As manufacturers and other industrial organizations continue to use the Industrial Internet of Things (IIoT), fundamental changes in industrial architectures are being seen.
According to a recent survey of companies in 2022, almost 62% of companies surveyed have already implemented or are in the process of implementing an industrial Internet of Things strategy (IIoT (IIoT World, 2022)).
What sets this paradigm shift apart from others is the shift from prioritizing automation equipment to focusing on data, as well as software-defined applications, peripheral architectures, and cloud solutions that control data movement in IIoT production systems.
This is a new focus on data and software-based solutions at full speed.
This year, for the first time, the average manufacturer will spend more on industrial software than on industrial automation equipment (OT hardware), according to the latest IoT Analytics industry report called Industrial Software Landscape 2022-2027 (see Figure 1).
Fig.1: – Industrial software market and operational technology market, 2022 (Internet of Things analytics)
Industrial-software-market-vs-operational-technology-market.jpg (72 KB)
The benefits of this transition to virtualized infrastructure, in terms of business maneuverability, improved production and streamlined operations, have been well described elsewhere.
A key aspect of this new reality is the use of cloud platforms to control the movement of data through IIoT deployments, particularly in terms of analytics, data storage, and so on.
The problem is that as IIoT systems generate more and more data at an ever-increasing rate, how will vendors manage, use, and stream data to the cloud platform of their choice?
Does this cloud platform have the capabilities, reliability and performance to retrieve data generated by today’s (and tomorrow’s) IIoT systems?
The average factory generates terabytes of data daily, and this volume is expected to grow rapidly (IBM, 2022).
With this in mind, manufacturers should consider the following criteria when evaluating different cloud services to obtain IIoT data.
Compliance with industry standards
This is probably the most important criterion that organizations must consider. Does this service meet published standards such as MQTT and Sparkplug?
There is a strong consensus that these two standards are key to the future of the Internet of Things.
Cloud services that support these standards must provide predictable performance and reliability that patented solutions simply cannot guarantee.
Moreover, non-compliant compatibility with other ecosystem solutions is compromised, threatening the manufacturer’s ability to work with partners and severely limiting its access to other technologies.
Finally, solutions that do not meet industry standards can lead to “supplier blocking” by giving the cloud platform excessive power over the strategic direction of the future IIoT manufacturer.
The only thing that almost any manufacturer can guarantee is that the amount of data they will generate continues to grow.
As a result, the ability of the cloud platform to scale to meet these ever-increasing demands is paramount.
Performance includes the number of connections that the cloud platform can support, as well as the number of messages per second.
The ability of the cloud platform to scale message implementations is also key.
A great example is MQTT topics, which are a form of addressing that allows MQTT customers to share information.
MQTT themes are structured in a hierarchy similar to folders and files in the file system.
The ability of the cloud service to analyze these messages at the level of performance required by the current program is important.
A separate issue from performance, the ability to provide high reliability in industrial or industrial conditions is a critical element for any cloud data service.
The data generated by IIoT systems does not stop, which means that the cloud platform must provide the appropriate set or reliability. Historically, this has been a big problem for cloud services.
The ability of organizations to own and monitor their own data is theoretically one of the main reasons for moving to data-driven analytical solutions, which are an integral part of most IIoT deployments today.
When evaluating cloud platforms that will connect to the IIoT network, you need to ask a few questions about the ability to monitor your data in action.
Do IoT devices turn into opaque “black boxes” when they connect to the cloud provider’s IoT service?
Observation of their environment
IoT / IIoT for detecting trends, anomalies and ensuring daily visibility is one of the main reasons for moving company data to analytical solutions in the cloud.
For example, if you want to make real-time analytics decisions, your cloud service provider’s analytics service should provide (or your systems) the visibility of that information.
Also, if your cloud provider offers to manage your IoT devices, you should ask if they can help you manage individual devices.
These capabilities are important because many IoT services can easily turn into “black boxes” that don’t show what’s going on with your data.
Vendor answers to these questions will help identify the vendor that meets your Observability needs.
Deployment rigidity is never a good look for IIoT-based cloud service.
Most modern services must support hybrid models for both local and cloud deployment for a single IIoT program.
Their service also needs to be flexible enough to adapt to the unique needs of your IIoT deployment.
In addition, all IIoT deployments have unique aspects inherent in their organization, so a good cloud data service should be able to host a network.
Examples include things like device connectivity and security policies.
Making the final decision
While there are certainly other criteria that an organization may need to successfully navigate the many cloud-based data solutions available today, these five criteria are still a powerful foundation to build on when building your assessment protocols.