Through years of growth and demand for Jason’s Deli’s “fast casual” sandwiches and salads, manual and insufficient data on customers and sales were still on the menu. Local sales managers logged customer info and delivery transactions into notebooks, and later transcribed them into spreadsheets. Sales forecasts couldn’t put a finger on all of the figures behind sales or lack thereof at certain locations, and the few business-side reports available provided views of customers that were incomplete.

The chain has cropped up quickly in the last few years, growing to 230 locations across 28 states and approximately $500 million in revenue last year, though the data systems on the back end throughout this expansion were undercooked. Enterprise Architect Chris Cosgrove joined the chain’s IT department in 2008 as part of a point of sale implementation that brought in the start of a data-based approach to customers and sales for its corporate-owned stores, which accounts for 60 percent of locations.

Over the course of two years, Cosgrove and his peers put in place the unified architecture for the sales systems. As that data streamed in for the first time, the C-suite saw promise in data-driven decisions across departments. Soon after, C-suite expectations for the next decade of chain growth required a much more information-rounded idea of customers and sales, with all of the accompanying data. More daunting than the executive expectations were what Cosgrove calls the “foreign concepts” of getting the data in a usable state.

Between January 2011 and April 2011, in-house data analysts and IT worked with the Jason’s Deli chief strategy officer to identify the business goals with the data and kick the tires on software and as-a-service options that could deal with the known or anticipated quality and integration issues. From a technical standpoint, the restaurant IT team implemented a master data management process using Pervasive Data Integrator, extracting data from sources like store point of sale figures and aggregating it within The customer side of that information was enriched in Pervasive Data MatchMerge, with D&B Direct and Melissa Data API in place for standardization.

“Going in, nobody in the company had done something of this scale at all. There was an understanding that there would be some issues with data quality, or that integration issues would come up,” Cosgrove says. “Still, like a lot of teams, we didn’t have the budget or staff for a data quality only team, and the company was pretty leery about overspending on IT.”

Early on, however, Cosgrove and his handful of dedicated data workers found that their static budget to follow through on the plans could cover the internal implementation but not the continuation of data quality efforts by outside consultants. Cosgrove and his boss rolled up their sleeves and developed a proof of concept to cover the tough sledding of meticulous data cleansing and other back-end and dire quality needs.

“Our POC test went well, so we washed our hands of the consultants. My admin and I had to do some developer learning, but it all worked out,” he says.

After a summer of data cleansing and architecture construction, the system was set for testing across a few sales areas. A couple of tweaks and about a month of training later, and the entire process was ready for rollout to their corporate stores. Sales data on this front was able to account a goal of $10.8 million in new sales months ahead of schedule, and a 5 percent bump in delivery sales also came into play subsequent to the new, unified streams of information. On the data side, 10 percent of the company’s data set has been deduplicated through ongoing use of the system.

Most rewarding has been the true view of customers, so far from the names and contact info jotted down in pen on notebook paper and then transferred to spreadsheets just years ago. The data has changed the restaurant chain’s view of their customer, as reports make their way up to executives and sales associates.

“[Executives] are finding all of these customers that they had no idea were contributing this much to our sales, especially those who are smaller individual transactions at a more constant pace. They were flying under the radar before. But now can get unified, single view of them and sales folks can focus on them and be more efficient,” Cosgrove says.

In the months ahead, Cosgrove says the goal is to add more of the data to marketers and outside marketing researchers and use it as a backdrop for selecting new store locations.

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