Case Studies F500 Wholesaler in U.S. Depends on Sigma to Meet SLAs

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	F500 Wholesaler in U.S. Depends on Sigma to Meet SLAs

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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 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.

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.

Impact #1	Employees now have direct access to live data in Snowflake, ensuring consistent and current data usage.
Impact #2	Sigma's cloud-native solution delivers unlimited scale and speed, eliminating the need for data summaries or aggregates.
Impact #3	The spreadsheet interface of Sigma allows for self-service data exploration, making ad hoc analytics accessible to all employees.

Impact #1

Employees now have direct access to live data in Snowflake, ensuring consistent and current data usage.

Impact #2

Sigma's cloud-native solution delivers unlimited scale and speed, eliminating the need for data summaries or aggregates.

Impact #3

The spreadsheet interface of Sigma allows for self-service data exploration, making ad hoc analytics accessible to all employees.

Benefit #1	The BI team reduced the time spent on answering ad hoc questions and extracting data by 20%.

Benefit #1

The BI team reduced the time spent on answering ad hoc questions and extracting data by 20%.

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Overview

F500 Wholesaler in U.S. Depends on Sigma to Meet SLAs

Operational Impact

Quantitative Benefit