When you purchase a can of Tremclad rust proofing paint from your local Canadian Tire store, Matt Roher is bound to know about it the very next day.
He’ll know what colour you chose, the can size, and what Canadian Tire branch you got it from.We’re moving from reporting yesterday’s news to predicting tomorrow’s events.Hung LeHong>Text
“This kind of data used to come to me three to four weeks after the fact. With our business intelligence (BI) software I get it in 24 hours over the Internet,” said Roher, category manager for Toronto-based Rust-Oleum Consumer Brands Canada, makers of the popular anti-rust paint Tremclad.
It was only two and a half years ago that Rust-Oleum deployed Hyperion Intelligence. The software from the Santa Clara, Calif.-based BI firm provided Rust-Oleum with web-based reporting and analysis implementation features.
But the BI tools in the hands of high-level managers like Roher are trickling down the corporate ladder.
“One of the big trends is to get BI in the hands of people in the front lines, not just the higher-ups,” said Hung LeHong, research vice-president specializing in BI at Gartner’s Mississauga office.
“BI developers are tailoring their applications for use by people in various positions across the enterprise,” LeHong said.
This trend bodes well for businesses like Rust-Oleum which delivers a wide array of paint products in hundreds of colours and sizes to thousands of outlets across the U.S. and Canada.
“We need to know as early as possible which colours are hot or what stores are selling our products well. This helps us to decide whether to cut back or increase delivery on a certain product,” Roher said.
Prior to using Hyperion Intelligence, Rust-Oleum managers waited three to four weeks for dot-matrix printed reports to come in from various areas. It would take another two weeks for the reports to get to the local sales force that would implement the changes needed.
“Now within three days, we have the info readily available in a format that can be broadcast to sales reps via the Internet. They can walk into a store with the stats and implement their plan,” Roher said.
As more and more employees gain access to BI tools, it becomes imperative that data “become consumable to everyone,” said Michael Turney, marketing strategy manager for BI software developer SAS Canada.
“People in the marketing department might need to access the same data that people in accounting have, but the marketing people need to see that information in a different way to understand it.” Turney said.
He said “role-based applications are gaining popularity with SAS customers because they come with screen views and dashboards that are specific to the user’s needs.”
Role-based applications not only enable users to see graphs or tables in a specifically meaningful manner to them, it also allows them to manipulate that information according to their role in the organization, Turney said.
“Clients want access to predictive models. The trend is moving from reporting yesterday’s news to predicting tomorrow’s events,” LeHong said.
For Roher it means “inputting current sales information to be able to forecast sales in a weekly, monthly or seasonal basis.”
Last year, Export Development Canada (EDC) helped Canadian entrepreneurs conduct over $51.9 billion in domestic and export sales. The federally mandated body also delivers financial and insurance services to clients that other lenders often deem to risky.
The EDC uses SAS’s data warehousing and BI intelligence software not only to disseminate information throughout the organization but to get a “complete and accurate picture of risk” as well.
Arthur Pelletier, director of client services for EDC said they have to quickly and accurately calculate risk factors such as portfolio concentrations, probability of default and credit migration.
Those calculations need complete and reliable credit data in the form of risk ratings, risk limits and board-approved limits.
Before using SAS’s software, the process to generate this crucial report involved 257 sub-process including data retrieval, data manipulation, verification and rework.
SAS automated 95 percent of those processes and eliminated the rest.
James Dorrance, EDC data project manager said the reduced workload freed up the equivalent of one full-time employee to do analytical work. It also reduced potential errors by eliminating points at which data is touched and manipulated.
When data comes from a variety of sources there’s often a risk of repetition, conflicting information, keying error and other inaccuracies.
“Adaptors of the technology were enthralled by the dashboards and wonderful templates, but soon found out that data was not coming at a level of quality that they needed,” Turney said.
“Another focus today is to provide our clients with quality of data that would give them one version of the truth.”
With Rust-Oleum, most inventory data is collected at a point of sale when a product’s barcode is scanned at the counter.
“Hyperion Intelligence also has features that enable us to track duplicated entries,” Roher said.
Turney said SAS applications uses data matching and standardization routines that organize data and identify duplicated names, addresses and contradictory and conflicting information.
“It’s back to the basics. To get accurate forecasts, you need accurate data,” Turney said.