It’s no surprise that companies implementing dynamic IoT solutions are doing so by harnessing the power of cloud computing. However, it may surprise you how cloud computing is benefitting these IoT initiatives. When referring to the cloud, many talk about its scalability, cost-effectiveness, and low maintenance, yet the cloud has so much more to offer than that.

When deciding how cloud computing might impact your IoT efforts, consider these surprising benefits.

• #1 The cloud facilitates data integration.

For years, enterprises have invested in big data initiatives, combining information from multiple sources to help their business make quicker, more accurate decisions. Most of these efforts have been focused on human-generated data stored in ERP, CRM, and other enterprise systems. These systems alone can generate a lot of data, and changes across a company (such as a merger or acquisition), can create a whole new set of data sources that need to come together for unified visibility.

To further complicate things, companies began adopting IoT as part of their data strategy to provide real-time information to these existing reporting systems, more context in terms of how the business is operating, and greater visibility into areas of the business that were not previously possible. For some companies, this is an opportunity to increase operational efficiency and streamline costs. For others, it can unlock new business models and revenue streams.

Now that data from traditional enterprise systems are layered in with data generated from sensors and connected equipment, companies are finding that IoT data has different characteristics than traditional enterprise data. The velocity and volume of this kind of data can overwhelm systems that are not prepared for it. It also requires some rearchitecting of data models because it represents different types of information that may not have been part of prior planning.

This is where cloud computing comes in. Because of the cloud’s ability to house large amounts of data, companies can process and store both data from their enterprise systems and their IoT devices in the same place. The cloud becomes a great aggregation point for all disparate systems, where companies can scale their efforts up or down with very few limitations. Organizations can then eliminate the need for integration and audits between systems that crop up when their data is stored separately.

• #2 Companies can count on the cloud for security and reliability.

In the past, industrial companies have questioned the protection of the cloud because they viewed it as losing the ability to touch and feel their data. Much like consumers who were reluctant to move their savings from under their mattress into a bank account, many businesses have held similar reservations about where to put their data. As a result, these companies have rejected cloud computing in favor of on-premise technology.

While there are still many people out there who view security in the cloud as a concern, actions from the leading cloud providers have started to sway these opinions. While most companies have a dedicated security professional (or several), cloud vendors like Microsoft and Amazon have hundreds. These massive security teams also follow best practices and industry-specific standards and obtain proper certifications out of obligation. Vendors also equip businesses using their cloud solutions with the tools they need to take ownership of the security of their data.

Those looking to include a cloud solution as part of their IoT deployments can count on its security too. As the security of the cloud itself is proven further, it also allows companies to more efficiently and securely interacts with their IoT devices. As you will see in greater detail below, the cloud is an essential component of any large-scale IoT initiative, so a comfortable and secure connection between data generation points is key.

On a similar note, cloud platforms undergo continuous auditing so that cloud service providers can make performance and security data readily available to customers. This data access helps businesses ensure proper security and performance across fleets of IoT devices. With the realization that cloud providers are putting substantial resources towards security, along with the undeniable benefits the cloud offers, companies have increasingly begun to view cloud solutions as a trusted and even preferred approach.

• #3 When paired with edge computing, the cloud offers the most significant business benefit.

Treating cloud and edge separately is a fairly standard business practice. But for all the essential workflows that the cloud enables, there are still advantages to integrating edge computing into a solution. Both cloud and edge offer different benefits in different types of environments, which often makes a distributed computing framework best suited for IoT deployments. Having differentiating services can involve different layers to compute at the edge – or the point where data is generated.

For example, consider a large factory with hundreds of pieces of equipment – each of which is effectively an edge endpoint, while the factory itself could represent another endpoint. In a deployment of this size, it would make sense to take the data generated from the equipment and aggregate it on the factory floor before sending it to the cloud.

Inserting this intermediate layer becomes critical because it reduces the number of direct connections and allows for filtering of information traveling into the cloud, which prevents unnecessary data from cluttering downstream analysis. Furthermore, if this factory only used cloud computing, they wouldn’t be able to react fast enough to the data generated on the equipment.

Delays stemming from data overload, as well as the distance between endpoint and analysis, slow response times, which can make a huge difference in both safety and quality scenarios. Including edge in computing, the framework allows businesses to extract insights and act faster than if the data had to travel to the cloud and back. This time-savings opens the door for real-time evaluation of data right on the equipment itself.

On the flip side, if the factory opted for an edge-only approach, they would lack the ability to get a full view of their operation. Without the cloud, they would only have on-site visibility into each piece of equipment individually, with no insight into how those endpoints were operating in relation to each other. To get this level of analytics, the factory would have to implement offline batch processing, and manually combine all the factory data.

In a surprise move, cloud vendors have begun moving toward offering some on-premise solutions to complement their cloud solutions. For example, Amazon has launched two products that are dedicated to edge computing: AWS IoT Greengrass, which offers an edge computing environment for larger devices, and Amazon FreeRTOS, which offers edge computing for microprocessors and microcontrollers. Microsoft has also rolled out comparable products, including Azure IoT Edge and Azure Sphere.

No matter the situation, distributed processing and selecting the right solution for your operation are key elements of a successful IoT initiative. Often, it’s a multi-tiered approach that uses different methods of computing based on strengths and weaknesses. Organizations that perform analytics both at the edge and in the cloud can see much more significant results, such as minimized costs and maximized performance.

Shifting views of the cloud will lead to greater IoT success.

As the cloud becomes more widely adopted across industries, a shift to multi-cloud environments will begin to gain momentum. Much like when companies stopped asking the question, “Windows or Linux?” The same paradigm is moving to the cloud. People who were pledging their allegiance to AWS or Azure, have now realized that different cloud providers have different strengths and that a more cohesive strategy is finding a way to glue them all together in a way that makes things seamless.

As the cloud landscape shifts, the IoT landscape changes too. More devices are introduced every day, creating a greater need for device management and tighter security. The cloud offers key benefits that help businesses implement IoT initiatives more effectively in industrial environments.

When utilized effectively and paired with edge computing, organizations are better able to match their computing to their business needs and act on insights in real-time. And making faster, more accurate decisions based on live operational data can create real business value and increase ROI.

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Over the last several years, the Internet of Things (IoT) has become a hot topic. Its appeal is universal across countless industries and it is positioned to be a significant trend for the foreseeable future. However, the understanding of what IoT is, how it helps businesses, and which technologies are required to implement it are anything but clear.

A lot of this is attributable to the lack of definition in its components and reference architectures for achieving use cases like predictive failure, data-driven diagnostics, and asset optimization. Businesses have been left to figure this out on their own, which often leads to less than satisfactory results. In many cases, the problem can be attributed to believing that one technology alone can deliver on the promise of IoT.

This is especially true for the field of data analytics, which is also garnering more attention than ever. It can certainly appear as though it single-handedly holds the key for extracting business value from IoT data. New data analytics startups are appearing on a daily basis and people with even a minor background in math are positioning themselves as data scientists. It’s like the California Gold Rush all over again, except this time the prize is constructed out of ones and zeroes. Whenever you see this much hype, it is always good to take a breath and look at the bigger picture. 

Early adopters of IoT viewed it mainly as a different term for machine-to-machine connectivity or M2M. This is understandable since a prerequisite for any IoT solution is the ability to collect and aggregate device and sensor data. Most often, this data would be presented in a dashboard and victory was declared. The problem is that viewing data in a dashboard requires a human to interpret the results and take manual action. That doesn’t scale to the ever growing demands of IoT – not to mention that IoT data can be a lot more complex than what is typically handled by business intelligence solutions (For a great explanation on the difference between data analytics and business intelligence, read this blog).

The missing ingredient is automation

To achieve business results, you must have a method of applying business logic to IoT data. Some of this logic might serve the purpose of transforming the data into a common format for further processing or routing it to an appropriate data store. It is also the mechanism that can drive actual business use cases. For example, applying logic to data can automate the process of root cause analysis by automating the diagnosis of a failed piece of equipment. By evaluating fault information, surrounding operational data and historical repair history, it is possible to determine the most probable root cause, which can then be used to identify the parts needed for the repair – all before ever dispatching a technician.

Once there is data connectivity and a distributed method of processing business logic, it is time for data analytics. It is what businesses commonly use to derive the rule sets that are then applied to the population of managed equipment. A perfect example of this is predictive failure. By creating a data model that represents how a piece of equipment operates, it is possible to determine the leading indicators that precede a fault. Many times this can be a complex set of variables that would be impossible for a human to catch on a dashboard. This is why automation is so important. Data analytics can tell you what to look for, but the rest of the solution is what helps you find that condition in your firehose of real-time data. 

If you combine predictive failure (something that happens before a fault occurs) with data-driven diagnostics (something that happens after a fault occurs), you have now unlocked true business value: improved up-time of a mission-critical asset. This is what really gets people excited about IoT and it should now be obvious that data analytics alone would not have been enough to deliver on it.

Data analytics not a “one-time event”

Another potential trap is thinking of data analytics as a one-time event, especially since operating conditions are always changing. Analytics run in the past may not match actual results in the future.  This concept of “drift” can have a significant impact on the effectiveness of real-time monitoring. To mitigate this condition, it is important to track how well rule sets are aligning to current data and have the system dynamically adjust parameters in response. For industries with heavy regulation, keeping track of the accuracy of data analysis is key for maintaining compliance.

The goal of any IoT system should be to extract business value from device and sensor data. In all cases, this is much more than just collecting and analyzing data. It is a combination of these elements, integrated into automated process and workflows, that lead to improved business outcomes.

The author is Senior Director of Products for Bsquare.

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