There are thousands of IoT platforms on the market, but they are not all created equal – in fact, far from it.

IoT platforms, generally, concentrate on data ingestion, the visualization of data, and then offer you the ability to perform simple analysis and allow you to trigger alerts and actions (manual or automatic) based on what’s happening in real-time.

Great. But, whilst some organizations claim this is an end-to-end Industry 4.0 IoT platform, it’s not.

The reason for this is that, whilst doing all of the above is critical, it falls short of being able to deliver the predictive element that’s needed if you’re to create automated, continuously improving solutions (i.e. what ‘Industry 4.0’ promises). No, the only way to be classed in this bracket, is if the platform incorporates Machine Learning and AI – let us explain why…

Why Machine Learning is critical in an ‘end-to-end IoT platform’

Machine Learning is different to normal programming. With normal programming you take certain steps to get to an answer, knowing where you want to get to and how to get there when you begin, e.g. when there’s a power spike, shut off your machinery.

With Machine Learning, however, you only know the answer you want to reach but don’t know how to or which is the best way to get to it. For example, when is there often a power spike, what’s causing it and how can machinery be better configured to prevent it? To get there, you create or deploy a number of pre-built algorithms designed for the purpose and give it the data where the answer lies, leaving it to find its own way to the answer before then comparing the results to see which works best.

(You can read more on the basics of what AI and Machine Learning are in an IoT solution – in plain English – in this blog).

Fundamentally, the addition of Machine Learning to an IoT platform gives you the power to transform and make significant, ongoing optimizations to your operations, enabling you to:

  • Analyze huge data sets to easily answer very complex questions with lots of variables by finding the data that matters
  • Once an algorithm is trained, get real-time intelligence into future performance or problems, as well as what to do to prevent them, so that you can take immediate action
  • Create algorithms that are always learning and delivering ongoing improvements to your operations that achieve real business goals
  • Use a single platform to improve every aspect of your business i.e. use it for multiple, interconnected use cases (e.g. energy efficiency, predictive maintenance, forecasting, load balancing, operations and maintenance crew performance etc.)

The addition of the predictive element allows for these very useful use cases:

  • Anomaly detection: power spikes, machine energy consumption pattern changes, long-term drift in a parameter, and theft.
  • Forecasting: energy consumption, electricity generation, and yield optimization.
  • Asset characterization: identifying equipment based on energy consumption signatures, model vs. model performance or across fleet analysis.
  • Condition monitoring and predictive maintenance: vibration detection, changes to energy consumption, and scheduling.

Types of ‘End-to-End’ platforms

A handful of companies have already gone a step further than IoT platforms that do data ingestion, visualization, management and control, incorporating Machine Learning and AI, too.

There are two types:

  1. Modular – these are generally the solutions that you see targeting the enterprise-end of the market from the likes of IBM, Microsoft etc. What they are under the hood is a standard IoT platform that you’re then able to tack on numerous additional modules and abilities to (like Machine Learning). What this means is that you’re actually buying an integrated stack of platforms and technologies which, invariably, actually consist of separate IoT monitoring and Machine Learning analysis platforms that are patched together to fit your needs at the time of deployment.
  2. In-built – there are a small number of all-in-one AI and IoT platforms that have integrated Machine Learning and IoT abilities (of which Rayven is one). With these, you get monitoring, management, control and predictive abilities out-of-the-box in the same platform that are capable of doing just about anything that you need them to do for any scenario at any given time. Where they often deliver is on ease of use, pre-built solutions and price-point.

From the description above, you might be able to guess why Rayven’s platform has the Machine Learning and AI engine in-built. 

The problem with modular IoT platforms

Commonly, modular platforms come with both business and technology limitations:

  • Because of the way that they’re designed (and what they’re for), they’re expensive. This is because they’re built to service hugely-complex enterprise-level businesses, with teams of people solely applied to run them, and require multiple modules stitched together to actually service your organization’s needs (read: the basic model rarely does everything you need it to do).
  • There are many moving parts and potential gaps between modules and hidden platforms. At best this adds complexity and limitations, at worst it causes problems and breakdowns.
  • They are complicated to use, with different interfaces and settings that need tuning.
  • They require a degree in computer programming to deploy and configure – sometimes even to run on a day-to-day basis.
  • They don’t provide you with guidance and aren’t tuned to particular use cases and industries. They’re capable of doing anything, but aren’t able to give you suggestions on improvements or have off-the-shelf Machine Learning models that you can easily adopt, adapt and build on – that’s what their consultants and services (read: expensive) are for.
  • If you want to further develop, integrate different use cases to your solution (e.g. you’ve done predictive maintenance and now want to add energy efficiency), then it’s much more difficult to do and may require ‘rewiring’ i.e. will come at a much greater added cost and disruption.

What to look for when choosing an IoT solution

Fundamentally, know what you’re looking for and compare like-for-like.

If you’re in search of a true end-to-end AI and IoT solution (if you’re not, that’s fine, too! You can read this blog on what is and isn’t an IoT problem if you want to check), then know what you’re looking for in a platform when you’re on the hunt and check on the ‘predictive’ element of its abilities.

After that, when shortlisting, make sure that the out-of-the-box, transformative IoT solution that’s easy-to-use and capable of growing with your business is actually that and that it won’t cost you a huge amount of time, money and effort to run or develop after deployment (unless you’ve large enough pockets to fund it).

We truly believe that the Rayven platform and our AI Dynamix predictive analytics modeller and engine is the best-in-market. It’s capable of doing anything, comes as-standard out-of-the-box and can be used by non-developers using a drag-and-drop interface to develop, tweak and/or deploy a complex Machine Learning algorithm, simply. What’s more, it’s inexpensive and quick-to-deploy, too.

At Rayven, one of our core goals is to democratize Machine Learning, so do get in contact if you’re interested in finding a true, all-in-one IoT and AI solution capable of delivering transformation now and into the future.