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F500 Wholesaler in U.S. Depends on Sigma to Meet SLAs
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Real Time Analytics
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Food & Beverage
Applicable Functions
- Business Operation
- Logistics & Transportation
Use Cases
- Predictive Maintenance
- Process Control & Optimization
- Supply Chain Visibility
Services
- Data Science Services
- System Integration
The Challenge
The foodservice distributor faced significant challenges in managing its vast dataset, which included multi-billion rows of service level data. Employees needed timely access to this data to conduct root cause analysis and resolve issues to meet SLAs. However, scale limitations and the inability to anticipate ever-changing data requirements hindered their ability to access all necessary data. The BI team spent 20% of its time answering ad hoc questions and extracting data, which needed continuous refreshing as issues evolved. This lack of timely data access negatively impacted employees' ability to understand and resolve issues, leading to missed SLAs, penalties, and customer retention and acquisition challenges.
About The Customer
The customer is a leading foodservice distributor in the United States, partnering with 300,000 restaurants and foodservice operators to help their businesses succeed. The company has a large dataset, including multi-billion rows of service level data, which is accessed by more than 2000 employees multiple times a day through the Service Level Impact dashboard in Tableau. The company aims to ensure fulfillments are achieved and SLAs are met, but faced challenges due to scale limitations and the need for timely data access.
The Solution
The foodservice distributor implemented Sigma, a cloud-native solution purpose-built for Snowflake and cloud data warehouses. This allowed employees direct access to live data in Snowflake, ensuring everyone worked with the same current data without stale extracts or conflicting insights. Sigma provided unlimited scale and speed, enabling employees to analyze and filter billions of rows of transactional data without rendering or latency delays. The spreadsheet interface of Sigma made iterative ad hoc analytics accessible to anyone, especially those accustomed to analyzing data in spreadsheets. Employees could now analyze data and create pivot tables in Sigma, quickly addressing potential issues before they became serious problems.
Operational Impact
Quantitative Benefit
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