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EY Retail Canada

Canadian Tire

Data Lake / Data Warehouse Modernization · Marketing Personalization

Canadian Tire had abundant customer data across many banners and business units, but over-centralized processes, duplicated data paths, uneven governance and limited advanced analytics access kept leaders from using that information quickly enough to drive personalization, reporting and business growth.

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165 data processes
20-plus Canadian Tire executives
34,000 employees
40 discovery meetings

Reported impact

Business outcome

Canadian Tire created a more agile customer-data foundation that improves data access, strengthens personalization and enables business teams to use analytics faster, supporting better merchandising, loyalty and customer-growth decisions across the enterprise.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

EY helped Canadian Tire redesign how customer data is governed, accessed and used so advanced analytics and AI could move closer to business teams. The work started with a data maturity assessment covering governance, accessibility, Power BI proficiency and advanced analytics and artificial intelligence capabilities. EY then mapped nearly 500 data paths and 165 processes, using that inventory to recommend a decentralized data-mesh model on the Microsoft Azure stack with a federated governance approach. The resulting architecture gives domain owners more local control while preserving shared standards, and it is supported by an expanded internal data and analytics organization staffed with new data scientists and managers. Power BI dashboards running on top of the new model help banners analyze product performance by SKU, while Canadian Tire's Triangle Rewards program uses richer real-time customer insights to target offers and future marketing opportunities more precisely.

Data flow

Customer data from many banners and business units is inventoried, realigned into the Azure-based data mesh and governed through a federated model. Business domains then consume the unified data through Power BI dashboards and programs such as Triangle Rewards to analyze product performance, buyer patterns and marketing opportunities.

Solution architecture

4 components · 3 layers
  1. Compute
    • Microsoft Azure stack Provides the shared cloud platform for the new decentralized customer-data architecture.
  2. Serving
    • Microsoft Power BI Presents dashboard insights that business units use for agile product and customer analysis.
    • Triangle Rewards Program Uses richer customer insights to calibrate offers, deals and marketing potential.
  3. Orchestration
    • data mesh construct Distributes data ownership to business domains while preserving common standards and governance.

Architecture clues

  • Canadian Tire created a new internal data and analytics organization to run the operating model.
  • EY recommended a decentralized data mesh on the Microsoft Azure stack rather than a centralized data platform.
  • Power BI is the business-facing analytics layer for new dashboards.
  • The governance model gives domain owners localized autonomy with shared standards.

Evidence from the source

"Canadian Tire's Triangle Rewards Program is now strategically calibrated to match products with buyer patterns"
"New dashboards on the Microsoft Power BI platform enable business domains to harness data in more agile ways."
"The proposed platform, built on the Microsoft Azure stack"
"To strategically leverage data as a competitive advantage, the EY team recommended Canadian Tire realign and configure its existing data architecture and technology investments in a data mesh construct."
"advanced analytics and artificial intelligence changed a two-lane road into an information superhighway"
Transparency Evidence & methodology 70/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value70/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. EY

    Canadian Tire customer story

    First-party
    https://www.ey.com/en_gl/insights/consulting/how-canadian-tire-leveraged-the-potential-of-customer-data-with-ai
    Source type
    Vendor-published customer story
    Published
    Apr 3, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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