Data Lake / Data Warehouse Modernization · IT Operations Automation · Real-time Analytics
Google Cloud Support revolutionized operations, moving from fragmented tools to a governed, AI-powered Looker environment supercharged by conversational analytics. In a global enterprise, support operations inherently rely on data, and having immediate access to accurate statistics is vital for identifying case volume drivers and monitoring performance.
Reported impact
Achieved scale of 500:1 user-to-employee ratio through self-service; 10x the speed of analysis for end users through conversational analytics; Improved stability by migrating to Looker core on Google Cloud; Reduced manual reporting for 5,000 monthly active users.
The implementation of conversational analytics in Looker has further supercharged this self-service model. By connecting the conversational analytics agent directly to the existing, governed Looker data model, the team ensured that AI-generated insights remained fully accurate and compliant. The investment in metadata and data hygiene, originally intended for human users, became AI-friendly, paying off exponentially when the agent launched. Users can now ask complex questions in plain language, such as "Show me average resolution times for Gemini Enterprise Agent Platform cases," eliminating the need to hunt through dozens of bookmarked dashboards or manage complex filters. The team selected Looker for its semantic layer and centralized governance features. This transition allowed the team to move away from a fragile on-premise instance to Looker Core on Google Cloud, providing the stability required for high-concurrency workloads.
stored or unified in BigQuery, orchestrated via Gemini Enterprise Agent Platform, and surfaced through Gemini Enterprise, Gemini, Looker
10x the speed of analysis for end users through conversational analytics
Achieved scale of 500:1 user-to-employee ratio through self-service
Improved stability by migrating to Looker core on Google Cloud
Reduced manual reporting for 5,000 monthly active users
This lack of governance made it nearly impossible to maintain a consistent 360-degree view of the customer experience across the organization’s nearly 5,000 monthly users.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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