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[00:01:43] And he studied originally out my neck of the woods at the University of New Hampshire for computer science and then a Masters in Management of Technology from M.I.T..

TP: [00:02:39] Sure, Tom. I hope I can do justice to that introduction. I’m blushing over here. So, yeah, I’d be happy to.

[00:02:48] As you said, I started in right out of school with a rabid thirst for A.I.. And at the time, I had a overblown idea of what I actually meant. And, you know, through my academic career was sort of underwhelmed by what we really meant in practical terms by A.I.. And as I left school was convinced that there must be somebody doing more, being a science fiction fan.

[00:03:18] I had very high expectations and I found one of the only games in town working with A.I., which back in the early 90s, late 80s was expert systems and rules based systems and kind of gotten involved with that company, really kind of writing banking software.

[00:03:37] And I don’t think anybody really ever thought they’d grow up to write banking software. Right.

[00:03:45] It feels like when these things you kind of fall into along the way. A lot of kids talking about that during career day at school.  I want to be a cowboy or write banking software.

TM: [00:03:57] Right, right. Right, right there. Next to firefighter and astronaut two. Absolutely right.

[00:04:06] It was a lot it was a roundabout way to get to living the dream. But what I what I did get the opportunity to do is to really see how big organizations that fundamentally are only about data when you think about a financial institution. There is no material product. Everything they have is information. That’s the only product they actually have. So the sophistication around data management, data handling, data processing is, I think, probably greatest in the world. Cutting edge. Maybe not. But at scale and sophistication, certainly in the 90s, they were really the only game in town. And and so I think it was a accidentally a good place to grow up technically. Looking at these really scaled business problems. And as I went through a couple of decades of of that evolution, we ended up doing more of the same kind of things, not only in banks and ended up in kind of health care insurance and those kind of tangential types of organizations. And we were deploying a and data management and all these things in use cases that were really compelling value propositions. Right. Because these are essentially only information processing organizations. And and so they spent a lot of money, did a lot of, you know, cutting edge things and they got a lot of value out. But over time, what I began to see, as, you know, these technologies and these approaches aren’t diffusing beyond these, you know, robust enterprises that clearly recognize unique value from them. In other words, there was no democratization of the technology and the approaches that people who didn’t have, you know, 50 million dollars to spend on a product project, weren’t getting a I weren’t getting, you know, robust data management. But that I think I’m not overselling it to you guys. You clearly see the value in being data driven. I think in almost any kind of company. So really, what I wanted to do was to figure out how do we take these 50 million dollar projects and all of the tools and techniques and technologies that we use in those and scale them so that they could be used by more people and that more people could get the value out of the data driven paradigm. And of course, while we were on this journey, at some point, we flipped from expert systems into machine learning as the standard bearer for A.I. And that became even more clear to me that having high quality, trustworthy, real information is absolutely critical to machine learning. And I think there’s a lot to be democratized on that. So microtia was really kind of founded to figure out how we drive that down market. And, you know, if I can use the Jeffrey Moore diffusion of innovation language to cross the chasm between these early adopters in the marketplace and and the mainstream of the market, which I think can really benefit from these tools to some of those early adopters.

TM: [00:07:18] And I assume you’re talking about the bigger companies with the multiple CAAMA budgets them. I, I imagine if for a lot of those they grow to a certain size and it’s like the digital systems, the machine learning, these types of things make sense because they’re there at scale and they if they have to do something and at a certain point they can’t just throw more people at the problems. When you’re talking about crossing the chasm and reaching a greater market, do you find it? Do you find it difficult to for the end customer to understand that they need or would be greatly benefited by some of the digital machine learning, even expert systems, backed processes?

TP: [00:08:25] Yeah, it’s good. It’s a great question, Tom, because, you know, classically we describe early adopters as, I think, having two primary features. One is that they have used cases that are unaddressed and very, very clear. So high are a lie. Kind of use cases and they’re nowhere to turn for an easy solution. And then they also tend to have an innovative mindset, which means there’s risk takers. They’re not that interested. And just following the status quo, they’re ready to break glass. They’re open to failure and experimentation. So it’s a it’s a combination of that sort of risk taking attitude towards things. And that high end use case. And you find people in a market who have one or the other and don’t become early adopters because without one or the other, you either lack the wherewithal, you know, the funding. In other words, to get something done or you lack the Coney’s out on your own and take a risk. You have to have both. And, you know, not to say that the other side of that chasm lacks both. But in general, what you’ll find is that on a scale they are lower in scale on both of those dimensions. Right. Tend not to be risk takers as much. They tend not to have as kind of clear use cases. And there’s often other solutions for the kind of problems that they see on a day to day basis. And in fact, I think it’s kind of a forest for the trees. Problem in the mainstream is that they’re so well served by things that they have a often a hard time seeing that there’s a better way to do it because they’ve got a good enough way of doing things right. So as you describe, you know, do they need education? Yes, they you always need education. But really what’s interesting is they don’t want to be educated about the details, the way early adopters do. Early adopters tend to geek out on it on detail. Right. They they they want to read specs. They want to be cutting edge. And the people in the mainstream are thinking more. Not that they’re stupid by any means, but they’ve got day jobs and they’ve got tools that already kind of work. And everybody around them is also using those tools. So there’s a real social difference between being an early adopter and being in the mainstream where you’re you’re kind of sticking your neck out to do something different than everybody else’s. And only over time in education, maybe you begin to realize, oh, you know, maybe there is an advantage to doing something a little bit different. So that’s where the education, I think, comes in. And if you can recall Jeffrey Moore’s work, there’s a bell curve. Right. And the first part of that long-tailed real bell curve is for early adopters. But, you know, there’s the peak of the bell curve and there is the beginning of the bell curve that’s crossed the chasm where people have maybe more reason to stick their neck out than, you know, people at the very top. But once you’re on the downside of that L curve, it’s people who just kind of copy what their friends are doing and don’t think too hard about it and just want to run it out. So from my side, the need for the commercialization effort on that is education. As you pointed out, and it’s kind of down risking things and taking a lot of the jargon out of the techniques and technologies so that you’re not talking about nerdy stuff with these guys.

[00:11:57] You’re really helping them envision how their business can be different. And I think a lot of, you know, tech buzz waves are crashing right now on that particular problem. You know, the big, big data has a buzz wave that came and went. I.T. has a buzz wave, came and went.

[00:12:12] But at the core of it, you know, I think we’re chipping away at this idea that data is a great way to run businesses of all kinds. And we just need to make it so that we drop the risk and educate people on their terms and then get better at showing people that there is an R y for them as well. And once we build momentum on the other side of that bell, curve it more and bell curve of adoption. I think we’ll see more uptake and then eventually it’s downhill. My God, I’d love to see that downhill side where people are just adopting because it’s the thing to do.

TM: [00:12:43] Yeah, that makes sense.  It’s like you’re not going to go to a restaurant owner and say, hey, how about an A.I. solution? Yeah, your business is they’ll say, well, I don’t want robots serving my customers. But if you go to them and say, here’s a package that’s going to take your ordering data and suggest menu items and price updates. Right. So you don’t have to think about it, right?

TP: [00:13:10] I think that’s right. And, you know, in some ways, I find that it takes the right kind of technologist and the right kind of culture of a company to bridge this gap, too, because I think it’s real easy for early adopters and the people who. We’re used to serving them to geek out so much that they kind of begin to build contempt for the people who don’t see the value immediately and kind of forget just how hard it was to get to the point where they see the value. So, you know, we just fundamentally never want to talk down or have a contemptuous attitude toward the people we’re trying to serve, because those guys really know their business as well. They’re smart people.

[00:13:51] They’re they’re at the forefront of what they’re doing. And it’s just that they are unaware yet of how the nerdy stuff we’ve been doing is applicable to them. And I think that’s a key. Part of the good fault here is to say, look, we want to learn from you just as much as we want to teach you what we know. And it’s the blend of those two things that democratized, right? It’s not it’s not me inculcating you. It’s like you inculcating me. It’s us working together and finding the goodness in the middle. And I think a lot of us common language and things like that. But I think you hit it on. Right. If you’re talking to a restaurant guy, well, you know, frankly, put aside the industry. At this moment, and maybe forever and ever. People are being asked to do more with less. What that means and is is different in different industries. But at the end of the day, automation as and telemetry and A.I. as the sort of nerdy implementations of those things have a lot to say about doing more with less, you know, doing doing better with fewer resources. At the end of the day, if you can find the right nouns to match with that verb in a particular industry, then you’ve got soup. And then it’s just really an implementation detail to close the gap.

[00:15:04] You know, that’s a really interesting point, because I think. The implementation or the organization, the way the ecosystem was set up.

[00:15:12] People are taking pieces of the technology stack and then, you know, we’ve had what we thought of as third party integrators in the past at this time.

TM: [00:15:24] In some senses, you know, we consider Microshare your thought process as a data strategy, as a service center at a we’ll get into the pit pivot with predictive cleaning and actually control building automation. But it is kind of threading this complex thread through the needle points, but really emphasizing a use case. And you’re right. I mean, I never thought of first market movers as like, why do we get into the specs? But that that makes sense in the past. I want to talk about the platform. They want to talk about the analytics module. They want a particular sensor. [00:16:04] But, you know, that’s just kind of – that is not solving a problem that is going to get into the details. And you’re right. Somebody who’s got a manufacturing facility just does not have the time for that. They wrote the burning issue.

TP: [00:16:20] Yeah, that’s right. And, you know, there’s there’s there’s not enough of that kind of innovative mindset to go around. Frankly, not everybody, even if they know they need innovation. Not everybody can find the right people to drive it because it’s a it’s a it’s a magic combination when you find those guys. And as a technologist, God is fun to work with those guys. Right.

TM: [00:16:40] But there’s not a team. Right. That’s a great one as well. I mean, made it extra or a lawyer by macro is conservative. [00:16:49] They have a strict guideline because that means, you know, quality control or risk. So they get paid to be like that.

TP: [00:17:00] That’s right. And, you know, following a formula so that you do things at highest quality is a time honored strategy in certain areas. And, you know, there’s plenty of places in the world where risk taking is inappropriate. Right. So you’re right. And I think these are just reflections of personal attitude, personal experience and industrial orientation. Right. So how is your industry going oriented? Like you said, you know, if you’re a legal mind or an accountant or a manufacturer, it may not be your best interest to be taken a bunch of risk and not following processes and everything else. But I think all humans kind of get the sense that, yeah, I mean, we we’ve had the idea that innovation is good beaten into us. Certainly in the West. Pretty, pretty thoroughly. Not everybody has the stomach for the actual sausage making. What kind of the reality of that situation? But meeting people where they are is the bridge to that. And as you said. Right, it’s it’s taking it’s it’s you know, in many ways, we all we’re all technologists get a little bit drunk dealing with those early adopters.

[00:18:13] We love talking about our stuff. And then we just kind of we get lulled into believing the next traunch people are going to be the same or maybe just incrementally different. And I think the reality is that the next traunch people are very different and you’re turning them off by talking about radio frequencies and training sets. And is this a neural net or know linear regression? They don’t want to know that stuff. So you start talking that way, kind of tune out. And, you know, this a great meeting and you never hear back from it. Right. So I think it’s recognizing that leap. And then, you know, it is incumbent upon us who have sort of grown up in and unfortunate these new technologies on the leading edge. We’ve got to meet them where they are because they don’t have time to meet us. And so it’s a lot of work, frankly, too, to tone that stuff down, democratize, learn, speak their language and bridge and historically Assize have done that for the world. And they do it well. But they do it extensively. Right. Because it’s it’s a bespoke process. And. And, yeah, I’m going to get a bespoke outcome, but I want to pay for it. And so, you know, the productize stuff is that next wave of saying, all right, we’re going to integrate but not charge you dollar for dollar around the effort to do the integration. I’m going to I’m going to integrate with an eye towards proctor zation, drive the cost down, drive choices out, but hopefully do it thoughtfully and say we’re going to take the best of breed. We’re going to leave you some latitude for those that really care to make some choices. But we’re gonna try to push to the side all these religious arguments that we have as technologists and say, look, let’s just pick the best we can now know that it’s good enough work where it’s not good enough or where we think, you know, the next horizon for improvements. But, you know, remove that from the fore and start talking about the are alive for these guys and really help them drive their business. And I think that’s that’s what microspheres has been doing. And I I have to say that increasingly we’re seeing good results from that. And and being a bridge also between device manufacturers and A.I. providers, helping them cross the chasm to talk to our customers by being, you know, the products bridge between those things. And there’s still innovation going on. But it’s more business innovation now than it is technical innovation. I mean, it turns me on. I think that’s pretty exciting.

TM: [00:20:44] Yeah, that makes sense. And if you think about a lot of companies that are scaling to the point where they need a solution like this to the point where it’s either, you know, a lot more hiring or we need to get out of automation in place. [00:21:05] They’re growing so fast that they don’t have the time to reach out and look at hardware and reach out and look at software options and do all these things, like you’re saying, they need something that can be delivered to them. And the reason why I would imagine the reason why when someone goes in and has the technical discussion with them, the guy who’s whose business is is growing to the point where he needs help. [00:21:31] I don’t have time to figure this out now.

TP: [00:21:34] I think you nailed it, Tom. That’s right. You know, if you can afford it, that means you’re busy. If you’ve got the time right, you probably can’t afford it. Right. Your business isn’t growing. It’s not thriving. It’s not challenging you. And why would you look for a new tool? But ironically, that’s when you have the time to look for a new tool. And so, yeah, you’ve got a perfect storm or. Right. We’re all humans and we’ve got a limited bandwidth. And the demands of daily business is tough. I mean, I’m at risk of dating. Our podcast together here, you know, we’re in the middle of this covered 19 crisis. And what is sure to generate that feeling in everybody’s case is the massive amount of chaos that has been injected. It’s not the only thing that has injected chaos and or will inject a cast. But this one is is global and it’s universal. Modi is not in chaos right now. So everybody’s got more than they can handle from a lot of different angles. And what’s interesting about our time right now is one of the, I think, durable notions about people adopting new technologies, whether those are literally, you know, bits and bytes, technologies, whether just new ways of thinking technologies is, you know, the past experience. Right. What I’ve used and what I’ve been successful with sometimes gets in the way of people thinking, taking the time and thinking clearly about, well, what would I use now? I mean, I know I’ve always been successful doing it this way. Is there a better way to do that now? There’s nothing like a global pandemic to throw all experience out the table. And everybody’s now on an even playing field saying, I’ve never seen anything like this before. So I’m less inclined to think just doing what I’ve always been doing is going to get me through. And so we live in an opportunity period as well. That sort of opens people up because there is no experience to draw upon. So I think that’s a perfect environment for data driven because who else am I gonna believe? There are no experts and something that’s completely unprecedented. And therefore, I think it’s a great time for our collective message of what data can help us here. It’s not the only answer. And I also think my background in a I for the NeuroFocus to a guy who just think they eyes and l know MRL thrives in an environment where there’s a lot of data and you got enough to train on. You’ve got enough to test with. And then a flow of steady data to actually apply your algorithms to. And that’s not a world that looks like unprecedented activity. Right. Some of it just started happening. Does not have a body of information built up about it. No kidding. Right. And this is the world more of expert systems where you’re counting on human intelligence to automate. And so I also see this as a perfect blend of those disciplines of expert system, rule-based, human asserted, expertize baked into a system to bootstrap the data management insight generation automation to feed.

TM: [00:24:45] Then over time, a more robust data stream for machine learning to begin to pick up and improve.

TP: [00:24:52] Could not have put it better. That’s this is this is exactly the message that I think a lot of folks need to hear and be thinking about, because you’re absolutely right. [00:25:02] A lot of times it’s we need a AI and the immediate first step is OK. Machine learning data. We have some data into the machine learning and it will give us amazing results. Wow. That’s that’s how a lot of AI implementations fail spectacularly [00:25:22] I think probably everybody listening to this has had a garbage in garbage out experience, right?

TM: [00:25:26] Yeah. In an expert systems, I think, you know. Well, it doesn’t sound as sexy as machine learning does these days. You know, they they provide tremendous amounts of benefit. If you’re moving to. They’re from essentially manual. Right. Tremendous. And it gives you really nice data at the end of it.

TP [00:25:50] That’s right. And what’s great, I think today, Tom, versus, you know, when we were doing expert systems in the 90s, is we now know that the target is to enrich Annell. So everything we do at microtia for. This is yeah, we’re doing a lot of expert system stuff for people because, hey, guess what? The experts in Cauvin, for instance, are scientific papers that are being revised and published every every minute. We’re talking. Right. So it is cutting edge needs to be flexible. It needs to change. And but we know that our target is to produce good quality data, that we can then begin to filter through amale over time to improve beyond what the experts are saying, to really use data to validate data to improve. And I see that as a virtuous cycle of of automation that we can tap into today. And it’s I think it’s just coincidental that I’ve had exposure to both sides of that, that we can bring it back. But I think most people like said, I think more people will become aware of that because, you know, I find it heartbreaking how many Amelle researchers have to make up the data before they can run their algorithms and put the data just doesn’t exist on a high enough quality. How many people are data scientists? But actually, they turn out to be data engineers because they spend 98 percent of their time worried about the data quality and where it came from. And, you know, I’m trying to remove all of that with Microshare. And a lot of that’s a lot of heavy lifting that most of the business doesn’t recognize. So sometimes some days it’s hard to say, yeah, we’re going to continue to invest in this track. But I know at the end of the day, real value will be unlocked by being able to provide high quality, high volume data to machine learning. And, you know, it’s it’s just priming the pump. So we spend a lot of time worried about that. Is the data high quality? Are we are we. Is this data we can trust? And then, you know, I don’t know if this is Segway point for you guys, but something I’m really passionate about is also making sure that the data we collect is done so in an ethically responsible way, doing that right from the get go. Because if it’s not baked in and it’s an afterthought, I don’t think that’s good enough. Yeah.

[00:28:04] Oh, Licalsi, it is. Let’s for the listeners, make sure their Microshare is, you know, this enterprise scale, iota’s solution and infection control, predictive cleaning, occupancy and asset tracking, smart building stuff. [00:28:21] Sacrificing privacy. So just a little bit back.

[00:28:25] We can look just from the name – Micro – share. This wasn’t intended to be correct.

[00:28:32] Safety, sanitation as it relates to then he pivoted there.

[00:28:36] But tell us you’re doing but also for anybody that’s an arts career. We’re seeing these technological advances. It will apply somewhere. And then how, Kyouko, that made you think?

[00:28:48] I think would be very interesting for people. Many people are hesitating with solutions. Well, what was your decision street to of go hard into that space?

[00:28:58] Yeah, man. You know, we’re we’re eight years old, so we’re fairly mature as a startup. But, man, the wounds feel still pretty raw. You know, it’s I think as anybody who really knows what tech entrepreneurship looks like, there’s very few unicorns and there’s a lot of real tough grinds. And ours has been a tough grind. You know, I think that and I think every entrepreneur thinks when they start that they’ve got it now. They’ve got a real true thesis and some proving in the marketplace. And I think, as we have found, there’s a lot of tuning necessary to really meet both the market in where they live and the time in which we live. Right. It’s where and when. And so we began really around the reasons called Microshare was really around this novel data management technology that we’ll talk about a little bit more later about. And, you know, I hate to say it, but I didn’t think so at the time. But it really was the cardinal sin of a good tech looking for a problem. It didn’t feel that way at the time. Maybe it’s more accurate to say we didn’t have the right problem that we were trying to solve when we first went to it. And, you know, the decision tree is always cleaner when you look back and try to describe it. It was messy and it’s probably messy still. But but getting clearer. I think the key forecast for me where, you know, if you’re an enterprise solution and you’ve got a novel data management choice, if you go to somebody and say, hey, you should convert your banking software to use this novel data management thing, they’re going to look at you like you got five heads. Hey, I’ve had this and a mainframe. I’ve had an Oracle database. I’ve had it in an X, Y, Z for five decades. Why the heck would I ever take a risk on a new technology to manage it? Right.

[00:30:53] I’m part sell real hard, sell what we came across was I hoti because what’s interesting about I.T. is even though, you know, there was this boom bust cycle and the buzz of it, there is value there and it is data that’s a little bit different and doesn’t already have a legacy home in an enterprise. You know, you go to a CIO at a Fortune 100 company, they’re not like, yeah, here’s here’s our I.T. data strategy. It’s was big five years ago. It’s not robust. It’s got, you know, all the I’s dotted, all the T’s crossed. That doesn’t happen. Everybody’s like, well, this is brand new. We don’t really know. I’m not going put into my Oracle database with my CRM data. So, you know, it opens the opportunity for something novel.

[00:31:41] I mean, I really appreciate the.

[00:31:44] So are you. Where was the secret sauce to where did your passion lies?

[00:31:50] Integrate or two degrees to enterprise scale solution?

[00:31:55] Where was where were you? Was it the tech with the privacy rules governing this engine? Yeah.

[00:32:02] Yeah. So my last stop in my career train was with Accenture. Those are probably familiar global massive systems integrator and had a great period with them. When I got to look around globally, which was a really fantastic vantage point for me, I’ve got to look around globally at what people were doing with the combination of a cloud data and helped a lot of people, but also got to see a lot of problems that just went unsolved. Right. So there were things that people were dealing with that were kind of like, we don’t really know what to do about this, so let’s kind of sweep it under one of those key things. And, you know, I will say it’s a an item that I personally resonated with me, so I latched onto it really was that deal about how do you run a data driven business without taking advantage of the people that generate the data, you know? And this is this is a paradigm that people are familiar with via online behaviors and trading and online data with controversies around Facebook and Cambridge Analytics and the impetus behind legislation in the EU and GDP are. And more lately. California, New York.

[00:33:25] You know, individuals are starting to get the sense that there are organizations that might be mistreating their information and are mystified by that and probably quite rightly alarmed by that.

[00:33:37] And, you know, you could see this coming for decades. And it was one of those kind of problems that felt to me like it needed to go back to first principles to solve. And it wasn’t something you could just skim over the top with a Band-Aid, because it was not one of these fundamental notions about how business ought to be done, about how we as humans act. And you know what? What does it really mean for a business or organization more generically to be data driven or fundamentally if it’s not based on an ethical approach? And I can’t feel good about it and probably nobody else can feel good about it. So that was one of those problems that just leaped out and said, yeah, we’ve got to do all these other things. We’ve got to collect good data. It’s got to be clean. It’s got to be dependable. It’s got to be shareable. It’s got to be blah, blah, blah, but. This elephant in the room about it’s also got to be ethically collected source to manage. How are you going to do that? I didn’t see anybody stepping forward and saying, yeah, we got that problem nailed. So even in the bespoke world where money’s no object, I’ll spend one hundred million dollars to solve it. I didn’t see anybody come up with anything clever. So I just think of myself. I admit that I think of myself as clever. But I, I thought I had a unique angle on taking some of the things we had done. Going back to first principles. Throw away a lot of the assumptions that people had made about data management and kind of go back to basics. As we built the core of what microtia became and how we do business on a lot of the surface stuff, right. Where we’re delivering Vallerie value for facilities managers who are running everything from office buildings to hospitals to mines and factories and and, you know, educational institutions are serving a lot of people with just ground floor basic problems that they need to solve at scale. But underneath we have this this company mission and this fundamental patent pending technology around. How do you manage that privacy problem in a way that I think nobody else is complete? And I’m pretty passionate about that.

[00:35:36] You know, it’s it’s an area that.

[00:35:39] Yes, data privacy gets a ton of attention, especially in the last several years.

[00:35:47] But it seems like it’s all this. I do want to say virtual data. But yeah. Online world type data.

[00:35:57] What am I putting in social media? My information that I’m putting into accounts, Web sites and that kind of thing.

[00:36:05] In this whole, you know, I Otey, let’s call it real world data.

[00:36:10] Physical remeasurement. Fisher, the physical sensor data.

[00:36:17] It’s like it’s it’s not even part of the conversation yet.

[00:36:20] It’s it’s a back seat and, you know, it’s brought up occasionally. But, you know, it is a small portion of the conversation about the online behavior. I think that’s a sign of our times about, you know, we we transact digitally.

[00:36:38] We express our concerns digitally and almost by its very nature, you need to be involved digitally to worry about something locally. Therefore, you know, it’s a self-fulfilling prophecy. But what I see as somebody educated in I.T. is and fundamentally at heart, I’m a libertarian, so I’m worried about the liberties of individuals. Philosophically, the threat of real world surveillance, I think trumps our digital footprint and surveillance. Now, a lot of data scientists who are listening to this are going to say yes, but we’re able to use digital behavior to predict real world behavior pretty well. And yet, you’re right. So there is that, too, right. So I’m not suggesting at all that digital data is something we ought not to worry about but cherish this idea of real world telematics is something that people really need to work wake up to, because at the end of the day, you have a choice to turn off your phone and turn off your computer. And you think you might now be undetected by, say, a rogue corporate or a rogue government or, you know, some other entity that might want to track and control. But you’re not. There’s surveillance all around us. And that data, I think, is much harder, much less personal.

[00:37:59] It’s harder to proceed because it’s it’s that blinking camera you probably didn’t notice is trained on you. It’s not a phone you’re holding in your very hand. Right. So it’s a little more abstract, but I think it is a greater threat. But simultaneously, I think it’s also a greater opportunity. And this is part of the balance that I see. You know, I’m very pro-business. I’m pro organization, a pro humanity. But I’m also very strong pro individual. Right. But I think you can balance those things. And I think real world data is the battleground, actually. And, you know, the world the war is already underway in many ways. Your social media outgassing is just today’s example of your CRM and banking information. In my analogy earlier, Google is not looking for a new way to manage their data. Facebook is not looking for a new way to manage the data. So they probably should be. They’ve gotten about this problem for for sure. But again, you know, this real world, I think is underestimated and is still new. There’s no paradigm that says we already know how to handle Latin and you’re disrupting that paradigm. So it’s a great opportunity for us to come in and say, look, let us start with this data in the right way. And if I might just highlight what I think is special about that one, to support businesses in being digitally driven and being data driven. Be that online or real world behaviors they’re measuring, I think is a societal good.

[00:39:27] I think it’s good for business. I think it’s good for governments. I think it’s good for individuals because it’s all about doing more with less. And we are very much in a world where we need to do better with the resources we’ve got because new resources are hard to come by.

[00:39:41] And guess what? People. People’s comfort, people’s standards of living, people’s safety. These these are real concerns. And how do you continue to advance the world from a perspective of human well-being while at the same time respecting the constraints? We have very real constraints on resource use so that we’re not burning our environment and wasting our resources. At the same time, we’re trying to lift people out of poverty and keep people safe. And in a lot of ways, that’s a frustrating equation. But I’ll tell you, the path through needs good quality data to make those decisions. I don’t know what the decision is. I don’t know what the data will tell us. But I do know that’s the path through. It’s not through human intuition and arguments online. And all that stuff is just too easy to politicize. Real data, I think, is the only path to making May striking the balance that we must make. If we’re going to balance these societal values. So I think that’s clear in my mind. So how do you do that? Well, you know, number one, that is you have to recognize the job is not to deprive organizations of data, because if you do that, they’re going to keep doing it the way they’ve always been doing it.

[00:40:49] And I don’t see that as any innovation. And you’re going to slow progress. And. Right, Tom, you don’t want to do that.

[00:40:56] Yeah. You don’t want to do that. And the human or your mistakes are limited. Right. So we want to supplement with good quality data. At the same time, we don’t want organizations to think it’s OK to steal the data. We don’t want them to think it’s OK to hide it, to do things behind the scenes. And in many cases. You know, a lot of people’s fears are overblown, but they are derived from, I think, a meaningful fear about things being done out of sight. That transparency at the end of the day is what’s really called for in many cases. And, you know, there’s going to be some bad actors. But the majority of business want to do the right thing.

[00:41:40] I really don’t think that they you know, I mean, about, you know, corporations being default evil or governments being default people is right. I think by default, they want to do the right thing. But, you know, it’s it’s it’s possible to do evil along the way. I think transparency really helps that.

[00:41:54] So what I want to be able to do is open up the ability for organizations to be data driven. But at the same time, give unprecedented visibility and control about the data that’s being collected by individuals so that they can see it. And they can they can they can make decisions at a granular level. Use it for this. Use it by that. This, but not that in this case. But not that case. And ultimately, maybe even participate in the value creation to the extent that that is something that is possible.

[00:42:25] Because as we go on and we increase more data driven value by automation, I think we do also need to keep in mind that we can’t be impoverishing people.

[00:42:36] And and at the end of day, participation in that value creation system, I think it’s something we need as a society figure out. I haven’t nailed that. I don’t know what the politics of that are, what the right way to do it is. But I do know, again, data is at the core of it. And so we need to begin to unlock it, experiment with it and find novel ways to address it. I think in a Microshares just we’ve chosen one and we’re trying to commercialize it and and see where it leads. And it opens up this opportunity to share data where I think otherwise that that activity would be underground.

[00:43:11] Yeah, that that makes sense and am I like. I think the point that you make around transparency, because that’s become a.

[00:43:21] Again, an issue in the digital world, right? You go to a Web site these days and on just about every Web site. Now there’s a little pop up comes from the bottom of the screen or at the top of the screen at the side of the screen or, you know, says, hey, we collect, we we use cookies. Are you OK with that?

[00:43:41] And it’s in in a lot of ways that transparency might be easier in the digital world because you can just throw a pop up in front of someone and they can say, OK, I don’t know what a cookie is, but fine. I want to read my article in the in the physical world.

[00:44:06] It’s the the the user interface is a lot broader.

[00:44:12] So if you’ve got a if you’ve got a camera, you know, that’s off 100 yards away and a motion sensor and, you know, what have you other sensing devices.

[00:44:27] What what is that what’s the path to transparency around those types of things?

[00:44:33] Tom, I asked a question. Right. So the physical world. The digital world. Right. I think there’s no mistake why there’s been so much innovation in software, because it’s easy. Relatively speaking, to start a software company, I can do it with a laptop. That’s that’s literally what I did. Right. There’s no asset in my career aside from a couple of laptops when we started this. And you can go a long way with no more than that. So it’s an easy area. The barrier is low to innovation there. But, you know, technologists in this space talk about I.T. and OTEY, information technology, operational technology. And in general, we talk about operational technology as being mostly non digital. We’re talking about in building space, it’s h back systems. But, you know, it might be the nuclear power plants reactor is O t shirt, you know, and there’s a whole group of people who worry nothing about those cool technologies. And really what we’re talking about is bringing those two things together. And I think T is the hint that these things actually aren’t divorced in reality. They are one and the same, that every piece of operational technology increasingly leaves a digital exhaust and I.T. has all of these cool tools. Yeah, we might have sharpened the tools on online behaviors, but these tools are at least as applicable to this digital exhaust from Otey world. And it’s kind of merging those two ideas together and then improving on that new branch that they create together. And that’s I think at the end of the day, if you take the buzz out of I.T., Internet of Things, what is it we mean? I actually think it is that intersection of information technology and operational technology. How do you take the best of those things, merge them and get, you know, some of the parts, you know, greater? And I think that that is at the heart of it what we’re up to in practical terms. And to Dan’s earlier question, I didn’t really envision that’s what we would be up to when we first started the company. I didn’t really imagine that we’d be as worried about bits as we are as bite’s.

[00:46:42] And I would say that at the moment my company has to be as equally worried about both because a lot of logistic problems that come along with dealing with the bits of operational technology, present challenges that didn’t exist and the pure I.T. world. And I think as we go along, more I.T. will bleed into into the physical world and the physical world. We come more blurred with the digital world. And in fact, we talk a lot about digital. Twinning is kind of the the buzz word, but it’s the idea that you can model the digital, you can model model the physical and the digital, and to use the tools that we know and love in I.T. to make decisions, gain insights and, you know, control and measure what’s going on in the physical world. And that, I think, is a maybe just a higher order thing.

[00:47:33] Over top of Bayati. You need the I.T. aspect to generate the digital exhaust and you bring the A.I. data management visualization insight into action tools that we’ve been doing in the big data space.

[00:47:47] Mix them together. You’ve got that next order, which I think is something like digital twin. I don’t know what the right word will be, but something like that that allows us to manage closer to the flexibility that we’re used to in digital with the physical in scope. Man, I think that’s where the next, you know, the rest of the century about innovating in that direction. And I think there’s a lot of sidel good to come out of that.

[00:48:13] Well, that I mean, that alone and I’ve got a background in the semiconductor industry and I think about like a lifetime testing, stress testing, that type of stuff and having a having a digital twin or a digital model like that that’s grounded in. Real world, real time collected data. Yeah, very valuable.

[00:48:38] Yeah. And so there’s there’s there’s hope for us, brother. So there’s a lot of semiconductor guys out there. I think we’ve lived in a in many ways. If you’ve been in silicon in any way, there’s been a either boom or bust. But the bust is fun. For more people than not. Yeah. And I think we’re gonna see the end of that because there’s been a lot of innovation in devices. And I am in a perfect position to take advantage of that. So thank you. Hardware guys for suffering through the last 10 years. Still innovating, even though I frankly, the world hasn’t paying that much attention to you. If you weren’t an iPhone, the. But we have this bedrock of innovation that is really not made the market impact yet. That, you know, is is based on investors who have been investing on the promise and really good engineers applying these very durable, very robust engineering practices, frankly, puts software guys to shame the amount of testing and and verification that they did.

[00:49:48] The Otey guys go through it. Right. So it’s it’s a lot more difficult to push out a fix.

[00:49:52] Yeah. And so get it get it right to start with. And there’s a there’s a much bigger downside if you’ve got your nuclear power plant chip wrong with your if your app gets the app store with with a bug. Right. So. Right. There’s all of that.

[00:50:06] And and the great thing is now that we’ve got those innovations and pent up innovation, I think is really what it is in the physical space and the digital coming together with it. I don’t know. Maybe I’m just optimistic. I think you’ve got to be to be an entrepreneur. But, man, I see it as a perfect storm. And we’ve got cloud allowing us to scale digital. We’ve got a pent up innovations in the physical world that haven’t seen the market. And really, it’s up to just somebody to bring them together, scale them up so people can depend on them, do risk them and then learn how to talk about them. So this next wave of business people can just adopt. And then, for God’s sake, I hope we learn the lessons of the past and do it ethically. Right.

[00:50:50] And also do it in a solution based format. Right. Where the buyer doesn’t take all the risk. A tremendous amount of risk at the time, culturally and quite honestly. Like you said, these issues are working on these tremendous technologies, but they haven’t been able to be consumed because they charge too much or they don’t tell the right story or they don’t share the risks. And I find anybody that’s willing to share the risk in the Aoki’s space. My approach here seems to be on the winning side of the equation.

[00:51:24] I hope that’s true. Obviously, selfishly, Dan, but I agree with you. You know, our our assessment of the market fits that in spades in that, you know, if you democratize the technology, you take solution space and reduce it. You’re also de risking the deployment. So if you have smaller variables, you can begin to use some of that OTEY knowledge to shrink the risks.

[00:51:53] But you also have to do the same on the business side. So I would say from microtia side, you know, I’m the CTO, but, you know, give my partners a lot of credit. I think we’ve innovated at least as much on the business model side to say how do we do this as a service in a way that makes us take a lot of the risk out of the hands of the business so that it doesn’t work out. That doesn’t cost them anything. Know, it becomes an operational expense. We hide a lot of the business degree and all of the technical sausage making in a way that does allow it to be solution oriented. And so you also get to try for the kind of fully actually you never really need to make a big commitment. You know, there’s you can if you want to. Right. In some ways, I see maybe our role as maybe Microshare isn’t the always I.T. solution for some of our customers. But I think we’re a great starter that we are giving people an easy way, low risk from a business and technology perspective to try it out. I see my job as showing them the value of the data and opening their eyes to this idea that data driven is real. Has our why it is a solution oriented band. And at the same time can be done ethically, Tom. And once those eyes are open, what’s educational? I think I think you can get business people hooked on it. And then, you know, if there’s other solutions to come that share more risk with the business. Maybe. Maybe. But where we are in the market right now, I think is right. Dan, we’ve got to help close that gap on all from. And I’ve said this for a while, if we believe, you know, in the I.T. space, if we believe that we really do have a great way to unlock it, why are we eating dog food? Why are we asking somebody else to make the bet? I just told my partners, if we really think this is true, we should open our own bag. You put our money where their mouth is and you know, it’s hard to open a bag. So at the end of May, we open Microshare instead. But I think it’s the same kind of notion. Let’s let’s let’s us make the bets, because we are in the best position to be confident about the outcomes.

[00:54:06] That’s right. I mean, and I think the persona of the organizations that mature enough, usually mature enough, that kind of stuff their toe.

[00:54:15] And they realize that data is important. And then the systemic issues are too large that they can throw bodies at it. And you’ve got to think differently with automation. I mean, I think it’s if the market is kind of turning towards these things, but you got to you know, it’s also very hard because you got to find the right person or company to adopt. Right. I mean, those there’s this great matchmaking.

[00:54:36] It’s almost you can think of it like marriage or something that that they’re willing to try and get and go. So I applaud you, you know, to let me win if you think about Microshare in terms of predictive cleaning and infection control. If this was adopted just in the past, then we would have contact tracing. So say, you know. So, yeah. Have you learned from the moment and go that this way of thinking and data can be very helpful for the public good in public health as well?

[00:55:13] And I think, you know, I’ve done a bad job of commercializing ourselves and using this opportunity to send a commercial, but we are working with some real innovators in the return to work space and working with them has been very rewarding. And obviously, we have a very compelling event to take advantage of. So there’s there’s a wind at our backs, but we’re using the stuff that we created newly in terms of contact tracing and using stuff that we’ve been working on for now years. What we really find is that it’s the combination of multiple sensor types brought together that really makes a difference. Right. We’re finding out that people care about cleaning maybe more than the virus does, but being visible with your cleaning efforts in a space helps put people at at ease. And that’s important. But as we get back to work, we’re finding that contact is important to track. But also things like air turnover, air quality probably had a disproportionate impact based on what we’re seeing in new science on whether we’re actually transmitting a virus. But at the end of the day, no one dimension is the key to improving us. And I think that’s the theme of data driven all together. It’s the more types of data, more types of high quality data that you can put together at once, the better view you have of the real world and the better decisions you can make. And, you know, I feel pretty good about where we are both as a business and as just a contributor to society, that we’ve got some innovators who are just a pleasure to work with who are driving this return to work, doing it in an ethical way and and helping us be part of that innovation cycle. Figure out what to really do here. And I don’t think it’s going to go away. I think the world knows that pandemics are possible and we’re going to enter a world where people want to know that their employers have thought about it, even if we get a magical vaccine for coated. Everybody knows it’s possible now. And so why would you go back to just doing things the way you used to? I think businesses will want to change. I think the best employees will want to work for people who care about their their long term health. And they’re watching how people are behaving right now. And yet none of us have exactly the answers. But that spirit of data driven experimentation that is in the marketplace, because there’s really no other choice. I think this is going to lead to more opportunities for everybody in the data marketplace. That is probably our audience for this for this broadcast in Louisville.

[00:57:52] My last question is, how do you envision the future or in the near future under this global context?

[00:58:01] Yeah. Good question. Good question. You know, it’s it’s it’s it’s wide open. So I’m opining in this case. But we spend a lot of time with industry pundits and customers and talking about this. And we were talking about it before the quarantine hit. And I think what we’re going to see is that the trends that had existed before are going to accelerate. And what were the trends? Well, you saw things like we were growing in popularity. You saw people increasingly using digital to disintermediation, the physical. So it was slow, surprisingly slow, but work from home or work remote and distributed workforces. I think it’s been a trend that’s been in place since the dot com boom. And I think this is really accelerating with with the quarantine where people are now realizing that, hey, I can get work done with people remote. But, you know, there’s some lessons to learn. Right. There’s some tools that are necessary. And guess what? Employers, I think, also realize my responsibility to my employees can’t end at the door to the corporate campus if work is extending into the home. And so does my accountability to my employees. And I’ve got to figure out how to do that. How do I.

[00:59:22] How do we ensure people are health healthy and safety and and productive when they’re at home and distributed? So I think what we’re going to see is the world begins to divest of large office, large campus, large centralized places where humans congregate, go more to distributed where, you know, regional offices become more popular. Flexible space that you saw with we work continues to accelerate. If I want people to get together once a month, I have a whiteboarding session. It’s really hard still to to do engineering without a whiteboard around. I find I don’t need an office just to hold whiteboard if I can just read one when I need one. And I think I will fill some of that gap too, to make it better and better to use. The tools are using now for this podcast to do normal business. And that’s going to continue. Right. And I think that also emphasizes people understanding work better, you know, clustering people together. You know, if we work together, then we ought to be housed together. And that reduces the risk of infection. Yes. But it also probably improves our happiness and productivity that we get to be with the people that we collaborate with. I think all of this is going to continue to drive change. You know, 2020 is going to be panic, calming the panic, getting starting to get back to work, I think. Twenty, twenty one. We’re going to see. Let’s start redesigning what we think about work and how we how the Otey environment adapts. And I think but 2022, I think we’re going to see data driven workplaces is going to be the norm and people are just going to want to continue to get better at it. Investment will continue and we’ll be a lot of work for people who listen to the podcast, I think.

[01:01:12] I think so, too. Into your point about coworking spaces like we work. I know there’s a lot of opinions out there that this pandemic is kind of been then nail in the coffin for we were particularly, but take their business practices kind of out of the picture for a minute, I think. I think coworking spaces like that in general will come back and thrive after after 2020 into 2021.

[01:01:49] Yeah, because a lot of companies basically we’ve we’ve tested the theory that remoteness can work for a lot of companies.

[01:01:59] The answer is yes, it works and it actually works pretty well.

[01:02:03] But there’s a lot of people that are realizing that working at home from the kitchen table or from the bedroom and with the distractions of the house isn’t always ideal. That’s right. But they don’t necessarily want to live, you know, in the same place that the corporate headquarters is. That’s right. And so you take that remote.

[01:02:29] CheckBox and then you go with where where do.

[01:02:32] Where do I as a, you know, a human want to live and, you know, spend time with my friends or family or, you know, what I like doing? And I think you’ll see, OK. I live out. Like, for me, I live out in Maine. The company that I’m with is out of Minneapolis.

[01:02:53] And at a certain point, you need an office space to either be quiet, like you said, get access to a whiteboard.

[01:03:01] If anybody has got a really good connected whiteboard solution where I can whiteboard with somebody who’s not in the same room with me, I want to talk about that because I’ve been looking for it for a while. See? See me if you find it. Right. Right. OK. I will. I will. That goes out to our listeners. Please let us know. But, you know, I think that I think that works really well because most of those people don’t want to sign a three year commercial lease and deal with utilities for an office space. And that’s what the coworking provides, a place to get away and get get your work done, maybe be more productive.

[01:03:38] I think one thing is entirely uncontroversial right now, Tom. No company is signing seven year leases for fifty thousand square feet of office space right now. That’s not happening. So what’s the business model to replace it? Because, you know, the reality is we’re also monkeys. And so we can’t escape our preference for physical interaction. And digital does a much better job than it used to. But still, I think, falls short and a lot of people’s minds. And so what we’re what we have to figure out is what the right blend is and then improve in that channel where it’s a combo. All right. Face to face and digital with a lot more digital than there used to be. But I really don’t think we’re gonna get out of face to face entirely. And then it also begs the question of, you know, white collar participation has been remote and increasingly remote for a while, but we all don’t have those kind of jobs. And is that fair? I think the other thing we see is, hey, you know, we’ve got meatpackers who are disproportionately being impacted by this disease. And that turns out, you know, this whole critical worker debate on my high horse about I call myself down. But, you know, it’s very possible to extend the benefits to people who must have some physical proximity because it’s more equipment oriented.

[01:04:55] There’s still Otey I.T. benefits to be made and distributed capabilities and work scheduling benefits and three printing. We’re also, I think, in a finitely, in a space where that can be prepared for innovation. And now I think we also have the incentive to do it around around quarantining and breaking it up. So I think the thing the trends that we saw in white collar work will now begin to expand and do hospitals and mines and factories. And we’ll be talking, you know, about I.T. much more, because if we all need the same machine to manufacture a chip or to sensibly cut up some meat, then there’ll be a lot of conversations about how best to use that machine and how many machines and where should they be and are they being utilized. And that’s the I think that’s the I TOTN next gen of helping these organizations redesign themselves. So everybody is safer. Everybody’s quality of life is improved. And we still, as a society keep functioning because we need digital and we need physical. There’s no getting around those two things.

[01:05:58] Yeah, I completely agree. Distributed manufacturing starts to come into the conversation, mentioned 3D printing, done some work with that. I mean, bringing manufacturing in, you know, different areas, shorten supply chain. So there’s a ton of benefits that start to cascade as you open up that conversation. Yeah, yeah. I think we could have a whole nother podcast episode on that topic.

Thanks, Tim. Thanks for joining us on this episode of All Things Data. If you enjoyed the show and want more, please subscribe on Apple podcasts, Stitcher and others or via RSS so you’ll never miss a show while you’re at it. If you’ve found value in this show, we’d appreciate a rating or a review. Until next time.