Local TV Broadcasting Leader in U.S. Optimizes Revenue with Sigma
公司规模
1,000+
地区
- America
国家
- United States
产品
- Sigma
- Snowflake
- Tableau
技术栈
- Cloud Data Warehouses
- BI Tools
- Data Analytics
实施规模
- Enterprise-wide Deployment
影响指标
- Customer Satisfaction
- Productivity Improvements
- Revenue Growth
技术
- 分析与建模 - 大数据分析
- 分析与建模 - 实时分析
- 功能应用 - 远程监控系统
适用行业
- 电信
适用功能
- 商业运营
- 销售与市场营销
用例
- 实时定位系统 (RTLS)
服务
- 云规划/设计/实施服务
- 数据科学服务
- 系统集成
关于客户
The customer is the fourth largest independent TV station owner in the U.S., operating 60 television stations across 42 markets. They share news and entertainment content and rely heavily on audience engagement metrics and performance data from sources like Nielsen and Comscore. The company ingests and analyzes billions of rows of operational and TV ratings data to understand audience engagement and measure both national and local media group performance. Their primary goal is to optimize content and advertising revenue by leveraging data-driven insights.
挑战
The fourth largest independent TV station owner in the U.S. faced significant challenges with their legacy data analytics architecture. The system required constant administrative oversight, and basic queries took hours to run. Adding new data sources could take up to three months, stifling data exploration and delaying insight discovery. The architecture couldn't handle the volume of data necessary for key revenue-driving reports, leading to abandoned projects. Scale and performance limitations made it impossible to effectively analyze data across sources, with filters taking an hour or more to load. Additionally, the company lacked a way for the BI team to collaborate on analytics with their line of business counterparts or quickly share new insights with TV station leaders.
解决方案
The company implemented Sigma, a cloud-native BI tool purpose-built for Snowflake and cloud data warehouses. This solution provided direct access to live data in Snowflake, ensuring that everyone was always looking at the same current data. Station leaders gained instant access to the information they needed to make informed decisions while data remained secure in Snowflake. Sigma's unlimited scale and speed allowed the BI team to analyze and filter billions of rows of ratings data across multiple sources without latency delays. The spreadsheet interface of Sigma enabled self-service data exploration, making iterative ad hoc analytics available to anyone. The BI team now has a single source of truth for data and can easily add new sources to analyses without help from the data team, accelerating and improving insights.
运营影响
数量效益
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