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F500 Manufacturer Accelerates Product Development with Sigma
Technology Category
- Analytics & Modeling - Big Data Analytics
- Application Infrastructure & Middleware - Data Visualization
- Functional Applications - Product Lifecycle Management Systems (PLM)
Applicable Industries
- Semiconductors
Applicable Functions
- Business Operation
- Product Research & Development
Services
- Software Design & Engineering Services
- System Integration
The Challenge
The Power & Performance Leads (PPL) team at a major U.S. semiconductor manufacturer faced significant challenges in managing and analyzing vast amounts of data. The team relied on millions of data points housed within Snowflake to build custom data center solutions. However, their existing workflow, which involved using Power BI and Excel, was inefficient and led to bottlenecks, wasted time, and slow product cycles. The disjointed workflow also caused unplanned ad hoc requests, constant reprioritization of tasks, and analyst frustration. The PPL team often had to work evenings and weekends to keep up with the demands, leading to an overworked and stressed team.
About The Customer
The customer is one of the largest semiconductor manufacturers in the United States, known for its advanced technology and innovation in the semiconductor industry. The company has a dedicated Power & Performance Leads (PPL) team responsible for predictions, modeling, product definitions, customer request matches, tuning, optimization, and verification of custom data center solutions. This team plays a critical role in ensuring the reliability and performance of the company's products, as any faulty solution can result in multi-million dollar problems. The PPL team relies heavily on data analysis to meet performance targets and deliver high-quality solutions to their customers.
The Solution
The PPL team adopted Sigma to address their data analysis challenges. Sigma, purpose-built for Snowflake and cloud data warehouses, provided the team with direct access to live data in Snowflake, eliminating the need for data extracts from the BI team. This ensured that the data remained safe and up-to-date. Sigma's cloud-native solution offered unlimited scale and speed, allowing the PPL team to analyze and filter all testing data in a single pane, reducing the potential for errors. The spreadsheet-like interface of Sigma enabled self-service data exploration, making it easy for the PPL team to perform iterative ad hoc analytics. This familiar interface allowed the team to quickly analyze data and address potential issues before they became serious problems. As a result, the company is now deploying Sigma across all product teams and leveraging it for cost modeling.
Operational Impact
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