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.
Built on Watson Data Platform, IBM Data Catalog is IBM’s next-generation, cloud-based enterprise data catalog. It promises to provide a central solution where users can catalog, govern and discover information assets, and it is designed to slash the time spent searching for and hesitating over sharing data, so that you can focus on extracting business value from your data assets.
Data governance is rarely seen as a glamorous topic, and even the mere mention of the ‘G’ word often inspires groans and yawns from non-specialists. But are they missing a trick? It’s possible that the failure to appreciate data governance comes from a lack of understanding about the value it can deliver, and just how important it is to future success.
Today, we’re going to attempt to address that gap in understanding. First, let’s define our terms: by data governance, we’re referring to the overall management of the availability, usability, integrity, and security of the data employed in an enterprise. A sound data governance program includes a defined set of procedures, a plan to execute those procedures, and people who are responsible for putting that plan into action. This might sound like a lot of work without much payoff—but the truth is that data governance plays a key role in ensuring that data is used to its full potential.
IBM’s aim with Watson Data Platform is to make data accessible for anyone who uses it. An integral part of Watson Data Platform will be a new intelligent asset catalog, IBM Data Catalog, a solution underpinned by a central repository of metadata describing all the information managed by the platform. Unlike many other catalog solutions on the market, the intelligent asset catalog will also offer full end-to-end capabilities around data lifecycle and governance.