Major Hurdles in Industrial IoT Streaming Analytics

The hurdle of integrating data with multiple consumers and applications is a classic data integration problem, very well solved within on-premises data centers. 

FREMONT, CA: IoT adoption has accelerated significantly in the last few years due to the availability of vast computing power, innovations in data-analytics technology, and the advent of machine learning and natural-language processing algorithms. IoT has opened a new era for customers to address their long-standing problem of connecting devices and leveraging the resulting data to positively influence decision-making processes. IoT also opens a unique spectrum of applications where customers can operationalize actions on the IoT devices in real-time—something that was not possible previously. Where the data comes from many IoT devices, there arise many challenges. Learn more here.

Provisioning Data Collection to Different Data Sources and Devices

The IoT world would be better with less diversity. Different platforms offer support for IoT devices, like built-in firmware in simple devices, firmware in ultra-expensive devices, programmable open-source, proprietary industrial clouds, or IoT computers. Like advanced analytics and artificial intelligence (AI) projects, many innovations fail because data collection is neglected. Often, the assumption is that data will wait to be picked up. The reality is that firms must get their hands dirty to get the data, especially from the field.

Data drift

Data drift the ongoing changes to data format and semantics over time. Transformations can be introduced without notice, probably the harmful scenario. Changes can be presented with partial information, changes to data format are described but leave out significant changes in the semantics of missing and default fields. In any case, data gathering needs to be aware somehow about this drift to avoid streaming out the garbage. 

Lack of Unlimited Network Bandwidth

One of the vital assumptions of big data is that firms can cope with massive data volumes because of inexpensive storage and technology that increases well. And this is true within on-premises data centers. People who have performed a severe big data project will probably understand the significance of considering network bandwidth as a limitation. It is not that complex to saturate internal routers while performing byte transfers into the data lake; it is relatively easy to saturate a company’s Internet bandwidth if doing byte transfers back and forth with the private cloud.

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