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Google Cloud Retail Nordics

Power

Data Lake / Data Warehouse Modernization · Enterprise Knowledge Search

Power needed a shared data and identity foundation across countries, stores, and SAP workloads so it could stop working from siloed data warehouses and launch AI experiments without heavy setup each time.

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Reported impact

Business outcome

Power unified enterprise data for thousands of employees and made it much easier to launch new AI pilots on top of retail and logistics information.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Power uses Google Cloud to address this challenge: Power needed a shared data and identity foundation across countries, stores, and SAP workloads so it could stop working from siloed data warehouses and launch AI experiments without heavy setup each time. BigQuery serves as the consolidated source of truth for previously siloed retail, logistics, and SAP data, serving as the storage layer. Looker gives 6,000 employees a common analytics layer on top of the centralized data foundation, serving as the serving layer. Vertex AI Search for commerce powers retail-search proof-of-concept work on the Danish site, serving as the serving layer. Gemini lowers the threshold for additional AI-driven proof-of-concept projects across the business, serving as the compute layer.

Data flow

SAP BDC and Datasphere data is lifted into BigQuery, Looker exposes one consistent view of the business across countries and functions, and Vertex AI Search for commerce plus Gemini operate on top of that foundation for new AI experiences.

Solution architecture

4 components · 3 layers
  1. Compute
    • Gemini lowers the threshold for additional AI-driven proof-of-concept projects across the business
  2. Storage
    • BigQuery serves as the consolidated source of truth for previously siloed retail, logistics, and SAP data
  3. Serving
    • Looker gives 6,000 employees a common analytics layer on top of the centralized data foundation
    • Vertex AI Search for commerce powers retail-search proof-of-concept work on the Danish site

Architecture clues

  • The centralized identity and infrastructure setup makes it possible to launch a PoC for 40 users with minimal effort
  • The unified data model spans stores in multiple countries and logistics operators
  • Vertex AI Search for commerce is already in production experimentation on Power’s Danish site

Evidence from the source

By using SAP BDC and Datasphere on Google Cloud, Power lifted on-prem SAP data into BigQuery without losing context.
Consolidating multiple data warehouses into a single source of truth built on BigQuery has made everything easier.
Power has been running proof-of-concept projects across Google Cloud AI capabilities, including Vertex AI Search for commerce on its Danish site and Gemini.
Transparency Evidence & methodology 71/100 reference value · 72% 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 value71/100
  • 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 specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence72%
  • 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 evidence15/20
    3 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • 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. Google Cloud

    Power customer story

    First-party
    https://cloud.google.com/customers/power
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived May 10, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Oct 9, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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