Tag: Industry

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What happens when you combine edge AI with metadata analysis in the cloud? The possibilities are endless. Previously unanswerable questions like “Are my factory workers staying safe?” or “How can I reduce losses in my retail chain?” suddenly come into focus.

Join us as we explain how to combine video analytics with virtually any data source to reveal new insights in this conversation with Chetan Gagil from the Edge AI team at Intel, and Lerry Wilson from Splunk, a leader in Big Data analytics. We discuss:

• Why edge AI is needed for a wide range of industries and use cases
• How Big Data combines video and other data to reveal hidden patterns
• How the Intel + Splunk team-up helps developers quickly deploy complex systems

Transcript

Chetan: How many people across my entire chain of, let’s say, 7-Elevens that I have deployed the solution in—how many people on an average entered without wearing masks between 5:00 p.m. to 7:00 p.m. on Saturdays? You can ask questions like that. OpenVINO is very good at providing the metadata, and Splunk can provide the time series, or the complex analytics part of it.

Kenton: That was Chetan Gadgil from Intel. And I’m your host, Kenton Williston, the editor-in-chief of insight.tech. A publication of the Intel^® Internet of Things Solutions Alliance, insight.tech is your go-to destination for IoT design ideas, solutions, and trends.

Today’s show is all about the convergence between video analytics and big data. I’ll be talking with Chetan, as well as Lerry Wilson from Splunk. Did you know that Splunk was into video? Well, neither did I, so I can’t wait to learn more.

But before we get to that, a quick note that we ran into problems with Chetan’s audio connection, so you’ll hear a big drop in his audio quality halfway through the podcast. But you’ll want to stick around for his technical insights in the second half. With that, let’s get to it!

Lerry, welcome to the show. Really glad to have you here. Could you tell me a little bit about your role at Splunk?

Lerry: Kenton, thank you so much for the opportunity and the invitation. Yeah. I’ve been at Splunk for about four and a half years. I live in what’s called our Global Strategic Alliances Organization, and I specifically focus on what I like to call innovations. An innovation area is where we take a look and identify key technologies that can be brought into Splunk and make us more valuable into new markets. So, really excited to share what we’re doing with Intel.

Kenton: Excellent. Speaking of Intel, Chetan, could you tell me a little bit about your role there?

Chetan: Sure. Thanks, Kenton, for having me here. I’m Chetan Gadgil. I’m part of Intel’s Internet of Things Group. We just call it IOTG in short. Within IOTG I am responsible for what is called as “Edge AI at Scale.” So Edge AI is basically our terminology for our artificial intelligence applications at the Edge, which is not in the cloud—things like IoT use cases.

My job is to apply my technical expertise in deep learning, or artificial intelligence, and help our partners adapt our deep learning technologies in their commercial grade offers. Once that integration is done, then I also work within Intel, as well as with the partners, to help them scale these solutions in different use cases and markets.

Kenton: Very interesting. I want to come back to a little bit of discussion about Edge AI, because, to me, it seems like not necessarily what Splunk is known for. But, first, I want to ask a bigger-picture question. When I think of Splunk, I think about big data expertise and not necessarily video analytics, which is the topic of our conversation today. So, Lerry, can you tell me how in the world did Splunk get together with Intel to tackle this new field?

Lerry: It’s actually a great question, Kenton, because when you talk to people about Splunk, they’re probably used to hearing most often we do all types of data and big data—except video or except imagery. That’s a true statement, because certainly Splunk is focused on machine data, human-readable data, and correlating that with a variety of other data sources. But we don’t look at data from a pixel-by-pixel perspective. How we got together with Intel is really driven by customers. What customers are realizing now in this digital age is that actually a huge part of their big data set is actually imagery.

Again, what Splunk is really good at is taking that metadata and the information around the GPS location—the time at which that image was captured or that video was shot—and we can correlate that and bring that into our system. Now, you add in the additional data that OpenVINO creates from Intel, where you’re now actually creating inferences, and you’re identifying the things and targets that are important to your business. That also becomes a digital signature that can move in to Splunk. Now we’re just bringing the digital side of imagery into our environment, helping you correlate it. So we’re expanding big data to actually include video now.

Kenton: That’s really interesting. So what you’re saying is essentially—answering the question that I was going to ask about Edge AI, which is that Splunk is not so much interested in storing and processing video as much as it is the metadata. Of course, the way you get that rich metadata is through doing analytics at the Edge to ascertain what it is that’s in your field of view. Chetan, I think this would be a great thing for you to speak to a little bit more. What is Intel doing in that area, and what is the OpenVINO thing that we just heard about?

Chetan: Sure. So, I’ll answer the second part of the question first, which is OpenVINO. OpenVINO stands for Open Visual Inference Neural Network Optimization Toolkit. It’s quite a mouthful, but that’s what the acronym stands for. What that means is, basically, if you think about some of the advances in deep learning. As an example, in 2018 AI crossed the threshold of being able to identify human faces more accurately than humans themselves. So, an AI can tell the difference between two faces much more accurately than humans. That was done in 2018.

What happens is these types of AI algorithms, especially computer vision, but many others too—in computer vision you need a lot of processing. In order to be able to do a lot of processing very fast, one solution is obviously you can buy more and more powerful, expensive hardware, but that hardware is typically not very easy to deploy in the field or at the Edge.

OpenVINO actually is able to optimize the complex models and just look at the essential parts, because many of these complex models, the way they’re built is by brute force computing, but the entire model is not always required. So it is able to shrink the model down into the most essential parts and still retain the accuracy that customers require. Then we are able to run it on different types of Intel hardware very, very efficiently.

Everybody, of course, is very familiar with the Intel CPUs. But then within the CPUs there’s what we call as the Integrated GPU. There is another hardware called VPU, or Vision Processing Unit, from Movidius, a company we acquired a few years ago. And new classes of hardware that Intel is now also working on and releasing as we speak that are optimized for AI. Regardless of which target hardware your model needs to run on, OpenVINO provides you that single abstraction layer, so as a customer of Intel you do not have to rewrite your application. You just pick whatever hardware is the right thing for that particular job, and your software will still continue to run with the best possible performance. That’s what OpenVINO does.

The first part of your question was, how do I look at the relevance of something like being able to integrate Splunk, and what benefit that brings to the industry. What I believe is that, say for example, Splunk is one of the most well known, as well as the most powerful tool that I have seen. Especially the types of things it does—being able to adjust data from different sources—a lot of existing integrations of vast ecosystem of experts who already know how to use it and deploy. It means that, from customer point of view, they already have a starting point, if Splunk is already there.

What OpenVINO gives us is that ability to convert different kinds of information that used to be opaque, earlier. So video data just used to be files, NVRs, and things like that—you have to actually have people looking into these video recordings to know what’s happening. But now you don’t need to do that. In real time, OpenVINO can actually turn what used to be opaque data into more transparent metadata, and you can actually then analyze.

For example, you can now start asking questions to Splunk like, “Tell me how many people across my entire chain of, let’s say, 7-Elevens that I have deployed the solution in—how many people on an average entered without wearing masks between 5:00 p.m. to 7:00 p.m. on Saturdays?” You can ask questions like that. OpenVINO is very good at providing the metadata, and Splunk can provide the time series, or the complex analytics part of it. They complement each other very, very well.

Kenton: Yeah, that makes sense. In fact, you mentioned something that I wanted to bring up, which is, I think the use of video has been growing dramatically in the last two years because of these capabilities. You’re talking about the ability to deploy analytics in a much more cost-effective and practical way.

I think that the pandemic has really accelerated that because there are all these new use cases. Things like, are people wearing masks? Are people maintaining social distancing? Even more advanced things, like contact tracing. So I’m wondering where you see the biggest need for video analytics right now, and if you see that changing as a result of the pandemic?

Lerry: I’ll go ahead and start with that response. We’ve seen a huge acceleration of some key trends that have happened during the pandemic. The greatest one, of course, is how do you protect not only the people that are employees or customers, but also the people that manually were sent out to take temperatures and do different types of things.

There’s this unbelievable connection in terms of how does technology help us now in the physical environment that this solution plays really well into. There’s an abundance of use cases, and throughout the rest of the time Chetan and I will talk about some of them. But I think the most important thing to recognize is that video and imagery plays an important part in an entire loop.

That first loop, like I said, is gathering data, or collection of information, or monitoring and observing an environment, and then being able to, again, use the inference models to actually find what you’re looking for very, very quickly and do the analysis with Splunk. But then as you go through the process and you come back out on the other end, we’re still in an environment where we as humans, before we flip a switch or turn something back on, there’s always a visual confirmation that everything has been done correctly.

I think that’s another important role that the infrastructure that people are investing in—the cameras—that they’re looking to do is just that final confirmation, for a human operator to say, “Okay, I see that things were done to assess this issue, and now I can mark this off comfortably and we can turn something back on, or we can continue with whatever process.” I think it’s the capture part of it, the observability, and the continuous piece of it, but also that taking advantage of all the technology that they gather, and then at the end making that confirmation visually of that environment that it’s safe to move forward.

Kenton: So, that’s real interesting, and honestly I’d like to dive into some of those applications. Like I said, I think there’s such a huge range of things that people are looking to do with video these days. I’m really interested to know where you’re seeing—both in terms of the specific applications, and also the industries that are deploying these applications.

Lerry: So, from a Splunk perspective this fits really well into our core area around security. Again, with this pandemic, and even before so, there’s a huge need to marry the cyber and physical data points. So, imagery is a great way to help secure—be that surveillance engine, be that observer—and that information is absolutely critical. I think that’s a foundational use case that we share already with Intel, the OpenVINO environment, and many of our customers. That’s truly horizontal. Then I think you can look across verticals, and you can start to see where other things are going in.

So, a specific industry—such as retail or manufacturing—where you’re looking at something being built and trying to make sure that the quality control measures are being followed. The combination of seeing and visualizing that information and being able to measure it against KPIs that you have for those processes—that’s really the power of this joint combination. Chetan, you want to talk a little bit about some of the other vertical use cases you guys are involved in and focus on?

Chetan: Lerry mentioned about the common use cases around security. At Intel, we also have a similar point of view in terms of the market opportunity. We call it situational monitoring, and it cuts across multiple verticals. It is not just limited to computer vision. Audio is another big use case—being able to detect audio signals and then be able to figure out what’s happening on metadata that can be generated out of classifying these audio signals. That’s another common use case.

In terms of the situational monitoring, it really has different applications in many, many industries. So, for example, in retail, loss prevention is a big use case for retail customers to be able to operate there—stores with maximum transparency and efficiency. In manufacturing, it happens to be in terms of worker movements. Worker safety is another common concern from customers that they can solve very effectively with real-time computer vision.

Another category of use cases that we are prioritizing is what we call as product inspection. Traditionally it was done visually—visual inspection, manual inspection. But now with computer vision, instead of just sampling and taking a random sample of some products, you can pretty much analyze every single product with AI, because all it takes is just more compute power. You don’t need humans to make those decisions of the quality aspect.

Another cool thing about this is the fact that with product inspection, it doesn’t have to be limited to just visual spectrum. X-rays can be analyzed, and so on, in terms of manufacturing. Another area where we are seeing a big demand from customers is analysis of medical images and such: MRIs and CT scans. Things like that. Even in these areas there are many types of analysis that computers are doing a much better job than experienced radiologists. So that’s another area where it is not as mature as some of the other things, but it is definitely getting very more mature, and they are picking up in demand.

Kenton: One thing I think is particularly interesting about all of these applications—and we’ve talked about this a little bit already—is the fact that, like you said, it’s more than just vision, in the sense of putting a camera that’s looking at the visual spectrum. It could be x-rays, but there also could be audio. There could be other things, like we mentioned at the top of the call, related to the positioning of things—GPS, or what have you.

So, I’m wondering, and I think this is probably a question for Lerry, if you could tell me a little bit more about how you are integrating these various data sources and drawing insights from a multimedia, if you will, data set?

Lerry: Absolutely. That’s what we get very, very excited about, Kenton. Again, we see this as very powerful data sources that, when combined with other types of things, will open up whole new insights and automation capabilities for our customers. What really makes Splunk unique in this area, as Chetan mentioned earlier, is our ability to quickly bring raw data streams in—any digital data, time-stamp, human-readable—and then search.

Splunk is really a search engine for machine data. So as you bring these other data sources in, and you start to query the data and information, you quickly can build visualizations or actions around the results of those searches that then continuously stream, and are made available, and can be pushed out to a wide variety of users. Everybody is looking at the same information to make the same decisions, and then you could even automate beyond that with different types of playbook.

So that unique capability to scale to incredible amounts of data and then correlate that and make it easier for analysts to investigate and understand. And now you add the capability of, “Hey, we’ve actually found things that you’re looking for” that OpenVINO brings into the environment. Now you can run back from that point and figure out—how did we get to that fault, or to that item, or to that area of interest, and what can we do to improve or automate around that?

Kenton: Interesting. So, Chetan, this makes me want to come back to you. What I’m hearing from Lerry is in the big data side you’re correlating and combining all these different types of data, but I’d love to hear a little more about on the Edge side. I think you were talking earlier about how OpenVINO compresses things down in terms of making the AI algorithms manageable at the Edge.

I think a big part of what you’re talking about there is the idea—to get a little technical—that we’re talking about mostly pushing inferencing to the Edge. So, I think across the system holistically a lot of what’s happening here that makes this work is putting the right kind of intelligence in the right place, and the right kind of data in the right place. Would you agree with that?

Chetan: You’re talking about exactly the right types of issues that customers have. Number one, moving data around is very expensive, so it is always better to analyze anything that happens close to where it happens, rather than trying to move it halfway across the world and get it analyzed in the cloud.

One example—just take a standard store. So, the pharmacy that I go to, it has probably got about 40, 50 cameras in a small area—and just that one pharmacy. Imagine a moderate-sized city: you’ll have millions of cameras, and there is no cloud in the world that can take all these camera feeds and analyze everything, move the data to the cloud. First of all, it’s going to be extremely expensive and, second of all it’s not even going to work.

So, having the analysis happen right where the data is being produced—so where the cameras are—it is going to be a lot more efficient. That’s the power of being able to shrink down the models, being able to provide more Edge-processing capabilities. We believe that over the next 10 to 15 years when this technology matures enough, 80%-90% of AI inferencing will actually happen at or near the Edge, and not in the cloud.

Kenton: That makes a lot of sense, and I think in particular if I extend these considerations, not only the practicalities of moving data around, but also the privacy concerns related to that. Inherently, if you’re not transmitting information that has any personally identifiable sort of data, in that it helps protect privacy. But, of course, there have been a lot of concerns throughout 2020 that people are starting to have about privacy, and about how public-facing video cameras are capturing their data. I wonder if you could speak a little bit to how Intel, how Splunk, are addressing these privacy concerns?

Chetan: Yeah, that’s a great question actually. There are three things that happen when you process information close to the Edge, where the cameras are. Number one, you’re not moving data where it doesn’t need to be there—for example, if data doesn’t need to be copied to a cloud where it can be hacked. You’re not opening up more surface area for attack of the data that is supposed to be privatized—number one.

Second thing is that, with AI inferencing, it’s better than humans taking a look at what other humans are doing, because inherently if an AI is looking at something it is private. Basically, an algorithm is looking at what’s happening. It’s not some human who is sitting in some security office and observing other people and how they are behaving. That’s another area.

In addition to that, the way most of these algorithms work, they work on these visual features. When people talk about facial recognition and use cases like that, the way it works is that they just classify a set of features, and it just happens to match to one person in that particular case. It’s not like a computer really knows who that person is—unless you physically map that person’s information with what the algorithm is doing. That’s the second protection that AI solutions bring in terms of the privacy.

Third, is it actually goes to the application there, and this is where Lerry can add some of his context as well. The application also then has to still do its job, which is making sure that all the other bits and pieces of correlated information that we’re managing—you’re only keeping and retaining at the minimal required bits, and not allowing situations, where if there is an attack or any information being stolen, it does not really compromise things that are not meant to be disclosed. For example, social security numbers, those types of things—at the Edge, nobody even stores those things. So there’s not even any opportunity for any attackers to steal that information if you have trust in that Edge AI.

Lerry: Kenton, that’s a great question, and it’s something that Splunk takes very, very seriously. Just to give you some context, when the COVID-19 hit we actually talked to about 200 senior executives, and without a doubt the number-one issue was regarding any type of data—whether imagery data or even machine data, IoT sensor data, any type of that data—the number-one issue was around privacy. Splunk’s roots are in security, and we understand that.

First and foremost, you have to recognize these companies are working within regional different types of requirements in terms of how data gets handled and how they manage it. Then it’s up to them to really manage that. How Splunk really helps them is making sure that they know that the data is theirs. They own the data. They are responsible for that data. We produce that data. In terms of exposing that data, we work with our customers very closely in terms of who sees what at what point, because at most times it is just data.

It’s not information about people—it’s not that information. It’s really just the data pieces that are going through. But at some point, if you’re responding to an incident based on the requirements that the customer has set in place and the protections that they’ve set in place, you want to be able to dive into that information and understand where the sources are and be able to manage it. In a physical environment that becomes a very, very sensitive issue that our customers are very, very attuned to.

Kenton: I’m really glad to hear from both sides these issues being taken so seriously. Like I said, this year has been a lot in the headlines, and I’m glad to see both Splunk and Intel really taking the right steps, I think, to address these concerns. The topic I’d like to close on is probably the most important topic, which are the dollars and cents.

As we’ve been talking about with the pandemic, there’s been a real acceleration of video use cases, and real need for all kinds of new technologies and techniques to ensure safety and security and the health of the public and employees and all the rest. But, at the same time, the pandemic has also put a real crunch on a lot of organizations’ budgets. So, I wonder if you can to talk to me about how these things can be deployed in a cost-effective manner that has a very quick ROI in these budget-constrained times?

Lerry: In that same discussion that we had with all of these senior executives, this was exactly the point that they brought to our attention as well—is, “Look, we are very constrained. If we’re fortunate, we’ll break even on our budgets for a little while.” The important thing about what we’re doing together is recognizing that a lot of the investment and infrastructure has already been made. So we’re just helping them, in many ways, leverage better use of the investments that they’ve already put into camera infrastructures—many camera-rich environments. I don’t have any exact numbers, but I think we can all agree there’s a significant amount of video that never gets looked at because of the human cost associated with that.

If we could actually demonstrate and show them the capabilities that we jointly have produced, that could be a great way for them to leverage that previous investment. I think the most important thing is recognizing that your digital journey is indeed a journey, and leveraging the data and the infrastructures you already have is where our customers are going, and being very smart about recognizing that—make these investments now, relatively small to the investments they’ve already made, and start to correlate these different silos of data. Then they’ll be much more efficient and ready to optimize and keep further investments as they move forward.

Chetan: Yeah, I agree with that, Lerry. We’re also seeing the same thing that most customers—they are looking at being able to leverage their existing investments rather than put in new investments, and how do you do that? With this type of integration, if you look at the install base of Splunk—which is vast—if you look at the install base of Intel hardware, it is also quite vast.

So, being able to then get the benefit of that to the customers by saying, “Okay, you don’t have to start by replacing or buying completely new infrastructure and all of that from day one.” You can start gradually by adding new capabilities to existing infrastructure, and the way you do that is by taking OpenVINO as the foundational AI element so you can actually leverage hardware to the best possible extent—leverage software investments, leverage expertise—that many of these customers already have Splunk. And then being able to integrate those two.

Then showing them how to create new solutions as the needs of the businesses evolve. As you pointed out, right now we’re in a stage where everything has slowed down. Mostly business…the least amount of destruction required, that’s what they’re looking at. But this is turning around. In 2021, as we start seeing more business turning around and starting to look at newer use cases, being able to create new experiences for their customers. Because one thing that the pandemic has changed is that we’ve realized now that new experiences are needed in many types of scenarios. So how do you do that? Now we are pretty well set in showing them a path to getting there.

Kenton: Yeah, absolutely. So—love to end on a positive note. Let me just give you folks some opportunity if there’s anything we haven’t touched on already that you’d like to add to the conversation.

Chetan: On an ending note, one thing I could say here is artificial intelligence is really a game-changing technology. What this opens up is a new set of opportunities. I mentioned computer vision is the most obvious one, or we can say the most visible one, by definition.

On the other hand, if you start looking at how these things are being applied in different ways, combined integrated and applied, one use case I did not mention was what we call control optimization and autonomy—meaning autonomous-mobile robots—and being able to combine the control aspects—like how something behaves in conjunction with what it observes. That’s pretty much what humans do.

Those types of integrations are becoming very, very required now, because then you can send robots into dangerous areas, or distributing medicines in a quarantined location. Use cases like that, which we never would have imagined a year ago, are now being talked about. That’s one big trend in terms of how people are thinking about these applications.

Second things is, how do you improve the ability of software or hardware manufacturers to create these types of applications? We have to break down the barriers. Instead of us having to spend years of development cycles, can we bring it down to months, or weeks, or whatever? What is the way to do that? What are some other development tools we can provide? That’s the second part—opportunity for the industry.

The third part is, how do you do it very, very cost effectively? I think your point [inaudible] customers, they’ll always have budget constraints, because it’s the way an opportunity costs at the very least. If you’re spending that “X” here, you don’t have that “X” to spend somewhere else. So how do we provide that range of different options for these customers to be able to deploy the solutions for their needs? Any solution should be able to not only scale up, but should be able to scale down to the requirement of the smallest customer.

Lerry: Chetan, that’s great. I think, in closing, the thing hopefully we conveyed today is the excitement and the opportunity around the technologies that we’ve combined. But how we get these into market—how do we make these use cases come alive? Intel and Splunk together are going to be relying heavily on a phenomenal ecosystem. Chetan referenced those manufacturers that will be able to provide boxes that are preconfigured to set these systems up into a customer environment.

It’s really also important that our systems integrators and the development community really embrace OpenVINO and Splunk together, and start to understand what is capable, and how there is an opportunity for them to play in this extremely growing market, and really come into an area that has not been explored. We’re working very diligently—and very excited to bring in the entire ecosystem from the developer’s side, from the system integrator, and the go-to-market side, to make sure that the customers not only get this great technology, but they also get it implemented correctly, they get it operating efficiently, and they can realize its value as quickly as possible.

Kenton: That’s great. Well, that just leaves me to say thanks to both of you for joining. So, thank you, Lerry, so much for being with us today.

Lerry: Thank you very much, Kenton. Appreciate it.

Kenton: And, Chetan, much the same to you. Thanks so much for your really valuable insights.

Chetan: Yeah. Thanks, Kenton. Thanks for having me here.

Kenton: And thanks to our listeners for joining us. If you enjoyed listening, please support us by subscribing and rating us on your favorite podcast app. This has been the IoT Chat podcast. We’ll be back next time with more ideas from industry leaders at the forefront of IoT design.

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Let’s face it: 2020 was a tough year. But systems integrators have a lot to look forward to in 2021. The tech industry’s rapid response to the global pandemic means that SIs can now offer their customers a wide array of return-to-work solutions. And as life gets back to normal, those same customers will be eager to continue the digital transformation efforts that were accelerated by the crisis.

Join us as we explore the opportunities with Tom Digsby, Senior Manager of the IoT and Data Solutions Group at Tech Data, a leading global distributor. We discuss:

• How SIs can position themselves for rapid growth in 2021
• How to quickly onboard complex new technologies like AI
• Why distributors are evolving into aggregators, and what this means for SIs

 

For more on this topic, read IoT Solution Factory Builds System Integrator Growth.

 

Transcript

Tom Digsby: “What is your killer feature? Why would I buy it from you versus partner X down the street?”

Kenton Williston: That was Thomas Digsby, an IoT expert from Tech Data, and I’m your host Kenton Williston, editor-in-chief of insight.tech. I’ll be talking with Thomas today about the ways systems integrators can find success in these uncertain times. Thomas, welcome to the show. What’s your role at Tech Data?

Tom Digsby: Sure. Hi, my name is Tom Digsby. I’m senior manager of the IoT and Data Solutions Group. I manage a group of vertical consultants, and technical consultants, and what our team does, is interface with partners. And what I mean, partners, is resellers. So, what we do is, we help from a vertical perspective, our resellers to understand IoT, and in a vertical context. So, it would be… Our focus is on healthcare, it’s on smart cities, it’s on industrial manufacturing and it’s also retail and commercial.

And we kind of have a little bit of a horizontal as well called smart buildings or smart spaces. Tech Data is a solutions aggregator, and we have a lot of value rather than just ordering from us. We have a solutions aggregator value where we have Practice Builder methodology. They help partners take a solution to market. We show them how to do that and take a solution to market in less than 90 days. We have the ability to bring new technology updates for solutions. We have prepackaged solutions that you can take to the market today. And we also have a vast ecosystem of partners that we can marry up different partners with.

Kenton Williston: That’s all really great. And I think there’s some really important things that you mentioned there. So, the first thing that stands out to me and in what you said just now is, the markets that you serve, including things like smart buildings. And of course, it’s not a surprise to anybody that building occupancy and all kinds of other things have been seriously impacted by the pandemic. And so, systems integrators, I think, regardless of what market they’re serving, are being asked to very rapidly deliver all kinds of new solutions, fever checking, and contact tracing and mask compliance, and all this kind of stuff on a very, very quick turn basis. So, I’m wondering from your perspective, how this has been impacting the business that systems integrators have, and where you see things trending going into next year.

Tom Digsby: Yeah. Great question. We have seen end users place their purchasing projects on hold because of the pandemic and for reserving and preserving their capital that they may need. The systems integrators have really had to shift their focus and we’ve helped do some education around COVID and the return to work, right? So, as a lot of these buildings are empty now, there has to be some safe measures put in place, and we have some solutions. We have about 20, what we call COVID return to work solutions. And these are focused on, for example, I’ll give you a few of them.

Temperature pre-screening, right? Someone comes into the building, you can grab their temperature and make sure they’re good. Telehealth, so, having a virtual conference with your doctor, air quality monitoring, social distancing, alerting as well, digital signage is another one that’s pretty critical for information going into a building that people need. I think when we’re looking at 2021, those are really going to be some of the really focused areas to look at when you’re a systems integrator. Those kinds of solutions make people feel safe. And when people feel safe, they kind of return to normalcy that we knew before the pandemic. It will take us a good portion. I would say the first half of next year, talking to companies that are returning to work and need to put some of these safeguards in place.

Kenton Williston: So it sounds like what I’m hearing is systems integrators should not expect to return back to their previous lines of business in the near future. That they’re still going to be at a lot of focus on the return to work and health and safety sort of elements. And that it’ll be strategically wise to plan for that going into next year.

Tom Digsby: Yeah, absolutely. I mean, there’s still going to be a little bit of integration work. The finishing up the projects that they have in flight already, but as new opportunities in new ways of returning to work, for instance, we had a partner approach us and said, “Hey, we need to look at the office as kind of like a hotel for the desks. I want to make sure that we’re cleaning the desk. I want to make sure that there’s not more than six people in this one area, and there’s not more than X number of people on the floor. I think he called it desk hoteling, if you will. So, those are… That’s an example of returning to work and some of the things that can be put into place technology-wise and plan for now, so that you can be ahead of the curve and actually talking and selling those kinds of systems.

Kenton Williston: Makes sense. And what about the role that systems integrators play in a solutions landscape? So, what I mean by this is, there is an ecosystem, it starts with the technology providers, let’s call them OEMs, ODMs, those sort of folks. Distributors like Tech Data, then take those solutions and deliver them to systems integrators who in turn work with their end customers. Do you see that food chain, so to speak, evolving in any way, either because of the response to the pandemic or for other reasons?

Tom Digsby: Yeah, I think we at Tech Data take our solutions aggregator role very seriously, and we think it’s more critical, and we have been more involved, more and more bringing solutions to product market rather than just buying and shipping a product, right? Our expertise is vetting the vendors, understanding the solutions that can be aggregated and brought to market. We have what we call an IoT solutions catalog. We can dive into that. We have probably 60 or so solutions in the catalog now that are by-vertical. We view it, and look at the landscaping changing a little bit, as we all know, no single vendor OEM or partner can deliver everything that one end user needs, right? We have an extensive ecosystem partnership with a lot of different types of skilled partners. They could be someone that implements or goes and does an assessment at a client site.

We can connect that partner to a person that’s trying to pitch the value to an end user, or we can also connect them with… When you’re looking at the solution, it has to have a business outcome. And if the solution doesn’t have a business outcome, it’s a science project and no one’s buying a science project. It has to have the business outcome. So the business outcome is usually analytics or AI driven. So we have ecosystems of partners that can help with that. So our value, when we’re looking at it from a distributor to how to help the changing market, especially when it comes to IoT and data solutions, is not only providing the technology, giving you a blueprint for how to deliver the solutions in our catalog, or you can bring your own and we’ll help you with that. And then helping you drive the business value through the analytics side of the house.

Kenton Williston: So that’s all really powerful. One thing actually has stood out more than anything. In a way it was just said it was a single word, which was the word aggregator. What in the world is an aggregator? How does that differ from a distributor?

Tom Digsby: Oh, that’s great. So, distributor has relationships with a lot of different vendors, right? And their primary role is to buy a product and make a little bit of margin, and ship the product. We have a solutions specialty practice. There’s four of them, four distinct solutions, especially practices that we’ve invested a lot of money into from Tech Data. One is security, one is cloud analytics and IoT are together, as you can imagine. So, those four specialty solutions practices are all focused on how do we deliver the value? How do we educate our partners, so that they can sell solutions rather than just ordering a point product? A lot of times when we get a call from an end user, sorry, a partner, they’ll say, “Hey, I need 16 tablets or 160 tablets.” And you’ll say, “Oh, well, what are you going to use that for?”

Tom Digsby: “Oh, we have a blah, blah, blah, solution that we’re going to take to market.” And you start digging into it. And it becomes part of a bill of materials that someone’s needing to fulfill a technology need, a business outcome need. So, we dug a little bit more and what it was, was they were wanting the tablets to be able to access the information for the manufacturing floor. So, well, we dug a little bit more and discovered that, “Hey, you need some sensors, you need some more information.”

And we basically cobbled together a solution for them as part of the business outcome to say, “Oh, you need to centralize this information. You need to be able to deliver it in this way. And you need to be able to see the data on those 260 screens, right? That you just wanted to order.” They were so appreciative that that’s kind of what spawned and helped the Practice Builder. The Practice Builder has been around for about 10 years for protect data. And we’re doing it in each one of those disciplines. Security, cloud, IoT, and analytics. So, our value is teaching our partners how to solution sell. What is that business value for the end user, not just buying product and shipping it.

Kenton Williston: So, I take it, this Practice Builder service you’re talking about, it sounds like there’s a whole toolkit there to help systems integrators succeed. Can you explain to me what exactly this Practice Builder service is?

Tom Digsby: Oh yeah. Yeah, sure. So, it’s two parts. One is we teach the value of what the Practice Builder is, what’s the timeline. We can help a partner take a solution to market. Whether it’s ours or theirs, and in less than 90 days, and when I say take it to market, everything is included. We know what your marketing looks like. We know what your operations within your business looks like. We know how much money that you’re going to need to supply from how do I sell this based on some business assumptions that we make.

We have a business tool that we use and what that simulator does, it sits down and understands the pricing, the cost of goods, your additive services, your third-party integration, your OEM price from all the different vendors that you’re aggregating. And it’s a one place to look at the whole picture of, “Hey, what does this cost and how much margin can I make out of it?” All in one place. So the tool and the process work together, hand in hand, and we’ve actually won some awards for the Practice Builder.

Kenton Williston: Wow. Well, that makes sense. I mean, it just sounds tremendously useful and thinking, particularly there’s two things that come to mind for me. So, one is just the aspect of how no one company is going to be the expert at everything. So, it sounds like a big part of what you’re doing is bringing to bear the technologies and expertise from a lot of different sources. So, a systems integrator can focus on their own specialized expertise, but be able to leverage the best in class for all the other areas that they’re not necessarily experts in and don’t want it or need to be experts in.

Tom Digsby: I think that’s a great summary. Absolutely.

Kenton Williston: Yeah. And then the other thing, that I think it’s really important about this to me, is just as the technology has progressed and you’ve mentioned AI a couple of times already, so, that’s a perfect example of a technology that can be incredibly complicated and doing things like detecting fevers, or contact tracing or anything like that. I mean, these are the sorts of applications that are challenging to develop. So, having a complete solution that can not only execute these kinds of tasks, but also integrate with a larger analytics framework to deliver business value. This is very, very complicated. So, having access to these sort of more complete end-to-end solutions, I imagine, is tremendously valuable.

Tom Digsby: Yeah, our partners are very appreciative for the value and all we ask them in exchange is, “Hey, we’re going to teach you this methodology, award-winning program, and all we ask you to do is, source the equipment from us, the software, the hardware, the things that are needed to put the solutions together. So that’s all we’re asking in return.”

Kenton Williston: A fair trade. The other thing that I have a question about here is, we’ve been talking about how Tech Data can source technologies from a lot of different providers, and how it’s extremely valuable, especially in these times where there’s a lot of uncertainty in the market has shifted very rapidly to have a ready-to-roll, end-to-end solution. But I imagine there’s still a lot of customization needed to address the particular set of customer needs. So, can you talk to me a little bit about how you worked with systems integrators to custom tailor these technologies for their end customers?

Tom Digsby: Oh, sure. So let me start out by going through a little bit of the Solutions Factory process. So, as we bring a solution, what we think is a great solution package together, we bring it through our Solutions Factory process, and that’s where we vet the vertical industry, the aggregation of the technology. We make sure that the business outcome is there, has it been deployed and really what’s the ROI? Because if there’s not an ROI, no one’s going to buy it. So, our solution factory processes, it really looks at a solution before we even bring it and announce it to our partners.

And we also have baked in the ability to roll out the solution with about 80% of it baked. If we look at… Let’s take equipment manufacturing, for example, okay? You can take the equipment manufacturing, you can say, I need sensors or, you know what? I’m going to talk to the equipment directly through the PLC card.

We leave that flexibility and so that, you may need to add some sensors and talk to the PLC card at the same time. So, how can you tweak the solution so that it fits the end user that you’re actually talking to? If it’s a, let’s say it’s a smart parking solution, the number of cameras, they may have existing cameras, we may need to add 20% more cameras. So, having the ability to aggregate all of that and be able to leverage existing equipment or adding new equipment, the business outcome is what’s driving the need for the solution. The flexibility and tweaking and modifying is what the systems integrators really appreciate when we’re talking about an end-to-end solution like that.

Kenton Williston: That totally makes sense. But I’m wondering about the other piece, which I touched on briefly earlier, which is the way different systems integrators have different areas of expertise. I’m betting that part of what you do is not just bringing together these technologies from different sources, but I’m betting you also help systems integrators find each other and other kinds of service providers to fill in wherever they don’t have the right expertise. Am I guessing correctly?

Tom Digsby: Oh, absolutely. Absolutely. So, when I talked to people about our different types of partners, or skills of different partners, I often draw out three circles. One on the left, one on the middle of the one on the right. And one on the left. I usually talk about implementation and assessments. So, if you need to go out and assess an environment for where should the cameras be placed, how many cameras need to be placed? How far apart are the cameras that need to be capturing the information? How are you going to aggregate that camera data? How many gateways do you need? Do you need switches? What kind of equipment is in place today? They may have a vendor preference that we need to take in consideration when we’re looking at that. So that’s the implementation or and assessment group. The resellers are, “Hey, I have an opportunity. I’m really good at creating demand. I can get face-to-face with a customer and I need things to sell.”

So, we have over 60 solutions in our catalog that are vertically focused, right? For IoT and analytics outcomes. So, those are the VARs. Those are the folks that are really good at selling and identifying opportunities, and then matching up the technology with what it is the client needs. And then on the right hand side, the third circle, is all about that business outcome. So, what is it that we need to capture? How is it that we need to capture it? It could be dashboards. It could be video feeds. It could be learning from the video itself and doing some AI interpretation of it.

It could be machine learning. So the analytics and AI and machine learning is a different group set of partners that we have. So, assessments, what I call reseller bars and analytics, AI type partners. We can cross-match. So, if an organization is looking for a skill set in any one of those three, that doesn’t have it, we have a vast ecosystem and contracts with the partners that can deliver those kinds of services. So, it’s really just a matter of a little bit of speed dating, and introducing them and saying, “Hey, guess what you’re doing? You have a need for that.”

Kenton Williston: Exactly. Exactly. Yeah. So, well, I’m wondering if I can add another access to your three circles chart here. Because we talked about sort of the, how people could be segmented in terms of their business focus. Whether there are VAR, for example, or something else, but there’s another axis that’s gotten a lot of attention over the last couple of years, which is how the sort of operational technology machine sensors or security cameras or whatever, is there any convert or more with information technology that sort of classic data center, cloud, all that kind of stuff. So, you do see that as really happening? And if so, how is it impacting systems integrators that are coming from each of those backgrounds?

Tom Digsby: Yeah, sure. So, we call it OT and IT. So, it’s the convergence of that, right? So, we look at OT and IT bringing the power to the customers, kind of what the phrase we use. That’s to say that, they can deliver the control to the customer. The IT team now has sensor and machine data, and can now look at normal processes and systems rigor and safety from a security perspective that the IT folks already have.

So integrating the OT and the IT together and operationalizing that, is very much needs that the type of partners that we have as well, to interface with them. So, when you look at the OT management aspect, they really reap the benefits of the technology that comes from IT. And when we’re talking about efficiency, knowledge, we’re looking at being able to schedule maintenance without impacting the bottom line, because now they have more real-time information from sensors and cameras and such. So, bringing that operational side of the house to bear here, is really a skill that can be leveraged almost immediately with all kinds of things that we talked about already.

Kenton Williston: So I think there, I would argue that a lot of what you’ve just described, is what I would consider to be digital transformation. So I’m wondering how you see that concept of factoring in here, especially because there’s been a lot of talk about the pandemic accelerating digital transformation.

Tom Digsby: Yeah. Yeah. I think you’re right. I think it is a layering kind of process. Digital transformation is definitely… It’s a multi-step process. And when you’re looking at improving the ability to talk through the equipment, or learn from the equipment, get the data from the equipment and then be able to autonomically monitor the plant efficiencies, for example, in a manufacturing environment. Once you have that, all kinds of things open up. When you have that base level of automation, that some plants don’t have right now, you can gain efficiencies, but more importantly, you can also create a revenue growth. And when I say revenue growth, meaning, if you have certain machine data, and you’ve gathered it over time, now that you’ve transformed your environment, you can actually monetize some of that data and put it into data sets. And you can actually offer that as a different revenue stream for the same kind of industry that the partner, the end user is in.

So, the end user can do that, or the SI can do that on their behalf. And then co-market the ability to take that data that’s learned, and sell it as a service. So, one can monetize the data after you’ve gone through a digital transformation. So, what I mean by that is, if you have a certain kind of machines, let’s say they’re more on the manufacturing floors, you’ve got algorithms, you’ve got things built in, you’ve got this kind of equipment. If someone has the same kind of equipment, they can learn from that and they will pay you for that. So they don’t have the pain and painstaking process to go through it themselves. And if they know what kind of dashboarding and operations that you’re looking at, and they can just tweak the information and the algorithms to make things better for their machine, they’ll actually pay for that.

Kenton Williston: Interesting. And that leads me to something else I wanted to ask you about, which is just the concept of agility. So, I think it’s fair to point out that we’re in some very uncertain times, obviously the pandemic created a lot of uncertainty, but there so much more. As we’re recording this, the outcome of the election is unclear. It’s kind of like everywhere you look, there’s just lots of question marks. And I think, persistence of integrators to succeed in this kind of environment, having a lot of agility is really important. And I’m wondering what you see as the keys to succeeding in this environment. Is it really about having differentiated offerings being able to separate yourself from the pack somehow, is it having access to these 90-day deployment kind of schedules? What’s kind of the key to success here?

Tom Digsby: Yeah. It’s a great segue into what I coach our partners on our Practice Builder. So, as part of that, you hit on a few topics there, having the right technology, having the right value prop. As part of our Practice Builder, one of the things we really home in on, and make the partner discern themselves is what we call, “What is your killer feature? Why would I buy it from you versus partner X down the street?” So, having that differentiation, if you’ve got 16 years of manufacturing experience, people want to know that. So, in your marketing materials, in your differentiation, you’ve got to be able to say that. And we capture that and we hone it even to a finer point in the Practice Builder. When we look at that, the kind of the Practice Builder accelerates the guesswork. You do it all on paper.

And what we’re doing is looking at the repeatable solutions because no one wants a one-off solution. You want to be able as a reseller or a systems integrator to say, “Hey, I could sell at least 80% of this over and over and over, right?” So, that’s what we call a repeatable solution. When you have the repeatable solution and all the profile and all the aspects of taking the services piece to actually deliver it.

We actually have the services piece, the cloud consumption, the hardware, any third-party assessments, all of that thing gets modeled in the Practice Builder in the business simulator. So, it kind of takes the guesswork out of it. So, if you can do it virtually and sit down with us and do that, it takes a lot of guesswork, but it also honed your message to a very sharp point, almost like a dart going into a dartboard. It takes the guesswork out of, well, what would we do here and really captures on your skills as a systems integrators. What can you deliver on that business value to the market?

Kenton Williston: Makes sense. And this leads me to ask, what Tech Data itself is doing to continue improving your value proposition and in particular, how you’re leveraging your relationship with Intel.

Tom Digsby: That’s a great question. I mean, there’s a lot of under-predictability, just like you said, right? We’re always looking at our role as an IoT solutions aggregator by gaining the insights from the vendors like Intel and the suppliers that we buy from. We look to strengthen our knowledge. We were having knowledge transfer the other day of how edge processing and what the software from Intel looks like. So, OpenVINO was one of our conversations. So, we were actually looking at some of the technologies and transferring.

And as I mentioned, I have some technical resources on the team. They were looking at it and going, “Wow, we could apply that here. And we could apply this here.” So, as our partners start to come to us, we share that knowledge and say, “You have a solutions that’s architected this way. Have you thought of replacing it or looking at it in this way, or a different way of bringing the value from the different vendors like Intel and looking at it from also from the industry perspective and looking at it from what are the AI analytics look like? What’s the processing for machine automation look like?

Tom Digsby: So, as you’re looking at it, we’re working with Intel to make sure that we’re identifying the solutions, and we’re map mapping that with the problems and the business outcomes from the catalog that we have, so that they can leverage the technology and our expertise and can really go to market. We support our partners in that way, and they appreciate our value.

Kenton Williston: I’m sure. I’m sure. So, we’re getting close to the end of our time. I’m just wondering if there is anything I’ve overlooked you wish I had asked you?

Tom Digsby: Well, I think if partners come to us with an idea, even it doesn’t have to be one of our solutions. As I mentioned that we have our solutions catalog. We have a Practice Builder. If you bring us a solution, we can still work with you. Just last week, we had a partner bring a solution to us that revolved around SAP environment. And I was like, “Oh yeah, we can absolutely do the same kind of methodology and same Practice Builder.”

And we did a Practice Builder with them last week. And so, it doesn’t have to be our technology. If you have a solution that you want to bring to market, and it has distinct business value, and some will actually buy it and you’ve implemented it, or need to take it to market in a repeatable fashion, we’ll work with you. So my little five-minute spiel here is about how we can help you take what you want to go to market with. That’s our value from Tech Data.

Kenton Williston: Wonderful. Well, with that, I’ll just say thanks so much, Thomas, for joining us today. Really enjoyed talking to you, and I’m sure our audience will enjoy this conversation as well.

Tom Digsby: Yeah, thanks for the time, Kenton. Appreciate it.

Kenton Williston: And thanks to our listeners for joining us. If you enjoy listening, please support us by subscribing and rating us on your favorite podcast app. This has been IoT Chat Podcast. We’ll be back next time with more ideas from industry leaders at the forefront of IoT design.