CASE STUDY
Data Lake Case Study: Automating Franchise Sales Journal Entries with AWS
A national, multi-brand restaurant operator with a clear ambition to grow started with 85 restaurants on the West Coast in 2014 and has expanded to over 300 locations across two major brands. The restaurant operator prides itself on positively impacting the lives of its team members and the communities they serve.
INDUSTRY
Restaurant (Quick Service and Fast Casual Sector)
CLIENT
National, Multi-Brand Restaurant Operator
BUSINESS CHALLENGES
The restaurant operator faced several challenges with their existing sales journal entry management:
- High costs: Third-party solutions managing journal entries incurred substantial annual maintenance fees.
- Slow responsiveness: Backlog of requirements and slow turnaround times.
- Limited data access: Only read-only access to their own critical data.
- Reporting limitations: Need for enhanced reporting capabilities before finalizing accounting entries.
- Data consolidation issues: Existing solutions couldn’t upgrade to modern data transfer methods, making data integration cumbersome.
TECHNOLOGY USED
- Amazon S3 (Storage)
- AWS Lambda (Compute)
- AWS Step Functions (Orchestration)
- AWS Glue (Transformation)
- Amazon Redshift Serverless (Database)
- Amazon Quick Sight (Reporting)
- AWS Secrets Manager (Credentials)
- Amazon DynamoDB (NoSQL Database)
SOLUTION
Ironside proposed a comprehensive AWS-based
data lake solution to address these challenges:
- Unified Data Platform: Consolidated all data sources into a modern AWS data lake, utilizing scalable connectors.
- Automated Data Pipelines: Built end-to-end pipelines for data collection, ingestion, and processing.
- Extensible Rules Engine: Developed flexible rules-based mechanisms for journal entry creation, enabling rapid updates.
- Data Transformation: Automated processes to classify and modify data periodically, maintaining accuracy and relevance.
OUTCOMES
This solution delivered significant benefits:
- Laid the groundwork for future data modernization and advanced reporting.
- Reduced operational costs by eliminating hefty vendor fees.
- Decommissioned SFTP servers, saving time and money.
- Enhanced agility for market-responsive data adjustments.
- Improved data accessibility and analytics potential via a robust data lake setup.
How Ironside Delivered
Ironside adopted a phased implementation process.
01
REQUIREMENT GATHERING
Developed a detailed Business Requirements Document.
02
SOURCE AND OUTPUT ANALYSIS
Reviewed data sources and current processes.
03
ARCHITECTURE DESIGN
Created the solution architecture and estimating AWS costs.
04
PIPELINE DEVELOPMENT
Built data ingestion and reconciliation tools
05
DEPLOYMENT
Rolled out the complete system, including JE generation and review via Quick Sight.
By engaging all stakeholders at each phase, Ironside ensured the project aligned with
client goals, demonstrating its ability to deliver scalable, innovative data solutions.
This project not only improved operational efficiency, but also established a strong
foundation for the restaurant operator’s future growth and technological advancements.
