What Are Edge Analytics and What Do You Need to Leverage Them?
Kyle Maxey posted on June 17, 2016 |
How can you make “edge analytics” work for your engineering application?

HP Enterprise (HPE) has recently introduced two new products, the Edgeline EL1000 and the Edgeline EL4000 Systems. According to HPE, both solutions are aimed at securing the computing giant a foothold in the growing “edge analytics” business.

And with that release, I’m led to my first question, what exactly are “edge analytics,” and who exactly is interested in them?

What Are Edge Analytics?

Provided you haven’t been living in a cave, you’ve likely heard of a trend called the Internet of Things (IoT), or as some have started to call it “smart, connected products.”

Well, edge analytics is a way of managing IoT data. To put it succinctly, edge analytics is the idea that data collected on the IoT is processed, at least in part, where it is collected. This reduces the reliance on data transfers to the cloud and can ensure that operations are still running if the Internet cuts out.

Why Is That Important?

For many companies these days, it’s vital to collect data from the machines that they use. These connected devices can then inform the company about its operations gathering raw materials, monitor environmental conditions, and manufacture goods or countless other important tasks in the field.

While it’s still a fledgling field, the IoT is a big idea. IoT imagines a world where any device (from a fridge to a CNC mill) can be connected to other devices or to its manufacturer, and even a local service center. With these connections in place, both the product owner and its maker can get up-to-the-minute information about how a product’s systems are behaving, if maintenance needs to be scheduled, or even if a customer just needs to go pick up a gallon of milk because they’ve run out. 

Your typical factory floor, now with more sensors!

Your typical factory floor, now with more sensors!

Edge analytics takes this idea and gives IoT immediate meaning. With edge analytics, users can leverage the sensors connected to their hardware in factories and out in the field to instantaneously translate raw data into actionable information.

But there’s the rub. Collecting all of this sensor data means that there’s going to be a ton of data produced. And it is unfeasible to send all this data to the cloud to be processed. Here’s where the analytics part of “edge analytics” comes in. It processes the data, at least in part, where it is collected.

So, What’s HPE Offering for Edge Analytics?

So, now that we’ve got a grasp of edge analytics, it’s time to ask the next question. If I’m interested in leveraging this kind of data, what do I need to make this happen?

Like any information processing scheme edge analytics requires two things—a hardware component and a software component. For HPE’s part, it’s offering the hardware in the guise of two systems, the Edgeline EL1000 and Edgeline EL4000.

In essence, HPE is offering a robust, no-nonsense machine that’s meant to crunch numbers on the front lines of your business. With that in mind, HPE has designed the Edgeline EL1000 and EL4000 to handle shock, vibration and extreme temperature all while completing its core function of delivering real-time analytics via graphical data visualizations. To build these visualizations, the Edgeline systems will support cutting-edge data capture, control, computing, storage and machine learning techniques to ensure that every bit that it’s sifting through is processed correctly.

But still, for those just wading into the world of data analytics, it can appear to be a spooky place. Who’s to say that machine learning algorithms can really replace an engineer on the ground?

As of now, the likes of PTC, a leader in the IoT space, has publicly endorsed HPE’s Edgeline products. In fact, at the company’s recent LiveWorx conference, PTC’s CEO Jim Heppelman gave a ringing endorsement of the hardware, saying that PTC’s involvement with the Edgeline product range will make HPE’s offerings stronger and more competitive.

How’s That?

Well, here’s where the software component comes in. If you dig deep into the struggle that’s at the heart of edge analytics, you’ll probably notice that no two companies are going to have the same deployment of sensor and apparatus working on the bleeding edge of their business. Because of that, HPE’s hardware may be able to crunch a company’s numbers, but the Edgeline system itself is going to need a customized software solution that can adapt to businesses’ needs. That’s where PTC’s ThingWorx IoT is useful. With PTC’s IoT tools, any company can quickly create analytic engines that can process the appropriate data that a business needs. Once these customized pieces of software are created, they can be deployed to Edgeline hardware and set out to do their job.

Suffice it to say, edge analytics is going to be an important part of making sense of the IoT. Without the ability to sift through the massive amounts of data that our connected products are going to generate, there’s almost no reason to have them linked at all. While I can’t say for sure if HPE’s Edgeline systems are the right fit for your operation, I can say they’re one of many solutions being developed by the biggest players in the IT field. With so much involvement from likes of HPE, Dell and others, it’s likely that devices like the Edgeline series are going to be a foundation for manufacturing and information technology in the future.

And hey, look at it this way—with all of these analytical tools churning away and being tinkered with in the hopes of making better decisions, one day some programmer might find a way to make artificial intelligence (AI) emerge from this loose network of connected devices called the IoT! It can happen, right?


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