Podcast #10: The Coming AI-Era of Connected Industry

Hello everyone and welcome back to our Momenta Edge podcast. This is Ed Maguire, Insights Partner at Momenta, and we like to bring you some of our favorite thinkers and entrepreneurs, and today we have the pleasure of Dale Calder who is the founder and CEO of RevTwo, but Dale also has an amazing history as a pioneer in Connected Industry, in working with industrial technologies. Dale, it’s great to have you on. 

Nice to be with you Ed, and the rest of the guys out there. 

Absolutely. I think it would be great if you could provide just a bit of your background. I think what a lot of people may not appreciate, they may know some of the work that you did immediately before RevTwo, but you’ve got a really interesting background and I’d love to hear a bit about what got you into technology, and the path that you followed that has brought you to RevTwo today? 

That’s a long path, so I’d better give the abridged version! I’m a technology guy, my background is all about technology. I like technology, not from the perspective of just the technology, I’ve always like technology from the perspective of what you can do with it. So, I fancy myself a little bit as an inventor, as a dreamer of new ways to do things, and technology is my canvas. My background, I started at the advent of the PC, I missed the mini-computer revolution at the time, but I came into the industry when the PC was starting to gain some prevalence and dominance. My background really was about initially applying that concept to distributed computing, to different areas, and industrial was the area that we operated on first with that. 

Then from the concept of processing power being made available, I got really heavily into the concept of information being made available through connectivity. As I started to get into the more entrepreneurial side of my activity, I founded a few companies that were really centralised on the concept of connectivity. My first one was a company named FactorySoft, we were the first guys to figure out how to make OLE for Process Control, known as OPC work, and we built a toolkit that was at one time used in almost every factory driver out there. That one was really about helping companies better manage connectivity from real-time assets into human hands, HMIs.  

Then as the internet started to take root, I saw an opportunity to not just leverage connectivity for moving information like say 10 ft, but to move it across the globe, and so I started a company called Axeda that was a pioneer in the Internet of Things, and between myself and my partner in the companies that I’ve started, a gentleman named Jim Hanson, we invented a lot of the technology that was involved in the IoT, especially on a wide area basis. 

And now I’ve taken the next step in my career. After we sold Axeda a few years back we started thinking about, now that data’s been moved all over the place, what do you do with it? And so, we’ve created a company called RevTwo that makes that happen. But ultimately my background on technology has been about moving information, and making it valuable, and that’s really been the two trends of what’s taken me to where we are today. 

What was it that really attracted you to industrial technology? Certainly, the technologies have been I guess developed in silos over many years, and it’s a pretty tough problem to solve. Was there a trigger that inspired you to go after the market with FactorySoft? 

Well it’s funny, my whole early career was in industrial technology. When I came out of college I knew I wanted to do one thing, I wanted to build products; I had an opportunity to build products with a company called, Wesley House Electric, and we made a distributed control system that was used to run big power plants, and this was back in the day when we made everything; we made boards, memory systems, screens, you’d make everything associated with these kind of computer systems, and these things were massive and expensive, but it was distributed. So, it had networking, it was really an interesting platform.  

So, early-on I got introduced to both the concept of making products, and making products for industrial-scale problems, and frankly I kind of loved it. I found them to be meaningful problems, I found them to be really interesting, they had a lot of real-time aspects, the things that they did were mission-critical, so it was important, and it really got me into the whole arena. I evolved off of the BCS system into doing basically a PC version of that, where we made operator consoles out of PCs so that you could put screens on, back in those days they were dock screens, and then eventually they were Window screens. So, those first two steps laid the foundation for at least understanding what people wanted to do with data from all these real-time sources.  

That’s what got me into the FactorySoft side. Once we realized that every company that was making an HMI system at that time had to write their own portfolio of drivers and maintain them, it was really an expensive task on the whole industry; the idea of leveraging an open standard for facilitating that connectivity was really compelling to us, and that’s what we got into FactorySoft about. 

What were some of the challenges with data at that point? I guess at this stage, this was the period where the initial sequel database wars were under way, and certainly on the enterprise there was a lot of initial groundwork that had been built. Were there some initial challenges in dealing with factory data and industrial data, as compared to what was going on in the business world, or traditional IT? 

Back in those days data was like it was locked-up in Fort Knox, it was in an area you just couldn’t get at it. Sometimes it was purposeful, sometimes it just wasn’t purposeful, it just wasn’t accessible. That really caused a lot of problems. If data doesn’t flow then humans have to flow to where the data is, in order to make modifications and changes. If you look back, the eighties was making computing power distributed, the nineties was really starting to lay the foundation of global connectivity. This whole idea of connectivity and then dataflow, and of course the databases were coming along at those times as well, making it, being able to remember it and making that all accessible. It really changed the game on how we architect, build and use systems. 

So, to me it was really the availability of data, making it something you can actually action on, see and interact with, but that was really the key innovation during those times. 

Who else was involved in trying to solve these problems? In that era, I think as you alluded, you were one of the first people to attack this problem; why didn’t these pain points have solutions from either some of the big tech guys or some of the big industrials? How would you characterise the state of the market at that point, and how it evolved? 

It’s funny, it’s not dissimilar from what it is today. The majors are solving problems with scale using the technology that has become mainstream, young companies are innovating on the technology side to solve problems in new and different ways, or to solve different problems that are currently being ignored, so the majors were originally doing things all with hand-built distributed system, solving the problems in the way in which they were solved in the seventies, and early eighties, but the companies like Wonderware and Intelusions, guys of that nature, those companies were all trying to do it in a more open and different way. That’s obviously become mainstream today, but back in those days, again you’re solving problems using a technology stack that was unproven in the industrial domain. It turned out to be a compelling solution to the way things needed to be done in factories, plants, and pretty much everywhere; so, it ended up eating the world, but back then it wasn’t so obvious. 

Were there challenges in convincing factory operators and manufacturers to change their thinking at the time? What were the key messages that you really had to articulate, and any key resistances that you had to overcome at the time, that ultimately would result in the broader acceptance of the approach you guys were taking? 

Well, the world hates and loves change, and industrial applications in particular. I would even broaden it as business-critical applications, change can be awfully scary. So, you have to have a compelling advantage in order to facilitate the risk of change. And so, those things I think are universal in the technology industry, it’s as true today as it was then; it was true when we stared applying the Internet technologies, it was true when we started applying PC technologies, its true now that we’re applying AI. This whole embracing new approaches to do things has a certain fear associated with it. 

But fortunately, because it brings compelling business advantages, and I think most people today have seen quite a few changes come in their lifetimes, they realise that ultimately you have to be on if not the forefront, at least a reasonable adopter of technology, or you get competed right out of the market. Though I think in the early days there was much more change aversion than there is today, I think today most companies have wised-up to the fact that some level of change is required, just to be in the game at all. If they’re not paying attention to it, they really run a risk of being at a huge competitive disadvantage. 

Industrial at one time it probably wouldn’t change for 10 or 15 years, it was a very static type of industry, and a very hard industry to bring new ideas and technology into it. If you look at industrial today where most people are always famous for saying, ‘Brown is the new green’, right! where you’re trying to pull new advantage out of existing systems, infrastructure, and facilities, technology and new technology is really the way that can happen. 

So, once you had achieved some success with FactorySoft, the next stage was starting to connect industry in a more profound and a deeper way. Could you talk about what the environment was that you saw, and the pain-points that really led you to take what you learnt from FactorySoft to start Axeda, and talk about how that had evolved, and the experience of putting that together into a company that was certainly the biggest acquisition that PTC had ever done and had provided the foundation for a complete transformation of their business. 

If you look at back then, this was in the 1990’s and it was a pretty exciting time, I really feel very fortunate that I was there at the early evolution of the internet. The Internet was everywhere, the Stock Market was booming off of Internet speculation, Pickstock.com and the Soft Puppets! All sorts of ideas were floating around about new ways of leveraging this global super-highway to do things. So, this was the environment that we lived in, it was vibrant.  

One day I was visiting a factory that bottled beer, I think the name of the company was Varia, it was in Europe, either Belgium or the Netherlands, I’m not 100 percent certain which. So, I’m in this bottling facility, I’m walking around, I don’t know how many of you guys have seen a facility like this, but it was a symphony of amazing complexity and precision, bottles running down the belt, these are giant machines that are filling these bottles at super-high speeds, and they’re filling them as the bottles are moving down the belt, and then capping and packaging, it’s just amazing. The thing that really stuck with me at the time as I’m watching this thing was, ‘If this thing breaks, I just don’t see how someone at a facility that is involved in making beer would have the sufficient expertise to fix the machine. The machine was at such a complexity level. 

Literally the light-bulb went off in my head right then and there that-that was a legitimate and compelling use of the Internet. That, instead of bringing the expertise to the facility and affecting the production adversely of that facility, whilst we waited for expertise to travel; we could take effectively the machine to the expertise using the internet, and fix it from far away, and really cut a lot of time out and create a lot of value in the process. That’s where the idea for an internet based IoT was born. That was the foundational inspiration behind Axeda, which we spent the next 15 years really embodying and building off of that initial vision. 

Can you talk about how the initial technology constraints changed, from your initial vision to the time that it had really matured as a business? What were some of the initial challenges, both technologically and in terms of actual implementation from a business perspective that you faced early, and how did that evolve over the time that you helped grow and build the company? 

The initial thought was, ‘You turn everything into a webserver and you interact with it’, like it’s a place on the worldwide web. So, from a technological perspective you can just imagine how stupid that is! Stupid in a lot of ways, it’s stupid in the security aspects of things, who wants to put their bottle machine on the worldwide web, and expose it to all sorts of mischief? So, the real first innovation that we had to do with Axeda to embody that vision was, we had to come up with a different way of doing it. We realised that instead of turning things into servers, we had to turn them into clients and go with the flow of how the Internet would work, and how the Internet worked securely. 

That was really the big innovation. We looked at the problem, I like to say we looked at it backwards. Everybody in the world that had done any sort of remote telemetry at time had always done it through modems where they called out, reached out to a physical asset, or through VPNs where they would call out and reach out to something, so it was always done from the company reaching out to something, and then having the thing in the wild answer. When we built Axeda we just did it in the exact opposite direction, we started out at the physical asset and we had it reach out to the central location, then we would facilitate the communication from there. So that was the technological breakthrough that allowed the company to operate.  

The thing I think Axeda had a big advantage over, one of reasons that we not only survive but thrive, is we had a clear vision on the problem we were solving, ultimately, we were facilitating flow of expertise to solve issues for companies and customers. That flow of expertise ultimately led us to not only move data over the cloud but to do things to effectively utilize it, and that first generation of solution was the reason that the company ended up being successful. Those were the main things, the main things were, we had to invent how to really communicate using IoT type technology, it didn’t exist at the time. Then the second thing was, now you’ve got it talking what do you do with it? That was a whole other range of innovations. 

Following up on that, did you have any standout customers or use cases that blazed a trail, which would essentially open-up the market to the uses of the data? How did you go from just connecting data to being able to really incorporate the vision of delivering greater business value? What were the types of people or ideas that moved that vision forward? 

I’m going to say something that may be a tad controversial, today IoT is still about moving data, most people don’t really have a clue what to do with it once they move it. So, I felt like that’s still an area that needed a lot of innovation, and needed a lot of focus, and RevTwo is really about monetizing the data; so, once you move it, what do you do with it? The business case that we ended up with at Axeda which was really driven off of almost that initial vision was, Axeda was kind of a human expertise platform, we delivered human expertise at a distance; so, we used the data to trigger a human’s attention, and then we provided mechanisms using that pie that would facilitate the flow of expertise back to an asset. So, think of it as the virtual truck roll. 

Ultimately the thing that was the initial core business case was to avoid truck rolls; how can I do something to compress time and distance, and avoid the movement of a human? If I can do that, I can save a lot of money for both myself and for my customers, that little closed loop activity was what Axeda was all about. 

The ability to completely I would say anticipate service, through that visibility. What’s interesting too is through this evolution, how that has made that transition to RevTwo, and I think this is a good time to talk about that, because you had learned all these business insights, employed all of these technologies, and really created an entire new way of thinking about connected devices and products. Now you’re launching RevTwo, I think you’ve got a really unique view of the problem, and I’d love to hear how what you learned at Axeda ceded that idea, that spark that led you to form RevTwo. 

At Axeda, people wanted to move human expertise at a distance without moving the humans, and that meant like I said, using the data to trigger them into action, and then providing capabilities to do things. I think if you look at the dynamic that we’re sitting at now, I guess there’s two pieces here, there’s data, and there’s humans. When you look at most heavy industry-type companies, the human part which we’ve always taken for granted is becoming a scarce commodity. Companies that sell service and maintain complex things have real issues staffing those types of roles today, I think the average age of the normal support, service and maintenance person in North America and Europe is 56 years of age. A lot of these companies are staring down the pipeline of somewhat of an Armageddon event, where their human expertise is going to walk out the door in 10-years, and they’re going to be left with nothing, they’re going to have real trouble of doing things without those humans. 

The trend that’s hitting now which I think is equivalent to the Internet, is AI. And so, what RevTwo is about is replicating the expertise, I like to call it ‘Know how’, of all those humans, but doing it in a way that can become a sustainable resource for the company, so doing it in an AI way. The thing that’s really innovative about AI versus how we’ve done things in the past, and even how we did things with a system like Axeda, if you look at the first generation of IoT platforms, the Axeda’s, and the ThingWorx and guys like that, we tried to present information to people, so we would move the data, present it to people, and let people do things to it that are interesting. 

The reason you present something to a human, because the human brain is pretty fabulous, they can do damn near anything, it learns from its mistakes, it can make leaps of logic from information that it sees to take meaningful action, it’s got a certain flexibility to it that program solutions do not. When you program something, that rigidity is oftentimes an impediment to a company getting its job done. A lot of times its useful, but in situations where expertise is valuable it’s pretty much an impediment. So, the benefit of AI is, AI represents a problem-solving technique that is a lot more fluid than anything we’ve had at our disposal in the past, and we’re able to take information, make decisions, and then learn from the activity of those decisions and update our criteria so that we make new decisions next time. That really promises to change the game on how IoT data can be leveraged, in both manufacturing and in wide area support and service operations.  

So, that’s really where we’ve taken RevTwo, we’ve invented a new technology, much like we did when we invented IoT, we invented a way to take what I consider now an available resource of IoT information, and then use it to make choices that can learn from the act of their choices, and to then apply action in a more automated way. So, RevTwo is about taking the know-how that your humans have and automating it from the decision-process all the way through to the action activity. We want to be able to capture that know-how and operationalize it in a way that doesn’t require people, because the people aren’t going to be there in 10-years, we need to be able to do this in an automated way. 

It’s also the problem of support, product support and service as well, that is a big pain point, can you talk a little bit about how you’ve looked at that, it is a business opportunity in a sense, but how the industry has looked at it, and currently where there are inefficiencies that can certainly be addressed. It’s horrifically difficult to get support on a lot of products, and it would seem that this is a market that’s right for some real new ways of looking at the problem. 

There was a recent study and I can get this for your comments later, where it came from. It was a recent study on support, and I think 60 to 70 percent of people would rather scrub a toilet than engage in a support interaction! 

Well not as bad as going to the dentist, but that’s pretty bad! 

You look at that and the thing I always think about, no one wakes up wanting to give bad support or to do things in a bad way, but yet they do. Part of the reason they do is because all of these support systems were designed from the optic of the agent, they’re designed from the call-center down. They’re designed to optimize the call-center and to process business, it doesn’t have to be a human interaction, it could be a business-to-business transaction, to process these things in somewhat of a factory orientation. You get level 1 where you do basic triage, into level 2 where you’re trying to get the stupid questions out of the way, and then level 3 where you really put the pedal to the metal, and you get someone who knows what they’re doing. But you have to go through a company’s line of defenses first. That’s a frustrating, time-consuming, expensive process. 

When you look at it from an IoT perspective, or from the product first perspective, you realize the product today knows all, they’re very smart products, they have all the information they require, and the human activity of pulling that information either from the customer or through remote-type technologies is really a waste of time; it’s there and its available for anybody that wants to query it. It’s an IoT resource today. We always start from that optic, we start it from the product and the customer first, and really what a customer wants is just a fast solution. We wanted to build a process that instead of navigating a call-center, chatting with the chatbot or an automated system, you could just ask for help, and then five-seconds later you get a solution on your doorstep that you can apply, and you can be back in business so to speak. 

So that’s the promise of leveraging this type of IoT data, and applying AI, to determine these types of solutions. RevTwo also invented another technology to complete the loop, we call them active solutions, which can be delivered to a customer in a locational facility, and then allow that customer to walk through an active repair scenario where something can actually be fixed. In the situation where maybe, there’s really a part broken, the system can take action, order the right replacement part, organise the field service person to come do the next activity, but all of that can happen without having to be dragged through the mud. 

What’s interesting is how you’re able to collect this data and be able to put it into context when there’s a support request that’s created. How do you go about training or applying the AI? You start with the data, you understand the connectivity that’s been kind of kind of table stakes, but can you talk about the process, what’s involved with incorporating AI to get you to that concept of these active solutions? 

That’s a great question Ed. Again, one of our innovations, most people are looking at AI today as something that’s in the domain of data-scientists. So, I sell to data-scientists, they show up some place, they do an analysis and they build a magic AI model, then they put it to deployment and everyone hopes it works. That’s not quite how a human brain works! You don’t usually have the data scientists in the middle when you’re doing something yourself. So, we wanted to create an environment that was more closely related to how the human activity works, and the way we do that is we gather the information that’s relevant to let’s say the way a human does their job today; so, if a human diagnoses the issue with data piece 1, 2, 3 and 4, then we want to feed our AI data piece 1, 2, 3 and 4. We identified the signals that we need based on the human analogue. Then the next step of it is there’s an exercise where you train the system to take issues and identify what the solutions were for those issues.  

The thing that makes RevTwo unique is we now have a correlation between issues, relevant data, and then ultimately once its trained, solution. That information is used to generate the model, and then the model can be predicted from that point forward. So, RevTwo takes kind of an industrial and dynamic look at AI, we use training in a way which can happen in two ways, it can happen just through system use, so as the system encounters new issues, solves new issues, and gets the feedback that the issues it solves is correct, that will go to the next generation of the model. Or, as you add new solutions for the system, and then maybe retroactively train them, or prospectively train them, that goes to the next iterations of the models. 

So, RevTwo automatically generates models once the training data has moved a sufficient distance to warrant re-execution of it, that can be daily, or it could be once a week, or it could be a few times a day, but ultimately its constantly learning and its constantly updating, and its doing all of this in an automated manner. So, what happens then in practice is the system starts out pretty good, you’re looking at maybe an 80 to 85 percent accuracy rate, and in over a short period of time you’re moving into the 90’s where you’re operating pretty much at the level of your best people. You can use that information to either autonomously fix things, or you can just use that information to improve the humans that you have and help them gain the expertise of the AI in their own activity, and cut out the triage time, cut out the diagnostic time. Ultimately the big benefit here is no data scientist required. The system teaches itself, and it teaches itself from developing the correlations between the relevant data, and the issues that it sees. 

And ultimately that creates a much more efficient way of delivering real support, and real service to customers as you go through that process. That makes an enormous amount of sense. 

What would be some of the real pain points that you see in the market, particularly whether its certain types of industries, or certain types of products, where you think there’s a real need for this type of support? I know it’s still early days, but can you point to any types of industries or types of situation where your most optimistic? 

We generally start in areas that have a couple of characteristics, they have some level of complexity; it’s a human thing today, it takes a human brain to analyse, so it has complexity and it has connectivity, so there’s local intelligence at the physical asset or location, and we can gain access to that information via connectivity. That would mean it either has an existing IoT infrastructure that’s been associated with it, or its relatively easy to instrument on an event by event basis. We have a light-weight IoT mechanism built into RevTwo that doesn’t require constant monitoring of something, but when an event occurs it can gather information and attach it with the event. So, it’s a lot more lightweight type of exercise. 

Ultimately the types of things that we focus on are, smart products, today we’re dealing with industrial smart products, so things that operate in industrial environments, medical environments, infrastructure, I used to call them, ‘The things that have high pissed-off factors’! When they don’t work everyone’s pissed-off. So, those are the areas that we focus on initially, and there’s value there from a couple of perspectives, one is its costly for the companies to deliver their expertise, they have enormous organizations and infrastructures, it’s really-really costly. And, its costly for the customers and the fact that downtime is expensive. So, what we do is we create a win-win scenario where we take downtime and hopefully move it into minutes versus much longer type of process. We cut down on a lot of the human labor associated with figuring out things by doing it in an AI way, and so it’s a win for both the companies that have to deliver that type of expertise, and for the companies that consume it. 

When we look at the market opportunities for RevTwo, we move from those complex products into consumer smart products, the things that your grandmother has to deploy but has no ideas how to, to smart factories and plants, and other types of areas where complex triage and taking action but in a much faster way, by creating a resource that is utilizable at scale, and doesn’t go home at night, is valuable. 

That’s true, the AIs don’t get headaches and they don’t get cranky!   

Where do you see this going? If you look to another 5 or 10 years, what impact do you think these types of technologies that AI could have across the businesses that you’ve transformed already, through being able to connect and gain visibility in analytics into the data, when you applied this next level of automation and insight, and almost anticipatory intelligence? How do you see things unfolding, and on the flip-side are there any challenges that customers and people at large are going to have to get past? 

Well, if you look at 20 years ago connectivity was crazy talk, and just moving data and having data for everything was crazy talk. Now you have the data, so now the next logical question is, ‘So what?’ I see this whole AI revolution as operationalizing the data, how to make this data valuable and to leverage the data to do things. In our domain, our focus with AI is around problem solving and rectification, so we’re looking at datasets in order to take action on problem resolution, and that’s our primary focus. Today, we do that in a cloud orientation, we pull the data in on an event, we take that stuff reactively, think of it as, ‘I feel ill, so what do I do?!’ You use RevTwo to diagnose and give you the pill to make you feel better, that’s the place where we are today. 

Our next step with it is we’re taking that AI that gets generated from that type of training activity, and now we can push that dynamically created AI out into the wild. We can push that into a kind of Edge orientation and use that AI to not just react to events but to anticipate them, so that we can look at a dataset of an asset and we can say this is now starting to have the DNA markers of trouble. The reason we know that is because we’ve trained the system to know what trouble looks like. So, if it doesn’t know what trouble looks like, if it’s a human’s vision of what trouble might be, that’s not always the same thing as what real trouble looks like. So, we can see pushing that out into a kind of computing environment at the Edge, where the AI can start to anticipate proactively these types of issues, and potentially take action then and there, or at least notify someone and then walk them through the action. You have to remember, RevTwo’ s not just about saying, ‘Hey, something seems wrong’, RevTwo says, ‘Hey, this is wrong, and this is what you do about it’, so it’s a lot more prescriptive.  

I think now, much like if you then roll back the past 20-years, connectivity and smarts has got pushed into everything; processors have got smaller and cheaper, you can buy a 15-dollar computer now for God’s sake, you can stick it in anything. Well guess what? AI is going on chips and that’s going to go in anything. So not only will this type of technology be utilized on a large scale, or even on mid-scale aggregating Edge type of apparatuses or devices, but they’ll actually be able to go into all the physical types of things that are being generated and built, anything executed on these types of AI chips that will be coming along. 

So, I just look at it that in the next 20-years you’re going to end up with not only just a universal communicating backbone, but you’re going to end up with a backbone that can communicate and self-diagnose its behaviour, and almost self-repair. Kind of like a board ship for Sci-Fi fans out there, it self corrects, it self-generates, its self-regenerates, and just makes things better. That’s where we see the technology. 

I’m giving a kind of a story a little bit more with the product orientation, but process is very much the same scenario. When you’re dealing with the process and you have data that’s coming in, it’s just coming in from multiple end-point types of environments, but processes have similar characteristics, things can go amiss and when they go amiss it’s still the same sort of diagnostic process; you have to get information, understand what things are doing and then figure out what the corrective action is. So, this type of AI will operate both a product at atomic levels, but also process levels. That’s where we see it going. 

It is pretty amazing to think that as we get more and more connectivity, and really just this assumption that these connective products are going to work. It’s hard to imagine – or maybe it’s easy to imagine a world where everything is connected, but then it all breaks down and you can’t fix it! It’s kind of what we’re dealing with now. But clearly, I think that scenario that you’ve outlined and that vision is where we’re going, and clearly the need and the potential risks of the reliance on all of these connected products and business processes that get embedded in them, you’ve got to be able to anticipate what’s going to knock them down and bring them back up. 

I think in 20-years you’re not going to talk to someone in a call center ever again, that whole thing is going to go away. 

Well, we barely do now anyway! This voice-response, they do everything they possibly can to connect you with somebody who’s not very helpful anyway. 

It’s really going to be the type of thing that you’re just going to find that when things go amiss, it’s going to be AI diagnosed, and there’s going to be an automated repair scenario or fixed scenario, it’s just going to be lickety-split.  

That’s a world that I would like to live in, so I’m pretty excited that you’re working to get us there.  

One of the questions I always like to ask as we wind down is a good recommendation that you’d like to share, it doesn’t have to be technology, but is there a good book or resource over the last year that you could recommend to our listeners? 

I’m a Sci-Fi fan, my family are all Sci-Fi fans as well, and so we’re always into that. I’m waiting for Martin on the Game of Thrones to release his next book, we’ve read the whole series. But on a business level, one of the challenges I have is I deal in new concepts, so I’m always inventing new technologies, I’ve invented things that didn’t exist before, and I’ve worked to explain those things to people, and help to them understand that a) it’s not scary, and b) it’s really, really beneficial. So, when you deal in new ideas you don’t have the whole infrastructure of a language around some of those ideas, you don’t have the infrastructure of talking about those ideas, so it can be really-really challenging to help explain what you’re about. 

I recently did a little bit of reading around that particular area where I wanted to learn how to convey those types of new ideas in a better way. One of the books that I read in that area was a book called, ‘Pitch Anything’, by Oren Klaff. 

Oh, that’s a great book, absolutely! I love that book, yeah. 

I found it extremely valuable. I can be longwinded, I love to talk, and I love to enjoy the conversational aspects of talking about how technology can make us all better, smarter, and more adept. The whole ‘Pitch Anything’ concept was beyond your typical pitch deck book. The thing I liked was it talked about how we perceive information and the step you have to undertake to open up people’s ears so to speak. I found that very-very interesting and helpful. 

That’s a great recommendation. He’s got some videos that are helpful, and I think it goes down to the idea that decision-making is almost an inherent reaction in people, where they don’t even listen to what people say, you have to position things, particularly if you’re making a product pitch, or an investment pitch. He did a lot of study and that’s a terrific recommendation. 

Dale, it’s always great talking to you, I think this has been enormously informative, and a great vision of where the world is going, and in context of the experience, and the innovation that you’ve delivered to the industry as we bring ourselves up to this point.  

I want to thank you for being with us today, and I want to thank all our listeners as well for listening to Dale Calder, CEO of RevTwo. 

I am Ed Maguire, Insights Partner at Momenta, and we thank you all again for another episode of the Momenta Edge podcast.