Download PDF
Xandr Uses Anodot for Real Time Monitoring of Its Massive Scale Marketplace
Technology Category
- Analytics & Modeling - Real Time Analytics
Applicable Functions
- Sales & Marketing
Use Cases
- Predictive Maintenance
- Real-Time Location System (RTLS)
Services
- Data Science Services
The Challenge
Xandr’s marketplace operates at a scale and complexity that are hard to fathom. The company serves multiple billions of ads every single day, handles 45 million transactions per second, and processes more than 175 terabytes of data. Xandr’s platforms make a lot of complex business decisions to reach the right customers for the marketers. When glitches occur and blank ads are served, all parties lose money. This has to be detected and resolved quickly before losses mount. The extensive nature of Xandr’s partnerships meant that issues could take a week or more to detect and resolve. Xandr’s infrastructure includes thousands of servers and hundreds of applications across its global data centers. The company used a variety of disparate tools to monitor the performance of the infrastructure itself as well as the delivery of the ads.
About The Customer
Xandr Inc. is the advertising innovator at the intersection of digital and TV. Xandr’s combined assets – including trusted partnerships, powerful data-enabled technology and automation, and identity solutions – power one of the world’s largest marketplaces for premium advertising. The company is headquartered in New York City with offices around the world. Xandr’s massive marketplace sits in the center of a global digital business ecosystem. Originally part of AT&T and Warner Media, Xander has access to all the brands, content, and direct-to-consumer relationships afforded by that affiliation. As Xandr becomes part of Microsoft through acquisition, the possibilities for reaching even more consumers, publishers, and marketers open up.
The Solution
Anodot ingests all of Xandr’s data feeds in near real time, learns the patterns and seasonality of individual metrics, correlates issues, and directs analysts right to the root cause. Xandr and its clients can respond quickly – normally in less than a day – to prevent small issues from having big business impacts. Xandr has its Anodot results integrated through Slack to automatically notify people like engineers, account managers, code managers, and solutions consultants. This helps get issues resolved faster by the people with the knowledge, skills, and customer relationships to get to the underlying cause.
Operational Impact
Quantitative Benefit
Related Case Studies.
Case Study
Remote Monitoring & Predictive Maintenance App for a Solar Energy System
The maintenance & tracking of various modules was an overhead for the customer due to the huge labor costs involved. Being an advanced solar solutions provider, they wanted to ensure early detection of issues and provide the best-in-class customer experience. Hence they wanted to automate the whole process.
Case Study
Predictive Maintenance for Industrial Chillers
For global leaders in the industrial chiller manufacturing, reliability of the entire production process is of the utmost importance. Chillers are refrigeration systems that produce ice water to provide cooling for a process or industrial application. One of those leaders sought a way to respond to asset performance issues, even before they occur. The intelligence to guarantee maximum reliability of cooling devices is embedded (pre-alarming). A pre-alarming phase means that the cooling device still works, but symptoms may appear, telling manufacturers that a failure is likely to occur in the near future. Chillers who are not internet connected at that moment, provide little insight in this pre-alarming phase.
Case Study
Aircraft Predictive Maintenance and Workflow Optimization
First, aircraft manufacturer have trouble monitoring the health of aircraft systems with health prognostics and deliver predictive maintenance insights. Second, aircraft manufacturer wants a solution that can provide an in-context advisory and align job assignments to match technician experience and expertise.
Case Study
Integral Plant Maintenance
Mercedes-Benz and his partner GAZ chose Siemens to be its maintenance partner at a new engine plant in Yaroslavl, Russia. The new plant offers a capacity to manufacture diesel engines for the Russian market, for locally produced Sprinter Classic. In addition to engines for the local market, the Yaroslavl plant will also produce spare parts. Mercedes-Benz Russia and his partner needed a service partner in order to ensure the operation of these lines in a maintenance partnership arrangement. The challenges included coordinating the entire maintenance management operation, in particular inspections, corrective and predictive maintenance activities, and the optimizing spare parts management. Siemens developed a customized maintenance solution that includes all electronic and mechanical maintenance activities (Integral Plant Maintenance).
Case Study
Leading Tools Manufacturer Transforms Operations with IoT
Stanley Black & Decker required transparency of real-time overall equipment effectiveness and line productivity to reduce production line change over time.The goal was to to improve production to schedule, reduce actual labor costs and understanding the effects of shift changes and resource shifts from line to line.
Case Study
Asset Management and Predictive Maintenance
The customer prides itself on excellent engineering and customer centric philosophy, allowing its customer’s minds to be at ease and not worry about machine failure. They can easily deliver the excellent maintenance services to their customers, but there are some processes that can be automated to deliver less downtime for the customer and more efficient maintenance schedules.
