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Streaming analytics goes mainstream

The key to deriving value from the Internet of Things

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Product Marketing Manager, IBM Analytics, IBM

As customer attention span tends to diminish, and as marketplaces tend to become crowded and competitive, real-time streaming analytics is evolving as a key area of focus for many businesses. Examples include a marketing manager in a telecommunications organization automatically sending offers to customers within seconds of them entering a shopping mall or a utility operations manager reducing operational and equipment cost by minimizing unplanned outages. Significant business value can be derived from streaming analytics in a wide array of areas.

Building contextual insights

We have been hearing about the Internet of Things for a few years now. It is an extension of the definition IBM provided in 2005 for a smarter planet: instrumented, interconnected and intelligent. According to the recent Bloor Market Report on Streaming Analytics 2016, a rapid increase in vendor and market activity is occurring for the Internet of Things that is driving wide industry adoption and propelling streaming analytics into the mainstream.

According to a report there are more than 6 million developers working on Internet of Things applications and another study forecasts that the Internet of Things market will grow to $4 trillion in 2016. These metrics speak to the growing importance of the Internet of things in the business world.

https://kapost-files-prod.s3.amazonaws.com/uploads/direct/1473719774-173-5266/Analytics_IoT_embed.jpgRating Internet of Things market use cases

Many Internet of Things use cases for streaming analytics exist that apply to many industries, and here are the top use cases cited in the Bloor Market Report: 

  • Preventive maintenance: Customers within this use case span different markets, including energy and utilities, oil and gas and vehicle telematics. The value to the customer is clear: reduce operational and equipment costs by minimizing unplanned outages and reduce the requirement for costly site and maintenance visits that can be avoided. 
  • Retail: Radio-frequency identification (RFID) sensors provide real-time inventory updates that help drive business processes for inventory and pricing optimization and optimization of the supply chain, logistics and just-in-time delivery. 
  • Smart energy: Streaming analytics has several deployments in the smart energy sector, from real-time monitoring of smart meters and smart pricing models for electricity to real-time sensor monitoring of wind farms. Streaming analytics improves efficiency and energy output by analyzing vast volumes of sensor data. 
  • Industrial automation: This use case combines streaming analytics and predictive analytics for optimizing manufacturing processes and product quality. Streaming analytics enables statistical analysis of the manufacturing process for organizations that have implemented six sigma and lean manufacturing techniques. Alerting and automated shutdown occur when quality levels are breached. 

Learn more about IBM Streams and why the marketplace and customers in a wide range of industries are deploying streaming analytics.