Situation
The business context
Multiple restaurant brands and channels generated enormous volumes of guest activity, but fragmented identities and an unreliable legacy platform limited the enterprise’s ability to connect transactions to guests, understand behavior, personalize experiences, and generate timely, actionable insights. Creating a complete view of the guest was the first step; making that data broadly accessible, timely, and actionable required a longer modernization journey.
Task
The leadership mandate
As a senior IT leader, I was responsible for developing and executing a multi-year data transformation strategy that would turn fragmented guest data into a strategic enterprise asset. It began by unifying identity and culminated in a modern data foundation capable of democratizing trusted insights across the enterprise.
Strategy
The strategic approach
My strategy was to build guest analytics as a sequence of expanding enterprise capabilities rather than approaching it as a single platform replacement. We would first resolve fragmented guest identity so transactions could be connected to people and behavior. We would then stabilize data movement before scaling consumption, establish a governed Lakehouse as the target architecture, and broaden access to trusted, timely insight. Each stage was designed to create immediate business value while establishing the foundation for the next, ultimately enabling personalization, loyalty, advanced analytics, guest-experience improvement, and AI.
Action
How the work moved forward
- Sequenced the multi-year roadmap: Aligned technology investment to the company’s vision for becoming more guest-centric and data-driven, then organized delivery around progressive capabilities that could create value while preparing the next stage.
- Established a unified guest identity: Connected more than 500 million transactions through a tokenized identity-resolution framework, creating actionable guest profiles for personalization, loyalty, and data-driven marketing.
- Stabilized data movement before scaling access: Replaced brittle PowerShell ETL processes with Azure Data Factory, improving the reliability and scalability of the integration foundation.
- Established and delivered the target architecture: Led the migration from Azure Synapse to a governed Databricks Lakehouse, improving platform economics and processing speed while enabling broader analytics and advanced AI use cases.
- Turned trusted data into business action: Expanded access to timely insight and transformed guest feedback and behavioral data into actionable information for improving guest experience and operator performance.
Result
The measurable difference
- 500M+ transactions unified through identity resolution, creating actionable guest profiles across channels.
- 95% reduction in integration failures, dramatically improving data reliability.
- 90% reduction in time-to-insight, enabling business teams to act on information substantially faster.
- 30% reduction in platform TCO through migration to the Databricks Lakehouse.
- Four consecutive quarters of year-over-year improvement in guest experience and operator performance scores directly attributable to guest feedback translated into actionable insights.
- Established the data foundation for personalization, loyalty, advanced analytics, and AI/LLM use cases.
Leadership insight
Transformational technology leadership is not simply about replacing legacy platforms, it is about creating a shared business vision, sequencing change around measurable outcomes, and enabling teams to turn technology into new organizational capabilities.