Markus is a product-focused entrepreneur, working with paradigm shifts in industries like data, fintech and content rights from HK, London, NY and SF. Markus has studied finance and information systems (MS) and is passionate about personal freedom. He’s CEO at Prifina – a company that believes data and applications will be separated in the future, where individuals gain value from their data and developers get democratized data access to build the best apps.
Ciprian Borodescu: I’m here with Markus Lampinen, CEO of Prifina. I’m super excited, and it’s an honor to have you as the very first guest on this podcast. Thank you so much for being here, Markus.
Markus Lampinen: Thank you so much. Like I said, I’m very happy to be the inaugural guinea pig here, but also to be chatting about some really, really cool things with you.
Ciprian Borodescu: Before we start, I have to say this. So, we go way back. We met, I think, in 2010 during Startup Bootcamp, and if I remember correctly, you were running the Grow VC podcast, right?
Markus Lampinen: That’s right. So that was some time ago. That was also an entirely different era. Like, of course, right now, where we are today, we have this COVID-19 environment. But it’s also funny to reflect back longer-term, like, if we think about 2010. I mean, most of the cloud computing environments were completely in development. That was like a completely different environment altogether. So yes, I was part of the Startup Bootcamp at the time.
Ciprian Borodescu: Exactly. That was in Copenhagen and you were a true pioneer of the podcasting industry that it is today. Do you remember how many episodes did you end up having?
Markus Lampinen: Well, I had a great team at the time and I’ve always been fortunate to have a great team. So I think we did over a couple of hundreds at the time. We were working with Equity Crowdfunding, we pioneered one of the world’s first equity crowdfunding marketplaces growvc.com. And then, I mean, I didn’t personally do all of the couple hundreds, I did some of them. But then we also got other people, some other great hosts take over and some great guests. We had US politicians, for example, talking about some of the securities regulations, we had some prominent investors and entrepreneurs, and so on, and so forth. So it was fantastic. But it’s also like, you know, just thinking kind of back, I mean, you’re absolutely right that in 2010 podcasts were far less popular than they are today.
Ciprian Borodescu: Yeah, that’s true. It’s amazing. And thank you, again, for accepting this invitation. This really means a lot to me. Alright, cool. So let’s start. Tell us a little bit about yourself and how you started with Prifina.
Markus Lampinen: Sure. Like you said, we go way back. We’re both entrepreneurs and I think, from my side, I founded a couple of different companies in different industries, like, I worked with content rights, I worked with equity crowdfunding. But from the crowdfunding then came the entire FinTech movement. So I ran an API back-office company called Difitek for about seven and a half years. That brought me to Silicon Valley. I’ve been here for about five and a half years now. But then, essentially, also working with various different banks, exchanges, lenders. And then, of course, lots of software developers building up new things in FinTech that also essentially opened up this opportunity in data for us. So when I moved on from Difitek and then the new team took over, then one of the coolest things that we saw in the market was essentially this, call it new era of data. Meaning that if we think ahead 10 years, then we see that there’s a very likely future where essentially data is portable, for various different reasons. And that’s something that we wanted to capitalize on, that we wanted to just build, essentially our small part of this feature.
Markus Lampinen: I think, from my own background with Difitek, we were already working with lots of developers, we had essentially an API-first approach. But even before we’ve kind of seen this benefit of open collaboration in the marketplace, and specifically, when we’re talking about something as monumental as the data industry, then that had to be the first and foremost approach; when we’re looking at essentially could we create, let’s say, an alternative for how data, and especially this Data Broker industry works, then I mean, for us, it was natural that that would have to be something that’s open source and also open collaboration because that’s not something that just one company does. So, that’s a little bit of a blurb in terms of where I come from, but I do think it is a really remarkable day and age in terms of where we are; for example, for the open-source movement, I think that we’re also starting to see and have really, really strong data in the last couple of years that open source, and especially this type of collaboration, that’s also become a far, outperforming asset class that’s getting a lot more investment into. So that’s something that we’re really excited about.
Ciprian Borodescu: You and I talked a little bit about the app within an app approach. Can you tell us a little bit about that? Because it’s a really new concept and even I’m trying to wrap my head around that.
Markus Lampinen: Yeah. So, let’s take a little bit of a step back – and I can just kind of walk through first in terms of where we come from with Prifina and then talk a little bit more about how we think about this app within an app concept. So, when I said that it’s essentially a new era for data, then in practice, what we mean is, we’re building a platform that has a consumer and a developer offering. The consumer offering is that the individual, so a person like you and I, we can essentially aggregate our data from the various different data platforms and bring it into what we call our own data cloud. So, effectively, right now, that means AWS, but in the future, it will mean something else. So you bring your data, you essentially use some of the marketplace APIs, the GDPR, export functions, whatnot, and then we create this data backup for you under your own control. But that also then allows you to have your own data, and then do cool things with that. So, we help you productize that into portable data profiles. So essentially, like, what type of traveler am I, what type of content do I like to engage with, and so on, and so forth. But then also that allows you to grant and revoke access to this type of data, or these types of programs. So that’s the consumer side. So, a consumer gets control, they can control consent, but they also get their data so that they can actually do something with it. And then for the developer/ the enterprise, this also means that effectively, you can build different experiences on top of this. Like, if you have your data be local to your customer, then you could ask that data to be able to understand better, for example, what type of traveler they are, or what type of sports equipment they’re into. But you could also essentially build apps that just fit on top of this local data.
Markus Lampinen: So we separate the type of apps into three different categories. One is effectively those types of profile apps, which just create a better experience based on what type of person this is like, what is their profile? So for example, I’m this type of traveler, and you know, me as Airbnb or whoever else I can create this type of an offer. So, that’s like this type of…Call it more superficial personalization, but superficial app anyway. The second one is effectively data widgets, which is like, for example, if I have all of this data locally with me, what type of analytics could I run? Like, what type of dashboards could I create. Like, can I create, for example, how my sleep pattern correlates with my financial spending? So, for example, if I have a hypothesis that when I sleep poorly, then I, for example, drink more coffee and spend more money, then, by creating that type of a correlation between sleep data and the financial spend data, I could actually plot that on a chart and create that type of a dashboard. So that’s the second one. And the third one is, effectively, that you can actually create entire apps that run as an app within an app. So, for example, you can create an app that keeps all of the data local, with the individual, and allows the individual, for example, to run this within their own cloud. So, this personal cloud that I mentioned earlier that has your data in it, if you could install local software, and then just run those locally, for example, without that data or that interaction between the data sharing and going in, that would be an example of an app within an app.
Markus Lampinen: So, just to keep kind of one tangible example, if there’s a developer group that’s working with us that’s creating a newsletter opt-out – so super simple functionality, but essentially, the way that they’re doing this is that they’re using an anonymous email functionality that we have within Prifina and that the user can take that anonymous email and then add that to various different mailing lists that they want to be part of. And then, all of those subscriptions, they go to that anonymous email, which is effectively just an email that we control. But that forwards, essentially, the subscriptions to their mailboxes. And within this app within app, they can manage that forwarding from this anonymous email to their own inboxes. But because they also do this, then they’re never exposing the underlying email but they also have all of the interactions, all of the content of the mail going through their personal cloud. So it goes essentially through the anonymous email, and it goes to this personal cloud, and there’s no exposure to their own Gmail or something like that. So that’s a very, very simplistic app, in a sense. But it’s also something that when we think about these types of apps, then it could be something as simple as that. It’s just essentially like your email guard that fits in your own app and then it just runs there, it doesn’t run on somebody else’s server, it essentially runs on your server, so to speak.
Ciprian Borodescu: This is super interesting and I think as developers, we need more use cases from you, guys, because if we understand these use cases, then we can come up with new approaches. So yeah, great job on that. And just for the sake of the conversation maybe push back a little bit and ask you, maybe, if I’m an online retailer, what is my incentive to basically embrace this change that Prifina is proposing?
Markus Lampinen: I think, for a lot of online retailers, merchants, whoever else, I think we have to start from the reality today that it’s very easy to think that everybody has lots of data but that’s actually not true. So, most companies, most brands, they actually don’t have a lot of data about their customers.
Ciprian Borodescu: I can confirm that. Yes, exactly. Yeah.
Markus Lampinen: So if you start from this reality, that if you aren’t Facebook, or Google or Amazon – I mean, you don’t have their data, you don’t have a lot of insight, like if you think about, for example, just yourself as a user, how many accounts do you actually keep up to date with actually accurate and lots of information? Maybe one or two.
Ciprian Borodescu: Not that much, exactly. Yeah.
Markus Lampinen: So essentially, starting from here, if you as a merchant could have your individual customer provide you with some more information about who they are, then you would probably be willing to give them a better offer because now you essentially know who they are so you can price your offer better. So, for example, one of the examples that we oftentimes see is, for example, is with travel. For example, booking hotels. The hotels themselves actually don’t have a lot of information about you because a lot of this data goes to the brokers. But if you were able to book with a hotel and tell them that in the last year, I actually stayed this many nights and spent this much money – in aggregates not exposing anything – like, I spent 110 nights in hotels, and I spent $72,000 in the last year on hotels that are comparable to yours. And now, I’m booking, let’s say, a two-night stay with you – if the hotel sees this, and they see that I’m booking a two-night stay with you, but actually, I travel quite a lot, then they can also see that I’m actually quite valuable as a client. So that’s something that for them it would be very easy to say that, “Well, I want this person to come back.” And then maybe instead of giving you the room that you booked, maybe they give you potentially a complimentary upgrade or something. So this is an example of a profile app effectively. So, it’s taking this data profile of who this traveler is, and creating a specific experience for them.
Markus Lampinen: Now, it doesn’t have to be like an upsell. It can also be a discount, it could be essentially a better offer. Like, I know this person is traveling to Singapore, they travel quite a lot, they’re here on a Saturday, why don’t I give them also this golf trip or whatever additional service? So, part of it’s really for the merchant to get a better understanding of who their customer is, effectively at no cost because it’s coming directly from their customer, and also at no risk because their customer is volunteering it. But then also, for the second part, from that data, you can actually be more competitive. So, we talk about the democratization of data, that if the customer is able to have this data with them, that means that essentially you as a merchant, or you as a retailer, you can also provide better data. And at the end of the day, that’s what we care about. Like, we really care about the end-user that if they can have better experiences, they can have better prices, better offers – that’s ultimately what we see that’s gonna resonate in the market. Because let’s be honest, I mean, we care about data, we care about privacy, but at the end of the day, most people don’t. Like, most people, they conceptually care about those things but at the end of the day, if you look at what actually moves the needle, what actually gets people to engage, then it’s always convenience and value. So that’s kind of where we have to go. But also, from your point of view, I mean, you guys work with a lot of merchants, and you guys work with a lot of people in this industry, so it’s really about also updating their game, that by being able to give, let’s say, a one-step-up offering and not just do the same thing that everybody else is doing, that’s a huge opportunity for people to create more specific value and more personal experiences for who their customers are.
Ciprian Borodescu: Yeah, it’s funny, because you mentioned that, in general, not a lot of companies have data. So, this is something that we’ve seen in the eCommerce industry as well; like any other industry, commerce is basically a long tail. And even now with the Coronavirus crisis, a lot of retailers are moving online. But these are new entries, they do not have yet huge amounts of data and thus, for them and for other smaller eCommerce businesses, data cannot really power a proper AI or ML system. And this is something that we’ve been trying to find the solution here, with the team at Morphl, and one solution is to aggregate the data from multiple eCommerce or online retailers. However, all that is problematic in more than one way, right? So, the solution that we ended up with can be federated learning. And without going into technical detail, basically, federated learning means that it eliminates the need for centralized data by relying on sub-models that are trained at the device level. So, in this paradigm, the user data never leaves the client, in contrast to the traditional paradigm of collecting, storing, and processing user data on backend servers. And I just have the intuition that we could use federated learning and include Prifina in the mix.
Markus Lampinen: I think it’s something that we also see that, in the typical data world, you start from essentially one massive database of millions of data points across lots of different people. So, what if you turn that the other way around, that you start with holistic and rich data points on an individual level, and how can you model base on that? You’re absolutely right to kind of tear out this problem because it’s not trivial, and that means that, for example, if it’s something that’s as compute and data-intensive as machine learning, you have to kind of break it down. So, at the end of the day, I try to kind of think about it from a big picture and then also a small picture. And I think from a small-picture point of view, you can do a lot with very basic modeling, like very, very basic, like 80/20 rule type of data, you can actually create quite a lot of good indicators for merchants. So, this is one of the things that’s really important for us that if essentially somebody uses this system, they have to get value right away, not in 90 days or 180 days; it has to be right away that when the user lands on the site, then you know a little bit more about the user, and then you can provide more value for them. And for the user it has to be one click – it can’t be any more difficult than that. So, you can start off with very basic things. Like, you take 10 data points – if you’re able to validate and cross-check 10 data points like age bracket, income bracket, demographic, where they live, some of the interesting groups and events, and so on and so forth, that they engage with, you already know quite a lot. And if this is data that you can trust, for example, it’s aggregate data over the last year – like this person has been at 17 events that kind of fall in this category, this person has spent X amount of dollars in this category – not exposing any raw data, not exposing anything sensitive, but just this type of behavioral data, that’s already quite valuable. But now, you’re absolutely right, that for training a predictable and statistically significant model, that’s something where essentially, it does require various different steps.
Markus Lampinen: But for us, a lot of the things that we start with is just very primitive access to holistic data. And then from there, I think that you can do a lot of different more sophisticated things. But just kind of thinking back, like, what we did in the FinTech company I ran, we worked with a lot of anti-money laundering and compliance systems. And that’s something where, for example, if you have to work with anti-fraud, or money laundering, or so on, then I mean, at that stage, you work in rules and thresholds, like, you work with statistical significance, you work with pattern recognition, all these different things. So, I think that that’s absolutely important. But at the same time, just thinking about where the merchants are today, and thinking about how can we help them take one step in the right direction, and then one step beyond there, and one step beyond there, then the first thing is if we can give them access to better data so that they can provide better value, that’s already essentially a game-changer. And then from there, they can optimize and they can train, and so on. But this is kind of where the open collaboration comes in as well that, for example, Kimmo, who’s our CTO in our company, he’s run three data companies, and he’s somebody that in his Ph.D., he studied neural networks in the late ‘90s – so these are all things that we were kind of also grappling with internally that we were thinking about, like, you know, if you start with the individual, and you have millions of individuals, how do you do that instead of, essentially, starting from one database with millions of data?
Markus Lampinen: I think Google has also been very visionary in this sector. I mean, of course, they have a lot of insight into the trends, and they have a lot of different projects in the works, they also have a lot of open-source libraries, and so on and so forth. But that’s actually a really, really cool, and just a very specific point about federated learning – that’s something that we’re also looking into, and I think that there are a lot of these types of initiatives that as we get into the new era of things, they’ll just become more and more valuable because, you know, for example, third-party cookies being depreciated, that’s not a small thing. That’s going to be quite a big shift.
Ciprian Borodescu: Yeah, exactly. Yeah. And you mentioned that you’re already working on an SDK for merchants? Can you please share more on that?
Markus Lampinen: Yeah. So if you think about what a merchant would need in terms of using something like this is two things. One is they would need, essentially, this type of referral system where their customer can create this profile. But then, they would need also the spec for what goes into that profile. So, that’s kind of where we’re going into different types of industries. Like, we worked with some travel, some media, we worked with some merchants and some financiers that are also a little bit more cross-sector. And it’s really just about kind of thinking through that, one, is the customer experience and then the other one is the productization of the data pipe. So, from a code point of view, it’s a very simple snippet, like it can be a button on their website, like, “Get offer using Prifina” or something like this – get an offer using your data profile, whatnot. It’s much more about the data pipeline, that you as a merchant, what type of things do you need to know to be able to offer, for example, a perk or an upgrade about your customer? So, that’s kind of the area that we’re now piloting with various different companies and then, we’re productizing that into certain types of key areas first. But also here, I mean, we’re doing a lot behind the scenes, and we’re publishing more and more openly into the public domain. But then, beyond that, with these pilots, we’re doing a lot in the behind the scenes to just kind of understand the data pipeline so that we can productize them because, yes, everybody is unique, but there’s also a lot of universality about this 80/20 rule that if you include, for example, age, income level demographics, some interest data, some behavioral data, some spend in certain categories, you can actually already do quite a lot in most industries. So, a lot of what we start with on the data pipeline is that there’s some universals, and then there’s some specific things that would be important for the merchant. But we want to take it to a certain point ourselves. But after that, we want to make a public domain because from our point of view, we’re not going to be the expert in 100 different verticals. So, we need not only partners, but we also essentially want to make sure that if somebody has a great idea for a data profile spec that they would be able to use, then they should be able to have control of that. And that’s something that we can then feed into our own roadmap and essentially just give distribution for. That’s the general logic that you should be able to onboard your users very easily, but they should also be able to then volunteer the right data profile for the use case that you’re building for them.
Ciprian Borodescu: Probably that’s the reason you just launched the use case blog series.
Markus Lampinen: We’ll have a lot of different things coming out from there. Like you mentioned earlier about use cases and examples. That’s absolutely why we did this because we’ve been working with these behind the scenes. Last year, we did lots of different pilots, but it’s going to be critical that they’re out in the open because we have a good amount of developers that are essentially engaging with us on a daily basis. But then, at the end of the day, there’s always an order of magnitude more that are following but aren’t doing anything, that aren’t talking to us, that don’t have inside information. So, that’s why we need to just highlight these three different types of apps that we have, and then what you can do. But then, one of the coolest things that I’m waiting for – or that I always love – is there’s a lot of things that we haven’t thought of that you can do. So that’s also an opportunity and a challenge for people that think that “Okay, if I had the opportunity to have direct-from-consumer data, provided that they, of course, give me the consent – but if I’m providing something valuable, then they might – then what can I do?” Like if I have, for example, different datasets from an individual person about their behavioral data, what type of insights could I give them? Or what type of experiences could I create them? Or what type of suggestions?
Markus Lampinen: So, I’ve been talking with a couple of my data scientist friends that are far smarter than I am about nudges, and incentives, and so on, and so forth. And one of the things is showcasing data and kind of creating insights and analytics from that, that’s step one. But then step two is, okay, if you know this, then what can you do? Like, what should you do? What should you change? And that becomes much more about understanding the individual, understanding their goals, and also understanding how can you help them get there. And if you think about, for example, just from your point of view at Morphl about the granularity of data, but if you can actually start from that point of view, that you not only understand a little bit more about the individuals, you also understand a little bit more about their goals because we don’t have that type of data yet today. And, of course, we don’t get there right away. But it’s just an interesting thing, that if you understand what the user actually wants, and then who the user is, then that’s something where developers can surprise us, that they can come up with some incredible things that we just didn’t think about.
Ciprian Borodescu: So I have two things to say about that. One, that perfectly aligns with the questions that we get from our customers, “Okay, you guys give us these predictions. This is lovely. What can we do with these predictions?” And on the other side with developers building applications on top of your SDK – and let me know when you guys are going to organize the hackathon in a few months’ time, that would be interesting.
Markus Lampinen: I mean, there are some things that are already going on in the background. Like, we have with some of the universities here in Silicon Valley, we have some things that work already. I mean, we’re already scheduled, but of course, with Coronavirus and everything, they’re postponed for now, or they’re virtual. I don’t think they’ve been rescheduled yet. But I actually think that that’s absolutely a fantastic idea because, on one side if you just start off with this very, very basic question that, you know, if you had all of the data in the world about your customer, and you could essentially ask them anything, what would you ask them? And then what could you provide for them? Like, what could you build with it? So that’s something that, you know, in some of our pilots, we’ve worked with data such as mobility data, meaning that, how do you get around in your everyday life? So, how much you drive, how much you take public transport, how much you ride a bike, how much you walk, and so on, and so forth. And then, putting a price on that, like, you spend this much on your car, you spend this much on public transport, and so on, and so forth. That’s kind of just a very, very simple commuter dataset; that just kind of gives you the patterns on a map, the price, and the vehicle. But based on that, you can already do a lot of different things. You can say that “Well, for example, you’re driving here where there seems to be lots of traffic. But if you took this car ride and turned it into a bike ride, you actually would be quicker”, or something like that. So, just kind of starting from the very, very primitive point of view, just showcasing that type of data – that dataset, for example, the mobility data, that’s also interesting for a couple of other use cases that we found out. One is this local purchasing profile – I think that’s actually already on the public GitHub – but this is essentially something that if you think about like coupon style offers, if you knew, essentially, the patterns that individuals have, you can say that “Hey, on your way to work, here’s an offer on a latte.” Something very, very simple. Or the other way around is that you turn these individual data points about commuter patterns, and then you turn them into city planning, that if you know how people move about in your city on an individual basis, and you know where they’re going and their patterns and so on, how can you essentially use that to feed, for example, a municipal planning or different types of things like that. So, it’s just kind of starting from very, very basics, and then building up.
Ciprian Borodescu: And, Markus, I have to ask you this because I get this question a lot. What is the industry, what is the vertical that you guys are focusing on? Is it retail? Is it financial? Is it media publishers? What is your answer to that?
Markus Lampinen: So, my answer right now is developers. That’s really what we care about because, at the end of the day, yes, we’re piloting in various different verticals, and yes, we have some preferences, and yes, our own go-to market is linked to one of those preferences, but that’s kind of just more, let’s say, for our company. But ultimately, at the end of the day, big picture-wise our focus is on developers. We want to give them the best tools, we want to give them the best SDKs, the best talks, the best code examples for how to provide and facilitate this data exchange in their own industries. So, I think that you will see from us that we’re going to go into one specific vertical, and that’s going to change over time, because we’re gonna go into one vertical, one core audience, and then we’re going to go step by step from there. But, at the same time, that developer commitment, that’s never gonna change. I mean, that’s always going to be front and center because if I think about this from a macro point of view, I think that ultimately, for enterprise adoption, like we talked about merchants and we talked about retailers, as well as developer adoption, I think that it’s going to be all developers. I think it’s going to be developers that adopt new models, they test them out, they prove them out, and then the enterprises adopt. Whether or not those developers are within the enterprises to start off with, or that they’re independent developers, or they’re developers as part of a third-party company that sells enterprises, I think it’s all developers.
Markus Lampinen: With my past company, I worked in large banks, I’ve sold to large banks, I’ve also sold to developers. I think just looking at where we are, especially now when people can’t take in-person meetings, and then just kind of extrapolating forward, I think it’s going to be developers that essentially have that catalyst power. But you’re absolutely right that focus is also one of these things that we have to make sure that the tools that we have, they’re as good as possible for specific verticals. But this is kind of where I would say that this industry knowledge comes in that it doesn’t require a lot at this stage. Like, for example, I really like eCommerce as just in general more horizontal mechanical priority, because essentially just getting the right SDKs and the right tools for the sellers as well as the platforms themselves, I think that that’s going to be incredibly important. But at the same time, thinking about the data, there’s going to be a universal aspect and then there’s going to be a specific aspect. And I would argue that the universal aspect of the data, as well as the model – so the technical model, as well as essentially everything that we put on top, like the federated learning, and different types of training sets or different types of support – I think that that’s actually going to be quite universal across the entire eCommerce sector. And I would argue that that specific aspect for different types of verticals, that’s going to be something that we want to empower those merchants or those developers to do themselves because I think it’s absolutely important, it’s absolutely critical, but at the end of the day, that’s somebody else’s business. That’s not our business. But you’re absolutely right, like, especially as an entrepreneur, you have to think about this in stages, like, you have to think about what is the zero to one, what is the one to two, and what is the two to 100. And then, especially for the zero to one, you have to pick your battles, you have to just choose one area where you get the right support. And then, after that, you choose the next one, and then you start scaling. But I think for us, yes, we’ll be working forward in one specific area and that’s something that we’re doing. But I think especially in the public domain, it’s going to be very, very consistent about the developers because that’s where we see that we can actually have this type of lever into the market and that developers can also create more and more of these meaningful experiences for their own customers.
Ciprian Borodescu: And I love that about you, guys, that you have a developer-first approach and focus. And since you mentioned developers, where can they reach out to you for ideas and comments?
Markus Lampinen: I would love for people to check out just the website – prifina.com – and then export from there. I mean, there’s a developer section, there’s different examples, there’s a blog series on Medium that has more color. There’s also the public GitHub, we actually call it Liberty Equality Data.
Ciprian Borodescu: Yeah, I love that.
Markus Lampinen: So, that’s something where you can just stay in the loop on things that we publish. There’s going to be more and more all the time. But then, I mean, just generally, checking out those things, that’s great. And I would love for you to just stay in the loop. But I would love to hear also more ideas. If you just start from this point of view that if your customer can tell you more about them, what could you build? That’s kind of the thing that’s really inspiring for us because we also have a developer Slack, which right now is via invitation, which means that you can apply and we’ll essentially accept you into the community. But we’re going to also be making that more public and we’re going to be making our own roadmap public. Just kind of blurring the lines more about these use cases. So, there’s a lot that you can check out. But I would love to kind of get especially more ideas about, like, if we’re able to democratize access to data, even if just for one use case or one person at a time, then what can you do? So that’s the open question. And I will definitely take you up on that kind of idea that it would be fantastic to run a sprint hackathon at some point, and just kind of figure out that, you know, if we have certain type of datasets and data profiles that we can essentially tailor then what could we come up with and 48 hours? Because I’m guessing that we could come up with something, on one side, fantastically provocative, but on the other side, also incredibly valuable. Because a lot of these things are not rocket science; it’s just about looking at what is a struggle that somebody has in their everyday life? How could you make that a little bit better if you knew a little bit more about the person as well as their goals?
Ciprian Borodescu: Cool. Alright. Well, on that positive note, Markus, it was a pleasure to have you. Thank you so much.
Markus Lampinen: Thank you so much for having me as well. And thank you everybody for listening. So, I look forward to continuing the discussion.