Data Analytics

Streaming Analytics for the Internet of Things

Share this post:

The core features comprising Watson Data Platform, Data Science Experience and Data Catalog on IBM Cloud, along with additional embedded AI services, including machine learning and deep learning, are now available in Watson Studio and Watson Knowledge Catalog. Get started for free at


According to the recent Bloor Research Report, the Internet of Things is driving wider industry adoption and propelling streaming analytics into the mainstream. There is a rapid increase in vendor and market activity in the Industrial Internet of Things (IIoT) and M2M, with significant deployments of streaming analytics in sectors such as healthcare, smart cities, smart energy, industrial automation, oil and gas, logistics and transportation. The characteristics of streaming analytics are particularly suited to the processing of sensor data: the combination of time-based and location-based data analysis in real-time over short time windows, the ability to filter, aggregate and transform live data, and to do so across a range of platforms from small edge appliances to distributed, fault-tolerant cloud clusters. Sensor data volumes have already reached a level where streaming analytics is a necessity, not an option.

Streaming analytics use cases for Internet of Things

  • Preventative maintenance has emerged as the leading use case in this sector and the one with the greatest potential. It includes customers across different markets, including vehicle telematics, oil and gas drilling equipment on remote rigs, conveyor belt wear, elevators, and pipeline leaks. Streaming analytics can help customers to reduce operational and equipment cost by minimizing unplanned outages, and reduce the requirement for expensive site and maintenance visits.
  • Retail – Streaming analytics helps with real-time inventory updates to drive business processes for inventory and pricing optimization, and for optimization of the supply chain, logistics and just-in-time delivery.
  • Smart Transportation – The model in transportation is now set towards usage-based pricing and operations. The future is certain to bring more use cases for usage based pricing, for example, tyre manufacturers  are experimenting with smart sensors in tyres to measure usage and wear, and smart city car share schemes that combine usage-based pricing models with real-time tracking and vehicle telematics.
  • Smart Energy – There are several deployments in the smart energy sector, from real-time monitoring of smart meters, smart pricing models for electricity, to real-time sensor monitoring of wind farms (which produce a vast volume of sensor data and where streaming analytics can drive a significant increase in efficiency and energy output). This is a new and attractive market with tangible business benefits for streaming analytics.
  • Industrial automation combines streaming and predictive analytics to optimize manufacturing processes and product quality. Where companies have implemented six sigma and lean manufacturing techniques, streaming analytics enables statistical analysis of the manufacturing process, with alerting and automated shutdown when quality levels are breached.
  • Healthcare – M2M services for improving client engagement have been around for many years without significant uptake, probably due to the shortcomings of SMS as a delivery mechanism. Smart sensors may unlock the potential. For example, where an SMS message can only remind a patient to take a pill, a smart sensor on a pill bottle can report continuously if a pill has been taken and when, even if the storage temperature is not correct.

Gain more information on IBM Streams by trying the cloud services version or IBM Streams Quick Start Edition and join the IBM Streams community.

More Internet of Things stories

Think fast with IBM Streaming Analytics

Learn how IBM clients are moving from batch analytics to real-time streams at Think 2018

Continue reading

Keep your streams flowing

Sign up for the new beta of IBM® Streaming Analytics to increase availability, boost performance and simplify scalability for high-speed data-flows

Continue reading

Upcoming changes to RStudio in Db2 Warehouse on Cloud

We've decided to deprecate built-in support of RStudio in all existing and future Db2 Warehouse on Cloud deployments starting April 24th, 2018, in favor of having DSX as an single, collaborative hub for your data science workflow.

Continue reading