How Do Cloud-Based Analytics Work?

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Cloud-based analytics is at the heart of every major technology company. Data analysis itself is a common activity in any business, but when it takes place in the “Cloud,” it can truly change the way that a company operates.

In the technology industry, “The Cloud” refers to a network or group of networks that give connected devices access to shared pools of resources like servers, applications, and services. The internet is an example of this type of network, and therefore, any internet connected device is a part of the cloud.

Cloud-based analytics provides a handful of tools that enable companies to extract insight from large data sets, present those insights in an intuitive and understandable way, and make it all available via a web browser.


Data Centers

Most cloud-based analytics systems run on a network of secure data centers, which are owned by some of the biggest tech corporations in the world including Google and Amazon. These data centers are regularly upgraded with most efficient computing hardware, and this is also what makes cloud services much more reliable.

The servers, in addition to other hardware at a data center, is what physically supports a cloud-based analytics platform. It makes data accessible at very fast speeds and ensures that if a company’s internal systems malfunction, all of the data will still be safe at the data center.


Cloud-Based Analytics

First and foremost, a cloud-based analytics system must be hosted on some sort of platform or website over the internet. Usually, this is a software system that takes data inputs, processes the information, and provides feedback. This is obviously dependent on the platform itself, but there is always something in common — the data that is collected gets uploaded to the “Cloud” and is safely stored so that it can be accessed by any internet-connected device.

In regards to manufacturing, a cloud-based analytics system usually connects with various hardware devices that all incorporate sensors and communication modules. These devices constantly collect information on the performance of the factory’s operations. The data that gets collected from a machine is uploaded to the cloud and managed on an analytics platform.

A business can choose to build their own, in-house platform, or they can decide to use a third-party service. Usually, a service is cheaper, and these services already have built out functionality that can benefit a company immediately.

The functionality that is provided via a cloud-based analytics platform is made possible through proprietary algorithms that can organize, process, and make meaning out of data. This data goes through various mathematical equations and produces values that are represented in intuitive ways to the users.

A cloud-based analytics platform can provide tremendous value for a company versus an offline version. By being connected to the internet, users will also have the ability to integrate offline data into their datasets with the click of a button, for far greater insight.


Cloud-Based Analytics and AI

Cloud-based analytics also paves the way for any future integration of artificial intelligence in a company’s operations. AI is only as powerful as the data that it is utilizing to make its decisions, so by having a digital data infrastructure, you are building the foundation for future tools that will be made available by service providers.

For example, in a manufacturing setting, it is important to track information about the performance of a machine. There are already tools that are available for manufacturers to track and upload this information to a cloud-based analytics platform. Once this data is uploaded, it is processed by the platform’s algorithms and learns about the machine.

In the field of AI, machine learning is a subset that refers to a computer’s ability to learn by itself. As more powerful machine learning algorithms are developed and made available, cloud-based analytics platforms will gain the ability to learn by on their own. One major benefit of this is that the platform is then able to more accurately predict future outcomes based on all of its previously collected data. This will translate into more efficiency, and allow you to avoid things like unplanned downtime. This is why having a cloud-based analytics system is so important.


Improved Functionality

The true value of cloud-based analytics comes from the improved functionality for the user. With data being uploaded to services that function over the internet, users gain more accessibility and value. Data is managed more efficiently and represented in more intuitive ways. Cloud-based analytics presents a big opportunity for any business, so if you have not made the transition to the cloud, this might be the best time for it.

Tools To Aid With Predictive Maintenance

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In the manufacturing industry, predictive maintenance refers to the use of big data and analytics to predict when a machine or system will need maintenance. This is made possible through the integration of smart sensor devices that can communicate with external programs to gather and process the necessary information.

Predictive maintenance is important in a smart factory because it allows a business to mitigate unplanned downtime. By having accurate predictions of when a machine may lose efficiency or malfunction, a company can plan ahead to ensure that the lost production from that machine will not influence their operations.

The tools that aid with predictive maintenance include everything from hardware plugins to areas of artificial intelligence (AI). These tools allow businesses and individuals to implement predictive maintenance in their factories.

Connected Machinery

First and foremost, to create a smart factory you will need to enable your machinery with the ability to connect to a central network. This can be through a wired medium, but most services make use of wireless communication. For example, Oden Technologies offers a simple data collection device that can be plugged into any machine or PLC to gather and wirelessly communicate data to their online platform.

A good way to view the machinery of a connected, smart factory is by likening it to the nervous system of a human. Let’s pretend that your factory is an organism, and for it to function, it needs the ability to detect information about itself and its environment. This IoT device and the machines that it is connected to makes that possible.

Cloud-Analytics Platform


The next tool that will aid with predictive maintenance is a cloud-based, analytics platform. In more simple terms, this is just an online service where you can connect you machinery to so that the true value of your data can be extracted.

The platform that a company decides to use for their connected factory is where all of the gathered data will be aggregated and processed. Depending on the algorithms that are in place, various insights will be provided to the user about the current and future state of the factory. If we go back to our human body analogy, the platform would be the mind while the machinery is the body.

Machine Learning

Machine learning is a subset of AI that enables a computer with the ability to learn by itself. It is also an important tool for manufacturers to use in predictive maintenance. When machine learning algorithms are applied to a smart factory, the system will be able to learn how different inputs correlate to outputs. This is necessary for predictive maintenance because it will allow a computer system to learn how the conditions of their operations are affecting the outcomes of those operations.

Over time, the AI will see that, for example, if a factory is operating at an average of 70% capacity for 2 weeks, there will be a 92% chance that machine X will underperform and need maintenance. Therefore, if the company wants to avoid this downtime occurrence, they can figure out what they need to do to make the malfunction less likely to occur.

Maintenance Scheduling

The main value that comes from predictive maintenance is that it allows you to figure out the optimum time frames for when maintenance should occur so that unplanned downtime can be avoided. The best platforms for predictive maintenance will automate as many processes as possible to a degree where the employee is empowered to make smarter and better decisions. The first step in this process is to determine when a system or machine may lose productivity or malfunction. Once this is detected, the next step is for the employees to fit the maintenance work into the proper time slot.

Scheduling to Save Money

Predictive maintenance, at its core, is a method for saving money. When a machine or system malfunctions, it can drastically affect the bottom line of a company. This is why it is important to have systems in place to be able to predict and plan for when these machines will need maintenance.

The best way to take advantage of the value that predictive maintenance provides is to make use of connected hardware and a powerful, software platform. It is important to make sure that whatever service and technology you are using has the features that makes this all possible.

Will Smart Factories Replace Humans? No.

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It is a common belief that the artificial intelligence (AI) revolution will be detrimental to the job market and lead to significant losses in jobs. In fact, some even believe that AI will completely outpace humans in all areas of productivity, which would result in a fully automated workforce.

It is true that people will lose jobs as automation increases. We have seen companies like McDonalds integrate digital kiosks to replace cashiers, and can imagine how self-driving vehicles will disrupt their industry, but the question for us is if smart factories will replace humans. The answer is no for 3 reasons: 1) Technological limitations, 2) Worker augmentation, and 3) Type of labor affected.

Narrow vs. General AI

The context that an AI system operates in is significantly more narrow than what people realize. The form of AI that is implemented in the products and services that we use today can be referred to as “narrow AI.” Narrow AI focuses on single functions and domains and is completely non-sentient.

When the average individual thinks of AI, they sometimes inflate their view based on the movies, tv shows, and articles they engage with. The type of AI that we see in movies is called general AI — a self-aware, AI system that can make general decisions about anything. This form of AI is only theoretical, at this moment, and if it is possible, we are still decades away from that reality.

One of the best uses for IIoT in a manufacturing plant is for the real-time performance tracking capabilities. When a factory connects their machines to an IIoT platform, the machines will start reporting data for how they are operating. The platform will provide a view that shows every input and corresponding output for all of the machines that are in operation.

Worker Augmentation

It is clear, at least for the next couple of decades, that robots and AI systems will not fully replace humans, but it still remains unclear how humans and machines can work together. In manufacturing, and specifically in a smart factory, there will always be the need for humans to work along with the autonomous systems in place.


There is even evidence that when a factory fully automates their operations, the number of employees that they had stayed the same or went up. This was evident for Siemens’ Amberg Electronics Plant in Germany where their productivity went up over 1000% by integrating a smart factory, all while maintaining 1200 employees throughout the process. This is also the trend with countries who are leading the way in smart factories. There is no evidence that with more automation in factories comes a trend of less employment in the workforce.

A Push For Higher Skilled Workers

There is one more reason why we will always have humans in the smart factory, and this has to do with the type of work that is performed in a factory. At this point, we have demonstrated why it is more realistic that humans will work alongside with robots and AI, but we know that it is still possible for people to be replaced through automation. So then, why can you not replace employees in a smart factory? Well, the answer is that you can, but not those who are highly trained and skilled.

Automation will impact workers who do repetitive tasks that could easily be replicated by a machine. Things like customer service, driving, and food service, among others, will be the main areas where jobs may be lost. In fields where more complex decisions must be made, there will always be a need for humans to make the final resolve, and this is the case with the smart factory.

A Combination Of Factors

There are many factors that go into whether or not a specific business will replace jobs with AI and automation. With the smart factory, however, it is clear that this will not be the case. Even when there are opportunities to replace an employee with AI, factories have shown that it makes more sense to re-apply those same employees to other tasks so that they remain productive, but this also can depend on the decisions of that company themselves.

At Oden, we are clear in our stance that we will always create technology for humans no matter how advanced automation technology becomes. We also look to work with companies that value this perspective, as well. The fact that AI is narrow, will augment workers, and would mainly replace repetitive tasks, leads to the conclusion that factory workers can look forward to the rise of the smart factory.