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Are Data Silos Holding Your IoT Strategy Back?
Rigid silos are preventing businesses from improving their productivity and quality by up to 30 percent by using new techniques in data processing and machine learning.
Once high-fidelity data is in the system, data scientists can use sophisticated machine-learning and deep-learning algorithms to predict failure of components before they can happen, as well as detect inefficiencies in the end-to-end process, detect production defects and more.
When tangible business results are seen in the main location, companies can extend this intelligence to remote edge locations. There are several options for edge-data platforms, ranging from cloud vendors to large technology conglomerates and startups. Companies are usually most successful when they are able to ship their applications and machine-learning models to the edge as is, rather than writing to an edge-specific platform.
Most critically, due to network limitations, it isn't feasible to send all data to a central location for analysis, so the edge system needs to be able to act independently of the core, while staying in tight synchronization. An architectural pattern that lends well to this is called "learn globally, act locally," which essentially delegates the heavy lifting of building predictive failure models to the core location where computing power is plentiful, while synchronizing those models with the edge sites so that the predictions can happen locally, eliminating latency and ensuring the process is up even if the network isn't.
Some think this type of solution is a pipe dream, but it isn't—companies are already adopting these solutions for the Industrial Internet of Things (IIoT) across multiple industries and use cases, including oil exploration, oil refining, mining, manufacturing and automotive.
Will Ochandarena is the director of product management at MapR Technologies, where he is responsible for user experience and the cloud. Prior to MapR, Will spent some time in the SeaMicro group at AMD, and was responsible for networking and cloud strategy. Before that, he was a product manager for the Nexus family of data-center switches at Cisco. Will has an engineering degree from Rensselaer Polytechnic Institute and an MBA degree from Santa Clara University.
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