How Tech Data EMEA Bridges the IoT Partner Ecosystem Gap

Posted on by Micah Kaplan

Digital is the name of the game these days—for pretty much every game in town, every business, every service. But digital transformation takes expertise and skills, and small- to medium-size companies can’t usually afford a big R&D department. They may not have the right resources in-house to deal with complex and interconnected technologies like computer vision, either. Fortunately, there is an ecosystem of IoT partners that can help.

But who services the service provider? Who sells to the channel reseller? Who integrates the systems integrator? That’s where Tech Data EMEA, a leading IT distributor and solutions aggregator, comes in. Its European Chief Technologist for Data and IoT Evan Unrue talks about overcoming barriers to entry for technology deployment, the solutions Tech Data supports, and its crucial Better Together alliance with Microsoft and Intel®.

What drives enterprises toward AI and IoT today?

Everything is digital these days. It’s forcing enterprise organizations to become more real time, more data driven, more informed, and more engaging across both their southbound channels down to the customer, and upstream with how they interact with their suppliers.

Data is also now absolutely critical to strategy. And rather than data just being something that’s generated as a byproduct of an application, data is driving applications. Look at supply chain, for example. Organizations are now having to deal with the fact that customers want to know where everything is, where it came from, is it ethically sourced, is it going to come tomorrow? All this requires an enormous amount of orchestration, which requires data and intelligence.

How have companies been doing in their efforts to deploy these kinds of technologies?

We’ve seen adoption really springboard over the last couple of years. Five years ago you had to be a Fortune 500, a Fortune 1000 company with an R & D budget to afford to do these projects and then drive them into the core of a business. And the SME market was suffering because it wasn’t able to do that. Or they were creating interesting projects, but they weren’t really driving them towards the business outcomes.

“All of the complex things that you can do with #AI or with #IoT at scale should be reserved for where the right budget exists, and where the payoff is big enough.” – @interestingevan, @TechDataEurope via @insightdottech

In the last couple of years, we’ve seen more success in companies being laser focused on outcome. A big part of that is due to the efforts of the vendor community—that’s the likes of Microsoft, the likes of Intel®—driving an immense amount of innovation through their ecosystems. And the ISVs and OEMs are solutionizing off the back of all that. So we’re seeing solutions come into the market that are more clear and concise in terms of the outcomes that you can drive with them.

What is Tech Data and its IoT partners doing to ease the barriers to AI and IoT?

For common industry challenges there should be off-the-shelf offerings that can be implemented by the average business stakeholder. We’ve seen a bit of a democratization around certainly AI, and around some of the application stack that sits in front of these IoT infrastructures. We have a big push towards solutions at Tech Data, and you only have to go on the websites of Microsoft or Intel® for two minutes to see their efforts around driving market-ready solutions, off-the-shelf solutions.

All of the complex things that you can do with AI or with IoT at scale should be reserved for where the right budget exists, and where the payoff is big enough. But if you are a small freight-logistics company, you’ll typically have the same problems of any other small freight-logistics company—whether it’s optimizing your fleet and their routes and their maintenance, or whether it’s tracking assets through the supply chain. Whatever it is, the solutions should be off the shelf and ready to take to market.

I think some of these complex technologies fall into the category of digital transformation as a whole, and attacking that can be a scary thing for a midmarket organization. So you have two approaches. You can either come with a top-down approach and have a uniform infrastructure that all these use cases can plug into; it’s a bit of a larger effort, but the payoff is there. Or you can look at the discrete parts of your business operations where you might have gaps in data, and then start to deploy these tactical solutions.

There’s a spectrum of solutions out there; the Intel® IoT Market Ready Solution program is one. But primarily it’s about taking proven solutions by ISVs, OEMs—companies that have deployed these solutions over and over with customers—and our job then is to provide the reach and scale of getting those solutions in front of a channel partner.

Can you talk about some specific use cases and technologies Tech Data supports?

Smart building is certainly a big area, and there are a few facets to that. Number one is how do you better manage and maintain a building? How do you reduce the cost of doing that? How do you get in front of problems as they’re happening? And within that area, energy is a big topic. Certainly a lot of companies are being tasked with being more proactive with their sustainability efforts.

Another one we’ve seen over the last few years is retail in the High Street looking to reassert its resiliency—giving people more reasons to come, being engaged with customers, and extending those customers’ digital journeys into the physical store. Better planning of store layouts, for example. Better optimization of marketing to the demographics on the ground leading to better conversions. A colleague of mine likes to use the term “phygital,” which is the combination of physical and digital, and this applies particularly well in the High Street. It’s being contextual in terms of how you interact with a customer, and that might be through signage, that might be through interactive displays and putting something in front of a customer that’s relevant.

How does your work with Microsoft and Intel® relate back to these solutions?

Those two organizations have coexisted in the enterprise space for a long time. And with regard to technologies like IoT, for example, there’s been a really strong focus from both of them to be promoting the industry applications around it.

If you look at Intel, it’s really been driving programs to simplify the development process, and help developers and organizations build these solutions at the edge—bringing on technology such as computer vision, using tool kits such as OpenVINO™, to create meaningful solutions. And you pair that with all the expertise and experience that Microsoft brings—and not just from the cloud. It ranges all the way from being able to do really complex and difficult, but very powerful and insightful things in very bespoke environments, through to very plug-and-play-type offerings that are really geared towards the midmarket. It’s kind of “one plus one equals five.” It’s a very powerful combination.

How is Tech Data bringing value to this alliance as an IoT solutions aggregator?

One of the big challenges historically, from a technology standpoint, has been understanding and identifying the multiple stakeholders required to bring these solutions together. So part of our job is to aggregate all the different technology players within that value chain, and to simplify the consumption of those solutions so resellers and customers don’t have to get bogged down by the technology.

As far as Intel goes, it’s a little bit removed from the coalface in terms of who’s selling what, and we connect them to that through our interactions with the resellers. And with Microsoft we’ve had a keen joint focus around IoT since the beginning. So I think there’s just strong alignment, a strong execution capability, and we complement each other in all the right ways.

What are some of the trends that businesses should be thinking about going forward?

One of the big things we’re seeing now is leveraging AI to make sure that insight into the data is driving what the action should be. This is what we see from a lot of the ISVs we work with. They’re focusing on what the action should be, what the outcome should be, rather than just gaining visibility and transparency into whatever is being monitored.

Computer vision looks like a really strong trend to me, just because it’s such a versatile technology. It can underpin countless use cases that could range all the way from traffic control and waste management and public safety from a smart city perspective; to gaining richer insights and stronger engagement from a retail perspective; all the way through to improving things like quality and safety in warehouse environments.

Are there any big IoT challenges that companies should be aware of?

I think the biggest thing is getting the support of the wider business. One of the challenges of IoT is that the first use case provided might not actually be the one that delivers the whole ROI. So it’s really important to try to understand who the different stakeholders are that are going to benefit from these solutions, and to bring them to the table. In retail offerings, for example, there are some things that just have to be done, regardless of ROI. Certainly retailers don’t want to have to close stores because they can’t meet conditions around, let’s say, proper social distancing.

But when it comes to looking at digital engagement within the store, when you start to bring it to the planning side of the business, and to the marketing side of the business, and you start to bring in, maybe, merchandising and third parties that advertise, and offer them space and data around footfall—then it helps the payoff become a lot more substantial in terms of justifying the business case.

I like to call it the IoT multiverse, because there are so many different dimensions in terms of the type of companies that play here—whether it’s the connectivity channels, the silicon channels, or the cloud channels.

Anything else you’d like to leave us with?

The importance of edge compute. We’ve seen technology architectures go from distributed to centralized, to distributed, to centralized, and back and forth. But this is one of the things that’s really become critical—certainly with the adoption of IoT, where you have mass amounts of data being generated at the edge, and that data might actually need to be processed, or might impact a process at the edge. Edge computing is becoming pretty critical because of the volume of data that can be created, and with an organization’s ability or need to drive action rather than just deliver data off the back of these solutions.

Whereas the cloud, whilst powerful and important for all of the things that I’ve mentioned in terms of getting a broad view across multiple assets, across multiple locations, having more horsepower behind you to drive deeper and richer insights—all of that’s important, but being able to automate and drive AI locally is also important. Microsoft acknowledges that actually some of the services it provides it needs to be able to push to the edge as well as having in the cloud, which is something that it does hand in hand with Intel.

Also, one of the things I’ve always said is that, as distributors, we have one of the most privileged positions in terms of being able to derive insight from the market. We get to see the hopes and dreams and fears of all of our vendors—what they’re trying to achieve and what their strategies are. And then, at the other end of the spectrum, we get to see what the partners are doing. Where their strategies have evolved or haven’t. Who are the early adopters, and who are the laggards? How do we help them move from one bucket into another and maintain relevance in the market?

And because we’re really sitting in between all of that, we are perfectly primed to bridge the gap between the vendors’ aspirations and their knowledge, their technologies and expertise, but also our understanding of those channels and how to drive adoption into the right technology spaces.

Related Content

To learn more about the IoT partner ecosystem, listen to Into the IoT Partner Multiverse with Tech Data EMEA. For the latest innovations from Tech Data, follow it on Twitter at @TechDataEurope and LinkedIn at TDSYNNEX.

 

This article was edited by Erin Noble, copy editor.

IoT Paves the Way Toward Smart Sustainability

Posted on by Micah Kaplan

Environmental sustainability is one of the most urgent topics today. At its foundation, it’s about lowering carbon footprint—a stairstep from where you are today to some future carbon neutrality.

In an interview with Michael Bates, Intel® Global Sales GM, Energy and Sustainability, we talk about the leadership role Intel® is taking through its own actions and technologies. For more than 25 years, Michael has worked at the leading edge of new concepts and business models in distributed energy, and the intersection of traditional energy and technology.

Michael shares the innovative ways in which the company enables an ecosystem of partners and end-customers to achieve their own smart sustainability initiatives.

Let’s start out by talking about Intel’s smart sustainability strategy.

The foundation for our strategy is wrapped around our RISE (Responsible, Inclusive, Sustainable, Enabling) 2030 goals and objectives. It is our north star, guiding us into 2022 and beyond. Not only in our own sustainability efforts and practices, but also how we’re enabling sustainability in the market.

We have a sense of responsibility in applying all the goodness at Intel and our partner ecosystem to act and address some of the biggest challenges in sustainability.

We have a sense of responsibility in applying all the goodness at @Intel and our partner ecosystem to act and address some of the biggest challenges in #sustainability. @IntelIoT via @insightdottech

When you talk about customers going down their own sustainability path, what does that look like?

The guiding principle is how to apply technology and solutions to go down that path. Sustainability has become the number-one topic in most, if not all, businesses. It’s a C-level conversation because it has so many implications, across the entire enterprise, all the way from the company brand to cost control. There’s also pressure from shareholders and financial markets that are also creating this sense of urgency.

And I think it has an impact on acquisition of talent. There’s a lot of passion to be part of the drive toward more sustainability and a lower carbon footprint.

When we think about sustainability, alternative energy sources and smart grids are top of mind. What’s happening in this area?

With these carbon-neutral energy sources like solar, wind, battery, the electricity distribution model clearly needs to undergo change, starting at the substation. We’re moving from an always-on, one-way flow of power to a highly distributed network across the other side of the grid. This two-way distributed power requires a level of edge compute that supports making real-time decisions using AI and machine learning tools.

Utilities are looking to develop a platform for the delivery of these new services. In markets like Europe, for example, many utilities are taking a very aggressive approach to this disruption. We are working with companies like Enedis in France, Iberdrola in Spain, and Enel, a utility in Italy. They see this as a great opportunity to expedite the transition to clean renewables. And while they are doing it themselves, they are taking these best practices to other utilities in other markets.

Companies that modernize the grid and their delivery of energy services are going to be the ones that are at the forefront of revenue generation.

What are private and public sector organizations doing to meet their green-energy objectives?

For one, businesses and educational institutions are building microgrids for their own facilities. They deploy these technologies on-site to help lower their carbon footprint, consume more green energy, and be more energy resilient.

And when they generate more power than they can use, it can be shared with the grid and be turned back to the market. Not only does it allow organizations to lower their carbon footprint but potentially open the opportunity to make some money while they’re at it.

This is a massive opportunity from an IoT solutions perspective. Imagine all the data needed to create those algorithms to optimize both sides of the energy equation, the building, the parking lot, the solar panels. We’re not at the very beginning, but it’s still very early. There are a lot of buildings out there that are going to get some funding to update and modernize in this way.

That’s a good segue to talk about sustainable, smart buildings specifically.

Smart building technology of course has been around for a while, and buildings are already fairly well instrumented. What’s needed is to consolidate all those different workloads onto a common platform. Then to look for those insights to drive energy efficiency, lower carbon footprint, and be more energy resilient. The impetus now is adding in the green-energy component, which requires a higher level of systems and data integration, and which a lot of our partners are fulfilling right now.

So same problem, and part of the solution is really driving that edge, computing edge, AI capabilities, high performance needed to do that.

But the amount of workload consolidation is heavy, and it needs significant compute power at the edge to optimize that. More importantly, it’s the almost  instantaneous decision-making that must happen. You can imagine, if you’re really relying on solar power, for example. In one area, the weather changes and it moves from cloudy in one to sunny in another, those changes happen instantaneously. And when you start aggregating a lot of that data, it’s clear to see that AI is the answer. To turn that intermittent, distributed energy into base load, like we were talking about.

Electric vehicles and the infrastructure to charge them is another hot topic. How is this market evolving?

It’s one of those chicken-and-egg conversations. Do you build out a network of fast-charging infrastructure and then the cars will come? Or do you wait for the cars and build it out based on demand?

With so many car manufacturers switching to EV models, the growth is exceeding projections up to this point. But the need to scale requires the certainty of being able to charge your vehicle and travel just like you do today. It must look and act like filling up a gasoline-powered car.

The big trend going forward is fast EV charging—10-15 minutes. On roadways, this will be scaled expeditiously through the funding that we get from infrastructure funding, both in the U.S. and Europe.

But the real driver is going to be the retailers that see charging your vehicles as a service. Just like you get Wi-Fi at Starbucks today. Imagine relying on a similar experience, whether it’s a plug-in or even a wireless EV charging station. Now, there’s a new revenue opportunity where customers pull in and purchase that retailer’s products at the same time.

Another interesting application that’s coming out of Japan is a mobile battery concept being tested. An automated mobile robot plugs in on the outskirts of town or in a dense city like Tokyo, where you can’t put solar panels. It charges up its batteries, and then gets dispersed to the parts of the city where the energy is needed, to fill in the peaks and valleys. And it knows where to go based on AI and real-time decision-making. It’s futuristic, but maybe not as far in the future as you might think.

This has been a great conversation. Is there anything you’d like to add that we didn’t get a chance to go over?

This is a call for action. No one has the ecosystem partners that Intel has, which has been built over 50 years. If we can work closer together and collaborate in this partner network, think about the real impacts that we can make. It could be impactful planet-wide.

We really do have an opportunity to do something here. It’s not theoretical, it’s not prohibitive, it’s needed. And I think the appetite is high for IoT solutions that help make it possible.

 

This article was edited by Christina Cardoza, Associate Editorial Director for insight.tech.

Hybrid Learning Propels Accessible and Equitable Education

Posted on by Micah Kaplan

For most people, the notion of formal education conjures up images of sitting at a desk and taking notes, surrounded by classmates. And yet the lessons that we learn from our teachers and peers extend far beyond the classroom walls and affect everything from our core values to our ability to function as members of society. In the words of 18th-century philosopher Immanuel Kant, perfection and hope lie “… in education, and in nothing else.”

In the hundreds of years that have followed, our systems have become much more formal, and disciplines have been disentangled. The global community has worked hard to ensure that every child has access to a high-quality education, irrespective of their background, location, or income level.

But only recently is education tech making accessibility, inclusion, and equality possible. Throughout the COVID-19 pandemic, we have seen a clear need to exploit digital transformation to avoid educational disruptions, foster access to students from all backgrounds, and arm teachers with the resources they need to bring lessons to life—in and out of the classroom.

Education Tech Leads the Way to a Hybrid Future

Cisco Systems, a worldwide leader in IT, delivers education solutions, powered by Intel® technology, which are leading the academic march toward a hybrid future. And I cannot imagine a better match. The former is an expert at building digital bridges and the latter at leveraging research and development to bring the impossible to life.

“One of the great benefits of hybrid is that it allows you to flex and maintain your operations,” says Brad Saffer, Global Education Lead at Cisco. “It also allows people to make the choice, whether you’re a faculty member, a staff member, or student, to where you want to be on a given day.”

In addition to connecting campuses, Cisco education solutions are streamlining administration, expanding learning models, enhancing safety and security, and fueling innovative research. Everyone in the ecosystem—students, teachers, and administrative staff—now have access to the same educational tools. Whether on-premises, hybrid cloud, or multi-cloud, the solution will adapt to each IT department’s needs, without jeopardizing the security of the whole.

At the core, we can find the Intent-Based Network (IBN) concept—a new paradigm relying on network virtualization and artificial intelligence to configure the network automatically, according to best practices, complying with any business request.

@CiscoIoT #education solutions are streamlining administration, expanding #learning models, enhancing safety and #security, and fueling innovative research. – @antgrasso via @insightdottech

“We build the platform so it’s scalable and secure. Those are two big pieces that are so critical, especially when you’re thinking about connecting districts across many different locations,” Saffer says. “We also integrate with our partners globally, with institutions that support and are tailored to education, providing specific solutions that we integrate with the platform.”

The architecture allows mobility, security, and collaboration in almost any underlying configuration about computational resources, network, and storage (Figure 1).

Cisco has many technology platforms that underlie IT education solutions
Figure 1. Cisco’s broad range of technology platforms underlie its flexible and secure education solutions. (Source: Cisco)

Notice how the solution fully adheres to their extended Cisco Digital Network Architecture (Cisco DNA)—an open architecture that accelerates and simplifies network management operations while reducing the associated risks and costs.

Beyond its outstanding architecture, I fell in love with the platform’s potential for social inclusion. During my discussion with Saffer, I discovered that the solution allows you to extend the physical limits of campus connectivity to adjacent areas, spanning entire neighborhoods and small cities. Offering a mixture of face-to-face and remote lessons, this gem allows those without a strong broadband connection at home to continue studying using an amplified campus signal.

Inclusive Education in Action

An excellent example of how technology can address inclusion problems and follow the changing landscape of learning is the Canutillo Independent School District in Texas. It faced a number of challenges when the pandemic forced a transition from the physical classroom to distance learning. With 6,200 students in 10 schools, 70% of the students did not have Internet access at home. Canutillo needed a way to curb the digital divide and keep these kids from falling behind.

The district worked with Cisco to deploy and test an “extended network,” enabling students to log onto private, secure Wi-Fi from home to continue their schooling. The solution includes Cisco Fluidmesh wireless backhaul, Cisco industrial switches, and Cisco Meraki outdoor access points.

Canutillo leadership foresees continued use of innovative technologies to meet the diverse needs of its students, enabling them to reach their full potential.

Embracing the Shift to Hybrid Learning

Given the cost savings and new features that allow the entire teaching process to be optimized, the asset can be amortized in a short period of time, yielding considerable return on investment.

Cisco’s hybrid education solutions have allowed academic institutions worldwide to not simply respond to, but embrace, the paradigm shift that disrupted the global education system more than two years ago. A mixture of face-to-face and remote lessons can ensure students benefit from a high-quality education, no matter their location, background, or income level.

Just as Kant maintained hundreds of years ago, education has the power to restore hope, enable us to look beyond mere appearances, and shape our lives and the very future of humanity.

 

This article was edited by Georganne Benesch, Associate Editorial Director for insight.tech.

Educational Tech Tools Power Smart Virtual Classrooms

Posted on by Micah Kaplan

It is very easy for a student to become distracted in a classroom setting. They might stare blankly out the window, count the minutes until the bell rings, or have secret conversations with classmates when the teacher isn’t looking. Add remote learning into the mix and the distractions are amplified. It’s all too easy to go on mute, turn off your camera, and forget about schoolwork.

This is not what schools had envisioned when deploying IoT technology and solutions. Leveraging IoT in education, while initially used out of necessity, is really meant to support learning—not block it.

“Technology should be used as a tool to assist education. It should not impact normal teaching plans or classroom management. It’s necessary that it is implemented in a way that is seamless and frictionless to learning,” says Jing Zhao, Marketing Planning Specialist for the Beijing Honghe Technology Group.

But the world is not turning back to a traditional education system. And universities and K-12 schools are left trying to find the right solution for the problem.

Smart Education Engaging Students Outside the Classroom

Rather than using educational tech to just broadcast a lecture, one tech-savvy university is using them in innovative ways. The university creates lessons that make learning material exciting, engaging, and interactive.

Thinking outside the box and screen, the Beijing Capital Institute of Physical Education took its introductory course on Winter Olympics out of the classroom and onto the ice rink. Instead of using a stark PowerPoint presentation to go over the history of the games, they set the class in a rink using real-life examples of different snow activities. The university even invited several athletes and coaches to teach and demonstrate various sports.

The class was broadcast to about 100 schools where tens of thousands of students and teachers were able to remotely access and engage in the course.

Because @HiteVision uses a #cloud + terminal platform to manage class participation, instructors can detect #students who need the most support and personalize the #educational experience for them. via @insightdottech

“Through this solution, a wide range of students were able to experience the charm of the ice project with their own eyes,” says Zhao.

This new online teaching technique is known as smart education, which uses technology as tools for teachers to provide an immersive learning experience.

The institute used the Honghe HiteVision Interactive Broadcasting Classroom solution to broadcast the Winter Olympics course from the arena. The solution supports hybrid educational models with a cloud + terminal platform. Key features include single “lecture classroom,” multiple “listening classroom” support, synchronized live teaching, two-way interactions, and insights into student participation. Teachers can control lessons and classroom interaction with a touch of the blackboard screen—simplifying the operation process and improving efficiency.

The solution uses Intel® technologies to ensure it has enough power and performance to record, transmit, and deploy lessons remotely.

“We make full use of the computing power in Intel CPUs and GPUs in order to realize the video-audio codec and provide smooth operations. Intel helps us provide remote interaction and courseware playback for remote interactive classrooms,” says Zhao.

Smart Education for All

The HiteVision solution is not only used to support a new form of learning. It is also used to address the education equity and expertise gap in China. Many students don’t have the same access to high-quality instructors as others. With the HiteVision solution, all students regardless of location or economic status can receive the resources they need on-demand.

“Although compulsory education is moving from a basic balance to a high-quality balance, some weak areas still have the problem of not offering enough courses prescribed by the state,” says Zhao. “This capability makes the solution a clear avenue for school districts to help promote equity and reduce poverty restrictions in the educational sector.”

Teachers can reach more than one classroom at a time as well as collaborate with other teachers on one platform. Because HiteVision uses a cloud + terminal platform to manage class participation, instructors can detect students who need the most support and personalize the educational experience for them.

“This capability makes the solution a clear avenue for school districts to help promote equity and reduce poverty restrictions in the educational sector,” says Zhao. “The solution automatically tracks test scores and student engagement to provide beneficial data to educators so they can alter their lesson plans and optimally address the needs of their students to ensure no one is falling through the cracks.”

In addition to student performance data, instructors get real-time insight into equipment health and operations so they can broadcast in the best streaming quality, Zhao explains.

The Honghe also works with systems integrators to create complete and customized distance learning solutions depending on the need. For instance, systems integrators explore different use cases and industries such as museums and libraries for shared lecture series and training initiatives.

Technology like HiteVision will be key as the evolution of education in China continues to rapidly advance.

“Technology itself has educational significance. It is not only a tool to promote social progress but also triggers a change in our way of thinking. Its educational nature is worth exploring and learning,” says Zhao.

 

This article was edited by Georganne Benesch, Associate Editorial Director for insight.tech.

AI Developers Innovate with Intel® OpenVINO™ 2022.1

Posted on by Micah Kaplan

Life just got a lot easier for AI developers with the latest release of the Intel® Distribution of OpenVINO™ Toolkit 2022.1—the most significant update since it launched in 2018. Packed with new features and capabilities, the new release is designed to tackle some of the most pressing artificial intelligence development challenges.

“OpenVINO 2022.1 builds on more than three years of learning from hundreds of thousands of developers to simplify and automate optimizations,” says Adam Burns, Vice President of OpenVINO Developer Tools in Intel’s Network and Edge Group. “The latest upgrade adds hardware auto-discovery and automatic optimization, so software developers can achieve optimal performance on every platform. This software plus Intel silicon enables a significant AI ROI advantage and is deployed easily into the Intel-based solutions in your network.”

What’s new with the Intel® Distribution of OpenVINO™ Toolkit

Tools like OpenVINO have become increasingly important over the past couple of years as organizations are under pressure to become better, smarter, and faster about the way they do things. AI has enabled them to digitally transform, gain deeper insights into their operations, and reduce manual effort.

But as a result, it’s changing the nature of developer work. For them to deal with the complexity of open architectures and ecosystems to work on a variety of supported devices, they need flexible, interoperable, and scalable tools.

“The idea is simplify, simplify, simplify, which allows the #developers to really focus on what they do best: innovate, create, and solve problems.” –@billpearson, @inteliot via @insightdottech

“The role of the developer becomes even more important than it’s been. We’ve got to make sure they’re equipped to deal with this new environment through tools, products, and the information that’s going to help them build their solutions that scale,” says Bill Pearson, Vice President of the Internet of Things Group at Intel.

OpenVINO 2022.1 aims to empower developers to create innovative AI-powered solutions. “The idea is simplify, simplify, simplify, which allows the developers to really focus on what they do best: innovate, create, and solve problems,” Pearson says. “AI is an opportunity for them to be able to go and embrace that new world and do some new things.”

The most notable features of the release include:

  • OpenVINO API 2.0 makes it easier for developers to adopt and maintain code, switch between frameworks, and reduce complexity. Intel® recommends AI developers migrate to 2.0 API to take advantage of new capabilities such as working with dynamic shapes and adding preprocessing operations to inference models. The team plans to extend these features in the future. A guide on how to transition to the new API 2.0 is available here.
  • Broader model support for natural language programming models, advanced computer vision, and PaddlePaddle models (Figure 1). Support for conversion and inference will be available for select PaddlePaddle models. The release also adds new pre-trained models with a new anomaly detection category and a focus on industrial inspection.
  • Portability and performance updates, including an AUTO device model, auto-batching functionality, and support for 12th Gen Intel® Core™ hybrid architectures. The new AUTO plugin automatically discovers available systems and their inferencing capacity while automatic batching functionality scales batch size based on XPU and memory. Additionally, new performance hints are available to configure systems with portability in mind.
Timeline chart depicting the popular AI frameworks OpenVINO supports.
Figure 1. OpenVINO 2022.1 supports popular AI frameworks so developers don’t have to compromise on tools or performance. (Source: Intel®)

To learn more about all the changes in OpenVINO 2022.1, check out the full release notes.

The Intel® Distribution of OpenVINO™ Toolkit In Action

The increase of AI adoption is evident in almost every industry, with organizations beginning to optimize and deploy AI to solve challenges in ways you wouldn’t expect.

San Diego-based restaurant automation company Vistry is using OpenVINO in quick-serve restaurants to measure performance and detect areas where it can speed up and improve quality of service. And with a wide range of AI models available to them, restaurant operators have been able to optimize their kitchen production system and better understand customer journeys within the drive-through.

“Most restaurants don’t have these big beefy GPU servers sitting on-premises, so OpenVINO allows us to use these models in these low- powered environments and then still run in kind of real-time fashion, which may be critical in a lot of different restaurant applications,” says Gabriel Ibagon, Head of Engineering and Co-Founder for Vistry.

The AI toolkit can also be found in malls, helping operators make smarter decisions and boost performance. For instance, Pathr.ai, a real-time spatial intelligence company, uses OpenVINO to help physical retailers provide ecommerce-like experiences and analytics in-store at an affordable price. Its spatial intelligence platform gives retailers the tools to understand customer behavior in real time, how many customers are shopping at a given moment, and which products struggle on the shelf.

“Adopting Intel CPUs and the Intel® Distribution of OpenVINO™ toolkit accelerated Pathr.ai’s deployment in multistory shopping centers in near-real time, allowing us to scale our solution across existing infrastructure and deliver cost and power savings to our client in a GDPR-compliant way,” says George Shaw, Founder and CEO of Pathr.ai.

As AI use cases and new ways to implement AI continue to expand into unexpected areas, all the new capabilities of OpenVINO will be key to providing organizations a clear path to success. “This is a toolkit that just helps developers deliver faster and more accurate results using AI and computer vision inference,” says Pearson.

 

This article was edited by Georganne Benesch, Associate Editorial Director for insight.tech.

Personalized Services Are the Future of Public Transit

Posted on by Micah Kaplan

Passengers used to be thrilled if reliable Wi-Fi were available on public transportation—and were willing to pay a premium. Today, always-on connectivity is a commodity, and customers expect it no matter how they travel, be it on trains, ferries, or buses.

This presents the transportation industry with both new challenges and new opportunities. Post COVID-19 pandemic, people are starting to travel again. But they have many choices and in a highly competitive environment, transit entities must go the extra mile to get passengers on board.

Transportation systems around the globe are being digitized, and on-board internet connectivity helps improve the passenger experience while adding new revenue streams, lowering costs, and improving daily operations. Passengera, a global provider of digital transportation solutions, focuses on the future of public transit, and makes these new opportunities possible.

“Our vision is to lead the path of mobility digitization and introduce a new level of travel experience, both for transport companies and their passengers,” says Jan Kolář, CEO of Passengera. “We aspire to increase the appeal of public transportation, because when more people decide to buy a train ticket rather than getting in their own car, our planet can be a greener one.”

Enhancing travel experience means providing entertainment to passengers who seek relaxation during their journey and stable high-speed internet connectivity to those who prefer to work—while delivering the trip information they need when they need it. With these services, the journey goes from meeting expectations to creating next-generation digital travel experiences.

With the @Pssngra #Fleet Connectivity and Infotainment Solution, operators gain insights into #passenger preferences and interests that can be used to personalize experiences in real time. via @insightdottech

Travel Technology Enhances Passenger Experiences

New technologies and AI in transportation provide all sorts of new ways for travel companies to increase revenue. In the past, operators didn’t have ways to know their customers—from demographics to infotainment preferences. With the Passengera Fleet Connectivity and Infotainment Solution, operators gain insights into passenger preferences and interests that can be used to personalize experiences in real time.

For example, if a passenger consistently watches animated movies screened on a train, the operator can deliver personalized information with similar characters and themes to their device over Wi-Fi. Or when people look up restaurant reviews at their destination, they can opt in to emails that suggest top dining options. And both of these scenarios present advertising opportunities not just to the transport company but to its partners as well.

Marketing options are practically endless and benefit both the passenger and transportation companies in the long term. For instance, passengers can order food from their personal devices with meals delivered right to their seat. Food sales grow if they receive special offers, discounts, and up-sell recommendations while ordering.

With its modular, centrally managed service platform, the Intel® technology-based Passengera solution makes these opportunities a reality (Video 1).

Video 1. Transport providers can offer personalized services well beyond traditional Wi-Fi. (Source: Passengera)

Riyadh Metro: Travel Tech Innovation in Action

The Riyadh Metro project is a great example of Passengera and its solution in action. Currently in the testing phase, it is one of the most extensive public transportation projects worldwide. With 85 stations, six Metro lines, 176 kilometers of tracks, and driverless trains, passengers will enjoy a cutting-edge experience from the moment they enter a station until they arrive at their destination.

From analysis, to design, to deployment and continuous support, Passengera delivers a connectivity and application hosting solution capable of seamless handover between trains and stations.

While traveling, people can view targeted advertising content on their personal devices and potentially even on the in-train screens. This focused advertising model is a plus for both customers and vendors alike. Travelers can learn about and take advantage of special offers. And when based on people’s locations and profiles, there’s greater potential for conversion rates and new revenue streams.

Another bonus for both the Metro and travelers is the higher likelihood of on-time transportation. Metro employees receive real-time analysis of train locations and passenger loads, as well as notifications of potential issues via centralized monitoring. The outcome is fewer crowds to manage and satisfied customers who are likely to use the Metro again and again.

And Passengera’s capabilities go beyond the complex connectivity solution deployed in Riyadh. For other customers all over the world, the platform also delivers other digital solutions, including infotainment platforms, monetization platforms, as well as management and monitoring systems (MMS). Through the Passengera infotainment platform, travelers can:

  • Personalize their experience by selecting a preferred language, tracking their current location, ETA, and wait times
  • Explore food options and ordering, and check destination weather conditions
  • Relax by watching a movie, playing games, or listening to music
  • Using audio guides to learn about their destination and journey

The Future of Public Transit

“Because Riyadh Metro is growing and expanding its transport options, it needs a travel technology framework that will scale in the future,” says Kolář. “Our solution allows the client to easily expand the deployment of new trains, lines, buses, and bus stops with minimal investments to purchase additional gateways. Also, they have the ability to adopt new technologies such as 5G, which will ease the expansion of connected services to buses and other ground transportation without the need for further communications infrastructure.”

As autonomous trains and metro systems become a reality, transportation companies and transit authorities are reimagining the future of digitization with innovative technologies.

“Without a driver in the vehicle, the digital communication systems will play an even more essential role in the future,” Kolář concludes. “Solutions like our business and communication platform, together with reliable connectivity, will become crucial for the era of autonomous vehicles. The way of interaction with passengers will be through digital communication.”

 

This article was edited by Georganne Benesch, Associate Editorial Director for insight.tech.

MLOps: The Path Forward for Scalable AI Workflows

Posted on by Micah Kaplan

Scalability is one of the most difficult parts of any AI project. First, it takes a huge amount of data to build and train an AI model. And there’s an immense complexity in synchronizing different jobs, models, and data set requirements. Challenges like these were the primary drivers of IT and engineering organizations’ adoption of DevOps design principles. But DevOps really can’t solve these AI scalability issues.

DevOps, a set of software development lifecycle (SDLC) management practices, reduce development time while delivering continuous, high-quality software updates so organizations can scale their engineering output. Given that AI and machine learning rely on repeated model iteration and algorithm optimization, any organization managing multiple AI projects simultaneously should use DevOps to handle things like version control and automated testing.

The catch is that AI projects are fluid. Different AI use cases require different tools, so any infrastructure that supports more than one AI application should be elastic. And the same data can be both the input and the output in an AI system where data and workflows in an ML project can exist on multiple parallel tracks simultaneously.

The DevOps model wasn’t designed to deal with these multi-tiered data and workflow hierarchies. Fortunately, veteran developers who created DevOps best practices decades ago are still tackling the problem today.

From DevOps to MLOps

To illustrate how unruly AI can become within an organization, let’s start with a story.

Chia-Liang “CL” Kao, an open-source developer for more than two decades, built SVK, a software version control system early in his career. In 2018, a nonprofit AI training academy brought Kao in to develop a back-end management system that would allow different groups of people to collaborate on shared data sets and share development resources without overwriting one another’s work or accidentally modifying someone else’s data. At the time, there was no equivalent process or architecture for machine learning workflows.

Kao’s decades of experience in DevOps development came in handy for this problem, which is larger than it might seem.

“They had 200 people in the first cohort and needed an automated way to manage all the resources for performing deep-learning tasks,” Kao says. “Otherwise, you would need 10 people to manually manage the environment to make sure the 200 students’ projects didn’t interfere with each other.

“How do you get the data, clean the data, aggregate the data, and organize data?” he asks. “And how do you manage your training library and workloads? And once you have the models, how do you keep track of them?”

The process of answering these questions led Kao and other members of the DevOps community to begin creating what would become MLOps.

The goal of a successful #MLOps deployment is to solve scalability issues so less time is spent configuring workloads and managing #data sets and more time focused on #AI outcomes. @InfusiAI via @insightdottech

Prepackaged, Open-Source MLOps

MLOps is an approach to AI lifecycle management that tailors the SDLC practices of DevOps to AI projects and workflows that need to scale. For instance, Kao founded InfuseAI, a company that builds PrimeHub AI, an open-source pluggable MLOps platform that supports a wide range of tools and software packages from within a single multi-tenant dashboard (Figure 1).

An example of central dashboard screen used to manage models, data sets, and tools
Figure 1. The PrimeHub AI platform allows organizations to manage and deploy multiple ML models, data sets, and tools from a central dashboard. (Source: InfuseAI)

The PrimeHub AI platform uses an API-centric architecture that helps users juggle multiple projects with different software requirements and data repositories without cross-connecting workflow silos. This starts with a prepackaged software stack comprising open-source and off-the-shelf tools that data scientists, AI developers, and IT professionals may already be familiar with:

  • Jupyter Notebooks for interactive development
  • Crane for building and pushing Docker containers
  • PipeRider and ArtiV for data version management and data set metadata tracking
  • Framework that enables 3rd-party apps to be seamlessly integrated into the ML workflow, such as model registry and data labeling tools
  • Streamlit for data focus and visualization

In addition, the platform supports multiple cloud-based offerings from AWS, Google Cloud, and Microsoft Azure.

As Kao explains, MLOps has to “embrace the diversity of different projects but standardize at a very high level so your tooling can support that practice.”

This is where APIs come into play that help integrate every part of an ML stack so different IDEs, libraries, data sources, and more can slide right into DevOps workflows built on top of the software infrastructure described above. They also enable the integration of other common workflow automation tools such as Jenkins servers, TensorFlow libraries, and even model optimizers like the Intel®OpenVINO ™ Toolkit.

“It’s like a central hub of all the tools that you need,” Kao says. “We are not going to re-implement different stages. There are going to be multiple tools. We’re not going to replace them all, but we are going to make them work seamlessly.”

A Matter of Scale

The goal of a successful MLOps deployment is to solve scalability issues so less time is spent configuring workloads and managing data sets and more time focused on AI outcomes.

For example, the PrimeHub AI platform was deployed in a large hospital where a small AI team used it to manage predictive ER capacity at the height of the COVID-19 pandemic. The application involved multiple ML models that tracked different patients’ conditions, predicted their likelihood of improvement, and determined how many beds would be available the next day. In parallel, the models needed to be constantly retrained to adjust for seasonal changes, refreshed pandemic data, and other factors, then verified and deployed.

Previously, all these moving parts created a silo between the AI developers and operations engineers that made updating models a weeks-long process. But with PrimeHub MLOps, it became an hour-long workflow that encompassed updating the model, testing it, and deploying it into production.

Extending the classic DevOps model to automate workflows, scale deployments, and make back-end control as simple as possible empowers this kind of organization to capture AI value and ROI. Without the shorter turnaround time, Kao says, “you’re just wasting your AI investment that’s sitting there idle.”

 

Into the IoT Partner Multiverse with Tech Data EMEA

Posted on by Micah Kaplan

Evan Unrue

[podcast player]

Do you have the right tools, budgets, and expertise in place to take advantage of all the IoT world has to offer? Probably not. Most businesses are dealing with constrained budgets, limited resources, and a small staff. But that can be extremely troubling in today’s modern and digital landscape as customers demand better, faster, and more advanced solutions and services.

Luckily, you don’t have to do it alone. There are multiple different partnerships and programs out there to help put IoT businesses on a successful path. Take Tech Data’s Better Together strategic alliance as an example. The company partnered with Intel® and Microsoft to leverage their unique knowledge and experience to create seamless end-to-end IoT solutions and improve business outcomes.

If you’re looking to learn more about how IoT partners can help you, what to look for in a partner, and the whole IoT solution provider ecosystem, then this podcast is for you.

Our Guest: Tech Data

Our guest this episode is Evan Unrue, European Chief Technologist for Data and IoT at Tech Data EMEA, a leading IT distributor and solutions aggregator. Evan works to make the world of IoT more accessible by fostering the proper IoT partnerships. He looks after the company’s technology strategy, evaluating and ensuring it has the right technology solutions and partners to support its distribution channel.

Podcast Topics

Evan answers our questions about:

  • (3:41) What drives enterprises to move toward IoT
  • (5:25) How successful companies have been at deploying this technology
  • (7:59) Easing the barrier of entry
  • (11:48) Solutions and use cases that support IoT development
  • (14:32) Tech Data’s Better Together Alliance with Microsoft and Intel®
  • (16:29) The value of IoT solutions aggregators
  • (18:37) What IoT businesses should think about moving forward
  • (22:38) Overcoming the biggest IoT business challenges

Related Content

For the latest innovations from Tech Data, follow it on Twitter at @TechDataEurope and LinkedIn at TDSYNNEX.

 

This podcast was edited by Christina Cardoza, Associate Editorial Director for insight.tech.

 

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Transcript

Kenton Williston: Welcome to the IoT Chat, where we explore the trends that matter for consultants, systems integrators, and enterprises. I’m Kenton Williston, the Editor-in-Chief of insight.tech. Every episode, we talk to a leading expert about the latest developments in the Internet of Things. Today, I’m talking about the power of partnerships.

There is so much that goes into IoT development that, at times, it can be difficult to even know how to get started. But you don’t have to go at it alone. With the right partners, you can find a clear path forward.

Joining me today to talk about the ways you can pull a team together is Evan Unrue from Tech Data. Evan, thanks for joining us.

Evan Unrue: Really excited to have a discussion. Great topics.

Kenton Williston: So tell me about Tech Data and your role there.

Evan Unrue: Yeah. So Tech Data is possibly one of the biggest companies not very many people have heard of unless they’re in the channel. So we are a global technology distributor, really, I mean, we’ve been around for decades as an organization, but historically focused more on, let’s say, the enterprise side of things: data center, but also consumer technology, the whole gamut, really. A lot of technology passes through our hands. We don’t directly service end customers, So we have a network of channel resellers, system integrators, service providers, and they address the market directly, and we support them in providing them with portfolio and access to skills and training and a gamut of other things to help them sell and develop their strategy around it.

And then northbound of us are all the vendors where we effectively act as an extension of their, I suppose, reach and scale in the market, feet on the street in terms of sales, supporting those partners, the marketing efforts, et cetera. So that’s Tech Data. We’ve been working around data and IoT technologies for the last five years or so proactively. And it’s been quite a journey I think, for everyone in the industry.

Kenton Williston: Fabulous. And what is Evan in this grand scheme of Tech Data?

Evan Unrue: Absolutely. So I have the grand title of European Chief Technologist for data and IoT. They give you the chief title when they want you to be a jack-of-all-trades, a master of none, but effectively what that means is I look after some of the technology strategy with respect to how we present that to the channel, supporting our sales efforts and specifically how we solutionize.

One of the challenges with technologies like IoT and AI is that you can kind of do everything with it if you have the will and the disposable income, shall we say. So, what do you do? And also it’s a really complex ecosystem of not just technologies but companies. So one of the things that we try and do is to really present those technologies through the lens of business outcomes so that our partners can easily articulate that to their customers. Natural language. I look after our solutions side of things for Europe around data and IoT, but also I get the joy of valuing technologies from tens of different technology supplies among us, to kind of see what’s out there, see what could be a good fit in our portfolio. And I get to work with the likes of Intel and Microsoft, which is, of course, a pleasure.

Kenton Williston: Lovely. You’ve made me very worried, because my title is the Editor-in-Chief for insight.tech, which I should mention is an Intel program. So now you’ve got me really worried that I’ve mastered absolutely nothing with that “chief” in there.

Evan Unrue: But you’ve become the Jack of all.

Kenton Williston: There you go. I’ll take it. So one of the things that I think is really interesting about what you’re saying here is, just like you said, AI, IoT: These concepts are showing up everywhere if you’ve got money to spend. Maybe even if you don’t have money to spend. People are thinking about where and how they might need to use these technologies. And what’s behind that, what’s driving so many enterprises to look into IoT projects?

Evan Unrue: Oh, I mean, it’s incredibly multifaceted, I would say, depending on kind of where you sit, but ultimately everything is digital these days. And if you look at kind of what drives the economy, that’s always, all the way down to the consumer, and just look at the way that people consume those technologies. It’s forcing enterprise organizations to become more real time, more data driven, more informed, and more engaging across both their southbound channels down to the customer, and upstream how they interact with their suppliers.

So, really, data is becoming absolutely critical to their strategy. It always has been really, if you look back to the last decade or so, but really now, rather than data just being something that’s generated as a byproduct of an application that’s used, data is driving the applications, which are becoming business critical. So it’s a slightly different paradigm.

You look at supply chain, for example, they’re fragile things. If you look at what Amazon’s done, they’ve kind of forced the market to become incredibly transparent to their customers, if you’re operating the supply chain at every stage.

So all of these organizations are now having to deal with the fact that customers want to know where everything is, where it came from, is it ethically sourced, is it going to come tomorrow? All this kind of stuff requires just a momentous amount of orchestration, which requires data and intelligence and all these kinds of things. So really it’s about becoming data driven and digital so you can be more agile and more robust and more engaging on both ends of the spectrum.

Kenton Williston: Yeah. Makes sense. And so it sounds like even more than, this is a thing that’s happening, you’re saying this is a thing that has been happening. Which leads me to ask, well, okay, how well have companies been doing in their efforts to deploy these kinds of technologies?

Evan Unrue: I mean, I think we’ve seen the adoption of these technologies really springboard over the last couple of years. I mean, when we as Tech Data first stepped into this space, it was kind of interesting because you had a lot of organizations that were nurturing science projects, but not really pinning a business case to them in the right way. If you look like five years and prior, unless you were a Fortune 500, Fortune 1000 company that had an R & D budget that could afford to do that and then drive these things into the core of a business.

A lot of the SME market was suffering for either not having the ability to do that, or they were creating these interesting projects, but ultimately they weren’t really driving towards the business outcomes that the business stakeholders were looking to drive for.

So I think what we’ve seen now over the last couple of years is a bit more success in companies being laser focused on the outcome. A big part of that as well is really through the efforts of the vendor community and ecosystem. So that’s the CSPs, the likes of Microsoft, the silicon companies, the likes of Intel that are driving just an immense amount of innovation development through their channels and ecosystems. And the ISVs and the OEMs that are solutionizing off the back of all of that, rather than it being a disparate tool kit that somebody has to pick up and try and figure out how these Lego bricks go together, we’re seeing solutions come into the market which are more clear and concise in terms of the outcomes that you can drive with them.

So I think awareness of what you could do and what you should do had been a challenge for a lot of organizations a few years ago. I think now a lot of the concepts have been proven behind a lot of the solutions that exist in the likes of Microsoft, Intel, and others that are really driving those use cases and solutions and ISV offerings to the forefront of awareness within the end-customer community.

So I think the last couple of years, and I won’t mention a specific causal event, but we’ve obviously been through a few things, the last two years specifically, which I think has forced companies to look at how they do things better with data and putting the right budget in place for it.

Kenton Williston: Yeah, absolutely. And it sounds like a big part of what is shifting here really has to do with barriers to entry, and the fact that if you’re a small- to medium-sized enterprise you can’t afford a huge R & D department to work on all these very complex, interconnected technologies. You may not even have the right expertise in-house to even know how to do that in many cases I would imagine. So can you go into a little bit more detail with some of the things that this ecosystem of partners, and I would say it includes certainly Tech Data, and it sounds like Microsoft and Intel are pretty important partners to you, so maybe we can focus in on the three of you together. What exactly it is you’re doing to ease this barrier to entry.

Evan Unrue: I’m going to apologize in advance for using a terribly overused term, “democratization.” But the reality is we have seen a bit of a democratization around, certainly AI, and around some of the application stack that sits in front of these IoT infrastructures. What we’re driving for collectively, we have a big push towards solutions at Tech Data, and you only have to go on Microsoft’s or Intel’s websites for two minutes to look at their efforts around driving market-ready solutions, off-the-shelf solutions, is that for the common industry challenges there should be off-the-shelf offerings which can be implemented or at least used by the average business stakeholder.

And actually all of the complex things that you can do with AI or with IoT at scale should be reserved for the, let’s say, the complex problems that exist, where the right budgets exist, and where the payoff is big enough for that, where you still need that expertise. But for an organization that has the same problems, that if you are a small freight-logistics company you’ll have the same problems typically of any other small freight-logistics company. There should be solutions there to address whatever it is you’re looking to do, whether it’s optimizing your fleet and their roots and their maintenance, or whether it’s tracking assets through that supply chain. Whatever it is, those solutions should be off the shelf and ready to take to market.

Kenton Williston: Yeah, that totally makes sense. And I’ll speak a little bit more. So the term you mentioned, “market-ready solutions,” this is a specific Intel-driven program, that there are actually these things you can acquire called market-ready solutions. And like you were saying, the thing that’s interesting about these is not just, one, that they’re sort of a prepackaged, off-the-shelf approach, but also, two, they’re things that have been proven in actual deployments already. So you kind of know what you’re getting into. And I think that’s one of the things that can be scary about these complex technologies, especially, again, if you don’t have in-house expertise to really determine what these solutions are capable of doing, knowing that they’ve been deployed by a similar business to yours I would think would be a pretty big advantage.

Evan Unrue: Absolutely. And it’s an interesting thing for an end customer to have to think about, because I think sometimes this falls into the category of digital transformation as a whole, which it certainly is a part of, and attacking that as a whole can be a scary thing for a midmarket organization if you weren’t born in the cloud, for example. So you have two approaches.

You can either come with a top-down approach and have a uniform infrastructure that all these use cases can plug into, which is a bit of a larger effort but the payoff is there. Or you can start to look at the discrete parts of your business operations where you might have gaps in data, your physical operations might be leaking money and you’re not sure how you want to get control of that, and you start to deploy these tactical solutions, and really that’s what a lot of this is for.

So yeah, I mean the Intel® IoT Market Ready Solution program has been, there’s a bit of a spectrum of solutions that are in there, but primarily it’s about taking proven solutions by ISVs, OEMs, companies that have deployed these solutions over and over with customers, and then driving all the right contents and material out to the market. So the likes of a Tech Data are being a solution aggregator. Our job is to then provide the reach and scale of getting those solutions in front of a channel partner. You can take them to their customers and make them aware of what’s possible.

Kenton Williston: So can you give me some specific examples of use cases that you’ve seen, and what kinds of solutions Tech Data has been supporting in these areas? So we can put a little meat on the bones for what these things might actually do.

Evan Unrue: Yeah, absolutely. So I think certainly some of the prevalent solutions that we’ve seen interest out in the market for in the last couple of years have been, I think, smart building is certainly a big area, and there’s a few facets to that. Number one is how do you better manage and maintain the building? How do you reduce the cost of doing that? How do you get in front of problems as they’re happening, rather than let them become lingering issues? But also within that, energy is a big topic. Certainly a lot of companies are being tasked with being more proactive with their sustainability efforts, but also being more cautious in terms of how people occupy the space when looking at their well-being and their safety in light of, let’s just say, recent events.

So smart buildings, energy, these particular areas have been real hot spots. So we are working with organizations like IAconnects, for example, just to name one, that has a great deal of experience and knowledge in this space. But also if you look at another impact that we’ve seen over the last few years is really retail in the High Street is now just looking to reassert itself in terms of being able to make sure that they are resilient on the High Street in terms of being able to deal with things. But also they are giving people more reasons to come to the High Street in terms of being engaged with customers and extending their digital journey into the physical store.

So we have companies like Ombori for example, and Wonder Store, where really they’re able to, number one, understand a customer’s journey for a physical-store environment, which is kind of like the Google Analytics–level of data: you get an e-commerce providing that back to the store so they can better plan their store layouts. They can better optimize how they’re marketing to the demographics they’re seeing, be more impactful, and also see better conversions, really supporting that growth, but also being more engaging. So, there’s a colleague of mine that likes to use the term “phygital,” which is the combination of physical and digital, which applies especially at the High Street.

And that’s, number one, being contextual in terms of how you interact with a customer. That might be through signage, that might be through interactive displays and identifying who they are, where they might sit in terms of demographics, and putting something in front of them that’s relevant, but also things like Lift and Learn technologies being able to interact with products in unique ways. So we’ve seen a whole gamut of things, also things like Smart City as well, and they’re helping improve citizen services such as parking, and all these kinds of things that become really key.

Kenton Williston: That’s really great. And one of the things that I’m wondering about here is, at the highest level you mentioned this Better Together alliance. Why have you formed this alliance, and why have you decided to work with Microsoft and Intel specifically, and how does that relate back to these solutions you’re talking about?

Evan Unrue: That’d be number one, just to mention Intel technology and Microsoft technology has kind of been ubiquitous in enterprise since year dot, certainly since the age of year.com. So the organizations have been coexisting in the enterprise space for a long time, but with regards to technologies like IoT, for example, there’s been a really, really strong focus from both organizations to be driving and promoting the industry applications around IoT, and in terms of the technology development of roadmap.

So if you look at Intel, for example, they’ve really been driving programs to push, I suppose, a simplification process through developers, through these organizations to help them build these solutions at the edge, bringing on technology, such as computer vision, using tool kits, such as OpenVINO to really help them accelerate their journey to create meaningful solutions. And you kind of pair that with all the expertise and experience that Microsoft brings, not just from the cloud, which ranges all the way from being able to do really complex and difficult but very powerful and insightful things in very bespoke environments, through to very plug-and-play-type offerings with our IoT central platforms, which is really geared towards midmarket.

You bring those together, and it’s kind of “one plus one equals five,” rather than two. It’s a very powerful combination. And because of that we are seeing a number of the solution providers that we work with have chosen Microsoft and Intel as the combination of choice. So it’s not just us that’s kind of chosen this. There’s a number of relationships that we have where Intel and Microsoft have been selected as the combination that they really feel is most powerful and impactful.

Kenton Williston: So that totally makes sense. And I guess maybe I should think about this the other way around: why have Microsoft and Intel chosen to get together with Tech Data in this alliance, and how is Tech Data bringing value to this as an IoT solution aggregator?

Evan Unrue: Yeah, so it’s an interesting question. And actually Intel have the IoT solution aggregator program, but before we’d even enter into that program, we’d started referring to ourselves as a solution aggregator. One of the big challenges historically, although improving in the market, has been trying to understand how to break through the multiple stakeholders from a technology standpoint that are required to bring these solutions together. So part of our job is to aggregate all the different technology players within that value chain, and simplify the consumption of those solutions.

And that’s been part of our strategy from the beginning, really, not just being a one-stop shop, but also adding value to that in terms of making sure that the ways resellers and customers can onboard the concepts of these solutions without having to get bogged down at the technology—that’s been at the forefront of our thinking.

From an Intel perspective, they’ve done great work fostering the developer in the ISV and the OEM community, but they’re also a little bit removed from the coal face in terms of who’s selling what, and one of the things that we do is we connect them to that through our interaction with the resellers, like we do for any vendor, really.

And from a Microsoft perspective, we’ve had a keen joint focus around IoT since the beginning of us stepping into this. So there’s some good history there. So all of the rights, goals, and motivations and vision are there in terms of them being common goals and having a common vision. But also we’ve been working with Microsoft and Intel at scale across multiple markets globally for a number of years. So we understand exactly how they want to execute this. And again, it’s very much aligned to our own goals. So I think there’s just strong alignment, strong execution capability, and we complement each other in all the right ways.

Kenton Williston: Excellent. So looking at everything we’ve been talking about so far, a lot of it has been kind of: where have we been? Where are we now? I’m curious, looking forward as you’re engaging with your customers, what some of the trends might be going forward that businesses should be thinking about as we get into the coming year?

Evan Unrue: Yeah. I mean, it depends what business you are ultimately, and where you sit. IoT can generate data and you can build dashboards with that data and gain a real-time view of what’s happening. And that’s powerful. One of the big things that we are seeing now, especially through this democratization-of-our-AI piece I talked about earlier, apologies for the buzzword again, but is leveraging AI to make sure that you are driving some insight around what the action should be from that data.

This is what we see from a lot of the ISVs that we work with. They’re focusing on what the action, the outcome should be, rather than just gaining some visibility and transparency into whatever it is you’re monitoring. So that’s a big kind of key thing.

Computer vision from a technology standpoint is really, I suppose, within the top three, two maybe aspects of what we’re seeing from a technology dimension that is the ultimate sensor. Because really it’s entirely programmable to be able to detect whatever you can detect through a camera. And these are use cases that could range all the way from traffic control and waste management and public safety from a smart city perspective, through to gaining deeper and richer insights, stronger engagement from a retail and store perspective, or simply just improving security from a store perspective or a public space, all the way through to improving things like quality and, again, improving things like safety if you’re working in warehousing environments.

So computer vision is a really strong one for me, just because it’s such a versatile technology that can underpin countless use cases, all in areas that people are asking for today. So that’s probably a big one for me.

Kenton Williston: Yeah, I absolutely agree. And like you said, I think it’s showing up basically everywhere because there are so many use cases, like you talked about earlier. For example, the idea of smart buildings and making them more efficient and safer, healthier environments. And there’s many things that go into that, but just even things like people counting. I’m old and gray enough that I remember when you used to have the little infrared beam that you would break going through a doorway. And that’s how people would know if somebody had come into the facility or not, and you could get some kind of count. We’ve come an awful long way from that now that you can do everything from telling, not just how many people are coming in, but what the demographics of the folks who might be entering a facility are, and are they congregating in one area more than you want or not.

And, like if you’re in a retail environment, where are they stopping to look at things, and just having this huge range of things you can do and just take one set of hardware, your cameras, and just do more and more and more interesting things with it over time, I think is incredibly powerful, and is really changing a lot of different businesses.

Evan Unrue: Absolutely. I talked earlier about vision and retail kind of providing Google Analytics for the physical space, just the wealth of ways it can improve engagement and planning and alignment to the customer journey and enable them to kind of A/B test and tweak different scenarios between stores. It’s just such a powerful thing, certainly in that space.

And obviously one area just to mention, which kind of feels like a no brainer for people that have been working around IoT for some time, but for maybe those that haven’t, any organization that has any asset that’s critical to their business and understanding, is it working? Is it healthy? Is it performing as it should? Is it where I expect it to be? Things like predictive maintenance and asset tracking and all these kind of things as well, these still remain really strong use cases. And whereas I suppose, again, two, three, four years ago, we were seeing a lot of pilots and whatever else, and the larger projects came from the larger companies. We’re now seeing this filter down into the midmarket again.

Kenton Williston: Yeah, absolutely. Which leads me to ask, we’ve talked about some of the hurdles folks have faced in the past, particularly around budgets. And I imagine that, looking forward, if we’re talking about new technologies that are even more unfamiliar, like computer vision, which I would imagine a lot of small and medium enterprises haven’t touched on at all to date, what some of the biggest challenges might be that companies may not be thinking about that they should be aware of as they’re getting into these areas.

Evan Unrue: I think the biggest thing for me, really, in this space is getting the support of the wider business. One of the challenges that was kind of expressed around IoT for some time is that actually the first use case provided might not be the one that delivers the whole ROI if you limit it to that one part of the business.

So really try and understand who the different stakeholders are that are going to benefit from these solutions and bring them to the table, bring them to the discussion. We talked about these retail offerings, for example, and actually it’s not always about ROI. There are some things that just have to be done. Certainly retailers don’t want to have to close stores because they can’t meet conditions around social distancing properly or understand their capacity properly. So that’s kind of a slightly different story, but when it comes to looking at digital engagement within the store and pulling their analytics, when you filter that across the business outside of just the store operators and you start to bring it into the planning side of the business, you start to bring it into the marketing side of the business, you start to bring in maybe merchandising and third parties that advertise and offer them space and data around footfall, and bring in a bit more of a community around those use cases that can benefit, then it helps the payoff become a lot more substantial in terms of justifying the business case.

So kind of one thing I would say is don’t be an island in exploring these innovations. Maybe you have to do some proof of value initially, but try and get the broader business engaged as soon as you can.

Kenton Williston: Yeah, absolutely. And going back to this whole idea about ecosystem, but I think it’s pretty interesting what you’re pointing out here is that SMEs can create sort of their own ecosystems in many cases; it’s not just about one use case, but look across all your businesses to see where this technology can help. And there’s sort of an internal ecosystem there in a sense. And then the partners that you’re working with in your facilities, there could be ways that sharing data, sharing capabilities can help both sides of the equation there.

Evan Unrue: Yeah. So I like to call it the IoT multiverse, because there’s so many different dimensions in terms of the type of companies that play here, whether it’s the connectivity channels or more in the silicon channels, or more just the cloud channels. A big part of what we’ve done is kind of brought that together as best we can.

And I suppose Microsoft and Intel power a lot of solutions out in the marketplace. And those are often realized through solution providers, vendors, ISVs that have a particular industry focus and expertise. So there are organizations, I mentioned one earlier, such as IAconnects. They have a really strong background. Their origins are in being an OTSI in the construction and the built environment space and the adhesive side of things. And through their own experience they developed into having their own IP, which really addressed the issue of, number one, being able to connect things that had been islands within the built environment before, initially maybe just to get visibility, but then to be able to drive control and automation. And ultimately you start to scale that out across companies with broad real estate. It gives them massive cost savings and benefits of being able to do that at scale.

So that’s kind of one. Then we work with organizations like Wonder Store, which are really focused on retail and providing, let’s say, that view of the customers: their movements through the physical space, how they’re flowing through a store, where they’re dwelling on certain things, where they might be looking at a particular piece of signage, how much of that footfall converted to point of sale conversion. And they’ve got a real expertise around that.

And then we have companies like Ombori, for example, which operate in a similar space, but they extend a little more into the engagement side of things. So things like interactive signage, being able to drive more actual control and automation off the back of understanding that footfall, but also being able to incorporate digital signage around the customer experience, extending the customer’s visibility of product beyond what’s on the shelf and then optimizing how they might consume that and purchase that rather than just having to stand in a queue or wait for customer service.

Kenton Williston: And so it sounds like a big area where Tech Data adds value is just connecting the dots, completing the discovery process so that you can help your customers understand even what’s possible because the scale and scope of what’s happening out there is developing so rapidly, and help them find the right solutions that will actually enable those use cases and help them be successful in deploying.

Evan Unrue: One of the things I’ve always said is that, as distribution we have one of the most privileged positions in terms of being able to derive insight from the market.

Number one, we get to see the hopes and dreams and fears of all of our vendors, big and small, and what they’re trying to achieve and what their strategies are. What their five year out plan might be, for example. And then on the other end of the spectrum we get to see what the partners are doing and where their heads are at. Where their strategies have evolved or haven’t. Who are the early adopters? Who are the laggards? How do we help them move from one bucket into another and maintain relevance in the market? And really sitting in between all of that we are perfectly primed to drive execution of a program like this, which can really bridge the gap between the vendors’ aspirations and their knowledge and their technologies and expertise, but also our understanding of those channels and how to drive adoption into the right technology spaces.

Kenton Williston: Makes sense. So Evan, I want to give you just a total wide-open question, which is we’ve covered a ton of really fascinating topics here. And I think this really has helped improve my understanding really of what’s happening in the small/medium enterprise space. And that’s amazing. And what I’m wondering is given all the complexities in this space and everything that’s changing so quickly, is there’s something happening really important that you say to yourself, “Man, I really wish Kenton had asked me about this.”

Evan Unrue: Yeah. I mean, I suppose one thing that we didn’t touch on so much, which is really at the heart of Intel’s play here, because their whole play isn’t just computer vision and powering ISVs and OEMs, they have a big push, and it’s not actually just a push it’s a pull in the market really, which they’re supporting, which is the importance of edge compute.

We’ve seen these technology architectures go from distributed to centralized, to distributed, to centralized, and back and forth. But one of the things that we’ve really seen become critical with, certainly the adoption of IoT, where you have mass amounts of data being generated at the edge and actually that data might need to be processed and impact a process at the edge. Edge computing, whilst there are still solutions that may not require it, such as asset tracking and other things, edge computing is becoming pretty critical with the volumes of data that can be created and with the organization’s ability or needs to drive action rather than just deliver data off the back of these solutions, where the cloud, whilst powerful and important for all of the things that we’ve discussed in terms of getting a broad view across multiple assets, across multiple locations, having more horsepower behind you to drive deeper and richer insights, all of that’s important, but also localization of being able to automate and drive AI locally is important.

And actually Microsoft realized that if you look at what they’ve done with things like Azure Stack with as Azure edge SDKs case, now all of those are in acknowledgement that actually some of the services they provide they need to be able to push to the edge as well as having at the cloud, which is something that they do hand-in-hand with Intel.

Kenton Williston: Yeah, absolutely. I think it gets back to a lot of what you were saying earlier about the long track record these companies have, and the accessibility of this technology, and the way that nobody really knows where it’s going to go next. It’s very helpful to have these reliable partners, Intel and Microsoft and Tech Data, who’ve been in the space for a long time and understand where things have been, where they are, and where they might go next. So with that, Evan, I want to thank you so much for joining us today. Really appreciate your insights and your time.

Evan Unrue: Oh, thank you. It’s been a great discussion, and thank you for hosting me.

Kenton Williston: And thanks to our listeners for joining us. To keep up with the latest from Tech Data, follow them on Twitter and LinkedIn at TD Synnex. If you enjoyed listening, please support us by subscribing and rating us on your favorite podcast app. This has been the IoT Chat. We’ll be back next time with more ideas from industry leaders at the forefront of IoT design.

 

The preceding transcript is provided to ensure accessibility and is intended to accurately capture an informal conversation. The transcript may contain improper uses of trademarked terms and as such should not be used for any other purposes. For more information, please see the Intel® trademark information.

This transcript was edited by Erin Noble, copy editor.

Unified Retail Digital Signage Gets Business Results

Posted on by Micah Kaplan

As consumers, we like to believe our purchasing decisions are entirely of our own volition. We don’t think about how a vibrant in-store ad entices us to make an impromptu purchase. Like when a display of glazed donuts hit with the perfect amber lighting propels us to buy the sugary confection along with the cup of coffee we intended.

But that’s the power of effective digital signage. It can seamlessly create immersive customer experiences that drive greater engagement and sales growth. Embed signage, a digital-signage software platform provider, delivers these experiences for leading brands.

The company’s powerful tools help businesses create engrossing content to get their message across loud and clear.

New Opportunities with Retail Digital Signage

Digital signage can engage customers, but many companies face challenges with managing content and devices.

Drew Harding, Sales and Marketing Lead for embed signage, says retailers often find it difficult to centrally manage digital-signage networks for many reasons. For example, different departments may embark on campaigns without engaging with the wider business. Or they may contract out to a variety of external agencies to create one-off campaigns—deploying bespoke software onto specific hardware.

Many companies have already experimented with different systems and low-cost, DIY solutions with limited success. They need a unified platform to bring all their devices together and control what content customers see and experience.

“A lot of the challenges are about bringing do-it-yourself scenarios into one centralized platform that provides a level of granularity and control while offering the ability to create those “wow” experiences easily,” Harding says.

Another challenge is the need to control multiple endpoints or locations, from a central location. And retailers need a solution that provides both technical and creative flexibility to bring on almost any resolution or type of screen technology. Embed facilitates the cloud-based management of an expanse of endpoints, allowing companies the flexibility to choose the right hardware for the desired experience.

@embedsignage facilitates the #cloud-based management of an expanse of endpoints, allowing companies the flexibility to choose the right hardware for the desired experience. via @insightdottech

Empowering Companies to Create Dynamic Content

The embed platform offers robust integration of data sources (JSON, XML, Excel for example), analytics, rule-based content playback, localized messaging, advanced content scheduling, and WYSIWYG layout design.

Customers also can access a range of analytics in the platform, such as stats for network downtime, status logs, content playback/proof of play and data related to each customer session for their interactive touchscreen experiences.

The solution has built-in security, with single sign-on, user provisioning, and two-factor authentication. Administrators can add custom user roles, create user groups and assign different levels of access. For example, they can give content creators access to media and layouts but designate device access only to their IT team for network management.

The embed platform relies on robust hardware for content playback including powerful Intel® processors to run high-resolution displays and multi-output players—seamlessly delivering content. But the company isn’t innovating just on the technology front—it also has some of the best customer support scores in the industry.

The company’s digital-signage software offers a layout builder packed with tools to create beautiful digital signage paired with advanced features like geofencing capabilities and conditional rules-based content. Companies can establish different rules to play certain content based on conditions such as weather or location, among many others through an intuitive user interface.

Engaging Digital Experiences at Krispy Kreme

Krispy Kreme is a prime example of how innovative digital-signage technology combined with great service can transform business results.

The company needed a single solution that was flexible enough to manage different devices, screen types, and customer experiences. With multiple store locations, diverse layouts, and different screens, it needed to deploy content for a variety of use cases.

“There were many different menu board configurations. There were LED screens going in,” Harding says. “And then there was the ambition for the future to create very unique cabinets and experiences related to presence detection, and LED lighting for a bit of theater around the production of donuts.”

Since 2018, embed has worked with Krispy Kreme to deploy digital signage across its UK and Ireland stores. The platform powers its entire digital screen estate, including digital menu boards, outdoor high bright screens in drive-throughs, and donut kiosk sales cabinets with presence detection capabilities.

Many stores also feature curved LED screens, LED banners, and an LED donut hole in the wall that helps Krispy Kreme share brand heritage stories and elevate its in-store experience. With these capabilities, and more, the company has been able to significantly increase like-for-like sales on some of its product lines, increase average order values, improve “brand love” scores, and efficiently manage and distribute content in-house (Video 1).

Video 1. With more than 1,000 digital-signage deployments in the UK and Ireland, Krispy Kreme uses retail digital signage for engaging customer experiences. (Source: embed signage)

Powering the Future of Digital Signage

This versatile solution is not only for retail. It can be deployed across different markets. Wembley Park in London is a great example. Wembley has used the platform to display digital art and wayfinding for major sporting events, so visitors can easily make their way around the park.

Embed signage can be used across other touchpoints and industries, as well. It can deliver health and safety information on factory floors and support wayfinding at corporate office campuses. The platform includes integration with Microsoft’s Power BI offering businesses a secure way to display their data visualizations.

While the company is helping customers reduce printing costs, improve operational efficiencies, and deliver incredible digital experiences, it’s also well aware that its digital installations all need power. Embed has focused on sustainability, carbon reduction projects, and funds the planting of new trees for every license it sells, while working toward a carbon-neutral future.

“We are constantly trying to use the sales revenue of digital signage to actually make the planet a better place,” Harding says.

As touchless experiences become more prevalent, especially in a post-pandemic world, more companies likely will turn to digital signage to engage customers, get their message across, and deliver an immersive experience. With its innovative digital-signage software, embed signage will be there to support them every step of the way.

 

This article was edited by Georganne Benesch, Associate Editorial Director for insight.tech.

Global Services Provide IoT Edge-to-Cloud Solutions

Posted on by Micah Kaplan

From shipping terminals to mines to the factory floor, companies are implementing transformational technologies to help keep workers safe and assets secure.

One global transportation company, for example, uses an IoT edge-to-cloud platform to secure automated cranes across 70 shipping terminals around the world. The company deployed an extended network to monitor the cranes as they move goods on and off vessels.

Another example is a mining company that uses wearable badges fitted with IoT sensors that can detect exposure to hazardous gases in the mine’s harsh environment. Real-time alerts are sent to a command center enabling operators to intervene immediately—proactively preventing incidents and protecting workers.

And for manufacturers, there’s a real drive to digitize the operational side of the business. Industrial IoT solutions such as real-time quality control and predictive maintenance help streamline operations, lower costs, improve energy efficiency, and increase overall business agility.

But IoT applications can be a challenge to deploy because many companies don’t have the tools, resources, or even the knowledge on where to start.

Global Thinking, Local Actions

Logicalis Group, a digital transformation solutions provider, is making applications like these possible—across industrial, transportation, smart city, and other markets. The company provides customized solutions and services for its global customers—from upfront consulting, to local implementation, and ongoing support. Logicalis sees itself as architects of change for its customers as they face the challenges of digital transformation.

“We have a long history of driving business value through innovative technologies,” says Richard Simmons, Group Technology Strategist at Logicalis. “And we are able to have a very close working relationship with our customers, to understand their culture and to make sure we’re aligning with it.”

A key part of the Logicalis value is helping customers deploy globally, and install, support, and manage locally. They bring together a focus on cloud, connectivity, and IoT devices.

The Eugenio platform underlies the end-to-end IoT solutions that Logicalis delivers. The platform abstracts connectivity, management, device security, storage, and data processing challenges. This enables asset visibility and performance analytics for applications such as predictive maintenance, worker safety, and energy efficiency.

At its core Eugenio is a set of integrated building blocks that enable customers to connect, securely collect and process #data, and turn that data into insights. @Logicalis via @insightdottech

Building Blocks for Edge to Cloud

At its core Eugenio is a set of integrated building blocks that enable customers to connect, securely collect and process data, and turn that data into insights.

Eugenio comprises three elements:

  • A modern data warehouse foundation that provides the data, analytics, and AI elements—needed to turn the data into useful insights
  • An extended network component based on Cisco technology, which provides secure connectivity for IoT and other devices
  • An IoT module that brings together Azure services, which provide the IoT functionality

“Under the covers, the platform is Azure, which allows us to provide managed services using the platform’s IoT AI, and other functionality,” says Simmons. “But even though Azure provides SaaS and PaaS services, there’s still complexity for our customers. You must use multiple services and then you need to manage and keep them up to date. And then there are the templates you provide over the top.”

On top of the product platform, Logicalis employs a three-step model to align, transform, and scale. The company engages with its customers in many ways. From running workshops, to upfront maturity assessments, identifying the devices needed, and how to leverage the connectivity they already have in place.

“We can identify any network, security, and cloud majority concerns before we even start,” says Simmons. “We don’t want to get partway through this process and find out they need to invest $1 million in their network and have their business case fall apart.”

Relying on Trusted Partners

To deliver these solutions Logicalis turns to its trusted partners. In particular, the company works with three global strategic companies: Intel®, Cisco, and Microsoft, whose products and technologies power the Eugenio IoT platform from the edge to the cloud.

“These are really strong partnerships for us. Intel provides the processing power that bolsters everything that we do, especially at the edge, while Cisco underlies the secure network connectivity.” says Simmons. “Azure is critical to delivering solutions, underpinning what we bring to market. In fact it’s driving our own transformation as a managed services provider.”

The pace of change is only going to increase as technology innovations continue to grow at full tilt. An ecosystem of partners allows Logicalis to work more holistically with its customers—building a foundation for continuous transformation. Partnerships enable the company to take a modular and service-oriented approach.

“Agility and innovation are our two key areas of focus,” says Simmons. “We have a history of driving with new technologies and supporting our customers as they deploy and drive value from the groundbreaking technology. We are the right size to support our customers to build in scale, but also have flexibility and agility.”

 

This article was edited by Georganne Benesch, Associate Editorial Director for insight.tech.