>> from the Silicon Angle Media Office in Boston, Massachusetts. It's the queue. Now here's your host. Still minimum. >> Hi, I'm student men and welcome to a special edition of Cube conversations here in our Boston area studio. Habito. Welcome to the program. First of all, to my right, a first time guests on the program Drew Clark, Who's the chief strategy officer? A click and welcome back to the program tomorrow on Carryon. Who's a senior vice president of enterprise data integration now with Click but new title to to the acquisition of Eternity. So thanks so much for joining us, gentlemen. >> Great to be here. >> All right, True, You know, to Nitti we've had on the program anytime we haven't click on the program, but maybe for audience just give us a quick level set on Click. And you know the acquisition, you know, is some exciting news. So let's start there and we'LL get into it. >> Sure, thanks. Teo and Click were a twenty five year old company and the business analytics space. A lot of people know about our products. Clint View, Click Sense. We have fifty thousand customers around the world and from large companies, too kind of small organizations. >> Yeah. Alright. Eso you No way. Talk a lot about data on our program. You know, I looked through some of the clique documentation. It resonated with me a bit because when we talk about digital transformation on our program, the key thing that different to the most between the old way of doing things the modern is I need to be data driven. They need to make my decision the the analytics piece of that s o it. Tomorrow, let's start there and talk about, you know, other than you know, that the logo on your card changes. You know what's the same? What's different going forward for you? >> Well, first, we were excited about that about this merger and the opportunity that we see in the market because there's a huge demand for data, presumably for doing new types of analytics business intelligence. They they's fueling the transformation. And part of the main challenge customers have organizations have is making more data available faster and putting it in the hands of the people who need it. So, on our part of the coming from eternity, we spend the last few years innovating and creating technology that they helped car organizations and modernize how they create new day. The architecture's to support faster data, more agility in terms ofthe enabling data for analytics. And now, together with Click, we can continue to expand that and then the end of the day, provide more data out to more people. >> S o. You know, Drew, it's interesting, you know that there's been no shortage of data out there. You know, we've for decades been talking about the data growth, but actually getting access store data. It's in silos more than ever. It's, you know, spread out all over the day. We say, you know, the challenge of our time is really building distributed architectures and data is really all over the place and, you know, customers. You know, their stats all over the places to how much a searchable how much is available. You know how much is usable? So, you know, explain a little bit, you know, kind of the challenge you're facing. And you know how you're helping move customers along that journey? >> Well, what you bring up stew is thie kind of the idea of kind of data and analytics for decision making and really, it's about that decision making to go faster, and you're going to get into that right kind of language into the right individuals. And we really believe in his concept of data literacy and data literacy was said, I think, well, between two professors who co authored a white paper. One professor was from M I t. The other one's from ever sin college, a communication school. Data literacy is the kind of the ability to read, understand, analyze and argue with data. And the more you can actually get that working inside an organization, the better you have from a decision making and the better competitive advantage you have your evening or wind, you're going to accomplish a mission. And now with what you said, the proliferation of data, it gets harder. And where do you find it? And you need it in real time, and that's where the acquisition of opportunity comes in. >> Okay, I need to ask a follow up on that. So when a favorite events I ever did with two other Emmett professors, yes, where Boston area. We're putting a lot >> of the >> mighty professors here, but any McAfee and Erik Nilsson talked about racing with the machine because, you know, it's so great, you know? You know who's the best chess player out there? Was it you know, the the human grandmaster, or was that the computer? And, you know, the studies were actually is if you put the grandmaster with the computer, they could actually beat either the best computer or the best person. So when you talk about, you know, the data and analytics everybody's looking at, you know, the guy in the ML pieces is like, OK, you know, how do these pieces go together? How does that fit into the data literacy piece? You know, the people and, you know, the machine learning >> well where you bring up is the idea of kind of augmenting the human, and we believe very much around a cognitive kind of interface of kind of the technology, the software with kind of a person and that decision making point. And so what you'LL see around our own kind of perspective is that we were part of a second generation be eye of like self service, and we've moved rapidly into this third generation, which is the cognitive kind of augmentation and the decision maker, right? And so you say this data literacy is arguing with data. Well, how do you argue and actually have the updated machine learning kind of recommendations? But it's still human making that decision. And that's an important kind of component of our kind of, like, our own kind of technology that we bring to the table. But with the two nitti, that's the data side needs to be there faster and more effective. >> Yeah. So, Itamar, please. You know Phyllis in on that. That data is the, you know, we would in big data, we talk about the three V's. So, you know, where are we today? How dowe I be ableto you know, get in leverage all of that data. >> So that's exactly where we've been focused over the last few years and worked with customers that were focused on building new data lakes, new data warehouses, looking at the clouds, building basically more than new foundations for enabling the organization to use way more data than every before. So it goes back to the volume at least one V out of the previous you mentioned. And the other one, of course, is the velocity. And how fast it is, and I've actually come to see that there are, in a sense, two dimensions velocity that come come together. One is how timely is the data you're using. And one of the big changes we're seeing in the market is that the user expectation and the business need for real time data is becoming ever more critical. If we used to talkto customers and talk about real time data because when they asked her data, they get a response very quickly. But it's last week's data. Well, that's not That doesn't cut it. So what we're seeing is that, first of all, the dimension of getting data that Israel Time Day that represents the data is it's currently second one is how quickly you can actually make that happen. So because business dynamics change match much faster now, this speed of change in the industry accelerates. Customers need the ability to put solutions together, make data available to answer business questions really faster. They cannot do it in the order ofthe month and years. They need to do it indoors off days, sometimes even hours. And that's where our solutions coming. >> Yeah, it's interesting. You know, my backgrounds. On the infrastructure side, I spent a lot of time in the cloud world. And, you know, you talk about, you know, health what we need for real time. Well, you know, used to be, you know, rolled out a server. You know, that took me in a week or month and a V m it reduced in time. Now we're, you know, containerized in communities world. And you know what? We're now talking much sort of time frame, and it's like, Oh, if you show me the way something was, you know, an hour ago. Oh, my gosh, That's not the way the world is. And I think, you know, for years we talked to the Duke world. You know what Israel time and how do I really define that? And the answer. We usually came up. It is getting the right information, you know, in the right place, into the right person. Or in the sales standpoint, it's like I need that information to save that client. They get what they need. So we still, you know, some of those terms, you know, scale in real time, short of require context. But you know what? Where does that fit into your customer discussions. >> Well, >> to part says, you bring up. You know, I think what you're saying is absolutely still true. You know, right? Data, right person, right time. It gets harder, though, with just the volumes of data. Where is it? How do you find it? How do you make sure that it's It's the the right pieces to the right place and you brought up the evolution of just the computer infrastructure and analytics likes to be close to the data. But if you have data everywhere, how do you make sure that part works? And we've been investing in a lot of our own Cloud Analytics infrastructure is now done on a micro services basis. So is running on Cuban eighties. Clusters it Khun work in whatever cloud compute infrastructure you want, be it Amazon or zur or Google or kind of your local kind of platform data centers. But you need that kind of small piece tied to the right kind of did on the side. And so that's where you see a great match between the two solutions and when you in the second part is the response from our customer's on DH after the acquisition was announced was tremendous. We II have more customer who works in a manufacturing space was I think this is exactly what I was looking to do from an analytic spaces I needed. Mohr did a real time and I was looking at a variety of solutions. She said, Thank you very much. You made my kind of life a little easier. I can narrow down Teo. One particular platform s so we have manufacturing companies. We have military kind of units and organizations. Teo Healthcare organizations. I've had just countless kind of feedback coming in along that same kind of questions. All >> right, Amaar, you know, for for for the eternity. Customers, What does this mean for them coming into the click family? >> Well, first of all, it means for them that we have a much broader opportunity to serve them. Click is a much, much bigger company. We have more resources. We can put a bear to both continuing enhance The opportunity. Offering is well as creating integrations with other products, such as collecting the click Data catalyst, which are click acquired several months ago. And there's a great synergy between those the products to the product and the collected a catalyst to provide a much more comprehensive, more an enterprise data integration platform, then beyond there to create, also see energies with other, uh, click analytic product. So again, while the click their integration platform consisting Opportunity and Click the catalyst will be independent and provide solutions for any data platform Analytic platform Cloud platform is it already does. Today we'LL continue to investigate. There's also opportunities to create unique see energies with some afar clicks technologies such as the associative Big Data Index and some others to provide more value, especially its scale. >> All right, eso drew, please expand on that a little bit if you can. There's so many pieces I know we're going to spend a little bit. I'm going deeper and some some of the other ones. But when you talk to your customers when you talk to your partners, what do you want to make sure there their key takeaways are >> right. So there is a couple of important points Itamar you made on the data integration platform, and so that's a combination of the eternity products plus the data catalysts, which was, you know, ca wired through podium data. Both of those kind of components are available and will continue to be available for our customers to use on whatever analytics platform. So we have customers who use the data for data science, and they want to work in our python and their own kind of machine learning or working with platforms like data robots. And they'LL be able to continue to do that with that same speed. They also could be using another kind of analytical visualization tool. And you know, we actually have a number of customers to do that, and we'LL continue to support that. So that's the first point, and I think you made up, which is the important one. The second is, while we do think there is some value with using Click Sense with the platform, and we've been investing on a platform called the Associative Big Data Index, and that sounds like a very complicated piece. But it's what we've done is taken are kind of unique kind of value. Proposition is an analytical company which is thehe, bility, toe work with data and ask questions of it and have the answers come to you very quickly is to be able to take that same associative experience, uh, that people use in our product and bring it down to the Data Lake. And that's where you start to see that same kind of what people love about click, view and click sense and brought into the Data Lake. And that's where Tamara was bringing up from a scale kind of perspective. So you have both kind of opportunities, >> Drew, and I really appreciate you sharing the importance of these coming together. We're going to spend some more time digging into the individual pieces there. I might be able to say, OK, are we passed the Data Lakes? Has it got to a data swamp or a data ocean? Because, you know, there are lots of sources of data and you know the like I always say Is that seems a little bit more pristine than the average environment. Eso But thank you so much and look forward to having more conversations with thanks to all right, you. And be sure to, uh, check out the cute dot net for all our videos on stew minimum. Thanks so much for watching

>> No. Yeah. Wait. >> Hi, everyone, and welcome to the big data. Deep Dive with the Cube on AMC TV. I'm Richard Schlessinger, and I'm here with tech industry entrepreneur and wicked bond analyst Dave Volonte and Silicon Angle CEO and editor in chief John Furrier. For this last segment in our show, we're talking about the future of big data and there aren't two better guys to talk about that you and glad that you guys were here. Let me sort of tee up the this conversation a little bit with a video that we did. Because the results of big data leveraging are only as good as the data itself. There has to be trust that the data is true and accurate and as unbiased as possible. So AMC TV addressed that issue, and we're just trying to sort of keep the dialogue going with this spot. >> We live in a world that is in a constant state of transformation, political natural transformation that has many faces, many consequences. A world overflowing with information with the potential to improve the lives of millions with prospects of nations with generations in the balance way are awakening to the power of big data way trust and together transform our future. >> So, Gentlemen Trust, without that, where are we and how big of an issue is that in the world of big data? Well, you know, the old saying garbage in garbage out in the old days, the single version of the truth was what you were after with data warehousing. And people say that we're further away from a single version of the truth. Now with all this data. But the reality is with big data and these new algorithms you, khun algorithmic Lee, weed out the false positives, get rid of the bad data and mathematically get to the good data a lot faster than you could before. Without a lot of processes around it. The machines can do it for you. So, John, while we were watching that video, you murmured something about how this is the biggest issue. This is cutting edge stuff. This is what I mean. >> Trust, trust issues and trust the trust equation. Right now it is still unknown. It's evolving fast. You see it with social networks, Stevens go viral on the internet and and we live in a system now with mobility and cloud things. Air scaling infinitely, you know, these days and so good day two scales, big and bad data scales being so whether it's a rumor on you here and this is viral or the data data, trust is the most important issue, and sometimes big data can be creepy. So a. This really, really important area. People are watching it on DH. Trust is the most important thing. >> But, you know, you have to earn trust, and we're still sort of at the beginning of this thing. So what has to happen to make sure that you know you don't get the garbage in, so you get the garbage. >> It's iterative and and we're seeing a lot of pilot projects. And then those pilot projects get reworked, and then they spawn into new projects. And so it's an evolution. And as I've said many, many times, it's very early we've talked about, were just barely scratching the surface here. >> It's evolving, too, and the nature of the data is needs to be questioned as well. So what kind of data? For instance, if you don't authorize your data to be viewed, there's all kinds of technical issues around. >> That's one side of it, But the other side of it, I mean, they're bad people out there who would try to influence, Uh, you know what? Whatever conclusions were being drawn by big data programs, >> especially when you think about big data sources. So companies start with their internal data, and they know that pretty well. They know where the warts are. They know how to manipulate. It's when they start bringing in outside data that this gets a lot fuzzier. >> Yeah, it's a problem. And security talk to a guy not long ago who thought that big data could be used to protect big data, that you could use big data techniques to detect anomalies in data that's coming into the system, which is poetic if nothing else, that guys think data has told me that that that's totally happened. It's a good solution. I want to move on because way really want to talk about how this stuff is going to be used. Assuming that these trust issues can be solved on and you know, the best minds in the world are working on this issue to try to figure out how to best, you know, leverage the data, we all produce, which has been measured at five exabytes every two days. You know, somebody made an analogy with, like something. If a bite was a paper clip and you stretched five exabytes worth of paper clips, they would go to the moon or whatever. Anyway, it's a lot of bike. It's a lot of actually, I think that's a lot of fun and back way too many times one hundred thousand times I lost track of my paper. But anyway, the best minds are trying to figure out, you know, howto, you know, maximize that the value that data. And they're doing that not far from here where we sit. Uh, Emmett in a place called C Sale, which was just recently set up, See Sail stands for the computer signs, an artificial intelligence lab. So we went there not long ago. It's just, you know, down the Mass. Pike was an easy trip, and this is what we found. It's fascinating >> Everybody's obviously talking about big data all the time, and you hear it gets used to mean all different types of things. So he thinks we're trying to do in the big data. Is he? Still program is to understand what are the different types of big data that exists in the world? And how do we help people to understand what different problems or fall under the the overall umbrella of big data? She sells the largest interdepartmental laboratory and mitt, so there's about one hundred principal investigators. So that's faculty and sort of senior research scientists. About nine hundred students who are involved, >> basically with big data, almost anything to do with it has to be in a much larger scale than we're used to, and the way it changes that equation is you have to You have to have the hardware and software to do the things you're used to doing. You have to meet them of comedy's a larger size a much larger size >> of times. When people talk about big data, they, I mean, not so much the volume of the data, but that the data, for example, is too complex for their existing data. Processing system to be able to deal with it. So it's I've got information from Social network from Twitter. I've got your information from a person's mobile phone. Maybe I've got information about retail records. Transactions hole Very diverse set of things that need to be combined together. What this clear? It says this is If you added this, credit it to your query, you would remove the dots that you selected. That's part of what we're trying to do here. And big data is he sail on. Our big data effort in general at MIT is toe build a set of software tools that allow people to take all these different data sets, combine them together, asked questions and run algorithms on top of them that allowed him to extracting sight. >> I'm working with it was dragged by NASA, but the purpose of my work right now is Tio Tio. Take data sets within Davis's, and instead of carrying them for table results, you query them, get visualizations. So instead of looking at large sets of numbers and text him or not, you get a picture and gave the motivation Behind that is that humans are really good into pretty pictures. They're not so that interpreting huge tables with big data, that's a really big issue. So this will have scientists tio visualize their data sets more quickly so they can start exploring And, uh, just looking at it faster, because with big data, it's a challenge to be able to visualize an exploiter data. >> I'm here just to proclaim what you already know, which is that the hour of big data has arrived in Massachusetts, and >> it's a very, very exciting time. So Governor Patrick was here just a few weeks ago to announce the Mass Big Data Initiative. And really, I think what he recognizes and is partly what we recognize here is that there's a expertise in the state of Massachusetts in areas that are related to big data, partly because of companies like AMC, as well as a number of other companies in this sort of database analytic space, CMC is a partner in our big data detail, initiatives and big data and See Sale is industry focused initiative that brings companies together to work with Emmet T. Think about it. Big data problems help to understand what big data means for the companies and also to allow the companies to give feedback. Tow us about one of the most important problems for them to be working on and potentially expose our students and give access to these companies to our students. >> I think the future will tell us, and that's hard to say right now, because way haven't done a lot of thinking, and I was interpreting and Big Data Way haven't reached our potential yet, and I just there's just so many things that we can't see right now. >> So one of the things that people tell us that are involved in big data is they have trouble finding the skill sets the data. Science can pick capability and capacity. And so seeing videos like this one of them, it is a new breed of students coming out there. They're growing up in this big data world, and that's critical to keep the big data pipeline flowing. And Jon, you and I have spent a lot of time in the East Coast looking at some of the big data cos it's almost a renaissance for Massachusetts in Cambridge and very exciting to see. Obviously, there's a lot going on the West Coast as well. Yeah, I mean, I'll say, I'm impressed with Emmett and around M I. T. In Cambridge is exploding with young, young new guns coming out of there. The new rock stars, if you will. But in California we're headquartered in Palo Alto. You know we in a chance that we go up close to Google Facebook and Jeff Hammer backer, who will show a video in a second that I interview with him and had dupe some. But he was the first guy a date at Facebook to build the data platform, which now has completely changed Facebook and made it what it is. He's also the co founder of Cloudera The Leader and Had Duke, which we've talked about, and he's the poster child, in my opinion of a data scientist. He's a math geek, but he understands the world problems. It's not just a tech thing. It's a bigger picture. I think that's key. I mean, he knows. He knows that you have to apply this stuff so and the passion that he has. This video from Jeff Hammer Bacher, cofounder of Cloud Ear, Watches Video. But and then the thing walk away is that big data is for everyone, and it's about having the passion. >> Wait. Wait. >> Palmer Bacher Data scientists from Cloudera Cofounder Hacking data Twitter handle Welcome to the Cube. >> Thank you. >> So you're known in the industry? I'LL see. Everyone knows you on Twitter. Young Cora heavily follow you there at Facebook. You built the data platform for Facebook. One of the guys mean guys. They're hacking the data over Facebook. Look what happened, right? I mean, the tsunami that Facebook has this amazing co founder Cloudera. You saw the vision on Rommedahl always quotes on the Cube. We've seen the future. No one knows it yet. That was a year and a half ago. Now everyone knows it. So do you feel about that? Is the co founder Cloudera forty million thousand? Funding validation again? More validation. How do you feel? >> Yeah, sure, it's exciting. I think of you as data volumes have grown and as the complexity of data that is collected, collected and analyzed as increase your novel software architectures have emerged on. I think what I'm most excited about is the fact that that software is open source and we're playing a key role in driving where that software is going. And, you know, I think what I'm most excited about. On top of that is the commodification of that software. You know, I'm tired of talking about the container in which you put your data. I think a lot of the creativity is happening in the data collection integration on preparation stage. Esso, I think. You know, there was ah tremendous focus over the past several decades on the modeling aspect of data way really increase the sophistication of our understanding, you know, classification and regression and optimization. And all off the hard court model and it gets done. And now we're seeing Okay, we've got these great tools to use at the end of the pipe. Eso Now, how do we get more data pushed through those those modeling algorithm? So there's a lot of innovative work. So we're thinking at the time how you make money at this or did you just say, Well, let's just go solve the problem and good things will happen. It was it was a lot more the ladder. You know, I didn't leave Facebook to start a company. I just left Facebook because I was ready to do something new. And I knew this was a huge movement and I felt that, you know, it was very gnashing and unfinished a software infrastructure. So when the opportunity Cloudera came along, I really jumped on it. And I've been absolutely blown away by the commercial success we've had s o. I didn't I certainly didn't set out with a master plan about how to extract value from this. My master plan has always been to really drive her duped into the background of enterprise infrastructure. I really wanted to be as obvious of a choice as Lennox and you See you, you're We've talked a lot at this conference and others about, you know, do moving from with fringe to the mainstream commercial enterprises. And all those guys are looking at night J. P. Morgan Chase. Today we're building competitive advantage. We're saving money, those guys, to have a master plan to make money. Does that change the dynamic of what you do on a day to day basis, or is that really exciting to you? Is an entrepreneur? Oh, yeah, for sure. It's exciting. And what we're trying to do is facilitate their master plan, right? Like we wanted way. Want to identify the commonalities and everyone's master plan and then commoditize it so they can avoid the undifferentiated heavy lifting that Jeff Bezos points out. You know where you know? No one should be required, Teo to invest tremendous amounts of money in their container anymore, right? They should really be identifying novel data sources, new algorithms to manipulate that data, the smartest people for using that data. And that's where they should be building their competitive advantage on. We really feel that, you know, we know where the market's going on. We're very confident, our product strategy. And I think over the next few years, you know, you guys are gonna be pretty excited about the stuff we're building, because I know that I'm personally very excited. And yet we're very excited about the competition because number one more people building open source software has never made me angry. >> Yeah, so So, you know, that's kind of market place. So, you know, we're talking about data science building and data science teams. So first tell us Gerald feeling today to science about that. What you're doing that, Todd here, around data science on your team and your goals. And what is a data scientist? I mean, this is not, You know, it's a D B A for her. Do you know what you know, sheriff? Sure. So what's going on? >> Yeah, So, you know, to kind of reflect on the genesis of the term. You know, when we were building out the data team at Facebook, we kind of two classes of analysts. We had data analysts who are more traditional business intelligence. You know, building can reports, performing data, retrieval, queries, doing, you know, lightweight analytics. And then we had research scientists who are often phds and things like sociology or economics or psychology. And they were doing much more of the deep dive, longitudinal, complex modeling exercises. And I really wanted to combine those two things I didn't want to have. Those two folks be separate in the same way that we combined engineering and operations on our date infrastructure group. So I literally just took data analyst and research scientists and put them together and called it data scientist s O. So that's kind of the the origin of the title on then how that's translating what we do at Clyde era. So I've recently hired to folks into a a burgeoning data science group Cloudera. So the way we see the market evolving is that you know the infrastructure is going to be commoditized. Yes, mindset >> to really be a data scientists, and you know what is way should be thinking about it. And there's no real manual. Most people aboard that math skills, economic kinds of disciplines you mentioned. What should someone prepared themselves? How did they? How does someone wanna hire data scientist had, I think form? Yeah, kinds of things. >> Well, I tend to, you know, I played a lot of sports growing up, and there's this phrase of being a gym rat, which is someone who's always in the gym just practicing. Whatever support is that they love. And I find that most data scientists or sort of data rats, they're always there, always going out for having any data. So you're there's a genuine curiosity about seeing what's happening and data that you really can't teach. But in terms of the skills that are required, I didn't really find anyone background to be perfect. Eso actually put together a course at University California, Berkeley, and taught it this spring called Introduction to Data Science, and I'm teaching and teaching it again this coming spring, and they're actually gonna put it into the core curriculum. Uh, in the fall of next year for computer science. >> Right, Jack Harmer. Bakar. Thanks so much for that insight. Great epic talk here on the Cube. Another another epic conversations share with the world Live. Congratulations on the funding. Another forty months. It's great validation. Been congratulations for essentially being part of data science and finding that whole movement Facebook. And and now, with Amaar Awadallah and the team that cloud there, you contend a great job. So congratulations present on all the competition keeping you keeping a fast capitalism, right? Right. Thank >> you. But it's >> okay. It's great, isn't it? That with all these great minds working in this industry, they still can't. We're so early in this that they still can't really define what a data scientist is. Well, what does talk about an industry and its infancy? That's what's so exciting. Everyone has a different definition of what it is, and that that what that means is is that it's everyone I think. Data science represents the new everybody. It could be a housewife. It could be a homemaker to on eighth grader. It doesn't matter if you see an insight and you see something that could be solved. Date is out there, and I think that's the future. And Jeff Hamel could talked about spending all this time and technology with undifferentiated heavy lifting. And I'm excited that we are moving beyond that into essentially the human part of Big Data. And it's going to have a huge impact, as we talked about before on the productivity of organizations and potentially productivity of lives. I mean, look at what we've talked about this this afternoon. We've talked about predicting volcanoes. We've talked about, you know, the medical issues. We've talked about pretty much every aspect of life, and I guess that's really the message of this industry now is that the folks who were managing big data are looking too change pretty much every aspect of life. This is the biggest inflexion point in history of technology that I've ever seen in the sense that it truly affects everything and the data that's generated in the data that machine's generate the data that humans generate, data that forest generate things like everything is generating data. So this's a time where we can actually instrument it. So this is why this massive disruption, this area and disruption We should say the uninitiated is a good thing in this business. Well, creation, entrepreneurship, copies of being found it It's got a great opportunity. Well, I appreciate your time, I unfortunately I think that's going to wrap it up for our big date. A deep dive. John and Dave the Cube guys have been great. I really appreciate you showing up here and, you know, just lending your insights and expertise and all that on DH. I want to thank you the audience for joining us. So you should stay tuned for the ongoing conversation on the Cube and to emcee TV to be informed, inspired and hopefully engaged. I'm Richard Schlessinger. Thank you very much for joining us.