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Case Studies: How are Enterprises Using Streaming Analytics

The insideBIGDATA Guide to Streaming Analytics is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting new area of technology. Many enterprises find themselves at a key inflection point in the big data timeline with respect  to streaming analytics technology. There is a huge opportunity for direct financial and market growth for enterprises by leveraging streaming analytics. Streaming analytics deployments are being engaged by companies in a broad variety of different use cases. The vendor and technology landscape is complex and numerous open source options are mushrooming. It’s important to choose a platform that will supply a proven and  pre-integrated, performance-tuned stack, ease of use, enterprise-class reliability and flexibility to protect the enterprise from rapid technology  changes. Maybe the most important reason to evaluate this technology now is that a company’s competitors are very likely implementing  enterprise-wide real-time streaming analytics right now and may soon gain significant advantages in customer perception & market-share. The complete insideBIGDATA Guide to Streaming Analytics is available for download from the insideBIGDATA White Paper Library.

insideBIGDATA_Guide_Streaming_Analytics_featureAs the technology grows in popularity, we see an increasing number of use case examples for how streaming analytics plays a significant role in cultivating competitive advantage. At a high level, there are a growing number of application areas such as IoT, mobile app analytics and call center monitoring and analytics. There are also a variety of horizontal applications coming to the forefront: customer experience, clickstream analytics, context-sensitive offers and recommendations, IT log analytics and security. In addition, we see an emergent list of verticals including:

  • Call center analytics
  • Predictive maintenance
  • Clinical care and patient management
  • Sensor data analytics
  • Fleet operations
  • Security
  • Fraud and anomaly detection
  • Gaming analytics
  • Churn analytics
  • Network traffic analysis and optimization
  • Manufacturing
  • Oil & Gas
  • Healthcare – Clinical
  • IoT
  • Transportation/logistics
  • Financial Services
  • Retail
  • E-commerce
  • Log analytics
  • Online advertising
  • Banking
  • Credit-line management
  • Insurance claim validation
  • Telecom

One particular use case that is growing in importance is business activity monitoring (BAM). Many large organizations maintain extensive, complex  processes with the need to make sure the systems are all saying the same thing. Reconciliation and auditing systems absorb data as it moves from  various systems and there’s a need to monitor and audit potentially hundreds of different parameters across the entire business process flow. This is a good example of applications built on top of a streaming platform to achieve real-time reconciliation. Such a system avoids having to find out hours or  days in the future that the systems were incongruent.

CASE STUDY – Call center monitoring

Call center monitoring with real-time customer interactions, rich context sensitive customer interactions is a good example of customer application  building on top of the streaming platform. Call center monitoring proved the immediate and quantifiable business benefits from real-time streaming analytics for the telecom/VOIP/Call-Center industry. Some of these benefits include: numerous person-months of productivity gain, customer  complaint resolution speed, customer satisfaction index and higher customer retention rates.

The successful streaming analytics solution allowed the call centers to process millions of minutes of calls per day across vast distributed networks around the globe, and also provided an infrastructure monitoring platform that allowed a unified view and analysis of events in real-time.

The solution included:

  • SLA Alerts: Service level alerts in real-time allow managers to escalate issues and resolve them as they are happening
  • Sentiment Analysis: The system performs real-time, multi-lingual classification and sentiment analysis of text data, including the ability to  generate alerts on email and conversations happening in real-time
  • Predictive Analytics: A reporting tool provides the ability to generate historical reports for future pricing models and requirement identification.  The reports can be viewed on the UI for analysis and enabling business decisions

CASE STUDY – Real-time multilingual classification and sentiment analysis of text

A major telecom company providing nationwide telecom services wanted a system that performs real-time, multi-lingual classification and sentiment  analysis of text data. The solution was required to allow storing, indexing, and querying petabytes (PBs) of data with a very high throughput. Some of the critical requirements were: ingest and parse high volume of data (15 TB per day) of varied types (e.g. weblogs, email, chat, and files), apply  real-time multi-lingual classification and sentiment analysis with very high accuracy (four nines), store metadata and raw binary data for querying,  with query response of 5s on cold data.

The successful streaming analytics solution included:

  • Rapid and accurate real-time text categorization and sentiment analysis
  • Adjustable text categorization for domain-specific classes
  • Multi-lingual support
  • Enhanced sentiment analysis to focus on feature-specific opinion mining
  • Linear scalability to increase the number of nodes in the cluster
  • Provision to add custom component for added functionalities

If you prefer, the complete insideBigData Guide to Streaming Analytics is available for download as a PDF from the insideBIGDATA White Paper Library, courtesy of Impetus.

 

 

 

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