Ottawa-based family discount chain Giant Tiger had good in-store systems for business intelligence, but the outmoded systems at the head office were incapable of providing the same advanced analytics for the management level.
The retailer had tried exception-based reporting and realized the benefits of key performance indicators in lieu of “ream and reams of reports,” said Giant Tiger’s executive vice-president and chief information officer, Mike Lewis. “However, our only method was hard coding this stuff, but of course you can do it that way but it’s very time consuming,” he said.
Before implementing an enterprise data warehouse (EDW) from Miamisburg, Ohio-based business intelligence technology vendor, Teradata Corp., Giant Tiger relied on an AS/400 system that served the company well for many years, but it “was like a set of good all-season radial tires and we’re starting to go through deepest snow, and it’s not able to do analytics,” said Lewis.
The head office systems had been long neglected because retail businesses tend to focus attention on store systems where the return on investment is greater, explained Lewis.
Besides deeper analytics, the new system is intended to make business intelligence a less frightening prospect for staff who, said Lewis, were often repelled by the complexity of Excel spreadsheets. “And we feel if we do that, we’re going to get them on board,” he said.
Teradata Canada president, Rick Makos, said that Giant Tiger’s need for the enterprise data warehouse reflects the fact that retail is very much a data-driven business and “data is really the lifeblood that a retailer runs their business on.”
Organizations fall in various levels of the business intelligence maturity scale, said Makos, but retailers are typically well beyond merely performing basic reporting. Advanced analytics, such as demand chain forecasting, is the focus of retailers like Giant Tiger. For instance, ascertaining individual store customer demand, and ensuring promotional items are in stock rely on a well-aligned supply chain.
Giant Tiger is still in the infant stages of the implementation, which it envisions will be more like a “journey” during which new capabilities and analytics are incrementally added as it moves its reporting to an enterprise data warehouse.
In its initial stages, that journey entails becoming acquainted with the newly-acquired technology, hiring a third-party consultant to aid with the process, and loading data in the data warehouse.
Besides acquiring the enterprise data warehouse, Giant Tiger had to also purchase a Retail Logical Data Model (RLDM) from Teradata to assist with designing a retail data model. The advantage to using an existing model, said Lewis, meant they didn’t have to recreate the different types of data that were required in the process.
Logical data models offer a “real quick start” for retailers who don’t already have a data model in place, said Makos, adding that Teradata’s retail data model in particular is mature given the ample years of working with retail clients.