The Data Driven Podcast was joined by Ben Emson, the Chief Technical Officer of Topolytics.  Topolytics is a data analytics business that is making the world’s waste Visible, Verifiable, and Valuable.  Topolytics’ customers use its WasteMap to help them gain visibility and control of environmental data immediately or over time, turning complex and varied metrics into useful information for management teams, regulators, customers, and communities making it engaging, sensible and actionable.

Some of the topics that Ben and I discussed:

  • Ben’s introduction to computers was a Commodore PET
  • His first owned computer was a Sinclair ZX Spectrum
  • How learning how to program a computer changes your mind-set
  • How using data is different than writing code
  • Data value increases through layers
  • How data can empower the building and strengthening of the circular economy
  • The necessity to ‘Innovate Out of Problems’
  • Data can be malleable

See the full transcript below:

Michael Waitze 0:01
Hi, this is Michael Waitze, and welcome back to the Data Driven podcast sponsored by is an artificial intelligence and machine learning application lifecycle management platform is built on an integrated set of frameworks and accelerators to help data scientists build cognitive solutions quickly and easily. Today, we are joined by Ben Emson, a Director and the Chief Technical Officer of Topolytics. Ben, how are you doing today?

Ben Emson 0:31
I’m very good. Thank you very much for having me on the show. Michael, very, very kind.

Michael Waitze 0:36
And I will say this to you sound great. It’s so great to be recording with someone who has a killer microphone.

Ben Emson 0:43
Oh, thank you. Yes. I guess a few years back, I created a whole load of online courses. And my MacBook microphone just wouldn’t cut it. So I did invest in a good one. So yeah,

Michael Waitze 0:57
Yeah, sound matters. Okay, makes a difference makes a huge difference. Okay, let’s get a little bit of your background for context. And then we can jump into the main topics.

Ben Emson 1:07
Yeah, so Ben Emson, CTO Topolytics. We’re a small tech company based in Edinburgh, Scotland. Yeah, we’re trying to sort of tackle the world’s world’s waste problem. So we’re trying to, you know, help enable a circular economy digitizing the circular economy. I’ve been involved for the last, I guess, four or five years now. But yeah, way back, I was involved, you know, a number of startups and a number of big organizations. And I guess technology has kind of been in my DNA. Although, from my parents’ side, they are complete technophobes. My brother can’t stand technology. You know, my father’s quite interested in it. But yeah, I was slightly the black sheep of the family from the sort of technology point of view.

Michael Waitze 2:00
I was gonna say, at some point, maybe you felt like the black sheep. But now technology is life. Right?

Ben Emson 2:06
Everybody knows. Absolutely. Yeah, absolutely. It just sort of, I really believe if you are, have technology skills, or even just the sort of the approach of thinking like a programmer, not necessarily as program be able to sort of stitch things together. I think that’s really important for this, you know, this where we are in the world at the moment, you know, you’re only going to be using more API’s and more services and more things, and you need to be able to sort of stitch them up and understand where things go wrong. So absolutely.

Michael Waitze 2:38
I mean, I spent my entire career trying to figure out how technology could make businesses more productive, but I could not program. I took some programming courses when I was in high school, but I just wasn’t interested in it, maybe because I wasn’t that good at it. But I knew that this mattered. And when I got to work, I said, this is what I’m going to focus on. And I did use tech a lot, but I could never program it. So I completely agree with you.

Ben Emson 3:01
I remember when I was at school, we had a little library, and I remember being fascinated by computers. And there was a some, one of the older boys was using a Commodore PET. And I remember sort of peeling back the books to sort of watch what he was doing and not wanting to sort of green screen. And then a little bit later, my father gave me a, he was very, you know, very encouraging of new things. And he gave me a Sinclair ZX Spectrum, the well. And that was it, I was just completely hooked. And I remember going off and, and asking the older boys how to sort of make a cursor move across the screen. And all these are getting these little snippets of code. And that I was just always fascinated by that. And so bumbled along, but eventually, I sort of taught myself to program and then got myself into university, doing programming and things and, and then and that was it, I was away. But it’s one of those. One of these things, it actually, I think changes your mindset. I remember when I first started learning, it was almost like I was sort of stretching this new kind of muscle that was in my brain. Once I’d sort of gone down that route, everything. You know, you look new when you’re trying to make decisions, you approach it with a very much a sort of logical approach. Certainly sort of been good for me through my life. But yeah, and it’s a good skill to have.

Michael Waitze 4:24
If you spend your entire life at work like living inside of algorithms or living inside of nested if-then statements. It just becomes part of your regular life as well. Right? So because I spent so much time on a trading desk, the whole world to me looks like some kind of trade, like, how do I get enough information to make the right decision? How do I create the right decision tree so I can figure out in real-time how to do this because that’s what I did every day as well. Go ahead. I interrupted you.

Ben Emson 4:49
Yeah, no, no, absolutely. And, and I wouldn’t also, I think like all these things, it’s a balance. Yeah. I think there is a real place for creativity. And it’s so easy just to sort of just have the logic and so on. But you need to almost see out of the corners of your eyes. And that’s where the innovation comes in, you know, being able to understand what’s going on in the environment or, you know, the sort of technology sector that you’re in the sector you’re in, and then just being able to apply things differently and look at the world through different lenses. Technology now, there are so many things that you can apply to problems that you would never think like waste, for example, if you just take a step back and look at that, and have the sort of technology skills, then then I think there are lots of opportunities open to people.

Michael Waitze 5:38
Do you still program now?

Ben Emson 5:40
Yeah, yeah. Yeah. Yeah, really. So I program in Python and Ruby, and originally did Java. And I’ve done a bit of C sharp. Oh, I did. I created a course on elixir as well, which was, which was quite fun. I like elixir.

Michael Waitze 6:00
Excuse me.

Ben Emson 6:00
Alexa. Yeah. Well, there have done that Alexa programming. I have programmed my Alexa at home. So

Michael Waitze 6:07
This is what happens when somebody from the United States speaks to somebody from the UK.

Ben Emson 6:11
Yeah, yeah.

Michael Waitze 6:12
We only hear the words we know. Are you originally from Edinburgh?

Ben Emson 6:17
No, no. So my father is in the military. And we moved all over the world. So I was actually born in Germany. But that was a sort of military hospital, right. And then we lived in Canada for two years, which was incorrect. I was it that kind of age, said 910 11, that kind of age. And my father would by his presence that we’re kind of out of our age range. So he bought us a motorbike and a buggy. And it was like a Honda Odyssey It was called. And we used to hair across the prairies, we were writing this big training area, which was like the size of Hampshire is massive. And we used to get across. And to start with we didn’t, it was very feral. We didn’t even have helmets. And I remember getting pulled over by the Canadian military police, for riding without helmets and stuff. And so we had to sort of go back to the cap in hand and, and do that. But it was a wonderful, wonderful kind of upbringing. And very, it taught you self-reliance, you had to sort of figure out how to look after yourselves. You know, when it was cold, it got down to minus 46 degrees centigrade. And any part of your skin that was exposed, you get frostbite in two minutes, after, you’d have to be very careful. But it was amazing.

Michael Waitze 7:30
And my dad was in the Air Force. So we moved around a lot, not outside the country. We were supposed to move to Japan with him. But in the end, that just didn’t happen. So I get Yeah, there’s this there’s a special kind of response you get from kids that move around a lot. There’s resiliency because you have to learn how to adapt to everything.

Ben Emson 7:50
Yeah, I think that’s a fantastic skill to have. Because we will always move because we’re always moving, we had to make new friends in these new areas. Yeah. And quite at an early age, I learnt how important a sense of humor was. It really breaks down barriers, especially if it’s not pointed at someone if it is, if it’s more pointed at yourself, then actually it breaks down barriers. And people sort of become, you know, much easier to talk to, and you can you know, helps make friends. Yeah, that’s always served me well, I remember doing an army course I was going to return to join the army. And having we tend to do this whole sort of, sort of two, three days exercise. And at the end, the lorry was just in front of us where we’re going to sort of finish. We’re jogging up this lorry. And just as we got there, it drove off. And, you know, we then have to do another 10-mile hike or something. And I just remember just being absolutely despairing. And then just thinking, I’m just gonna make a joke out of this. And I cracked I can’t remember I said, but I cracked some joke. And it just completely changed the mood of the situation with everybody else. Yeah, I kind of realized then that it’s a real bonding and powerful sort of social tool to developing yourself.

Michael Waitze 9:03
Right. So I actually said this morning to the guy that I was recording with as well. I said to him, you’ve got a great sense of humor, and I love talking to people with a great sense of humor. And he goes, Yeah, most people don’t get it though. But it was really, but again, it’s disarming, right? Because once you start laughing with somebody, everything kind of melts away. Anyway,

Ben Emson 9:23
Absolutely. Yeah.

Michael Waitze 9:24
Yeah. Anyway, so why did you end up moving to Edinburgh then?

Ben Emson 9:27
So my wife is Scottish, and we were living in London, I was working for O2 at the time, and her brother has a disease and she wanted to be with him. And we have three boys. And I thought Actually, you know what, the lifestyle for Edinburgh seems, seems, you know, much nicer and so on. So, so we moved up here, we sold a house in Edinburgh, in London and moved out here and I didn’t have a job to start with. So there is a little bit I have a sort of risk-taking kind of gene, I think that slightly gets me into trouble, but also opens up doors. And so when I moved up here, I created these online courses. You know, it seemed like you can make masses of money out of it. And of course, it’s never quite where you seem. So I only did two, either you have to sort of overcoming this kind of like, you know, the sort of personal barriers of, you know, whether you’re good enough and all that kind of stuff. And that’s when I kind of I, you know, I then started pushing that further and looking at creating an analytics platform for courses, and I started doing that. And in the meantime, I was beginning to run out of money. So I started doing contract work. And that’s where I met Mike. So Mike is the CEO of Topolytics, right. And I did a little bit of contract work for him, just to just remained. In fact, I think he found me through these courses, we work in this massive kind of tech hub in Edinburgh called CodeBase. Okay. And I was, obviously, COVID affected it. But there were about, you know, sort of 500 companies, I think, at one stage in here.

Michael Waitze 10:59
So it’s like Station F in Paris. Yeah.

Ben Emson 11:02
Yeah. And it’s fantastic. Those sort of places are fantastic. Because you meet people at the watercooler and, you know, you make friends and all of the team that we ended up the majority of the team we ended up employing, I just worked with or bumped into, and they’re all through CodeBase. And that made a massive difference. You know, you had a lot of trust. So we have a team of about 20, all sort of developers, you know, I’ve known well, and they’re all senior, and they’re all very capable, these communities are fantastic. I think

Michael Waitze 11:36
They really are I mean, I exist in one every day, I work at True Digital Park, the serendipity for me, it’s better than sitting on a trading floor, where you just going to run into people that are doing the exact same thing that you’re doing, which is fun, as well. But if you’re in one of these big tech-based co-working spaces, I’m pretty comfortable just walking over to anybody and say, What are you working on? What can I learn? Oh, you know, I have a podcast. And it’s like, it’s just so easy to do. And you’re right, you just become friends serendipitously. It’s really, really nice. You said you were building these courses. And it’s hard, right? Because I think in some cases, whether it’s online education, or podcasting, or even just a startup, we talked about this before, but sometimes it feels like you’re operating in a vacuum. No one knows. Yeah. So what made you think about building an analytics platform because I think it dovetails nicely into what you’re doing now? Yeah.

Ben Emson 12:28
So I bought these courses. And then, so I bought, I created these courses, you want to see how you’re doing, and you want to see how you compare to other courses. What I wanted to check was how my course was doing overtime. And so I would, you know, so I wrote a little simple tool that would kind of scrape the values off of, of my kind of particular area, and just record it. And then I figured, actually, why am I just doing it for one, one course, why not just do it for the whole lot. And so so so I did that. So you know, and then we started sort of gathering this information. And, and I’ve been doing a lot of work on Amazon, although we’re not topless x is currently hosted on Google. And I was really interested in that time in the data lakes and how that works. And as I started gathering some mistakes, get a restart, I started doing some analysis on it, I had a colleague and things I would work with on some of this. And that then really transposed to a lot of the things that we’re doing here to politics as well. So, you know, trying to sort of understand how to acquire all this information, how to do it in a responsible and repeatable way. And then you know, how to zoom. So handle data is awful. Because if you come from a sort of development point of view, development world, you know, you can be very structured in your code, you can sort of do all these code reviews. And you know, and that’s all quite nice. Data is much more slippery and much more kind of malleable, trying to sort of create these workflows and processes, I’ve found much harder. But we’ve now got some quite nice ways of doing it. But it was so So initially, when I was doing these things for myself, I slightly struggled. But again, you know, up here, we had had some very good people I was able to speak to, so we top latex, we won a competition with Google Cloud and sap. So that and that was this sort of circular economy 2030 ankles, they then gave us some resource to talk to, and that was, that was fantastic. And when you’re a small company, be able to sort of talk to, you know, people, you know, in the industry, and even though they’ve got a sort of bias towards their own products, is fantastic. You know, it really gives you a kind of leg up in a ways to go. And also, you know, be able to just experiment, you know, try things out is has been really important.

Michael Waitze 14:48
I want to understand this idea, that data value, data value increases through layers. What do you mean by this?

Ben Emson 14:54
So, one of the things that we sort of noticed or I noticed early on is that if you get that raw data coming in, obviously, there are all sorts of problems with it. So even raw data is probably wrong. Because you know, the people recording, it may give you a bias towards what they’re expecting to give you, and so on. So you can never completely trust it. So when you get this decision, obviously, you go through a cleaning process, and you have this nice kind of base layer, but then you can start combining it. So you can combine it with itself. And you can get stats and other bits and pieces. But what happens when you then start combining it with other reference data, and this is something that we’ve looked into and are doing at top politics a lot, is taking datasets, public, publicly available data sets, and then combining our customer data with it. And we start seeing all sorts of really interesting nuances and other things. And this is where I said, I hate to do that beginning, I actually really like the sort of magnification that you get, as you sort of build-up these levels. But you need to be very careful that you don’t make mistakes at the bottom there because they then ripple up. So once you sort of has that good base layer, then you can start applying algorithms to it, then you can start, you know, creating these interesting models. And then those can be combined with other things. So you know, and you need a platform where it’s open for experimentation. So obviously, in the data science world, that there’s a lot of things around notebooks, these Jupyter, notebooks, and so on. And we use, we use some of these, and we use some variants of that. And that’s really nice because it helps fix some of the problems with document documenting, you know, the data and the processes that you’re doing to it. But also you can put testing in it. And you can actually then export some of this stuff as, as libraries and things. So we use, you know, we’re beginning to use this a lot. And we sort of go through a bit of a kind of a workflow process to sort of figure out some of the things we tell people what type of Linux tablet x is now, so they can get a better sense of why you have all this data and maybe just touch on where the data comes from, and then how you’ve built and continue to build the waste map. Yes. So Topolytics is an organization that we are, we are building a data platform to make the world’s waste, visible, verifiable, and valuable. The whole premise really is that if you are going to create a circular economy, there are a number of building blocks that you need to get in place. And we think one of those building blocks is, is data platforms. And waste is one of those, those, those data platforms are one of those areas that building data platform be useful. Now, if you have these data platforms, as the sort of this sort of industry matures, not only will you be able to do interesting data, things or in your own area, like waste, but you can start having algorithms that can perhaps branch across these into, you know, maybe there’s another police platform around energy, and you can start combining these and you can then create a circular economy. And I think nowadays as humans, we have to start thinking more about the environment, we have to think, you know, how do we actually encode within our businesses and environmental factors? And I don’t mean it in a kind of greenwashing kind of, we need to have this balance, you know, as tribes have evolved, they’ve always sort of reuse and lived in harmony with nature. And I think we need to do that little bit more. Now. Nowadays,

Michael Waitze 18:43
Can we go through a few of these stages, though, and maybe again, just to simplify stuff, I like to create sort of a one-unit economy. So maybe if there’s like one waist producer, one, because you’re tracking the waist, right? If it’s visible, what you’re trying to do, I guess is follow the path of the waist, to where it’s going next. Maybe even from where it comes. So let’s say there’s just one place, we can call it a factory, we can call it whatever you want, where waste is getting produced, it leaves there. If there was one place in the world where it went, what type of place would that be? And then what type of data are you getting? Just to put it in terms of people can understand. So how does it become visible? What is the verifiable part? And if it’s valuable, that must mean that someone’s making money from it right? Or that it has a value that someone’s either willing to pay for it? Or turn it into something else? He just runs through those steps in that one sort of, you know, one strain economy, and then how data plays a role there.

Ben Emson 19:44
So, waste is, currently, companies, organizations produce waste, but everyone produces waste, and we call those producers. They’ll, they’ll have their bins and they’ll then get collected by a waste management company or a hauler. What will happen is they’ll pitch up on the schedule, usually pick up those bins, those trash, trash cans, and then carry that to a receiving site. And that will be either a transport hub, or it’ll be a site where they actually process that waste, and they’ll turn it into something, something else. So what they’ll do is they’ll either split it, so if it’s sort of a general bag of waste, they might sort of go through and pull out the plastic and the sort of metal and other things. And those will then start going down separate streams, and then they’ll go on to another site, which will then do something with that plastic. So maybe recycle that plastic, you know, do things with the metal, to recycling that metal, and so on. And there’ll be a whole bunch of stuff that will kind of leaks out and needs to go into, you know, go to landfill or, or go to incineration, and you can get energy from incineration as well. So there’s a whole chain of things that kind of go on these sort of waste management companies have this sort of contract where the conveyance contract where they will pick that waste up for you. And that’s often regulated. So in the UK, there’s, we have sort of waste transfer notes and consignment notes, which are, you know, for normal, nonhazardous, and hazardous waste. And there are other things as well. So that’s the sort of, you know, there’s a sort of lift, and there’s a sort of carry, and there’s a sort of drop of the material. And that’s kind of one, one kind of conveyance note, that’s one kind of docket, that kind of happens. And then there’s another one where it gets lifted from that that receiving site maybe and then gets sent on. And what happens is, you know, unlike in the sort of commercial world where you know, you’re, you’re a big manufacturer of some product, you’ll have a very good understanding of all the logistics, that sort of move for the materials coming in, and where that product then goes on to not cancel, you’ll be really kind of clarity about what happens there. But as soon as it becomes waste, it gets lost, in the system. And that’s partly, you know, due to how things are encoded, and, you know, some of the regulations and all sorts of different factors. And so, so you don’t have that view of where that material goes to, but that’s still a material that’s still can be reused, that can still be turned into something else. And there’s a lot of value in that. And that’s sort of gets a lot getting lost in the system, or it’s gonna be shipped off into us or other parts of the world, and getting turned it, you know, getting dumped in the oceans or other things. And, and actually, you know, within the industry, we’re all to blame to some degree, you know, the consumers need to be better, wanting things to be recycled, the producers need to, you know, make their products easier to, if you then link all of these, these hops together, right, you get a chain, but it’s very hard to link these jobs, because you’ll have one organization doing one part of it, and you’ll have another organization doing something else. And you know, what actually happens, the transformation of that material. So if you’re tracking a plastic bottle, all the way through, what actually happens to it. And that becomes very, very tricky. And the way we look at it at top politics is we say this is a data problem. And we think of it that this flow of material is like a flow of energy or flow of fluid. And if we use statistics and analysis, we can actually get an idea of how this material then flows through, this chain. And then by understanding what happens at each stage, to some degree, we’re able then to sort of build up this really interesting and powerful model of this chain that we can apply to many different things. How do you get the data though, so we get the data from a number of different sources. So firstly, the initial focus has been looking at working with waste producers, right, so we take the data that their waste management companies are producing, and we normalize it and clean it, and present it back to them in ways that are useful. And we then have our algorithms and so on. We also have, we’ve been working with the UK Government. So Defra, which is a Department of Environment, Food and Rural Affairs in the UK, put out a thing to sort of track the UK waste. And we built a pilot for them. We were one of two companies. And we have that’s just finished and we’ll hear how we go in the next few months, I guess. So we’re also talking to other organizers, sorry, other sorts of regulators in other parts of the world as well. And we’re also talking to waste management companies. So we’re looking at capturing data from producers from also from waste management companies, and also from regulators and all of this is adding to these different layers of data. And this is then becoming a very useful piece. So we’ll often get like, really strong connections and flows through certain parts of it. And we’ll have weak ones elsewhere. But the way we’ve designed the system is that we can handle that. And we put a confidence score into where that flow is strong and where that flow is, is weak.

Michael Waitze 25:22
And what is Topolytics trying to accomplish? Obviously, you have all this pretty incredible data, and whether it’s local, which is important, but then it turns regional. So it can be regional, if someone takes it from your locality and sends it to another region. Or and it’s also global, because if someone then takes it from here and sends it to the ocean, the Pacific Ocean off the coast of Japan, or sells it to somebody who wants it in Japan, who’s going to recycle it and turn it into cell phones or something. what’s the what’s the end goal? Really, once you create, it seems to me, here’s my view, here’s the way I envisioned it, it seems to me that like waste data, in a way, it’s almost like weather data, at some level, because it’s moving and it’s alive, and never sitting in just one place. And even if it gets to one place, it gets processed, it gets split up into things, it gets sent somewhere else it may get, like you said it may get reused. Right. So it’s always moving. And there must be like, you know, these mat this massive flow of just stuff but also of data that you’re getting? What is the end game that you’re trying to achieve when you have all this stuff? And is it predictive prescriptive in the sense that you’re writing all these algorithms try to figure out where it’s not just going to go, but we’re going to be the most valued as well.

Ben Emson 26:37
So firstly, there is inherent value in the material. And one of the things that, you know, we’ve had to, we keep kind of coming across sort of problems, and we’ve had some innovate our way out of things. And one of those is, is sort of descriptions of materials. So in Europe, you have these things called ew c codes, which is sort of European waste category codes, catalog code. So the problem with those is, they’re actually quite high level, and also being encompass sort of transformations and activities that have happened on those materials as well. And that’s not very good for just describing material. And also, if you start working in other other countries, you know, like in Asia and America, and so on, they’ve got other codes, you’ve got EPA codes, and in the US, and so on. And so we need a way of representing those internally. And one of the things that part of trying to sort of represent these internally within our system is actually what is the quality of that material as well. Now, if we, if we can, if we can get some kind of understanding of what you know, the quality of those things are getting, we actually start having a resource map, we know where there are high concentrations of good quality materials in certain areas. And this is beginning to move towards, you know, potentially powering a marketplace. And it’s something that we haven’t, you know, we’re not focusing on at the moment, but we do see that there is a lot of value in the data that we’re getting. And some of that may be sort of powering other types of sort of applications and so on. But ultimately, in advance your question that what we’re trying to do is to help enable a circular economy, you know, try to actually, you know, companies that produce organizations that produce waste materials, how do they get those back, and reuse those in an efficient way? How can they reduce their carbon footprint? And how can they help, you know, when they’re designing their products, what materials, you know, are the best materials to use from a recycling point of view, you know, at any particular area in in the UK, we can tell you, what percentage of material of that material goes to sort of landfill, or incineration or whatever. And if you changed it for, say, cardboard, or, you know, plastic bottle and change it for cardboard, you know, that would, you know, a greater percentage would go into recycling, for example. So, so they’re things that are beginning to sort of, we’re beginning to be able to see, and that’s only because we look at things that are much greater detail than just the sort of standard IE WC codes. So, again, if you have the whole chain, so if you know what the producer is putting into their bit and so if you have understand that bin composition, and then there are things like sub DRS schemes, so deposit return schemes and so on, were actually get very fine grained information about that initial, that initial bin. And even though then the waste management company will sort of encode it in the Z, WC codes, you can stop following the flows of those different material streams through the system. And you can then start seeing where they split and where they go off to. And again, we have a very patchy sort of, I sort of often say It’s like a sort of Swiss cheese, but we can start making predictions of what’s happening in the halls. And you know, to start with, some of those are not that accurate. But, you know, as we get more data coming in, that accuracy only increases. And, you know, we understand more about the relationships and other things. And then also, one of the other things is, is that we start having the ability to look at sort of indices of things as well. So we have an understanding of how well waist measurement companies are doing in terms of precession, we understand the amounts of materials, the different streams of these materials. And there’s all sorts of interesting, you know, that there’s a lot of value that starts coming out, when you start, you know, you can start looking at these things creatively.

Michael Waitze 30:48
Yeah, I mean, you said marketplace, right, you said, you’re not there yet. But this is where things start getting really interesting, right, because the more data you get, obviously, the more you understand where every sort of little granular component is going, like you said, right from the bin to wherever it’s gonna go. But also, because it has value, if you can create a marketplace, then you can create real time bidding and offering this stuff. So I can then change the place or the location where I’m sending stuff like you can disincentivize people, for putting things in a landfill, which is just a terrible result. And then also improve the circular economy, while still making money, through a monetary incentive for people to then buy and sell this stuff. As if it were just a regular commodity. And at some level, if you have as much information on throw away plastic, I’m just making it up because I don’t have the right terminology. But don’t throw away plastic, as they have on, you know, oil futures. You can create a whole market around the trading and the indices of these and just create a tight value for all that stuff. Write a real bid and offer spread that super tight where people know where it’s trading. That’s actually kind of cool, because it incentivizes people, then, you know, again, you said no greenwashing. But, you know, in a way, who cares, as long as it’s good for the environment helps the circular economy? I got, I don’t care what you call it.

Ben Emson 32:05
Yes, I you know, and I can’t point out, we’re not we’re not doing a marketplace, the moment we’re really focusing on, you know, providing as much kind of producer value to the producers, and the waste management companies and the regulators as we as we possibly can. Because really understanding that data and really be able to sort of help them, you know, because all this data is in many different formats. And, and just getting it in in the right place in the right place in the right shape is very hard. And that’s really where we’re focusing at moment. But there are a load of outcomes from the servers, loads of through applications and marketplaces is just one of them. Where we think that there is a lots of value. And this is things that we’ll be exploring over time. You know, and I’m particularly interested in, you know, as you mentioned, incentives, how can you How can you sort of incentivize people to do the right kind of behaviors? How can you incentivize, to give data that, you know, helps, you know, with the relationships that we’re interested in, and so on. So, there’s a load of little pieces of that puzzle. And I think some we have looked at blockchain and things previously, but it’s not something that we’re involved in at the moment. But I think there is potential for things around that. Not necessarily in the tracking, but in this sort of incentivizing good behavior.

Michael Waitze 33:32
Yeah. So you could use a smart contract, like an aetherium based smart contract, right, that says, if this gets triggered, then you get this.

Ben Emson 33:39
Yeah, absolutely. Yeah.

Michael Waitze 33:40
Yeah, kind of interesting. So do you want to give one more example. And then I’ll let you go of the kinds of things you’re thinking about doing staying away from the marketplace, which we already talked about.

Ben Emson 33:50
So part of what we’re really trying to understand at the moment. So we have this algorithm that we’ve been developing this waste chain algorithm. And a lot of that is tied to sort of the relationships between organizations, but also the sort of triggers of different data sets. So, you know, I mentioned this a reference data, you know, we have things like water permits and licensing sites in the UK. But actually, when you start combining these with other things like weather that has some really interesting outcomes. So for example, when a lorry picks up a load of cardboard. In the UK, you can’t leave that lorry in a warehouse overnight, because there’s a fiber so you have to leave it outside. Now, if it rains, rains, then that cable gets wet and the moisture content goes up, and then the value of that material then changes. Now, if we’re able then to start looking at all of these, these sites and so on, we can get an idea about how efficiently they are processing some of this this data. Now one of the one of the sources Side effects of this is, well, if you can, you know, make predictions down this chain, you don’t have to do that right now you can do that at some point in the future. So you have a sort of scenario planning side effects. And so one of the things that we’re looking at is, you know, if you’re an organization, what is the sort of the impact of adding a new waste processing site? Or what is the impact of, you know, changing all your materials, and how that goes down. But interestingly, you can kind of turn it the other way around and say, Well, you know, I’m, I’m at the far end, you know, I’m up to one of these receiving sites, and someone has sort of put in a, you know, I’ve got a big bundle of plastic or something, and someone has put some, you know, material that shouldn’t be in there, like a, like a nappy, or a kind of used thing. I want to know where that’s come from. And we can start looking at how we then go back up that chain, there’s some really interesting bits that we’re playing around with that. So scenario planning is probably one of them.

Michael Waitze 36:00
That sounds super cool. Okay, I’m gonna let you go. I can feel the enthusiasm. Actually, I can feel the enthusiasm here. We’re gonna have to get you back on again to keep talking about this because there’s a lot more to cover. I really want to thank you, Ben emson, the director and the Chief Technical offshore politics. This was super great.

Ben Emson 36:15
Thank you very much. I very much enjoyed it. And I’ve been listening to your podcast. So keep up the good work.

Michael Waitze 36:21
Thank you.