Blazeclan Modernized Data Architecture for a Customer, Reducing Vendor Lock-In and Data Pipeline SLA with AWS and Snowflake

About Customer

Customer is a fast-growing global restaurant company that operates across 32 countries with over 9,500 stores. With a mission to deliver exceptional food and a joyful eating experience, they manage 19 different restaurant brands. The company is dedicated to staying ahead in a competitive industry by embracing innovation and improving operational efficiency across its locations worldwide.

Challenge

Customer faced several challenges with its outdated legacy systems, which were hindering agility and slowing down innovation. Key business challenges included: 

• Lack of Self-Service Reporting: Business users lacked the ability to independently create reports and access real-time insights without relying heavily on IT teams. This resulted in delays and limited data-driven decision-making. 

• Fragmented Reporting: Existing reporting was based on Rolling Base KPIs, which only offered partial and incomplete views of the business. This led to inaccurate understanding of critical metrics and decision-making delays. 

• Data Gaps: There was no direct access to data from Non-Traditional Stores, creating significant gaps in business intelligence and preventing holistic analysis across all operations. 

• Inflexibility of Legacy Systems: The legacy system’s rigidity and inefficiencies made it difficult to respond quickly to changing business needs, limiting the company’s ability to innovate and scale rapidly. 

Solution

Blazeclan conducted a detailed assessment of the customer’s existing environment and proposed a Data Lake concept to address both current and future data needs of Customer. 

Key elements of the implemented solution include:

• Data Audit and Migration: A comprehensive data audit was conducted, followed by the migration of POS and labor data to Snowflake, providing a scalable cloud-based platform for enhanced data management. 

• Global Data Model Rollout: A Global Data Model was deployed to standardize data definitions, KPIs, and metrics across the organization, ensuring consistency in reporting and decision-making. 

• Standardized Dashboards and Reports: Self-service dashboards were introduced, offering real-time KPI reporting at daypart and channel levels, empowering business users to make informed decisions independently. 

• Data Access and Quality Assurance: Direct access to data from non-traditional stores was implemented, along with email notifications for missing store files for a given day and predefined quality checks per store, improving data accuracy and availability. 

• Legacy Report Rationalization: Existing reports were migrated from the RAD system to Snowflake, streamlining the data pipeline and reducing reliance on outdated systems. 

• End-to-End Documentation: Complete documentation was provided to ensure transparency and facilitate long-term maintenance. 

This Data Lake-based solution addressed both immediate and long-term data needs, offering the organization a more agile, scalable, and future-proof data infrastructure. 

Results

The new data architecture delivered the following key outcomes: 

• Reduced Vendor Lock-in: A 70% reduction in vendor lock-in by transitioning from RAD to Snowflake, enhancing flexibility. 

• Improved Data Pipeline Efficiency: A 30% reduction in SLA for data pipelines, speeding up information flow and decision-making. 

• Empowered Business Users: Real-time self-service dashboards and standardized reports enabled faster, data-driven decisions. 

• Enhanced Visibility: Consistent, reliable data improved insights, strategic planning, and operational visibility. 

Conclusion

By embracing cloud technologies like AWS and Snowflake, Customer successfully modernized its data management systems, reducing inefficiencies and empowering teams across the organization. This transformation enabled faster decision-making, more accurate insights, and a scalable infrastructure that supports future growth. The new system has not only improved reporting and data accessibility but has also fostered a culture of data-driven decision-making that aligns with the company’s mission of bringing joy to every meal. 

The project underscores the importance of modernizing legacy systems to remain agile and competitive in today’s fast-evolving, data-driven business landscape. 

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