IoT Analytics shared an insightful report on the rise of B2B software marketplaces. It underlines the shift from a direct sale model to software marketplaces. Why is that excellent news for IoT providers? Marketplace offers several opportunities from the buyers’ perspective, including: choosing and comparing solutions from various vendors, low entry barrier (subscription instead of a significant upfront investment), out-of-the-box working solutions (short time-to-verification and -market). Challenges The marketplace model promotes subscription-based offerings.

It takes courage to stop learning and start implementing commercial IoT solutions! It is all about handling the responsibility and leaving your comfort zone. Working in the Internet of Things domain requires constant learing. Keeping up-to-date with various communication solutions, hardware capabilities, and cloud offerings demands infinite effort. The learning never ends for IoT professionals. Or is that only an insecure mindset speaking in our heads? I totally agree with Socrates, “The more I know, the more I realize I know nothing.

In my previous article, I covered the Data Structure aspect of the Digital Twin. Today, I would like to extend that topic further and provide more practical insights. For the purpose of our discussion, I propose to think about the Digital Twin as the Data Proxy. What do I mean by the Data Proxy? That is an abstract way of accessing various types of data. Let’s take the current state of the monitored equipment as an example.

My take on the IoT 2023 review by IoT Analytics: The IoT market has matured, and the hype cycle is over. That is actually a good thing. Cloud providers’ layoffs in the IoT domain prove that there is no one-size-fits-all IoT solution. That is also a good thing. Fired IoT professionals will design and build specialized offerings solving particular needs. Public cloud providers offer generic IoT capabilities, far from working out-of-the-box solutions.

In my previous article, I introduced the concept of Digital Twin. The state of a Digital Twin (DT) relates to data gathered from sensors and external systems. That setup ensures automated, up-to-date digital representation of monitored entities. Today, I would like to focus on the Data Structure aspect of DT. Designing that Data Structure is an iterative, manual process. We extend the Data Structure to improve the functionalities provided by DT.

Digital Twin is a virtual representation of a physical device. It consists of Data Structure, Logic, and Visualization (in my opinion, an optional part of the Digital Twin). We use sensors to gather data about the monitored device and store obtained information using the Data Structures of Digital Twin. We can get additional information about the device from External Systems and also store it using the Digital Twin’s Data Structure.