(upbeat music) >> Hey, welcome back to Las Vegas. Lisa Martin here, covering Coupa Insp!re 2022. theCUBE is really happy to be here at this event. About 2,500 folks are here, which is great to see. I have two guests from Microsoft with me. Please, welcome Jonathan Allen, the director of global network modeling design and planning, and Cassie Wang, senior global network model and design engineer. Guys, thanks so much for joining me today. >> No problem. Thanks Lisa for having us. >> Thank you. >> So let's talk about what's going on at Microsoft, the Microsoft supply chain. Supply chain is a term that's on everyone's lips these days for some interesting reason, but talk to me a little bit about the Microsoft supply chain and how does it scale to meet the needs of business? >> Yeah, Lisa, it's really an interesting design at Microsoft. When you look at all the products we service, from Xbox consoles, controllers, Xbox games, Xbox Live cards, service devices for retail customers, for consumer customers and commercial customers. And then the way we go to market through distributors, retailers, and direct to consumer homes, we have to have a supply chain that actually executes across all the products and customer needs based on seasonality. When you think about our products, Xbox console heavy Christmas, heavy consumer, heavy retail commercial devices for service, heavy quarter ends, heavy periods of time back to school. So, we have to have a supply chain that effectively works across all of our products, all of our customers, and all the differences analogies that we have to manage. >> And do so globally? >> And do so globally. >> So talk to me about the transformation. That's a word that we talk a lot about digital transformation, right? >> Yes. >> Before COVID, now we've seen the acceleration of digital transformation during COVID, we've seen challenges with the supply chain. Talk to me about Microsoft supply chain journey from a digitalization perspective, what you guys have gone through. >> Yeah, absolutely. Data is the key. And I have a philosophy which is around managing a business by facts and figures. And so, when Cassie first came on about a year and a half ago, our focus was on digitizing our supply chain. So how do you take our physical supply chain, digitize it in a way that you have a digital mapping and a duplication of what's happening physically in a digital way across the supply chain. So about every single day, we're grabbing in about 500 gigabytes of data, that then allows us to understand the physical and the virtual world of our supply chain, to understand how it's moving, how it's executing and how it's delivering. As for example, we were able to, when the war began in Ukraine, to understand where our trains were, how they were moving, and if they were continuing to move versus stopping. On the second side, we're leveraging that data now to make decisions about where our supply chain is today, which is really focused in the changing environments that are real time occurring. That's driving opportunities, whether it's about reducing carbon, whether it's driving cost down or whether it's servicing the customers to make real time decisions, while at the same time planning for three to five years out based on our growth, our projections, and making sure we'll have the right infrastructure partner supply chain in place to service with those changes in growth. >> Basically you need a crystal ball? >> Basically. >> Essentially? >> Yes. >> And Cassie, it sounds like from what Jonathan just said, you joined the team during the pandemic? >> Yes. >> So, during a time of massive change? >> Fully remote, yeah. Talk to me a little bit about that and some of the opportunities that you saw in helping the supply chain modernization. >> Yeah, definitely. So when I joined Microsoft, it's great time. And it's all the risks and challenges and dynamic changing environment that's really involved. So we spent a long time, like from the time I joined Microsoft, we spent the time to set up this digital chain of our supply chain. So really to transform what is happening physically to how do we see it digitally. So just to bring the visibility of the supply chain. So the great thing is we are able to leverage the tool from Coupa, the digital transformation and also supply chain design optimization tool to help us really build the digital twin, and also the model for Microsoft device supply chain. >> Now, interesting comment. So when I met Casie, the first time I met her, was in person when I interviewed her. Second time I met her in person was here at Coupa, and I was afraid I wouldn't recognize her. (all laughing) >> Of course, challenges of last year. Talk to me about speaking of challenges, talk to me about some of the challenges that Microsoft saw and said, "We need a partner like Coupa to help us eliminate these challenges. We don't have time. Real time is no longer nice to have. We've got to be able to transform, so we have that visibility in real time." >> Absolutely. When you think about time, time and decisions, overnight, cities get locked down in China, cities get locked down in Europe. And if you wait days or wait hours, that could be the difference between product on a boat, product on a plane, or product not arriving to support your customer needs. >> Right. And then the question is knowing that with that real time, how are you making decisions real time to change, to alternate airports? Making changes on the products you're making to make sure that, I was making this but now I should make this, because I have a risk of getting product to show. >> And you've got to do all that with very limited amount of time. And of course, cause there's the consumer. I mean, we think about the Microsoft on the business side but the consumer side, you mentioned some of the consumer products you don't offend the Xbox, the service consumers. One of the things that was really in short supply during the pandemic and probably still is to some degree, is patience. >> Yes. >> The consumer experience is so critical for a brand. >> Correct. >> And as is the employee experience. >> Yes. >> Talk to me a little bit about, from a supply chain digitization perspective, what was some of the executive sponsorships? Who were some of those executive sponsors that were involved in going, "Yeah, we need to move in this direction with Coupa, and it's got to be now."? >> The real supporter behind that is, my manager, Jeff Davidson, and then his leader, which is Donna Wharton, where they are truly about what are we doing next? How are we going to leverage the tools and the capabilities that are provided by others that allow us to do our job? So let's be clear on, let's use those that are designed to do what they're supposed to do, and then build where we need to. And that was the big difference, the digitization of the data, create the data, create the information so that we could then leverage the tools to create the information, right? And that information is then about bringing the facts, the information and the data forward, to have very fact-based conversations, which is back to manage the business by facts and figures. >> Right. Well, Cassie, one of the things that we've also learned in the last couple years, is that every company is a data company. If they're not a data company they're probably not going to be around. I even think of my grocery store and all that data that they have on me to be able to surface up. What did I buy last time, and I want to buy that again? Talk to me a little bit about why was Coupa the right choice to help facilitate this data strategy so that the visibility and the supply chain and the ability to tweak things on demand is there? >> Yeah. So, the main stuff that we are leveraging from Coupa are the data group and also the supply chain group. So data group enable us to really, for the people who do not have a intensive data manipulation backgrounds, they can use data group very straightfowardly to work on the data so they can build, they can grab the data transactional level and aggregate to the leadership level to see data in different aspects, tell the trends to get the key information. So that's the power of getting the massive data on a level that's like everybody can say, "Oh, wow! This is what it means." And another is definitely leveraging the data to get into a model, which is what we just talked about, the digital twin of our physical supply chain. So, we are able to like make analysis based on very easy design, like sensitive analysis, what-if analysis, to test out what our future supply chain can be. And what is the cost benefits? What is all the impacts on the on the lead times? On the carbons? So, yeah. So that's the power of leveraging the data. >> Speaking of carbons, how is Microsoft working towards being carbon negative, zero waste? What's some of the things that are going on there from a corporate responsibility perspective? >> Yeah, that's a really important one. As known about two years ago, we came out with a pledge to be carbon neutral by 2030. >> 2030. >> And so, the company as a whole is doing massive initiatives from different groups, but specifically in supply chain, we're constantly focusing on cutting our carbon footprint, whether it's the way we're making the products and designing the products, whether it's the way that we're designing our warehouses. So for example, just recently, we launched a Carbon Neutral DC in Europe, which is all solar panel based. We're about to do that as well in one of our US operations. We're working on other things that allow us to think about alternative pallets that eliminate the weight of wood, to a much lighter pallet that has a huge carbon reduction when you think about shipping things via the air and the carbon impact there. So, everything that we work on is really around three things; service, cost and sustainability. And our biggest objective is really taking all three of those objectives and trying to bring them closer to each other so that the decisions aren't as large against each other when you make one versus the other. That's our objective. So, how do we continue to move that ball forward, challenge the paradigms of the old, that we're so accustomed to and really move forward to changing? >> How does Coupa help with that? >> Oh, I can't say that, yeah. >> Yeah, so one of the actual dimensions, Microsoft our goal is to achieve carbon neutral by 2030. So traditionally, the trade off might be between cost and service, right? >> Okay. And now, the carbon is the most important priority. So the trade off, the balance, are between cost, service, time and carbon. So one of the great thing that Coupa can help us is in the network modeling. There is actually objective for lowering the carbon emissions. So that can be the top priority that you wanted to solve through your network modeling like in parallel to cost, to service. So you can just like very straightforwardly put more weight into carbon when you're making your decisions, like that can be a higher penalty cost when you have more carbon emissions. It's like a very straightforward way to translate the carbon goal into some quantifiable goal into the modeling and data. >> Jonathan, I'm curious from a Microsoft strategic partnership perspective, how important is it from Microsoft to partner with companies that have that strong commitment to help facilitate being carbon neutral by 2030, having a strong ESG initiative? >> It's critical. Microsoft for the most part is an outsourced supply chain in which we measure partners across the network. We have our partners run our distribution and centers, we have outsource manufacturing, we have outsourced logistics. And it's important that we're working with them about what their plans are, because they're just simply an extension of the Microsoft supply chain. >> Right. >> Right. They're not not just companies we work with, they're companies we partner with, to think about how can we change the future? What are the alternatives that we can do? How do we think about alternative fuels? How do we think about alternative shipping ways? How do we think about creating density in the network? So one of the biggest things when you really think about optimization is really around creating deensity. How do I create more with less, and make sure I'm taking, for every dollar spent, for every shipment made, I maximize it to its fullest, and leave no waste behind it? That's the goal. And so, partners challenging us is probably the most important piece because they're on the front line. They actually see our shipments, they see our loads, they see the work we're doing and how it's translating to their environment. And it's important that they give us that hard feedback back that allows us know where we're not meeting the bar. >> Got it. Cassie, you guys are giving a presentation in about a couple of hours. Talk to me about some of the things that the audience, like if you had to summarize the top three takeaways that the audience is going to learn from the top, what would they be? >> I think the first is sustainability. So we want everybody to know that this is the key mission for Microsoft. That's one of the priorities for the next eight years for Microsoft to achieve. And the second is just how Coupa can help us achieve that goal. And how do we leverage the the applications, the tools, the cutting edge technologies for us to achieve a sweet balance between sustainability and technology supplychain? >> I think one of the greatest things about conferences like this, is that Coupa is great with that customer centricity, is it the opportunity to hear from the voice of the customer? What challenges you had? Why you chose Coupa? How you resolved them? And that crystal ball that you talked about in terms of where we're going from here. I think that there's so much value. I'm sure in what you're going to share today with the audience. Jonathan, last question for you, for other folks in any industry that are about to embark on, or are in the midst of a supply chain, digital transformation, what's your advice? What recommendations would you give? >> For me, it's really about two things. First and foremost is about creating data. Focus on data, not an answer, not a conversation. What is the information that you require? And then the second piece about that is then how do you make sure you stitch it together? And how you create, whether it's manufacturing data, whether it's purchase order data, whether it's sales order data, whether it's shipment data, whatever it is, making sure that you can stitch end-to-end together, because each individual decision by itself, may be right, but could be wrong, because ultimately, it's about the decision for the whole, not the decision for the one. And then making sure you focus on the cultural change, which is around, it's just not my area, it's just not my thing, it's about the end, it's about the planet, it's about Microsoft, it's about the customer, it's about the future, and making sure you're really really focused on making that change, right? Not my change. >> Right, and Rob Bernstein even alluded to that a little bit this morning in his keynote talking about one of the things that Coupa breaks is silos. >> Yes. >> Organizations that, cause to your point, something might be really good for sales or operations, but not good for marketing or logistics, for example, need to be able to have that visibility across, but also another thing that Coupa is famous for is collaboration. >> Correct. >> Being able to enable that collaboration across lines of business, across teams, across partners. >> Yep. And an important statement of that is, when you think about change, think of it like a stream, right? Streams, they create pathways with persistence. When you believe in something and you're truly behind it, just stay the path, right? There'll be a time and a place, cause sometimes the decisions just aren't now, but they will become. There's a lot of things that, for example, myself and Cassie are constantly working on, that might not be right now, but they will be right in the future. And it takes sometimes, just the right opportunity, the right situation, but the key is making ysure you understand those things so when those opportunities present themselves, you can just step in. >> Yep. Another thing we've learned, I think in the last two years, I'm losing count, is it's not a matter of if, but when. >> Correct. >> And you can apply that general statement to pretty much anything these days. >> Absolutely. >> Guys, thank you so much for joining me talking about Microsoft's transformation of the supply chain, the digital twin that you've created. Have a great time in your session. I'm sure folks are going to learn a lot from you. >> Thank you very much. >> Thank you so much. >> All right, my pleasure. For Jonathan Allen and Cassie Wang, I'm Lisa Martin. You're watching the the CUBE's coverage of Coupa Insp!re 2022 from Las Vegas. Stick around, be right back with my next guest. (upbeat msuic)

>>Yeah, yeah. Welcome back to the third session in our building, A vibrant data ecosystem track. This session is unleash the power of your cloud data warehouse. So what comes after you've moved your data to the cloud in this session will explore White Enterprise Analytics is finally ready for the cloud, and we'll discuss how you can consume Enterprise Analytics in the very same way he would cloud services. We'll also explore where analytics meets cloud and see firsthand how thought spot is open for everyone. Let's get going. I'm happy to say we'll be hearing from two folks from thought spot today, Michael said Cassie, VP of strategic partnerships, and Vika Valentina, senior product marketing manager. And I'm very excited to welcome from our partner at AWS Gal Bar MIA, product engineering manager with Red Shift. We'll also be sharing a live demo of thought spot for BTC Marketing Analytics directly on Red Shift data. Gal, please kick us off. >>Thank you, Military. And thanks. The talks about team and everyone attending today for joining us. When we talk about data driven organizations, we hear that 85% of businesses want to be data driven. However, on Lee. 37% have been successful in We ask ourselves, Why is that and believe it or not, Ah, lot of customers tell us that they struggled with live in defining what being data driven it even means, and in particular aligning that definition between the business and the technology stakeholders. Let's talk a little bit. Let's look at our own definition. A data driven organization is an organization that harnesses data is an asset. The drive sustained innovation and create actionable insights. The super charge, the experience of their customers so they demand more. Let's focus on a few things here. One is data is an asset. Data is very much like a product needs to evolve sustained innovation. It's not just innovation innovation, it's sustained. We need to continuously innovate when it comes to data actionable insights. It's not just interesting insights these air actionable that the business can take and act upon, and obviously the actual experience we. Whether whether the customers are internal or external, we want them to request Mawr insights and as such, drive mawr innovation, and we call this the for the flywheel. We use the flywheel metaphor here where we created that data set. Okay, Our first product. Any focused on a specific use case? We build an initial NDP around that we provided with that with our customers, internal or external. They provide feedback, the request, more features. They want mawr insights that enables us to learn bringing more data and reach that actual data. And again we create MAWR insights. And as the flywheel spins faster, we improve on operational efficiencies, supporting greater data richness, and we reduce the cost of experimentation and legacy environments were never built for this kind of agility. In many cases, customers have struggled to keep momentum in their fleet, flywheel in particular around operational efficiency and experimentation. This is where Richie fits in and helps customer make the transition to a true data driven organization. Red Shift is the most widely used data warehouse with tens of thousands of customers. It allows you to analyze all your data. It is the only cloud data warehouse that sits, allows you to analyze data that sits in your data lake on Amazon, a street with no loading duplication or CTL required. It is also allows you to scale with the business with its hybrid architectures it also accelerates performance. It's a shared storage that provides the ability to scale toe unlimited concurrency. While the UN instant storage provides low late and say access to data it also provides three. Key asks that customers consistently tell us that matter the most when it comes to cost. One is usage based pricing Instead of license based pricing. Great value as you scale your data warehouse using, for example, reserved instances they can save up to 75% compared to on the mind demand prices. And as your data grows, infrequently accessed data can be stored. Cost effectively in S three encouraged through Amazon spectrum, and the third aspect is predictable. Month to month spend with no hitting charges and surprises. Unlike and unlike other cloud data warehouses, where you need premium versions for additional enterprise capabilities. Wretched spicing include building security compression and data transfer. >>Great Thanks. Scout um, eso. As you can see, everybody wins with the cloud data warehouses. Um, there's this evolution of movement of users and data and organizations to get value with these cloud data warehouses. And the key is the data has to be accessible by the users, and this data and the ability to make business decisions on the data. It ranges from users on the front line all the way up to the boardroom. So while we've seen this evolution to the Cloud Data Warehouse, as you can see from the statistic from Forrester, we're still struggling with how much of that data actually gets used for analytics. And so what is holding us back? One of the main reasons is old technology really trying to work with today's modern cloud data warehouses? They weren't built for it. So you run into issues of trying to do data replication, getting the data out of the cloud data warehouse. You can do analysis and then maintaining these middle layers of data so that you can access it quickly and get the answers you need. Another issue that's holding us back is this idea that you have to have your data in perfect shape with the perfect pipeline based on the exact dashboard unique. Um, this isn't true. Now, with Cloud data warehouse and the speed of important business data getting into those cloud data warehouses, you need a solution that allows you to access it right away without having everything to be perfect from the start, and I think this is a great opportunity for GAL and I have a little further discussion on what we're seeing in the marketplace. Um, one of the primary ones is like, What are the limiting factors, your Siegel of legacy technologies in the market when it comes to this cloud transformation we're talking about >>here? It's a great question, Michael and the variety of aspect when it comes to legacy, the other warehouses that are slowing down innovation for companies and businesses. I'll focus on 21 is performance right? We want faster insights. Companies want the ability to analyze MAWR data faster. And when it comes to on prem or legacy data warehouses, that's hard to achieve because the second aspect comes into display, which is the lack of flexibility, right. If you want to increase your capacity of your warehouse, you need to ensure request someone needs to go and bring an actual machine and install it and expand your data warehouse. When it comes to the cloud, it's literally a click of a button, which allows you to increase the capacity of your data warehouse and enable your internal and external users to perform analytics at scale and much faster. >>It falls right into the explanation you provided there, right as the speed of the data warehouses and the data gets faster and faster as it scales, older solutions aren't built toe leverage that, um, you know, they're either they're having to make technical, you know, technical cuts there, either looking at smaller amounts of data so that they can get to the data quicker. Um, or it's taking longer to get to the data when the data warehouse is ready, when it could just be live career to get the answers you need. And that's definitely an issue that we're seeing in the marketplace. I think the other one that you're looking at is things like governance, lineage, regulatory requirements. How is the cloud you know, making it easier? >>That's That's again an area where I think the cloud shines. Because AWS AWS scale allows significantly more investment in securing security policies and compliance, it allows customers. So, for example, Amazon redshift comes by default with suck 1 to 3 p. C. I. Aiso fared rampant HIPPA compliance, all of them out of the box and at our scale. We have the capacity to implement those by default for all of our customers and allow them to focus. Their very expensive, valuable ICTY resource is on actual applications that differentiate their business and transform the customer experience. >>That's a great point, gal. So we've talked about the, you know, limiting factors. Technology wise, we've mentioned things like governance. But what about the cultural aspect? Right? So what do you see? What do you see in team struggling in meeting? You know, their cloud data warehouse strategy today. >>And and that's true. One of the biggest challenges for large large organizations when they moved to the cloud is not about the technology. It's about people, process and culture, and we see differences between organizations that talk about moving to the cloud and ones that actually do it. And first of all, you wanna have senior leadership, drive and be aligned and committed to making the move to the cloud. But it's not just that you want. We see organizations sometimes Carol get paralyzed. If they can't figure out how to move each and every last work clothes, there's no need to boil the ocean, so we often work with organizations to find that iterative motion that relative process off identifying the use cases are date identifying workloads in migrating them one at a time and and through that allowed organization to grow its knowledge from a cloud perspective as well as adopt its tooling and learn about the new capabilities. >>And from an analytics perspective, we see the same right. You don't need a pixel perfect dashboard every single time to get value from your data. You don't need to wait until the data warehouse is perfect or the pipeline to the data warehouse is perfect. With today's technology, you should be able to look at the data in your cloud data warehouse immediately and get value from it. And that's the you know, that's that change that we're pushing and starting to see today. Thanks. God, that was That was really interesting. Um, you know, as we look through that, you know, this transformation we're seeing in analytics, um, isn't really that old? 20 years ago, data warehouses were primarily on Prem and the applications the B I tools used for analytics around them were on premise well, and so you saw things like applications like Salesforce. That live in the cloud. You start having to pull data from the cloud on Prem in order to do analytics with it. Um, you know, then we saw the shift about 10 years ago in the explosion of Cloud Data Warehouse Because of their scale, cost reduced, reduce shin reduction and speed. You know, we're seeing cloud data. Warehouses like Amazon Red Shift really take place, take hold of the marketplace and are the predominant ways of storing data moving forward. What we haven't seen is the B I tools catch up. And so when you have this new cloud data warehouse technology, you really need tools that were custom built for it to take advantage of it, to be able to query the cloud data warehouse directly and get results very quickly without having to worry about creating, you know, a middle layer of data or pipelines in order to manage it. And, you know, one company captures that really Well, um, chick fil A. I'm sure everybody has heard of is one of the largest food chains in America. And, you know, they made a huge investment in red shift and one of the purposes of that investment is they wanted to get access to the data mawr quickly, and they really wanted to give their business users, um, the ability to do some ad hoc analysis on the data that they were capturing. They found that with their older tools, the problems that they were finding was that all the data when they're trying to do this analysis was staying at the analyst level. So somebody needed to create a dashboard in order to share that data with a user. And if the user's requirements changed, the analysts were starting to become burdened with requests for changes and the time it took to reflect those changes. So they wanted to move to fought spot with embrace to connect to Red Shift so they could start giving business users that capability. Query the database right away. And with this, um, they were able to find, you know, very common things in in the supply chain analysis around the ability to figure out what store should get, what product that was selling better. The other part was they didn't have to wait for the data to get settled into some sort of repository or second level database. They were able to query it quickly. And then with that, they're able to make changes right in the red shift database that were then reflected to customers and the business users right away. So what they found from this is by adopting thought spot, they were actually able to arm business users with the ability to make decisions very quickly. And they cleared up the backlog that they were having and the delay with their analysts. And they're also putting their analysts toe work on different projects where they could get better value from. So when you look at the way we work with a cloud data warehouse, um, you have to think of thoughts about embrace as the tool that access that layer. The perfect analytic partner for the Cloud Data Warehouse. We will do the live query for the business user. You don't need to know how to script and sequel, um Thio access, you know, red shift. You can type the question that you want the answer to and thought spot will take care of that query. We will do the indexing so that the results come back faster for you and we will also do the analysis on. This is one of the things I wanted to cover, which is our spot i. Q. This is new for our ability to use this with embrace and our partners at Red Shift is now. We can give you the ability to do auto analysis to look at things like leading indicators, trends and anomalies. So to put this in perspective amount imagine somebody was doing forecasting for you know Q three in the western region. And they looked at how their stores were doing. And they saw that, you know, one store was performing well, Spot like, you might be able to look at that analysis and see if there's a leading product that is underperforming based on perhaps the last few quarters of data. And bring that up to the business user for analysis right away. They don't need to have to figure that out. And, um, you know, slice and dice to find that issue on their own. And then finally, all the work you do in data management and governance in your cloud data warehouse gets reflected in the results in embrace right away. So I've done a lot of talking about embrace, and I could do more, but I think it would be far better toe. Have Vika actually show you how the product works, Vika. >>Thanks, Michael. We learned a lot today about the power of leveraging your red shift data and thought spot. But now let me show you how it works. The coronavirus pandemic has presented extraordinary challenges for many businesses, and some industries have fared better than others. One industry that seems to weather the storm pretty well actually is streaming media. So companies like Netflix and who Lou. And in this demo, we're going to be looking at data from B to C marketing efforts. First streaming media company in 2020 lately, we've been running campaigns for comedy, drama, kids and family and reality content. Each of our campaigns last four weeks, and they're staggered on a weekly basis. Therefore, we always have four campaigns running, and we can focus on one campaign launch per >>week, >>and today we'll be digging into how our campaigns are performing. We'll be looking at things like impressions, conversions and users demographic data. So let's go ahead and look at that data. We'll see what we can learn from what's happened this year so far, and how we can apply those learnings to future decision making. As you can already see on the thoughts about homepage, I've created a few pin boards that I use for reporting purposes. The homepage also includes what others on my team and I have been looking at most recently. Now, before we dive into a search, will first take a look at how to make a direct connection to the customer database and red shift to save time. I've already pre built the connection Red Shift, but I'll show you how easy it is to make that connection in just three steps. So first we give the connection name and we select our connection type and was on red Shift. Then we enter our red shift credentials, and finally, we select the tables that we want to use Great now ready to start searching. So let's start in this data to get a better idea of how our marketing efforts have been affected either positively or negatively by this really challenging situation. When we think of ad based online marketing campaigns, we think of impressions, clicks and conversions. Let's >>look at those >>on a daily basis for our purposes. So all this data is available to us in Thought spot, and we can easily you search to create a nice line chart like this that shows US trends over the last few months and based on experience. We understand that we're going to have more clicks than impressions and more impressions and conversions. If we started the chart for a minute, we could see that while impressions appear to be pretty steady over the course of the year, clicks and especially conversions both get a nice boost in mid to late March, right around the time that pandemic related policies were being implemented. So right off the bat, we found something interesting, and we can come back to this now. There are few metrics that we're gonna focus on as we analyze our marketing data. Our overall goal is obviously to drive conversions, meaning that we bring new users into our streaming service. And in order to get a visitor to sign up in the first place, we need them to get into our sign up page. A compelling campaign is going to generate clicks, so if someone is interested in our ad, they're more likely to click on it, so we'll search for Click through Rape 5% and we'll look this up by campaign name. Now even compare all the campaigns that we've launched this year to see which have been most effective and bring visitors star site. And I mentioned earlier that we have four different types of campaign content, each one aligned with one of our most popular genres. So by adding campaign content, yeah, >>and I >>just want to see the top 10. I could limit my church. Just these top 10 campaigns automatically sorted by click through rate and assigned a color for each category so we could see right away that comedy and drama each of three of the top 10 campaigns by click through rate reality is, too, including the top spot and kids and family makes one appearance as well. Without spot. We know that any non technical user can ask a question and get an answer. They can explore the answer and ask another question. When you get an answer that you want to share, keep an eye on moving forward, you pin the answer to pin board. So the BBC Marketing Campaign Statistics PIN board gives us a solid overview of our campaign related activities and metrics throughout 2020. The visuals here keep us up to date on click through rate and cost per click, but also another really important metrics that conversions or cost proposition. Now it's important to our business that we evaluate the effectiveness of our spending. Let's do another search. We're going to look at how many new customers were getting so conversions and the price cost per acquisition that we're spending to get each of these by the campaign contact category. So >>this is a >>really telling chart. We can basically see how much each new users costing us, based on the content that they see prior to signing up to the service. Drama and reality users are actually relatively expensive compared to those who joined based on comedy and kids and family content that they saw. And if all the genres kids and family is actually giving us the best bang for our marketing >>buck. >>And that's good news because the genres providing the best value are also providing the most customers. We mentioned earlier that we actually saw a sizable uptick in conversions as stay at home policies were implemented across much of the country. So we're gonna remove cost per acquisition, and we're gonna take a daily look how our campaign content has trended over the years so far. Eso By doing this now, we can see a comparison of the different genres daily. Some campaigns have been more successful than others. Obviously, for example, kids and family contact has always fared pretty well Azaz comedy. But as we moved into the stay at home area of the line chart, we really saw these two genres begin to separate from the rest. And even here in June, as some states started to reopen, we're seeing that they're still trending up, and we're also seeing reality start to catch up around that time. And while the first pin board that we looked at included all sorts of campaign metrics, this is another PIN board that we've created so solely to focus on conversions. So not only can we see which campaigns drug significant conversions, we could also dig into the demographics of new users, like which campaigns and what content brought users from different parts of the country or from different age groups. And all this is just a quick search away without spot search directly on a red shift. Data Mhm. All right, Thank you. And back to you, Michael. >>Great. Thanks, Vika. That was excellent. Um, so as you can see, you can very quickly go from zero to search with thought Spot, um, connected to any cloud data warehouse. And I think it's important to understand that we mentioned it before. Not everything has to be perfect. In your doubt, in your cloud data warehouse, um, you can use thought spot as your initial for your initial tool. It's for investigatory purposes, A Z you can see here with star, Gento, imax and anthem. And a lot of these cases we were looking at billions of rows of data within minutes. And as you as your data warehouse maturity grows, you can start to add more and more thoughts about users to leverage the data and get better analysis from it. So we hope that you've enjoyed what you see today and take the step to either do one of two things. We have a free trial of thoughts about cloud. If you go to the website that you see below and register, we can get you access the thought spots so you can start searching today. Another option, by contacting our team, is to do a zero to search workshop where 90 minutes will work with you to connect your data source and start to build some insights and exactly what you're trying to find for your business. Um thanks, everybody. I would especially like to thank golf from AWS for joining us on this today. We appreciate your participation, and I hope everybody enjoyed what they saw. I think we have a few questions now. >>Thank you, Vika, Gal and Michael. It's always exciting to see a live demo. I know that I'm one of those comedy numbers. We have just a few minutes left, but I would love to ask a couple of last questions Before we go. Michael will give you the first question. Do I need to have all of my data cleaned and ready in my cloud data warehouse before I begin with thought spot? >>That's a great question, Mallory. No, you don't. You can really start using thought spot for search right away and start getting analysis and start understanding the data through the automatic search analysis and the way that we query the data and we've seen customers do that. Chick fil a example that we talked about earlier is where they were able to use thoughts bought to notice an anomaly in the Cloud Data Warehouse linking between product and store. They were able to fix that very quickly. Then that gets reflected across all of the users because our product queries the Cloud Data Warehouse directly so you can get started right away without it having to be perfect. And >>that's awesome. And gal will leave a fun one for you. What can we look forward to from Amazon Red Shift next year? >>That's a great question. And you know, the team has been innovating extremely fast. We released more than 200 features in the last year and a half, and we continue innovating. Um, one thing that stands out is aqua, which is a innovative new technology. Um, in fact, lovely stands for Advanced Square Accelerator, and it allows customers to achieve performance that up to 10 times faster, uh, than what they've seen really outstanding and and the way we've achieved that is through a shift in paradigm in the actual technological implementation section. Uh, aqua is a new distributed and hardware accelerated processing layer, which effectively allows us to push down operations analytics operations like compression, encryption, filtering and aggregations to the storage there layer and allow the aqua nodes that are built with custom. AWS designed analytics processors to perform these operations faster than traditional soup use. And we no longer need to bring, you know, scan the data and bring it all the way to the computational notes were able to apply these these predicates filtering and encourage encryption and compression and aggregations at the storage level. And likewise is going to be available for every are a three, um, customer out of the box with no changes to come. So I apologize for being getting out a little bit, but this is really exciting. >>No, that's why we invited you. Call. Thank you on. Thank you. Also to Michael and Vika. That was excellent. We really appreciate it. For all of you tuning in at home. The final session of this track is coming up shortly. You aren't gonna want to miss it. We're gonna end strong, come back and hear directly from our customer a T mobile on how T Mobile is building a data driven organization with thought spot in which >>pro, It's >>up next, see you then.

>>Live from San Diego, California It's the Q Covering Koopa and Cloud Native Cot Brought to you by Red Cloud. Native Computing Pounding and its ecosystem >>Welcome back, toe Gorgeous. SAN Diego, California This is Q Khan Cloud Native Khan. 29 years. I'm still minimum. My co host is John Troyer, and this is the end of three days water wall coverage over 12,000 and 10 d Having a welcome to the program. Cassie, Who's the executive director at the national headquarters for v. American Red Cross. Matt. Thank you, American Red Cross. And thank you so much for joining us. >>Yeah. Thanks for having me. Uh, it's been a great conference so far. Uh, you know, we're here to share our story where as an end user on our journey with Cloud native with kubernetes Andi how that helps Red Cross do what we do, which is help people in need a cz best we can every day. >>Eso no matter what industry I talked to, everybody's dealing with change. There's always more things happening. American Red Cross. I mean, you know, it feels like I hear American Red Cross mentioned Maur a cz Time goes on because you know everything from, you know, things related to climate through, you know, global events and the like. So maybe before we get into some of the tech, just give us, you know, you know your role there and how kind of the changing world impact your organization. >>Sure. So my role is to support a few different business units. One is biomedical marketing. We try to recruit blood donors, too. Give blood at Red Cross Blood Dot or GE and other channels. That's obviously a significant part of what we d'oh were major player in the blood supply market in the US we provide service is to the armed forces, you know, in that regard as well. So that's part of it. Part of it is I work with humanitarian service is group as well to recruit financial donors on recruit volunteers. That's primarily through Red Cross. That or GE a T least as faras my group goes on, then corporate brand marketing and chapter related marketing and communications. So all that happens through Red Cross that Oregon Red Cross blood dot organ some related platforms on those our flagship brand products. >>Okay, And what led to the American Red Cross being part of this cloud. Native computing, Yeah, system. >>Our journey is a lot like, you know, a lot of other folks. We had a very, you know, monolithic type of architecture. We had all of these different business units with the different priorities, different timelines, different needs wrapped up into one big monster of a platform that, you know, kind of bundled up risk for everybody in this one platform. And, uh, you know, we'd always have collisions of priorities, mostly not to mention the resource issues of who's gonna work, you know, on what? At what time. And so a few years ago, we started talking about breaking that down. And, um, we've been lucky to have some technical leaders that are very aware of and welcoming to new cloud native technologies. We decided at that time to pursue, you know, a cloud native architecture. And what we have today in a few years later, is two years worth of being in production with a platform that runs on Amazon. We take advantage of a lot of the native orchestration tools there for running our clusters. And we've been able to service, you know, those different needs in a much more nimble way. We can release something for a Red Cross blood dot or without risking much on the financial donation side or on the volunteer recruitment side. And likewise, you know, for those other groups, we can kind of separate out the risks for each of those groups. And that's that's been a great, great benefit. >>You've been on the the vendor side. The for profit side is I t very different at the non profit. If you're looking, people are looking down, you get >>higher. Yeah, You know, I have been doing it a long time, a lot of different perspectives. But I think you know what I tried to do. And I would. I think I've seen work best is when I t is not the ticket taker, you know, integrated with the business. I'm very fortunate to have some business partners at Red Cross that collaborate. You know, every day we're having conversations every day. We have some people on our team that feel as though they're accountable for business outcomes, not just, you know, doing cool technology things, you know, For example, you know, multiyear evolution of process related to being more agile. We've got so much more integration and communication with business teams have gone from, you know, something like one release every five months now due to a weak, you know, and I think we could do more. It's just we don't have the need to do more. Um, and that's a huge, huge, big lift. You know, there's a lot of conversations that need to happen. Should make that work. >>Yeah, it's all a journey, right? We're all we're all improved. Continuous improvement, but so follow up there. So as a 90 leader for a very large organization, you know, they're one of the things people are saying this year. Wow. The conference is big. So many new technologies. So many new company somebody open source projects. You know, you're in the middle of this journey. You can't screw it up, right? That would be disastrous. So how do you How do you How did you and your organization look at new technologies and pick out which do technologies to try and incorporate them into your stack and your portfolio? >>Right. So we wanted to be a cloud native. We wanted a do, um, you know, focus on projects that where we knew there were skills in the marketplace, uh, that we could acquire at our price point. You know, we try to be good stewards of donor dollars at the end of the day, you know, all the money we have comes from folks like you and you guys who support Red Cross, you know, and thank you very much for all that generous support. And so we try to spend that money, uh, you know, very carefully. Way have some people who are, uh, you know, employees on our team made about 25 or so. But one of the great things we've been able to do with some of these technologies now is we have a program called Code for Good. It's a volunteer work force where we're here recruiting volunteers with the skill set that you know, they have a day job, but they have an interest in supporting Red Cross. Uh, maybe not financially. Maybe not with their blood, but they can give us some time on their skills, and we run it like an open source project. We set out a road map of features for six months or so. We have planning sessions, we say. Listen, you know if you can sign up for a feature that because you have two hours this week to work. Great. You have six hours. Great. You just had a baby, and you're not available for three months. Fine. You know, we we wanna have a, you know, a bench of people that can self select based on their time commitment, what to work on. And somehow it's been been working Great. You know, we started this in June. We have about 30 volunteers now on. We've already delivered an app for slack. That is kind of a workplace app where you can, you know, if your organization works with us, you can donate right from slack. You can give a schedule of blood donation, appointment, do things like that. >>I love that model. It's something that, you know we've looked at years ago. That kind of micro participation, if you will. You know, You think it's like, you know, Wikipedia wouldn't have been built if it wasn't for everybody. Just spending a little bit of time on it. Uh, I'm curious. Does something like participating with you know, this ecosystem I have generalized tools that people know and can plug in with, as opposed to, you know, having to know your direct stack Is that helpful To kind of be able to recruit people into that environment? What? What are the kind of most needed skills on dhe usages that you're recruiting? >>It is. You are learning. Curve at this point is much smaller than it was on our previous platform Because of the fact that we're using technologies people are familiar with, um, you know, things like Docker we use a lot. We just started evaluating Prometheus, another C N C F project for monitoring some non proud systems. Hopefully that'll graduate into production systems. So from a technology standpoint, yes, yes, we find that, you know, the people we talked with can walk in and be productive sooner. You know, there's still the Red Cross specific things they need to know about how we do business. But, um, you know, at least at this point, is that and not some proprietary system that they also have to learn >>any learnings that you've had participating in the c m. D. F. With the rollout of the technologies that you share with your peers, >>you know, I love the sea. NCF is very maintainer driven, You know, uh, and and user driven. I heard today at one of the analyst panels. I did. I think maybe 30% of people here are end users. >>That's a pretty >>large number. Um, you know, the fact that we can come here and learn about technologies meet people, meat vendors meet some of the people contributing code. Um, it's a lot different than you know, maybe some some summit sponsored by a for profit vendor that wants to, uh, you know, generate leads and sell you things. It feels much more community driven here and open to lots of different perspectives. >>So now what you looking forward to in the next few years? Both in terms of your stack and maybe coming back >>to Cuba? Yeah, way. It's funny. We've started to see other parts of Red Cross come to us toe, learn about kubernetes because the vendors they work with are mentioning these things. And and we have been early adopters, as far as you know, where across goes our group. Um and I think it's great if we can expand usage of, um, cloud native technologies to other parts of the organization on really get some economies of scale. So that's part of what we're trying to do is kind of internal, uh, consulting knowledge sharing collaboration on then, as far as what we're doing on our team way. Just really want to focus on. We're on a stable point in the platform, and then we want to do some things around monitoring and alerting that. Reduce those incident outages, too. Nothing. Hopefully, um, and work on that. >>You're working on a few projects that are that are being worked on here for That >>s So you have this Prometheus project. Like I said, we're piloting that, uh, you know, I would say in four or five months time, we'll know if that's going to be something we can, you know, put some more investment into >>All right, that want to give you the final word. Red cross dot org's code for good. I believe. The web. >>Yes, yes. >>What else? >>Your code for the number four. Good on. You know, if you're interested in volunteering, we need technical skills. We need team leadership skills, product owner skills, eh? So it's not just about you know, developing features and ops engineers as well. So thanks for your time. I want to say hi to my daughter, Peyton. It's late on the East Coast, so go to bed now, but thanks, folks. >>All right. What? Well, Matt, and actually, that is the final word for our day one of coverage for John Troyer. I'm stupid. And be sure to join us tomorrow. We've got two more days water wall coverage here. Lots of great speakers. Really appreciate. We've got the end users on. And, Matt, thank you so much. And, you know, great mission. The code for good. We definitely hope that the community here, you know, reaches out in connection Participates s Oh, that's it for today. Fixes all for watching.

>> Narrator: Live from Orlando, Florida, it's The Cube, covering PentahoWorld 2017. Brought to you by Hitachi Ventara. >> Welcome back to The Cube's live coverage of PentahoWorld, brought to you, of course, by Hitachi Ventara. I'm your host, Rebecca Knight, along with my cohost, Dave Vellante. We're joined by Reni Waegelein, he is the IT manager of Veikkaus. Thanks so much for coming on The Cube Reni. >> Thank you for having me here. >> So, Veikkaus is the Finnish national betting agency wholly owned by the government. >> Yeah. >> Tell us more. >> Yeah we have, we used to have like three companies, now we are merged as one and we operate every money gaming thing, all the money gaming in Finland. So that includes from casino to lottery, to scratch tickets, sports betting, horse betting, whatever that is, and we gather money, of course, pay out some good winnings as well. But everything we make under the line, that goes to good causes, and I mean everything. >> And you are IT manager. >> Reni: Yeah. >> So what does, what are your responsibilities? >> Yeah, responsibilities like the developing the whole of the idea things we have, from architecture to doing the IT procurement development, and harnessing how we work. >> So the public policy on betting is, hey, let's have a single state-run monopoly. >> Reni: Yep. >> And we'll take the winnings and put it to the public good, right, makes sense. >> Reni: Yep. >> And is there any competition from internet, for example? >> Of course, yes, and the internet, well, it's like a full competition, although we are a legally-based company in Finland and we operate and sell only to Finnish people. The people itself, they have all the freedom to choose whoever they want to play with, so in that sense, it's full competition and have been so for many years. >> So you have to have great websites. >> Reni: Yep. >> Great customer experience, >> Reni: Yep. >> User experience. >> Reni: Yeah. >> Competitive rates, all that stuff. >> Reni: Yep. >> Okay so, and good analytics. (laughing) I mean that industry is obviously very data heavy. >> Reni: Yep. >> Always has been. So how do you analytics and data to compete? >> So we have been doing, like, the product analytics for quite a long time and then we established a customer-ship. So in Finland we have a 5.4 million habitats, and we sell only for the 18+ year old people, and at the moment we have more than 2 million registered customers already. So, you can imagine that we have that vast amount of data from the customer, and we use that data, for example, promoting the service, promoting games, targeting, making some recommendation. We build our own recommendation engine, for example, and utilize all of that kind of data. But, as you know, the gaming is also like a two-edged sword, that's a happy side, but there's also the dark side. So it does cause problem, so we try also to use the data so that we want to identify the bad patterns when somebody is about to lose control of gaming. So we use also the same data that we want to see, for example, for these players who want to see all the activities of marketing, for example, we don't want anybody to get into problems because of gaming. >> So that's a really interesting tension here, is that you obviously want to make money in this, but you also have to watch out for the Finnish society. And as you said, if there's a compulsive gambler or an addicted gambler, you need to act, I mean, is that? >> Yeah, yeah that's really big part of our responsibility, and if we didn't have any data or if we couldn't process it fast, we couldn't know who is problematic gambler and who is not. Since vast majorities, of course, is enjoying it, it's a nice habit. Play a game of poker every now and then or go to the casino for once or twice a month, for example. But then there's the small portion of people who we want to protect so that they don't get into the debt. That's not our intention. >> And the level of protection that you provide, is you stop marketing to them, is that right or? >> Reni: Yeah, yeah. >> It's not like you intervene in some other way. >> Yeah, of course, we want to promote that if you want, you can stop and close your account, or this kind of activities. >> So you promote cutting the cord basically? >> Yeah, yeah, yeah. So instead of marketing, we say that this might be a problem to use, so yeah. >> Let's take a break. >> You should take a break, yeah. >> So, as Dave was saying, you're really, because you are competing with private entities you really have to have a great interface, great customer experience, great rates. How much does this put Veikkaus really on the vanguard of this kind of technology, more so than what other government agencies are doing, in the sense that, you really have to stay on the cutting edge of these things. >> Yeah, we have to be like double-backed, you say. >> So how much do you then you talk to the health agency, or other government agencies about what you're doing and sharing the best practices about capturing customer attention? >> We are actually talking more to the new players out in the field who already live and breath true to data, so that's where we can learn and, I would say that we are also in to like a lottery area itself but also in quite many other industries as well. So we have been doing this for awhile, so we have had the luxury that we have already gathered some experience and opened some paths and, well, maybe learned also from the hard way how not to do it. We of course didn't succeeded in the first runs but you just have to go and have a trial and error in some areas as well. >> And you have multiple data sources obviously, maybe talk about how you're handling those data sources, are you ingesting, how you ingest those into Pentaho, what you do with it, how you're operationalizing the analytics. Where does Pentaho fit in that whole process? >> Yeah Pentaho we use, that's like ETL process, so to get this 360 view of the customer, we have like a various data sources. After the merger, we tripled the amount of different sources, and I think more than quadrupled the amount of data. So of course, just to make the data and work of the analysts easier, we need to make some transformations to the data and in that area the Pentaho has it's place. And in the future, what we are also expecting like the future versions to help us with is the tech in the more real time data. So for example, we can put in the real time data feed for the one physical place so they can see like which machines are used well, which are not, or is there any other activities that they can learn right in their place. >> So are you in the process of instrumenting the machines at this point? >> Reni: Yep. >> And so you're putting, how does that work, is it rip and replace, is it some kind of chip that you put into the machine? How do you instrument the machine? >> It's a good thing, so that we have actually we design our own slot machines, even. >> Dave: Okay, okay so. >> So we, we can like build up from the ground up. >> Dave: Design it in. >> Yeah. We designed the hardware supports like, it's, they are big IOT machines. >> Dave: Right. >> But also the software will support us. >> And then you've got connectivity, is it hard-wired? Is it physical or is it wireless connectivity? >> We use, well, whatever is available, so... >> Dave: Depends. >> Yeah, yeah. And when we are developing like a new type of games, for example, when the slot machines should have like online all the time, like jackpot available, then of course, we have to think about what's the quality of service of the network, as well. So far, we have been like using whatever is available. >> So what does the data architecture look like? I wonder if you could paint the picture, so you've got the machines, let's just use slot machines as an example. So you have the slot machines, you've instrumented those, you're doing real time analytics there, and maybe talk about what kinds of things you do there? And then where does the data go? How much data, do you persist the data? Maybe talk about that a little. >> Yeah so we get like the slot machines and other resources as well, and have like Kafka Hadoop area where we collect everything. Then there's a Pentaho doing the ETL work and we store the, all the data that goes through it to the Vertica. So we have HP's Vertica there, in that Vertica they've like lots of users, they have like a SAS analytics, use that and the Hadoop as well, so then we have some reporting, financials, finance department they also utilize it. But then we are also building up some new things like Apache's Kudu is one thing that we want to set up there just to make the life of analysts much more easier so they are the moment having little bit hard time in some areas how to utilize the data, and especially how to use like the different analyst tools from different cloud vendors for this data since we are still at the moment on premise, so everything is on premise partly because of the government requirements. >> Dave: Okay. >> So some part of the data they require that we keep it in within the Finland. >> Right so could we call that your private cloud? >> Reni: It's not private cloud yet. >> It's not, okay. >> But we're, we are going. >> Dan: Someday. >> Yeah, yeah. >> It will be a private cloud, okay, so you have edge device, which is the slot machine, and then you do you send all the data back to Vertica or no, probably not, right, I mean. >> Not yet. >> Dave: But do you want to? >> But it will be. >> Dave: Really? >> Yeah, it will be. Of course we have to make some decision like what data will be important and what is not, so not all the data is valuable, but especially when it's like connected somehow to the customer, or the retailer as well, that data we also keep like more than a year. So we are not doing all the analytics just for a short time of data but also want to seek out the long trends and make new hypothesis out of it. >> And the Vertica system is essentially your data warehouse, is that right? >> Reni: Yeah. >> Okay. And then are you doing sort of, well you mentioned recommendation engine so you're doing some >> Reni: Yeah. form of it. That's a form of AI, as far as I'm concerned. Are you doing that, where are you doing that? Is you doing that in your data center, and is that another layer of the data pipeline or is that done in the? >> Yeah, it's done partly on site but also in AVS. >> Yeah >> So we used Amazon services in some areas where we can use those, so the recommendation for example, and part of the cost of AI, that's part, some blocks are also on the AWS. >> So it's a three tier. >> Reni: Yeah. >> So there's the edge, then there's the aggregation at Vertica, and then there's the cloud modeling and training that goes on, and Pentaho plays across that data pipeline, is that right? >> Yeah, yeah, it's our one major player in our data platform in this sense so that it will take care quite a many different kind of transactions so that we have the right data in the right place. >> Dave: All right I'm done geeking out. (laughing) >> All right, so Reni before the cameras were rolling, we were talking a little bit about the difficulties of cultural change within these organizations and you were talking about something that you're working on in Finland that's not necessarily related to Veikkaus, can you tell our viewers a little bit about what you're doing? >> Yeah, we are also setting up a Teal Finland, so promoting this like next phase of organizational, well you cannot call it belief, but vision and perspective so we want to also promote these kind of activities. So I know that especially with the big data movement, you have also seen the cultural changes so not the normal organization ways of working are not, just are not efficient enough so you have to liberate today, you have to give the freedom, how to use the data, what kind of hypothesis, what kind of activities are done, and this cultural change is also with the Teal movement. It's like getting next big leap so this is, well it's a side project but it's also really heavily work related. >> And how open is the Finnish tech community to these ideas, I mean is there an adversarial relationship within the people who don't necessarily welcome the change, I mean how would you describe it? >> I believe it's a really open, we have already, I believe, a handful of companies who work and who operate by this, from this perspective and more is popping out. And we are establishing one cooperative, like to support this movement, and maybe to create new spinoffs which can be for profit. >> All right, let's get to the heart of the matter here, (laughing) how do I beat the house? >> I knew you were going there, Dave. >> Just, just between us. >> I knew it. (laughing) >> Obviously I'm kidding but different games have different odds. >> Reni: Yeah. >> Right, I mean, and those are, you're transparent about that, people know what they are, but what are the best odds? Is it slots, best chance of winning, or poker, or... >> Yeah, slots is good side and also whenever you go to Cassie you know, it has a top notch, so 90 point something, so... >> Of probabilities and, >> But of course I have to say that the house wins eventually, so yeah, yeah. >> The bookeys always win so. >> Rebecca: Right exactly. >> So the higher the probability, the lower the pay out, and reverse, presumably, right? >> Reni: Yeah, yeah. >> The lottery would be. >> Lottery you're a check out if you're yeah. >> Dave: Low odds. >> Low odds but, >> Dave: Telephone numbers if you win. >> Yeah. >> Dave: Yeah. >> But David, you can't win if you don't play, okay, just saying, just saying. >> And every week there's somebody who wins. >> Rebecca: Right! >> Yeah. So why it cannot be me, or you? (laughing) >> Or me, or me, maybe! >> So what do you do to the guys who count cards, you like break arms or you put them in jail, no? >> It's Finland, this is no, no, come on. >> Nobody does that, right? >> Reni: No, no, no. But of course, yeah that's probably something we could in future also to use data more efficiently than we use it at the moment, so that's one part like how people behave versus machines behave. So for example in the online poker, the card counting program, that's one problem I think every, for the industry. >> Dave: Right. >> Are you working with behavioral finance experts in this to sort of understand people's behavior when it comes to this? >> Yeah we work, for example, with psychologists to understand this and the same goes with problematic gambling as well so you have to know about how people behave. >> And do you have customers outside of Finland or is it pretty much exclusively? >> No, sorry, it's exclusive club, you have to move to, you know you have to move to Finland. (laughing) And then we welcome you. >> Awesome. >> He's going to immigrate, I think, any day now. Well Reni, >> Reni: But hey, it's one of the best countries. >> Thank you so much for coming on The Cube, it was a lot of fun talking to you. >> Yeah, thank you. >> I'm Rebecca Knight, for Dave Vellante, we will have more from PentahoWorld just after this.