehouse. That makes the BICC more strategic in the requirements process. Kowalecki, also sitting on the business side, has earned a large measure of latitude in writing new processes and maintaining the data in the data warehouse.

“It makes my job both more and less complicated than it used to be,” Kowalecki shares. “It simplifies because I can do my work without having to rely on IT to free up time for me. It’s more complicated, because the more you can do, the more you get to do.”

Internally, the biggest win is a much-improved version of truth. “When we go into an executive review and see posted revenue, the first question is, ‘where did you get this revenue from?’” Russo says. “We’ve decided as an organization that the cube revenue is what we’re going to report, and we tie back to every customer and to every freight bill that passes through for that customer.”

For reviews, the SIM system led to another portal view for trigger reports, which reflect a sales representative’s complete book of business and are color coded to show what’s up and what’s down at a glance. “It helps our representatives make decisions on how to manage their own book of business,” explains Russo. Now, marketing is intersecting cube revenue with contact information and segmenting to create target lists of the types of business sales might want to go after in the future, all in a centralized system.

Next Steps

You get the impression that Pitt Ohio is a company that prides itself on openness to new ideas and supports people like Russo and Kowalecki who are allowed to roll with the changes, embrace what works and not linger on what doesn’t.

Russo is now working to grow the footprint of the BICC. “We’re rolling out new services, and the business is always expanding so our warehouse has to change to reflect that. We’ve mastered our sales universe, but that is only one slice of our business.” She intends to expand its influence by looking at other types of data in the organization, whether in operations, billing or other areas that can extend the single version of truth throughout.

I noticed that Pitt Ohio maintains a Web presence that is open and friendly to outsiders, that company revenues have grown steadily and impressively since 2002 and the company took three first-place awards and best in show at this year’s Transportation Sales and Marketing Association conference.

The net result almost has to be due to customer service, which has led to a remarkable 97 percent on-time delivery success, all the more impressive once you understand what goes on behind the scenes. It’s automation that leads to business improvement without neglecting the human element of business processes that comes first.

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