Normalized claim
Report development time reduction: 95% decrease
decrease in report development time of 95%
GCPEvidence: Medium50/100
Waterscan: embedded analytics for water management (Looker powering Waterline)
Use case typeCustomer experience analyticsUpdated Jun 13, 2026
Waterscan replaced Excel-based data models with Looker to deliver faster, more flexible reporting for its Waterline product and internal teams. The embedded analytics implementation connects Looker to Waterline, builds a data model, embeds dashboards, and configures customer permissions. Waterscan also uses Looker to automate invoice exception detection, alerts, and workflow handling across finance, retail services, operations, and account teams.
- Organization
- Waterscan
- Industry
- Energy & Utilities
- Location
- United Kingdom
- Published
- June 2026
Reported outcomes
−95%
report development time reductionTime & speed
100 hoursinitial implementation time2,400 USDreport build cost+100%customers improving water efficiency+100%employee self-service analytics adoption
Strategic outcomes
New product / capabilityBuilt premium embedded analytics offeringSpeed & agilityAccelerated report development and changesCustomer experience & trustEnabled customer-specific self-service dashboardsBetter decisions & insightImproved invoice exception detection and validation
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Initial implementation time: 100 hours decrease
two developers less than 100 hours to complete
Normalized claim
Report build cost: 2,400 USD decrease
each report built or amended cost Waterscan more than $2,400
Normalized claim
Customers improving water efficiency: 100% increase
helped 100% of customers who use Waterline to lower their water usage and costs
Normalized claim
Employee self-service analytics adoption: 100% increase
today 100% of Waterscan’s employees have self-service access to analytics with Looker
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Waterscan
- Provider
- GCP
- Maturity
- Unknown
- Linked source
- Google Cloud Customer Story
No explicit deployment-stage evidence found.
Customer identity supportedSource describes one deploymentMaturity evidence evaluated
Primary read
Use case focus
Showing 3 of 4
- 1Embedded Analytics
- 2Business Intelligence
- 3Workflow Automation
- Waterscan relied on Excel spreadsheets for business intelligence, which was inflexible and hard to scale as customer demand grew.
- The team needed near real-time analytics for both internal users and Waterline customers, plus better invoice error detection and exception handling.
- Report development and amendments were slow and expensive, taking up to four weeks and costing more than $2,400 per change.
- Waterscan chose Looker for embedded analytics and built a premium reporting offering through Waterline.
- The team connected Looker to Waterline, built the data model, embedded dashboards, and configured permissions for customers.
- The finance team uses Looker to log invoices automatically and trigger overdue email alerts, while retail services uses it to identify process exceptions and manage validations.
- The platform provides self-service analytics and customer-specific dashboards with benchmarking and real-time usage data.
- The initial implementation took two developers less than 100 hours.
- Report development time decreased by 95%.
- Waterscan said one report previously cost more than $2,400 to build, with extra cost for amendments.
- 100% of customers using Waterline were able to improve water efficiency and save water and money.
- 100% of employees gained self-service access to analytics.
Architecture
Looker is embedded into the Waterline product and sits on top of Snowflake. Waterscan built the data model, embedded dashboards, and permissioning for customers, with automated invoice logging, alerts, and exception handling workflows supporting internal teams.
Sources & evidence1
Evidence: Medium50/100Evidence strength
- Customer explicitly identified
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: Jun 7, 2026Publisher: Google CloudEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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