By Juan Orlandini / 19 Nov 2021 / Topics: Data and AI Devices Application development Hardware
As an example, one could start with an all-Microsoft Azure IoT deployment from beginning to end. Take a silly idea based on a real-life need: A device that will tell you when your steak (or burger) is cooked to the perfect temperature. To start, you take a camera that reads the temperature on your grill and sends you a message. The prototype could be to get an Azure IoT-enabled platform, connect it to a camera, send the data to Azure, and have the Azure Machine Vision system decode the temperature. Then, that gets converted to a number that gets relayed to a web page. Your phone then connects to this web page and BAM! You have an internet connected grill.
But that’s not where the process ends. The next step is to make an iterative improvement by thinking of a better way to reach your desired outcome.
What if I use a thermocouple in the meat and read the temperature directly? Then, only send that information to the cloud for it to notify me. Now we’re doing better.
But what if I improve the gizmo further by making sure it’s rugged, cheaper and hacker proof (you don’t want internet trolls to ruin your steak). Now we’re getting somewhere.
Then you say hmm… I could productize this and sell a $20 gizmo with a $5/month subscription and it will tell me much more for that fee…
Then sell millions.
By the way, that silly little idea isn’t actually that silly. That little gizmo is an IoT-enabled meat thermometer called a Meater Pro — and I never grill without it. (Note: Although I love using Meater Pro, neither I, nor Insight, have any affiliation or sponsorship relationship.)
There’s others that are similar — so the difference becomes, which gives the better experience? That’s a different story though.
That product development journey could have gone a different path. It could have started with the same premise but instead of Azure, you use something like the frameworks that Altair or many others provide. Similar story: Get a cheap device that I can tinker with. Send the data to my data center that is running the Altair software. Go through the same iterations of refinement. Eventually end up with a similar product, but the data is now being processed in a data center you own, via software provided by Altair instead of Azure.
Which is better? That’s an economics and innovation discussion that should be considered carefully. You might be surprised at which is better (no judgement either way).
The important part of this is understanding that the “discover”, “evaluate”, “secure”, “support” and “scale” steps are the same.
While use cases for intelligent edge will differ from one client to the next, knowing each implementation will follow roughly the same trajectory helps make the process of preparing for and executing the project simpler.
But why are we so interested in the intelligent edge right now? The pace of business has evolved to be much faster than before — staying competitive means staying on top of your consumer needs, outpacing other players and creating operational agility. Edge intelligence, usually Internet of Things (IoT) arrays equipped with edge computing technology, helps clients do that.
It gives companies the ability to gain, process and apply data for accelerated business success where it matters the most for the bottom line. The results of these types of implementations range from improved operational visibility to faster data analysis, to more productive automation and autonomy, to advanced Artificial Intelligence (AI)-enabled processes.
So, what are the essential steps for achieving scalability in the intelligent edge journey?
1. Discover
This is the initial part of the process in which we work with the client to identify the organization’s business goals. We ask where we see the potential to apply intelligent edge infrastructure to most likely do one of three things: drive new revenue, optimize operations or augment an existing experience.
2. Evaluate
The next step is nailing down just one idea or application to create a rapid prototype for testing. Homing in on this one idea allows us to create a solid proof of concept that can be iterated on quicky to validate whether the business outcome can be met by this project.
3. Secure
Making sure that you secure your intelligent edge infrastructure is critical. This requires hardening your requirements, developing a formal strategy for secure devices, identifying compute locations and platforms, etc.
Also, security is part of the development journey that you have to take. You must make sure they’re building in a security focus right out of the gate. Bolting on security after the fact is a recipe for trouble.
4. Support
Next, you must make sure support, tooling and processes are in place for deployment, operations and ongoing development. This step can be a key blocker in scaling your deployment, so spend time planning for this from the very get beginning.
5. Scale
Finally, you’re ready for deploying rapidly and at scale. And “scale” will mean different things for different clients and different projects, whether five or 5,000 locations. Regardless of the end goal, all of the previous steps have to be met before sustainable scalability can be achieved.
Properly planned and implemented, the intelligent edge is capable of unlocking incredible Return on Investment (ROI) for organizations committed to following the tried-and-true roadmap it takes to succeed.
In our work with clients, we’ve seen some outstanding success stories — all of which illustrate how, despite the edge journey being different at a detail level for each client, the process is actually very similar for every scenario.
When a client works with Insight for intelligent edge initiatives, the process starts with the Align phase, in which our intelligent edge experts spend about two weeks walking the client through defining project parameters and outlining requirements for moving forward. We work with clients to help them choose a use case, determine what kinds of architectures and support are best, validate the use case, and walk through the process to deployment and scale. A typical engagement progresses as follows:
Preparation
Align session
Executive readout
The full journey to scaled success continues on through the Envision & Plan phase, the MVP Build phase, and the Iterate, Scale & Manage phase.
Learn more about creating success with intelligent edge solutions, explore specific use cases and dive deeper into what it takes to deploy at scale — watch the video linked below.
Chief Technology Officer, North America and Distinguished Technologist, Insight
Juan is Insight’s chief technology officer, North America, and one of Insight’s distinguished technologists. He is a 30-plus-year veteran of the IT industry and has designed and deployed enterprise computing, storage, data protection, virtualization and hybrid cloud solutions. Juan evaluates next-generation technologies for Insight and works with enterprise clients, assisting them in architecting and selecting strategic roadmaps. In his current role, Juan designates champions of the technology community within Insight and drives events that promote thought leadership, professional development and knowledge sharing.
