Case Study Archives - StreamAnalytix

To power real-time decision making on large data sets, enterprises need an expert team, high-performing hardware systems, and a scalable ETL solution that can accelerate development and deployment of ETL frameworks, while swiftly accommodating changing business needs.

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Business activity monitoring (BAM) provides end-to-end visibility of business processes, enabling enterprises to make better-informed decisions and quickly address problem areas. A BAM framework is used for continuous event correlation, real-time alerts, and monitoring KPI statistics for business activities in real-time.

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Pre-emptive fault prediction can help manufacturers avoid business losses and is therefore gaining importance across all industries. Accurate and on-time maintenance requires predictive insights on the functioning of equipment, next breakdown forecast, primary faults and their causes, and reasons for downtime. These insights enable businesses to ensure fault-free production.

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Cable TV service providers worldwide are facing immense competition for customer retention and new customer acquisition, not only from traditional players, but also from a new breed of digital players like Netflix, Amazon Prime, Roku, and more.

These digital players collect vast amounts of customer data and use predictive analytics and machine learning to deliver highly personalized, contextual, and content-driven interactions. Conventional cable companies feel the pressure to make use of similar technologies and tools to stay competitive and know what their customers truly want.

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To keep up with the new digital consumer and remain competitive, the auto insurance industry is increasingly investing in connected car solutions to offer simplified, transparent, and flexible products and pricing options.

A leading auto insurance provider chose StreamAnalytix to ingest, transform, enrich, analyze and store automotive telematics data in real-time to build an end-to-end analytics application for driver profiling and individual risk assessment, and subsequently offer dynamic, usage based, plans to its customers.

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Insider threats are one of the biggest cybersecurity risks to banks today. These threats are increasingly becoming more frequent, more difficult to detect, and more complicated to prevent. A large US-based bank chose StreamAnalytix to identify and prevent insider information security threats across sensitive applications in its retail banking and wealth management divisions.

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Today, airlines have access to a wealth of customer data. The ability to utilize this data in real-time can lead to revenue enhancement and customer satisfaction through a better understanding of demand and specific market trends, presenting customers with personalized and relevant offers, and proactively resolving issues raised with the contact center.

A major US airline chose StreamAnalytix to efficiently manage, analyze, and draw actionable real-time insights from its continuously growing and complex customer and operational data.

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A leading cloud-based communications technology company that offers hosted contact center services needed a way to improve performance metrics, eliminate the guessing game of problem resolution and dramatically increase customer satisfaction. In an effort to improve performance metrics such as call abandonment rate, average speed of answer, and average call length, the client wanted to monitor the activities of every call in real-time.

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Enterprises generally need to compromise with running and maintaining multiple batch processes on the accumulated data due to throughput and management constraints. The overall business process turnaround can be improved if the data is made available after processing in real-time. A real-time system of such a large scale requires easy provisioning and monitoring.

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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 client was looking for a solution that allows storing, indexing, and querying Petabytes (PBs) of data with a very high throughput. Some of the critical requirements were:

1. Ingest and parse high volume of data [250M (15 TB) records/day] of varied types (for example, weblogs, email, chat, and files)

2. Apply real-time multi-lingual classification and sentiment analysis with very high accuracy (four nines)

3. Store metadata and raw binary data for querying

4. Query SLA - 5s on cold data

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