Google Cloud Support uses Looker + Gemini Enterprise Conversational Analytics to scale support BI and speed insights

Google Cloud Support centralized fragmented support BI in Looker on Google Cloud with BigQuery and Gemini Enterprise conversational analytics. The team shifted to governed semantic metrics and self-service conversational analytics to reduce BI bottlenecks, improve consistency, and speed decision-making for about 5,000 monthly active users.

Industry
Tech & Comms
Published
June 2026

Reported outcomes

10x

analysis speedTime & speed

500 :1user-to-BI-staff ratio30 minutesdaily time saved60 minutesdaily time saved−20%escalation rate+100%initial response requirements met

Strategic outcomes

Scale & capacityExpanded self-service BI capacityCustomer experience & trustEstablished a single source of truth for metricsBetter decisions & insightEnabled faster decision-making from conversational queriesCustomer experience & trustImproved customer support responsivenessInnovation & cultureShifted to a train-the-trainer self-service model

Catalog median for time & speed deployments: +75% across 183 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 3

  • 1Conversational analytics
  • 2Executive analytics
  • 3Data platform modernization
  • Fragmented BI caused metric drift and a lack of a single source of truth.
  • The BI team was a bottleneck because every new dashboard tile required a manual query.
  • The environment needed to scale to thousands of users while preserving governance and trust.
  • Migrated support BI to Looker Core on Google Cloud with a governed semantic layer.
  • Connected conversational analytics directly to the governed Looker model so AI answers use accurate metrics.
  • Enabled a train-the-trainer self-service model and plain-language querying through Looker Explores.
  • User-to-BI-staff ratio improved from 100:1 to 500:1.
  • Analysis became 10x faster for end users.
  • Leaders and managers saved 30 to 60 minutes daily.
  • Escalation rates were reduced by 20% and initial response requirements were met at near 100%.
Architecture

The support BI team migrated from a homegrown decentralized tool to Looker Core on Google Cloud, using Looker's governed semantic layer on top of BigQuery. They connected Gemini Enterprise conversational analytics directly to the governed Looker data model so AI responses reflect trusted metrics and support self-service querying through Looker Explores.

Sources & evidence1
Groundedness: 5/5Type: Customer StoryPublished: Jun 27, 2026Publisher: Google CloudEvidence: PrimaryConfidence: High

AI-generated summary. Verify important details with the linked sources before relying on this case.

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