Data warehousing solution transforms analytics for US restaurant chain, enabling real-time insights and smarter decision making
A prominent US restaurant chain aimed to enhance its data management and decision-making capabilities by implementing an efficient data warehousing solution. The restaurant previously attempted to deploy a Zoho-based solution that did not quite meet their requirements, left out integration of several key data sources, and became cost-prohibitive for additional new features, functionalities, analyses, and dashboards.
Fuld & Company used a more robust, flexible, and scalable data warehousing setup leveraging Azure cloud including delta lakes, data marts, and functional and cross-functional dashboards. The goal was to ensure that the restaurant chain could make smarter decisions in areas such as pricing, demand planning, operational forecasting, and any other future use case through real-time insights.
Fuld implemented a robust, scalable data warehousing solution that delivered the following results:
- 15+ Data Sources Integrated: Over 15 data sources, including sales transactions, inventory data, customer feedback, and supply chain information, were integrated to provide a comprehensive view of the restaurant’s operations
- 12+ AI Models Deployed: Over 12 AI models were deployed to optimize pricing strategies, predict customer demand, and improve forecasting for smarter resource allocation
- Hourly Incremental Refresh: An hourly incremental data refresh provided near-real-time insights while enabling quick data processing, minimal resource utilization, and enabling timely decision-making in dynamic business areas like inventory management and pricing adjustments
How We Did It
Fuld & Company worked with the CTO and technology team to understand the current deployment, capture current shortcomings, and finalize requirements that would not only migrate the warehouse but will also allow for future enhancements.
- Migration Design and Planning
Fuld & Company conducted a comprehensive gap analysis, identifying critical shortcomings in scalability, data integration, and cost inefficiencies. To address these, the team designed an Azure Data Lake and Delta Lakes architecture, enabling real-time analytics and AI capabilities. A phased migration plan was developed to ensure a seamless transition, minimizing operational disruptions and ensuring business continuity.
- Migration Implementation and New Integration
The migration process began with data extraction and cleansing, ensuring historical data was standardized and normalized. An Azure-based data warehouse was then deployed to optimize performance and scalability. ETL pipelines using Azure Data Factory were automated to enable structured transformation and governance. Finally, real-time dashboards were developed, providing both functional and cross-functional stakeholders with actionable insights for decision-making.
We additionally integrated over 10 data sources, covering sales transactions, inventory management, store staffing, customer feedback and helpdesk, and supply chain information. This integration created a unified view of the restaurant’s operations, ensuring more informed decision-making across all business areas.
- Real-Time Data Refresh with Full Observability
All refresh and transformation jobs were with scheduled, sequenced, and optimized for performance. We adopted an hourly incremental refresh to ensure that the strategic stakeholders had access to the most up-to-date information for their decision making.
- Advanced Analytics with AI Models
With the data warehouse set up in the first 3 months, our data scientists started working on training custom AI models to support crucial business processes such as pricing optimization, demand planning, staffing, and forecasting. These models allowed the restaurant chain to predict customer demand more accurately, adjust pricing strategies, and allocate resources efficiently based on real-time insights.
- Scalability and Flexibility
The data warehousing solution was built with scalability and self-service in mind. This allowed the restaurant chain to seamlessly expand its data capabilities as the business grew and add new data sources. Further, the self-service datasets and dashboards, and curated trainings to upskill and train corporate analysts empowered client teams to be more independent in their analysis and decision making. This flexible design and architecture facilitated integration of advanced analytics and enhancements in a planned manner without compromising decision making.