Getting a handle on IoT data
In the coming years, enormous volumes of machine-generated data from the Internet of Things (IoT) will emerge. If exploited properly, this data – often dubbed machine or sensor data, and seen as the next evolution in Big Data – can fuel a wide range of data-driven business process improvements across numerous industries.
For example, consider the following:
· Product monitoring: Manufacturers use sensor-data analytics to monitor product health and issues, before they lead to operational downtime.
· Usage-based or “pay-as-you-drive” insurance: Insurance companies use data generated from sensors in automobiles to offer drivers rates based on the amount of driving they do, their driving habits, and even where they drive and park. In addition, insurance companies can now perform predictive modelling on vehicle data to identify lower- and higher-risk customers, leading to more informed decisions in setting premiums.
· Predictive and preventive maintenance: Airlines use data from airplane sensors to proactively manage maintenance, improve reliability, reduce unplanned service work, and mitigate risk.
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There is no doubt that IoT is a huge opportunity, and organisations that put IoT to work can increase revenues, cut costs, and improve efficiencies and customer satisfaction. But it’s not enough to just collect massive amounts of data. To capitalise on IoT and implement data-driven business models, organisations need a platform that helps them generate connected intelligence by collecting, managing and analysing huge volumes of sensor data in a cost-effective and scalable manner.
The first step in this process – data collection and integration – remains a challenge because there is currently a lack of common (vendor and platform-agnostic) connectivity standards in the industry. In fact, we view this as a factor inhibiting wider IoT adoption.
For these reasons, it is critically important to utilise a Big Data platform that can consume or read many diverse data sources, streamlining and accelerating data integration. This will enable the delivery of connected intelligence from Big Data for both the business and its IT operations. In addition, IoT data must be able to be loaded and queried simultaneously to avoid missing out on real-time, immediately actionable insights. By the time the data is loaded into a database and analysed, an organisation may have missed a critical chance to respond or act upon a small window of opportunity with a connected product.
The cloud represents a bright spot and opportunity for IoT initiatives in terms of ease of adoption and the journey for businesses to transform technology into a service broker model in today’s hybrid IT environment. Cloud-based analytic capabilities bring IoT to the masses for all businesses, enabling them to get up and running more quickly, easily and cost-effectively. The cloud as a deployment model makes years of intellectual property immediately available to anyone that seeks to incorporate IoT into their Big Data strategy.
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Beyond these advantages, today’s cloud-based analytics platforms offer critical capabilities for structured IoT data, including columnar storage (which means the analytic engine reads and retrieves only the needed columns, yielding faster results against larger data sets); aggressive data compression (which supports very fast parallel load and query times); scalable, multimode infrastructure (which eliminates single points of failure); and integration with the market’s leading open source software for statistical computing.
As a final note, the cloud enables companies to tie all its data resources together – structured and unstructured – to generate connected intelligence from all types of data, including IoT data in greater context.
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