IoT promises are depending on unlocking data trapped in enterprise PLM systems

IoT promises are depending on unlocking data trapped in enterprise PLM systems

IoT became a huge buzzword over the past few years. I’m following this topic and sometimes I feel lost in the number of trends, products, technologies, opportunities and… failures. Yes, failures are quite popular in IoT space as much as many fast promises to deliver fast IoT ROI. It didn’t happen yet.

Matt Turk, VC at FirstMark published a very interesting overview – Growing Pains: The 2018 Internet of Things Landscape. If you’re in IoT business, it is must have reading for you. I found lot of interesting examples and data points. It smells the failure…

For proponents of the Internet of Things, the last 12-18 months have been often frustrating. The Internet of Things (IoT) was supposed to be huge by now.  Instead, the industry news has been dominated by a string of startup failures, as well as alarming security issues.  Cisco estimated in a (controversial) study that almost 75% of IoT projects fail.  And the Internet of Things certainly lost a part of its luster as a buzzword, easily supplanted in 2017 by AI and bitcoin.

But after all, it gave more details and diverse analysis of IoT trends and opportunities. It pointed out on many potential in everything related to enterprises and Industrial internet of thing (IIoT). Here is my favorite passage:

In the enterprise and industrial IoT (IIoT) world, too, machine learning and AI have become a key topic.  Unlike their consumer IoT cousins that require a big commercial “hit” with their product to be able to gather enough data for truly meaningful AI, IIoT companies can leverage their industrial customers’ data.  Many machines, assembly lines and oil rigs already have thousands of sensors on them. Of course, obstacles abound, both technical (the data is often “trapped” and hard to extract) and cultural (transitioning from decades of statistical analysis on small samples to a new software driven approach, in a context where failure can be catastrophic).  However,  AI can be a complete game changer in those industries

Trapped data. This point is very much resonating with everything I thought about IoT development led by PLM vendors in the past few years. As posted earlier, IoT will demand re-architecture of enterprise PLM backbones. Current architectures are very much focusing on controlling data and don’t provide enough openness and ability to interlink PLM data with IoT systems to glean data intelligence.

What is my conclusion? PLM data management paradigms will have to change, otherwise highly valuable data will be trapped in enterprise PM systems. It will slow down PLM IoT development and will make enterprise manufacturing companies to look for alternative solutions to manage data and connects dots between virtual and physical products in the future IIoT world. Just my thoughts…

Best, Oleg

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Disclaimer: I’m co-founder and CEO of OpenBOM developing cloud based bill of materials and inventory management tool for manufacturing companies, hardware startups and supply chain. My opinion can be unintentionally biased.




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