Remaking Industry Podcast: Demistifying Data Analytics & Simplifying Your Successes

Dec. 14, 2021
We connect with Uptake CEO Kayne Grau to dispel the myth of complexity with data analytics and explore ways to keep it simple.

In this episode we connect with Uptake CEO Kayne Grau to dispel the myth of complexity with data analytics and explore ways to keep it simple, regardless of the size of the company or the mountain of data generated by the assets. 

Chris: Welcome to the "Smart Industry" podcast, Remaking Industry, where we dive deep into the tools, techniques, and technologies that are accelerating digital transformation. All right, welcome. My name is Chris McNamara, Editor in Chief with "Smart Industry." Today, we are looking at keeping things simple. We're chatting with Kayne Grau, who is as of this recording in mid-October 2021, newly the CEO of Uptake. Kayne, thanks for joining us.

Kayne: Hey thanks, Chris. I appreciate you having us.

Chris: Yeah, let's get to know you a little bit first. Tell me what's the last concert you went to?

Kayne: Cody Jinks, about three weeks ago. I got really fortunate to... I think it's my first concert actually in a year, but I'm a very, very big country fan and flew into Richmond to see a buddy and saw Cody Jinks at the Richmond Raceway.

Chris: Perfect, perfect. Talk to me about this industry that we're in. What's the most appealing thing about this industry? Why do you work in this space?

Kayne: Well, you know, a couple of things. Number one is I'm a data guy at heart, and I've kind of built my career around data. And I just am absolutely intrigued about how, I would say, greenfield this entire space is with the OEMs building this newer machinery that has, obviously, an unbelievable amount of data coming off now and what we can do from a preventative, what we can do from a predictive, what we can do from emissions, elimination, or clean energy perspective. It's fun. I mean, you know, I've been in health care, I've been in retail, been in insurance, been an automotive, and it's not to take away from any one of those industries, but I just feel like in this industrial intelligence space, it just feels so cutting edge, so new, and, you know, what we're doing it's really thought-provoking. And I think that it has... You know, what I love about it is from a safety and from a sustainability perspective, there's a lot of good in it. It's not, you know, just for-profit perspective. There's a lot of really good things that we're doing to help the world. So, you know, it's just been really fun, you know, learning the industrial intelligence space.

Chris: Yeah, yeah. You know, I mean, that is something that is always in the back of everyone's mind, I guess, but it's interesting to hear it stated so explicitly. In addition to boosting profits and revenue and things like that, some of the main missions of all these smart approaches is reduced emissions, cleaner operations, safer operations, putting people in dangerous spots less frequently or less often than they need to be, all that good stuff. And sometimes that gets overshadowed by some of the more common wins associated with some of these smart operations, but those are no less valid. It's interesting for you to say it that way. Give me a quick update. What is Uptake? And how do you envision that Uptake changing under your watch? What do you plan to do a little differently there?

Kayne: Yeah. So we're a 7-year-old company. And we're the leader in the industrial intelligence space. And, you know, essentially, what we believe is that machines across, you know, heavy industries are more connected than ever before, just what we were talking about. And these sensors generate just an enormous amount of data that live in tons and tons of places across the customer's enterprise. So we take the data to help heavy industries solve, basically, their toughest challenges, which is insight into the performance of the machine. So, we serve oil and gas, chemical energy, transportation, manufacturing, rail, and the equipment dealer space from our Caterpillar days. So, yeah, we're basically what we were just talking about. We're enabling businesses to, you know, lower their maintenance costs, improve the reliability, mitigate risk, and then obviously, you know, what's dear I think to my heart is enhancing the sustainability.

Chris: Yeah, yeah. Excellent. You know, we always celebrate the technological advancements and the amazing tools that are at play in this space and the cutting-edge smarts that are behind everything. But central to making these ideas work is simplicity. I know that's... In your bio you talk about part of your mission is keeping simplicity central to industrial intelligence. What do you mean by that? And why is it important to you?

Kayne: It's unbelievably important for a number of reasons. Number one is, our customers do not have the time nor the patience to look at really complex data and go through endless tools and endless sources of tools to make business decisions. I mean, in most cases they need the business decision made in a prescriptive manner, let alone a reactive or proactive manner. And so, I've got 20 years of really in-depth product experience. And what I've always found is never ever assume that you know the answers and within the four walls of your building, and you have to be out talking to the customer, understanding the customer need, finding pattern recognition, and how do you make basically, every attribute on the screen important? And so, what I really focus on within the company is every pixel, you know, every letter being the most important thing on the screen, and the stuff that's really sort of irrelevant is just removing it.

And so, the goal is that I wanna basically take your asset, I wanna solve your business problem in the least amount of clicks as possible, and I want you to be so excited by the tool that you will be the advocate not only of your plant or, you know, your fleet, but you'll be the advocate for the entire organization. And you want to see this across the enterprise. And so, customer loyalty, customer engagement, our NPS score, all of these things are really, really important. But it all goes back down to the simplicity. It's like, I always use the analogy of like, which I've just always been a great fan of or, you know, even Apple in the greatest sense of, you know, I open the box, I bump two phones, my backup starts, my resource starts, I'm using my phone within 10 minutes. It's like, I just shouldn't be calling Verizon, I shouldn't be calling Apple, I shouldn't be going on the internet to find, you know, how to do something. Like, it's that simple. And so, simplicity is...

Kayne: I'm just gonna say like, so for me it's, you know, how do we just make the product simple?

Chris: Does industrial intelligence lend itself to that simplicity or is it a challenge? Do you have to very purposefully, very strategically, you know, establish your networks and your processes to keep the complexity at bay?

Kayne: So, it's a great question and it lends itself to probably the type of industry and the maturity of the industry. So, there's industries, I would say, that are set sort of in the way where the obtaining of the data and getting the data can sometimes be a little more complex where there's other industries where the data is available and the configuration can be done in minutes and hours versus, you know, a day, or a week. What I will say is that was really the crux behind obtaining, ShookIOT back in January was their ability to obtain data in those more complex places, get the data into either their environment or our environment, and allow the manipulation of the data, and to basically answer their questions in almost a real-time manner. So the simplicity, I think, really comes with the toolsets that the company is offering.

Chris: Okay. You also use a phrase to talk about revolutionizing the way we use industrial analytics. What does that mean? And, you know, let me play devil's advocate. Isn't industrial analytics so young of a field that we're not ready to revolutionize things, or is that statement not true at all, or does the speed with which this field changes prompt, you know, considering overhauling it much more frequently than other approaches?

Kayne: So, I think it really depends on how you look at the world, in the sense of's called the industrial 4.0 movement. You can look at, you know, what we're doing at Uptake is one, you know, one piece. We're a cog, you know, obviously, in the larger wheel. But here's what I get really intrigued by is stretch the brand a little bit on everything that's happening around the human workforce optimization, the asset optimization, the workflow optimization, and when you bring that all together, and you really start to look at some of the revolutionary things that are taking place, holistically, I think that's where like, I think that's honestly where the magic happens, where a lot of the fun happens. And so, you know, you were asking about, like, where Uptake is going. And not to jump sort of to that question, but I think we' know, we've embarked on this industrial intelligence in what we're doing with data.

But I'll tell you that I'm keeping a very keen eye on the human asset optimization because in some sense if the humans aren't doing what they need to be doing, and even with technology and the advancement of technology and you can say, you know, well, what should the human be doing in the manufacturing plant in 5 to 10 years, well, who knows? But I can tell you that they're just as relevant as our asset optimization right now. So if I think of that, I think of what we're doing at Uptake. And then I actually think downstream in automating case workflow, and be able to build cases on the fly, and be able to understand, you know, is the part available today, or do you need to order the part, or do you have too many parts, or can we provide you the intelligence to tell you like, "Don't buy this many parts in the future," that downstream aspect, none of it is connected? And I think Uptake is in this prime place to connect a lot of these dots that I just talked about in the 4.0 space. And so, that's where I sort of dream about, like in three to five years is we're really, sort of this large connectivity tissue of all of these different things that are happening within the 4.0 space.

Chris: Interesting. What's a misconception? What's the most common misconception you encounter when dealing with clients about industrial analytics? Do people overestimate the complexity? Do people underestimate the amount of steps that need to be taken to make insights truly valuable? What do people get wrong?

Kayne: I think the change management aspect is probably front and center, right? How hard it's going to be to change. Changing the process, in trusting the data and saying, well, you know, how much data do you have? And how accurate is your predictive models or your preventative models going to be? And so, you know, that's a big leap of faith, right? Like you've done this thing, you've done this process the same way, whether you have a tool, you don't have tool, or you have internal data that you love and trust, what does this company coming out of, you know, out of left field? Like, what do they, like, why do they know my business any better than I do? So building that trust and getting them to adopt a different process in a different world takes time. And I've seen this across multiple sectors. And I'm confident that we will get the mind shift to happen, but it doesn't happen overnight.

Chris: Yeah. We are pretty familiar with what you guys do. I'm always intrigued by different projects you're working on. What's the most unusual application? What's the most unusual client that you or your team has worked with? I love the stuff that you guys do with locomotives. I feel like that's such an interesting combination of like, kind of, an antiquated asset with a modern operation approach. What's the most unusual situation you've worked on recently?

Kayne: Hard to say on unusual. I mean, I would say, you know, I would tell you what I am learning a lot more about maybe than unusual is the work that needs to be done within our federal practice and our federal sector, and really around the Environmental, Social, Governance, the ESG type stuff. And what I've learned is almost that it's like Y2K all over again, that there is a time clock right now around climate change, clean energy, anything really relating to ESG, and companies are gonna be held accountable to it. And I think in the most simplistic form is let's almost pretend like it's a credit score, is that they want solutions where you push a button and it gives you a score, and that's gonna basically be like, are you inline or not with whatever, you know, let's just call it at the highest level with whatever the social governance is going to say that the score needs to be?

And I feel like within our federal sector that's the type of stuff to me that it...we're not even remotely thinking about today. And why I say we, I think the world is not necessarily thinking about today on the level that we need to be thinking about within three to five years. And then I would say, the one other area I would definitely say is cyber. I mean, we are heavily focused on integrating various different cyber-type solutions into our product. I don't think you can be disconnected. And I think what some of the companies are doing now around cyber in keeping our grid safe, and keeping our energy sector safe, for all intents and purposes, keeping the world safe, that goes into the tools no different than a component having to break. We wanna know, you know, is there malicious activity happening on a machine. Is there, you know, how is penetration testing getting done? So I think having the ability to bring that type of data in for decision making is no different than anything else that a company is worried about right now.

Chris: Interesting. Very cool. Kayne Grau of Uptake, thank you for joining us on the "Remaking Industry" podcasts today. Very sharp stuff.

Kayne: Thanks, Chris.

Chris: And to our listeners, as always, we remind you to go out and make it a smart day.