>>Welcome back to the cubes, continuous coverage of AWS reinvent 2021. I'm Lisa Martin. We are winning one of the industry's most important in hybrid tech events this year with AWS and its enormous ecosystem of partners to life sets we have going on right now. There's a dueling set right across from me, two remote studios over 100 guests on the program. We'll be digging into really the next decade of cloud innovation. I'm pleased to welcome two guests that sit next to me here. We've got Aaron Arnold Santa's partner at BCG gamma and a diesel associate director of data science at BCG gamma guys. Welcome to the program. Thanks for having us. I D let's go ahead and start with you. Give us the low down what's going on at BCG gamma. >>We are focused on building responsible, sustainable, and efficient AI at scale to solve pressing business problems. >>Good. We're going to dig into that more. There was a lot of talk about AI this morning during the keynote yesterday as well. And you know, one of the things Aaron that we talked about the last day and a half is that every company, these days has to be a data company, but the volume of data is so great that we've got to have AI to be able to help all the humans process it, find all of the nuggets that are buried within these volumes of data for companies to be competitive. You talk about a sustainable efficient let's go ahead and talk about what do you mean by efficient AI? It sounds great, but help unpack what that actually means. And, and how does an organization in any industry actually achieving? >>Yeah. So when we talk about efficient AI, we're really talking about resilience, scale and adoption. So we all know that the environment in which AI tools and systems are deployed change and update very frequently, and those changes and updates can lead to errors, downtime, which erode user trust. And so when you're designing your AI, it's really critical to build it right and, and ensure it's resilient to those types of changes in the operational environment. And that really means designing it upfront to adhere to company standards around documentation, um, testing bias as well as approved model architecture. So, so that piece is really critical. The other piece about efficient AI is we're really talking about using better code structure to ensure that and enable that teams can search learn, um, and really clone AI IP to bring AI at scale across company silos. So what efficient AI does is it ensures that companies can go from proof of concept and exploration to deploying AI at scale. The final piece is really about solving the right business problems quickly in a way that ensures that users and customers will adopt and actually use the tool and capability >>That adoption there is absolutely critical. And >>You know, when we, when we're talking about AI, most of the time we're talking about three components and we call it like the ten twenty, seventy rule, 10% of the change is really about the better AI algorithms that are coming out. 20% is a better architecture, the technology, all of those components, but 70% of it is really about how are we influencing our business partners to make better decisions? How are we making sure AI is built right into the operational decision flow? And that's really, when we start talking about better AI, we move it away from kind of our pet project, buzzword bingo, into decision operational flows, you know, and, and there's, there's a journey there, there's a journey that we all are on. You see the evolution of AI right now. And I th and I liken it a lot to, um, myself when I'm, I'm a big football fan, right. And I've fantasy football is like my passion. I see. And when I look at the decision-makings, I've made 10 years ago versus now, now I actually have my own models. I'm running against it. I'm, I'm very much into the details of what is the data telling me, but, um, it's not until I bring that together with my decision making process, that really makes it so that I have bragging rights on Sundays. >>I wouldn't want to compete against Aaron. I mean, you know, I've got a lot of friends that do fantasy football, but I don't think they're taking, they're actually doing data-driven approaches as you are. One of the things I'm glad that you talked about the 10, 20, 70 formula for in dividing investments in AI. One of the things that really surprised me, and I'm looking at my notes here, because I was writing this down was that you said 10% AI and machine learning algorithms, 20% software and technology infrastructure, 70% though is also change management. That is hard, especially the speed with which every industry is operating today. What we've seen in the last 22 months, we've seen a massive acceleration to the cloud, every business pivoting, many times where our customers, in terms of understanding the challenges that they can solve with AI, given the fact that we're still in such a dynamic global environment, do what are you seeing? >>So I think it's actually quite, bi-modal some companies, including the public sector are really leaning in and exploring all the different applications and all the different solutions. Unfortunately, if they're not emphasizing that 70% on change management and the culture change and user adoption, those are substantial, but you don't get the return on the investment. Right. On the other hand, the other part of that bi-modal distribution is there are folks who are still really reluctant because they have made investments and it hasn't right. Brought about the change that they were hoping for. And so I think it's really critical to bring that holistic approach to bringing AI and advanced analytic tools to really change the way, you know, a company's attacking its problems and bringing solutions to its users and customers. >>Yeah. I like it a lot to us as us, as adults have when we teach our kids about math, right? Like less of my time with my own kids is focused on teaching them, the principles, the, and all those things, but it's more teaching them to be comfortable. Why are they learning math? What are they doing? How is that going to prepare them to be more competitive and, uh, later on in life. And so, and then the same thing's happening in this evolution in AI, right? There is this big tech and AI transformations that are happening. But the questions we need to ask ourselves within is are we taking the time to make sure our companies and our people are on the journey with us and that they understand that this is going to be better for them and give them a competitive advantage. >>That's critical. We know we've talked a lot in Alaska. We talk a lot about every show about people, process technologies and people as part of that. But I've definitely seen more of a focus. I think the last two and a half days of the people emphasis going, we have to have, we have to upskill our people. We have to train our people. We have to make sure that they're understand how this technology can partner with them and enable them rather than take things away. So it's nice to hear you talking about the big focus there being on the people that is because without that, then a deed to your point, a lot of those projects aren't successful >>And not only, I think the other piece there in terms of bringing the user along for the journey is you don't want them to feel like this is just another tool, right? Another part of their, in addition to their workflow, right? You want to take the burden away. You want it to really, um, not add, but to, to their, to their list of, of daily tasks, but subtract and make it easier. Right. And I think that that's really critical for a lot of companies as well. >>I think along with what you're talking about, we have to teach people to be responsible. So it's, it's one thing to do the job better, but it's another thing to be responsible because in today's world, we have to think about our responsibilities back to our communities, to our consumers, to our shareholders and into ultimately to the environment itself. And so responsive as we are thinking about AI, we need to think differently too, because let's face it. Data is fuel and we can accidentally make the wrong decisions for the globe by making the right decisions for stakeholders. We have to do a better job of understanding the why we're doing what we're doing, what we're doing, and not only the, the intended consequences of our decisions, but also the unintended consequences. And then we need to be responsible in the ways that we're using AI and that we're transparent in our use thereof. >>Yeah, Aaron, I think that's incredibly critical. I think responsible AI, um, has to be at the heart of, of AI transformation. And one of the interesting things that we have found is that organizations perceive their responsible AI maturity to be substantially higher than it actually is. Right. And less than 50% of organizations that have, you know, a fully implemented AI at scale, do not have a responsible AI, um, capability. And so at BCG, we've been working quite hard to integrate our gamma responsible AI program into the big AI transformations, because it's so critical. It's so absolutely important. And, and really that there's a lot of facets to that. But one of that, one of the critical ones is an ensures the goals and the outcomes of the AI systems are fair and unbiased and explainable, which is so critical. Um, I think it also ensures best that we follow best practices for data governance to protect user privacy, which I think is another critical, um, piece here, as well as minimizing any negative social or environmental impact, which again, I it's, it's just gotta be at the forefront of AI development. What about, >>And I think that there's a tech part to that too. So like one thing that we're working on called a gamma facet is really, you know, for the longest time in this AI transformation, AI was kind of a black box and it's kind of mystical, but we, we optimize our results. The transformation, when we talk about better, AI is, uh, the decision maker is in the center and knows the outcome. And so we make it a clear box. And so they're really, we're working a lot on, you know, the most common Python packages, uh, to make them more clear too, so that the business user and the data scientist understands the decisions that they're making and how it will impact the company and longer term society. >>What about the sustainability front? I mean, it's clear that I can understand why you have the 10 20, 70 approach that, that 70% is really important. There are companies that think they're much farther advanced in terms of responsible use of responsible AI responsibly than they really are. Um, but you know, we talk about sustainability all the time. It's a buzzword, but it's also something that's incredibly important to you to companies like AWS. I imagined a companies like yourself, where does, what does sustainable AI look like and how to organizations implemented along with responsible AI efficient AI? >>Yeah, I think it's the question in some ways right now, given everything that's happening around the world. And so AI for sustainability is, is really critical. I think we all have a part to play in this fight, um, to ensure our, our global environment. So I think we need to use the same AI expertise, the same AI technology that we bring to maximize revenue and minimize cost, um, to, to minimizing a company's footprint. Long-term I think that's really critical. One of the things we've seen is that 85% of companies want to reduce their emissions, but less than 10% of them know how to accurately measure right. Their footprint. And so we've been focusing on AI for sustainability across a couple of different pillars. The first is measuring the current impact from operations. The second is data mining, um, for optimal decisions to reduce that footprint. And the third is scenarios to plan better strategies to alter our impact. >>Excellent. Well, there's so much work to be done, guys. Thank you for joining me talking about what BCG is doing for responsible, efficient, ethical, and sustainable AI, a lot of opportunities. I'm sure for you guys with AWS and your list of clients, but we thank you for taking the time out to talk with us this morning. So much. I write for my guests. I'm Lisa Martin. You're watching the cube, the global leader in live tech coverage.

>> Voiceover: Live from Las Vegas it's theCUBE covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel along with its ecosystem partners. >> Welcome back to Las Vegas everybody. We're here live at the the Sands Convention Center. You're watching theCUBE, the leader in live tech coverage. We go out to the events and extract the signal from the noise. This is re:Invent 2019, the seventh year theCUBE has been here. I'm Dave Vellante with my co-host Justin Warren. Sesh Iyer is here, he's the Managing Director and Senior Partner at DCG and is joined by Allen Chen who's the Associate Director of Software Engineering at BCG. Gents, welcome to theCUBE, good to see you. >> Thank you >> Thank you for having us >> So we're going to talk about AI, we're going to talk about machine intelligence, digital transformation, but I want to start with this concept that you guys have put forth and you're putting it to action with some of your clients I'm sure, of this bionic organization. You know it's a catchy term, but what's behind it? What's a bionic company? >> So if you think about the next 10 years we believe that it's going to be the era of the bionic organization. Where the bionic organization is essentially humans and machines coming together. The bio and the nic, right. We believe that we are at a point now where the power from AI, the power from machines combined with the intrinsic human potential coming together delivers a very, very different set of outcomes. We get to outcomes largely on three fronts. The first is around customer experiences and relationships, you take that to a really new level. The second thing is in operations, you drive to a lot more productive set of operations through automation, and the third thing is innovation. The rate of innovation is just going to increase significantly, and we are seeing a lot of that today at re:Invent here. >> So you're optimists for the future, right? You don't want to pave the cow path, you don't want to protect the past from the future, but at the same time people are concerned, right. Machines are replacing humans, and they always have but for the first time in history it's with cognitive functions. So I'm sure you guys are having these conversations with your clients, maybe that's one of the blockers is that sort of perceptions that it's going to cause too much disruption. But maybe you could talk about that. How do I get to become a bionic organization? What are some of the barriers that I have to go through? >> I think the biggest thing is we are actually getting to an organization where technology continues to augment the human. So it's not substituting or replacing the human it's really augmenting the human. So how do we take human performance and organizational performance to a next level by bringing them together? So it's always about them coming together. When we think about barriers the real barriers actually are organizational models and old ways of working. They are legacy technologies. It's the lack of access to data that we can leverage to actually convert that into insightful outcomes using AI. And the lack of talent, so we really are at a point where we don't have enough digital and AI talent out there . Andy today talked about training as one of the core tenants of what you do to take an organization to leverage technologies that we have today, so those are the things that are barriers today that we're working with our clients on to overcome, to be able to extract the full potential of what we can do. >> Allen maybe you could talk a little bit about BCG's AI business, how do you guys look at it? Maybe share that with our audience. >> I mean as Sesh mentioned, the bionic organization really has two parts, right. It has the human element and it has the machine element and AI is really the thing that underpins the backbone for the machine element, but you can't really disconnect it from the human, because you know as we see with our clients if you just do the algorithms themselves the algorithms can't change the business, right. You can't remove the algorithms from the context of the business. The people who need to make the cultural changes, the organizational changes, the priority changes to actually put those algorithms into action. So we of course, as a company that helps clients go through this transformation, we have to usher along the human change but for the AI and machine learning change, we bring a lot of the best talent that we have. We've got 850 data scientists and engineers around the world helping our clients go through this transformation and you know we build lots of really, really interesting technology. For example, we've got a platform called SOURCE AI that we use to facilitate the building of these AI models and these advanced analytics use cases to accelerate at least the machine portion of that journey. >> Do you have a discrete AI business, a practice, or is it part of sort of a client's digital transformation where you bring in that expertise? >> Yeah, so within BCG we have a group called BCG GAMMA which is the arm of the company that focuses solely on AI and machine learning use cases. But the thing is, our model isn't just to kind of embed ourselves into your company and try to like take root and be there forever. We want to empower these companies to kind of kickstart their journey so we can go in, we help them get started, prove out a few use cases and then we actually train and transfer them so that they can make sure that the programs that we helped plant the seeds for end up being long term, sustainable programs for them. >> Dave: Teaching 'em to fish? >> Exactly >> When we think about what really drives impact and outcome and clients, it's all about bringing together the different capabilities that we have. So we have our heritage strategy consulting business. We have GAMMA, which is our AI at-scale, data analytics business. We have BCG Digital Ventures which is all about incubating new companies and taking them out of market. And then we have our Platinion team which is all about driving new architectures, new technologies, in terms of driving adoption at client. So all these capabilities typically come together at a client for us to deliver impact at the end of the day. >> Examples of sort of where you've implemented? Some successes? >> So I think, I think one great example that we have is around when you think about customer experiences and customer engagement, we have recently done a piece of work with United Airlines that's actually getting showcased here at AWS re:Invent where we really used personalization technology that we have with our partner Formation.Ai to really deliver a new level of customer experience and engagement for United customers, right. So we call it Miles Play and you can actually, I don't know if you guys are United customers, I know you guys travel a lot, >> Dave: Of course, everybody we also, so Miles Play is a way in which we have actually really leveraged AI and gamification inside of the United app to really drive a different level of experience for customers. So that's one example, there are many, many others. >> Yeah, we are here at AWS re:Invent as you point out, and the talk of transformation was part of the keynote this morning with Andy Jassy. A lot of that is around organizational change, but this is also a cloud show, so how does this work that BCG's doing with AI, how does that interact with the cloud and how does that link into that idea of organizational transformation? >> So when we think about, again I'll go back to that bionic organization, we see as we move towards this new organization that's bringing together bringing together data, technology as well as organization constructs, there are four things that we think of. We think about purpose at the core. So what is the reason that an organization exists, and how to we make that alive, and bring it alive? I think there's a second around data and technologies. So what can you do with AI, what can you do with data, how do you really drive modular technologies to adopt them to drive change? And then there's a third around people and organization. So how do you drive new organizational models to get an organization to deliver to the potential? And how do you bring new talent? And you know Andy talked about re-skilling today or training people, and then lastly leadership. How do you bring in a different style of leadership, we call it jazz leadership, where you really have to bring different parts of your organization to, and help them orchestrate to get to an outcome, rather than a more command and control style approach. So all of these are the pieces that we see coming together and that's what we work with our clients on to move them from where they are today to where they will be in the next 5 years. >> Allen you have software in your title, so I'm curious as to what kind of tooling that you guys have built, that you apply in your client situations? >> Yeah, so we work with a lot of different clients in a lot of different industries, and in a lot of different use cases and even though we treat every client as a unique situation there are patterns that begin to emerge and we want to make sure that, you know in order to provide the most value to our clients we want to be able to quickly prove out wins and use cases. And one of the ways that we're able to do that is building software products that facilitate those things. And so we've got data scientists that go through this whole machine learning pipeline even though the use cases are different, the challenges are kind of the same no matter what so you go through the process of how do you get access to data, right? Once you have access to data, how do you begin experimenting with models? Once you've experimented, how do you begin to consolidate the knowledge of the team to start evaluating models in a collaborative way? And once you have a model that you decide is good, how do you deploy that into a client environment? In many cases, it's going to be cloud because in order for these clients to really see the value of these AI programs, it's got to scale and so we work very closely with partners like AWS to ensure that we can bring the most scalable AI solutions to bear for our clients. And so we build platforms like SOURCE AI to facilitate that entire journey from data access all the way to deployment at scale. And then depending on the verticals, we also have other products that are most use case specific So we work with a lot of airlines to actually do airline scheduling for their airplanes, gate scheduling, routing bags. And so while we have SOURCE AI underpinning the platform, airlines have very, very unique problems of their own that are very, very interesting to solve and so we built products to cater to those industries specifically as well. >> One other piece that I would add is for the retail industry for example, markdowns is a big topic. So how do you get the best price for the given inventory that you have. We again have AI based solutions that drive markdowns and take the profitability of the revenue of a client to a better level than they're at. >> One of the things that we see is many of our clients want to get increasingly close to their customer to have that one-on-one relationship that traditional marketing can never afford you, right. So with things like markdown and personalization, we can gather all this data, use the latest AI techniques and begin to start giving offers and discounts and promotions and offers to people on a one-to-one basis, rather than marketing to a cohort of people. >> So a lot of these are functional areas, particular problem domains that have particular technological solutions, and then the pace of technology continues to change. We've seen that for decades. But it seems that this transformational agenda that we need to have, has a lot more to do with the humans and that problem doesn't really seem to have changed to me in the last several decades. BCG's been around for a very long time. Became famous back in the '80s for doing a lot of the same sort of transformational ideas how do you transform your organization? So what is it that is about, what is it about cloud and AI today that's changed the nature of organizational change? What the change in there? >> So my sense is My sense is, if you think about maybe there are two points to make here, and then Allen you should add on. I think one is, it's always easy to bring AI and data and do a proof of concept, right? And to show that something has potential. Taking that potential to impact and outcomes requires it to move to being at scale. So one of the big changes that we are seeing is we have to take these AI technologies and really deliver them at scale. So that's one piece of it. I think the other piece that really becomes important is leveraging AI for the right context in which you're applying the solve for. So you need to go into targeted spaces, as Allen said, certain use cases that have huge impact and go after it and deliver value there. As opposed to trying to do something a lot more expansively. So how do you now go into specific industries and identify unique areas that have a lot of promise and potential, and then put your energy against that to get to again impactful outcomes. Right, he had that example around markdowns. We've talked about airline optimization, we have talked about personalization. All of these are good examples of very targeted areas that have a lot of potential to really drive value. >> Yeah, like one of the things that I see that cloud has changed the transformation process is just the ability for us to very quickly experiment with new use cases, right. In terms of the types of tools and building blocks that cloud vendors like AWS provide us, you know we could think of an idea, an AI powered use case one day, and we could start cranking the gears on it the next day and if it works, we could just start scaling it up. And if it doesn't, we turn it off and it's a very, very kind of low regret, low risk kind of thing. Whereas back in the day where everybody is building data centers, in order to try something new you have to capitalize the cost of actually buying all this hardware, filling up your data center, staffing it, and then if it turns out that that use case didn't pan out, well now you've got loads of hardware that's just kind of costing you tons of money every day. With the cloud, we can just move so much more quickly and take a lot more bold risks. >> It's the cost of, I think it's the cost of experiments and the speed with which you can bring teams to get to outcomes. Right, so Andy again talked today about an integrated development environment for data scientists. How do you really bring data scientists, get them to start working on something, experiment with it, start to show some potential and then really scale it? Those are things that we believe, you know cloud has really immensely changed. The other thing is access to massive, massive data sets. Again Andy today talked about how different data sets can be brought into Amazon and the ability to do that easily today. So how can you really create value from these billions, and billions of rows of data that are sitting out there in your enterprise and converting that into something meaningful. >> So that approach and that philosophy of sort of low risk, pick a winner, scale it first of all, the CFO loves it, I think generally the organization is going to see value. They can, it's tangible. However, I think about digital disruption and if you think about the successful digital companies they've got data at their core. So my question to you is, are you helping these sort of incumbents? You mentioned United, I'm sure there are many others you work with. Are they able to sort of transform and put data at the core, become a digitally transformed organization before somebody disrupts them? You know will those, maybe not quick hits, but those focused projects, will they ultimately lead to an outcome that transforms them in a way that Jassy was sort of putting forth today? >> I would, I think so. I think that's the promise of the next five years. So if I think about, when we talk about a bionic organization, we talk about 30 to 50 processes that that organization will have. I mean my sense is those processes will have 50 to 60% of the components that are driven by AI or data. So if you think about an incumbent today working in manual processes, legacy systems, they are going to actually move to leveraging AI and data and new ways of working to transform that legacy environment into a next gen technological environment but also ways of working, and then bringing all of that together to drive a very different level of engagement with the customer, experiences with the customer, how they actually run their operations, do it much faster, reduce cycle time, and then also the rate and pace of innovation, right. You can see today the number of new features that got released on AWS and it's all been in a year and there are like 30 of them. So how do you really drive to that level of rate and pace of innovation. You'll see all of those happening in all of these traditional industries over the next five years. >> And if they don't move, they're going to probably be in big trouble. >> Sesh: They are going to be in big trouble, they're going to die >> All right, guys thanks so much for coming on theCUBE. It was great conversation, great to have you. >> Our pleasure, thank you >> Yeah, thank you so much for the time. >> All right, keep it right there everybody we'll be back with our next guest right after this short break. Dave Vellante for Justin Warren from AWS re:Invent 2019. Right back (electronic music)

(inspiring music) >> Welcome back to The Cube's coverage here in Las Vegas. I'm John Furrier for re:Mars coverage. Two days of live action, a lot of things happening in space, robotics, automation, and machine learning. That's Mars spelled backwards, but that's machine learning, automation, robotics and space. Got a great guest, Jens Ortmann, associate director at Boston Consulting Group, also known as BCG. Jens, welcome to The Cube. >> Thank you very much. >> So tell me what you're working on. You've got a very cool project you're working on, 'Involved'. Take us through what it is, explain what the project is. >> Yeah, so I'm part of the data science unit within BCG Gamma and I'm focusing on solving business problems for the automotive industry. What I would like to talk about is actually a small internal site project that we were building. It's a conversion rate engine, where we built an advanced analytics tool that computes the conversion rate for car dealerships, at scale. So for every single car dealer in a market, we can compute the conversion rate. >> John: What is a conversion rate? Can you explain that? >> So a conversion rate is very simple. It's actually out of the people that come into your car dealership, how many do you, as a car dealer, manage to sell a car to? >> So, what's your sell, through monthly kind of- >> Per visitor that come into, so your walk-ins. >> So, physical? >> Physical, yeah. So this was for physical stores. It's actually a key metric for sales performance for car dealerships, or for the automotive manufacturers to be aware of. >> So I'm watching here in the show floor at re:Mars, you've got the 'Just Walk Through', which is Amazon's 'take whatever you want and go', are you seeing you're getting analytics on like people coming in, you can see them, there's a drop off rate? Take me through how it works, the challenges because I don't envision like, "Oh, so they walked in and they left but they didn't leave with a car." It's not take and walk out, it's not grab and go. But the concept of using computer vision, I can imagine it being a popular thing. So how do you measure this, people coming in? >> It's actually a big challenge that we learned when we were doing this project. Traditionally, people were measuring it with like these laser sensors but the signal is very, very messy. Now when we wanted to do it at scale, we partnered with an Israeli startup called Play Sense, who aggregate mobile phone data. So we used mobile footfall data to measure how many people visit a store. So it actually is a combination of three main data sources to get to the conversion rate. One, as I mentioned, the mobile footfall data, the second one is building footprints, actual outlines of buildings that we source from the cadastral agency that we need to use it to cut out the footfall data to get the visitors. And the third one, of course, is sales that we get from the official car registration data. Then we combine those to have the key numbers. >> Is there a facial recognition involved in this? >> There's no facial recognition involved. >> So the tire kickers that come in and kick the tires and leave, but might come back. Is that factored in too, or? >> So there is a lot of pre-processing going on to really only get the signals from visitors. So filtering out people that maybe come into the store after hours, cleaning crews, people that come into the store every day, people that work there, they would be in the footfall data. So we applied some logic to identify exactly those people that are most likely actually visitors interested in buying a car >> Well everyone can relate to buying a car, obviously. I wanted you to step back and you mentioned scale. Can you scope the scale of the problem for us? How big is this observation space? What systems are involved? 'Cause when you say scale, I'm thinking all the dealerships in the aggregate. Or, is it by franchise or is it anonymous data? Can you scale the scope of this thing, or scope the scale? >> So we built this as a prototype for the German market and we used the top 10 car brands in Germany. They have around 10,000 car dealerships, for which we all have data. The actual mobile phone footprint data, it's a lot more. I think it was 30 million data points. >> Are you triangulating that? How does that mobile data work? Signal? >> So the mobile data is coming through apps. So mobile apps where you allow the app to track your location. >> Got it, okay. >> That gets anonymized and then you have these mobile data aggregators, like Play Sense. >> Got it, okay. >> That sell the data on. >> So you have to plug into a lot of systems? >> Yes. >> To make all this work. >> Yes and a lot of different data sources. >> And how easy is that? What's the challenge there? Is it cloud integration? How are you guys pulling this together? >> So we build it as a prototype initially, based on our own internal infrastructure, using basic Python and regular cloud infrastructure to process the data. >> All right, so I'm looking at my notes here. Data sets, you have a lot of data sets. What kind of analytics are you running on that? Can you share some examples? >> So I have to be careful since we filed a patent on this but a lot of the thing is actually in data processing, making sure that the data points we get are accurate and usable for this, and then differentiating between the different types of businesses that people are running. So there is on the one hand, you have the problem of outliers, basically filtering out when numbers don't make sense. On the other hand, there is a lot going on in the business itself. Like what do you do when a car dealership sells cars of multiple brands? You see only one visitor seeing cars of different brands but you see sales for two different types of brands. So this is just two examples of some of the processing that we had to implement to make this happen. >> So where can people find out information on this project? Or is it pretty much not public? Are you sharing anything publicly? >> So currently, we have held off the publication on this because we filed a patent on it. We're now about to go to market, building out a solution for the US as well, to then bring this to clients. >> What do you think about this show here at re:Mars? What's your assessment of the vibe? What's it like? Share with the folks who aren't here, what's your takeaway? >> It's really fun. It's really impressive. And it has a great, really inspiring vibe of cool innovative solutions. >> Yeah, you get the creative geniuses, you got the industrial geniuses, you got the software geniuses, all kind of coming together, and they're real people and they're here as a community. To me, the positive future vibe of this show, really is resonating in the keynotes and the energy. It's a forward thinking, positive message. And it's not marketing, this is the vibe. >> Exactly, I think it's something we really need at the moment. >> Yeah, we can solve all of the global problems by going to the moon and Mars. First the moon, then Mars. Who knows, maybe the breakthrough is there. >> People solve a lot of fundamental issues along the way that'll help in a lot of different areas as well. >> I wonder if I'll be alive when there's tourism in the moon. I was just joking with the folks earlier, "Oh yeah, I left that part on Earth, I have to go get it." Cause there's going to be a whole infrastructure there. Construction, all in good time. Okay, what's next for you guys? Tell me what's next on the project. You got a patent pending, so you're a little bit tight lipped and quiet on the secret sauce, I get that. What's next for the vision of the project? >> So this is just one example of how we can use this. Especially this footfall data set in an innovative way in the automotive industry. What we would like to look into is getting more details. Currently, we only see a single data point for a visitor. What would be interesting to understand, also, like the journey of visitors. Did they visit other car dealerships? Or, where are they from? What demographics do they come from? If you can tie that to a geographic location. And then on the business side as well, linking this for example, for companies to marketing campaigns. Does advertisement catch on? Do discounts catch on? Do they drive more people into the stores? Do they drive more sales? How does it affect conversion rate? Also, benchmark within the network, how different car dealerships are performing, how different brands are performing. And then eventually, everything is going to online. This can also be a foundation to set a baseline for online sales, which is still at the very early stages in the automotive industry. >> Yeah, I think there's a lot of reference implementations here for other applications, not just dealerships, all footfall traffic. That's interesting. The question I have for you, and the final question really before we wrap up, is the convergence of online, offline, physical, virtual. It's pretty clear we're living in a hybrid steady state right now, with all the post pandemic and the innovations pulled forward. So, having a device on me, IOT device or phone, will be a big part of things. So I'm buying online and I'm walking in, I'm one presence, virtually and physical. How do you guys see that around the corner? What's next there? Because I can see that coming together in my mind. >> It is. I mean, we can see it happen at Tesla. Tesla barely has any physical dealerships anymore, they have showrooms and do all the sales online. And I think that has a large impact on the industry at the moment. Driving the more traditional manufacturers also to think about what can be and what can be in a digital and online first world. >> Yeah, well this is happening. Well, Jens, thanks for coming on. I appreciate the commentary on re:Mars. Thanks for sharing your perspective and sharing about your project at Boston Consulting Group, also known as BCG. >> Thank you very much. >> Very reputable firm. Okay, that's the Cube coverage here at re:Mars. I'm John Furrier, your host. Two days of wall to wall coverage here. It's a great show. Machine learning, automation, robotics, and space, Mars. Of course, you got Reinvent, the big show, and at Reinforce, the security show. You got the space-software-robotics show, security. And then of course Reinvent is the big show. The Cube covers it, all three will be here. So keep watching here for more coverage. We'll be right back. (gentle inspiring music)