Learn how IoT-enabled analytics provides the intelligence needed to facilitate faster decision-making at the every level, in every type of organization.
Bjorn Andersson is the Senior Director of Global IoT Marketing at Hitachi Vantara. He is a leader in IoT and industries marketing, accelerating go-to-market (GTM) for innovative solutions that combine advanced data analytics, artificial intelligence, IoT and deep industry expertise to ultimately enable safer, smarter and healthier societies.
Jeremy Brisiel is Chief Creative Officer at Swelloquent Arts. An award-winning writer, producer and host, he has more than 20 years of experience delivering creative content to audiences around the world.
He is an expert on IoT edge technologies (software and hardware) and use cases to drive messaging and highlight differentiation through compelling content and sales tools that inform and highlight the value of our solutions. Working with plant managers, controls engineers, reliability managers, CIOs, CTOs, CSOs and CDOs, he helps them through the adoption curve of digital technology, to drive meaningful change in their operations.
This is the studio next podcast. I'm your host, Jeremy Brisiel. I-O-T are three letters that have had a lot of impact in the last several years and will continue to do so. The internet of things is filled with things. That's the technical aspect of it. We're talking much more about it in much more detail with two great guests today. Let's meet them. Introduce yourself and let us know what you do.
I'm Bjorn Andersson, I head up marketing for our Lumada applications, which is mostly in the IoT space here.
All right. Welcome Bjorn.
Hi, my name is Steve Garbrecht. I'm the Director of Product Marketing for IoT solutions, especially as it applies to edge solutions for IoT.
Well, those are both great parts of this conversation. Hitachi's Lumada solutions for digital innovation and Lumada Edge. I mean those are enormous parts of NEXT 19. But, before we get into of details of that, let's get into what IoT means. I think it's almost like DataOps in some way. Everybody has a slightly different point of view. IoT, Bjorn, what is it?
There's a lot of hype out there with IoT, ranging from sort of what you can see in your fridge when your home ran out of milk or something that's more on the consumer side. Hitachi is more focused on what really makes a difference in business and for society. So, it's really the way I think about it. It's the link between the digital side and the physical world. So, it's really taking those sensor data coming in and making sense of that and also using that to make decisions and impact the physical world, as well.
All right, well that's a great description there. Steve. I need to change your add. No, I wouldn't change it. I don't think that'd be a fair thing to do. What else did you add?
Yeah. I think Bjorn definitely got it right. It always involves equipment. It also involves playing some more IT type technologies to it. So: analytics to be able to really massage that information coming from that equipment and provide more visibility and more insights to what actually is going on with devices and with operations and things. Because things can be many things. They can be a process, they could be a physical piece of equipment. Even people can be things within it, within this construct.
I think you speak to it and we can talk about that right away, which is, with it being so device driven and machine driven, it's happening fast. Like, if there's always a human element, there was always a human element. It would slow it down a little bit. There would be some person who had to connect these things, but now everything's connected automatically. How fast is this happening?
You know, you can look at fast in multiple ways, also. It's fast in terms of how fast the data comes in and much faster than the human can really handle. But, it's also fast in terms of how the technology is moving forward. IoT really relies on multiple different technologies. The compute power, the networking, what you can do with AI, for example. What we're seeing is all of these are feeding on each other. So you're accelerating the rate of development and that, in turn, impacts the outcomes that we see. So, the end result is sort of exponential in terms of how it really impacts you, But also, going back to the data: Really all of it is about data. And so it's coming in much faster than the old ways of structured data and so on, from all of those sensors where you can have thousands of sensors and it's thousands of data streams coming in that you need to do something with. And that's especially apparent on the edge where Steve is working,
Steve is out there on the edge.
I'm always at the edge. What's interesting too is, we're seeing a lot of the data is coming in really quickly and there's a lot of it, but also we're trying to get to the point where we're going to give information to people more quickly to make decisions, right? So, they don't have to think through things quite as much. They can be given an answer, a prediction of something that might happen or instructions on what to do based upon something that's going to happen. So, they can very quickly come to a resolution as part of using the technology.
And that is ultimately the goal, right? We all recognize that there are way more things connected. There's way more data coming in, there are way more ways to process that data. But if you don't come up with a result, then you're back to just being stuck, anyway. If it's still just a coin flip personally, then I could have done that without all that stuff between me and the result. So it's great to see that that's exactly part of it. To some degree, there's an enormous amount of hype … like it's all going to be connected. You'll know where your milk is, you'll know the best way to get to the traffic to go get to the store. Your Apple Pay will do it with the phone and it'll all be tracked and then you're budgeted automatically and you'll have the milk paid for the next time you go, which should be great. That's exactly what's going to happen. But I think that's a large amount of hype versus reality. Where are we in that cycle?
Yeah. So we've been on that hype curve. We're very high on the hype curve. I think we're getting into reality now where we're starting to see they're really, impactful outcomes from IoT. And, like I said before, we're focused on those kinds of outcomes from a Hitachi standpoint. So what you can do for business in terms of being more effective, more efficient in what you do, but also impact important goals for both people and companies out there.
And that is ultimately the goal. The customer's end goal is the goal of this. So if the hype outweighs the reality, we're in big trouble.
Exactly. On what we're showing here at the conference, also, is those real goals that we have achieved with customers. We have had multiple customers here talking about what they had done with Hitachi in this IoT space.
Yeah. We're also trying to kind of fine tune it a little bit on how we're approaching IoT projects. A lot of them so far have been just pilots, right? People started off with an idea and kind of tried to take it to the next level and some of them, a lot of them actually, failed because they haven't been able to scale that overall solution to an entire organization or across their enterprise. And a lot of that's because they don't start off with the people to really understand the capabilities that the organization has. Do they just need some basic visibility? You know, start collecting data and looking at it in some kind of a context. Or, are they farther advanced where they've already started to do some kind of analysis of things and now they need more advanced analysis? They need to power their data scientists and their data managers to do more things as part of that. So, you know, it really is important to understand who you are as a company and what your capability is as you approach instituting IoT.
I think that's a really good point because you're sort of starting with the outcome. What are we really trying to do? And then you're sort of backroom with what kind of technology they are going to use, who are the people involved and how do you operationalize this? So it's not a science project. It's actually something that you can have as a regular part of your process going forward.
Regular part of process, not science project is a great way to talk about the cycle of it because that's the most humane way. And yet we have to deal with people in a fundamental way. And so I want to kind of veer that way now. Because Steve, you mentioned … I want to touch on that a little bit more. So there are ways to be successful with IoT, but there are barriers to that success and it comes in many different forms. Steve, maybe you can talk more a little bit about that: What do you see as the biggest barriers to success with IoT?
What people are discovering, I think, around IoT … I think it started off kind of as we've got all these sensors and now we can enable equipment to provide more information about how it's operating, how to make it better. And we have the cloud where we can start to scale up and scale down very quickly. We can do a lot of computing up in that environment. But the reality is that in between those two things, there are networks, there are different organizations and there are different data storages that need to happen, right? So, that's where the edge comes in, where they're finding that they don't have to take all the information, they're gathering off this equipment if it's in the field or if it's in a plant environment, for example, and lifted all up to the cloud. They don't need to. Because what they can do is start doing some analytics right at the edge and enable the operations teams at these critical locations to be able to react to things very quickly. And then they can convert that data over to maybe more summary information or combine it with some other information and send it up to the enterprise where it can further be massaged and leveraged as part of that. So as part of that, there's a lot of costs of moving data, right? So, you know, part of what we're concentrating on Hitachi is putting the right technology solutions in place to manage the data at every level and to enable decision-making at every level for every type of organization.
It's remarkable as we speak about how the success and the challenge are one and the same. The success can be technology and people and the challenge is also the technology and the people, right? If the organizational culture isn't ready for this, then there's no device that is going to convince them that this is the way to go.
Exactly. And sometimes you have to actually have to start with that. Do the change management up front, figuring out sort of how, if this project here is successful, how will I change the way I do my work going forward.
With that in mind, what stage would you say this market segment is in? Where are we on the IoT segment?
I think it varies depending on who you talk to and what kind of expectations they have. Some people have advanced quite far on this path in terms of instrumenting their environment, getting the data, starting to analyze it and actually using that to make many decisions. Others have not even got to a point where they have connected machines or devices out there. So they need to do that first. And then others may actually have more data than they think. For example, in manufacturing you may have data associated with one machine from a certain vendor and you have other machines from other vendors and you have data in silos. And one good way to get started is to get the data out of those silos to start to analyze it across. So it's a good first step to do that.
It makes sense.
Yeah. The other thing is, the operations teams are starting to discover more capabilities coming from it that they weren't aware of. So, some people think that they're further along down the path, but they may only just be scratching the surface of what's possible. And when I talk about that, you know, things like massive data stores of information and starting to bring in new types of data, like audio and video with time series data … that they're used to. Being able to really run analytics, real powerful analytics to sort through these, these types of data to provide intelligence about what's going on. And then, making them even smarter where it's not just about predicting something. It's actually telling me exactly what I need to do, like asking Alexa, what should I do Alexa? And that AI becomes smarter and smarter. So, you know, you might ask also ask the question, where are we in the state with AI today and where will we be 10 years from now? You know, I think we're just starting to scratch the surface.
Yeah. And the other thing we're starting to see: We're often engaged with customers with a certain problem we were wanting to solve, whether that's in manufacturing or maintenance or something like that. With our solutions today, we can actually mix and match between the different solutions we have. So, for example, if you go into manufacturing area and want to optimize the state of the actual manufacturing process, we can bring in our video solutions also into work or safety for that same customer. So, we can expand the scope, but also give more value to the customer.
Right. Well, I mean that's, that's a remarkable description of the various places in organizations are in the segment rather than the whole segment being, it's so mature, or not mature level. It's really the individual relationship to that. So with that in mind, the easiest question you'll have all day is: What do you do? Like somebody was somebody like: You know what, this makes a lot of sense to me. I'm at this point, I have no connected devices. All connected. I'm in a sea of data. It's not a lake or a swamp or a delta. It's a giant sea and I can't get out of it. What do you say? What, do you recommend as steps for someone who wants to get involved?
The way I usually put it is that you need to look at: What are you trying to do? What's a business outcome you're looking for? And then from there, figuring out: What kind of data do you need? What do you have already? What do you need to do to get the data you need for those kinds of outcomes? And then figure out: Do you need to deploy an AI solution here or is it some other type of data analytics there? So start with the outcome and then figure out what you need in terms of data and analytics behind that.
Yup. IoT is definitely in my mind, not a rip and replace type of an approach. A lot of people have systems in place today, operation systems, automation. They've got some data already in, inside of their systems, so we can add to what they've already got and extend it as part of that. In many cases, it's a hybrid solution between IT and OT to actually make that happen. Right? And then also when you start to apply technologies, what specific technology do you need to use to leverage that? Are you, is it networking you need, is it a data management, data storage, is it analytics? You know, it's add to what you already have and build out those specific use cases like Bjorn was talking about.
Yeah. I would add to that. Also, teeing off from your written not rip and replace. It needs to be connected with your existing business systems. So, it can't be another silo out there and you'd do something different with IoT. It needs to connect it with your business. So, you can deliberate on the outcomes you're looking for.
Oh, that's great. Thank you guys so much for your insights. It's an enormous topic in three little letters in IoT, and I think you guys have done a great job of laying out a lot of different strategies for how to get involved and how to solve what you need to solve. So I appreciate that. Steve, you weren't, thanks for being with us.
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