Podcasts > Strategy > Ep. 069 - Building business models instead of technology
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Ep. 069
Building business models instead of technology
Ron Rock, CEO, Microshare
Thursday, September 24, 2020

In this episode, we discuss how to monetize data for multiple user groups with different needs and simplify IoT deployments with end to end productised solutions.


Ron is the CEO of Microshare. Microshare provides Data Strategy as a Service, enabling our clients to quickly capture previously hidden data insights that produce cost savings, sustainability metrics and business opportunities. Our solutions create a Digital Twin of your physical assets, providing comprehensive picture of their performance, the risks they face going forward, and the steps required to produce maximum returns from these assets. https://www.microshare.io/


Erik: Welcome to the Industrial IoT Spotlight, your number one spot for insight from industrial IoT thought leaders who are transforming businesses today with your host, Erik Walenza.

Welcome back to the Industrial IoT Spotlight podcast. I'm your host, Erik Walenza, CEO of IoT ONE. And our guest today is Ron Rock, CEO of Microshare. Microshare provides enterprise scale IoT integration for buildings, with solutions ranging from sustainability to energy management, to infection control and predictive cleaning. In this talk, we discussed how to monetize data that have value for multiple groups of users. We also explored the benefits of simplifying deployments by bringing productized end to end solutions to market rather than discrete technologies.

If you find these conversations valuable, please leave us a comment and a five-star review. And if you'd like to share your company's story or recommend a speaker, please email us at team@IoTone.com. Thank you. Ron, thank you for joining us today.

Ron: Thank you, Erik. My pleasure.

Erik: So Ron, before we get into the business, be great to understand a bit more of your background and how you ended up founding Microshare back in 2017. What were some of the more, let's say, influential positions that led you then towards setting up Microshare?

Ron: Well, I pretty much been a lifelong entrepreneur. I like to say I start companies because I'm virtually unemployable. I'd like to stay in Philadelphia. I like to spend time in London as well. And finding a job that lets me do everything that I get interested in naturally, somebody with ADD and lots of ideas, you have to end up building companies that support the kinds of things that I like to do.

So I've had a series of startups, earliest one was in 1988, was one of the first companies to remanufactured toner cartridges for laser printers. I've had some big successes over the last few decades. Also, I've had big failure. I had a dotcom that just exploded in spectacular form; was a horrible experience. And I like to say I spend more time dwelling on that one failure than I do the successes that I've had over the years.

In addition to being an entrepreneur, I had a couple corporate jobs. I was fortunate enough to get a job in the credit card industry in the mid-90s. So I learned a lot about financial services, and that's been one of the cornerstones of my career. The other is I had a job in the early 90s with tele services, the temporary help firm, and learning about auxiliary labor and how we think about managing most expensive resource on our P&L and creative ways to do that.

And then finally, in the late 90s, I was working at a software firm up in Boston called Pegasystems, and that's where you just learn how to bring all of this together in an enterprise software way. So, it's been a balance of entrepreneurial and enough big enterprise experience to understand and be able to connect and empathize with who has ended up being my customers over the years.

Erik: And now I think Microshare, you are styling those different worlds: the technology world, the service world. Explain a bit the core value proposition here. So you're serving quite a range of markets, a lot of different asset classes, but they all, I suppose, share some common needs. What's the kernel here?

Ron: So, Microshare actually was started seven years ago. And we started under a different name. We were Point.IO. And the genesis of Point.IO and now Microshare, my two partners and I have been together since 1999. In 2003, I started a company called Knowledge Rules. I grew that company globally and sold it to Accenture in 2010. And our clients were some of the biggest companies in the world: GE, American Express, her Majesty's Revenue and Customs, which is the IRS of the UK.

And so our pedigree, our background was big, resilient, scalable, secure, regulated data, the kind of systems that we all just take for granted. The ATM machine just works, we don't think about all of the software and code behind these core tenants of society. So when we left Accenture and decided to start the new, new thing, we were intrigued with the intersection of cloud, mobile, and enterprise. Enterprise spent the last four decades and trillions of dollars locking everything down.

And literally, overnight, they were being asked to take the mainframe and move it to the cloud. They were being asked to take that desktop and move it to whatever device we got for Christmas that year, or new iPhones or Androids. And we're suddenly being asked to take advantage of these brand new SaaS, something that didn't even exist eight years ago, these SaaS offerings like salesforce.com or DocuSign. So how do you blend all that data together that regulated, secure, and all these new things going on outside your firewall? That was the genesis of Point.IO. We had a lot of experience helping big global clients do just that.

And so we built out a software platform that represented, collectively, the careers of the three founders. And we've learned a lot about how to do those kinds of projects. Along the way, one of our largest customers, about five years ago, was going to get into IoT, Internet of Things. And specifically, a form of IoT called LPWAN, low-power wide-area network sensors, and I won't get too much into the tech right now.

But the headline is that all of a sudden in the last five years, there are cloud infrastructure providers like Amazon, Google, Microsoft, that provide this super low cost infrastructure. And we have a whole global industry growing around these low power sensors, these sensors that are many times under $20 with a five year battery life. And suddenly, we realized there was an opportunity to put billions of these sensors all across the market. We did a hard pivot about three and a half years ago. We renamed the company Microshare. How do you share the right data with the right people at the right time with complete control and compliance?

And we realized that in IoT, unlike our previous lives, where we were looking at enterprise, maybe thinking about integrating with 200, 300, 400 other datasets in IoT took the problem and put it on steroids. Now I needed to integrate with maybe tens of millions of different kinds of datasets to bring it all together.

And because we were aligned with some Fortune 2,000 companies who were also getting into that space, we also were aligned with a lot of companies in Europe, who were manufacturing, the sensors. There's a global organization called LoRa, that was founded by Semtech. And LoRa is the fastest growing IoT network in the world. Most of Europe is covered with LoRa wave radio waves. India is covered with radio waves. There are many companies looking to do it in the US and Canada. And one of my partner's became the chairman of the marketing committee for the LoRa Alliance.

And so we were able to position our software, which again, we're software guys, we're not hardware. We were able to position our cloud software platform as the glue to hold all these pieces together. And suddenly, with that, our business started to really take off. And that's where Microshare is today.

Erik: And so what does that mean? Does that mean that if I'm a sensor manufacturer, or maybe I'm a company that purchased 10,000 sensors and deployed them on some assets, that I can then take the data coming off of my sensors, and I can maybe monetize this or make this available to my partners or some other class of users using through LoRa? But then on top of Microshares, is that the value proposition to enable the transfer of this data in a secure manner?

Ron: You're right, yes, but with a giant caveat. We were at a LoRa conference in Vancouver three years ago, and some of the biggest companies in the world were there. I mean, the LoRa Alliance includes IBM, Orange, British Telecom, AT&T, and Verizon. All the big boys are in this alliance. And everybody was there and after one or two cocktails at happy hour, everybody looked at everybody else, no matter whether you were big or small, and they said hey, got a customer. The whole market was we realized, and we were guilty of this as well.

Erik, everybody was at that conference selling camshafts, brakes, tires, and engines. And the realization we had is the market didn't want that. The market wanted a car. And so, all of the complexity of IoT: the sensor, the communications, the security, the data analytics, the dashboards, we packaged all of that into business solutions. And suddenly, within six months, we were selling occupancy, predictive cleaning, smart parking, environmental monitoring.

I like to say tongue in cheek, most of my customers today can’t spell IoT. They don't know or care that that's what they bought. They bought a business solution. Imagine going and leasing a car, I give you the keys, you go out ready to go, and there's no tires on the car. And they say, oh, no, you got to go over there and buy tires. Well, that's what was going on in the IoT industry. I had to go to a communications provider. I had to go to a software provider. I needed to go to a cloud provider. I needed to find a sensor.

And then the other issue is most of these sensors, the data in and of itself, is not that valuable. I don't want a smart appliance. I want a smart building. Or I don't want a smart building. I want a smart city. So I need to bring millions of data sources together. And of course, each one different formats, different standards, different data ownership, you touched on something else really interesting in that question about data monetization and data sharing.

We decided to focus on commercial real estate as a vertical. We could have gone anywhere with this platform. But we decided to stay in a non-regulated space. Remember, again, our pedigree is financial services, insurance, health care. But we shied away from those industries because of all the regulation. And we said you know what, let's look at commercial real estate. Everybody in the world has it. It's typically your most expensive spend on your income statement after staff. It's traditionally been underserved from a technology perspective. And when we thought about real estate, we also said let's include hospitals without touching the patient, airports without touching the plane, manufacturing, warehousing distribution, college campuses, virtually any building other than residential.

And so when we looked into the real estate space, it turns out, there's lots of parties that want that data. Increasingly, the people that loan you $100 million to build a skyscraper, they're building into the covenant of their bonds. Hey, we want to see how this assets performing real time. We don't want to wait until the building owner gets informed by the tenant that they're going out of business. So we can begin tracking the real time liveliness of this building.

Secondly, the people that own the building, for the same reason want to know. The people that occupy that building, they want to know exactly how their assets being utilized, the people that clean that building. Well, now if I can start aligning all of my supplies and my cleaning with space that's actually getting used, I have the ability to lower my cost but improve the quality of service complaint in corporate world, Europe and North America before COVID-19 dirty bathrooms.

Well, every day, the team shows up and cleans the same bathrooms in the same order every day, whether they use them or not. Here's a radical idea. Why don't I just clean the bathrooms that are being used? And why don't I clean them more frequently, and align those resources with utilization? And then finally, if a fire alarm goes off, emergency responders want to know who's in that building.

So take a conference room, for example. The finance people want to know. The owners want to know. The tenants want to know. The people cleaning the building want to know. And in an emergency responders want to know, I'm not going to have five sensors in every conference room. I'm going to have one. And then the question becomes, how do I share that data with the right party at the right time? Some people are entitled to it for free. Some are going to be entitled to it because they contractually signed up for it. Some are going to pay for it. That is the core of the Microshare value proposition. That being said, that's our software. That being said, as exciting as that is Erick, nobody was buying it, nobody cared.

Because right now we're still in the process of putting this digital infrastructure, what we call digital twinning, this digital twinning infrastructure in place, which is the billions of sensors, bringing all of these inanimate objects to life, living, breathing, buildings and spaces, and campuses, all of that. None of that is in place yet. And because of LoRa, and the low cost nature of LoRa and the accompanying cloud infrastructure, we've been able to package all of this and begin delivering that at scale.

Erik: I love that origin story, because I encounter so many startups that have great technologies, great ideas, real pain points but don't have proper solutions. Like you said, they're going to market with a great set of breaks, but then the customer looks at it and says, this looks like a lot of work: you have to talk to 10 different vendors and somehow pieces together.

Ron: In the last two years, I've been talking to a lot of entrepreneurs. I speak a lot at various colleges around entrepreneurism and the programs. How do you do this, right? Seven years ago, I went out to a Silicon Valley investment banker, a venture capital, I was so proud of our technology. I thought, man, wait till they get a loan to this. And I walked into this office in Sand Hill Road, and the guys are sitting at this conference room and they're staring me down.

And I go in and I get to slide three on the 15 slide deck. The big guy at the end of the table, he just says stop. And he takes my deck, which I printed out for them. He taps it on the table, and he tosses it across the table back at me. And he said, Ron, we don't invest in technology. He goes, anything I can dream up, I've got a global force of programmers that can build it for me. We only invest in scalable business models. And I remember leaving thinking that was the biggest jerk I ever met. Now, seven years later, I think he's the smartest guy I've met. Because it's the scalable business model that the entrepreneur needs to be successful.

Everything else, you can beg, borrow, steal, buy everything else. But you've got to have that solution that a market understands and they're willing to pay for, and you can deliver a lot of it. That is the point of combustion for startup companies. And if I look at our history, starting out at Point.IO, and pivoting to Microshare. Even when we pivoted to Microshare and we said hey, we're going to be in the IoT business, we were selling a software platform, nobody wanted to buy a software platform. There was no market for it.

I mean, there were occasional, very expensive, what my partner would call science projects, where people were interested in what you were doing, they hire you, they give you a lot of time and material, hourly rates. But that's not a scalable business. That's a consulting engagement. And at the end of the day, it doesn't generate a growing business. And so, it was the epiphany of packaging, absolutely baking our software in the middle.

So, at the end of the day, our core mission as a team, drive as much data through our SaaS software platform possible. But in order to do that, we needed to back up and wrap a whole lot of other things around that, so suddenly, people care about consuming our software. So scalable business model, if there's one thing anybody takes away from this podcast, let it be that.

Erik: Well, let's get into the business model a bit here, then because you've just described a wide array of people that find value out of these solutions that are deployed. And then the question is, well, who's paying for it? I mean, is it the financial institution? Is the asset owner? Is it the occupant? Is it is it you? I mean, it could also be that you place it there bear that cost and then rent out the data? What is the business model that has worked best for you, and maybe you've experienced or experimented with a few so far?

Ron: We have. So if you look at the real estate industry, as I've defined it, and you look at those five pillars of interest, there are people they're interested in buying the data or think they're entitled to the data. But who's going to actually pay for the infrastructure to put in place? And we also adopted a disruptive pricing strategy. We provided as a pure OpEx model, it's a per sensor per month fee, all in.

Going back to my analogy, we're not charging you extra for tires. Well, in IoT, we're not charging you extra for backhaul communications. We're not charging you extra for your cloud consumption or your storage. We're not charging you extra for your apps. The whole thing is one clean bundle. And it's a per sensor per month. We share our pricing. You can get a discount based on two things: the number of sensors and the duration: 12, 24, 36, or 60 months. So it's a pure OpEx model which takes a lot of the complexity out of getting the infrastructure in place. And for us, we like that because we're building an annuity, we're building a long term revenue stream.

Within the commercial real estate space, as we defined, there's all of those constituents. We find that the people financing the buildings, they're interested. But they're just as happy to buy the data or they have all the power. They're loaning you the money so they can bake that into the covenant.

The building owners increasingly are interested in buying this because it's a way for them to keep an eye on their tenants and how their assets are performing. But what we found early on, Erik, in the value chain is the people that really are in trouble are the facilities management companies, the [inaudible 20:56], the Sercos, the Sodexo’s, the ISSS. The companies that are hiring global workforces of minimum wage employees, a high churn, they physically show up in these buildings, and they clean, they supply, they repair.

Companies like Karelian in the UK was too big to fail, went out of business three years ago. And they were billions of dollars in the UK. You look at these facilities management folks, and they've got a business model that's upside down. They've missed the wave of technology and innovation. So they have to get efficient.

This idea that I shared with you how do I align my cleaning resources with my utilization to increase customer satisfaction and decrease cost? We have clients in the UK a company will go and bid to a big global bank, they'll have a couple of million square feet, and companies will bid for the cleaning contract. Increasingly, they're bidding the contract at a breakeven or a loss. And then the only way they make extra money is with service level agreements, customer satisfaction, responsiveness. Well, how do you do any of that if you're not measuring the actual utilization and real time feedback and occupancy of space?

So early on, pre-COVID-19, we found that the facilities management of everybody in real estate, that seemed to be the low hanging fruit. They can most easily justify putting in this infrastructure, because they get to save money, increase margin, and then begin negotiating with all the other folks in the ecosystem who want that data as to how they're going to be compensated for the data. So that was the primary go-to market.

That being said, it's not binary, all in. So we found some tenants. We have a couple of global clients that people would recognize, where they decided that they were going to sensor up their buildings. And what's the ROI for them? What we're finding is that office space in general, before COVID-19 is only 50% utilized or less. And we were working with some large clients in the UK, where they were under a lot of pressure to build a new building. And a new building out in the suburbs of London all in, that's a £15-20 million endeavor. There's the dirt. There's the building. There's the parking. There's space.

Well, before I do that, I better make darn sure that the space I have is being used.  And guess what? With our occupancy sensors, we found it wasn't being used. We found there were other reasons why there were popular parts of the building. And there were unpopular parts of the building. And so yeah, the popular parts always seemed overcapacity, but they weren't leveraging and maximizing all the other space that they had.

The other thing that's changing a lot is how many parking spaces do I need? As Millennials come into the workforce, more people riding their bike, much more people taking public transportation, dynamics like that change. What about by bathrooms? As we go to more unisex bathrooms and more flexible workforce where we're moving away from 9-5 and more dynamically li scheduling, folks, so all of the things that we take for granted as the normal office building are now up for grabs.

And you can't answer any of that without putting granular level sensors in around occupancy, predictive cleaning, environmental monitoring, leak detection, waste management, all of these things, refrigeration doors. How many times people are using the cafeteria? How do you beginning to alarm utilization of my building with a weather forecast? I can now look at the weather and determine how many people are going to be in the building three days from now, I begin to develop those patterns. And I can adjust all of my consumable resources, from food and supplies, heating and air conditioning, electric consumption. All of that I can begin to align with actual utilization.

So we found that building owners, we have several clients that have stepped up in a big way. As we've moved to a COVID-19 world, we see building tenants becoming more and more the lead customer. We see the facilities management companies scrambling to keep up and it's a big opportunity for facilities management if they get it right. But right now, there's a huge demand post-COVID-19 to make building safe again. And that has certainly changed our business model.

Erik: I'm sure this is not all of the use cases that you actually do cover. But the ones that you've already covered, this is quite a bit of territory in terms of the range of sensors involved, and to some extent the operational experience required, right? We do a little bit of sourcing here at IoT ONE, somebody needs sensors for some particular use case, they'll contact us, we'll go through this process. I mean, it's not brain surgery, but it requires a bit of work. And I imagine for you, this is a fairly significant operational, also, maybe a risk.

But if you have a flat fee, you have to make sure that field that you're offering meets service requirements so that the sensors operate efficiently in the building, and you have to negotiate these contracts. What do you have, like a database of hardware that you've vetted for different solutions, maybe different quality grades, depending on customer requirements? Or how do you piece together each of these solutions and determine what is the right fit for a particular situation?

Ron: So remember, we started putting all of our energy behind the LoRa ecosystem. And while our software can ingest, annotate, and store and share data from any source, we decided, again, when one of the components of a scalable business model is defining very clearly what you do at scale. Every one of the sensors that we go to market with is a LoRa sensor. Our software platform is front ended with all kinds of API's. We now work with global SI firms, like Atos, like Cognizant, like PWC, who build custom applications and custom integration on top of our platform, because you can absolutely do all that. But I don't want to be in that business. I want to be in the software business.

So my software has my Azure platform. It's built around Azure IoT Hub and all that infrastructure. It's already pre-integrated into Power BI and data bricks and all that cool stuff. And on the front end, remember, my partner was chairman of the marketing committee of the LoRa Alliance, we had the ability for the last three years to meet sensor manufacturers who are coming to the alliance with products before they were even available yet. In some cases, we even had the opportunity to influence them, where we had customers that had a need, and we needed to get a sensor to accommodate.

And so at a macro level, all of our sensors, you can integrate our platform to any, but the ones that we sell are all LoRa based. And within LoRa, there are some standards. The analogy I use though is anybody that had Bluetooth headsets or Bluetooth connected to their car 15 or 12 years ago knows that it was always a little quirky. Nowadays, it just works. But there was a time you'd have your car set up and you'd go into your buddy's car, and your Bluetooth didn't work. Or you got a new set of headphones and you're hooking it up to an Android or an iOS phone and it was quirky. LoRa is where Bluetooth was 10 years ago.

So while there's a standard, there's a lot of nuance in all of these. And also there is no standard around data formatting, data packing, data encryption. So we developed something called a Data Domain module. And when we pick a best of breed sensor, we create a data domain module that takes all of those anomalies out of it. So regardless of the formatting, the packing, the encryption, the content, whatever it may be, by the time it gets into our Azure instance. It's a well-documented, annotated piece of data in a standard JSON format that's ready to get consumed in any way that end users will need it to be.

So that's how we ended up vetting our sensors. We found a lot of sensors that just didn't work. We found a lot of sensors that worked well. And we've managed to align ourselves with the manufacturers so that we get that influence, we get that relationship, so we can work under the covers with the manufacturer to get things done.

One of our most strategic partnerships is a French company called CarLink. We've made the front page of the Financial Times, The New York Times, BBC in the last three months, because of our contact tracing solutions, that was co-developed was one of the parts that I'm describing, called CarLink.

Erik: In that contact tracing use case points towards more of a macro type solution, so some of these are fairly micro, you want to know the status of a bathroom that's only maybe interesting to a certain group of people. But some of these so contact tracing will be one. Potentially, energy efficiency, there are others that might be interesting across a larger dataset. So if you have visibility into 10,000 buildings and what's happening in 10,000, buildings, there might be some data set that at a macro level is interesting to a particular buyer but that's not interesting at a micro level.

Because I suppose the business model that you've described, usually, probably the buyer is operating more or less at a micro level, like identifying how their specific data points are useful to a known group of stakeholders. But you have the opportunity to look at it this potentially from a macro level and say, okay, we've deployed this across X-thousand buildings, what can we do with that level of data? Is that something that you're looking into now, or that you would plan to look into as an extension business model?

Ron: We absolutely are. We always think that the Microshare business model has a lot of legs for the future. Going back to my comment about microsharing of the data, and that nobody was buying it, we were way ahead of our skis. We know the problems and the opportunities that customers are going to have, over the next three to five years as they start deploying.

If folks go to our website, you'll find some whitepapers around data ownership framework, around data monetization and global data mart. And so we've thought through a lot of the complexities of a dynamic real time data exchange that will allow people to buy and sell data across all different kinds of industries, both personally and professionally. And we meet with a lot of companies around the world that are thinking about those kinds of use cases.

Right now, as it relates to COVID-19, none of us knew what contact tracing was 15 weeks ago, and all of a sudden, everybody in the world is trying to figure out contact tracing. And we took contact tracing, and we bundled it with five of our other solutions for a clean equal safe campaign. And we're helping companies all around the globe, some of them really big, begin delivering non-cellphone based, non-app based contact tracing. And we're using a combination of Bluetooth and LoRa to make that happen.

And we started talking to all of our clients about the idea that contact tracing is a global problem. It's not a competitive advantage if you're in mining, or utilities, or manufacturing, or food distribution. The data around how human beings behave, and how we interact with each other, and how often we need to be reinforced to avoid contact tracing, there's some global macro trends there.

And so it's early on, Erik, but we're trying to convince all of our growing list of customers. And I that is defined MicroShare since the second week of March has been COVID-19, contact tracing and clinical safe. And we're trying to defer to work with our customers to begin participating in a weekly program, where we all contribute and consume the data to try and figure out best practices for avoiding contact events, and doing what we can to help shut down the spread of the virus.

And the spread of the virus has really three key components. It's occupancy, too many people in one space. So how do I monitor that real time? It's air quality. We now know that certain temperatures and humidities are more conducive for the virus to spread or not. And the third is contact events: people that are closer than six feet for more than 10 minutes. So if we can get that data at a granular level across the globe, across many different industries and begin understanding human pattern behavior on that, we think that's going to be a big win for everybody to at least help get people back into warehouses and factories where we need them, but making sure that we're keeping them as safe as possible.

Erik: And it's definitely a shared burden, like you said, I mean, the competitive advantage, well, if the factory next to me shuts down, or my competitor shuts down, maybe that's a little bit of an advantage. But really, I just want everybody to be operating, like a functional economy again. There's a company that you might be interested in, it's called Terbine, T-E-R-B-I-N-E, and they do basically a IoT data warehouse. They use public sources. So they're focused primarily on transportation, and then also things like weather data. And then similar to as you explain, they capture all the metadata, and then they allow monetization of this.

I just got the notice from the CEO yesterday, that they're now opening this up to commercial data sources. So maybe it's worth looking into this and seeing whether this would be a potential channel to bring your data out to the market. But I don't know if it would be a competitive solution, I think right now not a competitive solution.

Ron: I will definitely look into them. Our core value proposition, again, is the data, the annotation that control the audit. As you begin to monetize the data, how do you create the infrastructure to allow that to happen?

I like to use the analogy, the traffic outside your building, right at this second is worth a penny. The traffic in front of your building 10 minutes ago is worth a 10th of a penny. The traffic in front of your building for the last 10 years, it never goes to zero. So data has a high value, the closer you are to real time, the more valuable the data is. So how do I think about selling access to real time data? And how do I manage those contracts at a global scale to allow that exchange to happen? And they're the kind of things that we think are coming down the road.

Obviously, I was working with a company, your personal healthcare data. You've got a wearable. You've got your eye watch. You've got shirts. You got Nike with sensors in it. And right now we consider all that data to be private. Well, what if I came to you and said, hey, Erik, I'm going to charge you $19 a month, and you're going to give me all that data? You say, well, why would I do that? Well, because we're going to monitor that data and we're going to monitor 100,000 men just like you, your age, your weight, your activity level, everything about you.

And every time one of them has a heart attack, their data looks just like yours a day before. What if I could start predicting your health or catastrophic health events before they happen? All of a sudden, there's a huge value exchange. And suddenly, what was personal and private data that you wouldn't share with anybody, suddenly, you not only share, but you pay somebody to do something with it.

And we think that's going to happen with homes and cars and cities and operations everywhere. And some of this data is competitive. And some of it like the COVID-19 is not. It's driving efficiencies around ESG and sustainability. We're not going to reduce our carbon footprints as a planet, unless we start sharing all kinds of data around best practices and how we're doing that. And so we think that we're right at the beginning of this global data sharing opportunity. And we've got some patents pending around exactly how we do that at scale.

Erik: Just to dive a little bit deeper into the business model, you mentioned earlier that it's roughly a price per sensor, subscription per sensor. What is the range that we're looking at? I'm sure we have quite different costs of hardware here, potentially different service costs for you as well. But can you give us a rough range for what that might look like for a couple different solutions?

Ron: Sure. So again, take as much friction out of the process as possible for customers to buy. We did something radical. We took most of the sensors that are in the highest demand, occupancy, cleaning, environmental monitoring, contact tracing, asset zoning, we threw all of those sensors into what we call category one. And every sensor in category one is exactly the same price. Even though on the backend, we have very different cost structures, we found ourselves couple quarters ago with procurement people painstakingly worried about a leak detection sensor and how many versus occupancy sensors. We realized, wait a minute, let's just take all this off the table.

So 99% of a solution that customers want are all category one; mix and match as many as you want of any one of them, so exactly the same price. They range is based on volume of commitment and term, the two variables as high as $12 a month and is cheapest $2 a month. We have found that to be disruptively cheap. We just beat out to global well known competitors in a deal in Australia and procurement went all out. It was a very exhaustive analysis. Not only did we check every box has the best solution, they said, oh, by the way, of all of the final proposals, you guys were by far the cheapest.

So we really are trying to be the best, the fastest, the most inexpensive. So with that kind of pricing, when you get to certain volumes at $2 a month a sensor, I like to say to many customers, come on, folks, that's almost free.

Erik: I mean, if you have some sort of fairly easily calculable headcount cost or OpEx cost, their energy, whatever that might be, then it should be pretty easy to figure out the business case at those price points, right?

Ron: That's right. And at those price points, people are tying this to our earlier conversation, Erik. Most people right now are still trying to justify that within return on investment, a cost savings. The real opportunity is that $2 a month sensor can probably generate $100 a year of data monetization. So that's where it's going to be so cool to see the data monetization movement come in, on top of the infrastructure. You can't cost save your way into innovation.

Cost savings is a temporary band aid fix. If I save you 10% this year, the only thing that happens is next year your budgets been cut by 10%. And then somebody says what are you going to do for me this year? You can't keep cost saving your way to a competitive advantage or a sustainable company. You have to innovate. You have to create new business models and new revenue streams. And all of that layers on top of, but at $2 a month, I can get my technology in the door just on the cost savings you're going to get right now and then keep working with you to get you to cross the chasm into data sales.

There's legalities around who owns it, privacy, regulation, different countries, different states have different laws. So there's a lot of things that get in the way of pure data monetization, but we're going to overcome them. The analogy I use is now you can take your Visa or MasterCard and go anywhere in the world and buy anything and you're totally isolated from government regulation, currency exchange, all of that. Visa, MasterCard took all that complexity, and made it so simple for you to use. We're going to get there with data as well. We're just 15 years behind the Visa, MasterCard networks.

Erik: I mean, we encounter this all the time that there's a long list of valid but challenging to quantify benefits. And then there's the direct cost savings and end of the day, those are still where the decisions being made, even if the buyer understands the other benefits, but just because they're challenging to quantify and there's not a strong track record there to point to.

But I think as you said this data monetization, that's the big win here. And it probably needs to be as simple for the customer as yes or no, maybe also with some check the box, what type of metadata do you want to share, how much do you want to anonymize this. And then you or somebody else would go and actually do the job of monetizing the data. What do you think is a timeline for you to have this kind of solution that that your customers can just opt in and say, please monetize my data and then just deposit whatever earnings there are that are my share into my account? What would be the timeline there?

Ron: I think that's three to five years away. The technology is there. It's not a technology problem. It's building a global marketplace in a very sensitive topic. GDPR is the data privacy laws in all of Europe right now. Within the United States, states are adopting their own privacy laws. So California is the most egregious right now in the States. We see the battle is going on between Google and Apple and the positionings, and the posturing that they're taking on all of that.

So, putting it into a simple example, in real estate, if I own a skyscraper in Manhattan, and I'm the building owner, and I put sensors on that skyscrapers so I know how many people are going in and out of that building and I know how many people are walking in front of the sidewalk in that building, is that my data?

Well, suppose I'm leasing the ground level to Nike, and Nike announces a new sneaker tomorrow, and I've got data that shows what traffic was walking in front of the Nike store the day before. And what traffic people are walking into the Nike store the day of, is that Nikes data? Or is that the landlord's data? It gets really sensitive.

And of course, companies like Thomson Reuters, they want to buy that data all day long, because anybody that gets an early insight into whether Nike’s new product launch is successful or not has a competitive advantage to buy or sell on the market. And now we're talking real value. The challenge you have is who owns that data at the end of the day?

So a simple box checking yes or no, most people are going to check no, because they scratch their head and say, I don't know if I'm allowed to check yes. And that's going to be, I think, the biggest hurdle.

We were working with a company in the UK, they monitor your energy consumption in your apartment. And they came up with some really cool technology. It's a single sensor on the inbound power line. What they discovered is when your refrigerator turns on, there's a reverberation back through the power line, and their sensors, were able to pick it up. The digital signature for your refrigerator was different than your vacuum cleaner, different than your teapot.

So suddenly, they knew what appliances you were using when. Turns out when your refrigerator motor was going to go bad, it changed the digital signature. They took all this data and dumped it through machine learning. And that's how they begin to identify these patterns. So all of a sudden, they had all this incredible data about how you use all of the stuff in your apartment. And they realize that British Gas wanted that information, the electric company wanted that information, the appliance manufacturers wanted that information, here's real time how your stuffs getting used the repair network wanted that information.

So they found that they could monetize the data. But they weren't sure it was their data to sell. So what they did, it was very clever. And they went to their customers and they said, hey, we think we can sell your data about your utilization, we're always going to keep it anonymize. They don't know that it's Ron or Erik, and oh, by the way, we'll split the revenue with you. They proactively began to build out a marketplace.

My example with wearables and the exchange of value for your healthcare information. And the example I used earlier, you can bet, Nike gets a sense that their stocks being shorted or sold, sooner than anybody else, Nike’s going to get to the bottom of where that data came from. And all of a sudden, you're going to have a conversation with your landlord and say, whoa, whoa, wait a minute, you violated our privacy rules, that was not your data to sell. So I see that as the example of the biggest barrier.

And so that's why I think it's five years or more, because most of the outreaches I'm getting right now from the press and from industry analysts, people want to talk about data ownership and data privacy. And COVID-19 is going to put this on steroids because contact tracing is suddenly front and center. And so who owns your data while you're at work, potentially infecting other fellow employees? Is that your data or is it your company's data? There's no fast fix to these conversations.

Erik: Well, this is going to be a fascinating one. We have so much data on online platforms, on Facebook and Twitter and so forth, which these platforms are able to monetize, simply because they were they were new enough in a new enough territory that there's no regulatory framework. And unfortunately, you're operating in public spaces where there's a lot of legacy. So hopefully, this is figured out. For the government that does figure this out, first, I think the big competitive advantage in terms of actually allowing companies to scale up solutions, because this is certainly going to be likely a situation where whether it's China, Europe, the US, some region is going to be significantly ahead of the others. So let's root for the US here.

Ron: I also think this isn't a one and done. I think the data ownership is a 20, 30, 40 year journey, just like the Visa MasterCard network is always improving and different rules for merchants and returns and fraud. And it's not like we solve the problem and we're done. I think social events over time like what we saw with 911, how people put their personal privacy rights aside to allow the government to track phone conversations. So, there are moments in time where we loosen our standards, and then they come roaring back again.

And I think the companies like Facebook and Google, frankly, most consumers have no idea what they're doing and how they're doing it. We all blindly hit ‘Accept’ and get on with using their products. And so there's a growing awareness now around data, but we're just at the beginning. And so as that awareness grows, as people come into the workforce, you're just going to see the attitudes and the laws and the ebb and flow and change.

One of the powers of Microshare is our platform is designed with all the knobs and dials. Whatever's changing, if I need to monitor by region, by state by industry, by new regulation, it all is configurable on the platform. And that's one of the reasons you end up using a Microshare versus diving in and building these solutions bespoke.

Erik: Ron, we're at the hour here, I know you are, you're jumping onto next call, really a fascinating company that you built. Wish you a tremendous amount of success in the future. Thank you for taking the time today.

Ron: Erik, thank you great questions. Really appreciate it. And yeah, stay safe.

Erik: Thanks for tuning in to another edition of the industrial IoT spotlight. Don't forget to follow us on Twitter at IotoneHQ, and to check out our database of case studies on IoTONE.com. If you have unique insight or a project deployment story to share, we'd love to feature you on a future edition. Write us at erik.walenza@IoTone.com.

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