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Google Cloud Travel and Hospitality Singapore

Wego

Document Processing · Real-time Analytics · System Migration

Wego transitioned from fragmented, inconsistent data silos to a unified analytical framework using Google Cloud and Looker. By leveraging Looker's semantic layer, Wego has democratized data access for all departments, from finance to product, while laying the foundation for an AI-driven agentic future in travel analytics.

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Reported impact

Business outcome

Achieved organization-wide data consistency by centralizing definitions for heartbeat metrics like Conversion Rate through LookML; Empowered stakeholders across every team to build charts and explore data without needing SQL knowledge; Reduced time-to-insight by allowing leadership and product teams to perform live data exploration during business meetings; Streamlined operational efficiency through automated Slack...

  • Cost Reduction
  • Productivity Improvement
  • Revenue Growth
  • Scalability

Solution

Today, Looker is woven into the fabric of Wego's strategic multi-cloud infrastructure, with all analytical power concentrated within Google Cloud. This setup allows Wego to utilize BigQuery for its scalable pay-per-use model, where costs are driven by querying value rather than storage. This flexibility ensures that as Wego scales, their infrastructure costs remain aligned with their actual data consumption. Additionally, the team leverages Vertex AI as a seamless endpoint for deploying machine learning models directly to product teams, bridging the gap between data science and user experience. Before adopting Looker, Wego faced a critical hurdle common to rapidly growing digital enterprises: a pervasive lack of data trust. The company relied on a basic, SQL-based business intelligence tool that required manual coding for every individual visualization.

Data flow

stored or unified in BigQuery, processed with Vertex AI, and surfaced through Looker

Solution architecture

4 components · 4 layers
  1. Compute
    • Vertex AI Hosts and orchestrates machine learning and generative AI capabilities inside the solution.
  2. Storage
    • BigQuery Stores and queries the analytics and AI-ready datasets that power the workload.
  3. Serving
    • Looker Provides the governed semantic layer, dashboards, and self-service analytics used by business teams.
  4. Governance
    • LookML Defines governed metrics and semantic models so teams and AI workflows use the same business logic.

Architecture clues

  • # Wego scales travel insights and consistency with Looker's centralized semantic layer
  • Achieved organization-wide data consistency by centralizing definitions for heartbeat metrics like Conversion Rate through LookML
  • Looker has dramatically increased the efficiency of the data team. We've taken data exploration from a 'data team privilege' to something the whole organization can do hands-on, including the CEO.
  • Products: Google Cloud BigQuery Looker Vertex AI

Evidence from the source

Achieved organization-wide data consistency by centralizing definitions for heartbeat metrics like Conversion Rate through LookML
Empowered stakeholders across every team to build charts and explore data without needing SQL knowledge
Reduced time-to-insight by allowing leadership and product teams to perform live data exploration during business meetings
Streamlined operational efficiency through automated Slack reporting, providing a trusted and consistent language for daily product standups
Wego transitioned from fragmented, inconsistent data silos to a unified analytical framework using Google Cloud and Looker.
Transparency Evidence & methodology 70/100 reference value · 80% confidence · 2 sources

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 depth20/25
    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 scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence80%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency9/10
    2 source references 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

    Wego customer story

    First-party
    https://cloud.google.com/customers/wego
    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
  2. Wego

    About Us - Wego's Mission, Core Values, Offices, Growth and Innovation

    First-party
    https://company.wego.com/

    We are 30 nationalities (out of about 300 employees!).

    Source type
    Official company website
    Supports
    • Company size

Date evidence

  • First archived Mar 26, 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 Mar 18, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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