>>Hi, everyone. Welcome to the cubes presentation of the AWS startup showcase the theme. This episode is data as code, and this is season two, episode two of the ongoing series covering exciting startups from the ecosystem in cloud and the future of data analytics. I'm your host, John furry. You're getting great featured panel here with AWS heroes, Lynn blankets, the CEO of Lindbergh Lega consulting, Peter Hanson's, founder of cloud Cedar and Alex debris, principal of debris advisory. Great to see all of you here and, uh, remotely and look forward to see you in person at the next re-invent or other event. >>Thanks for having us. >>So Lynn, you're doing a lot of work in healthcare, Peter you're in the middle of all the action as data as code Alex. You're in deep on the databases. We've got a good round up of, of topics here ranging from healthcare to getting under the hood on databases. So as we'll start with you, what are you working on right now? What trends do you see in the database space? >>Yeah, sure. So I do, uh, I do a lot of consulting work working with different people and, you know, often with, with dynamo DB or, or just general serverless technology type stuff. Um, if you want to talk about trends that I'm seeing right now, I would say trends you're seeing as a lot, just more serverless native databases or cloud native databases where you're seeing these cool databases come out that really take advantage of, uh, this new cloud environment, right? Where you have scalability, you have plasticity of the clouds. So you're not having, you know, instant space environments anymore. You're paying for capacity, you're paying for throughput. You're able to scale up and down. You're not managing individual instances. So a lot of cool stuff that we're seeing, you know, um, with this new generation of, of infrastructure and in particular database is taking advantage of this, this new cloud world >>And really lot deep into the database side in terms of like cloud native impact, diversity of database types, when to use certain databases that also a big deal. >>Yeah, absolutely. I like, I totally agree. I love seeing the different types of databases and, you know, AWS has this whole, uh, purpose-built database strategy. And I think that, that makes a lot of sense. Um, you know, I want to go too far with it. I would, I would more think about purpose-built categories and things like that, you know, specialize in an OLTB database within your, within your organization, whether that's dynamo DB or document DB or relational database Aurora or something like that. But then also choose some sort of analytics database, you know, if it's drew it or Redshift or Athena, and then, you know, if you have some specialized needs, you want to show some real time stuff to your users, check out rock site. If you want to, uh, you know, do some graph analytics, fraud detection, checkout tiger graph, a lot of cool stuff that we're seeing from the startup showcase here. >>Looking forward to unpacking that Lynn you've been in love now, a healthcare action with cloud ops, the pandemic pushes hard core on everybody. What are you working on? >>Yeah, it's all COVID data all the time. Uh, before the pandemic, I was supporting research groups for cancer genomics, which I still do, but, um, what's, uh, impactful is the explosive data volumes. You know, when you there's big data and there's genomic data, you know, I've worked with clients that have broken data centers, broken public cloud provider data centers because of the daily volume they're putting in. So there's this volume aspect. And then there's a collaboration, particularly around COVID research because of pandemic. And so you have this explosive volume, you have this, um, need for, uh, computational complexity. And that means cloud the challenge is it, you know, put the pedal to the metal. So you've got all these bioinformatics researchers that are used to single machine. Suddenly they have to deal with distributed compute. So it's a wild time to be in this space. >>What was the big change that you've seen with the, uh, the pandemic and in genomic cloud genomic specifically what's the big change has happened. >>The amount of data that is being put into the public cloud, um, previously people would have their data on their local, uh, capacity, and then they would publish their paper and the data may or may not become available for, uh, reproducing the research, uh, to accelerate for drug discovery and even variant identification. The data sets are being pushed to public cloud repositories, which is a whole new set of concerns. You have not only dealing with the volume and cost, but security, you know, there's federated security is non-trivial and not well understood by this domain. So there's so much work available here. >>Awesome. Peter, you're doing a lot with the data as a platform kind of view and platform engineering data as code is, is something that's being kicked around. What are you working on and how does platform engineering change as data becomes so much more prevalent in its value proposition? >>Yeah. So I'm the founder of cloud Cedar and, um, we sort of built this company out, this consultancy all around the challenges that a lot of companies have got with getting their data sorted, getting it organized, getting it ready for other use cases, such as analytics and machine learning, um, AI workloads and the like. So typically a platform engineering team will look after the organization of a company infrastructure, making sure that it's coherent across the company and a data platform, engineering teams doing something similar in that sense where they're, they're looking at making sure that, uh, data teams have a solid foundation to build upon, uh, that everything's quite predictable and what that enables is a faster velocity and the ability to use data as code as a way of specifying and onboarding data, building that, translating it, transforming it out into its specific domains and then on to data products. >>I have to ask you while you're here. Um, there's a big trend around data meshes right now. You're hearing, we've had a lot of stuff on the cube. Um, what are practical that people are using data mesh, first of all, is it relevant and how are people looking at this data mesh conversation? >>I think it becomes more and more relevant, uh, the bigger the organization that you're dealing with. So, you know, often times in the enterprise, you've got, uh, projects with timelines of five to 10 years often outlasting technology life cycles. The technology that you're building on is probably irrelevant by the time that you complete it. And what we're seeing is that data engineering teams and data teams more broadly, this organizational bottleneck and data mesh is all about, uh, breaking down that, um, bottleneck and decentralizing the work, shifting that work back onto, uh, development teams who oftentimes have got more of the context and a centralized data engineering team. And we're seeing a lot of, uh, Philocity increases as a result of that. >>It's interesting. There's so many different aspects of how data is changing the world. Lynn talks about the volume with the cloud and genomics. We're hearing data engineering at a platform level. You're talking about slicing and dicing and real-time information. You mentioned rock set, Alex. So I'd like to ask each of you to answer this next question, which is how has the team dynamics changed with data engineering because every single company's impacted. So if you're researchers, Lynn, you're pumping more data into the cloud, that's got a little bit of data engineering to it. Do they even understand that is that impacting them? So how has data changed the responsibilities or roles in this new emerging area of data engineering or whatever you want to call it? Lynn, we'll start with you. What do you, what do you see this impact? >>Well, you know, I mean, dev ops becomes data ops and ML ops and, uh, you know, this is a whole emergent area of work and it starts with an understanding of container technologies, which, you know, in different verticals like FinTech, that's a given, right, but in bioinformatics building an appropriately optimized Docker container is something I'm still working with customers now on because they have the concept of a Docker container is just a virtual machine, which obviously it isn't, or shouldn't be. So, um, you have, again, as I mentioned previously, this humongous skill gap, um, concepts like D, which are prevalent in ad tech FinTech, that's not available yet for most of my customers. So those are the things that I'm building. So the whole ops space is, um, this a wide open area. And really it's a question of practicality. Um, you know, I have, uh, a lot of experience with data lakes and, you know, containerizing and using the data lake platform. But a lot of my customers are going to move to like an interim pass based solutions. If they're using spark, for example, they might use to use a managed spark solution as an interim, um, step up to the cloud before they build their own containers. Because the amount of knowledge to do that effectively is non-trivial >>Peter, you mentioned data, you mentioned data lakes, onboarding data into lake house architectures, for instance, something that you're familiar with. Um, this is not obvious to some verticals obvious to others. What do you see this data engineering impact from a personnel standpoint? And then ultimately how things get built, >>You know, are you directing that to me, >>Peter? >>Yeah. So I think, um, first and foremost, you know, the workload that data engineering teams are dealing with is ever increasing. Usually there's a 10 X ratio of, um, software engineers to data engineers within a business and usually double the amount of analysts to data engineers again. And so they're, they're fighting it ever increasing backload. And, uh, so they're fighting an ever increasing backlog of, of, uh, tasks to do and tickets to, to, to churn through. And so what we're seeing is that data engineering teams are becoming data platform engineering teams where they're building capability instead of constantly hamster wheels spinning if you will. And so with that in mind, with onboarding data into, uh, a Lakehouse architecture or a data lake where data engineering teams, uh, uh, getting wins is developing a very good baseline of structure where they're getting the categorization, the data tagging, whether this data is of a particular domain, does it contain some, um, PII data, for instance, uh, and, and, and, and then the security aspects, and also, you know, the mechanisms on which to do the data transformations, >>Alex, on the database side, those are known personas in an enterprise, a them, the database team, but now the scale is so big. Um, and there's so much going on in databases. How does the data engineering impact organizations from your standpoint? >>Yeah, absolutely. I think definitely, you know, gone are the days where you have a single relational database that is serving operational queries for your users, and you can also serve analytics queries, you know, for your internal teams. It's, it's now split up into those purpose-built databases, like we've said. Uh, but now you've got two different teams managing it and they're, they're designing their data model for different things. You know? So L LLTP might have a more de-normalized model, something that works for very fast operations and it's optimized for that, but now you need to suck that data out and get it elsewhere so that your, your PM or your business analyst, or whoever can crunch through some of that. And, you know, now it needs to be in a more normalized format. How do you sort of bridge that gap? That's a tough one. I think you need to, you know, build empathy on each side of, of what each side is doing and, and build the tools to say, Hey, this is going to help you, uh, you know, LLTP team, if we know what, what users are actually doing, and, and if you can get us into the right format there, so that then I can, you know, we can analyze it, um, on the backend. >>So I think, I think building empathy across those teams is helpful. >>When I left to come back to, you mentioned a health and informatics is coming back. Um, but it's interesting, you know, I look at a database world and you look at the solutions that are out there. A lot of companies that build data solutions don't have a data problem. They've never, they're not swimming in a lot of data, but then you look at like the field that you're working in right now with the genomics and health and, and quantum, they're always, they're dealing with data all the time. So you have people who deal with a lot of data all the time are breaking through New Zealand. People who are don't have that experience are now becoming data full, right? So people are now either it's a first time problem, or they've always been swimming in a ton of data. So it's more of what's the new playbook. And then, wow, I've never had to deal with a lot of data before. What's your take? >>It's interesting. Cause they know, uh, bioinformatics hires, um, uh, grad students. So grad students, you know, use their, our scripts with their file on their laptop. And so, um, to get those folks to understand distributed container-based computing is like I said, a not non-trivial problem. What's been really interesting with the money pouring in to COVID research is when I first started, some of the workflows would take, you know, literally 500 hours and that was just okay. And coming out of FinTech, I was, uh, I could, I was blown away like FinTech is like, could that please take a millisecond rather than a second? Right. And so what has now happened, which makes it, you know, like I said, even more fun to work in this domain is, uh, the research dollars have really gone up because of the pandemic. And so there are, there are, there's this blending of people like me with more of a big data background coming into bioinformatics and working side by side. >>So it's this interesting sort of translation because you have the whole taxonomy of bioinformatics with genomics and sequencers and all the weird file types that you get. And then you have the whole taxonomy of dev ops data ops, you know, containers and Kubernetes and all that. And trying to get that into pipelines that can actually, you know, be efficient, given the constraints. Of course, we, on the tech side, we always want to make it super optimized. I had a customer that we got it down from 500 hours to minutes, but they wanted to stay with the past solution because it was easier for them to go from 500 hours to five hours was good enough, but you know, the techies want to get it down to five minutes. >>This is, this is, we've seen this movie before dev ops, um, edge and op operations, you know, IOT, world scenes, the convergence of cultures. Now you have data and then old, old school operations kind of coming up. So this kind of supports the thesis. That data as code is the next infrastructure as code. What do you guys, what's the reaction there for you guys? What do you think about that? What does data's code mean? If infrastructure's code was cloud and dev ops, what is data as code? What does that mean? >>I could take it if you like. I think, um, data teams, organizations, um, have been long been this bottleneck within the organization and there's like this dark matter of untapped energy and potential waiting to be unleashed a data with the advent of open source projects like DBT, um, have been slowly sort of embracing software development, lifecycle practices. And this is really sort of seeing a, a big steep increase in, um, in their velocity. And, and this is only going to increase and improve as we're seeing data teams, um, embrace starter as code. I think it's, uh, the future is bright for data. So I'm very excited. >>Lynn Peter reaction. I mean, agility data is code is developer concept CICB pipeline. You mentioned it new operational workflows coming into traditional operations reaction. >>Yeah. I mean, I think Peter's right on there. I'd say, you know, some of those tools we're seeing come in from, from software, like, like DBT, basically giving you that infrastructure as code, but applied to that data realm. Also there have been a few, like get for data type things, pack a derm, I believe is one and a few other ones where you bring that in and you also see a lot of immutability concepts flowing into the data realm. So I think just seeing some of those software engineering concepts come over to the data world has, has been pretty interesting >>What we'll literally just versioning datasets and the identification of what's in a data set. What's not in a data set. Some of this is around ethical AI as well, um, is a whole, uh, area that has come out of research groups. Um, mostly AI research groups, but is being applied to medical data and needs to be obviously, um, so this, this, this, um, metadata and versioning around data sets is really, I think, a very of the moment area. >>Yeah, I think we, we, you guys are bringing up a really good kind of direction that's happening in data. And that is something that you're seeing on the software side, open source and now dev ops. And now going to data is that the supply chain challenges of we've been talking about it here on the cube and this, this, um, this episode is, you know, we've seen Ukraine war, but some open source, you know, malware hitting datasets is data secure. What is that going to look like? So you starting to get into this what's the supply chain, is it verified data sets if data sets have to be managed a whole nother level of data supply chain comes up, what do you guys think about that? >>I'll jump in. Oh, sorry. I'll jump in again. I think that, you know, there's, there's, um, some, some of the compliance requirements, um, around financial data are going to be applied to other types of data, probably health data. So immutability reproducibility, um, that is, uh, legally required. Um, also some of the privacy requirements that originated in Europe with GDPR are going to be replicated as more and more, um, types of data. And again, I'm always going to speak for health, but there's other types as well coming out of personal devices and that kind of stuff. So I think, you know, this idea of data as code is it's, it goes down to versioning and controlling and, um, that's, uh, that's sort of a real succinct way to say it that we didn't used to think about that. We just put it in our, you know, relational database and we were good to go, but, um, versioning and controlling in the global ecosystem is kind of, uh, where I'm focusing my efforts. >>It brings up a good question. If databases, if data is going to be part of the development process has to be addressable, which means horizontally scalable. That means it has to be accessible and open. How do you make that work and not foreclose it with a lot of restrictions? >>I think the use of data catalogs and appropriate tagging and categorization, you know, I think, you know, everyone's heard of the term data swamp, and I think that just came about because that everyone saw like, oh, wow, S3, you know, infinite storage. We just, you know, throw whatever in there for as long as we want. And I think at times, you know, the proliferation of S3 buckets, um, and the like, you know, we've just seen, uh, perhaps security, not maintained as well as it could have been. And I think that's kind of where data platform engineering teams have really sort of, uh, come into the, for, you know, creating a governance set of buckets like formation on top. But I think that's kind of where we need to see a lot more work with appropriate tags and also the automatic publishing of metadata into data catalogs so that, um, folks can easily search and address particular data sets and also control the access. You know, for instance, you've got some PII data, perhaps really only your marketing folks should be looking at email addresses and the like not perhaps your finance folks. So I think, you know, there's, there's a lot to be leveraged there in formation and other solutions, >>Alex, let's back up and talk about what's in it for the customer, right. Let's zoom back and saying reality is I just got to get my data to make sure it's secure always on and not going to be hackable. And I just got to get my data available on river performance. So then, then I got to start thinking about, okay, how do I intersect it? So what should teams be thinking about right now as I look up all their data options or databases across their enterprise? >>Yeah, it's, it's a, it's a good question. I just, you know, I think Peter made some good points there and you can think of history as sort of ebbing and flowing between centralization and decentralization a lot of times. And you know, when storage was expensive, data was going to be sort of centralized and Maine maintained, sort of a, you know, by the, uh, the people that are in charge of it. But then when, when S3 comes along, it really decreases storage. Now we can do a lot more experiments on it. We can store a lot more of our data, keep it around and do different things on it. You know, now we've got regulations again, we were, we gotta, we gotta be more realistic about, about keeping that data secure and make sure we're, we're doing the right things with it. So it's, we're gonna probably go through a period of, of centralization as we work out some of this tooling around, you know, tagging and, and ethical AI that, that both Peter. And when we're talking about here and maybe get us into that, that next wearable world of de-centralization again. But I, I think that ebb and flow is going to be natural in response to, you know, the problems of the, the other extreme, >>Where are we in the market right now from progress standpoint, because data lakes don't want to be data swamps. You seeing lake formation as a data architecture, as an example, where are we with customers? What are they doing right now? Where would you put them in the progress bar of, of evolution towards the Nirvana of having this data sovereignty? And this data is code environment. Are they just now in the data lake store, everything real-time and historical? >>Well, I can jump in there. Um, SQL on files is the, is the driver. And so we know when Amazon got Athena, um, that really drove a lot of the customers to really realistically look at data lake technologies, but data warehouses are not going away. And the integration between the two is not seamless. No, we, we are partners with AWS, but we don't work for them. So we can tell you the truth here. Um, there's, there's work to it, but it really, for my customers, it really upped the ante around data lake, uh, because Athena and technologies like that, the serverless, um, SQL queries or the familiar quarry, um, uh, libraries really drove a movement away from either OLTB or OLAP, more expensive, more cumbersome structures, >>But they still need that. Oh, LTP, like if they have high latency issues, they want to be low latency. Can they have the best of both worlds? That's the question. >>I mean, I w I would say we're getting, you know, we're getting closer. We're always going to be, uh, you know, that technology is going to be moving forward, and then we'll just move the goalpost again, in terms of, of what we're asking from it. But I think, you know, the technology that's getting out there, you can get, get really well. And then, you know, just what I work in the dynamo DB world. So you can get really great low latency. So, you know, single digit millisecond LLTP response times on that. I think some of the analytics stuff has been a problem with that. And there, there are different solutions out there to where you can export dynamo to S3, and then you can be doing SQL on your FA your files with Athena Lakeland's talking about, or now you see, you know, rock set of partner here that that'll just ingest your dynamo, DB data, you know, make all those changes. So if you're doing a lot of, uh, changes to your data and dynamo is going to reflect in Roxanna, and then you can do analytics queries, you can do complex filters, different things like that. So, you know, I, I think we continue to push the envelope and then we moved the goalpost again. But, um, you know, I think we're in a, a lot better place than we were a few years ago, for sure. >>Where do you guys see this going relative to the next level? If data as code becomes that next agile, um, software defined environment with open source? Well, all of these new tools with serverless things happening with data lakes are built in with nice architectures with data warehouses, where does it go next? What happens next? If this becomes an agile environment, what's the impact? >>Well, I don't want to be so dominant, but I have, I feel strongly, so I'm going to jump in here. So, so I, um, I feel like, you know, now for my, my, my most computationally intensive workloads, I'm using GPS, I'm bursting to GPU for TensorFlow neural networks. So I've been doing quite a bit of exploration around Amazon bracket for QPS and it's early. Um, and it's specialty. It's not, you know, for everybody. And the learning curve again is pretty daunting, but, um, there are some use cases out there. I mean, I got ahold of a paper where some people did some, um, it was a Q CNN, um, quantum convolutional neural network for lung cancer images, um, from COVID patients and the, the, uh, the QP Hugh, um, algorithm pipeline performed more accurately and faster. So I think, um, bursting to quantum is something to pay attention to. >>Awesome. Peter, what's your take on what's next? >>Well, I think there's still, um, that, that was absolutely fascinating from Lynn, but I think also there's, there's, uh, you know, some more sort of low-level, uh, low-hanging fruit available in, in the data stack. I think there's a lot of, there's still a lot of challenges around the transformation there, getting our data from sort of raw landed data into business domains, and that sort of talks to a lot of what data mesh is all about. I think if we can somehow make that a little more frictionless, because that that's really where the like labor intensive work is. That's, that's kinda dominating, uh, data engineering teams and where we're sort of trying to push that, that workload back onto, um, you know, software engineering teams. >>Alice will give you the final word. What's the impact. What's the next step? What's it look like in the future? >>Yeah, for sure. I mean, I've never had the, uh, breaking a data center problem that wind's had, or the bursting the quantum problem, for sure. But, you know, if you're in that, you know, the pool I swim and of terabytes of data and below and things like that, I think it's a good time. It just like we saw, you know, like we were talking about dev ops and, and pushing, uh, you know, allowing software engineers to handle more of, of the operation stuff. I think the same thing with data can happen where, you know, software engineering teams can handle not just their code, not just, you know, deploying and operating it, but also thinking about their data around the code. And that doesn't mean you won't have people assist you within your organization. You won't have some specialists in there, but I think pushing more stuff, even onto the individual development teams where they have ownership of that. And they're thinking about it through all this different life cycle. I mean, I'm pretty bullish on that. And I think that's an exciting development >>Was that shift, what left with left is security. What does that mean to >>Shipped so much stuff left, but now, you know, the things that were at the end are back at the end again, but, uh, you know, at least we think we can think about that stuff early in the process, which is good, >>Great conversation, very provocative, very realistic and great impact on the future data as code is real, the developers I do believe will have a great operational role and the data stack concept and impacting things like quantum, it's all kind of lining up nicely. Um, and it's a great opportunity to be in this field from a science and policy standpoint. Um, data engineering is legit. It's going to continue to grow and thanks for unpacking that here on the queue. Appreciate it. Okay. Great panel D AWS heroes. They work with AWS and the ecosystem independently out there. They're in the trenches doing the front lines, cracking the code here with data as code season two, episode two of the ongoing series of the 80, but startups I'm John for your host. Thanks for watching.

(bright music) >> Welcome back to the CUBE presents the AWS Startup Showcase The Next Big Thing in cloud startups with AI security and life science tracks, 15 hottest growing startups are presented. And we had a great opening keynote with luminaries in the industry. And now our closing keynote is to get a deeper dive on cracking the code in the enterprise, how startups are changing the game and helping companies change. And they're also changing the game of open source. We have a great guest, Katie Drucker, Head of Business Development, Madrona Venture Group. Katie, thank you for coming on the CUBE for this special closing keynote. >> Thank you for having me, I appreciate it. >> So one of the topics we talked about with Soma from Madrona on the opening keynote, as well as Ali from Databricks is how startups are seeing success faster. So that's the theme of the Cloud speed, agility, but the game has changed in the enterprise. And I want to really discuss with you how growth changes and growth strategy specifically. They talk, go to market. We hear things like good sales to enterprise sales, organic, freemium, there's all kinds of different approaches, but at the end of the day, the most successful companies, the ones that might not be known that just come out of nowhere. So the economics are changing and the buyers are thinking differently. So let's explore that topic. So take us through your view 'cause you have a lot of experience. But first talk about your role at Madrona, what you do. >> Absolutely all great points. So my role at Madrona, I think I have personally one of the more enviable jobs and that my job is to... I get the privilege of working with all of these fantastic entrepreneurs in our portfolio and doing whatever we can as a firm to harness resources, knowledge, expertise, connections, to accelerate their growth. So my role in setting up business development is taking a look at all of those tools in the tool chest and partnering with the portfolio to make it so. And in our portfolio, we have a wide range of companies, some rely on enterprise sales, some have other go to markets. Some are direct to consumer, a wide range. >> Talk about the growth strategies that you see evolving because what's clear with the pandemic. And as we come out of it is that there are growth plays happening that don't look a little bit differently, more obvious now because of the Cloud scale, we're seeing companies like Databricks, like Snowflake, like other companies that have been built on the cloud or standalone. What are some of the new growth techniques, or I don't want to say growth hacking, that is a pejorative term, but like just a way for companies to quickly describe their value to an enterprise buyer who's moving away from the old RFP days of vendor selection. The game has changed. So take us through how you see secret key and unlocking that new equation of how to present value to an enterprise and how you see enterprises evaluating startups. >> Yes, absolutely. Well, and that's got a question, that's got a few components nestled in what I think are some bigger trends going on. AWS of course brought us the Cloud first. I think now the Cloud is more and more a utility. And so it's incumbent upon thinking about how an enterprise 'cause using the Cloud is going to go up the value stack and partner with its cloud provider and other service providers. I think also with that agility of operations, you have thinning, if you will, the systems of record and a lot of new entrance into this space that are saying things like, how can we harness AIML and other emerging trends to provide more value directly around work streams that were historically locked into those systems of record? And then I think you also have some price plans that are far more flexible around usage based as opposed to just flat subscription or even these big clunky annual or multi-year RFP type stuff. So all of those trends are really designed in ways that favor the emerging startup. And I think if done well, and in partnership with those underlying cloud providers, there can be some amazing benefits that the enterprise realizes an opportunity for those startups to grow. And I think that's what you're seeing. I think there's also this emergence of a buyer that's different than the CIO or the site the CISO. You have things with low code, no code. You've got other buyers in the organization, other line of business executives that are coming to the table, making software purchase decisions. And then you also have empowered developers that are these citizen builders and developer buyers and personas that really matter. So lots of inroads in places for a startup to reach in the enterprise to make a connection and to bring value. That's a great insight. I want to ask that just if you don't mind follow up on that, you mentioned personas. And what we're seeing is the shift happens. There's new roles that are emerging and new things that are being reconfigured or refactored if you will, whether it's human resources or AI, and you mentioned ML playing a role in automation. These are big parts of the new value proposition. How should companies posture to the customer? Because I don't want to say pivot 'cause that means it's not working but mostly extending our iterating around their positioning because as new things have not yet been realized, it might not be operationalized in a company or maybe new things need to be operationalized, it's a new solution for that. Positioning the value is super important and a lot of companies often struggle with that, but also if they get it right, that's the key. What's your feeling on startups in their positioning? So people will dismiss it like, "Oh, that's marketing." But maybe that's important. What's your thoughts on the great positioning question? >> I've been in this industry a long time. And I think there are some things that are just tried and true, and it is not unique to tech, which is, look, you have to tell a story and you have to reach the customer and you have to speak to the customer's need. And what that means is, AWS is a great example. They're famous for the whole concept of working back from the customer and thinking about what that customer's need is. I think any startup that is looking to partner or work alongside of AWS really has to embody that very, very customer centric way of thinking about things, even though, as we just talked about those personas are changing who that customer really is in the enterprise. And then speaking to that value proposition and meeting that customer and creating a dialogue with them that really helps to understand not only what their pain points are, but how you were offering solves those pain points. And sometimes the customer doesn't realize that that is their pain point and that's part of the education and part of the way in which you engage that dialogue. That doesn't change a lot, just generation to generation. I think the modality of how we have that dialogue, the methods in which we choose to convey that change, but that basic discussion is what makes us human. >> What's your... Great, great, great insight. I want to ask you on the value proposition question again, the question I often get, and it's hard to answer is am I competing on value or am I competing on commodity? And depending on where you're in the stack, there could be different things like, for example, land is getting faster, smaller, cheaper, as an example on Amazon. That's driving down to low cost high value, but it shifts up the stack. You start to see in companies this changing the criteria for how to evaluate. So an enterprise might be struggling. And I often hear enterprises say, "I don't know how to pick who I need. I buy tools, I don't buy many platforms." So they're constantly trying to look for that answer key, if you will, what's your thoughts on the changing requirements of an enterprise? And how to do vendor selection. >> Yeah, so obviously I don't think there's a single magic bullet. I always liked just philosophically to think about, I think it's always easier and frankly more exciting as a buyer to want to buy stuff that's going to help me make more revenue and build and grow as opposed to do things that save me money. And just in a binary way, I like to think which side of the fence are you sitting on as a product offering? And the best ways that you can articulate that, what opportunities are you unlocking for your customer? The problems that you're solving, what kind of growth and what impact is that going to lead to, even if you're one or two removed from that? And again, that's not a new concept. And I think that the companies that have that squarely in mind when they think about their go-to market strategy, when they think about the dialogue they're having, when they think about the problems that they're solving, find a much faster path. And I think that also speaks to why we're seeing so many explosion in the line of business, SAS apps that are out there. Again, that thinning of the systems of record, really thinking about what are the scenarios and work streams that we can have happened that are going to help with that revenue growth and unlocking those opportunities. >> What's the common startup challenge that you see when they're trying to do business development? Usually they build the product first, product led value, you hear that a lot. And then they go, "Okay, we're ready to sell, hire a sales guy." That seems to be shifting away because of the go to markets are changing. What are some of the challenges that startups have? What are some that you're seeing? >> Well, and I think the point that you're making about the changes are really almost a result of the trends that we're talking about. The sales organization itself is becoming... These work streams are becoming instrumented. Data is being collected, insights are being derived off of those things. So you see companies like Clary or Highspot or two examples or tutorial that are in our portfolio that are looking at that action and making the art of sales and marketing far more sophisticated overall, which then leads to the different growth hacking and the different insights that are driven. I think the common mistakes that I see across the board, especially with earlier stage startups, look you got to find product market fit. I think that's always... You start with a thesis or a belief and a passion that you're building something that you think the market needs. And it's a lot of dialogue you have to have to make sure that you do find that. I think once you find that another common problem that I see is leading with an explanation of technology. And again, not focusing on the buyer or the... Sorry, the buyer about solving a problem and focusing on that problem as opposed to focusing on how cool your technology is. Those are basic and really, really simple. And then I think setting a set of expectations, especially as it comes to business development and partnering with companies like AWS. The researching that you need to adequately meet the demand that can be turned on. And then I'm sure you heard about from Databricks, from an organization like AWS, you have to be pragmatic. >> Yeah, Databricks gone from zero a software sales a few years ago to over a billion. Now it looks like a Snowflake which came out of nowhere and they had a great product, but built on Amazon, they became the data cloud on top of Amazon. And now they're growing just whole new business models and new business development techniques. Katie, thank you for sharing your insight here. The CUBE's closing keynote. Thanks for coming on. >> Appreciate it, thank you. >> Okay, Katie Drucker, Head of Business Development at Madrona Venture Group. Premier VC in the Seattle area and beyond they're doing a lot of cloud action. And of course they know AWS very well and investing in the ecosystem. So great, great stuff there. Next up is Peter Wagner partner at Wing.VX. Love this URL first of all 'cause of the VC domain extension. But Peter is a long time venture capitalist. I've been following his career. He goes back to the old networking days, back when the internet was being connected during the OSI days, when the TCP IP open systems interconnect was really happening and created so much. Well, Peter, great to see you on the CUBE here and congratulations with success at Wing VC. >> Yeah, thanks, John. It's great to be here. I really appreciate you having me. >> Reason why I wanted to have you come on. First of all, you had a great track record in investing over many decades. You've seen many waves of innovation, startups. You've seen all the stories. You've seen the movie a few times, as I say. But now more than ever, enterprise wise it's probably the hottest I've ever seen. And you've got a confluence of many things on the stack. You were also an early seed investor in Snowflake, well-regarded as a huge success. So you've got your eye on some of these awesome deals. Got a great partner over there has got a network experience as well. What is the big aha moment here for the industry? Because it's not your classic enterprise startups anymore. They have multiple things going on and some of the winners are not even known. They come out of nowhere and they connect to enterprise and get the lucrative positions and can create a moat and value. Like out of nowhere, it's not the old way of like going to the airport and doing an RFP and going through the stringent requirements, and then you're in, you get to win the lucrative contract and you're in. Not anymore, that seems to have changed. What's your take on this 'cause people are trying to crack the code here and sometimes you don't have to be well-known. >> Yeah, well, thank goodness the game has changed 'cause that old thing was (indistinct) So I for one don't miss it. There was some modernization movement in the enterprise and the modern enterprise is built on data powered by AI infrastructure. That's an agile workplace. All three of those things are really transformational. There's big investments being made by enterprises, a lot of receptivity and openness to technology to enable all those agendas, and that translates to good prospects for startups. So I think as far as my career goes, I've never seen a more positive or fertile ground for startups in terms of penetrating enterprise, it doesn't mean it's easy to do, but you have a receptive audience on the other side and that hasn't necessarily always been the case. >> Yeah, I got to ask you, I know that you're a big sailor and your family and Franks Lubens also has a boat and sailing metaphor is always good to have 'cause you got to have a race that's being run and they have tactics. And this game that we're in now, you see the successes, there's investment thesises, and then there's also actually bets. And I want to get your thoughts on this because a lot of enterprises are trying to figure out how to evaluate startups and starts also can make the wrong bet. They could sail to the wrong continent and be in the wrong spot. So how do you pick the winners and how should enterprises understand how to pick winners too? >> Yeah, well, one of the real important things right now that enterprise is facing startups are learning how to do and so learning how to leverage product led growth dynamics in selling to the enterprise. And so product led growth has certainly always been important consumer facing companies. And then there's a few enterprise facing companies, early ones that cracked the code, as you said. And some of these examples are so old, if you think about, like the ones that people will want to talk about them and talk about Classy and want to talk about Twilio and these were of course are iconic companies that showed the way for others. But even before that, folks like Solar Winds, they'd go to market model, clearly product red, bottom stuff. Back then we didn't even have those words to talk about it. And then some of the examples are so enormous if think about them like the one right in front of your face, like AWS. (laughing) Pretty good PLG, (indistinct) but it targeted builders, it targeted developers and flipped over the way you think about enterprise infrastructure, as a result some how every company, even if they're harnessing relatively conventional sales and marketing motion, and you think about product led growth as a way to kick that motion off. And so it's not really an either word even more We might think OPLJ, that means there's no sales keep one company not true, but here's a way to set the table so that you can very efficiently use your sales and marketing resources, only have the most attractive targets and ones that are really (indistinct) >> I love the product led growth. I got to ask you because in the networking days, I remember the term inevitability was used being nested in a solution that they're just going to Cisco off router and a firewall is one you can unplug and replace with another vendor. Cisco you'd have to go through no switching costs were huge. So when you get it to the Cloud, how do you see the competitiveness? Because we were riffing on this with Ali, from Databricks where the lock-in might be value. The more value provider is the lock-in. Is their nestedness? Is their intimate ability as a competitive advantage for some of these starts? How do you look at that? Because startups, they're using open source. They want to have a land position in an enterprise, but how do they create that sustainable competitive advantage going forward? Because again, this is what you do. You bet on ones that you can see that could establish a model whatever we want to call it, but a competitive advantage and ongoing nested position. >> Sometimes it has to do with data, John, and so you mentioned Snowflake a couple of times here, a big part of Snowflake's strategy is what they now call the data cloud. And one of the reasons you go there is not to just be able to process data, to actually get access to it, exchange with the partners. And then that of course is a great reason for the customers to come to the Snowflake platform. And so the more data it gets more customers, it gets more data, the whole thing start spinning in the right direction. That's a really big example, but all of these startups that are using ML in a fundamental way, applying it in a novel way, the data modes are really important. So getting to the right data sources and training on it, and then putting it to work so that you can see that in this process better and doing this earlier on that scale. That's a big part of success. Another company that I work with is a good example that I call (indistinct) which works in sales technology space, really crushing it in terms of building better sales organizations both at performance level, in terms of the intelligence level, and just overall revenue attainment using ML, and using novel data sources, like the previously lost data or phone calls or Zoom calls as you already know. So I think the data advantages are really big. And smart startups are thinking through it early. >> It's interest-- >> And they're planning by the way, not to ramble on too much, but they're betting that PLG strategy. So their land option is designed not just to be an interesting way to gain usage, but it's also a way to gain access to data that then enables the expand in a component. >> That is a huge call-out point there, I was going to ask another question, but I think that is the key I see. It's a new go to market in a way. product led with that kind of approach gets you a beachhead and you get a little position, you get some data that is a cloud model, it means variable, whatever you want to call it variable value proposition, value proof, or whatever, getting that data and reiterating it. So it brings up the whole philosophical question of okay, product led growth, I love that with product led growth of data, I get that. Remember the old platform versus a tool? That's the way buyers used to think. How has that changed? 'Cause now almost, this conversation throws out the whole platform thing, but isn't like a platform. >> It looks like it's all. (laughs) you can if it is a platform, though to do that you can reveal that later, but you're looking for adoption, so if it's down stock product, you're looking for adoption by like developers or DevOps people or SOEs, and they're trying to solve a problem, and they want rapid gratification. So they don't want to have an architectural boomimg, placed in front of them. And if it's up stock product and application, then it's a user or the business or whatever that is, is adopting the application. And again, they're trying to solve a very specific problem. You need instant and immediate obvious time and value. And now you have a ticket to the dance and build on that and maybe a platform strategy can gradually take shape. But you know who's not in this conversation is the CIO, it's like, "I'm always the last to know." >> That's the CISO though. And they got him there on the firing lines. CISOs are buying tools like it's nobody's business. They need everything. They'll buy anything or you go meet with sand, they'll buy it. >> And you make it sound so easy. (laughing) We do a lot of security investment if only (indistinct) (laughing) >> I'm a little bit over the top, but CISOs are under a lot of pressure. I would talk to the CISO at Capital One and he was saying that he's on Amazon, now he's going to another cloud, not as a hedge, but he doesn't want to focus development teams. So he's making human resource decisions as well. Again, back to what IT used to be back in the old days where you made a vendor decision, you built around it. So again, clouds play that way. I see that happening. But the question is that I think you nailed this whole idea of cross hairs on the target persona, because you got to know who you are and then go to the market. So if you know you're a problem solving and the lower in the stack, do it and get a beachhead. That's a strategy, you can do that. You can't try to be the platform and then solve a problem at the same time. So you got to be careful. Is that what you were getting at? >> Well, I think you just understand what you're trying to achieve in that line of notion. And how those dynamics work and you just can't drag it out. And they could make it too difficult. Another company I work with is a very strategic cloud data platform. It's a (indistinct) on systems. We're not trying to foist that vision though (laughs) or not adopters today. We're solving some thorny problems with them in the short term, rapid time to value operational needs in scale. And then yeah, once they found success with (indistinct) there's would be an opportunity to be increasing the platform, and an obstacle for those customers. But we're not talking about that. >> Well, Peter, I appreciate you taking the time and coming out of a board meeting, I know that you're super busy and I really appreciate you making time for us. I know you've got an impressive partner in (indistinct) who's a former Sequoia, but Redback Networks part of that company over the years, you guys are doing extremely well, even a unique investment thesis. I'd like you to put the plug in for the firm. I think you guys have a good approach. I like what you guys are doing. You're humble, you don't brag a lot, but you make a lot of great investments. So could you take them in to explain what your investment thesis is and then how that relates to how an enterprise is making their investment thesis? >> Yeah, yeah, for sure. Well, the concept that I described earlier that the modern enterprise movement as a workplace built on data powered by AI. That's what we're trying to work with founders to enable. And also we're investing in companies that build the products and services that enable that modern enterprise to exist. And we do it from very early stages, but with a longterm outlook. So we'll be leading series and series, rounds of investment but staying deeply involved, both operationally financially throughout the whole life cycle of the company. And then we've done that a bunch of times, our goal is always the big independent public company and they don't always make it but enough for them to have it all be worthwhile. An interesting special case of this, and by the way, I think it intersects with some of startup showcase here is in the life sciences. And I know you were highlighting a lot of healthcare websites and deals, and that's a vertical where to disrupt tremendous impact of data both new data availability and new ways to put it to use. I know several of my partners are very focused on that. They call it bio-X data. It's a transformation all on its own. >> That's awesome. And I think that the reason why we're focusing on these verticals is if you have a cloud horizontal scale view and vertically specialized with machine learning, every vertical is impacted by data. It's so interesting that I think, first start, I was probably best time to be a cloud startup right now. I really am bullish on it. So I appreciate you taking the time Peter to come in again from your board meeting, popping out. Thanks for-- (indistinct) Go back in and approve those stock options for all the employees. Yeah, thanks for coming on. Appreciate it. >> All right, thank you John, it's a pleasure. >> Okay, Peter Wagner, Premier VC, very humble Wing.VC is a great firm. Really respect them. They do a lot of great investing investments, Snowflake, and we have Dave Vellante back who knows a lot about Snowflake's been covering like a blanket and Sarbjeet Johal. Cloud Influencer friend of the CUBE. Cloud commentator and cloud experience built clouds, runs clouds now invests. So V. Dave, thanks for coming back on. You heard Peter Wagner at Wing VC. These guys have their roots in networking, which networking back in the day was, V. Dave. You remember the internet Cisco days, remember Cisco, Wellfleet routers. I think Peter invested in Arrow Point, remember Arrow Point, that was about in the 495 belt where you were. >> Lynch's company. >> That was Chris Lynch's company. I think, was he a sales guy there? (indistinct) >> That was his first big hit I think. >> All right, well guys, let's wrap this up. We've got a great program here. Sarbjeet, thank you for coming on. >> No worries. Glad to be here todays. >> Hey, Sarbjeet. >> First of all, really appreciate the Twitter activity lately on the commentary, the observability piece on Jeremy Burton's launch, Dave was phenomenal, but Peter was talking about this dynamic and I think ties this cracking the code thing together, which is there's a product led strategy that feels like a platform, but it's also a tool. In other words, it's not mutually exclusive, the old methods thrown out the window. Land in an account, know what problem you're solving. If you're below the stack, nail it, get data and go from there. If you're a process improvement up the stack, you have to much more of a platform longer-term sale, more business oriented, different motions, different mechanics. What do you think about that? What's your reaction? >> Yeah, I was thinking about this when I was listening to some of the startups pitching, if you will, or talking about what they bring to the table in this cloud scale or cloud era, if you will. And there are tools, there are applications and then they're big monolithic platforms, if you will. And then they're part of the ecosystem. So I think the companies need to know where they play. A startup cannot be platform from the get-go I believe. Now many aspire to be, but they have to start with tooling. I believe in, especially in B2B side of things, and then go into the applications, one way is to go into the application area, if you will, like a very precise use cases for certain verticals and stuff like that. And other parties that are going into the platform, which is like horizontal play, if you will, in technology. So I think they have to understand their age, like how old they are, how new they are, how small they are, because when their size matter when you are procuring as a big business, procuring your technology vendors size matters and the economic viability matters and their proximity to other windows matter as well. So I think we'll jump into that in other discussions later, but I think that's key, as you said. >> I would agree with that. I would phrase it in my mind, somewhat differently from Sarbjeet which is you have product led growth, and that's your early phase and you get product market fit, you get product led growth, and then you expand and there are many, many examples of this, and that's when you... As part of your team expansion strategy, you're going to get into the platform discussion. There's so many examples of that. You take a look at Ali Ghodsi today with what's happening at Databricks, Snowflake is another good example. They've started with product led growth. And then now they're like, "Okay, we've got to expand the team." Okta is another example that just acquired zero. That's about building out the platform, versus more of a point product. And there's just many, many examples of that, but you cannot to your point, very hard to start with a platform. Arm did it, but that was like a one in a million chance. >> It's just harder, especially if it's new and it's not operationalized yet. So one of the things Dave that we've observed the Cloud is some of the best known successes where nobody's not known at all, database we've been covering from the beginning 'cause we were close to that movement when they came out of Berkeley. But they still were misunderstood and they just started generating revenue in only last year. So again, only a few years ago, zero software revenue, now they're approaching a billion dollars. So it's not easy to make these vendor selections anymore. And if you're new and you don't have someone to operate it or your there's no department and the departments changing, that's another problem. These are all like enterprisey problems. What's your thoughts on that, Dave? >> Well, I think there's a big discussion right now when you've been talking all day about how should enterprise think about startups and think about most of these startups they're software companies and software is very capital efficient business. At the same time, these companies are raising hundreds of millions, sometimes over a billion dollars before they go to IPO. Why is that? A lot of it's going to promotion. I look at it as... And there's a big discussion going on but well, maybe sales can be more efficient and more direct and so forth. I really think it comes down to the golden rule. Two things really mattered in the early days in the startup it's sales and engineering. And writers should probably say engineering and sales and start with engineering. And then you got to figure out your go to market. Everything else is peripheral to those two and you don't get those two things right, you struggle. And I think that's what some of these successful startups are proving. >> Sarbjeet, what's your take on that point? >> Could you repeat the point again? Sorry, I lost-- >> As cloud scale comes in this whole idea of competing, the roles are changing. So look at IOT, look at the Edge, for instance, you got all kinds of new use cases that no one actually knows is a problem to solve. It's just pure opportunity. So there's no one's operational I could have a product, but it don't know we can buy it yet. It's a problem. >> Yeah, I think the solutions have to be point solutions and the startups need to focus on the practitioners, number one, not the big buyers, not the IT, if you will, but the line of business, even within that sphere, like just focus on the practitioners who are going to use that technology. I talked to, I think it wasn't Fiddler, no, it was CoreLogics. I think that story was great today earlier in how they kind of struggle in the beginning, they were trying to do a big bang approach as a startup, but then they almost stumbled. And then they found their mojo, if you will. They went to Don the market, actually, that's a very classic theory of disruption, like what we study from Harvard School of Business that you go down the market, go to the non-consumers, because if you're trying to compete head to head with big guys. Because most of the big guys have lot of feature and functionality, especially at the platform level. And if you're trying to innovate in that space, you have to go to the practitioners and solve their core problems and then learn and expand kind of thing. So I think you have to focus on practitioners a lot more than the traditional oracle buyers. >> Sarbjeet, we had a great thread last night in Twitter, on observability that you started. And there's a couple of examples there. Chaos searches and relatively small company right now, they just raised them though. And they're part of this star showcase. And they could've said, "Hey, we're going to go after Splunk." But they chose not to. They said, "Okay, let's kind of disrupt the elk stack and simplify that." Another example is a company observed, you've mentioned Jeremy Burton's company, John. They're focused really on SAS companies. They're not going after initially these complicated enterprise deals because they got to get it right or else they'll get churn, and churn is that silent killer of software companies. >> The interesting other company that was on the showcase was Tetra Science. I don't know if you noticed that one in the life science track, and again, Peter Wagner pointed out the life science. That's an under recognized in the press vertical that's exploding. Certainly during the pandemic you saw it, Tetra science is an R&D cloud, Dave, R&D data cloud. So pharmaceuticals, they need to do their research. So the pandemic has brought to life, this now notion of tapping into data resources, not just data lakes, but like real deal. >> Yeah, you and Natalie and I were talking about that this morning and that's one of the opportunities for R&D and you have all these different data sources and yeah, it's not just about the data lake. It's about the ecosystem that you're building around them. And I see, it's really interesting to juxtapose what Databricks is doing and what Snowflake is doing. They've got different strategies, but they play a part there. You can see how ecosystems can build that system. It's not one company is going to solve all these problems. It's going to really have to be connections across these various companies. And that's what the Cloud enables and ecosystems have all this data flowing that can really drive new insights. >> And I want to call your attention to a tweet Sarbjeet you wrote about Splunk's earnings and they're data companies as well. They got Teresa Carlson there now AWS as the president, working with Doug, that should change the game a little bit more. But there was a thread of the neath there. Andy Thry says to replies to Dave you or Sarbjeet, you, if you're on AWS, they're a fine solution. The world doesn't just revolve around AWS, smiley face. Well, a lot of it does actually. So (laughing) nice point, Andy. But he brings up this thing and Ali brought it up too, Hybrid now is a new operating system for what now Edge does. So we got Mobile World Congress happening this month in person. This whole Telco 5G brings up a whole nother piece of the Cloud puzzle. Jeff Barr pointed out in his keynote, Dave. Guys, I want to get your reaction. The Edge now is... I'm calling it the super Edge because it's not just Edge as we know it before. You're going to have these pops, these points of presence that are going to have wavelength as your spectrum or whatever they have. I think that's the solution for Azure. So you're going to have all this new cloud power for low latency applications. Self-driving delivery VR, AR, gaming, Telemetry data from Teslas, you name it, it's happening. This is huge, what's your thoughts? Sarbjeet, we'll start with you. >> Yeah, I think Edge is like bound to happen. And for many reasons, the volume of data is increasing. Our use cases are also expanding if you will, with the democratization of computer analysis. Specialization of computer, actually Dave wrote extensively about how Intel and other chip players are gearing up for that future if you will. Most of the inference in the AI world will happen in the field close to the workloads if you will, that can be mobility, the self-driving car that can be AR, VR. It can be healthcare. It can be gaming, you name it. Those are the few use cases, which are in the forefront and what alarm or use cases will come into the play I believe. I've said this many times, Edge, I think it will be dominated by the hyperscalers, mainly because they're building their Metro data centers now. And with a very low latency in the Metro areas where the population is, we're serving the people still, not the machines yet, or the empty areas where there is no population. So wherever the population is, all these big players are putting their data centers there. And I think they will dominate the Edge. And I know some Edge lovers. (indistinct) >> Edge huggers. >> Edge huggers, yeah. They don't like the hyperscalers story, but I think that's the way were' going. Why would we go backwards? >> I think you're right, first of all, I agree with the hyperscale dying you look at the top three clouds right now. They're all in the Edge, Hardcore it's a huge competitive battleground, Dave. And I think the missing piece, that's going to be uncovered at Mobile Congress. Maybe they'll miss it this year, but it's the developer traction, whoever wins the developer market or wins the loyalty, winning over the market or having adoption. The applications will drive the Edge. >> And I would add the fourth cloud is Alibaba. Alibaba is actually bigger than Google and they're crushing it as well. But I would say this, first of all, it's popular to say, "Oh not everything's going to move into the Cloud, John, Dave, Sarbjeet." But the fact is that AWS they're trend setter. They are crushing it in terms of features. And you'd look at what they're doing in the plumbing with Annapurna. Everybody's following suit. So you can't just ignore that, number one. Second thing is what is the Edge? Well, the edge is... Where's the logical place to process the data? That's what the Edge is. And I think to your point, both Sarbjeet and John, the Edge is going to be won by developers. It's going to be one by programmability and it's going to be low cost and really super efficient. And most of the data is going to stay at the Edge. And so who is in the best position to actually create that? Is it going to be somebody who was taking an x86 box and throw it over the fence and give it a fancy name with the Edge in it and saying, "Here's our Edge box." No, that's not what's going to win the Edge. And so I think first of all it's huge, it's wide open. And I think where's the innovation coming from? I agree with you it's the hyperscalers. >> I think the developers as John said, developers are the kingmakers. They build the solutions. And in that context, I always talk about the skills gravity, a lot of people are educated in certain technologies and they will keep using those technologies. Their proximity to that technology is huge and they don't want to learn something new. So as humans we just tend to go what we know how to use it. So from that front, I usually talk with consumption economics of cloud and Edge. It has to focus on the practitioners. And in this case, practitioners are developers because you're just cooking up those solutions right now. We're not serving that in huge quantity right now, but-- >> Well, let's unpack that Sarbjeet, let's unpack that 'cause I think you're right on the money on that. The consumption of the tech and also the consumption of the application, the end use and end user. And I think the reason why hyperscalers will continue to dominate besides the fact that they have all the resource and they're going to bring that to the Edge, is that the developers are going to be driving the applications at the Edge. So if you're low latency Edge, that's going to open up new applications, not just the obvious ones I did mention, gaming, VR, AR, metaverse and other things that are obvious. There's going to be non-obvious things that are going to be huge that are going to come out from the developers. But the Cloud native aspect of the hyperscalers, to me is where the scales are tipping, let me explain. IT was built to build a supply resource to the businesses who were writing business applications. Mostly driven by IBM in the mainframe in the old days, Dave, and then IT became IT. Telcos have been OT closed, "This is our thing, that's it." Now they have to open up. And the Cloud native technologies is the fastest way to value. And I think that paths, Sarbjeet is going to be defined by this new developer and this new super Edge concept. So I think it's going to be wide open. I don't know what to say. I can't guess, but it's going to be creative. >> Let me ask you a question. You said years ago, data's new development kit, does low code and no code to Sarbjeet's point, change the equation? In other words, putting data in the hands of those OT professionals, those practitioners who have the context. Does low-code and no-code enable, more of those protocols? I know it's a bromide, but the citizen developer, and what impact does that have? And who's in the best position? >> Well, I think that anything that reduces friction to getting stuff out there that can be automated, will increase the value. And then the question is, that's not even a debate. That's just fact that's going to be like rent, massive rise. Then the issue comes down to who has the best asset? The software asset that's eating the world or the tower and the physical infrastructure. So if the physical infrastructure aka the Telcos, can't generate value fast enough, in my opinion, the private equity will come in and take it over, and then refactor that business model to take advantage of the over the top software model. That to me is the big stare down competition between the Telco world and this new cloud native, whichever one yields in valley is going to blink first, if you say. And I think the Cloud native wins this one hands down because the assets are valuable, but only if they enable the new model. If the old model tries to hang on to the old hog, the old model as the Edge hugger, as Sarbjeet says, they'll just going to slowly milk that cow dry. So it's like, it's over. So to me, they have to move. And I think this Mobile World Congress day, we will see, we will be looking for that. >> Yeah, I think that in the Mobile World Congress context, I think Telcos should partner with the hyperscalers very closely like everybody else has. And they have to cave in. (laughs) I usually say that to them, like the people came in IBM tried to fight and they cave in. Other second tier vendors tried to fight the big cloud vendors like top three or four. And then they cave in. okay, we will serve our stuff through your cloud. And that's where all the buyers are congregating. They're going to buy stuff along with the skills gravity, the feature proximity. I've got another term I'll turn a coin. It matters a lot when you're doing one thing and you want to do another thing when you're doing all this transactional stuff and regular stuff, and now you want to do data science, where do you go? You go next to it, wherever you have been. Your skills are in that same bucket. And then also you don't have to write a new contract with a new vendor, you just go there. So in order to serve, this is a lesson for startups as well. You need to prepare yourself for being in the Cloud marketplaces. You cannot go alone independently to fight. >> Cloud marketplace is going to replace procurement, for sure, we know that. And this brings up the point, Dave, we talked about years ago, remember on the CUBE. We said, there's going to be Tier two clouds. I used that word in quotes cause nothing... What does it even mean Tier two. And we were talking about like Amazon, versus Microsoft and Google. We set at the time and Alibaba but they're in China, put that aside for a second, but the big three. They're going to win it all. And they're all going to be successful to a relative terms, but whoever can enable that second tier. And it ended up happening, Snowflake is that example. As is Databricks as is others. So Google and Microsoft as fast as they can replicate the success of AWS by enabling someone to build their business on their cloud in a way that allows the customer to refactor their business will win. They will win most of the lion's share my opinion. So I think that applies to the Edge as well. So whoever can come in and say... Whichever cloud says, "I'm going to enable the next Snowflake, the next enterprise solution." I think takes it. >> Well, I think that it comes back... Every conversation coming back to the data. And if you think about the prevailing way in which we treated data with the exceptions of the two data driven companies in their quotes is as we've shoved all the data into some single repository and tried to come up with a single version of the truth and it's adjudicated by a centralized team, with hyper specialized roles. And then guess what? The line of business, there's no context for the business in that data architecture or data Corpus, if you will. And then the time it takes to go from idea for a data product or data service commoditization is way too long. And that's changing. And the winners are going to be the ones who are able to exploit this notion of leaving data where it is, the point about data gravity or courting a new term. I liked that, I think you said skills gravity. And then enabling the business lines to have access to their own data teams. That's exactly what Ali Ghodsi, he was saying this morning. And really having the ability to create their own data products without having to go bow down to an ivory tower. That is an emerging model. All right, well guys, I really appreciate the wrap up here, Dave and Sarbjeet. I'd love to get your final thoughts. I'll just start by saying that one of the highlights for me was the luminary guests size of 15 great companies, the luminary guests we had from our community on our keynotes today, but Ali Ghodsi said, "Don't listen to what everyone's saying in the press." That was his position. He says, "You got to figure out where the puck's going." He didn't say that, but I'm saying, I'm paraphrasing what he said. And I love how he brought up Sky Cloud. I call it Sky net. That's an interesting philosophy. And then he also brought up that machine learning auto ML has got to be table stakes. So I think to me, that's the highlight walk away. And the second one is this idea that the enterprises have to have a new way to procure and not just the consumption, but some vendor selection. I think it's going to be very interesting as value can be proved with data. So maybe the procurement process becomes, here's a beachhead, here's a little bit of data. Let me see what it can do. >> I would say... Again, I said it was this morning, that the big four have given... Last year they spent a hundred billion dollars more on CapEx. To me, that's a gift. In so many companies, especially focusing on trying to hang onto the legacy business. They're saying, "Well not everything's going to move to the Cloud." Whatever, the narrative should change to, "Hey, thank you for that gift. We're now going to build value on top of the Cloud." Ali Ghodsi laid that out, how Databricks is doing it. And it's clearly what Snowflake's new with the data cloud. It basically a layer that abstracts all that underlying complexity and add value on top. Eventually going out to the Edge. That's a value added model that's enabled by the hyperscalers. And that to me, if I have to evaluate where I'm going to place my bets as a CIO or IT practitioner, I'm going to look at who are the ones that are actually embracing that investment that's been made and adding value on top in a way that can drive my data-driven, my digital business or whatever buzzword you want to throw on. >> Yeah, I think we were talking about the startups in today's sessions. I think for startups, my advice is to be as close as you can be to hyperscalers and anybody who awards them, they will cave in at the end of the day, because that's where the whole span of gravity is. That's what the innovation gravity is, everybody's gravitating towards that. And I would say quite a few times in the last couple of years that the rate of innovation happening in a non-cloud companies, when I talk about non-cloud means are not public companies. I think it's like diminishing, if you will, as compared to in cloud, there's a lot of innovation. The Cloud companies are not paying by power people anymore. They have all sophisticated platforms and leverage those, and also leverage the marketplaces and leverage their buyers. And the key will be how you highlight yourself in that cloud market place if you will. It's like in a grocery store where your product is placed and you have to market around it, and you have to have a good story telling team in place as well after you do the product market fit. I think that's a key. I think just being close to the Cloud providers, that's the way to go for startups. >> Real, real quick. Each of you talk about what it takes to crack the code for the enterprise in the modern era now. Dave, we'll start with you. What's it take? (indistinct) >> You got to have it be solving a problem that is 10X better at one 10th a cost of anybody else, if you're a small company, that rule number one. Number two is you obviously got to get product market fit. You got to then figure out. And I think, and again, you're in your early phases, you have to be almost processed builders, figure out... Your KPIs should all be built around retention. How do I define customer success? How do I keep customers and how do I make them loyal so that I know that my cost of acquisition is going to be at least one-third or lower than my lifetime value of that customer? So you've got to nail that. And then once you nail that, you've got to codify that process in the next phase, which really probably gets into your platform discussion. And that's really where you can start to standardize and scale and figure out your go to market and the relationship between marketing spend and sales productivity. And then when you get that, then you got to move on to figure out your Mot. Your Mot might just be a brand. It might be some secret sauce, but more often than not though, it's going to be the relationship that you build. And I think you've got to think about those phases and in today's world, you got to move really fast. Sarbjeet, real quick. What's the secret to crack the code? >> I think the secret to crack the code is partnership and alliances. As a small company selling to the bigger enterprises, the vendors size will be one of the big objections. Even if they don't say it, it's on the back of their mind, "What if these guys disappear tomorrow what would we do if we pick this technology?" And another thing is like, if you're building on the left side, which is the developer side, not on the right side, which is the operations or production side, if you will, you have to understand the sales cycles are longer on the right side and left side is easier to get to, but that's why we see a lot more startups. And on the left side of your DevOps space, if you will, because it's easier to sell to practitioners and market to them and then show the value correctly. And also understand that on the left side, the developers are very know how hungry, on the right side people are very cost-conscious. So understanding the traits of these different personas, if you will buyers, it will, I think set you apart. And as Dave said, you have to solve a problem, focus on practitioners first, because you're small. You have to solve political problems very well. And then you can expand. >> Well, guys, I really appreciate the time. Dave, we're going to do more of these, Sarbjeet we're going to do more of these. We're going to add more community to it. We're going to add our community rooms next time. We're going to do these quarterly and try to do them as more frequently, we learned a lot and we still got a lot more to learn. There's a lot more contribution out in the community that we're going to tap into. Certainly the CUBE Club as we call it, Dave. We're going to build this actively around Cloud. This is another 20 years. The Edge brings us more life with Cloud, it's really exciting. And again, enterprise is no longer an enterprise, it's just the world now. So great companies here, the next Databricks, the next IPO. The next big thing is in this list, Dave. >> Hey, John, we'll see you in Barcelona. Looking forward to that. Sarbjeet, I know in a second half, we're going to run into each other. So (indistinct) thank you John. >> Trouble has started. Great talking to you guys today and have fun in Barcelona and keep us informed. >> Thanks for coming. I want to thank Natalie Erlich who's in Rome right now. She's probably well past her bedtime, but she kicked it off and emceeing and hosting with Dave and I for this AW startup showcase. This is batch two episode two day. What do we call this? It's like a release so that the next 15 startups are coming. So we'll figure it out. (laughs) Thanks for watching everyone. Thanks. (bright music)

(soft music) >> From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is theCUBE Conversation. >> Hi, I'm Stu Miniman coming to you from our Boston area studio here for a CUBE conversation. Really like when we can dig into help some of the nonprofits in our industry, going to be talking about, training, helping other people lift up their careers. Happy to welcome to the program, first time guests, Jerome Hardaway. He's the founder of vets who code coming down from Nashville, Jerome, I seem to remember a time where I was able to travel. I did some lovely hiking even saw bear last time I was down in Nashville. Thanks so much for joining us. Roger that. Thank you, a funny story. I saw a cow on the loose while driving on the highway yesterday. So not much has changed. (Jerome laughs) Thank you guys for having me. >> Yeah, it is a little bit of strange times here in the Covert area. I live kind of suburban Massachusetts area. One of my neighbors did report a small bear in the area. I'm definitely seeing more than just the usual, what kind of wild turkeys and the like that we get up in New England, but let's talk about Vets Who Code. So, you're the founder, the name doesn't leave much up for us to guess what you do, but tell us a little bit as to the inspiration and the goals of your organization. Roger that, Vets Who Code is the first veteran founded, operated and led, a remote 501 C three that focuses on training veterans regardless where they are and modern age of technologies. Our stack right now, I would say is focused more towards front-end DevOps with a lot of serverless technologies being built-in. And that's pretty much what exactly what we do well. >> Well awesome, I had been loving digging into the serverless ecosystem the last few years. Definitely an exciting area, help us understand a little bit, who comes and joins this? What skill set do they have to have coming in? And explain a little bit the programs that they can offer that they can be part of. >> Yeah, cool. So we run Vets Who Code like a mixture between a tech company or a tech nonprofit, I guess, using those practices while also using military practices as well. And the people that come in are veterans and military spouses. And we try to use what we call a pattern matching practice, showcasing like. Hey, these are the things, he's been in military. This is how it translates to the tech side. Like, our sit reps is what you guys would call stand up. Kanban is what we would call like systems checks and frag orders, Op orders, things like that, or, our SLPs. So we turn around, we just train them, retrain them. So that way they can understand the lingo, understand how things, how you code, move and communicate and make sure that these guys and girls, they know how the work as JavaScript engineers and a serverless community. As of right now, we've helped 252 veterans in 37 States get jobs, our social economic impacts, then I think it's at 17.6 million right now. So it all from the comfort of their homes, that's like the cool and free, and those are like the coolest things that we've been able to do. >> Wow, that's fascinating. Jerome, I heard something that you've talked about, leveraging the military organizational styles. I'm just curious, there's in the coding world a lot of times we talk about Conway's law, which is that the code will end up resembling the look of the organization. And you talk about DevOps, DevOps is all about various organizations collaborating and working together. It seems a little bit different from what I would think of traditional military command and control. So is that anything you've given any thought to? Is there some of the organizational pieces that you need to talk to people about? Moving into these environments compared to what they might've had in the military. >> Negative, I think the biggest misconception that we have is that people, when you're talking about how the military moves, they're thinking of the military of yesteryear of 20, 30, 40 years ago. They're not thinking of global war on terrorism veterans and how we move and things like that. We understand distributed chains. We understand cause we call, that's what we've done at CENTAF and CENTCOM in Iraq and Afghanistan. So we honored, like we are already doing a lot of this stuff, we just naming it different. So that's part of the thing that we have as an advantage as the, cause all the people who are educators, there are veterans who learn how to code and they've been working in industry and they know. And so when they're teaching, they know the entire process that a veteran's going to go through. So that's how now we focus on things. And so the organizational structure for us first term to second term veterans is pretty normal. If you're coming out within the last, heck 10 years. (Jerome laughs) >> Yeah, absolutely. That's wonderful. And I I've had the opportunity to work with plenty of people that had come from the military. Very successful in the tech industry, definitely tend to be hard workers and engaged in what they'r doing. Curious, you talked about being able to do this remotely and then it is free. What's the impact of the current global pandemic? Everything that's happening here in 2020 been on what you're doing in your resources. >> Of the impact, unfortunately, I mean, not unfortunately, fortunately it has been nothing but positive. It's been crazy, we've gotten more applications. We have people are seeing that during, I was the crazy person in the room, when in 2014, when I was saying nonprofits should move to remote first protocols. So that way they could have greater impact for less, with less financial resources. And back then I was the, like what are you talking about? This is the way we've always done. Well now everybody was scrambling to try to figure out how to help people without being in same room with them. We were like, Oh, okay, lt's do today. So we got an influx of people applying, influx of people, sending me, trying to get into our next cohort in August. It's just, the biggest thing that has happened for Vets Who Code is yet, it's been a really positive experience for us, which is really weird to say, but I think it has, my doomsday Murphy's law style of preparing, I assume that anything that can go wrong will go wrong. So I try to prepare for that. So being open source, being serverless, being having everything in a manner to where--in case I was out of the pot, out of the situation, other people operate having this distributed teams, or there are other leaders that can take over and do things. It's all stuff that, I guess I got from the military. So, we were know we were prepared because there was absolutely zero pivot for us. If anything, it has been more resources. We've been able to dive deeper in more subjects because people have had more time, but, we can do, we can dive deeper into AWS. We started a lunch and learn every two weeks. We actually have a lunch and learn next week with Dr. Lee Johnson. And she's going to be talking, we open that to it by all juniors and entry level devs, developers, regardless of whether you're a veteran or not, we just throw it on Twitter and let them get in. And the focus will be on tech ethics. We all know, right now we've been leading the charge on trying to make sure people are supercharging their skills during this time frame. So that's what, it's been very positive. I've been working with magazine, front-end masters. It's been awesome. >> Well, that's wonderful. Wish everyone had the mindset coming into 2020, because it does seem that anything that could go wrong has, (both laugh) I'm curious, once people have skilled up and they've gone through the program, what connections do you have with industry? How do you help with job placement in that sort of activity? >> That is the most asked question, because that is the thing that people expect because of code schools, because of our educational program protocols. We don't really need that issue because our veterans are skilled enough to where to hiring managers know the quality that we produce. I live in Nashville and I've only been able to place one veteran that I've trained locally in the community because of fame companies have snatched up every other veteran I've ever trained in the community, so things like that, it's not a problem because no, a usually 80% of our veterans have jobs before they even graduate. So you're literally picking up, picking people who, they know they have the potential to get a bit companies if they put the work in and it's just as they come, we actually have people. I think a company reached out to me yesterday and I was like, I don't even have people for you. They already have jobs. (jerome laughs) Or I'm in a situation now where all my senior devs are looking for fame companies. Cause that's one of the things we do is that we support our veterans from reentry to retirement. So we're not like other code schools where they only focus on that 30 to 60 to 90 days, so that first job, our veterans, they keep coming back to re-skill, get more skills, come up to the lunch and learns, come to our Slack side chats to become better programmers. And once they're, so we've helped several of our programmers go from entry-level dev to senior dev, from absolutely zero experience. And so, I think that's the most rewarding thing. When you see a person who they came in knowing nothing. And three years later, like after the cohort safe they got their job and then they come back after they got the jobs, they want to get more skills and they get another job and then they come back. And the next thing, my favorite, one of my favorites Schuster, he starts at a local web shop, a web dev shop in Savannah, Georgia. And then next thing, oh, he's on Amazon, he's at Amazon three years later and you're like, Oh wow, we did that, that's awesome. So that's the path that we do is awesome. >> I'm curious, are there certain skill sets that you see in more need than other? And I'm also curious, do you recommend, or do you help people along with certain certifications? Thinking, the cloud certifications definitely have been on the rise, the last couple years. >> I feel like the cloud, the cloud certifications have been on the rise because it's expensive to like test for that stuff. If a person messes up, unless you have a very dedicated environment to where they can't mess up, they can cost you a lot of money, right? So you want that certain, right? But for us, it's been, we just focused on what we like to call front-end DevOps. We focus on Jamstack, which is JavaScript, APIs and markup, also along with a lot of serverless. So we're using AWS, we're using, also they're, they're learning Lambda functions, all this stuff. We're using a query language called GraphQL. We're using Apollo with that query language. We're using some node, React, GET, Speed. And a lot of third party API has to do like a lot of heavy lifting cause we believe that the deeper dive that a person has in a language and being able to manipulate and utilize APIs that they can, the better they will be, Right? So, same way that colleges do it, but a more modern take like colleges, they give you the most painful language to learn, which is usually like C right? Where you had to make everything a very low-level language. And then you're going through this process of building. And because of that, other languages are easier because you felt the pain points. We do the same thing, but with JavaScript, because it's the most accessible, painful language on earth, that's what I called it with Wire magazine last year anyway. (jerome laughs) >> So Jerome, you've laid out how you you're well organized. You're lean and financially, making sure that things are done responsibly. We want to give you the opportunity though. What's the call to action? Vets Who Code, you're looking for more people to participate. Is it sponsorships? Work in the community, look to engage. >> Roger that, we are looking for two things. One, we're always looking for people to help support us. We're open source, we're on GitHub sponsors. Like the people who we we're up, we're open source. But the people that do most of our tickets are the students themselves. So that's one of the best things about us. there is no better move, feeling that having something in production that works, right? It actually does something right? Like, Oh, this actually helps people, right? So we help have our veterans like actually pull tickets and do things like that. But, we also, we build, we're building out teams that they're on all the time as well. We have our new tutorials team or veterans. They literally built front facing tutorials for people on the outside. So that way they can learn little skills as we also have podcasts team and they're always podcasting, always interviewing people that in community, from our mentors to our students, to our alumni. And so just, let's throw our podcasts on Spotify. Let's do some codes, the best Code podcast and sponsor song get up. >> Wonderful, Jerome. We want to give you the final word. you're very passionate. You've got a lot interested, loved hearing about some of the skill sets that you're helping others with. What's exciting you these days? What kind of things are you digging into, beyond Vets Who Code? >> Oh man, everything serverless dude. As a front-end, as a person who was full stack and move to front-end. This has never been a more exciting time to learn how to code because there's so many serverless technologies and is leveling the playing field for front-end engineers, just knowing a little bit of like server-side code and having DevOp skills and being able to work in a CLI, you can do like Jamstack and the people that are using it. You have Nike, you have governments. It's just, it's such an exciting time to be a front-end. So I'm just like, and just seeing also how people are like really turning towards wanting their data more open source. So that's another thing that's really exciting for me. I've never been a person that was very highbrow when it came to talking about code. I felt like that was kind of boring, but seeing how, when it comes to like how code is actually helping normal, average everyday people and how the culture as a whole is starting to get more hip to how, API is like our running the world and how tech is being leveraged for. And it gets them, I'm on fire with these conversations, so I try to contain it cause I don't want to scare anyone on TV, but we could talk like, we could talk hours of that stuff. Love it. >> Well, Jerome, thank you so much for sharing with our community, everything you're doing and wonderful activity Vets Who Code, definitely call out to the community, make sure check it out, support it. If you can and tie so much in Jerome, I've got a regular series I do called Cloud Native Insights that are poking at some of those areas that you were talking about serverless and some of the emerging areas. So Jerome, thanks so much for joining, pleasure having you on the program. >> Roger that, thank you for having me. >> All right. Be sure to check out thecube.net for all of the videos that we have as well as Siliconangle.com for the news an6d the writeups, what we do. I'm Stu Miniman and thank you for watching theCUBE. (soft music)

>> Live, from Barcelona Spain, it's theCUBE, covering Cisco Live! Europe. Brought to you by Cisco and its ecosystem partners. >> Welcome back to theCUBE, here at Cisco Live! 2019 in Barcelona, Spain, I'm Stu Miniman, happy to welcome to the program a first-time guest, but someone I've known for many years, Jason Edelman, who is the founder of Network to Code. Jason, great to see you, and thanks for joining us. >> Thank you for having me, Stu. >> Alright, Jason, let's first, for our audiences, this is your first time on the program, give us a little bit about your background, and what led to you being the founder of Network to Code. >> Right, so my background is that of a traditional network engineer. I've spent 10+ years managing networks, deploying networks, and really, acting in a pre-sales capacity, supporting Cisco infrastructure. And it was probably around 2012 or 13, working for a large Cisco VAR, that we had access to something called Cisco onePK, and we kind of dove into that as the first SDK to control network devices. We have today iPhone SDKs, SDKs for Android, to program for phone apps, this was one of the first SDKs to program against a router and a switch. And that, for me, was just eye-opening, this is kind of back in 2013 or so, to see what could be done to write code in Python, Seer, Java, against network devices. Now, when this was going on, I didn't know how to code, so I kind of used that as the entrance to ramp up, but that was, for me, the pivot point. And then, the same six-week period, I had a demo of Puppet and Ansible automated networking devices, and so that was the pivot point where it was like, wow, realizing I've spent a career architecture and designing networks, and realizing there's a challenge in operating networks day to day. >> Yeah, Jason, dial back. You've some Cisco certifications in your background? >> Sure, yes, CCIE, yeah. >> Yeah, so I think back, when this all, OpenFlow, and before we even called it Software-Defined Networking, you were blogging about this type of stuff. But, as you said, you weren't a coder. It wasn't your background, you were a network guy, and I think the Network to Code, a lot of the things we've been looking at, career-wise, it's like, does everyone need to become coders? How will the tools mature? Give us a little bit about that journey, as how you got into coding and let's go from there. >> Yeah, it was interesting. In 2010, I started blogging OpenFlow-related, I thought it was going to change the world, saw what NICRO was doing at the time, and then Big Switch at the time, and I just speculated and blogged and really just envisioned this world where networks were different in some capacity. And it took a couple years to really shed light on management and operations of networking, and I made some career shifts. And I remember going back to onePK, at the time, my manager then, who is now our CEO at Network to Code, he actually asked, well, why don't you do it? And it was just like, me? Me, automate our program? What do you mean? And so it was kind of like a moment for me to kind of reflect on what I can do. Now, I will say I don't believe every network engineer should know how to code. That was my on-ramp because of partnership with Cisco at the time, and learning onePK and programming languages, but that was for me, I guess, what I needed as that kick in the butt to say, you know what? I am going to do this. I do believe in the shift that's going to happen in the next couple years, and that was where I kind of just jumped in feet first, and now we are where we are. >> Yeah, Jason, some great points there. I know for myself, I look at, Cisco's gone through so much change. A year ago, up on stage, Cisco's talking about their future is as a software company. You might not even think of us as networking first, you will talk to us about software first. So that initial shift that you saw back in 2010, it's happening. It's a different form than we might have thought originally, and it's not necessarily a product, but we're going through that shift. And I like what you said about how not everybody needs to code, but it's this change in paradigms and what we need to do are different. You've got some connections, we're here in the DevNet Zone. I saw, at the US show in Orlando last year, Network to Code had a small booth, there were a whole bunch of startups in that space. Tell us how you got involved into DevNet, really since the earliest days. >> Yes, since the early days, it was really pre-DevNet. So the emergence of DevNet, I've seen it grow into, the last couple years, Cisco Live! And for us, given what we do at Network to Code, as a network-automation-focused company, we see DevNet in use by our clients, by DevNet solutions and products, things like, mentioned yesterday on a panel, but DevNet has always-on sandboxes, too. One of the biggest barriers we've seen with our clients is getting access to the right lab gear on getting started to automate. So DevNet has these sandboxes always on to hit Nexus API or Catalyst API, right? Things like that. And there's really a very good, structured learning path to get started through DevNet, which usually, where we intersect in our client engagement, so it's kind of like post-DevNet, you're kind of really showing what's possible, and then we'll kind of get in and craft some solutions for our clients. >> Yeah, take us inside some of your clients, if you can. Are most of them hitting the API instead of the COI now when they're engaging? >> Yeah, it's actually a good question. Not usually talked about, but the reality is, APIs are still very new. And so we actively test a lot of the newer APIs from Cisco, as an example. IOS XE has some of the best APIs that exist around RESTCONF, NETCONF, modeled from the same YANG models, and great APIs. But the truth is that a lot of our clients, large enterprises that've been around for 20+ years, the install base is still largely not API-enabled. So a lot of the automation that we do is definitely SSH-based. And when you look at what's possible with platforms, if it is something like a custom in Python, or even an ANSEL off the shelf, a lot of the integrations are hidden from the user, so as long as we're able to accomplish the goal, it's the most important thing right now. And our clients' leaderships sometimes care, and it's true, right? You want the outcome. And initially, it's okay if we're not using the API, but once we do flip that switch, it does provide a bit more structure and safety for automating. But the install base is so large right now that, to automate, you have to use SSH, and we don't believe in waiting 'til every device is API-enabled because it'll just take a while to turn that base. >> Alright, Jason, a major focus of the conference this year has been around multi-cloud. How's that impacting your business and your customers? >> So, it's in our path as a company. Right now, there's a lot of focus around multi-cloud and data center, and the truth is, we're doing a lot of automation in the Campus networking space. Right, automating networks to get deployed in wiring closets and firewalls and load balancers and things like that. So from our standpoint, as we start planning with our clients, we see the services that we offer really port over to multi-cloud and making sure that with whatever automation is being deployed today, regardless of toolset, and look at a tool chain to deploy, if it's a CI/CD Pipeline for networking, be able to do that if you're managing a network in the Campus, a data center network, or multi-cloud network, to make sure we have a uniform-looking field to operations, and doing that. >> Alright, so Jason, you're not only founder of your company, you're also an author. Maybe tell us about the, I believe it's an update, or is it a new book, that recently got out. >> Yes, I'm a co-author of a book with Matt Oswalt and Scott Lowe, and it's an O'Reilly book that was published last year. And look, I'm a believer in education, and to really make a change and change an industry, we have to educate, and I think the book, the goal was to play a small part in really bringing concepts to light. As a network engineer by trade, there's fundamental concepts that network engineers should be aware of, and it could be basics and a lot of these, it could be Python or Jinja templating in YAML and Git and Linux, for that matter. It's just kind of providing that baseline of skills as an entrance into automation. And once you have the baseline, it kind of really uncovers what's possible. So writing the book was great. Great opportunity, and thank you to Matt and Scott for getting involved there. It really took a lot of the work effort and collaborated with them on it. >> Want to get your perception on the show, also. Education, always a key feature of what happens at the show. Not far from us is the Cisco bookshop. I see people getting a lot of the big Cisco books, but I think ten years ago, it was like, everybody, get my CCIE, all my different certifications updated, here. Here in the DevNet Zone, a lot of people, they're building stuff, they're building new pieces, they're playing in the labs, and they're doing some of these environments. What's your experience here at the show? Anything in particular that catches your eye? >> So, I do believe in education. I think to do anything well, you have to be educated on it. And I've read Cisco Press books over the years, probably a dozen of them, for the CCIE and beyond. I think when we look at what's in DevNet, when we look at what's in the bookstore, people have to immerse themselves into the technology, and reading books, like the learning labs that are here in the DevNet Zone, the design sessions that are right behind us. Just amazing for me to have seen the DevNet Zone grow to be what it is today. And really the goal of educating the market of what's possible. See, even from the start, Network to Code, we started as doing a lot of training, because you really can't change the methodology of network operations without being aware of what's possible, and it really does kind of come back to training. Whatever it is, on-demand, streaming, instructor-led, reading a book. Just glad to see this happen here, and a lot more to do around the industry, in the space around community involvement and development, but training, a huge part of it. >> Alright, Jason, want to give you the final word, love the story of network engineer gone entrepreneurial, out of your comfort zone, coding, helping to build a business. So tell us what you see, going forward. >> So, we've grown quite a bit in the past couple years. Right now, we're over 20 engineers strong, and starting from essentially just one a couple years ago, was a huge transformation, and seeing this happen. I believe in bringing on A-players to help make that happen. I think for us as a business, we're continuing to grow and accelerating what we do in this network automation space, but I just think, one thought to throw out there is, oftentimes we talk about lower-level tools, Python, Git, YAML, a lot of new acronyms and buzzwords for network engineers, but also, the flip side is true, too. As our client base evolves, and a lot of them are in the Fortune 100, so large clients, looking at consumption models of technology's super-important, meaning is there ITSM tools deployed today, like a ServiceNow, or Webex teams, or Slack for chat integration. To really think through early on how the internal customers of automation will consume automation, 'cause it really does us no good, Cisco, vendors, or clients no good, if we deploy a great network automation platform, and no one uses it, because it doesn't fit the culture of the brand of the organization. So it's just, as we continue to grow, that's really what's top of mind for us right now. >> Alright, well Jason, congratulations on everything that you've done so far, wish you the best of luck going forward, and thank you so much, of course, for watching. We'll have more coverage, three day, wall-to-wall, here at Cisco Live! 2019 in Barcelona. I'm Stu Miniman, and thanks for watching theCUBE. (electronic music)

(upbeat electronic music) >> Narrator: From the VMware campus in Palo Alto, California, it's theCUBE covering Women Transforming Technology. >> Hi, I'm Lisa Martin with theCUBE. We are on the ground at VMware in Palo Alto, with the third annual Women Transforming Technology event and I'm very excited to be joined by the CEO of Women Who Code, Alaina Percival. Alaina, nice to have you here. >> Hi, thank you very much for having me. >> So tell me about Women Who Code. You co-founded it a while ago. Give us a little bit of a background about what your organization is. >> Yeah, Women Who Code is the largest and most active community of technical women in the world. Our mission is to see women excel in technology careers, and that's because we have a vision of women becoming executives, technical executives, founders, board members, and of course through a pathway of being software engineers. >> So Women Who Code started, originally, back in 2011 as a community. Tell me a little bit about the genesis of that and what you've transformed it into, today. >> Yeah, it started off as a local community, and it was just a space to get together with other technologists, and what we started to see is it was this thing that was just fun and kind of our little secret for, you know, that first year, and we realized-- at one point I said, "Hey other women around the world deserve to have this, as well." And, that's really where the focus to grow globally came about and focus on women: building on their skills and building up their leadership skills and if you invite software engineers to a leadership and networking event, they won't come, but we hold an average of five free technical events every single day, throughout the world, and at those events, they're primarily technology events where we weave in a little bit of leadership and networking, but it feels authentic and its an event that software engineers are excited to be. >> Five events per day, that's incredible. So, VMware became a partner back in 2015, when you had around nine or 10,000 members. Now, today, its over 137,000 global members. Talk to us about the strategic partnership with VMware and what that's enabled Women Who Code to achieve. >> Yeah, we can't accomplish what we accomplish without the partners that support us. We try not to charge our members for anything. So, those 1,900 events we put on last year were free. We've given away $2.8 million in our weekly newsletter of scholarships, and conference tickets, encouraging our community to go out there in the broader tech community and we can do those things, we can launch in the cities that we can launch in, we can elevate women as leaders around the world, but we can only do that through partners, and VMware is one of our founding partners and what that took is someone in executive leadership to see who we could be, because we're very small, and we were very local when we came to VMware and talked to them about what our vision was and what we were going to accomplish and I say now, what I said back then, is we've only scratched the surface of what we are going to achieve. >> There's some commonalities, some parallels that Women Who Code has with VMware. You know, this is the third annual Women Transforming Technology event at VMware here and its sold out within hours. Walking into that room it's very empowering. The excitement and the passion are there and you just start to feel a sense of community. Tell me about the parallels that you see with VMware and some of the visions that they share about, not just raising awareness for the diversity gaps and challenges, but also taking a stand to be accountable in that space. And what they announced this morning with Stanford, with this massive $15 million investment in this Innovation Lab of actually wanting to dig deep into these barriers to help identify them to help eradicate them. What are some of the visionary similarities with Women Who Code and VMware? >> Yeah, so what you see with that is you know, you're investing in someone or an organization that already has the potential. Our average age of our community is 30. We have a lot of trouble claiming that you achieve what you achieve in your career, because of us. We know we play a part in it, but we know that potential, that raw power, exists within you, and when someone sees and knows that that's there and gives you what you need to be able to harness that potential, you are able to achieve great things, global things. You're able to change the world, and that's what we do for our members and their careers, and that's what our partners, like VMware do for us. >> I saw on your website: 80% of members experience a positive career impact, after joining Women Who Code. 80% of women, that's huge. >> Yeah, and a lot of that comes from the people that you connect with, the sense of belonging. We had a women at the end of Hackathon, in Manila come up to our leaders, there, and she started crying. She said, "I was about to leave the industry and I realize I have a place." And that sense of belonging that you get from coming to a Women Who Code event that's very welcoming, it can really help to override all of those unconscious biases that you encounter every day, throughout the course of your career, and it helps you to realize, "I'm not alone. There's a lot of really smart, talented women in the tech industry, who want me to be in my job and being in my job isn't just for me. I'm lifting up the people around me, as well." >> So one of the things that we hear a lot about is a lot of focus on STEM programs and getting young girls interested in STEM fields to study in college, but another thing that's huge is the attrition rates. Women are leaving technology at alarming rates, and a lot of people think it's to go off and have children, and it's actually not the case. What are some of the things that have surprised you about women kind of in that, maybe, mid-stage of their career that are leaving, and how can Women Who Code help to impact that, positively? >> Yeah, so what you're speaking to is definitely the data showing that women are leaving their technical careers at a rate of 50% at the mid-career level, and they're leaving their overall careers, if you aggregate women in careers, at a rate of 20% over a 30 year period, so that gap is huge and the industry is a great industry for women. You've got a lot of job security, a lot of job opportunity, a lot of flexibility. All of these things are great for women and their careers, but what you're encountering is often being the only, or one of the only, and you really don't overcome that, until you're getting above 20%, 25%, 30% of that feeling of being the only on a team, and what I think is the biggest issue with women coming into their careers at what kind of wears you down is the unconscious bias. It's something that you encounter on a daily, or multiple times a day basis. That thing that if you complained about a single one of them, you'd be the weird person who complains, at your company. And so, what Women Who Code really does is: one, it helps to create a sense of belonging, it helps to build domain-specific and non-domain-specific skills, it helps you to envision your career, not just the next step in your career, but the step after that, and the step after that, so it's really working to combat those things that you're to, on a daily basis, to provide that sense of community, to remind you, you do belong, and to really help you envision and achieve your career goals, long-term. >> So you have about 137,000 members, globally. And when we had Lily Chang on earlier, she was talking about the Shanghai and Beijing and kind of what that sort of thing meant to her going back there now, on the board. Tell us, maybe give me an example of a real shining star, who joined Women Who Code and was able to get that support, and that guidance, and that camaraderie to continue to be successful, and actually be promoted, and succeed. >> Yeah, so one example that I love is a woman came up to me at an event, last year, and she said, "Hey Alaina, I was going to the Women Who Code Python events, and I now, today, because of what I learned, ended up choosing a path in data science. I'm a senior data scientist, and this year, I'm being flown across country to speak, as an expert in data science. I would not be in this career path, without Women Who Code." Another story that I love is a woman who came up to me at a Hackathon and she told me her story that she had joined Women Who Code, in February, and she was going to our events and kind of figured out what she wanted to do, and by the summer she had transitioned into a new job, gotten a job with The Weather Channel, as a software engineer, and she was making more than double any salary that she had had prior to that. >> Wow. >> And so its career direction, competing job offers, which really increases your likelihood of having a higher salary, those are kind of two examples that I love. The one thing that we haven't talked about is our leadership program. We have a global leadership program, which really actions you to build skill-based volunteering and become a local tech leader. It opens up lines of communication between you and executives at your company. You often get called in as a thought leader at companies. You typically will receive a promotion or a pay increase, at a higher rate than you would otherwise. Some of our leaders get press mentions, get invited to be speakers at conferences, or even advisors on advisory boards. And so, when I look at the stories that are coming from our leaders, one of my favorite stories is a woman in Atlanta. She had a master's in CS. She was inside of the box, you know, the person that every company wants to hire. She was incredibly shy, and when she stepped up as a Women Who Code leader she said, "Oh Alaina, I'm going to be the worst leader." And, okay you've got this. At her first event, she stoop up and she was like, "My name's Erica. Feel free to ask me questions," and kind of sat down, as quickly as possible, but she stood in the front of that room. She began to be perceived by the community, and by herself, as a leader. And in under one year, she was invited, she didn't even apply, to speak at three different tech conferences, and she went from barely being able to say her name in front of a nice community to giving a talk to a standing-room-only crowd. >> Wow, very impactful. And is that for other opportunities that you guys deliver, in terms of public speaking, or was that because she was able to, through Women Who Code, to start to get more confidence in her own capabilities and in her own skin? >> Experience, confidence, self-perception, community-perception, I had one lead at our community tell me that she became a leader at Women Who Code, by regularly attending events. One day, the leader was running late, so she said, "Oh, well, you know I can probably get this started. I've been coming enough," so she went and stood at the front of the room, welcomed everyone, got everything going, said our pitch and she said, by the end of that three-hour event, people thought she was a leader and she began to think, "Oh yeah, I'm a leader," and she says, "Hey, I know that I can get an interview anywhere I want. I know that this opens doors for me." I had one leader tell me that she interviewed with SpaceX, and they specifically told her in the interview that they were impressed with her Women Who Code leadership and that was one of the reasons they were interviewing her. >> Wow, what have been some of the things that have really blown you away, in the few years that this organization has been around? >> It's just the individual stories. It's, every step of the way, the impact that it has in the lives of our leaders in our community. And I honestly feel, everyday, that I get to do this for a job. >> With what VMware announced this morning, with Stanford and this huge investment that they're making into Women's Leadership and Innovation Lab, to look at some significant barriers that women in technology are facing and to identify those barriers that we can then eradicate, what are some of the things that you're looking forward to, from that research and how you think that can actually benefit Women Who Code? >> Yeah, I'm very excited to see what comes out from there. I think we need a lot more research to help us to understand at what point things are happening and what things you can be doing that really help to overcome. I think that combining research with the real-world, in-person action that Women Who Code does and the work that we do with our community would have an even bigger impact. >> I also think what it speaks to is accountability. You know, a very large, very successful, 20-year-old organizations standing up saying, "We actually want to study this," and I think that there's a message there of accountability, which is, I think, a very important one that other organizations can definitely learn from. >> Yeah, I think that also they're going to an organization outside of them and funding that. And so, the research that comes out of there might come back and say, "You're doing this wrong. This is how you can be doing it better." And so, the fact that they're willing to make an investment and say, "Hey, we want to see this better, not only for us. It's not just going to be internal. This data's going out to the world." That's an investment in global change. That's not just holding that in at a personal or organizational level. >> Right, so in addition to that news that came out today, what are some of the things that you're going to walk away, from this third annual Women Transforming Technology event going, "Ah, that was awesome. Now, this gives me even more ideas for Women Who Code." >> Yeah, I think this is a great opportunity to connect with, especially, women who are in leadership positions and figure out how we can better service women at the higher tiers of their career, because you don't stop needing support, and you don't stop growing your career, once you become a director or a vice president. You continue to invest in your career, and you continue to needs support. And so, I'm really looking for ways that we can better serve those women. >> And hopefully, we start to see that attrition number at 50% start to come down. >> Alaina: Definitely. >> Alaina, thanks so much for your time. It was a pleasure to chat with you, and we wish you continued success with Women Who Code. >> Thank you. >> Thank you for watching. I'm Lisa Martin with theCUBE, on the ground at VMware, for the third annual Women Transforming Technology event. Thanks for watching. (funky electronic music)

(electronic pop music) >> Hi, I'm Peter Burris. Welcome to a Wikibon Action Item Quick Take. One of the biggest challenges that all cloud players face is how to bring more developers into the ranks. Jim Kobielus, Amazon did something interesting to, or I should say, AWS did something interesting this week. Tell us about it. >> Well, they haven't actually done it, Peter, but there is rumor that they're doing it. Let me explain. Darryl Taft, who's a very well-seasoned veteran reporter with TechTarget now... Darryl reported that AWS is "appealing to the masses" with a low-code development project. I think that's exciting. He's got it on strong background that they've got Adam Bosworth, formerly of Microsoft, heading up their low-code tool development effort. I think one of the things that AWS is missing is a strong tool for developers, especially professional developers, trying to rapidly build cloud applications, and also for the run-of-the-mill business user who wants to quickly put together an application right in the Amazon cloud. I'm impressed that they've got Adam Bosworth, who was very much one of the drivers behind the Access database at Microsoft, going forward. So going forward, I'm looking forward to seeing, hopefully, they say they've been developing it since last summer, AWS... I'm hoping to see an actual low-code tool from AWS that would bring them into this space in a major way, really to encourage more development of cloud applications running natively in the very sprawling and complex AWS world. >> All right, so, AWS being rumored to expand their attractiveness to developers. This has been a Wikibon Action Item Quick Take. (electronic pop music)

[Music] from the cube studios in Palo Alto in Boston it's the cube covering the IBM thing brought to you by IBM welcome back everybody you're watching the cube and our continuous coverage of IBM think Digital 20/20 events it's we've been wall-to-wall for a couple days now and and we bring in you all the action a bass fall is here here he is the global league for quantum education and open science at IBM quantum gave great to see you thanks for coming on yeah thanks for having me here Dave you're very welcome love the discussion on quantum but I gotta say so I'm reading your bio in your bio I see quantum algorithms experimental quantum computation nanoscale device fabrication cryogenic measurements and quantum software development hardware programming etc so you're obviously qualified to talk about quantum but but how how can somebody learn about quantum do I have to be like a rocket scientist then understand this stuff so Dave this is one of the things that I'm very passionate about it's also my job to make sure that anyone can learn about quantum computing today so primarily what I'm focused on is making sure that you don't need a PhD to program a quantum computer when I was going through my graduate studies trying to learn quantum computing I needed access to a lab so I have to go to graduate school to do this but in 2016 IBM put a quantum computer on the cloud in that dramatically changes the field it allows access to anyone from the world with just an internet connection to program a quantum computer so the question I'm trying to answer on a daily basis now is the question that you asked how do I learn to program a quantum computer well I'm trying to make several resources available for you to do that okay well let's talk about those resources I mean you have quantum you have access to quantum computers I talked to Jamie Thomas the other day she said that you guys it's all available in the IBM cloud I can't even I can't even imagine what the infrastructure behind that looks like but as a user I don't have to see that so how do I get access to this stuff so there are several quantum computers available on the cloud now and every time I think about this it's fascinating to me because I needed access to a lab to access these things but now you don't you can go to quantum computing dot ibm.com and get free access to several quantum computers now the question becomes if I give you this access to the quantum computers how do you learn to program them the software that you use to program them is called kiss kit just like we've made access to the quantum computers open for everyone our software is also open source you can access it by going to Kiska torgue that's QIS ki t org and if you go in particular to Kiska org slash education we've put together a textbook to help you go through everything that you'd learn in a classroom about quantum algorithms and to start programming the real quantum systems yourself so everything's ready for you to program immediately what was the it can you give me the quantity IBM want them - computing URL again yeah that's quantum - computing IBM com once you create an account there you immediately get access to several quantum computers which is an impressive thing to think about the cryogenics that you mentioned earlier the hardware the software all of it is ready for you to take advantage of but I gotta ask you I know it's sort of off topic here but but if I had to look under the covers I'm gonna see some big cryogenic unit with a bunch of cables coming in is that right that's exactly it very cold inside that's right so the way to here's the way to think about it outer space is about 200 times colder than room temperature and the temperature where the chip the quantum chips it's is another 200 times lower than that so we're talking very cold here we're talking only 15 Mille kelvins above absolute zero that's zero point zero one five degrees above absolute zero so it's a very cold system and you'd have several wires that are going down into this coil system to try to communicate with the quantum ship well and what's exciting to me about this whole thing Abe is it is it brings me back to the sort of the early days of computing and the you know huge rooms and now look where we are today and so I would expect that over the next many decades you're going to see sort of similar advanced advances in quantum and being able to actually execute at somewhat higher temperatures and in miniaturization it's very exciting time and we're really obviously at the very very early innings but I want to ask you just in terms of if if I'm a programmer and I'm a Java programmer can I actually come in and start using quantum if you what do I need to know to get started so you need to know two things the first thing is you need to be familiar with any programming language the easiest programming language to pick up today by far is Python so kiss kit is built based on Python so if you're able to quickly catch up with a few things in Python and we have a chapter dedicated to this topic in our textbook that's the first thing the second thing is simply having the ability to learn something new simply being excited about this field once you have those two together you can learn quantum computing very quickly within a few months the question then becomes catching up with the research and reading research papers that can take some time but for us to be able to talk through a quantum program takes only a few a few days of reading let's talk about what some of the folks are doing with quantum we talked again to Jamie Thomas and she gave me some examples not surprisingly you know you saw for instance some some examples in pharmaceutical and to the other obvious industries but then banking came in it's a but what what is it what are people doing with quantum today maybe you could add some color to that primarily most of the working quantum today is focused on understanding how to take problems in industry whether it is to understand how to simulate molecules whether it is to understand how to optimize a financial portfolio taking those problems and mapping them onto a quantum computer so that they can get solved so you'll see various various industries exploring how to take their problems and map onto a quantum computer so one one exciting one that I'm seeing a lot of progress in is chemistry learning how to simulate molecules using these quantum computers as someone with a physics background for me the exciting thing to see here is also how people are using these quantum computers which fundamentally are taking advantage of quantum mechanics to simulate other quantum systems so to understand nature better by using nature itself so this is another exciting progress that we're seeing in the field so exciting both from industry and from educational and science purpose so obviously it's a fascinating field and people would you say with curiosity it can get excited about it but but let's say I actually want you know some some kind of career in part of I mean what well how would people sort of get involved do you see you know on the horizon that this is gonna be something that is actually gonna be a vocation for you know young folks that want to get involved I could not tell you how challenging it is to find people who have the right combination of quantum computing knowledge and classical programming knowledge so in order to be able to take full advantage of the quantum systems today we need people who understand both the hardware and the software to some level and there is an extreme shortage of that kind of talent so the work that I'm focused on is exactly this problem of solving the workforce development problem so we're trying to make sure that people have access to anything that they need in order to be able to program a quantum computer and to learn how to then map their own problems into these quantum computers in the future the question becomes let's say we now understand how to use quantum computers to make financial portfolio optimization every bank in the world is going to want someone to implement this in their systems which immediately creates lots of jobs so this is going to become something that's in demand once it becomes possible on a on a large quantum computer so today is the right time to learn how to work with these quantum systems so that when the time comes that there are industries that are needing quantum skills you're ready to be hired for those positions okay so big skills gap you kind of gave an example in financial services where maybe some of the other things that you hope that that people are going to be able to do over time with these skills I cannot under I cannot over us overstate how important it is to learn how to simulate chemistry problems on these quantum computers that will have impacts anywhere ranging from whether it's drug design whether it's making better efficient solar panels more efficient batteries there are many applications where you'll see impact from these so the there are many industries that can benefit from understanding how to work with quantum computers that's something exciting I'm looking forward to see you know you read in the press that you know we're at least a decade away you know from from quantum being a reality but you're giving some examples where it's sort of here today I feel like it's going to come in layers you know not gonna be one big bang it's gonna come over time but but maybe you could you know frame that for us in terms of how you see this market developing I don't even want to call it a market but just this technology developing into a market what what has to take place and what kind of things can we expect along that journey sure so I think it's very important to keep in mind that quantum computers are fairly young technology so we're improving the technology as we go and there has been dramatic improvement in the technology itself but we're still learning as we go so one of the things that you'll find is that all of the applications work that's being done today is exploring how to take advantage of the quantum computer in some way if I immediately gave you a fully functional perfect quantum computer today you wouldn't even know what to do with it right you need to understand how to map problems on to that quantum computer so in preparation for that time several years away you'll see a lot of people trying to learn how to take advantage of quantum computers today and as they get better and better learning how to take advantage of whatever incremental progress is being made so as much as it seems like quantum computers are several years away many people are learning how to program them today just in preparation for that time when they're ready for use and my understanding is we're gonna get there with you know hybrid models today you're using you know traditional microprocessor technology to sort of read and write data from quantum that's likely going to continue for quite some time maybe maybe indefinitely but but but perhaps not right so Dave the important thing to remember is that a quantum computer works jointly with a classical computer if you ask me the question of how do i optimize my portfolio the numbers that I would need to compute with our classical there's nothing quantum about them these are numbers so there's classical information that you then have to take and map on to the quantum computer and then once the quantum computer is done you have to take the data out of that computer and then turn it back into classical information so you'll always have a quantum computer working jointly with a classical computer the question now is how do you make those two work together so that you can extract some benefit that you couldn't have attained with just the classic what do you see is the big sort of technical challenges that you're paying attention to you paying attention to I mean is it getting more you know qubits is a coherence working at higher temperatures what are the things that you see is as the the scientists are working on to move things forward so one of the things that I can do immediately Dave if you and I agreed right now is we can go to the lab and take a quantum chip and put a thousand cubits on that quantum chip that's fine we can do that immediately the problem that you'll find is that it doesn't matter that you have a thousand cubits if the qubits are not good quality cuteness so the technology should focus on improving the fundamental qualities of the qubits themselves before scaling them up to larger numbers in addition to that as you're scaling to larger and larger numbers new problems come into the picture so making better qubits scaling up seeing how the technology is doing learning new things and then scaling farther up that seems to be the model that's working today so in addition to monitoring the quality of the qubits themselves I'm monitoring within the technology how people are implementing solutions to scaling problems in addition to that another important problem that deserves a lot of attention is the question of how do you make good software that can take problems and map them onto quantum computers in in quantum computing when I say I'm running upon a program really what I'm doing is building a quantum circuit and then running that quantum circuit on the real device well if that circuit has certain operations in it maybe you want to tailor the way you transfer that circuit onto the device in a way that takes full advantage of the device itself but then in order to do that you need to write good software so improvements in the software along with improvements in the quantum technology itself will be how we get to success and at IBM we're focused on finding a metric that wraps all of these things together and it's called quantum volume and we're seeing improvements in the quantum volume of our systems as we go yeah Jamie talked about that you're essentially taking the key metrics and putting them into a you know a single observable metric that obviously you can track over time so I want to ask you about security a lot of people are concerned that the quantum is just going to blow away everything that we know cryptography and all the you know the the passwords and security systems that we we've put in place is that a legitimate concern will quantum you both get us into that problem and take us out of that that problem I wonder if you could talk about that so there are two ways to think about this problem one is just fundamentally if you ask me what does it take to put the the cryptography that has our bank accounts safe over the internet connections that we use it takes roughly about a thousand good cubits okay if I tell you a thousand good cubits that doesn't seem like a lot of work but when you think about it what it really requires is an overhead of about a thousand cubits for each qubit that we have today so the numbers of qubits that you need are in the millions in order to put the the kind of cryptography that we're using today at stake so certainly there's a long way to go that's one aspect of the story the other aspect of the story is that we should never underestimate the progress of technology so even though the time when we can use Shor's algorithm which is the algorithm that can be used to break the cryptographic algorithms like RSA even though that's several years away you still want to be ready for that time and what that means is if you have sensitive information today you need to be making sure that that information itself is protected with quantum resistant cryptographic techniques so that when the time comes you can't use a quantum computer to get back the data from today and break so two perspectives one is we're quite a while away from this kind of danger but at the same time it doesn't mean we should be complacent today we should be taking preparations make sure that our critical information is protected yeah that's so that that makes a lot of sense but when you say we're a ways away or we are we decades away we years away we can you and you quantify that in any reasonable way it's hard to speculate on that number so I'll refrain from giving you a specific timeline just to give you an idea the quantum bits that were in development ten years ago had a coherence time so the amount of time that they can store the quantum information of roughly a hundred times smaller than they are today and ten years ago if you asked people how do we get to a hundred times better qubits nobody would have been able to give you a clear answer you could have guessed some ways but nobody would have been able to tell you we'll get there in ten years but we did so instead of coming up with estimates of timelines that depend on what we know today it's probably a better idea to monitor the technology as it goes and keep adapting we're probably talking this century where we're talking to the century hopefully it is my last mission to enable enough people to learn quantum such that it happens within my life very exciting field a I can't thank you enough for helping us educate the audience and and my and myself personally really I'm I'm so fascinated by this it's something that you know jumper and I and the team have been really focused on and I think it's really time to your point the start digging and start learning you've given us some resources there give us give them give us those two reasons one more time there's there's the IBM site and the the the the the queue kit site use that site what are those again just those to wrap so you can access the quantum computers at quantum - computing ibm.com and once you're there the way to learn how to program these quantum computers is by using kiss kit which you can learn about by going to kiss kit org slash education once here at that education page you can access our textbook which we make open-source it's a textbook that's co-written with professors in the field and is open source so it's continually getting updated you can access that textbook at tisket org slash textbook if you go to our youtube channel you'll find several videos that allow you to also learn very quickly so kiss gets YouTube channel is another great place to look so lots of resources and that's kiss kit with a Q which is why I wrote it that way so alright exact thanks so much it was great to see you stay safe and next time hopefully we'll see you face-to-face and you can draw some some cool pictures to help me understand this even better Dave it was nice talking with you I look forward to learning quantum programming with you yeah Cheers and thank you for watching everybody this is the cubes coverage of the IBM think 2020 digital event experience we'll be right back Brennan for this short break [Music] you

okay welcome back live in Las Vegas is the cube ibm's information on demand conferences q exclusive coverage SiliconANGLE will keep on here live I'm John furry the founder of silicon Hank I'm Joe mykos Dave vellante co-founder Wikibon org our next guest is a Fred Balboni global leader business analytics optimization IBM GBS global business services you know obviously big data is powering the world I mean just can demand for information and solutions is off the charts afraid welcome to the cube anything there's a services angle here where you know services matters because one in the channel partner is this good gross profit for helping customers implement solutions that they have demand for so you've a combination of a market that's exploding with demand people know it's a game changer with big data analytics cloud is obviously right there in the horizon in terms of on prem of Prem then you've got now see mobile devices bring your own device to work which is thrown off more data okay and then people want to be in all the different channels the social business so you know CIO to CEO says hey this new wave is here if we don't think about it now and get a position and understand it the consequences of not doing anything might be higher than they are so we've heard that how do you look at that and what are you guys doing what's the strategy give us a quick update and from from GBS i think that the to make this successful first of all it services is important it's the last mile you know that means the point you may it's the last mile and without without that you cannot ever deliver the value the the really interesting challenge that every executive faces is you need to be able to we can easily get our head around big data technology and I shouldn't trivialize that but you can go and understand the technology what's possible in big data you can also get your head around analytics and the analytics algorithms and the kind of insights that can be drawn from that the real challenge is how do you articulate what's kind of possible to a client because many of the use cases are very niche and so clients often say yet that's right but it's big it's possibly bigger than that yeah that's right it's possibly bigger than that the other issue or the other challenge to get we've got a hurdle we've got a jump on me articulate this to the businesses clients businesses think in terms of process you don't think in terms of data you know you don't go talk to a CIO CEO and say you know tell us what's the key attributes of your customer and they don't think that way they can talk to you about servicing a customer or selling to a customer or managing customer complaints so that the processes but the data it's a tough thing so the first part the services is so crucial in this is being able to articulate the value of analytics and big data to a client in the businesses terms so it becomes a boardroom conversation kind of so that's that gets the program started and then quickly being able to fill in with use cases because clients don't want this to be they don't want to start from a blank sheet of paper and they don't like going to give me some quick wins here so it's kind of those timetable what kind of timetables mmmm back in the 80s 90s when client-server rolled out it was months and months yeah project management meetings roll out the Oracle systems roll out the big iron now I mean I'll see maybe shorter spurts little different hurdles what's the timetable only some of these horizons for these quick wins okay so project implementation I come on now let's let's know it's it's I think that that we're measuring project implementations in weeks I think cloud-based technology allows us to provision environments on the order of a couple of weeks and that used to be on the order of five to six months so I think that's going to that accelerates everything and that also allows you to do a lot of a lot more speed to value get applications or analytics use cases up there much more rapidly one two as you start to build these portfolio of use cases and if they're built on acceleration tools I mean acceleration so you've got those code sets that are already there that you can add you can jump on top of I mean you can get these use cases up there in 6-8 weeks we have one we have an example a really large major company i'd rather not i'd rather not because it's not externally referenceable but a really a significant client that had on the order of more than more than 5 million discreet customers and doing detailed customer analytics on their customer base against their products and we were able to get that baby up and running in three and a half months now that two to three years ago traditional logic would have told you that was a nine to twelve month project and by the way you know ten years ago that would have been a 18 to 24 month project yeah so I think that yeah we're moving much more rats the expectation now too I mean the customers realize that too right the absolute not but but there's one thing I want to talk about this it's still this is the one thing that if you'd asked me what's most important this speed thing allows you to go rapidly to places but you you better have a navigation roadmap on where you're going because if you're going to do all kinds of little code drops that's great but you want to make sure you're getting leverage so you're going somewhere so therefore there's a scale but this is where roadmapping becomes really really important for every the technology side of the business you have to have a technology roadmap the other thing that's really important out of this is if you don't let's use the client-server example you used because this kind of has a you know we've all been here right here we've all lived seen this movie before yeah if you if you don't in the build this roadmap another thing that happens do you remember when CIOs finally said okay I'm taking control this client servicing sure what do they end up with they ended up with all these departments of computing in the costs work going astronomical so if you've got a road map you can also address the issues of managed services because you don't the least thing you want to be is having all these data Mart's that are scattered everywhere because you get no economies you get no economies of it but a cloud would bring you you get Noah kind you get no economies and being able to do that and you end up having to have all these maintenance teams you know that maintenance and by the way analytics by its nature has constant maintenance little adjustments and changes you're getting new economies of that because they're all managed is discrete units so therefore there's a lot to be as you build this roadmap you've got to think about the managed services environment as well so Fred you talked about earlier clients don't think in terms of data they think in terms of their business process is that a blind spot for clients because there are some companies Google for example that does think in terms of data in your view should clients increasingly be thinking in data terms or does our industry have to evolve to make the data map to business process I actually I kind of just take it as a thick I don't I don't I don't choose to question why I just accept it um i but i would say i which i would say customer's always right I just I just think the industry i thought that definitely but i think just the industries at a stage where you know we've always you know back in the old days of you know i'm going to show my age here but you know the procedure division in the data division oh my god looked at all and and and we you know the procedure division is where you actually did all the really and i think if the reason is we got understand the paradigm under which modern computing was created I don't to be like we go into history lesson but the paradigm under which modern computing was created was that we use computers to automate tasks so we've always taken this procedural approach which went then we went to process reengineering and that became a boardroom conversation so just I think we've conditioned over the last 40 years businesses to think about using technology to gain business efficiency they've always thought in terms of process so that's why this data element yeah companies like Google founded on analytics clearly have got a whole different headset in a different way to approach these which gives them a built-in bias when they address the problems they've got in their businesses sure but you don't come a decline saying hey you got to rethink the way in which you look at data you come in and say let's figure out how we can exploit data in your biz erect what we do it two ways we do it two ways first of all let me not dress let me not dress monton up as lamb at the end of the day it's its data its data okay now the question is how you articulate that and it's twofold we tend to I like to use a metaphor to describe the data so if its customer that the metaphor we've been using recently is DNA DNA strands to be able so you use a metaphor that there's a language that the business can relate to and you can create a common language very easy one in that way you can have an account because you're never going to drag a CEO into your fourth normal form data model so so therefore you've got to you've got to talk a language one number two you talk about as a collection of use cases so you use use cases as a vehicle to have the process conversation and because with the use case you also can talk business outcomes benefits and you can tell kind of a story you don't have to drag them through the details of the process but you can tell them a story whether it's you know I if you can understand called detailed called detailed data records and the affinities you can understand the social networks and therefore you can reduce churn within your telco customer base as an example quick but if you follow I do so you talked about its little use cases and they begin to understand wow what's possible and then you talk about their data as a DNA chain and they get I got it I actually need to get the DNA chain if I'm going to actually think about think about my customer base or my product base or whatever the lingua franca the business is still the businesses language it doesn't result of data but data can enrich the conversation in a way that can lead to new outcomes the data in rich's the conversation when you talk about the business outcomes that are created as the part of the use case well it's like a three third order differential equation but i go back i watch this yeah i just go say your tweet your epic soundbite machine just can't type fast enough on the crowd chat it's good for good for Twitter viewing yeah I've just opened a Twitter account please look me up I'm looking for friends I promise to start posting you got people watching all right all right so so in terms of customers right give us a little bit peak of some of the customer responses when you when you open the kimono show them the road map you know the messaging around on IBM right now is pretty tight here at IOD last year was good this year is better you look really unified face to the customer when you show them the road map what's the feeling they get it they feel like okay I got some trust IBM's got some track record history do they is the is the emotion more of okay where do I jump in how do I jump in there doing it and this little shadow IT going on all over the place we know with Amazon out the area so so when you're in there you've got to have these are conversations what do they like and what's that what's the level of response you get from CIOs and then also the folks in the trenches so there's always a question which there's a couple of questions first of all is how can I get how can I get value from this and that in that and that's you know a I'm tightly coupled to my existing transaction processing which is kind of like if you will call that turbocharged bi and and which is which is where so many people have come from is this turbocharged bi environment and listen that's an important part of your reporting business you need to do that to keep the wheels on the question is as you move to this notion of analytics giving you great insight then then you've got to say okay I need to go from turbocharged bi to really augmented components so clients I'd say there's a large there's a large group of people that are right now moving from turbocharged bi to the notion advanced use cases so there's this some disco a large discussion right now how do I show me do use cases by which i can I can rapidly that would be advanced how to linux up the calling advance limit well no we have well 60 60 use cases industry-based use cases that we as a services business put together on top of that we have about seven or eight key code fragments that we uses accelerators I mean we call them wink we call them assets and we just them up as accelerators but their code fragments that we bring to a client as the basis that we put on top of the the blue stack of technology to actually get them a speed to value because we really want to be able to get clients up and running within this notion of non idealities it's like literally being best practices in the form of technology to the customers well you're on an IBM thing I mean dare I called an application no I wouldn't dare call it an application we're not in that business but the point is is that it is it's starting to feel like an application because it's really moving down these unreal integrated solution is really where we going it's an accelerant this code correct so it's leverage the economies of scale is every success breeds that's exactly it more and then on top of that we would have that just don't throw a few other things that we do to accelerate these things we actually have five what we call signature solutions which is services software together with a piece of services code coming together to solve a problem we've got that round risk and fraud around customers I mean some specific very narrow things if somebody wants to you know because often IT departments they want to buy something they want to buy something they don't want to go down the parts they want to buy something and so fine here's a package solution let's go buy something um and then last but not least one thing we haven't talked much about but I always like to throw this out there because I think this is one of the things they and we didn't talk about it much in the main 10 or any better sessions but let's not forget about IBM research I'm really proud to report to you now since we started this category we've done 61st of a kinds with IBM Research so this is about client says I've got this problem i think it's unachievable i cannot solve this problem you know help me map in my oil exploration like things that are considered big problems big problems let's let's apply this group that does patent factory you know that IBM is but 15 years in a row let's apply those people to my our problems and we have 60 we have 16 so we do about 15 to 20 a year so it's not like we like we're not cranking these out like I'm hundreds of thousands of licenses but it's where basically our services business our software business and IBM Research go work on solving a client specific problem you heard Tim Buckman this morning when he was asked to know why IBM that was said IBM Research was the first answer that's right he gave we talked to him about that on the cube you know in his is insane me as a customer and we you know we always love to hear from customers I mean you know the splunk conference just had was just last week as an emerging startup because probably well aware of those guys they have customers that just say just glowing reports you get to the same same set of customers you know he is someone of high-caliber at the command and control in his healthcare mission and he's automating himself he it's and essentially creating this new data model that allows it to be pushed down to be listen you've got to do this and I'll tell you why you remember the the governance discussion is it was well I'm most excited about is the governance discussion five to eight years ago was an arcane discussion available of data modelers and like what do we do the governance discussion is quickly moving into the language of our business people and the reason is because they're beginning to do you remember the days of accounting systems when they say we want our accounting department to focus on analyzing the numbers and not collecting and forming the numbers well we're here again and if you've got good data governance you can focus on creating the insights and determining what actions you want from the insights as opposed to questioning the numbers and questioning the validity and the heritage of the number the validity and the heritage of the numbers and in this place everywhere yep financial services companies are the most stressed about it because the validity and heritage is required when you want to prove a compliance to a federal statute yes but it means everywhere if you're a consumer packaged goods company and you don't believe that sales are down in a certain market or a certain chain store first thing they do is they start challenging the numbers if you have good governance you can now start that you can now start to trust these systems of record but let's talk about data quality data quality but it's also the governess in the death of mindset is much broader iteration right how we said the first you know that folks from the nonprofit said you want to go on the record but he's basically saying I'll say basically when you put stuff out when you package and then bring it out it still might have some flaws in the data quality but it's the iteration is transformational but once that's in market saying that's changing he things prepare pre-packaging data and then bringing it in is not the better approach but I want to ask you about the your what you just said about this governance conversation that is date the core of this debate around the data economy what is the data economy in your mind given what you do the history that you've lived through we've seen those movies now the cutting edge new wave that will create new well for new ways change from transform business all that stuff's great but what is the data conn what does that mean to business executives that they're focusing on outcomes is is it changing data governance is it changing the value chains is it changing what's your thoughts on that the data economy is about discovering those points of leverage that that the data tells you that your instincts don't the data tells you that your instincts don't one of my favorite stories three years ago four years ago we were called in and clients said this is my problem the going and problem was I got to take 200 million dollars out of my advertising spend budget two hundred million dollars out of my advertising spend was he's a retailer end and the problem is is out of my 600 million dollar advertising budget the problem I have is also have all kinds of interesting theories and models that my agencies have told me I'm not quite sure do I just take 200 off the board across the board do I take 200 off to minimize my risk just spread it around how do i how do I manage the process and what we actually did was we built a super super set of sophisticated analytics which tied to their transaction systems but also tied to their social media system so we also understood and what we did was we were able to understand which customer cohorts responded to which media types then we added one more parts of the model which is we understood the trending in the cost of free-to-air cable radio internet all the different media types and as we looked at the cost models of them and we understood which customer cohorts responded to which media types we suddenly realized that they were super saturated in certain media types they could like doubled their spin and they wouldn't got want any lift in the advertised in their in their sales what we did was we got 200 million out of their budget and increase they got 300 million incremental sales that Christmas season because we help them get really smart about the play let me tell you I tell us privately i maked media buyers look at me like like I'm like a pariah yeah but but it is actually really you know really started to rethink now there's just a really great example because I think we've all can relate to that but that's the data economy where you find these veins of gold in these simple correlations and from that simple correlation you can instantly go and your business you can get the lift listen I can get five percent I IBM get five percent ten percent lift in some small segment business I've got the volume that's going to make a significant difference to my share one small piece of data could open up a window kind of had with Jodie Foster we would contact words like one piece of data opens up a ton of new data I mean that totally is leverage and it changes the game for that customer and and that to me is that is the guts of the data economy identifying those correlations and and what we're finding is our most recent study we just released it here the thing the IB the IBM Institute for business value big data and analytics study w IBM com it's the Institute for bit I bv study on big data just released and said 75 percent of all companies that are outperforming their peers have said big data analytics is one of the key reasons and the human component not to put are all on machines it's really about it's an ardent science its a mix of both the math and the human piece well you know there's this notion of not only do you create the insight but you've got to take action on the insight you know it's not enough to know if I could predict for you who's going to win tonight's basketball game you still got to place the bet you still have to take action on the inside and so therefore this notion of action to insight is all about trust trust in the insight trust in the data and trust in the technology that the business trust the technology and it's until you take that leap of faith remember when the Indiana Jones movie when he liked the leap of faith and you've got to like to step out and take that leap of faith once you take that leap of faith in you suddenly have trust in the data so that's that trust to mention and that's a human thing that's not a that's that's not a that's an organizational thing that is not a lot of technology in that one okay Fred we gotta wrap up i'll give you the final word for the folks out there quickly put a bumper sticker on iod this year's and put on my car when I Drive home what's that bumper sticker say for this year it's not all about the technology but it starts with the technology ok we're here live in Las Vegas we're going to take about that bet that was going to win the games and I will be the sports book later this is the cube live in Las Vegas for information on demand hashtag IBM iod this tequila right back with our next guest if the short break exclusive coverage from information on demand ibm's premier conference we write back the q