Podcast #206 Hyper-Scaler

 TRANSCRIPT

 Ken: Good day, and welcome to episode 206 of our Momenta Digital Thread podcast series. Today I'm pleased to host Praveen Rao, Global Head of Industrial IoT and Analytics in A*S Worldwide Specialist Organization. In his current role, Praveen is responsible for growth strategy, growth initiatives, and partner ecosystem for the Internet of Things and AIML business in the manufacturing and supply chain vertical. Before A*S, Praveen spent eight years at IBM as a Managing Director, responsible for manufacturing and supply chain solutions leveraging IoT analytics and blockchain technologies. Before IBM, Praveen served as Managing Partner, CTO, and CIO and has consulted with several Fortune 500 companies. Praveen has a Bachelor of Science and a Master of Science from Michigan Tech in Mechanical Engineering and an MBA from the University of Chicago. He lives in Atlanta, Georgia, with his wife and two daughters and enjoys reading, hiking, and playing tennis and ping pong. Praveen, welcome to our Digital Thread podcast.

[00:01:47]

Praveen: That's a wonderful introduction. Thank you, Ken, for having me. It's truly a pleasure to be here. I'm looking forward to spending time with you today.

[00:01:55]

Ken: As well, as well. This is certainly setting up to be a very interesting conversation, given many of the changes at some of your pure Cloud computing companies. I'm excited to have this. We call this the Digital Thread podcast, so we always kick off by asking about your own digital thread, i.e., the one or more thematic threads that define your digital industry journey. What are your digital threads?

[00:02:19]

Praveen: It's a very interesting question. First, I would like to think of a digital thread as a closed loop, a system or information exchange between the physical and digital worlds. The intent is to transform how products go through their entire lifecycle, in the case of assets from their parts, and how they work in operations. The main purpose of the digital thread is to ensure it enables access to data and actionable insights. With that definition, my digital thread started very early, probably even in my undergrad dates. I've always been a curious kid of the block, based on being an early adopter of technology. As I reflect upon myself, one thing that comes to me is that I always gravitate towards looking at complex challenges and breaking them into bite-sized chunks and solving them by leveraging technology from the very early days. This curiosity led me to pursue engineering, as you mentioned. Even right after my first master's in mechanical engineering, my first job was with PTC- whom you know well. I joined the professional services group, and I was a road warrior. At that time, we would travel almost five days a week when there was an inflection point. Things were moving from a 2D to a 3D world, and CAD CAM, CAE, and PLM systems were going through a growth spurt. That's a time that I consulted with so many companies on their- all the way from the whole product lifecycle, the CAD-CAM; that's when I was exposed to many of the shopfloor technologies, a lot of optimization, digitization, and automation of information. I was exposed to CNC, DNC technologies, and all. Interestingly, I was in a conference room pilot in one of those difficult assignments, and the company president walked up to me with a CD. At that time, all the software was sold through CD, if you remember.

Holding a CD, he tells me- probably, he shows me an 18-wheeler standing outside, and he says, "This CD and your services are costing me more than that 18-wheeler standing outside. Are you aware of that?" I said, "Yeah, but that 18-wheeler is not going to optimize and automate your manufacturing and operations for you." That's how I ended up at my second job at the DTD, where I was hired to automate their entire system. That was when the design for manufacturing was popular, where the manufacturing information was put in right in the design stage. Then it was lights out; manufacturing was getting much traction and much robotics. I did that, we did some- that's where I was a CIO, CTO for several years. Then after that, I started my company, LeadStrong, along with a couple of others that helped a lot of business transformation, leading me to IBM. That's where I was at the forefront of some emerging technologies, IRP analytics, and Blockchain. I was hired into Global Business Services as well also consulted with 20 Fortune 100 companies. Later, I joined A*S for the last two years, leading the Industrial IoT and Analytics.

[00:06:01]

Ken: What a great track record and experience platform you've developed that time. Interestingly, I got my start in CNCs way back when, and it's rare to hear somebody say DNC, so direct numerical control. You've dated yourself at that; you probably were one of those guys like me punching paper tape way back in the day. I enjoyed that you mentioned LeadStrong, a company you co-founded. You went from school to large companies to being an entrepreneur relatively early, which is cool. How did your experience leading your company help prepare you for your subsequent successes?

[00:06:40]

Praveen: It's interesting that up till that point, till I started the company, I was an engineer, and then I, as a consultant, I was also a technologist, solving problems, optimizing, running a cost center- was all that I've experienced. Then, I returned and got my MBA from the University of Chicago. I always had this urge to do something more and to be able to solve problems at a larger scale or build something that transcends more than one company that I was working for. That's how I started the company. The backstory behind that is that one of the CIOs I met wanted to hire me, and I said, "Would it be okay if I come in through my own company?" That's how the company started. But again, the unique thing about being an entrepreneur, as you know, is that it certainly has its ups and downs. But it opened up the aperture of thinking.

Not only are you transforming others and employing people, but also, it's a tremendous opportunity to look inside and transform yourself. I learned from that experience that the key takeaway is customer obsession because the customer is the boss. You don't have that security or anything where anybody- so, every day is like a job interview, and you have to prove yourself every day. Then it also teaches you independent thinking because once you remove that comfort zone of where your paycheck comes from next month, you think differently, right? You must prove yourself because you have to earn your seat at the table; it gives you that unique perspective. Also, it frees you up to do the right thing, think bigger, and have confidence in your capabilities. The third thing is, again, it challenges you to push your boundaries because when you're an entrepreneur, you're sometimes doing different things. Again, you're out there, selling and delivering many different things. You also learn that you often do things that may not pan out right at this sign. But again, you start planting the seeds, you start solving, and you're meeting people. The ability to relay that freedom of- to be able to think differently and think at a different scale. Those are all the things that helped me because coaching and learning have always come to me naturally; I was there. But to be able to do that at a different level and with a customer obsession and to do it with an outcome in mind, it's something that helped me all through my career afterward as well.

[00:09:30]

Ken: They often talk about a startup as being the best real-world MBA one possibly can take, and I think everything you've talked about here, from the aperture to customer obsession to earning your seat at the table, all certainly affirm this thought of an early formal education via your real-world MBA, so certainly advise that.

[00:9:51]

Praveen: That's something that I advise everybody to experience because it opens up the way a person would think about different things.

[00:10:00]

Ken: I certainly agree. So much so that my son, who is 16, I've been advising him on what his startup needs to be to prepare for life in the real world versus the academic there. You joined IBM in 2013, with a series of senior roles over eight years. Big data, analytics, predictive asset optimization, and Blockchain- a formative time in manufacturing and asset analytics. If you had to summarize this experience into three key lessons learned relative to this space, what would they be?

[00:10:33]

Praveen: That's a big question. IBM was a unique experience; I still treasure some of the opportunities and things we'd done while I was at IBM. To give you context, I joined IBM right after starting this company, running it for about nine years. IBM asked me to lead their Technology Service Strategy Line, and that was a time- everything like the Cloud was also becoming popular. If you remember, the buzzword, Ken, was very popular. Cloud Analytics, mobile and social- IBM has four different service lines for that; they needed somebody to come in and integrate them and consolidate it, go-to-market strategy for all four of them, and then come up with a solution and methodology that resonated with the customers. That's what I did; later, that role evolved into big data and analytics. Then, Watson Analytics came in, and Blockchain was added.

Certainly, we were doing a lot of first-of-a-kind- even the technology at that time was cutting edge, but many things needed to evolve. If I look back on some of the key lessons I've learned and my experiences- especially when dealing with these emerging technologies that haven't been widely adopted, execution is your best salesperson in the field. Because there is always this notion of death by POCs because, especially whenever a new, shiny object comes in, there will be a lot of demand in the market. A lot of people want to know what it is. But only some people are capable or fully invested in seeing it through. If you think about it, Watson- when that Jeopardy came in was a huge hit. But there was much hard work underneath to make that happen, so it needed to get to a certain level before you could scale. That's the risk that many companies make, especially when trying out new things. It also means that you need to work with early adopters and innovators who are willing to forgive you when the technology at its infancy and willing when you're trying to prove something. The method that we adopted at IBM that worked was called garage methodology. Essentially, you don't put all your eggs in one basket and then try to scale. Instead, you try a whole bunch of ideas in minimalistic settings and then build consensus around which one will scale based on- by looking at the potential of the outcomes in the minimalistic setting. It also means having strong stakeholder support both at the customer and internally at your company. That's essential, especially when trying out some of these newer technologies.

The third and most important thing I learned was that the ecosystem matters because you're not only disturbing or disrupting the value chain, but you must also understand how we are disrupting. Because it's only sometimes the one that eliminates the waste wins, you also need to consider some of the relationships in the value chain and how everything works together. For example, we did an interesting track-and-trace solution using Blockchain at a big global ocean carrier. It was revolutionary. It eliminated the waste in the middle because what happens in ocean freight is that sometimes the physical goods travel faster than the paper trail that is associated with them. But what we learned was when we deployed this one- so this was essentially putting out a lot of the middlemen, the freight forwarders, out of business. Certainly, they were all up in arms, so we needed to give them a path where they would still be relevant, or they would be part of this. Otherwise, you would end up making a whole bunch of enemies and be fighting a battle you weren't thinking of fighting. That's another thing, understanding the ecosystem and going in with partners, not trying to do it all yourself. Having that support system is also super critical.

[00:15:15]

Ken: All good learnings will certainly lead you to A*S in 2021. Now, I'm curious. What attracted you to the company and tell us a little bit about your remit there.

[00:15:27]

Praveen: As you know, A*S has always been a gold standard for logistics supply chain, and it is known as a place where innovations happen, and that, too, happens at scale. When A*S reached out to me to lead their industrial vertical for some of these emerging technologies, IoT and analytics- it was too good to pass up, mainly because of the size and scale that A*S operates and the kind of reach that A*S has globally. Then, A*S's ability to attract partners to build that ecosystem that I talked about earlier. Mine is a global role at A*S. In a single sentence, it is to steer the industrial IOT and analytics offerings, both A*S and our partners, so that we can bring in IoT workloads and then help drive revenue and operations as per the growth of our business. As part of this role, I'm responsible for the global go-to-market strategy, the partner ecosystem, the solutions we roll out in the market, the field readiness, and all the things around Industry 4.0 initiatives we do for manufacturing and the supply chain.

[00:16:56]

Ken: That's quite a remit that you've got. You mentioned earlier that taking up such an opportunity would have been a bad thing. As we look across the operating technology areas, the so-called hyper scalers: A*S, Microsoft Azure, and Google Cloud, to name just three- have had a significant impact on the industrial IOT space. You might have just hit on it in terms of IoT workloads a moment ago, but what's A*S's strategy here?

[00:17:27]

Praveen: A*S's strategy has been consistent, if you think about it, from its inception. A*S is driven by 16 leadership principles; there were 14, and then two got added in 2021. In all this leadership, customer obsession is the first and most important thing. Almost 90% of what we do, the services we release, come directly from the customer demand or request. A*S is not a company that goes in; essentially, we look at it and are customer obsessed. We need to look at what competitors are doing and try to mimic it. Instead, everything we do is working backward from the customer challenges, breaking those into actionable use cases, aligning those challenges with an A*S service or a partner solution, and then going ahead and helping them globally.

We have a presence in more than 240 countries. The general strategy is to advise our customers to start small but think big and move fast. Then certainly, industrial manufacturing is a huge growth area for us. Suppose you look at most of the analyst reports. In that case, they say industrial is by far the largest data producer- one of the McKinsey reports talked about 1800 petabytes of data being produced per year, which is more than double the second closest industry. Then, 90% of the data is locked on-prem, so that's certainly a huge area. That's why we are rolling out many of the solutions that you see. We have 14 IoT services and four AIML services, and many of these services are now also part of our partner solutions stack. Many of our partners modernize their stack using A*S services to make bringing data to Cloud more optimal. That's the general strategy, working back from the customer use cases and then matching them with our capabilities, either A*S' or partner's capabilities, and then bringing those IoT workloads into A*S.

[00:19:58]

Ken: Your thoughts triggered two things. Number one is a book recommendation, and that is by the same name. "Working Backwards" by Colin Bryar and Bill Carr. "Insights, Stories, and Secrets from Inside Amazon." A well worthwhile read if you want to learn some of the things that Praveen has just mentioned: leadership principles, KPIs, why they don't use PowerPoint, etc. The book is quite informative. The other is when you mentioned on-prem data; sadly, the founder of OSI PI, Pat Kennedy, passed yesterday, at least at the time we're recording this. For anybody who knows the extent of data historians and on factory floors everywhere in the world, they are mainly OSI PI System. In that regard, we've lost a true visionary and entrepreneur. Now, back on topic, we noted the downsizing of recent- I'll call it the equivalent I-IoT or manufacturing teams that your peers at Microsoft and Google Cloud. How should we think about these moves relative to the Industrial IoT?

[00:21:08]

Praveen: I look at that question in two parts. Why are there more layoffs in tech than in the other industries? Then, why are some of the two large, hyper scalers reducing or moving away from these emerging technologies? The first one is well-evident. Certainly, some people overestimated the growth and the viability of the growth over a sustained period. But no other means or measure improves productivity like tech would do. I expect tech to come back roaring, maybe after Q1. Who knows? But certainly, the tech is here to stay. Second, why are some of these hyper scalers slowing down? If you look at industrial IoT and analytics, it's not for the weak heart. The sales cycle can be long, sometimes as much as 4 to 14 months, and they need a proven solution. I don't think Google had that much of a play in IoT; they were experimenting with this field. If you look at the number of customers and the path they're given, and again, there's an outsider view, you have to have much patience to win this and have credibility and have built several years of sustained success. Microsoft- still has a play, but it's just organized differently. They overestimated some of the demand as well.

But in general, the industrial area is complex and hard. If you have been traditionally used to the IT way of working and have been through the IT mindset, bridging this gap with OT and IT takes a different mindset. That's something we take pride in because for many of the things we roll out we also have A*S, one of our biggest consumers. As you know, A*S also operates some of the largest warehouses and factories. We see the value, and we prove it. As I mentioned, customer obsession is number one. We ensure that the things we roll out are in the customer's best interest and that they work. That's the difference.

[00:23:35]

Ken: To put a point on that, I was impressed that A*S rolled out something called Monitron a few years ago. It's a full-stack, A*S -branded Predictive Maintenance Solution. I saw it as an interesting possible shot across the bow of operating technology companies because it was a full-stack or a full-stack offering from the sensor to the gateway to Cloud connectivity and done effectively on an as-a-service basis. What's the story behind Monitron, and what lessons do you think it's taught A*S?

[00:24:10]

Praveen: I think you said it earlier. The idea is that if you take a nascent field, like industrial, which has much potential, is also complex, and involves many people, how do you grow in this market? The greatest solutions are always the simple solutions, the world has proven again and again; that's what Monitron is trying to prove in the industry. The backstory is that Monitron was born out of our own struggles in Amazon warehouses. It's in the distribution center in Germany; it had 26 kilometers of conveyor belt that was moving the material across. What would happen at that time was if one of those conveyor belts broke down, it would be a huge problem for us in terms of meeting the time-to-order or delivery commitments that the warehouse had. This came out of that pain.

Monitron is a simple device under connectivity; it's a full-stack solution for those who don't know. It only requires a small number of systems, integrators, professional services, or TSI. It's a two-inch device. You can purchase the whole stack with five sensors for $725. It is magnetic, too; you can essentially take that and stick it to anything with a rotary component- rotating equipment, pumps motor. It analyzes vibrations and temperature, and then the data connect- it also comes with a gateway, so you don't need to purchase another gateway. The data from the sensors go into the gateway via Bluetooth connectivity. Then that data gets analyzed using some of our machine learning analytics capabilities. You can also combine that with more advanced capabilities, like lookout for equipment to detect the anomalies in your operations and have some clustering to figure out which one needs attention. Essentially, it's a simple-to-use solution, much needed in the industrial space, that can do several things and then give you early warnings regarding any impending disruptions in your operations. The lesson is that things tend to grow when you come up with a simple solution and market it attractively. We've seen tremendous growth for Monitron in the industry in general. We have also gotten demand from many OEMs trying to white-label Monitron and then roll it out as part of their complex stack. This is a unique case where the sensors, gateways, and insights are all part of the same solution stack and seamlessly work together. Monitron has been a phenomenal story for A*S.

[00:27:27]

Ken: I would agree, although I only know some of the performance measures behind it. But what I was interested in having- certainly, experience the space, and now, having read the book, it explains a lot about how Amazon tries things and continues to grow. Going back to your point about OT as a long-term commitment and a long-term play, not similar to enterprise IT in some senses, and so, doing these full stack solutions, in many ways, is, I think, almost emblematic of that long-term focus that Amazon has. Thus, it's unsurprising that you guys have kept up even though your competitors have. I have to ask; A*S recently rolled out several new industrial and manufacturing-oriented offerings here. What are some of the more notable in your perspective?

[00:28:18]

Praveen: There are always things happening in A*S. In general, some of the new offerings, I can bucket them in two broad categories. One is the A*S services that we roll out, which could be either used by builders as Lego blocks so that they can put together a solution aligned to a use case or, essentially, a solution that we put together by leveraging some of these services that align with the use case, either working with a partner or as a guidance that we provide to our customers. Some of the notable ones that I could think of off the top of my head are- In April of 2022, we GA'd A*S TwinMaker, which is a digital twin solution for the industrial market. We recommend several use cases, and people are also using this in many different ways that we have yet to think about, especially when you combine this capability with our partner solutions. Matterport has 3D visualizations, even within buildings or industrial spaces. It has been an amazing journey. Then, we also have- as you know, we recently announced Bedrock, which is generative AI, which we expect will be interesting, seeing how many use cases and how all our customers would use it. It certainly has the capabilities. We also released RoboRunner, it's another industrial service to help orchestrate the robots within the warehouse or four walls, for that matter of fact, based on other things. Lastly, we also released a new supply chain solution called Galaxy, which is expected to grow and has gotten tremendous interest from the field.

[00:30:13]

Ken: Excellent. Lots of great offerings, and as you said, it's always a constant stream of new value-added offerings coming, so we will look forward to those next ones as well. In closing, I always like to ask where you find your inspiration.

[00:30:29]

Praveen: That's a big question. The inspiration comes from many different places. The three places I can think of are from my travels, I'm just meeting with customers, partners, and even colleagues- that's a lot of wonderful stories and very talented people I meet. I am inspired by listening to them and hearing their stories and journey. Then the second area is my own family.

As you know, I have two little girls. One is going 15, the other is 12. They inspire me to be the best version of myself. When I look at myself in their eyes and in their shoes. The third thing is, as I mentioned, curiosity has always been a driver for me. I'm always listening to something or reading something professionally and others to help me stay grounded. I'm also a long-time meditator, so this is my advertisement for people to meditate. The world will be a better place if everybody in the world meditates. If you're looking for certain books, I highly recommend them- on the professional side, I don't recommend reading many books. Instead, I suggest reading fewer books and reading them multiple times so that you ingrain the learnings from that book and you can absorb those things. That's been my approach. I select certain books and often read them repeatedly to absorb them.

One such book is "Power of Now" by Eckhart Tolle. It's a wonderful book if you still need to read it. It teaches you that some of the faculties we have in our mind, which are our biggest assets, can also be detrimental if we don't use them right and could be a source of negativity, anxiety, and stuff like that. Just realizing all we have now is wonderful to help ground us and do more. Another thing is "Thank You for Being Late" by Thomas Friedman. It's a wonderful book on how Mother Nature is resilient, how things operate, and how Mother Nature has guided innovations all through. If only we learn from that and then operate, our approach would be very different. That's what I do when I have free time. Certainly, just people. People are the biggest source of inspiration.

[00:33:16]

Ken: Two great book recommendations. Interestingly, I'm not a big fan of re-reading what I've read before, but I have read the "Power of Now" at least twice. Anything that Thomas Friedman writes is always worthwhile and a re-read, to your point, so I appreciate that. Praveen, thank you for sharing this time and these insights with us today.

[00:33:39]

Praveen: Thank you, Ken, for having me on your show. It's a pleasure speaking with you, and I look forward to our future interactions.

[00:33:47]

Ken: As well, and it's been a pleasure speaking with you. This has been Praveen Rao, Global Head for Industrial IoT and Analytics at A*S. Thank you for listening, and please join us for the next episode of our Digital Thread podcast series. Thank you, and have a great day. You've been listening to the Momenta Digital Thread podcast series. We hope you've enjoyed the discussion, and as always, we welcome your comments and suggestions. Please check our website at momenta.one for archived versions of podcasts, as well as resources to help with your digital industry journey. Thank you for listening.

[The End]