18 | | February 2016 | Autosphere.ca

10499453-8.375x11.125-4C-MALIBU-TECH-003.indd 10499453-003-4C-16

1SWOP

8.375” x 11.125”8.375” x 11.125”

7.125” x 9.875”8.625” x 11.375”100%

--Tiffany.Punnett

None--Jennifer Green

GM-Commonwealth TorontoNone

2-3-2016 3:20 PM2-3-2016 3:20 PM

Ferreira, Jamy (TOR-MCL)

Macintosh HD:Users:jamy.fe...11.125-4C-MALIBU-TECH-003.inddFleet Digest

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Louis

Cyan, Magenta, Yellow, Black

1 Visit onstar.ca for coverage maps, details and system limitations. Services vary by model, conditions and geographical and technical restrictions. OnStar with 4G LTE connectivity is available on select models and in select markets. Available Wi-Fi hotspot requires a data plan. 2 Vehicle user interfaces are products of Apple and Google and their terms and privacy statements apply. Requires compatible smartphone and data plan rates apply. Available on select vehicles for the 2016 Model Year.

THE ALL-NEW 2016 CHEVROLET MALIBU

The all-new 2016 Chevy Malibu is the perfect workspace for those on the move. With advanced technology like OnStar 4G LTE with Wi-Fi®1 you can stay connected with clients, customers and employees. Plus with Apple CarPlay™2 and Android Auto™2 compatibility you’re never more than a click away from emails, appointments and directions. The Chevy Malibu accelerates your company on and off the road – the mid-sized car that means full-size business.

See fl eet.gm.ca for Business Elite dealers in your area.

ULTIMATE CONNECTIVITY FOR ULTIMATE PERFORMANCE

S:7.125”

S:9.875”

T:8.375”

T:11.125”

B:8.625”

B:11.375”

PHOT

O: A

RI

Predictive Analytics

Prescription for Profit?Getting the most from big data.

BY KRYSTYNA LAGOWSKI

I f you want to know where you’re going, first, you have to know where you’ve been. That’s why predictive

analytics has become such a hot topic for fleet management.

“You can use the data you have and find the factors or variables that act as predictors for certain outcomes,” says Doug Peters, Advanced Analytics Product Leader at Element Fleet Management. “But it’s only one aspect of what we’re looking to do in the fleet space – some-times it’s prescriptive, where you can look at a set of data and predict where you’re going to more effectively manage your operation and reduce your costs.”

Now that it’s possible to capture a high volume of near real-time data through telematics, there’s a great deal of infor-mation on how a physical vehicle is per-forming. “We can also use location data, which can be combined with other sources like fuel card data, transactional data from maintenance vendors, to build bet-ter predictive models,” says Peters. “For example, we’re combining those data sets to define and better identify downtime in an asset, which can certainly impact your operation significantly.

“We can combine data assets and where we’re looking to reduce costs by identi-fying patterns in the data and driver’s behaviour.”

Tool setAccording to Harvey Smith, Director of Product Development at ARI, predictive analytics are part of a larger tool set. “You can have all the data in the world, but you have to change it from being interesting

to being impactful,” he says. “That’s when it becomes meaningful, and can give you insight and value.”

The first stage of predictive analytics begins with descriptive data, examining an event that has just happened. Then there’s diagnostic, which reviews why the event happened. “In the predictive stage, you want to know what will happen next,” says Smith. “And ultimately, you move to prescriptive, what can you do to prevent it? And there’s the value. The quicker you capture data and deliver it to the right person in a timely manner with visualiza-tions, the quicker you can take decisions and actions to correct it, that’s bringing value and impact to the data.”

Smith notes that usually, it’s only a certain percentage of a fleet that really affects the bottom line. “That’s who you want to iden-tify and manage, not the percentage who are operating correctly,” he says. “So you need to restructure the data to tell a story and generate a foundational base for an analysis.” That means creating an “apples to apples” scenario. A Ford F150 operat-ing in Florida will have different patterns to one being operated in Alberta.

Drill downThere could be drivers who are wearing out brakes faster than normal. “Because it’s apples to apples, I want to look at the same driving patterns with the same ve-hicles to identify what’s going on. “Is it harsh braking?” asks Smith. “What other things does it predict?”

It’s also possible that drivers who don’t change their brakes as frequently aren’t necessarily better drivers, but are

waiting until there’s a metal on metal situation. “They’re not driving the vehicle hard, they’re just not having the brakes changed, and now instead of a brake re-pair, it’s a much costlier expense,” Smith says. “Frequent brake changes could also indicate inferior parts, or that the vehicle is being overloaded or indicate certain driver behavior. You have to restructure the data, drill down into it, so you can base your analysis on that, and find com-monality.”

“They’re not driving the vehicle hard, they’re just not having the brakes changed, and now instead of a brake repair, it’s a much costlier

expense.”Harvey Smith, Director of Product

Development, ARI

T E L E M AT I C S & S O F T W A R E SPECIAL FILE