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.
Change doesn’t stop, so neither should your analytics. You could capture the most crucial, valuable insight of all—but if you don’t identify and act on it while it’s still valid, or before your competitors do, it’s worth nothing.
Imagine you’re an electronics company that has sunk thousands of hours and millions of dollars into building a profile of the perfect customer for a new product release. Before you can claw back your investment with a wildly successful launch, a rival comes along and disrupts the entire industry with an innovative device like no one has ever seen before. All that effort and resources expended… all for nothing.
IBM announced a series of upgrades and new offerings to Watson Data Platform, an integrated set of tools, services and data in the IBM Cloud that enables data scientists, developers and business teams to gain intelligence from data.
As a data scientist, you are probably spending a lot of time cleansing, shaping and formatting your data before you can do the analysis. According to a recent report, data scientists spend up to 80 percent of their time finding and preparing data. And 57 percent of data scientists said that cleaning and organizing data is the least enjoyable part of their job. The problem isn’t just limited to data scientists. Business analysts face similar struggles to obtain the data they need to build reports—often having to wait weeks for their IT team to extract data from the source systems.