About the Customer
Our client, one of the world’s largest fast-food restaurant chains, operates at an extraordinary scale where every decision around menu design, staffing, inventory, and promotions must be informed by data and informed fast. With thousands of restaurants, complex global supply chains, and millions of daily customer interactions, analytics performance directly impacts revenue, cost efficiency, and customer experience.
As our client accelerated its digital and AI ambitions, its existing analytics foundation began to show structural limits. What followed was not a simple platform upgrade but a strategic re-architecture of its enterprise data backbone designed to support real-time intelligence, AI at scale, and global growth.
When Data Volume and Velocity Outgrow the Platform
The client’s data ecosystem was generating massive volumes of operational, transactional, and customer data across restaurants, digital channels, and supply chain systems. However, the existing AWS Redshift-based architecture struggled to scale elastically with this growth.
Key challenges emerged across both technology and business dimensions:
- Query performance degradation during peak demand periods such as promotions and holidays
- Delayed demand forecasting impacting supply chain responsiveness and inventory planning
- Limited ability to operationalize AI and machine learning due to fragmented, batch-oriented data pipelines
- Rising infrastructure and licensing costs driven by over-provisioning to manage peak workloads
These constraints directly affected core business capabilities; menu personalization lagged behind customer expectations, forecasting accuracy declined during high-variance demand cycles, and operational decisions were often reactive rather than predictive.
Our client needed a platform capable of supporting real-time analytics, AI-driven decision-making, and global scalability without introducing additional operational complexity.
Designing an AI-Ready, Cloud-Native Data Architecture
The one of the world’s largest fast-food restaurant chain partnered with Onix and Google Cloud to modernize its data and analytics stack, aligning platform capabilities with clearly defined business outcomes.
The transformation centered on migrating to BigQuery as the enterprise analytics foundation, complemented by Gemini-powered AI capabilities to accelerate insight generation and support advanced use cases across personalization, forecasting, and workforce optimization.
The architectural strategy focused on three core principles:
- Decoupling scale from performance through serverless analytics
- Embedding AI directly into the data platform, not as an afterthought
- Enabling enterprise-wide intelligence, from headquarters to individual restaurants
This approach allowed the client to move from periodic, batch-driven reporting to near-real-time, decision-grade analytics.
Business Outcomes Driven by Platform Modernization
The modernization delivered measurable, business-critical results across revenue growth, operational efficiency, and cost optimization.
Revenue and Customer Experience
- 25% improvement in menu personalization accuracy, enabling more relevant offers and higher average order value
- Faster response to customer preferences across digital ordering and in-store channels
Operational and Supply Chain Performance
- 20% improvement in demand forecasting accuracy, reducing waste and improving inventory alignment
- 15% improvement in staffing optimization, supporting leaner operations while maintaining service quality during peak demand
Cost and Innovation Efficiency
- 30% reduction in infrastructure and licensing costs, eliminating over-provisioning and freeing capital for innovation
- Improved analytics agility enabled faster experimentation and roll-out of new digital initiatives globally
Together, these outcomes demonstrated how platform-level decisions translate directly into competitive advantage.
Why Google Cloud Was the Strategic Platform Choice
Our client selected Google Cloud to overcome the limitations of its existing hyperscaler and support the next phase of enterprise modernization.
Key decision factors included:
- BigQuery’s ability to deliver consistent performance at global scale without capacity management
- Integrated AI capabilities with Gemini, enabling faster transition from data to intelligence
- Alignment with the client’s long-term vision for enterprise AI, edge computing, and real-time decision-making
Google Cloud provided not just technical differentiation but a platform aligned to measurable business outcomes.
Onix: Turning Architecture into Business Impact
Onix played a pivotal role in translating platform capabilities into operational success.
The global restaurant-chain leader chose Onix for:
- Proven expertise in large-scale, enterprise data migrations
- Deep specialization in Google Cloud data and AI services
- A joint “One Google” delivery model that reduced risk and accelerated time to value
- Purpose-built Wingspan tools that streamlined migration, optimization, and governance
Onix ensured the modernization delivered not just a new platform but a durable, future-ready data foundation.
Building the Data Backbone for the Next Generation of QSR Intelligence
One of the world’s largest fast-food restaurant chain’s transformation underscores how modern data architectures enable smarter, faster, and more predictive operations at a global scale.
With Google Cloud and Onix, our client has:
- Established an AI-ready data backbone for enterprise-wide intelligence
- Enabled rapid rollout of personalized, data-driven customer experiences
- Created a scalable analytics platform designed to evolve with future innovation
This shift positions the world’s largest fast-food restaurant chain to lead the next era of QSR transformation, where data, AI, and agility work together to serve customers better, operate smarter, and compete faster in an increasingly digital world.
