Applying Sumo Logic to machine data ensures the highest service levels for the world’s biggest financial institutions
公司规模
Large Corporate
地区
- America
- Europe
- Asia
国家
- United States
- United Kingdom
- India
产品
- Sumo Logic
- ELK Stack
- Amazon Web Services (AWS)
技术栈
- Machine Data Analytics
- Cloud Computing
- Log Management
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Customer Satisfaction
- Digital Expertise
技术
- 分析与建模 - 实时分析
- 应用基础设施与中间件 - 数据交换与集成
- 基础设施即服务 (IaaS) - 云计算
适用行业
- 金融与保险
- Software
适用功能
- 商业运营
- 质量保证
用例
- 机器状态监测
服务
- 云规划/设计/实施服务
- 系统集成
- 培训
关于客户
Cardlytics is an advertising platform built within the banks’ digital channels. Through its partnership with financial institutions worldwide, the company has insight into purchase data from more than 100 million monthly active users, representing two in five card swipes, or roughly $2.3 trillion in annual spend. Each night the company ingests all of this raw data. This then serves as the source for deep analytics carried out by a team of 70 specialists to identify which consumers may be interested in receiving certain rewards based on their past spend. Once the detailed analysis is complete, Cardlytics uses this purchase intelligence to create offers and target marketing campaigns for a broad array of advertisers within the banks’ digital channels. These programs are more widely known as ‘Chase Offers’ or ‘BankAmeriDeals’ as two examples. Consumers activate the advertisers’ offers within their bank’s online banking site or mobile app. Later, when they shop at those merchants using their credit or debit card, they will see the reward credit on their next bank statement. Through this process, Cardlytics has saved consumers more than $245 million to date.
挑战
Maintaining separate, dedicated information processing environments for financial institutions is an exceptionally intricate endeavor that generates massive amounts of machine data. Cardlytics sought a comprehensive, technology-backed strategy to aggregate and manage this information across the entire organization. The new solution would need to enable users to correct defects without requesting extensive hand-holding; it would also need to satisfy stringent support and service level obligations for the company’s most important clients.
解决方案
Cardlytics replaced an internally maintained open source Elasticsearch, Logstash, and Kibana (ELK) stack with cloud-native machine data analytics platform Sumo Logic. The company immediately began ingesting machine data for billions of API transactions per day. With the upgraded application in place, Cardlytics then granted access to Sumo Logic to multiple teams within the organization. From the start, the company defined two primary use cases for Sumo Logic: dashboard visualization and alerting. Cardlytics created individual dashboards for each financial institution. These dashboards monitor client-specific key metrics, such as load per Web server, response times for Web servers, error rates, response times that exceed certain thresholds, and SLA compliance. To power its extensive alerting needs, Cardlytics configured several Sumo Logic alerts that run against log files. These alerts create notifications if there’s a deviation from normal response times and traffic patterns. Should a priority 1 support incident arise, Sumo Logic features prominently in the company’s ‘war room’ and is one of the key resources that the team consults when trying to address the issue. In many cases, Cardlytics has been able to leverage Sumo Logic to find and fix flaws before customers even notice that something has gone awry.
运营影响
数量效益
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