IIoT Platform: Cloud or Edge?

There is a lot of confusion with the buzzwords edge and cloud. There are a few different ways to see it. 

Basically, cloud is a virtual system somewhere in a data center, and basically edge is a solution that runs inside, on-premise in the plant and near the machine. 

Another way to see it—we view edge as dealing with data live. Edge data will help the people that work within the workshop. The cloud is more historized data, so it is more supporting the staff people that will need to leverage the historical information in different types of equations. 

So, the main difference between edge and cloud is more a question of “Do I need to use my data live? Or do I need to use my historical information?”

When thinking about how to invest your digitalization, Industry 4.0, Smart Factory, or other Big Data type budget, oftentimes, companies start with a research phase. They hope to uncover the best IIoT platform, AI, cloud offer, or connected edge that will completely revolutionize, transform, and uproot the habits of their factories into data-driven powerhouses.

While many Industrial IoT vendors promise these results, there are some things you should consider prior to the research IIoT solutions—starting with the ‘why’. Why are you searching for a solution? What problem(s) are you trying to solve? What is your end goal? What is your current connectivity? How will you know you’re successful?

Getting Started with Digital Transformation: Determine your Baseline

By first creating a SWOT analysis, or conducting an assessment of your digital maturity to determine current connectivity and opportunities for improvement. With these tools, you can begin to identify what you might actually be looking for—whether it is an edge, cloud, or hybrid IIoT solution. As excellent as a quick internet search can be for quick answers, it can also lead you to the companies with the largest marketing budget who catches you by paying their way to show up at the top of a results page.

Choosing the Right Cloud or Edge IIoT Solution

When you’ve done an honest review of what you have, what you need, and established a vendor audit (see what to look for from a security standpoint here), you’ll likely be able to narrow your needs to a short-list of providers.

Frequently, companies instantly rush to a cloud vs. edge solution. Should we start with a cloud computing and analytics solution to get a better understanding of our processes and give teams easier access to data? Or do we leverage edge computing and analytics to prevent scrap or machine breakdowns causing unexpected delays?

While you will want to consider these questions—it can make sense to think of your needs: Do you need historical data that you can leverage to learn from your past to drive progress forward? Or is it more about faster reaction time to data hot off the line? In a perfect world, you would probably want a little bit of each scenario. Cloud analytics enable you to learn from past performances while edge analytics help you react in real-time. And while having both is attainable, you want to build out an IIoT implementation that will not only achieve quick wins to drive momentum but also lay the foundation for solid OT/IT alignment that spans departments and a solution that can scale to all locations.

Implementing Edge & Cloud Analytics

Developing a digital transformation road map is critical—you want to crawl before you walk and walk before you run. For many manufacturers, this could be as straightforward as getting connected. Do you have end-to-end visibility of your process from raw materials to finished goods? If not, it might make sense to look at edge computing devices and software to help bridge the gap in your visibility.


However, what if your main concern is the amount of time that your analysts and process engineers spend cleansing data instead of solving problems? If this is the primary need, a cloud solution makes sense. Empower teams with an advanced analytics platform and cloud solution that helps you drive continuous improvement efforts.

When thinking about scaling, regardless of whether you start with cloud, edge, or a hybrid model to close the loop, you should think big picture. What does your company need right now and what is part of the 1, 3, or 5-year plan? What do your best plants do well and is there an opportunity to start demonstrators (or pilot projects) and identify use cases?

With so many options on the market, it’s important to first understand your current state, your future goals, and your timeline. First and foremost you need to push closer to your digitalization efforts—automate what can be automated and then continue to tackle your goals. Find an IIoT platform that allows you to grow within the constraints and goals of your organization so that you can find a robust, all-inclusive solution when the time is right.


What is an IIoT Platform?

Manufacturers need to easily access data from their production processes to take advantage of the new inroads available with Industry 4.0. Driven by the need to identify, analyze, and optimize industrial data, many manufacturers are considering (or have already adopted!) an Industrial Internet of Things (IIoT) platform to leverage the power of Machine Learning, AI, and Big Data discoveries.

IIoT Platform Considerations

While there are plenty of examples of best practices for implementing an IIoT platform, these five less-obvious approaches may be the key to helping drive a successful implementation. From choosing the right platform to multi-facility rollouts, these considerations can help you transition from a successful implementation to a successful Industry 4.0.


IIoT Platform Cyber Security

As organizations move more of their data to the cloud, you’ll need an IIoT platform that protects you against cyber security threats. From the way your platform processes your data to how your employees access that data, cyber security cannot afford to be an afterthought in your journey to Industry 4.0. Learn some of the best practices in protecting your organizational data against imminent cyber security threats.