>>Thanks. Hey, everyone, welcome back to the cubes. Continuing coverage of snowflakes. Summit 22 live from Caesars Forum in Las Vegas. Lisa Martin here. I have three guests here with me. We're gonna be talking about Snowflake Ventures and the snowflakes start up Challenge. That's in its second year. I've got Harry Glaser with me. Co founder and CEO of Model Bit Start Up Challenge finalist Damon Bryan joins us as well. The CTO and co founder of Hyper Affinity. Also a startup Challenge Finalists. And Stephane Williams to my left here, VP of Corporate development and snowflake Ventures. Guys, great to have you all on this little mini panel this morning. >>Thank you. >>Thank you. >>Let's go ahead, Harry, and we'll start with you. Talk to the audience about model. But what do you guys do? And then we'll kind of unpack the snowflake. The Snowflakes challenge >>Model bit is the easiest way for data scientists to deploy machine learning models directly into Snowflake. We make use of the latest snowflake functionality called Snow Park for python that allows those models to run adjacent to the data so that machine learning models can be much more efficient and much more powerful than they were before. >>Awesome. Damon. Give us an overview of hyper affinity. >>Yes, so hyper affinity were Decision Intelligence platform. So we helped. Specifically retailers and brands make intelligent decisions through the use of their own customer, data their product data and put data science in a I into the heart of the decision makers across their business. >>Nice Step seven. Tell us about the startup challenge. We talked a little bit about it yesterday with CMO Denise Pearson, but I know it's in its second year. Give us the idea of the impetus for it, what it's all about and what these companies embody. >>Yeah, so we This is the second year that we've done it. Um, we it was really out of, um Well, it starts with snowflake Ventures when we started to invest in companies, and we quickly realised that there's there's a massive opportunity for companies to be building on top of the Lego blocks, uh, of snowflake. And so, um, open up the competition. Last year it was the inaugural competition overlay analytics one, Um, and since then, you've seen a number of different functionalities and features as part of snowflakes snow part. Being one of them native applications is a really exciting one going forward. Um, the companies can really use to accelerate their ability to kind of deliver best in class applications using best in class technology to deliver real customer outcomes and value. Um, so we've we've seen tremendous traction across the globe, 250 applicants across 50. I think 70 countries was mentioned today, so truly global in nature. And it's really exciting to see how some of the start ups are taking snowflake to to to new and interesting use cases and new personas and new industries. >>So you had 200 over 250 software companies applied for this. How did you did you narrow it down to three? >>We did. Yeah, >>you do that. >>So, behind the scenes, we had a sub judging panel, the ones you didn't see up on stage, which I was luckily part of. We had kind of very distinct evaluation criteria that we were evaluating every company across. Um and we kind of took in tranches, right? We we took the first big garden, and we kind of try to get that down to a top 50 and top 50. Then we really went into the details and we kind of across, um, myself in ventures with some of my venture partners. Um, some of the market teams, some of the product and engineering team, all kind of came together and evaluated all of these different companies to get to the top 10, which was our semifinalists and then the semi finalists, or had a chance to present in front of the group. So we get. We got to meet over Zoom along the way where they did a pitch, a five minute pitch followed by a Q and A in a similar former, I guess, to what we just went through the startup challenge live, um, to get to the top three. And then here we are today, just coming out of the competition with with With folks here on the table. >>Wow, Harry talked to us about How did you just still down what model bit is doing into five minutes over Zoom and then five minutes this morning in person? >>I think it was really fun to have that pressure test where, you know, we've only been doing this for a short time. In fact model. It's only been a company for four or five months now, and to have this process where we pitch and pitch again and pitch again and pitch again really helped us nail the one sentence value proposition, which we hadn't done previously. So in that way, very grateful to step on in the team for giving us that opportunity. >>That helps tremendously. I can imagine being a 4 to 5 months young start up and really trying to figure out I've worked with those young start ups before. Messaging is challenging the narrative. Who are we? What do we do? How are we changing or chasing the market? What are our customers saying we are? That's challenging. So this was a good opportunity for you, Damon. Would you say the same as well for hyper affinity? >>Yeah, definitely conquer. It's really helped us to shape our our value proposition early and how we speak about that. It's quite complicated stuff, data science when you're trying to get across what you do, especially in retail, that we work in. So part of what our platform does is to help them make sense of data science and Ai and implement that into commercial decisions. So you have to be really kind of snappy with how you position things. And it's really helped us to do that. We're a little bit further down the line than than these guys we've been going for three years. So we've had the benefit of working with a lot of retailers to this point to actually identify what their problems are and shape our product and our proposition towards. >>Are you primarily working with the retail industry? >>Yes, Retail and CPG? Our primary use case. We have seen any kind of consumer related industries. >>Got it. Massive changes right in retail and CPG the last couple of years, the rise of consumer expectations. It's not going to go back down, right? We're impatient. We want brands to know who we are. I want you to deliver relevant content to me that if I if I bought a tent, go back on your website, don't show me more tense. Show me things that go with that. We have this expectation. You >>just explain the whole business. But >>it's so challenging because the brothers brands have to respond to that. How do you what is the value for retailers working with hyper affinity and snowflake together. What's that powerhouse? >>Yeah, exactly. So you're exactly right. The retail landscape is changing massively. There's inflation everywhere. The pandemic really impacted what consumers really value out of shopping with retailers. And those decisions are even harder for retailers to make. So that's kind of what our platform does. It helps them to make those decisions quickly, get the power of data science or democratise it into the hands of those decision makers. Um, so our platform helps to do that. And Snowflake really underpins that. You know, the scalability of snowflake means that we can scale the data and the capability that platform in tangent with that and snowflake have been innovating a lot of things like Snow Park and then the new announcements, announcements, uni store and a native APP framework really helping us to make developments to our product as quick as snowflakes are doing it. So it's really beneficial. >>You get kind of that tailwind from snowflakes acceleration. It sounds like >>exactly that. Yeah. So as soon as we hear about new things were like, Can we use it? You know, and Snow Park in particular was music to our ears, and we actually part of private preview for that. So we've been using that while and again some of the new developments will be. I'm on the phone to my guys saying, Can we use this? Get it, get it implemented pretty quickly. So yeah, >>fantastic. Sounds like a great aligned partnership there, Harry. Talk to us a little bit about model bit and how it's enabling customers. Maybe you've got a favourite customer example at model bit plus snowflake, the power that delivers to the end user customer? >>Absolutely. I mean, as I said, it allows you to deploy the M L model directly into snowflake. But sometimes you need to use the exact same machine learning model in multiple endpoints simultaneously. For example, one of our customers uses model bit to train and deploy a lead scoring model. So you know when somebody comes into your website and they fill out the form like they want to talk to a sales person, is this gonna be a really good customer? Do we think or maybe not so great? Maybe they won't pay quite as much, and that lead scoring model actually runs on the website using model bit so that you can deploy display a custom experience to that customer we know right away. If this is an A, B, C or D lead, and therefore do we show them a salesperson contact form? Do we just put them in the marketing funnel? Based on that lead score simultaneously, the business needs to know in the back office the score of the lead so that they can do things like routed to the appropriate salesperson or update their sales forecasts for the end of the quarter. That same model also runs in the in the snowflake warehouse so that those back office systems can be powered directly off of snowflake. The fact that they're able to train and deploy one model into two production environment simultaneously and manage all that is something they can only do with bottled it. >>Lead scoring has been traditionally challenging for businesses in every industry, but it's so incredibly important, especially as consumers get pickier and pickier with. I don't want I don't want to be measured. I want to opt out. What sounds like what model but is enabling is especially alignment between sales and marketing within companies, which is That's also a big challenge at many companies face for >>us. It starts with the data scientist, right? The fact that sales and marketing may not be aligned might be an issue with the source of truth. And do we have a source of truth at this company? And so the idea that we can empower these data scientists who are creating this value in the company by giving them best in class tools and resources That's our dream. That's our mission. >>Talk to me a little bit, Harry. You said you're only 4 to 5 months old. What were the gaps in the market that you and your co founders saw and said, Guys, we've got to solve this. And Snowflake is the right partner to help us do it. >>Absolutely. We This is actually our second start up, and we started previously a data Analytics company that was somewhat successful, and it got caught up in this big wave of migration of cloud tools. So all of data tools moved and are moving from on premise tools to cloud based tools. This is really a migration. That snowflake catalyst Snowflake, of course, is the ultimate in cloud based data platforms, moving customers from on premise data warehouses to modern cloud based data clouds that dragged and pulled the rest of the industry along with it. Data Science is one of the last pieces of the data industry that really hasn't moved to the cloud yet. We were almost surprised when we got done with our last start up. We were thinking about what to do next. The data scientists were still using Jupiter notebooks locally on their laptops, and we thought, This is a big market opportunity and we're We're almost surprised it hasn't been captured yet, and we're going to get in there. >>The other thing. I think it's really interesting on your business that we haven't talked about is just the the flow of data, right? So that the data scientist is usually taking data out of a of a of a day like something like Smoke like a data platform and the security kind of breaks down because then it's one. It's two, it's three, it's five, it's 20. Its, you know, big companies just gets really big. And so I think the really interesting thing with what you guys are doing is enabling the data to stay where it's at, not copping out keeping that security, that that highly governed environment that big companies want but allowing the data science community to really unlock that value from the data, which is really, really >>cool. Wonderful for small startups like Model Bit. Because you talk to a big company, you want them to become a customer. You want them to use your data science technology. They want to see your fed ramp certification. They want to talk to your C. So we're two guys in Silicon Valley with a dream. But if we can tell them the data is staying in snowflake and you have that conversation with Snowflake all the time and you trust them were just built on top. That is an easy and very smooth way to have that conversation with the customer. >>Would you both say that there's credibility like you got street cred, especially being so so early in this stage? Harry, with the partnership with With Snowflake Damon, we'll start with you. >>Yeah, absolutely. We've been using Snowflake from day one. We leave from when we started our company, and it was a little bit of an unknown, I guess maybe 23 years ago, especially in retail. A lot of retailers using all the legacy kind of enterprise software, are really starting to adopt the cloud now with what they're doing and obviously snowflake really innovating in that area. So what we're finding is we use Snowflake to host our platform and our infrastructure. We're finding a lot of retailers doing that as well, which makes it great for when they wanted to use products like ours because of the whole data share thing. It just becomes really easy. And it really simplifies it'll and data transformation and data sharing. >>Stephane, talk about the startup challenge, the innovation that you guys have seen, and only the second year I can. I can just hear it from the two of you. And I know that the winner is back in India, but tremendous amount of of potential, like to me the last 2.5 days, the flywheel that is snowflake is getting faster and faster and more and more powerful. What are some of the things that excite you about working on the start up challenge and some of the vision going forward that it's driving. >>I think the incredible thing about Snowflake is that we really focus as a company on the data infrastructure and and we're hyper focused on enabling and incubating and encouraging partners to kind of stand on top of a best of breed platform, um, unlocked value across the different, either personas within I T organisations or industries like hypothermia is doing. And so it's it's it's really incredible to see kind of domain knowledge and subject matter expertise, able to kind of plug into best of breed underlying data infrastructure and really divide, drive, drive real meaningful outcomes for for for our customers in the community. Um, it's just been incredible to see. I mean, we just saw three today. Um, there was 250 incredible applications that past the initial. Like, do they check all the boxes and then actually, wow, they just take you to these completely different areas. You never thought that the technology would go and solve. And yet here we are talking about, you know, really interesting use cases that have partners are taking us to two >>150. Did that surprise you? And what was it last year. >>I think it was actually close to close to 2 to 40 to 50 as well, and I think it was above to 50 this year. I think that's the number that is in my head from last year, but I think it's actually above that. But the momentum is, Yeah, it's there and and again, we're gonna be back next year with the full competition, too. So >>awesome. Harry, what is what are some of the things that are next for model bed as it progresses through its early stages? >>You know, one thing I've learned and I think probably everyone at this table has internalised this lesson. Product market fit really is everything for a start up. And so for us, it's We're fortunate to have a set of early design partners who will become our customers, who we work with every day to build features, get their feedback, make sure they love the product, and the most exciting thing that happened to me here this week was one of our early design partner. Customers wanted us to completely rethink how we integrate with gets so that they can use their CI CD workflows their continuous integration that they have in their own get platform, which is advanced. They've built it over many years, and so can they back, all of model, but with their get. And it was it was one of those conversations. I know this is getting a little bit in the weeds, but it was one of those conversations that, as a founder, makes your head explode. If we can have a critical mass of those conversations and get to that product market fit, then the flywheel starts. Then the investment money comes. Then you're hiring a big team and you're off to the races. >>Awesome. Sounds like there's a lot of potential and momentum there. Damon. Last question for you is what's next for hyper affinity. Obviously you've got we talked about the street cred. >>Yeah, what's >>next for the business? >>Well, so yeah, we we've got a lot of exciting times coming up, so we're about to really fully launch our products. So we've been trading for three years with consultancy in retail analytics and data science and actually using our product before it was fully ready to launch. So we have the kind of main launch of our product and we actually starting to onboard some clients now as we speak. Um, I think the climate with regards to trying to find data, science, resources, you know, a problem across the globe. So it really helps companies like ours that allow, you know, allow retailers or whoever is to democratise the use of data science. And perhaps, you know, really help them in this current climate where they're struggling to get world class resource to enable them to do that >>right so critical stuff and take us home with your overall summary of snowflake summit. Fourth annual, nearly 10,000 people here. Huge increase from the last time we were all in person. What's your bumper sticker takeaway from Summit 22 the Startup Challenge? >>Uh, that's a big closing statement for me. It's been just the energy. It's been incredible energy, incredible excitement. I feel the the products that have been unveiled just unlock a tonne, more value and a tonne, more interesting things for companies like the model bit I profanity and all the other startups here. And to go and think about so there's there's just this incredible energy, incredible excitement, both internally, our product and engineering teams, the partners that we have spoke. I've spoken here with the event, the portfolio companies that we've invested in. And so there's there's there's just this. Yeah, incredible momentum and excitement around what we're able to do with data in today's world, powered by underlying platform, like snowflakes. >>Right? And we've heard that energy, I think, through l 30 plus guests we've had on the show since Tuesday and certainly from the two of you as well. Congratulations on being finalist. We wish you the best of luck. You have to come back next year and talk about some of the great things. More great >>things hopefully will be exhibited next year. >>Yeah, that's a good thing to look for. Guys really appreciate your time and your insights. Congratulations on another successful start up challenge. >>Thank you so much >>for Harry, Damon and Stefan. I'm Lisa Martin. You're watching the cubes. Continuing coverage of snowflakes. Summit 22 live from Vegas. Stick around. We'll be right back with a volonte and our final guest of the day. Mhm, mhm

(cheerful orchestral music) >> Hi, I'm Peter Burris and welcome once again to the CUBE Conversation from our studio in Palo Alto, California. With all CUBE Conversations, we pick a topic and we get down to the meat of it and we're going to do that today as well. The topic today is the role that data's playing in an organization, but even more importantly, the changes that the organization has to make to take advantage of data assets. And to have that conversation, we've got Harry Glaser, who's the CEO and Co-Founder of Periscope Data, with us today. Harry, welcome to theCUBE. >> Hey, it's great to be here, thank you Peter. >> So introduce yourself. Who are you? >> Yeah, I'm Harry, I'm the Co-Founder and CEO of Periscope Data. So, we started this company gosh, six years ago in my CTO's second bedroom. And we've scaled it to about 1,000 customers, about 150 employees, all in downtown San Francisco. >> And Periscope Data does? >> Yeah, we make a platform for data teams. So data teams play this increasingly important and powerful role in organizations where they drive the way the company makes decisions with data. And we make their system of record, their source of truth, the platform that they use to do all their work within those organizations at places like HBO, and Uber, and Harvard. >> Okay, so let's talk about data teams. >> Sure. >> 'Cause it starts there. A lot of organizations are trying to adopt practices >> Yup >> Associated with better utilization of data and are failing partly because it's catch-as-catch-can. >> Sure >> It's everybody's responsibility to figure out what data is, where it came from, what the value is, and how their going to use it. >> That's right. >> That sounds to me like, the notion of a data team says, No, we have to bring some degree of >> Yup >> At least centralization in thinking >> Yup. To make sure that we're exploiting data properly. Have I got that right? >> Yeah, that's exactly right Peter. So, often an organization will start working with data 'cause someone somewhere in the organization wants to. Maybe it's marketing. They hire a business analyst into marketing, then sales decides to do it, then they hire someone into sales. But to your point, it's catch-as-catch-can. So, marketing is looking at one view of data from the marketing world. Sales is looking at another view of data from the sales world. They get into big fights about what the truth is. There's no-one to arbit. It goes all the way up to the CEO, who has no fucking idea what's going on with this fight. Right? And that ultimately gets solved when you hire a centralized data team, ideally reporting to a Chief Data Officer, who can form a source of truth and form best practices in the organization for how they work with and make decisions with data. >> And presumably, take some responsibility for diffusing those practices >> Yeah >> And diffusing the data about the data to the rest of the organization. So you get more common utilization. >> That's right. So if you fast-forward a few years, now all the data is centralized and the data team is centralized, and they have formed a source of truth. Maybe they got into a fight about how many leads marketing delivered to sales. Marketing analyst says 10, sales analyst says we only got five. Now you have one source of truth, one piece of data that tells you how many leads. Analysts farmed out to the rest of the organization but farmed out from that central team where they have best practices and sources of truth. >> Okay so, presumably, there is some degree of maturity or questions of maturity >> Yeah >> Associated with these teams. Let's start with day one, I'm going to do this. What's the difference between that and someone who's a little bit further along? >> Yeah >> What's the first thing that a corporation needs to do day one? >> Yeah, day one is, if you're doing it right, day one people do all kinds of things that turn out to be wrong. But day one, if you do it right, you hire that Head of Data first and you empower them first to build that organization and to build that sort of center of excellence. A big mistake that you'll see is either hiring data people to fuse the organization or hiring a data team but stuffing it somewhere like Finance or IT. Those are service organizations. They are not driving their own, sort of, source of truth and business practice through the organization. You want your data person reporting to a COO or a CEO, and you want them to be empowered throughout the organization. >> So the way I've always thought about chief, and you tell me if this corresponds. >> Sure >> A chief is an individual who's in a business, who has responsibility for generating a return on the assets under their control. So the Chief Financial Officer is responsible for generating returns on assets. >> Yeah >> Returns and capital. You know, the COO is returns on people and the operations of the firm. Chief Data Officer, presumably then, would be responsible for generating a differential >> Yeah >> Return on data assets. >> Absolutely. So the CDO will take control of all the data being generated by all the various systems in the firm. Right? And then they'll be, like to your point, they'll be responsible for generating a return. Which is the returns from the improved decision making at the company. If we spend all this time hiring this data department and spend all this time logging and storing this data and we don't actually make better decisions at the company. What is the point? The whole point is that everyone else at the company now has an ability to make much better decisions. And that, those decisions lead to profits which are the return on the data. >> So, I'm the CEO Board, I don't have this today, my first job is to hire a CDO >> Yes. >> And give them responsibility for increasing the returns on the data assets within my business. >> So you'll, yes. >> So lets talk about, one year later >> Yeah. >> We've hired a bunch of people. How is a more mature data team operating? >> Yeah, so there's a number of things and they all happen in lockstep. You will have data people, who are much more mature and advanced in their careers and their skill sets. People are advancing in their maturity. Your first analyst might be really good with Excel pivot tables. Few years later, you're going to have people under the Chief Data Officer who are data scientists who work with machine learning and AI capabilities, miles beyond your Excel pivot tables. So that's one thing. The other thing is how are decisions made throughout the company? So on day zero, your chief revenue officer maybe comes into a meeting and goes, you know we're going to sell this way, because this is how we sold this way at my last company, and I know it's the right way to sell based on my years of experience. Fast forward five years and they're going to go, the data shows me that we need to sell in a different way. That we've been selling in a couple of different ways and this is the most profitable way, and I can see that in the data. And so the CEO should expect as a return on hiring this data organization, the people who are coming to him or her with their decisions are backing it up with data, as a result of this CDO and their organizations work. >> Yeah, and that doesn't diminish the value of that Chief Revenue Officer's experience. >> Of course not >> But it just gives them an opportunity to test a proposition, see if it worked, test a proposition, and improve things over time, right? >> A good test is, the CDO and their people should be the most popular people in the organization. Everyone loves them, because they bring free value all the time. The CRO has probably almost certainly comped on the revenue they generate for the company. So if they've got data scientists helping them generate more revenue, that's awesome, that's money for the CRO. That's great, so they should be very popular. If you have a CDO who's getting in everyone's way and causing friction, you probably don't have a good one, or something is wrong. >> Okay so the CDO is now installed, their power and their influence and their authority is accepted >> Yep >> By the organization. >> Yep >> Practices are changing. >> Yep >> Now, the next level of maturity, what are they focusing on? >> Yep >> Let me give you a little bit of a background as I think about this because I look back at history and you see over and over and over functions that have processes. >> Yep >> Some that come from the outside, some might be developed inside, and they try to instantiate, they try to manifest those processes in software. >> Yeah >> Because it helps improve the productivity of their people, the certainty of the operation, the certainty of execution. >> Yep >> So I'm into this process, but it's taken me some time. What do I do to accelerate my maturity? >> Yeah so I think there's a number of things that it's driven from the people. But if you start, you know, day zero, maybe you can't even get the data that you want or you don't know that you want the data. The CDO helps you, gets the data and helps show you what it is, and you at least understand the data and you can start making some decisions. Then they start joining the data together. So maybe I was like, okay, now I can see what I'm spending on marketing and what the return is, whereas I couldn't before. But you still can't say, okay what's my total spend to acquire a customer? Until you merge the marketing and the sales data. So now you merge into a single source of truth, you resolve all the conflicts and differences between the organizations, that's good. Then you start predicting the future. And this is where the CDO kind of takes control of the discussion. Because previously we were going, maybe we started from a place of sales and marketing, we wanted a thing and can't have it. Then, the CDO staffs up, builds the technology and answers the questions, and this is where they get popular. But then they start driving the discussion. Well, hang on, I can hire some data scientists and I can build some machine learning, and I can actually predict based on all the inputs, run all the scenarios for the future of the business, and go, this scenario is best. So let's actually invest this way. And so now they're proactively bringing differentiated value based on technology that the company, and the capabilities of the company, did not even know about until they started hiring this team. And so you know, in a very mature organization the data team is actually driving the business towards what decisions they should be making, and is kind of in a much more powerful position, even than some of the other chiefs. >> But I want to talk a little bit about that notion of the future. >> Yeah sure. >> Because as someone who has been something of a student of the way that business uses data historically, it's interesting that a lot of the OLTP generation was recording what happened. >> Yes >> So it's really using technology to better understand the past. >> Yep. >> And then personal productivity in many respects, was how do I build a consensus amongst different thinkers about what's going to happen a little bit further in the future. >> Sure. >> So the Excel pivot table, often is used to forecast two, three years out. >> Yeah. >> Get people to agree that that's where we want to go. >> Yep. >> But you're talking about a more immediate notion of a future. The future that's relevant to the Chief Revenue Officer. >> Yeah. >> Like in the next quarter, or the next couple of quarters, have I got that right? >> It's both. I would say yes the Excel pivot table is used to forecast the future, typically in a relatively straight line fashion from what's happened in the past, and that's great. But when you really have a mature data team and you really have a strong source of truth, you might say actually, you know, the thing that drives revenue more than anything is not the historical revenue trend, but it's the number of active users of your product. Let's say, for example. Or does the viewership of your video get past the halfway mark? Those are your best customers and if we can drive more of those customers we get a sort of differentiated value. And so that requires a more sophisticated technical approach than the simple Excel pivot table. >> Right but still, at the end of the day what you're doing is you're allowing data to drive your next action. >> Yes, that's right. >> And that's different from a historical process orientation. >> That's right. >> Where you let the process drive your next action. >> That's exactly right. And to your point you end up requiring a more agile organization, because you're going to be getting more and more insights over time, and changing direction based on those insights. As oppose to saying, here's my process, let's just run the process. >> Okay so, you've mentioned a couple of times the notion of a system of record for the CDO. >> Yeah >> And you know, ERP was kind of the CFO's software platform for running the finance of the business. What role does Periscope Data play in the world of the CDO? >> Yeah I mean I think your analogy is exactly right. So all of these chiefs, all of these departments will have their systems of record. ERP for the finance team, CRM for the sales team, marketing automation system for the marketing team etc. And we provide that system of record and that source of truth for the data team. And that looks like a lot of different things. Tooling around, integrating the data so that you can build a single source of truth with data. Storage options, in fact, multiple storage options for the data itself, so that you can run the analyses. The actual system that runs the analyses, so you might be writing SQL code or Python code in the product to perform the analyses, integrations with machine learning systems so that you can predict the future. And all the different ways that you want to share and publish the data out in the organization, all that happens together in Periscope Data, Chief Data Officer is managing all of those workflows so they can manage the whole flow of data through the organization within the product. >> So as a CEO, you know, pretend CEO right now. >> Sure. >> I hire my CDO, I empower them to generate a return on data. >> Yep. >> I give them the authority to do so. >> Yep. >> And at some point in time I have a team that's being diffused into the organization, but all this can be accelerated if I get the software that will help my people be more successful. >> Yes, in fact I would say you probably can't get past a certain level of maturity without differentiated software like Periscope Data, because it simply breaks. The volume of data you want to be working with in that top end of the maturity curve is so large, and the sophistication is so large, that you really do need differentiated tooling at that point. >> Okay so how is this going to change industries? >> (laughs) Yeah. >> So I've got all this stuff organized, because I have a thought I want to run by you. >> Sure. >> But from your perspective, how is this going to change the notion of industry? >> Yeah so I think that in every industry you at this point have sort of digital disruptors and you have the old guard. And the old guard is not necessarily dead, and I think you can see, we were talking moments ago about Walmart and the transformation they've made to digital and how that's become a real focus of the company. Great example of a company from the old guard that is by no means dead. But you do have to embrace the idea that the way you made decisions 10 years ago is not the way you're going to make decisions now. And by hiring this organization and empowering them with differentiated tooling, what you can do is have a much more data driven culture as a result. So you will watch them, as they get more mature with data, transform the way your company makes decisions. And it is a cultural change, right? The company becomes much more nimble and agile, probably has less management hierarchy and fewer layers, all of that kind of stuff. And it enables you to survive and thrive in a world where you are constantly being challenged by new digital disruptors. >> Yeah and I'll tell you, here's my observation on the whole concept of industry. Industry is a general way of describing how assets are organized. >> Makes sense. >> So a financial services forum has certain classes of asset, so your airplane manufacturer, >> Yep. >> Certain classes of assets, or a bottling company. And you can look at each of these different industries and say, oh, they have this common approach to thinking about what is valuable and how the assets get work, perform work. >> Yep. >> Data reduces asset specificity. >> Yep. >> Asset specificity is the degree to which an asset can be applied to a limited number of purposes. >> Yep. >> Data reduces that, makes assets more programmable, gives us a better job of monitoring. >> Yep. >> If we think about that so that the industry is a function of assets, therefore asset specificity, as more people do more data it reduces the barriers. It takes certain respects, it limits the impact of industry, and you end up with new types of competitors. >> Yep. >> New types of disruptors, that you didn't know about. What do you think about that? >> I think that makes sense, I mean we were talking also moments ago about the return on these assets right? And so, the CFO of a major public company will be primarily responsible for investing the company's financial assets across the globe, in a way that maximizes the return on those financial assets or minimizes the loss of those financial assets. And similarly with data, you will start to think about data as an asset, it will be the CDO's primary asset and the return on that investment in that asset will be the profits from the better decisions across the company that you wouldn't have had, if you hadn't had a CDO to steward those assets. >> And the options that are created. >> Absolutely. >> So it's a profit now, but also the additional options that are created. >> Yep, yep. >> And that's where the industry notion starts to get very fuzzy. >> And like all assets, the return on those assets will compound over time. We'll get the increased optionality, we'll make better decisions. You know, because of the increased optionality and the better decisions, there's now even more optionality, we make a good decision again, right? Then it starts to build on itself, and you end up in a much better position relatively quickly. >> Okay so Harry, one last thought, one last question. What's 2019 hold for Periscope Data? >> (laughs) A lot of growth first of all, so it's nice to be a high growth technology startup, lots of good things happen. But it is a little sort of mind boggling, how much the company changes and how much the team changes, the software changes every sort of six months. And so we will almost certainly double or more our business again, we will move into, I mean, I've mentioned some of our customers, Uber and HBO and Harvard. That is indicative of a trend where we are starting to work with larger and larger customers, and real true enterprise customers for the first time. So I expect that trend to accelerate. And I will say the conversations for us are getting easier, when we started six years ago and we were talking about platform for data teams, people were like, data teams? You know? And now I think everybody understands that there's a big wave happening, and that's been sort of propelling the company forwards. So that's been a lot of fun. >> Alright, Harry Glaser, CEO and co-founder of Periscope Data, thanks very much for being on theCUBE. >> Thank you Peter, I appreciate it. >> You bet. And once again, I'm Peter Burris, and this has been another CUBE conversation. Until next time. (cheerful orchestral music)