This is an overview of how a 95 year old Retailer set out late to become a data driven retailer and the impact it had on the digital transformation of the organization. The objective of this presentation will outline the need for advanced Analytics and Real time reporting coupled with AI and Machine learning to find insights and action them in retail across our Supply Chain and Digital interfaces whether online or in-store. The were business challenges in adoption, technical skills gap and then the culture around data and its interpretation. GIOVANNI PIZZOFERRATO, VP Data Technology, Canadian Tire Corporation

1. Data technology transformation and its impact on digital
Gio Pizzoferrato, VP Data Technology
Canadian Tire Corporation

2. • Retail segment, financial services
division and CT REIT
• Canadian Tire, FGL, Mark’s, PartSource,
Gas+, CT Financial Services and CT REIT
• Approximately 1,700 locations
• 2017 revenue $13.4 billion CAD
For over 95 years, Canadian Tire Corporation
(CTC) has proudly offered products and services
to help Canadians from coast-to-coast navigate
the landscape we know and love so well.
CTC is one of Canada’s most recognised and
trusted brands and the place that Canadians turn
to for the Jobs and Joys of Life in Canada
Canadian Tire Corporation
Goal to be #1 retail brand in Canada by 2022

3. Legacy data technology
Cleansed, modelled, governance and assurance
Slow delivery of high value to business
ETL
ENTERPRISE DATA WAREHOUSE
ETL
landing
staging
marts
appliance
ETL
ETL
ETL
ETL
solutions

4. Digital reinvention of retail
Consumer behaviour being shaped by online experiences
Frictionless retail across all channels online, mobile, physical and
voice assistant
Personalised offerings and experiences
Data & advanced analytics is the enabler

5. Big Data Platform
Big Data
schema on read
Fast prototyping, experimental, searching for insights
Can one platform service both insights and an Enterprise Data
Warehouse supporting fast, high value delivery?

6. ServiceLibrary Event
Factory
Products
Vaults
Files
Info.
Lab.
On-Premises Clusters
Cloud Clusters
Query Report App. Visualise Learn
a b
c d
JSON
Sources
data lake

7. Vaults
Files
Info.
Lab.
Factory
ServiceLibrary Event
Products
Everything ingested as raw files
Organise in governed, read only, semantic vaults
Self service analysis from this enterprise memory
data lake

8. Vaults
Files
Info.
Lab.
Factory
ServiceLibrary Event
Data Products
Analytic models deployed as information marts
High value reasoning to digital as data products
Continuous Integration & Delivery (CICD)
data lake

9. What have we learned? so far…
1
3
2 Vaults and governance
Planning and preparation in policy, process and meta-data capture are
critical.
Skills transfer EDW to lake is hard
You have to unlearn pretty much everything.
This is the hardest challenge faced to date.
Integrate the lab and factory
Build an analytic development lifecycle. The self service outcomes
challenge business change and IT release management. DevOps
automation is essential

10. digital solutions impact
ATLAS our in-house developed search engine that gives us
a strong competitive advantage. Machine learning &
natural language processing embedded with 95 years of
retailing experience, application online and instore.
New loyalty program Triangle Rewards with
Canadian Tire Money earned and redeemed across
banners. Foundational to this effort was our master
customer profile.
Marketing and assortment decisions with
inputs from probability models that capture
how weather affects our businesses..

11. What’s next?
Did we connect
with our latest
campaign?
Any changes to
sales during
playoffs?
We want to democratise access to data in
our organisation
The focus is on ideas, raising the organisation’s idea potential
with the application of artificial intelligence to remove barriers
Make it simple for employees to discover and share ideas, with
effortless exploration of secured, trusted enterprise
information.
Should I buy
more from this
supplier or wait?