Google Cloud Software / SaaS Australia

ROLLER

Data Lake / Data Warehouse Modernization · Real-time Analytics · System Migration

Saved ROLLER's internal payments team days of manual data aggregation work per week, reducing the task to minutes Eliminated engineering bottlenecks by transitioning from manual SQL scripting to a self-service data model

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Enabled more than 3,000 venues to access self-service embedded analytics without needing third-party BI tools

Reported impact

Business outcome

Saved ROLLER's internal payments team days of manual data aggregation work per week, reducing the task to minutes; Enabled more than 3,000 venues to access self-service embedded analytics without needing third-party BI tools; Eliminated engineering bottlenecks by transitioning from manual SQL scripting to a self-service data model; Facilitated the launch of ROLLER iQ, a conversational AI chatbot that queries Looker...

Solution

With a robust data foundation established, ROLLER is now leading the attractions industry into a future defined by generative AI and seamless data accessibility. The team recently launched 'ROLLER iQ,' an innovative conversational AI chatbot built on Google Cloud, using Vertex AI that brings data to life through natural language, with Gemini 3 Flash + Gemini 3.1 Pro for deeper questions and insights. This project was a major focus for the data engineering and ML teams, who leveraged their deep knowledge of ROLLER's data to build a tool that outpaced traditional product development timelines. By integrating directly with Looker APIs, ROLLER iQ ensures that every answer provided in a chat is perfectly consistent with the official dashboards. This breakthrough allows venue managers to ask complex questions and receive immediate, trusted answers, making data interaction more intuitive than ever before.

Data flow

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

Solution architecture

5 components · 4 layers

Compute
- Vertex AI Hosts and orchestrates machine learning and generative AI capabilities inside the solution.
Storage
- BigQuery Stores and queries the analytics and AI-ready datasets that power the workload.
Serving
- Gemini Powers natural-language reasoning, summarization, generation, or assistance inside the workflow.
- Looker Provides the governed semantic layer, dashboards, and self-service analytics used by business teams.
Governance
- LookML Defines governed metrics and semantic models so teams and AI workflows use the same business logic.

Architecture clues

Facilitated the launch of ROLLER iQ, a conversational AI chatbot that queries Looker APIs for consistent data
Successfully migrated from DataBricks to an integrated BigQuery and Looker data stack
conversational AI with Looker

Evidence from the source

Eliminated engineering bottlenecks by transitioning from manual SQL scripting to a self-service data model

Enabled more than 3,000 venues to access self-service embedded analytics without needing third-party BI tools

Facilitated the launch of ROLLER iQ, a conversational AI chatbot that queries Looker APIs for consistent data

Saved ROLLER's internal payments team days of manual data aggregation work per week, reducing the task to minutes

Successfully migrated from DataBricks to an integrated BigQuery and Looker data stack

Transparency Evidence & methodology 85/100 reference value · 79% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

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 value85/100

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 impact15/25
1 metric describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence79%

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 evidence20/20
5 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 specificity10/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

Google Cloud
ROLLER customer story

First-party
https://cloud.google.com/customers/roller

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 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 19, 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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