Updated: Jul 2, 2019
Back in 2011, Cisco predicted that by 2020, there would be more than 50 billion devices connected. It's 2019 already and while we're still about 25 billion devices shy of that objective, I thought it would be a good time to reflect on how much progress has been made in IoT - and the challenges that still have yet to be overcome.
Connectivity chips, modules and gateways: Today, IoT solution architects and developers have a wide range of options in this arena. Cost and size continue to go down, making it easier and easier for IoT ideas to go from the drawing board to a proof of concept. We've even seen IoT modules that can fit in a device the size of a hearing aid. Nowadays, virtually anything can be connected.
Wireless communications: From Wi-Fi to bluetooth to LP-WAN to LTE/cellular, wireless communications are becoming more robust, cheaper and able to travel even longer distances than ever before.
Integration complexity: To fully realize ROI from IoT projects, most companies must figure out how to integrate existing and new machine and sensor investments with business applications. All of this boils down to a lot of programming and customization that require additional technical skillsets and additional cost. It's no surprise then that the top reasons why most IoT POCs fail include budget or schedule overruns and lack of expertise.
Lack of standards: Unlike IT equipment, most machines have been developed on proprietary platforms and software. Standardization is something that has been talked about for years - but getting machine manufacturers to agree on a common standard may be many light-years away and will still not address the issue of existing industrial machine investments, many of which still may have a decade or more of field usability.
Security: For most CIOs and IT admins, the invasion of billions of connected machines can seem like an endless assault from the Night King and his army of wights. In fact, a 2017 report by the BBC found that most enterprises underestimated the number of connected devices by anywhere from 30-40%. Since most existing machine platforms were not designed with the sophisticated security and processing capabilities as IT equipment and most device OEMs are still in the early stages of addressing security in their new products, security continues to be a top concern for organizations considering an IoT deployment.
AI and machine learning: For companies to realize the full potential of AI and machine learning, IoT data provides a perfect opportunity to enhance these new technologies and improve the ecosystem of IoT innovations at the same time.
IoT platforms and software solutions: In the not too distant past, it seemed as if everyone and their grandmother was introducing an IoT platform. Most of these solutions were all inclusive, expensive and proprietary; the result was that customers still had to spend significant technical resources and time to integrate these solutions with their systems, hardware and data. With many organizations today converging around AWS and Azure as platforms, new software solutions are focused on delivering robust functionality for specific elements of the IoT system architecture. This means that the promise of IoT is not only accessible to the largest companies - but to small and middle market enterprises as well.
Data Management: Data is at the heart of creating value from connecting machines. Data management recognizes that the volume of data being created in IoT networks requires a different and more automated approach to minimize data loss and increase IoT business velocity. Today, its estimated that nearly 70% of data goes unused. Just imagine how the businesses of tomorrow could benefit as more effective tools for managing data in the IoT network emerge.
Today, more and more industries and companies are looking at IoT innovations to help them reduce costs and improve customer and business outcomes. Here at Machinechat our focus is on delivering ready-to-deploy network-integrated data management solutions that will enable IoT solution architects and developers to integrate machine data faster. We believe that solving this problem -- getting the right data to the right applications at the right time - will go a long way to helping IoT solution architects deploy more successfully (and more cost-effectively). Want to learn more? Sign up to stay up to date on the latest news from Machinechat!