(upbeat music) >> Hello everyone. Welcome to theCube's presentation of the "AWS Startup Showcase." The topic this episode is AI and machine learning, top startups building foundational model infrastructure. This is season three, episode one of the ongoing series covering exciting startups from the AWS ecosystem. And this time we're talking about AI and machine learning. I'm your host, John Furrier. I'm excited I'm joined today by Robert Nishihara, who's the co-founder and CEO of a hot startup called Anyscale. He's here to talk about Ray, the open source project, Anyscale's infrastructure for foundation as well. Robert, thank you for joining us today. >> Yeah, thanks so much as well. >> I've been following your company since the founding pre pandemic and you guys really had a great vision scaled up and in a perfect position for this big wave that we all see with ChatGPT and OpenAI that's gone mainstream. Finally, AI has broken out through the ropes and now gone mainstream, so I think you guys are really well positioned. I'm looking forward to to talking with you today. But before we get into it, introduce the core mission for Anyscale. Why do you guys exist? What is the North Star for Anyscale? >> Yeah, like you mentioned, there's a tremendous amount of excitement about AI right now. You know, I think a lot of us believe that AI can transform just every different industry. So one of the things that was clear to us when we started this company was that the amount of compute needed to do AI was just exploding. Like to actually succeed with AI, companies like OpenAI or Google or you know, these companies getting a lot of value from AI, were not just running these machine learning models on their laptops or on a single machine. They were scaling these applications across hundreds or thousands or more machines and GPUs and other resources in the Cloud. And so to actually succeed with AI, and this has been one of the biggest trends in computing, maybe the biggest trend in computing in, you know, in recent history, the amount of compute has been exploding. And so to actually succeed with that AI, to actually build these scalable applications and scale the AI applications, there's a tremendous software engineering lift to build the infrastructure to actually run these scalable applications. And that's very hard to do. So one of the reasons many AI projects and initiatives fail is that, or don't make it to production, is the need for this scale, the infrastructure lift, to actually make it happen. So our goal here with Anyscale and Ray, is to make that easy, is to make scalable computing easy. So that as a developer or as a business, if you want to do AI, if you want to get value out of AI, all you need to know is how to program on your laptop. Like, all you need to know is how to program in Python. And if you can do that, then you're good to go. Then you can do what companies like OpenAI or Google do and get value out of machine learning. >> That programming example of how easy it is with Python reminds me of the early days of Cloud, when infrastructure as code was talked about was, it was just code the infrastructure programmable. That's super important. That's what AI people wanted, first program AI. That's the new trend. And I want to understand, if you don't mind explaining, the relationship that Anyscale has to these foundational models and particular the large language models, also called LLMs, was seen with like OpenAI and ChatGPT. Before you get into the relationship that you have with them, can you explain why the hype around foundational models? Why are people going crazy over foundational models? What is it and why is it so important? >> Yeah, so foundational models and foundation models are incredibly important because they enable businesses and developers to get value out of machine learning, to use machine learning off the shelf with these large models that have been trained on tons of data and that are useful out of the box. And then, of course, you know, as a business or as a developer, you can take those foundational models and repurpose them or fine tune them or adapt them to your specific use case and what you want to achieve. But it's much easier to do that than to train them from scratch. And I think there are three, for people to actually use foundation models, there are three main types of workloads or problems that need to be solved. One is training these foundation models in the first place, like actually creating them. The second is fine tuning them and adapting them to your use case. And the third is serving them and actually deploying them. Okay, so Ray and Anyscale are used for all of these three different workloads. Companies like OpenAI or Cohere that train large language models. Or open source versions like GPTJ are done on top of Ray. There are many startups and other businesses that fine tune, that, you know, don't want to train the large underlying foundation models, but that do want to fine tune them, do want to adapt them to their purposes, and build products around them and serve them, those are also using Ray and Anyscale for that fine tuning and that serving. And so the reason that Ray and Anyscale are important here is that, you know, building and using foundation models requires a huge scale. It requires a lot of data. It requires a lot of compute, GPUs, TPUs, other resources. And to actually take advantage of that and actually build these scalable applications, there's a lot of infrastructure that needs to happen under the hood. And so you can either use Ray and Anyscale to take care of that and manage the infrastructure and solve those infrastructure problems. Or you can build the infrastructure and manage the infrastructure yourself, which you can do, but it's going to slow your team down. It's going to, you know, many of the businesses we work with simply don't want to be in the business of managing infrastructure and building infrastructure. They want to focus on product development and move faster. >> I know you got a keynote presentation we're going to go to in a second, but I think you hit on something I think is the real tipping point, doing it yourself, hard to do. These are things where opportunities are and the Cloud did that with data centers. Turned a data center and made it an API. The heavy lifting went away and went to the Cloud so people could be more creative and build their product. In this case, build their creativity. Is that kind of what's the big deal? Is that kind of a big deal happening that you guys are taking the learnings and making that available so people don't have to do that? >> That's exactly right. So today, if you want to succeed with AI, if you want to use AI in your business, infrastructure work is on the critical path for doing that. To do AI, you have to build infrastructure. You have to figure out how to scale your applications. That's going to change. We're going to get to the point, and you know, with Ray and Anyscale, we're going to remove the infrastructure from the critical path so that as a developer or as a business, all you need to focus on is your application logic, what you want the the program to do, what you want your application to do, how you want the AI to actually interface with the rest of your product. Now the way that will happen is that Ray and Anyscale will still, the infrastructure work will still happen. It'll just be under the hood and taken care of by Ray in Anyscale. And so I think something like this is really necessary for AI to reach its potential, for AI to have the impact and the reach that we think it will, you have to make it easier to do. >> And just for clarification to point out, if you don't mind explaining the relationship of Ray and Anyscale real quick just before we get into the presentation. >> So Ray is an open source project. We created it. We were at Berkeley doing machine learning. We started Ray so that, in order to provide an easy, a simple open source tool for building and running scalable applications. And Anyscale is the managed version of Ray, basically we will run Ray for you in the Cloud, provide a lot of tools around the developer experience and managing the infrastructure and providing more performance and superior infrastructure. >> Awesome. I know you got a presentation on Ray and Anyscale and you guys are positioning as the infrastructure for foundational models. So I'll let you take it away and then when you're done presenting, we'll come back, I'll probably grill you with a few questions and then we'll close it out so take it away. >> Robert: Sounds great. So I'll say a little bit about how companies are using Ray and Anyscale for foundation models. The first thing I want to mention is just why we're doing this in the first place. And the underlying observation, the underlying trend here, and this is a plot from OpenAI, is that the amount of compute needed to do machine learning has been exploding. It's been growing at something like 35 times every 18 months. This is absolutely enormous. And other people have written papers measuring this trend and you get different numbers. But the point is, no matter how you slice and dice it, it' a astronomical rate. Now if you compare that to something we're all familiar with, like Moore's Law, which says that, you know, the processor performance doubles every roughly 18 months, you can see that there's just a tremendous gap between the needs, the compute needs of machine learning applications, and what you can do with a single chip, right. So even if Moore's Law were continuing strong and you know, doing what it used to be doing, even if that were the case, there would still be a tremendous gap between what you can do with the chip and what you need in order to do machine learning. And so given this graph, what we've seen, and what has been clear to us since we started this company, is that doing AI requires scaling. There's no way around it. It's not a nice to have, it's really a requirement. And so that led us to start Ray, which is the open source project that we started to make it easy to build these scalable Python applications and scalable machine learning applications. And since we started the project, it's been adopted by a tremendous number of companies. Companies like OpenAI, which use Ray to train their large models like ChatGPT, companies like Uber, which run all of their deep learning and classical machine learning on top of Ray, companies like Shopify or Spotify or Instacart or Lyft or Netflix, ByteDance, which use Ray for their machine learning infrastructure. Companies like Ant Group, which makes Alipay, you know, they use Ray across the board for fraud detection, for online learning, for detecting money laundering, you know, for graph processing, stream processing. Companies like Amazon, you know, run Ray at a tremendous scale and just petabytes of data every single day. And so the project has seen just enormous adoption since, over the past few years. And one of the most exciting use cases is really providing the infrastructure for building training, fine tuning, and serving foundation models. So I'll say a little bit about, you know, here are some examples of companies using Ray for foundation models. Cohere trains large language models. OpenAI also trains large language models. You can think about the workloads required there are things like supervised pre-training, also reinforcement learning from human feedback. So this is not only the regular supervised learning, but actually more complex reinforcement learning workloads that take human input about what response to a particular question, you know is better than a certain other response. And incorporating that into the learning. There's open source versions as well, like GPTJ also built on top of Ray as well as projects like Alpa coming out of UC Berkeley. So these are some of the examples of exciting projects in organizations, training and creating these large language models and serving them using Ray. Okay, so what actually is Ray? Well, there are two layers to Ray. At the lowest level, there's the core Ray system. This is essentially low level primitives for building scalable Python applications. Things like taking a Python function or a Python class and executing them in the cluster setting. So Ray core is extremely flexible and you can build arbitrary scalable applications on top of Ray. So on top of Ray, on top of the core system, what really gives Ray a lot of its power is this ecosystem of scalable libraries. So on top of the core system you have libraries, scalable libraries for ingesting and pre-processing data, for training your models, for fine tuning those models, for hyper parameter tuning, for doing batch processing and batch inference, for doing model serving and deployment, right. And a lot of the Ray users, the reason they like Ray is that they want to run multiple workloads. They want to train and serve their models, right. They want to load their data and feed that into training. And Ray provides common infrastructure for all of these different workloads. So this is a little overview of what Ray, the different components of Ray. So why do people choose to go with Ray? I think there are three main reasons. The first is the unified nature. The fact that it is common infrastructure for scaling arbitrary workloads, from data ingest to pre-processing to training to inference and serving, right. This also includes the fact that it's future proof. AI is incredibly fast moving. And so many people, many companies that have built their own machine learning infrastructure and standardized on particular workflows for doing machine learning have found that their workflows are too rigid to enable new capabilities. If they want to do reinforcement learning, if they want to use graph neural networks, they don't have a way of doing that with their standard tooling. And so Ray, being future proof and being flexible and general gives them that ability. Another reason people choose Ray in Anyscale is the scalability. This is really our bread and butter. This is the reason, the whole point of Ray, you know, making it easy to go from your laptop to running on thousands of GPUs, making it easy to scale your development workloads and run them in production, making it easy to scale, you know, training to scale data ingest, pre-processing and so on. So scalability and performance, you know, are critical for doing machine learning and that is something that Ray provides out of the box. And lastly, Ray is an open ecosystem. You can run it anywhere. You can run it on any Cloud provider. Google, you know, Google Cloud, AWS, Asure. You can run it on your Kubernetes cluster. You can run it on your laptop. It's extremely portable. And not only that, it's framework agnostic. You can use Ray to scale arbitrary Python workloads. You can use it to scale and it integrates with libraries like TensorFlow or PyTorch or JAX or XG Boost or Hugging Face or PyTorch Lightning, right, or Scikit-learn or just your own arbitrary Python code. It's open source. And in addition to integrating with the rest of the machine learning ecosystem and these machine learning frameworks, you can use Ray along with all of the other tooling in the machine learning ecosystem. That's things like weights and biases or ML flow, right. Or you know, different data platforms like Databricks, you know, Delta Lake or Snowflake or tools for model monitoring for feature stores, all of these integrate with Ray. And that's, you know, Ray provides that kind of flexibility so that you can integrate it into the rest of your workflow. And then Anyscale is the scalable compute platform that's built on top, you know, that provides Ray. So Anyscale is a managed Ray service that runs in the Cloud. And what Anyscale does is it offers the best way to run Ray. And if you think about what you get with Anyscale, there are fundamentally two things. One is about moving faster, accelerating the time to market. And you get that by having the managed service so that as a developer you don't have to worry about managing infrastructure, you don't have to worry about configuring infrastructure. You also, it provides, you know, optimized developer workflows. Things like easily moving from development to production, things like having the observability tooling, the debug ability to actually easily diagnose what's going wrong in a distributed application. So things like the dashboards and the other other kinds of tooling for collaboration, for monitoring and so on. And then on top of that, so that's the first bucket, developer productivity, moving faster, faster experimentation and iteration. The second reason that people choose Anyscale is superior infrastructure. So this is things like, you know, cost deficiency, being able to easily take advantage of spot instances, being able to get higher GPU utilization, things like faster cluster startup times and auto scaling. Things like just overall better performance and faster scheduling. And so these are the kinds of things that Anyscale provides on top of Ray. It's the managed infrastructure. It's fast, it's like the developer productivity and velocity as well as performance. So this is what I wanted to share about Ray in Anyscale. >> John: Awesome. >> Provide that context. But John, I'm curious what you think. >> I love it. I love the, so first of all, it's a platform because that's the platform architecture right there. So just to clarify, this is an Anyscale platform, not- >> That's right. >> Tools. So you got tools in the platform. Okay, that's key. Love that managed service. Just curious, you mentioned Python multiple times, is that because of PyTorch and TensorFlow or Python's the most friendly with machine learning or it's because it's very common amongst all developers? >> That's a great question. Python is the language that people are using to do machine learning. So it's the natural starting point. Now, of course, Ray is actually designed in a language agnostic way and there are companies out there that use Ray to build scalable Java applications. But for the most part right now we're focused on Python and being the best way to build these scalable Python and machine learning applications. But, of course, down the road there always is that potential. >> So if you're slinging Python code out there and you're watching that, you're watching this video, get on Anyscale bus quickly. Also, I just, while you were giving the presentation, I couldn't help, since you mentioned OpenAI, which by the way, congratulations 'cause they've had great scale, I've noticed in their rapid growth 'cause they were the fastest company to the number of users than anyone in the history of the computer industry, so major successor, OpenAI and ChatGPT, huge fan. I'm not a skeptic at all. I think it's just the beginning, so congratulations. But I actually typed into ChatGPT, what are the top three benefits of Anyscale and came up with scalability, flexibility, and ease of use. Obviously, scalability is what you guys are called. >> That's pretty good. >> So that's what they came up with. So they nailed it. Did you have an inside prompt training, buy it there? Only kidding. (Robert laughs) >> Yeah, we hard coded that one. >> But that's the kind of thing that came up really, really quickly if I asked it to write a sales document, it probably will, but this is the future interface. This is why people are getting excited about the foundational models and the large language models because it's allowing the interface with the user, the consumer, to be more human, more natural. And this is clearly will be in every application in the future. >> Absolutely. This is how people are going to interface with software, how they're going to interface with products in the future. It's not just something, you know, not just a chat bot that you talk to. This is going to be how you get things done, right. How you use your web browser or how you use, you know, how you use Photoshop or how you use other products. Like you're not going to spend hours learning all the APIs and how to use them. You're going to talk to it and tell it what you want it to do. And of course, you know, if it doesn't understand it, it's going to ask clarifying questions. You're going to have a conversation and then it'll figure it out. >> This is going to be one of those things, we're going to look back at this time Robert and saying, "Yeah, from that company, that was the beginning of that wave." And just like AWS and Cloud Computing, the folks who got in early really were in position when say the pandemic came. So getting in early is a good thing and that's what everyone's talking about is getting in early and playing around, maybe replatforming or even picking one or few apps to refactor with some staff and managed services. So people are definitely jumping in. So I have to ask you the ROI cost question. You mentioned some of those, Moore's Law versus what's going on in the industry. When you look at that kind of scale, the first thing that jumps out at people is, "Okay, I love it. Let's go play around." But what's it going to cost me? Am I going to be tied to certain GPUs? What's the landscape look like from an operational standpoint, from the customer? Are they locked in and the benefit was flexibility, are you flexible to handle any Cloud? What is the customers, what are they looking at? Basically, that's my question. What's the customer looking at? >> Cost is super important here and many of the companies, I mean, companies are spending a huge amount on their Cloud computing, on AWS, and on doing AI, right. And I think a lot of the advantage of Anyscale, what we can provide here is not only better performance, but cost efficiency. Because if we can run something faster and more efficiently, it can also use less resources and you can lower your Cloud spending, right. We've seen companies go from, you know, 20% GPU utilization with their current setup and the current tools they're using to running on Anyscale and getting more like 95, you know, 100% GPU utilization. That's something like a five x improvement right there. So depending on the kind of application you're running, you know, it's a significant cost savings. We've seen companies that have, you know, processing petabytes of data every single day with Ray going from, you know, getting order of magnitude cost savings by switching from what they were previously doing to running their application on Ray. And when you have applications that are spending, you know, potentially $100 million a year and getting a 10 X cost savings is just absolutely enormous. So these are some of the kinds of- >> Data infrastructure is super important. Again, if the customer, if you're a prospect to this and thinking about going in here, just like the Cloud, you got infrastructure, you got the platform, you got SaaS, same kind of thing's going to go on in AI. So I want to get into that, you know, ROI discussion and some of the impact with your customers that are leveraging the platform. But first I hear you got a demo. >> Robert: Yeah, so let me show you, let me give you a quick run through here. So what I have open here is the Anyscale UI. I've started a little Anyscale Workspace. So Workspaces are the Anyscale concept for interactive developments, right. So here, imagine I'm just, you want to have a familiar experience like you're developing on your laptop. And here I have a terminal. It's not on my laptop. It's actually in the cloud running on Anyscale. And I'm just going to kick this off. This is going to train a large language model, so OPT. And it's doing this on 32 GPUs. We've got a cluster here with a bunch of CPU cores, bunch of memory. And as that's running, and by the way, if I wanted to run this on instead of 32 GPUs, 64, 128, this is just a one line change when I launch the Workspace. And what I can do is I can pull up VS code, right. Remember this is the interactive development experience. I can look at the actual code. Here it's using Ray train to train the torch model. We've got the training loop and we're saying that each worker gets access to one GPU and four CPU cores. And, of course, as I make the model larger, this is using deep speed, as I make the model larger, I could increase the number of GPUs that each worker gets access to, right. And how that is distributed across the cluster. And if I wanted to run on CPUs instead of GPUs or a different, you know, accelerator type, again, this is just a one line change. And here we're using Ray train to train the models, just taking my vanilla PyTorch model using Hugging Face and then scaling that across a bunch of GPUs. And, of course, if I want to look at the dashboard, I can go to the Ray dashboard. There are a bunch of different visualizations I can look at. I can look at the GPU utilization. I can look at, you know, the CPU utilization here where I think we're currently loading the model and running that actual application to start the training. And some of the things that are really convenient here about Anyscale, both I can get that interactive development experience with VS code. You know, I can look at the dashboards. I can monitor what's going on. It feels, I have a terminal, it feels like my laptop, but it's actually running on a large cluster. And I can, with however many GPUs or other resources that I want. And so it's really trying to combine the best of having the familiar experience of programming on your laptop, but with the benefits, you know, being able to take advantage of all the resources in the Cloud to scale. And it's like when, you know, you're talking about cost efficiency. One of the biggest reasons that people waste money, one of the silly reasons for wasting money is just forgetting to turn off your GPUs. And what you can do here is, of course, things will auto terminate if they're idle. But imagine you go to sleep, I have this big cluster. You can turn it off, shut off the cluster, come back tomorrow, restart the Workspace, and you know, your big cluster is back up and all of your code changes are still there. All of your local file edits. It's like you just closed your laptop and came back and opened it up again. And so this is the kind of experience we want to provide for our users. So that's what I wanted to share with you. >> Well, I think that whole, couple of things, lines of code change, single line of code change, that's game changing. And then the cost thing, I mean human error is a big deal. People pass out at their computer. They've been coding all night or they just forget about it. I mean, and then it's just like leaving the lights on or your water running in your house. It's just, at the scale that it is, the numbers will add up. That's a huge deal. So I think, you know, compute back in the old days, there's no compute. Okay, it's just compute sitting there idle. But you know, data cranking the models is doing, that's a big point. >> Another thing I want to add there about cost efficiency is that we make it really easy to use, if you're running on Anyscale, to use spot instances and these preemptable instances that can just be significantly cheaper than the on-demand instances. And so when we see our customers go from what they're doing before to using Anyscale and they go from not using these spot instances 'cause they don't have the infrastructure around it, the fault tolerance to handle the preemption and things like that, to being able to just check a box and use spot instances and save a bunch of money. >> You know, this was my whole, my feature article at Reinvent last year when I met with Adam Selipsky, this next gen Cloud is here. I mean, it's not auto scale, it's infrastructure scale. It's agility. It's flexibility. I think this is where the world needs to go. Almost what DevOps did for Cloud and what you were showing me that demo had this whole SRE vibe. And remember Google had site reliability engines to manage all those servers. This is kind of like an SRE vibe for data at scale. I mean, a similar kind of order of magnitude. I mean, I might be a little bit off base there, but how would you explain it? >> It's a nice analogy. I mean, what we are trying to do here is get to the point where developers don't think about infrastructure. Where developers only think about their application logic. And where businesses can do AI, can succeed with AI, and build these scalable applications, but they don't have to build, you know, an infrastructure team. They don't have to develop that expertise. They don't have to invest years in building their internal machine learning infrastructure. They can just focus on the Python code, on their application logic, and run the stuff out of the box. >> Awesome. Well, I appreciate the time. Before we wrap up here, give a plug for the company. I know you got a couple websites. Again, go, Ray's got its own website. You got Anyscale. You got an event coming up. Give a plug for the company looking to hire. Put a plug in for the company. >> Yeah, absolutely. Thank you. So first of all, you know, we think AI is really going to transform every industry and the opportunity is there, right. We can be the infrastructure that enables all of that to happen, that makes it easy for companies to succeed with AI, and get value out of AI. Now we have, if you're interested in learning more about Ray, Ray has been emerging as the standard way to build scalable applications. Our adoption has been exploding. I mentioned companies like OpenAI using Ray to train their models. But really across the board companies like Netflix and Cruise and Instacart and Lyft and Uber, you know, just among tech companies. It's across every industry. You know, gaming companies, agriculture, you know, farming, robotics, drug discovery, you know, FinTech, we see it across the board. And all of these companies can get value out of AI, can really use AI to improve their businesses. So if you're interested in learning more about Ray and Anyscale, we have our Ray Summit coming up in September. This is going to highlight a lot of the most impressive use cases and stories across the industry. And if your business, if you want to use LLMs, you want to train these LLMs, these large language models, you want to fine tune them with your data, you want to deploy them, serve them, and build applications and products around them, give us a call, talk to us. You know, we can really take the infrastructure piece, you know, off the critical path and make that easy for you. So that's what I would say. And, you know, like you mentioned, we're hiring across the board, you know, engineering, product, go-to-market, and it's an exciting time. >> Robert Nishihara, co-founder and CEO of Anyscale, congratulations on a great company you've built and continuing to iterate on and you got growth ahead of you, you got a tailwind. I mean, the AI wave is here. I think OpenAI and ChatGPT, a customer of yours, have really opened up the mainstream visibility into this new generation of applications, user interface, roll of data, large scale, how to make that programmable so we're going to need that infrastructure. So thanks for coming on this season three, episode one of the ongoing series of the hot startups. In this case, this episode is the top startups building foundational model infrastructure for AI and ML. I'm John Furrier, your host. Thanks for watching. (upbeat music)

(light airy music) >> Hello, everyone, welcome to theCUBE's presentation of the AWS Startup Showcase, AI and machine learning. "Top Startups Building Generative AI on AWS." This is season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talking about AI machine learning. We have three great guests Bratin Saha, VP, Vice President of Machine Learning and AI Services at Amazon Web Services. Tom Mason, the CTO of Stability AI, and Aidan Gomez, CEO and co-founder of Cohere. Two practitioners doing startups and AWS. Gentlemen, thank you for opening up this session, this episode. Thanks for coming on. >> Thank you. >> Thank you. >> Thank you. >> So the topic is hype versus reality. So I think we're all on the reality is great, hype is great, but the reality's here. I want to get into it. Generative AI's got all the momentum, it's going mainstream, it's kind of come out of the behind the ropes, it's now mainstream. We saw the success of ChatGPT, opens up everyone's eyes, but there's so much more going on. Let's jump in and get your early perspectives on what should people be talking about right now? What are you guys working on? We'll start with AWS. What's the big focus right now for you guys as you come into this market that's highly active, highly hyped up, but people see value right out of the gate? >> You know, we have been working on generative AI for some time. In fact, last year we released Code Whisperer, which is about using generative AI for software development and a number of customers are using it and getting real value out of it. So generative AI is now something that's mainstream that can be used by enterprise users. And we have also been partnering with a number of other companies. So, you know, stability.ai, we've been partnering with them a lot. We want to be partnering with other companies as well. In seeing how we do three things, you know, first is providing the most efficient infrastructure for generative AI. And that is where, you know, things like Trainium, things like Inferentia, things like SageMaker come in. And then next is the set of models and then the third is the kind of applications like Code Whisperer and so on. So, you know, it's early days yet, but clearly there's a lot of amazing capabilities that will come out and something that, you know, our customers are starting to pay a lot of attention to. >> Tom, talk about your company and what your focus is and why the Amazon Web Services relationship's important for you? >> So yeah, we're primarily committed to making incredible open source foundation models and obviously stable effusions been our kind of first big model there, which we trained all on AWS. We've been working with them over the last year and a half to develop, obviously a big cluster, and bring all that compute to training these models at scale, which has been a really successful partnership. And we're excited to take it further this year as we develop commercial strategy of the business and build out, you know, the ability for enterprise customers to come and get all the value from these models that we think they can get. So we're really excited about the future. We got hugely exciting pipeline for this year with new modalities and video models and wonderful things and trying to solve images for once and for all and get the kind of general value and value proposition correct for customers. So it's a really exciting time and very honored to be part of it. >> It's great to see some of your customers doing so well out there. Congratulations to your team. Appreciate that. Aidan, let's get into what you guys do. What does Cohere do? What are you excited about right now? >> Yeah, so Cohere builds large language models, which are the backbone of applications like ChatGPT and GPT-3. We're extremely focused on solving the issues with adoption for enterprise. So it's great that you can make a super flashy demo for consumers, but it takes a lot to actually get it into billion user products and large global enterprises. So about six months ago, we released our command models, which are some of the best that exist for large language models. And in December, we released our multilingual text understanding models and that's on over a hundred different languages and it's trained on, you know, authentic data directly from native speakers. And so we're super excited to continue pushing this into enterprise and solving those barriers for adoption, making this transformation a reality. >> Just real quick, while I got you there on the new products coming out. Where are we in the progress? People see some of the new stuff out there right now. There's so much more headroom. Can you just scope out in your mind what that looks like? Like from a headroom standpoint? Okay, we see ChatGPT. "Oh yeah, it writes my papers for me, does some homework for me." I mean okay, yawn, maybe people say that, (Aidan chuckles) people excited or people are blown away. I mean, it's helped theCUBE out, it helps me, you know, feed up a little bit from my write-ups but it's not always perfect. >> Yeah, at the moment it's like a writing assistant, right? And it's still super early in the technologies trajectory. I think it's fascinating and it's interesting but its impact is still really limited. I think in the next year, like within the next eight months, we're going to see some major changes. You've already seen the very first hints of that with stuff like Bing Chat, where you augment these dialogue models with an external knowledge base. So now the models can be kept up to date to the millisecond, right? Because they can search the web and they can see events that happened a millisecond ago. But that's still limited in the sense that when you ask the question, what can these models actually do? Well they can just write text back at you. That's the extent of what they can do. And so the real project, the real effort, that I think we're all working towards is actually taking action. So what happens when you give these models the ability to use tools, to use APIs? What can they do when they can actually affect change out in the real world, beyond just streaming text back at the user? I think that's the really exciting piece. >> Okay, so I wanted to tee that up early in the segment 'cause I want to get into the customer applications. We're seeing early adopters come in, using the technology because they have a lot of data, they have a lot of large language model opportunities and then there's a big fast follower wave coming behind it. I call that the people who are going to jump in the pool early and get into it. They might not be advanced. Can you guys share what customer applications are being used with large language and vision models today and how they're using it to transform on the early adopter side, and how is that a tell sign of what's to come? >> You know, one of the things we have been seeing both with the text models that Aidan talked about as well as the vision models that stability.ai does, Tom, is customers are really using it to change the way you interact with information. You know, one example of a customer that we have, is someone who's kind of using that to query customer conversations and ask questions like, you know, "What was the customer issue? How did we solve it?" And trying to get those kinds of insights that was previously much harder to do. And then of course software is a big area. You know, generating software, making that, you know, just deploying it in production. Those have been really big areas that we have seen customers start to do. You know, looking at documentation, like instead of you know, searching for stuff and so on, you know, you just have an interactive way, in which you can just look at the documentation for a product. You know, all of this goes to where we need to take the technology. One of which is, you know, the models have to be there but they have to work reliably in a production setting at scale, with privacy, with security, and you know, making sure all of this is happening, is going to be really key. That is what, you know, we at AWS are looking to do, which is work with partners like stability and others and in the open source and really take all of these and make them available at scale to customers, where they work reliably. >> Tom, Aidan, what's your thoughts on this? Where are customers landing on this first use cases or set of low-hanging fruit use cases or applications? >> Yeah, so I think like the first group of adopters that really found product market fit were the copywriting companies. So one great example of that is HyperWrite. Another one is Jasper. And so for Cohere, that's the tip of the iceberg, like there's a very long tail of usage from a bunch of different applications. HyperWrite is one of our customers, they help beat writer's block by drafting blog posts, emails, and marketing copy. We also have a global audio streaming platform, which is using us the power of search engine that can comb through podcast transcripts, in a bunch of different languages. Then a global apparel brand, which is using us to transform how they interact with their customers through a virtual assistant, two dozen global news outlets who are using us for news summarization. So really like, these large language models, they can be deployed all over the place into every single industry sector, language is everywhere. It's hard to think of any company on Earth that doesn't use language. So it's, very, very- >> We're doing it right now. We got the language coming in. >> Exactly. >> We'll transcribe this puppy. All right. Tom, on your side, what do you see the- >> Yeah, we're seeing some amazing applications of it and you know, I guess that's partly been, because of the growth in the open source community and some of these applications have come from there that are then triggering this secondary wave of innovation, which is coming a lot from, you know, controllability and explainability of the model. But we've got companies like, you know, Jasper, which Aidan mentioned, who are using stable diffusion for image generation in block creation, content creation. We've got Lensa, you know, which exploded, and is built on top of stable diffusion for fine tuning so people can bring themselves and their pets and you know, everything into the models. So we've now got fine tuned stable diffusion at scale, which is democratized, you know, that process, which is really fun to see your Lensa, you know, exploded. You know, I think it was the largest growing app in the App Store at one point. And lots of other examples like NightCafe and Lexica and Playground. So seeing lots of cool applications. >> So much applications, we'll probably be a customer for all you guys. We'll definitely talk after. But the challenges are there for people adopting, they want to get into what you guys see as the challenges that turn into opportunities. How do you see the customers adopting generative AI applications? For example, we have massive amounts of transcripts, timed up to all the videos. I don't even know what to do. Do I just, do I code my API there. So, everyone has this problem, every vertical has these use cases. What are the challenges for people getting into this and adopting these applications? Is it figuring out what to do first? Or is it a technical setup? Do they stand up stuff, they just go to Amazon? What do you guys see as the challenges? >> I think, you know, the first thing is coming up with where you think you're going to reimagine your customer experience by using generative AI. You know, we talked about Ada, and Tom talked about a number of these ones and you know, you pick up one or two of these, to get that robust. And then once you have them, you know, we have models and we'll have more models on AWS, these large language models that Aidan was talking about. Then you go in and start using these models and testing them out and seeing whether they fit in use case or not. In many situations, like you said, John, our customers want to say, "You know, I know you've trained these models on a lot of publicly available data, but I want to be able to customize it for my use cases. Because, you know, there's some knowledge that I have created and I want to be able to use that." And then in many cases, and I think Aidan mentioned this. You know, you need these models to be up to date. Like you can't have it staying. And in those cases, you augmented with a knowledge base, you know you have to make sure that these models are not hallucinating. And so you need to be able to do the right kind of responsible AI checks. So, you know, you start with a particular use case, and there are a lot of them. Then, you know, you can come to AWS, and then look at one of the many models we have and you know, we are going to have more models for other modalities as well. And then, you know, play around with the models. We have a playground kind of thing where you can test these models on some data and then you can probably, you will probably want to bring your own data, customize it to your own needs, do some of the testing to make sure that the model is giving the right output and then just deploy it. And you know, we have a lot of tools. >> Yeah. >> To make this easy for our customers. >> How should people think about large language models? Because do they think about it as something that they tap into with their IP or their data? Or is it a large language model that they apply into their system? Is the interface that way? What's the interaction look like? >> In many situations, you can use these models out of the box. But in typical, in most of the other situations, you will want to customize it with your own data or with your own expectations. So the typical use case would be, you know, these are models are exposed through APIs. So the typical use case would be, you know you're using these APIs a little bit for testing and getting familiar and then there will be an API that will allow you to train this model further on your data. So you use that AI, you know, make sure you augmented the knowledge base. So then you use those APIs to customize the model and then just deploy it in an application. You know, like Tom was mentioning, a number of companies that are using these models. So once you have it, then you know, you again, use an endpoint API and use it in an application. >> All right, I love the example. I want to ask Tom and Aidan, because like most my experience with Amazon Web Service in 2007, I would stand up in EC2, put my code on there, play around, if it didn't work out, I'd shut it down. Is that a similar dynamic we're going to see with the machine learning where developers just kind of log in and stand up infrastructure and play around and then have a cloud-like experience? >> So I can go first. So I mean, we obviously, with AWS working really closely with the SageMaker team, do fantastic platform there for ML training and inference. And you know, going back to your point earlier, you know, where the data is, is hugely important for companies. Many companies bringing their models to their data in AWS on-premise for them is hugely important. Having the models to be, you know, open sources, makes them explainable and transparent to the adopters of those models. So, you know, we are really excited to work with the SageMaker team over the coming year to bring companies to that platform and make the most of our models. >> Aidan, what's your take on developers? Do they just need to have a team in place, if we want to interface with you guys? Let's say, can they start learning? What do they got to do to set up? >> Yeah, so I think for Cohere, our product makes it much, much easier to people, for people to get started and start building, it solves a lot of the productionization problems. But of course with SageMaker, like Tom was saying, I think that lowers a barrier even further because it solves problems like data privacy. So I want to underline what Bratin was saying earlier around when you're fine tuning or when you're using these models, you don't want your data being incorporated into someone else's model. You don't want it being used for training elsewhere. And so the ability to solve for enterprises, that data privacy and that security guarantee has been hugely important for Cohere, and that's very easy to do through SageMaker. >> Yeah. >> But the barriers for using this technology are coming down super quickly. And so for developers, it's just becoming completely intuitive. I love this, there's this quote from Andrej Karpathy. He was saying like, "It really wasn't on my 2022 list of things to happen that English would become, you know, the most popular programming language." And so the barrier is coming down- >> Yeah. >> Super quickly and it's exciting to see. >> It's going to be awesome for all the companies here, and then we'll do more, we're probably going to see explosion of startups, already seeing that, the maps, ecosystem maps, the landscape maps are happening. So this is happening and I'm convinced it's not yesterday's chat bot, it's not yesterday's AI Ops. It's a whole another ballgame. So I have to ask you guys for the final question before we kick off the company's showcasing here. How do you guys gauge success of generative AI applications? Is there a lens to look through and say, okay, how do I see success? It could be just getting a win or is it a bigger picture? Bratin we'll start with you. How do you gauge success for generative AI? >> You know, ultimately it's about bringing business value to our customers. And making sure that those customers are able to reimagine their experiences by using generative AI. Now the way to get their ease, of course to deploy those models in a safe, effective manner, and ensuring that all of the robustness and the security guarantees and the privacy guarantees are all there. And we want to make sure that this transitions from something that's great demos to actual at scale products, which means making them work reliably all of the time not just some of the time. >> Tom, what's your gauge for success? >> Look, I think this, we're seeing a completely new form of ways to interact with data, to make data intelligent, and directly to bring in new revenue streams into business. So if businesses can use our models to leverage that and generate completely new revenue streams and ultimately bring incredible new value to their customers, then that's fantastic. And we hope we can power that revolution. >> Aidan, what's your take? >> Yeah, reiterating Bratin and Tom's point, I think that value in the enterprise and value in market is like a huge, you know, it's the goal that we're striving towards. I also think that, you know, the value to consumers and actual users and the transformation of the surface area of technology to create experiences like ChatGPT that are magical and it's the first time in human history we've been able to talk to something compelling that's not a human. I think that in itself is just extraordinary and so exciting to see. >> It really brings up a whole another category of markets. B2B, B2C, it's B2D, business to developer. Because I think this is kind of the big trend the consumers have to win. The developers coding the apps, it's a whole another sea change. Reminds me everyone use the "Moneyball" movie as example during the big data wave. Then you know, the value of data. There's a scene in "Moneyball" at the end, where Billy Beane's getting the offer from the Red Sox, then the owner says to the Red Sox, "If every team's not rebuilding their teams based upon your model, there'll be dinosaurs." I think that's the same with AI here. Every company will have to need to think about their business model and how they operate with AI. So it'll be a great run. >> Completely Agree >> It'll be a great run. >> Yeah. >> Aidan, Tom, thank you so much for sharing about your experiences at your companies and congratulations on your success and it's just the beginning. And Bratin, thanks for coming on representing AWS. And thank you, appreciate for what you do. Thank you. >> Thank you, John. Thank you, Aidan. >> Thank you John. >> Thanks so much. >> Okay, let's kick off season three, episode one. I'm John Furrier, your host. Thanks for watching. (light airy music)

>> Hello everyone. Welcome to theCUBE's coverage of International Women's Day. I'm John Furrier, host of theCUBE been profiling the leaders in the technology world, women in technology from developers to the boardroom, everything in between. We have two great guests promoting in from Malaysia. Nancy Wang is the general manager, also CUBE alumni from AWS Data Protection, and founder and board chair of Advancing Women in Tech, awit.org. And of course Kate Watts who's the executive director of Advancing Women in Tech.org. So it's awit.org. Nancy, Kate, thanks for coming all the way across remotely from Malaysia. >> Of course, we're coming to you as fast as our internet bandwidth will allow us. And you know, I'm just thrilled today that you get to see a whole nother aspect of my life, right? Because typically we talk about AWS, and here we're talking about a topic near and dear to my heart. >> Well, Nancy, I love the fact that you're spending a lot of time taking the empowerment to go out and help the industries and helping with the advancement of women in tech. Kate, the executive director it's a 501C3, it's nonprofit, dedicating to accelerating the careers of women in groups in tech. Can you talk about the organization? >> Yes, I can. So Advancing Women in Tech was founded in 2017 in order to fix some of the pathway problems that we're seeing on the rise to leadership in the industry. And so we specifically focus on supporting mid-level women in technical roles, get into higher positions. We do that in a few different ways through mentorship programs through building technical skills and by connecting people to a supportive community. So you have your peer network and then a vertical sort of relationships to help you navigate the next steps in your career. So to date we've served about 40,000 individuals globally and we're just looking to expand our reach and impact and be able to better support women in the industry. >> Nancy, talk about the creation, the origination story. How'd this all come together? Obviously the momentum, everyone in the industry's been focused on this for a long time. Where did AWIT come from? Advancing Women in Technology, that's the acronym. Advancing Women in Technology.org, where'd it come from? What's the origination story? >> Yeah, so AWIT really originated from this desire that I had, to Kate's point around, well if you look around right and you know, don't take my word for it, right? Look at stats, look at news reports, or just frankly go on your LinkedIn and see how many women in underrepresented groups are in senior technical leadership roles right out in the companies whose names we all know. And so that was my case back in 2016. And so when I first got the idea and back then I was actually at Google, just another large tech company in the valley, right? It was about how do we get more role models, how we get more, for example, women into leadership roles so they can bring up the next generation, right? And so this is actually part of a longer speech that I'm about to give on Wednesday and part of the US State Department speaker program. In fact, that's why Kate and I are here in Malaysia right now is working with over 200 women entrepreneurs from all over in Southeast Asia, including Malaysia Philippines, Vietnam, Borneo, you know, so many countries where having more women entrepreneurs can help raise the GDP right, and that fits within our overall mission of getting more women into top leadership roles in tech. >> You know, I was talking about Teresa Carlson she came on the program as well for this year this next season we're going to do. And she mentioned the decision between the US progress and international. And she's saying as much as it's still bad numbers, it's worse than outside the United States and needs to get better. Can you comment on the global aspect? You brought that up. I think it's super important to highlight that it's just not one area, it's a global evolution. >> Absolutely, so let me start, and I'd love to actually have Kate talk about our current programs and all of the international groups that we're working with. So as Teresa aptly mentioned there is so much work to be done not just outside the US and North Americas where typically tech nonprofits will focus, but rather if you think about the one to end model, right? For example when I was doing the product market fit workshop for the US State Department I had women dialing in from rice fields, right? So let me just pause there for a moment. They were holding their cell phones up near towers near trees just so that they can get a few minutes of time with me to do a workshop and how to accelerate their business. So if you don't call that the desire to propel oneself or accelerate oneself, not sure what is, right. And so it's really that passion that drove me to spend the next week and a half here working with local entrepreneurs working with policy makers so we can take advantage and really leverage that passion that people have, right? To accelerate more business globally. And so that's why, you know Kate will be leading our contingent with the United Nations Women Group, right? That is focused on women's economic empowerment because that's super important, right? One aspect can be sure, getting more directors, you know vice presidents into companies like Google and Amazon. But another is also how do you encourage more women around the world to start businesses, right? To reach economic and freedom independence, right? To overcome some of the maybe social barriers to becoming a leader in their own country. >> Yes, and if I think about our own programs and our model of being very intentional about supporting the learning development and skills of women and members of underrepresented groups we focused very much on providing global access to a number of our programs. For instance, our product management certification on Coursera or engineering management our upcoming women founders accelerator. We provide both access that you can get from anywhere. And then also very intentional programming that connects people into the networks to be able to further their networks and what they've learned through the skills online, so. >> Yeah, and something Kate just told me recently is these courses that Kate's mentioning, right? She was instrumental in working with the American Council on Education and so that our learners can actually get up to six college credits for taking these courses on product management engineering management, on cloud product management. And most recently we had our first organic one of our very first organic testimonials was from a woman's tech bootcamp in Nigeria, right? So if you think about the worldwide impact of these upskilling courses where frankly in the US we might take for granted right around the world as I mentioned, there are women dialing in from rice patties from other, you know, for example, outside the, you know corporate buildings in order to access this content. >> Can you think about the idea of, oh sorry, go ahead. >> Go ahead, no, go ahead Kate. >> I was going to say, if you can't see it, you can't become it. And so we are very intentional about ensuring that we have we're spotlighting the expertise of women and we are broadcasting that everywhere so that anybody coming up can gain the skills and the networks to be able to succeed in this industry. >> We'll make sure we get those links so we can promote them. Obviously we feel the same way getting the word out. I think a couple things I'd like to ask you guys cause I think you hit a great point. One is the economic advantage the numbers prove that diverse teams perform better number one, that's clear. So good point there. But I want to get your thoughts on the entrepreneurial equation. You mentioned founders and startups and there's also different makeups in different countries. It's not like the big corporations sometimes it's smaller business in certain areas the different cultures have different business sizes and business types. How do you guys see that factoring in outside the United States, say the big tech companies? Okay, yeah. The easy lower the access to get in education than stay with them, in other countries is it the same or is it more diverse in terms of business? >> So what really actually got us started with the US State Department was around our work with women founders. And I love for Kate to actually share her experience working with AWS startups in that capacity. But frankly, you know, we looked at the content and the mentor programs that were providing women who wanted to be executives, you know, quickly realize a lot of those same skills such as finding customers, right? Scaling your product and building channels can also apply to women founders, not just executives. And so early supporters of our efforts from firms such as Moderna up in Seattle, Emergence Ventures, Decibel Ventures in, you know, the Bay Area and a few others that we're working with right now. Right, they believed in the mission and really helped us scale out what is now our existing platform and offerings for women founders. >> Those are great firms by the way. And they also are very founder friendly and also understand the global workforce. I mean, that's a whole nother dimension. Okay, what's your reaction to all that? >> Yes, we have been very intentional about taking the product expertise and the learnings of women and in our network, we first worked with AWS startups to support the development of the curriculum for the recent accelerator for women founders that was held last spring. And so we're able to support 25 founders and also brought in the expertise of about 20 or 30 women from Advancing Women in Tech to be able to be the lead instructors and mentors for that. And so we have really realized that with this network and this individual sort of focus on product expertise building strong teams, we can take that information and bring it to folks everywhere. And so there is very much the intentionality of allowing founders allowing individuals to take the lessons and bring it to their individual circumstances and the cultures in which they are operating. But the product sense is a skill that we can support the development of and we're proud to do so. >> That's awesome. Nancy, I want to ask you some never really talk about data storage and AWS cloud greatness and goodness, here's different and you also work full-time at AWS and you're the founder or the chairman of this great organization. How do you balance both and do you get, they're getting behind you on this, Amazon is getting behind you on this. >> Well, as I say it's always easier to negotiate on the way in. But jokes aside, I have to say the leadership has been tremendously supportive. If you think about, for example, my leaders Wayne Duso who's also been on the show multiple times, Bill Vaas who's also been on the show multiple times, you know they're both founders and also operators entrepreneurs at heart. So they understand that it is important, right? For all of us, it's really incumbent on all of us who are in positions to do so, to create a pathway for more people to be in leadership roles for more people to be successful entrepreneurs. So, no, I mean if you just looked at LinkedIn they're always uploading my vote so they reach to more audiences. And frankly they're rooting for us back home in the US while we're in Malaysia this week. >> That's awesome. And I think that's a good culture to have that empowerment and I think that's very healthy. What's next for you guys? What's on the agenda? Take us through the activities. I know that you got a ton of things happening. You got your event out there, which is why you're out there. There's a bunch of other activities. I think you guys call it the Advancing Women in Tech week. >> Yes, this week we are having a week of programming that you can check out at Advancing Women in Tech.org. That is spotlighting the expertise of a number of women in our space. So it is three days of programming Tuesday, Wednesday and Thursday if you are in the US so the seventh through the ninth, but available globally. We are also going to be in New York next week for the event at the UN and are looking to continue to support our mentorship programs and also our work supporting women founders throughout the year. >> All right. I have to ask you guys if you don't mind get a little market data so you can share with us here at theCUBE. What are you hearing this year that's different in the conversation space around the topics, the interests? Obviously I've seen massive amounts of global acceleration around conversations, more video, things like this more stories are scaling, a lot more LinkedIn activity. It just seems like it's a lot different this year. Can you guys share any kind of current trends you're seeing relative to the conversations and topics being discussed across the the community? >> Well, I think from a needle moving perspective, right? I think due to the efforts of wonderful organizations including the Q for spotlighting all of these awesome women, right? Trailblazing women and the nonprofits the government entities that we work with there's definitely more emphasis on creating access and creating pathways. So that's probably one thing that you're seeing is more women, more investors posting about their activities. Number two, from a global trend perspective, right? The rise of women in security. I noticed that on your agenda today, you had Lena Smart who's a good friend of mine chief information security officer at MongoDB, right? She and I are actually quite involved in helping founders especially early stage founders in the security space. And so globally from a pure technical perspective, right? There's right more increasing regulations around data privacy, data sovereignty, right? For example, India's in a few weeks about to get their first data protection regulation there locally. So all of that is giving rise to yet another wave of opportunity and we want women founders uniquely positioned to take advantage of that opportunity. >> I love it. Kate, reaction to that? I mean founders, more pathways it sounds like a neural network, it sounds like AI enabled. >> Yes, and speaking of AI, with the rise of that we are also hearing from many community members the importance of continuing to build their skills upskill learn to be able to keep up with the latest trends. There's a lot of people wondering what does this mean for my own career? And so they're turning to organizations like Advancing Women in Tech to find communities to both learn the latest information, but also build their networks so that they are able to move forward regardless of what the industry does. >> I love the work you guys are doing. It's so impressive. I think the economic angle is new it's more amplified this year. It's always kind of been there and continues to be. What do you guys hope for by next year this time what do you hope to see different from a needle moving perspective, to use your word Nancy, for next year? What's the visual output in your mind? >> I want to see real effort made towards 50-50 representation in all tech leadership roles. And I'd like to see that happen by 2050. >> Kate, anything on your end? >> I love that. I'm going to go a little bit more touchy-feely. I want everybody in our space to understand that the skills that they build and that the networks they have carry with them regardless of wherever they go. And so to be able to really lean in and learn and continue to develop the career that you want to have. So whether that be at a large organization or within your own business, that you've got the potential to move forward on that within you. >> Nancy, Kate, thank you so much for your contribution. I'll give you the final word. Put a plug in for the organization. What are you guys looking for? Any kind of PSA you want to share with the folks watching? >> Absolutely, so if you're in a position to be a mentor, join as a mentor, right? Help elevate and accelerate the next generation of women leaders. If you're an investor help us invest in more women started companies, right? Women founded startups and lastly, if you are women looking to accelerate your career, come join our community. We have resources, we have mentors and who we have investors who are willing to come in on the ground floor and help you accelerate your business. >> Great work. Thank you so much for participating in our International Women's Day 23 program and we'd look to keep this going quarterly. We'll see you next year, next time. Thanks for coming on. Appreciate it. >> Thanks so much John. >> Thank you. >> Okay, women leaders here. >> Nancy: Thanks for having us >> All over the world, coming together for a great celebration but really highlighting the accomplishments, the pathways the investment, the mentoring, everything in between. It's theCUBE. Bring as much as we can. I'm John Furrier, your host. Thanks for watching.

>> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (upbeat music) >> Welcome back to Barcelona, Spain, everyone. It's theCUBE live at MWC '23, day three of four days of CUBE coverage. It's like a cannon of CUBE content coming right at you. I'm Lisa Martin with Dave Nicholson. We've got Dell and VMware here. Going to be talking about the ecosystem partnerships and what they're doing to further organizations in the telco industry. Please welcome Jared Woodrey, Director of Partner Engineering Open Telecom Ecosystem Lab, OTEL. Odded Solomon is here as well, Director of Product Management, VMware Service Provider and Edge Business Unit at VMware. Guys, great to have you on the program. >> Thank you for having me. >> Welcome to theCUBE. So Jared, first question for you. Talk about OTEL. I know there's a big announcement this week, but give the audience context and understanding of what OTEL is and how it works. >> Sure. So the Open Telecom Ecosystem Lab is physically located at Round Rock, Texas, it's the heart and soul of it. But this week we also just announced opening up the Cork, Ireland extension of OTEL. The reason for our existence is to to try and make it as easy as possible for both partners and customers to come together and to re-aggregate this disaggregated ecosystem. So that comes with a number of automation tools and basically just giving a known good testing environment so that tests that happen in our lab are as close to real world as they possibly can be and make it as transparent and open as possible for both partners like VMware as well as customers. >> Odded, talk about what you're doing with Dell and OTEL and give us a customer example of maybe one that you're working with or even even mentioning it by a high level descriptor if you have to. >> Yeah. So we provide a telco cloud platform, which is essentially a vertical in VMware. The telco cloud platform is serving network function vendors, such as Ericsson, Nokia, Mavenir, and so on. What we do with Dell as part of this partnership is essentially complementing the platform with some additional functionality that is not coming out of the box. We used to have a data protection in the past, but this is no longer our main business focus. So we do provide APIs that we can expose and work together with Dell PPDM solution so customer can benefit from this and leverage the partnership and have overall solution that is not coming out of the box from VMware. >> I'm curious, from a VMware perspective. VMware is associated often with the V in VMware, virtualization, and we've seen a transition over time between sort of flavors of virtualization and what is the mix currently today in the telecom space between environments that are leveraging what we would think of as more traditional virtualization with full blown Linux, Windows operating systems in a VM versus the world of containerized microservices? What does that mix look like today? Where do you see it going? >> Yeah, so the VMware telco cloud platform exists for about eight years. And the V started around that time. You might heard about open stack in addition to VMware. So this has definitely helped the network equipment providers with virtualizing their network functions. Those are typically VNF, virtualized network functions, inside the VMs. Essentially we have 4G applications, so core applications, EPC, we have IMS. Those are typically, I would say maybe 80 or 90% of the ecosystem right now. 5G is associated with cloud native network functions. So 5G is getting started now, getting deployed. There is an exponential growth on the core side. Now, when we expand towards the edge of the network we see more potential growth. This is 5G ran, we see the vRAN, we see the open RAN, we see early POCs, we see field trials that are starting. We obviously has production customer now. You just spoke to one. So this is really starting, cloud native is really starting I would say about 10 to 20% of the network functions these days are cloud native. >> Jared, question for you. You mentioned data protection, a huge topic there obviously from a security perspective. Data protection used to be the responsibility of the CSPs. You guys are changing that. Can you talk a little bit about how you're doing that and what Dell's play there is? >> Yeah, so PowerProtect Data Management is a product, but it's produced by Dell. So what this does is it enables data protection over virtual cloud as well as the physical infrastructure of specifically in this case of a telecoms ecosystem. So what this does is enables an ability to rapidly redeploy and back up existing configurations all the way up to the TCP and TCA that pulls the basis of our work here with VMware. >> So you've offloaded that responsibility from the CSPs. You freed them from that. >> So the work that we did, honestly was to make sure that we have a very clear and concise and accurate procedures for how to conduct this as well. And to put this through a realistic and real world as if it was in a telecoms own production network, what did that would actually look like, and what it would take to bring it back up as well. So our responsibility is to make sure that when we when we provide these products to the customers that not only do they work exactly as their intended to, but there is also documentation to help support them and to enable them to have their exact specifications met by as well. >> Got it. So talk about a little bit about OTEL expansion into Cork. What you guys are doing together to enable CSPs here in EMEA? >> Yeah, so the reason why we opened up a facility in Cork Island was to give, for an EMEA audience, for an EMEA CSPs and ability to look and feel and touch some of the products that we're working on. It also just facilitates and ease especially for European-based partners to have a chance to very easily come to a lab environment. The difference though, honestly, is the between Round Rock, Texas and Cork Island is that it's virtually an extension of the same thing. Like the physical locations can make it easier to provide access and obviously to showcase the products that we've developed with partners. But the reality is that it's more than just the physical location. It's more about the ability and ease by which customers and partners can access the labs. >> So we should be expecting a lot of Tito's vodka to be consumed in Cork at some point. Might change the national beverage. >> We do need to have some international exchange. >> Yeah, no, that's good to know. Odded, on the VMware side of things. There's a large group of folks who have VMware skillsets. >> Odded: Correct. >> The telecom industry is moving into this world of the kind of agility that those folks are familiar with. How do people come out of the traditional VMware virtualization world and move into that world of cloud native applications and serve the telecom space? What would your recommendation be? If you were speaking at a VMUG, a VMware Users Group meeting with all of your telecom background, what would you share with them that's critical to understand about how telecom is different, or how telecom's spot in its evolution might be different than the traditional IT space? >> So we're talking about the people with the knowledge and the background of. >> Yeah, I'm a V expert, let's say. And I'm looking into the future and I hear that there are 80,000 people in Barcelona at this event, and I hear that Dell is building optimized infrastructure specifically for telecom, and that VMware is involved. And I'm an expert in VMware and I want to be involved. What do I need to do? I know it's a little bit outside of the box question, but especially against the backdrop of economic headwinds globally, there are a lot of people facing transitions. What are your thoughts there? >> So, first of all, we understand the telco requirements, we understand the telco needs, and we make sure that what we learn from the customers, what we learn from the partners is being built into the VMware products. And simplicity is number one thing that is important for us. We want the customer experience, we want the user experience to be the same as they know even though we are transitioning into cloud native networks that require more frequent upgrades and they have more complexity to be honest. And what we do in our vertical inside VMware we are focusing on automation, telco cloud automation, telco cloud service assurance. Think of it as a wrapper around the SDDC stack that we have from VMware that really simplifies the operations for the telcos because it's really a challenge about skillset. You need to be a DevOps, SRE in order to operate these networks. And things are becoming really complex. We simplify it for them with the same VMware experience. We have a very good ability to do that. We sell products in VMware. Unlike our competition that is mostly selling professional services and support, we try to focus more on the products and delivering the value. Of course, we have services offering because telcos requires some customizations, but we do focus on automation simplicity throughout our staff. >> So just follow up. So in other words the investment in education in this VMware ecosystem absolutely can be extended and applied into the telecom world. I think it's an important thing. >> I was going to add to that. Our engagement in OTEL was also something that we created a solutions brief whether we released from Mobile World Congress this week. But in conjunction with that, we also have a white paper coming out that has a much more expansive explanation and documentation of what it was that we accomplished in the work that we've done together. And that's not something that is going to be a one-off thing. This is something that will stay evergreen that we'll continue to expand both the testing scope as well as the documentation for what this solution looks like and how it can be used as well as documentation on for the V experts for how they can then leverage and realize the the potential for what we're creating together. >> Jared, does Dell look at OTEL as having the potential to facilitate the continued evolution of the actual telco industry? And if so, how? >> Well, I mean, it would be a horrible answer if I were to say no to that. >> Right. >> I think, I honestly believe that one of the most difficult things about this idea of having desired ecosystem is not just trying to put it back together, but then also how to give yourself choice. So each time that you build one of those solution sets like that exists as an island out of all the other possibilities that comes with it. And OTEL seeks to not just be able to facilitate building that first solution set. Like that's what solutions engineering can do. And that's generally done relatively protected and internally. The Open Telecom Ecosystem seeks to build that then to also provide the ability to very easily change specific components of that whether that's a hardware component, a NIC, whether a security pass just came out or a change in either TCP or TCA or we talked a little bit about for this specific engagement that it was done on TCP 2.5. >> Odded: Correct. >> Obviously there's already a 2.7 and 3.0 is coming out. It's not like we're going to sit around and write our coattails of what 2.7 has happened. So this isn't intended to be a one and done thing. So when we talk about trying to make that easier and simpler and de-risk all of the risk that comes from trying to put all these things together, it's not just the the one single solution that you built in the lab. It's what's the next one? And how do I optimize this? And I have specific requirements as a CSP, how can I take something you built that doesn't quite match it, but how do I make that adjustment? So that's what we see to do and make it as easy and as painless as possible. >> What's the engagement model with CSPs? Is it led by Dell only, VMware partner? How does that work? >> Yeah, I can take that. So that depends on the customer, but typically customers they want to choose the cloud vendor. So they come to VMware, we want VMware. Typically, they come from the IT side. They said, "Oh, we want to manage the network side of the house the same way as we manage the IT. We don't want to have special skill sets, special teams." So they move from the IT to the network side and they want VMware there. And then obviously they have an RSP process and they have hardware choices. They can go with Dell, they can go with others. We leverage vSphere, other compatibility. So we can be flexible with the customer choice. And then depending on which customer, how large they are, they select the network equipment provider that the runs on top. We position our platform as multi-vendor. So many of them choose multiple network functions providers. So we work with Dell. So assuming that the customer is choosing Dell. We work very closely with them, offering the best solution for the customer. We work with them sometimes to even design the boxes to make sure that it fits their use cases and to make sure that it works properly. So we have a partnership validation certification end-to-end from the applications all the way down to the hardware. >> It's a fascinating place in history to be right now with 5G. Something that a lot of consumers sort of assume. It's like, "Oh, hey, yeah, we're already there. What's the 6G thing going to look like?" Well, wait a minute, we're just at the beginning stages. And so you talk about disaggregation, re-aggregation, or reintegration, the importance of that. Folks like Dell have experience in that space. Folks at VMware have a lot of experience in the virtualization space, but I heard that VMware is being acquired by Broadcom, if it all goes through, of course. You don't need to comment on it. But you mentioned something, SDDC, software-defined data center. That stack is sometimes misunderstood by the public at large and maybe the folks in the EU, I will editorialize for a moment here. It is eliminating capture in a way by larger hyperscale cloud providers. It absolutely introduces more competition into the market space. So it's interesting to hear Broadcom acknowledging that this is part of the future of VMware, no matter what else happens. These capabilities that spill into the telecom space are something that they say they're going to embrace and extend. I think that's important for anyone who's evaluating this if they're concern. Well, wait a minute. Yeah, when I reintegrate, do I want VMware as part of this mix? Is that an unknown? It's pretty clear that that's something that is part of the future of VMware moving forward. That's my personal opinion based on analysis. But you brought up SDDC, so I wanted to mention that. Again, I'm not going to ask you to get into trouble on that at all. What should we be, from a broad perspective, are there any services, outcomes that are going to come out of all of this work? The agility that's being built by you folks and folks in the open world. Are there any specific things that you personally are excited about? Or when we think about consumer devices, getting data, what are the other kinds of things that this facilitates? Anything cool, either one of you. >> So specific use cases? >> Yeah, anything. It's got to be cool though. If it's not cool we're going to ask you to leave. >> All right. I'll take that challenge. (laughs) I think one of the things that is interesting for something like OTEL as an exist, as being an Open Telecom Ecosystem, there are going to be some CSPs that it's very difficult for them to have this optionality existing for themselves. Especially when you start talking about tailoring it for specific CSPs and their needs. One of the things that becomes much more available to some of the smaller CSPs is the ability to leverage OTEL and basically act as one of their pre-production labs. So this would be something that would be very specific to a customer and we would obviously make sure that it's completely isolated but the intention there would be that it would open up the ability for what would normally take a much longer time period for them to receive some of the benefits of some of the changes that are happening within the industry. But they would have immediate benefit by leveraging specifically looking OTEL to provide them some of their solutions. And I know that you were also looking for specific use cases out of it, but like that's a huge deal for a lot of CSPs around the world that don't have the ability to lay out all the different permutations that they are most interested in and start to put each one of those through a test cycle. A specific use cases for what this looks like is honestly the most exciting that I've seen for right now is on the private 5G networks. Specifically within mining industry, we have a, sorry for the audience, but we have a demo at our booth that starts to lay out exactly how it was deployed and kind of the AB of what this looked like before the world of private 5G for this mining company and what it looks like afterwards. And the ability for both safety, as well as operational costs, as well as their ability to obviously do their job better is night and day. It completely opened up a very analog system and opened up to a very digitalized system. And I would be remiss, I didn't also mention OpenBrew, which is also an example in our booth. >> We saw it last night in action. >> We saw it. >> I hope you did. So OpenBrew is small brewery in Northeast America and we basically took a very manual process of checking temperature and pressure on multiple different tanks along the entire brewing process and digitized everything for them. All of that was enabled by a private 5G deployment that's built on Dell hardware. >> You asked for cool. I think we got it. >> Yeah, it's cool. >> Jared: I think beer. >> Cool brew, yes. >> Root beer, I think is trump card there. >> At least for folks from North America, we like our brew cool. >> Exactly. Guys, thank you so much for joining Dave and me talking about what Dell, OTEL, and VMware are doing together, what you're enabling CSPs to do and achieve. We appreciate your time and your insights. >> Absolutely. >> Thank you. >> All right, our pleasure. For our guests and for Dave Nicholson, I'm Lisa Martin. You watching theCUBE live from MWC '23. Day three of our coverage continues right after a short break. (upbeat music)

(relaxing music) >> Hello, everyone. Welcome to theCUBE's coverage of International Women's Day. I'm John Furrier, your host. We got a great lineup of of guests this program and this segment, we got talking about hot company called, MessageBird. We got three amazing executives and leaders. Rachel Thornton, who's the chief marketing officer, and Andrea Euenheim, Chief People Officer, and Asha Thurthi, Chief Product Officer, We've got the CMO, Chief People Officer, and the Chief Product Officer. We've got everyone who's building that company. This is about building a startup culture that empowers women in tech. Ladies, thanks for coming on and thanks for taking the time. >> Thank you, John, for having us. >> Rachel, you know, you've seen big organizations, you're the CMO at AWS, now at MessageBird. This is a world where now there's new standards, you've got global culture, you can start off anywhere. A lot of things involved in being a C-suite leader, from not only marketing product to customers, but building a product, hiring the right team, team dynamics, power dynamics. So as female leaders, you guys are building that culture that empowers women to not only find their voices, but to use their voices to lead. What's the secret? What are you guys doing? Give us a taste of what's it like right now. Give us a feeling for what's going on in this world for you guys right now. >> I'll go first. I actually want to say that I was the, when MessageBird was building out their team, I was super excited to join because I was so impressed with the fact that the product officer was a woman, the HR officer was a woman. It was so great to see women in those leadership roles and I was just really positive and bullish on that. I felt like any company that was really building out that leadership team and thinking about being conscious of how do we have diverse perspectives and doing that is only going to make the product better. So I was super excited to join and I have really, really enjoyed being on a leadership team where I think we're 50% women. I think that is true. Like it's half women, which is really amazing. >> And that's to be the standard because I mean, software is in every product. Digital transformation is everyone and the world is not 17% women. I mean, let's just face it. So this is really a product issue as well and team issue because I mean, it just makes sense. I mean, this is really still, the industry's behind, this is a big problem. >> But I do think that, like I said, watching what's happening here, it gives me hope. Actually, it makes me inspired for to see other companies adopt it. I think, you know, both Asha and Andrea and you guys chime in, have just, you know, they're doing great jobs as leaders. I feel like we're all sort of, you know, able to speak, able to share our voice and able to inspire the folks in the company when they see that. >> Asha, talk, wait, Asha, could you weigh in on this because people matter in companies and now you have work at home, remote, you're seeing very successful configurations of teams, technical to business across the board, building products and working as a team. What's your take on this and what's your perspective? >> No, no, great question. The time is now. I really feel like the time has come for women to take what's really due for them and not just because we're women, because we are equally strong and contributing on the table. So I'm super excited for the generation that's to come because great voices really represent great customers because customers come in all different shapes and forms and people who are building the products, plus running companies should represent the customers, that end of the day, buy your products. So voice on the table is extremely important and so is making an attempt to make sure that you are hiring across all walks of life, all the way from sea level to even at IC level, ensuring that there is inclusion and diversity from a representation perspective. >> You've got the keys to the kingdom there as the product officer, Chief Product Officer, you know, you got to interface with engineering, you got to interface with the customers and like I mentioned earlier, the products are used by everyone now. This is all the, what's your experience been? What have you learned? Because again, a lot of engineers are male dominated and around the world, teams are male. What's your experience? How do you blend that together? How do you bring that harmony and so, and productivity? >> Yeah. >> Yeah. No, like I think the first thing is I think acknowledging the current state, which is women in tech, specifically because you asked about my role, continues to be a challenge. Women in tech be it in the product side of the equation or design side of the equation or engineering side of the equation, I think continues to be a challenge. I think all companies will have to lean in, especially starting education from STEM degrees, going forward to see how do you kind of make an effort to ensure that women in technology is not as high of a barrier that it used to be. Women in color in technology is not as high of a barrier as well. And how do you kind of make sure that when you are hiring, when you are advocating for your company, when you're setting up your interview loops, to actually setting up the right platform for all of these employees to thrive. You are ensuring that every walk of this, is kind of including women and making sure that all voice are voices are represented. Andrea, Rachel, I'd love your take as well because products just one piece of this whole equation where you build product. I'm kind of curious to see how you-- >> Andrea, weigh in, because this is like a hiring thing too. Like if you have a special test, like, okay, do we have the right makeup this person's going to, is there a bro test for example? I've heard companies have that where they have this kind of special questions that identify bros 'cause they don't want that in their culture. Is there a playbook? Is there a best practice in sourcing and identifying and interviewing loops? For instance, we just heard, Asha, that was great on the product side, Andrea, this is a big challenge. Putting teams together, having the right cohesive harmony, talent, looking for people, having the right interview loops, identifying that bro or the right makeup that you want to bring in or interview out. What's the strategy? How do you put these teams together because this is the real secret sauce. >> Not sure whether it's a secret sauce, but I think what we have shown that message for, is that we have made a very conscious effort and decision to start leading by example from the top to really build a leadership team that is already combining all the great traits on top of a good diversity in the team. Not only from a gender but also a skills and personality point of view. And then, from there, really making a planful intentional way down to say where do we hire which talent? What is it that we're still missing as a piece of the puzzle to really make the right decisions on leadership but also team compositions to really look at what's the customer needs, how can we build great products, how can we also compose great engineering teams to meet those expectations that our customers have and how do we build for the future? And that needs to happen in all different parts of the organization to really see that we can make a great effort across the board. >> And, you know, Rachel, last year, your talk inspired a lot of folks in conversation around sponsorship and you talk about networking and mentoring, but you highlighted sponsorship, I remember that clearly and that got great play in the conversation. So it's not just mentoring for mentoring's sake, there's also sponsorships. So there's really identifying, hiring, and then working with. And according to McKenzie's report that you guys are highlighting at me, MessageBird one in four C-suite leaders are identified as women and and with more hurdles to climb every day, especially at a startup. >> And I think that's why it has to be a combination of how do you think about your team composition? How do you think about your leadership composition? So all the things that Andrea just said, but then how do you make sure, as you're bringing folks in, you're constructing the right loop so people feel like this is a great place they want to be a part of, that it represents a diverse group of people. And then once they're in, how do you mentor people? What's the mentorship program you put in place? But the sponsorship program, I think like, sponsorship as well as mentorship also matters because you want to make sure when you identify folks in the organization, that you feel are ready for the next step, that you have identified as high potential, how do you come together as a leadership team and have a program that sponsors them, that gets them training or maybe it's executive coaching but also just makes them visible to leaders across the organization. So when it's time to put together the case for that promotion or maybe that new project or that that new group they would lead, everyone is aware of them and everyone has had some sort of interaction with them. So it really is building the right sort of sponsorship framework to help people get the kind of visibility and the kind of support they need to then unlock their potential in other areas. Whether, again, that's promotion or just taking on new groups or taking on new projects. >> Awesome. Well, you guys are fabulous. >> And in addition to this. >> Oh, go ahead, go ahead. >> No, in addition to this, I think it's also what is critical. Even though we're not the biggest company without Amazon and not Microsoft, but I think it's still important to also give exposure to the great people that we have, to make sure that everybody has visibility, everybody has a voice, and to make sure that we can then build sponsorship and mentorship across the different levels and teams and to build a great succession pipeline to really make sure that people can be considered for the next big project that is coming independent of any skill that they might have. But being a voice and having the experience that counts as most important. >> I love that inclusion, you jumped ahead. I wanted to get some questions 'cause you guys are a great group here. I guess the first question I had on the list here, is for you guys, what does it take to build an environment of inclusion? Because that's really key where female identified employees aren't just asked to questions, they take risks, they ask the right questions, they get involved, they're heard, they're recognized. What's it take to build that kind of environment? >> I can go, I think two things come to mind. One, I would say is commitment. Like commitment at the top. That you're not just going to lip sync, but you're going to walk the talk, that this is important to you as a company and who you stand or what you stand for as a human being. And you are going to put in the effort as a leadership team at the top to actually set the right example. Like MessageBird, I think Rachel said in her intro, 50% of the C level is women and you start right there. The second thing I would say is giving our people voice, you know, giving them confidence. Women because of, I don't know, thousands of years of social conditioning as such, hesitate to kind of speak up. So setting the right example, giving them the voice and encouraging them to take the challenges even if they're sponsored or not, to kind of make sure that they're willing to try new things and be not afraid of risk as much, I think is also super important. >> I think that's very, it is so, so true about the voice and about encouragement and just, I think all, you know, making sure people feel like across, you know, the entire organization, that they feel like they have a voice, their voice can be heard. And that we as a leadership team are supportive in those environments and people feel like I can take risks, I can't ask questions, I can push the envelope in terms of, "Hey, do you know, do we agree with this point? Is there room for discussion?" I think when people see that that's encouraged and it's encouraged for everyone, that's powerful. >> The McKinsey study had a lot of data in there. What's the summary on that on the people side? Obviously, the women are underrepresented, one in four, the C-suite leaders are women, but there's also people who are climbing through the ranks. I mean, what's the big takeaway from the McKinsey study beyond the obvious one in four stat? Is there any other messages in there that people should pay attention to? >> I think Asha said it really well with building the pipeline at the top. And I think that's something that we all think about every day. I think Andrea and her team do such a great job in helping us with that, but that is huge. Like, you're going to, you have to really think how can you build that pipeline out? And I think encouraging people, women, underrepresented groups, everyone to just think what do I want to do? What are the companies out there that I think would be great to work for? How can I find the right environment to support me? I think that's important and I think that helps build that pipeline. >> Okay. When you're a startup, you're a lot different than the big company, right? So the big companies are different. You guys are growing, startups are a lot about, you know, don't run a cash, hard charging, creative, teamwork. But it could be tough under fire. The startup, what's the learnings? How do you guys look at that and how do you guys manage that? Because it's super impart of the culture, of where the phase of these startups are in. >> I think the advantage that we have is we're not a big company. So I think in that way, there is a way to really build a culture of empowerment and us making decisions together and independent of where you come from, what experience you have, it's really what you can bring to the table. It's not having the fear of political cohesion. >> Yeah. >> That you have in larger corporations at times. To really build that great team that we are building right now. To say, all that matters to us is to build great products for our customers. And there's a lot of discussion about quota and one in four and I know large corporations are a lot more tied to meeting requirements that are depending on national laws and whatever, which is sometimes required to force a change in culture and how to do business. But I think us as a company, we just see a strong, strong benefit in not worrying about the gender. It's really like making an effort at the beginning to build the culture and the company that is just looking for a great team and a great culture independent of quotas. >> Actually, on the product side, Asha, on the product side. I want to get your thoughts because I know from startups, you know, being done a few myself, product market fit is huge, right? So you got, that's the goal and there's a lot of pressure. Rachel, you got to go to put the go to market together and you got to build the product. If you don't hit it, you got to br agile, you got to be fast, which could cause a lot of friction. You know, it's 'cause people got to reset, regroup. It's not for the faint of heart. How do you, pipeline folks, women are great for that. Are people aware? Do you have to, are people ready for it? Is there a training? How do you get someone ready or is there a test if they're startup ready? >> No, no, it's a great question. So like, we have a value at the company that's called move 200 miles an hour. All startups, I think, will totally resonate with this. As Andrea was saying, it's a balancing act. >> John: Yeah. >> How do you ensure that you're moving 200 miles an hour, but at the same time ensuring that you're hiring the right people who ultimately represent the customer. One example, Rachel and I were talking about this earlier, we actually represent 40% of the B2C emails that send globally. Imagine as the audience who's receiving one of these emails, think your favorites, you know, brand in Nordstrom that's actually sending you an email on the other end. Think about the customer on the other end. So it does require company commitment to ensure that the people you hire, represent ultimately the customer you're going after. So even if you're a startup, that's moving 200 miles an hour with lesser resources than any other bigger company, you have to commit to actually ensuring that your team has the right diversity. Starting all the way from sourcing to ensuring that this person is thriving and getting hopefully promoted to one day replace all of us. Let's put it that way. >> Rachel, weigh in on the startup velocity, challenges, dynamism, thoughts. >> You go, Andrea. >> It's not for everyone, you know, in that way, but it's something that if you find the right environment and the right people who thrive in such an environment like we do, it's magic. And building on that magic that we have is so powerful that we cannot afford giving voice to one group that is stronger than the others. We're counting on each other and this is a key element to who we are and how we want to build going forward. >> Rachel, your reaction, you're in a startup scene, whitewater rafting, heavy. Speed. >> It is very different. It's very different. But I love it. And what Andrea said is totally true. I think it isn't for everyone, but when you find a great organization and when you find a great group of people, it is magic. You know, it just, it's amazing the things you can do and it is a palpable feeling in the company when everyone is, you know, working on the same thing and excited about the same thing. >> You know, it's interesting about startups, not to take a tangent here, but a lot of startups just, it's not as much resource as a big company that that department doesn't exist. A lot of people doing multiple things. Wait a minute, someone doesn't write my emails for me, doesn't do the PowerPoints. Where's the marketing department? Where's the big budgets? There's a lot of juggling and a lot of versatility required, but also, there's opportunities to identify talent that could be hired for something that could move into something else. And this is part of the growth. And that's one side. On the other side, and this is a question, I promise, there's burnout, right? So you have burnout and fatigue, whether it's cultural and, or, I don't see an opportunity to really, truly a lot of aperture for new opportunities. So can you guys share your thoughts on this dynamic? Because in startups, there's a double-edged sword that could be burnout or there could be opportunity. >> I'll go and then I'll have Asha on the product side. I think that's true everywhere. I don't know, it could be that in some startups, it's exasperated, but I think that actually is true whether you're in a big company or a small company. I think, you know, depending on the industry, depending on the company size, depending on what you're going after, you know, you have to be clear about what it is you're going to deliver, how you're going to do it. And I do think it's important that everyone be able to say for themselves, "Hey, I'm excited about this product or I'm excited about this company and here's what I'm going to do," but I'm also going to make sure that I'm not putting myself in such a way that it does, you know, burnout does happen, but I don't think it can confine it to startups. I think it can happen anywhere. >> Okay. Yeah, exactly. We've seen that now. >> Yeah, I couldn't agree more. John, you've three moms on the call and definitely, we've all kind of come out of Covid into this space. I'm not going to lie, it's really hard. >> Yeah. >> It's really hard, actually balancing and juggling multiple different priorities that you have to. Especially in a startup world, when you move so many different miles an hour and you don't have enough support around you, it is really hard. The one advice I do have for women, which I kind of tell myself very repeatedly, is it's completely okay to be honest, I have taken an intentional action to be a lot more vulnerable over the years. Talk about, you know, having to pick up my child or, you know, having to spend the evening out when I need to spend time with my family. And being open about it because when I do it at the top, I can accept the space for enough people to talk about it a as well. So really, helping women set their own boundaries without feeling guilty about it. Because by nature, we end up, you know, taking care of everything around us. So how do you take care of yourself, fill your cup first so you don't burnout, to your question, I think is extremely critical. >> Yeah. Yeah, that's a really great point. Good point. I think about honesty and transparency comes in with boundaries, but also empathy. I think a lot of people, there's a lot of awareness now to this factor of teamwork and remote and creativity. Productivity is kind of a new, not new thing, but it's kind of more forefront and that's super important. How do you guys promote that? Because you still got to move fast, you got to schedule things differently. I mean, I find myself much more schedule oriented and it's hard to coordinate. How do you guys balance that because it's a management challenge, an opportunity at the same time to have that inclusivity vibe. >> I think on the empathy part on balancing, I just think you have to focus on it. It has to be a conscious choice. And I think, you know, sometimes we do it great and I only speak for myself. Sometimes I do it great, sometimes I don't. But I definitely think you have to focus on it. Think about it, think about where are you, you know, where are you scheduling things, what are you doing? How are you making sure you're thinking about your team, thinking about the, you know, the example you're providing or the example you're setting. >> Thoughts on the boundaries and when does something not a boundary, when it's not productive. 'Cause, you know, so I got my boundaries and they're like, "Wait, whoa, whoa, stay in your lane." No one likes to hear that. Stay in your lane thing. I mean, not to say that that people shouldn't stay in their lane. I just find that a little bit off-putting like, you know, stay in your lane. That sounds like a, it's against the culture. What do you guys think about how people should be thinking about their norms in these environments whether it's inclusivity and diversity? What are some of the areas to stay away from and what are the areas to promote in terms of how they'll communicate these boundaries and, or, good lanes, I should say. I mean, maybe I shouldn't say, stay in your lane's a bad thing, but, so it could be more off-putting. >> I can touch on something which is what can you do more of? I really resonated so much with Rachel's comment from last year on sponsorship. I am the product of sponsorship so it really resonates with me. Also, wouldn't even be sitting here with these two wonderful women and you. In addition to that, I think allyship, I think that's extremely important. What I would love to, you know, see everybody set the right example on is promoting a lot more of allyship where you kind of encourage, not just women, underrepresented minority, knowing really well the backgrounds that they come from and the, you know, situational context around it and seeing how can you be a great ally. And what great ally looks like for me is simple things. If you're in a meeting full of people and you see the underrepresented folks not talking or sharing their voice, how can you, as the senior person in the room, and you know, any person in the room, actually share the voice out and get their thoughts. If you can have many different people present in your company, all hands or what have you, what other forums that can be, how do you ensure that it's not just you always, but like you're putting in the spotlight on other people and, you know, when calibrations come in, when recruiting comes in, how do you ensure that your loops are diverse? So long story short, how do you ensure that you are setting the right example even if you don't belong to one of these groups, that I think do more of. >> Well, that's a great call out on the allies on mentorship programs and support networks. These are important. How should someone go forward and build a mentorship program and support networks so people can help each other out? Is there a way you guys have found best practices, Rachel and team? Is there a strategy that works well? >> Actually, Asha has some great examples here, so I'm going to toss it over to her. >> Thank you, Andy. Team, like this is what I would love for everyone to do more of. Like, we just kicked off 2023, why not make it a goal for this year? Let's seize the year to ensure that, you know, I'll start off with tech, especially where women are underrepresented. We ensure that all of your rock stars, all of your women, at least have a mentor, either within the organization or you reach out to your network externally and pair this person up with a mentor. What ultimately helps us, people having somebody they can bounce off their ideas off, get tips, get advice on how to tackle a particular situation. So really, pairing people up to ensure that they have a way to kind of bounce off ideas and see how can they elevate themselves, I think will go a long way. >> I mean, this is a big problem. Rachel, you've been a leader, you've seen this happen before. How do people climb through the ranks successfully? And you've seen people, maybe, fail a little bit. Is there a best practice or advice you could share with folks that are out there watching and listening on, you know, how to be savvy on climbing through the ranks, whether it's finding mentors being the right place at the right time. I always have the old saying, you know, "Hang around the basketball rim and you'll get a rebound." So is it timing, is it placement? What's your best practice advice for coming through the rim? >> I have a little, and then again, I think I've been very impressed with the team Asha built and just the things that she's done in her career. And I think that for women in tech, that's crucial. I would just say overall, finding your voice, using your voice, but also thinking about who's around you, who's supportive, who are the mentors or who are the people you would love to either mentor or have mentored you. And be sure to speak up and and make that known. And then I also think, don't be afraid to, like I said, use your voice, ask questions. Don't be afraid to also help people up. I think, Asha, what you said a few minutes ago is so true. Like, if there are folks in the room that aren't, you know, as vocal, that you know have amazing ideas, be sure that you're there to help them up, to help them with their voice 'cause you want to make sure that it just brings more to the conversation. >> Asha, you're running a product group, that's a big challenge. What's your thoughts on that? Can you share your opinion? >> Yeah, imposter syndrome is a real thing. I would definitely say confidence is self-taught is what I have really learned over the years and really kind of knowing that the next person to you may not be any smarter than you or may not be any less smart than you. So really, treating everybody as an equal around you and finding that inner strength and inner voice to be able to speak for yourself and to be able to share your ideas and do the best that you possibly can. Bring the A game and when you need help, asking for it. So really, just knowing that and taking initiative and we're here to help. >> Awesome. Andrea, you're here. I want to get your thoughts on building out a mentoring program and networks for women so they can have this great environment. What's it take to do that? I mean, it's hard to do. Building out meaningful networking mentorship program and sport network for women to help each other's hard. What's your experience? >> I think you need some strong leaders within an organization who are willing to sponsor and support. You need somebody to start it. It's usually senior female leaders who are kickstarting a networking environment and some good groups to have some great impact and then, also making sure that they get the visibility to see we accomplish great things together. We raise the topics that not everybody would see. And really bringing the other voice to the table to have like contradicting perspectives on what a company should do on the product side, but also on the general strategic side of things. And then building from there to say, "How can we also build great project teams that support these ideas and to really get the momentum going." Not big programs, but will really impact all communities that will push the topics. >> Awesome. Well, great, great, great panel here. Building a startup culture that empowers women in tech. You guys are amazing. Final question, rapid fire, go down the line. We'll start with Rachel, Andrea, Asha. What's it take to have that kind of success for startup? If you could share quickly what your advice is for people watching and succeeding in a startup. >> I would say focus, intention, and commitment. >> John: Andrea. >> I would say courage, backbone, authenticity. >> I couldn't agree more with Rachel. Focus and commitment. It is for me too. >> Well, you guys are amazing. Congratulations. And MessageBird, again, great ratios. You guys are succeeding. You're a standard for the industry and congratulations and thank you for taking the time on theCUBE's coverage National Women's Day. We also have women in data science at Stanford, with other programs going on today. It's a big day. Thank you very much for coming on. Really appreciate it. Thank you. >> Thank you, Jim. >> Okay, this is theCUBE's coverage of international news. I'm John Furrier, your host. Thanks for watching. (relaxing music)

(lively music) >> Hello, everyone. Welcome back to the Supercloud presentation here. I'm theCUBE, I'm John Furrier, your host. What a great segment here. We're going to unpack the networking aspect of the cloud, how that translates into what Supercloud architecture and platform deployment scenarios look like. And demystify multi-cloud, hybridcloud. We've got two great experts. Amir Khan, the Co-Founder and CEO of Alkira, Atif Khan, Co-Founder and CTO of Alkira. These guys been around since 2018 with the startup, but before that story, history in the tech industry. I mean, routing early days, multiple waves, multiple cycles. >> Welcome three decades. >> Welcome to Supercloud. >> Thanks. >> Thanks for coming on. >> Thank you so much for having us. >> So, let's get your take on Supercloud because it's been one of those conversations that really galvanized the industry because it kind of highlights almost this next wave, this next side of the street that everyone's going to be on that's going to be successful. The laggards on the legacy seem to be stuck on the old model. SaaS is growing up, it's ISVs, it's ecosystems, hyperscale, full hybrid. And then multi-cloud around the corners cause all this confusion, everyone's hand waving. You know, this is a solution, that solution, where are we? What do you guys see as this supercloud dynamic? >> So where we start from is always focusing on the customer problem. And in 2018 when we identified the problem, we saw that there were multiple clouds with many diverse ways of doing things from the network perspective, and customers were struggling with that. So we delved deeper into that and looked at each one of the cloud architectures completely independent. And there was no common solution and customers were struggling with that from the perspective. They wanted to be in multiple clouds, either through mergers and acquisitions or running an application which may be more cost effective to run in something or maybe optimized for certain reasons to run in a different cloud. But from the networking perspective, everything needed to come together. So that's, we are starting to define it as a supercloud now, but basically, it's a common infrastructure across all clouds. And then integration of high lift services like, you know, security or IPAM services or many other types of services like inter-partner routing and stuff like that. So, Amir, you agree then that multi-cloud is simply a default result of having whatever outcomes, either M&A, some productivity software, maybe Azure. >> Yes. >> Amazon has this and then I've got on-premise application, so it's kinds mishmash. >> So, I would qualify it with hybrid multi-cloud because everything is going to be interconnected. >> John: Got it. >> Whether it's on-premise, remote users or clouds. >> But have CTO perspective, obviously, you got developers, multiple stacks, got AWS, Azure and GCP, other. Not everyone wants to kind of like go all in, but yet they don't want to hedge too much because it's a resource issue. And I got to learn this stack, I got to learn that stack. So then now, you have this default multi-cloud, hybrid multi-cloud, then it's like, okay, what do I do? How do you spread that around? Is it dangerous? What's the the approach technically? What's some of the challenges there? >> Yeah, certainly. John, first, thanks for having us here. So, before I get to that, I'll just add a little bit to what Amir was saying, like how we started, what we were seeing and how it, you know, correlates with the supercloud. So, as you know, before this company, Alkira, we were doing, we did the SD-WAN company, which was Viptela. So there, we started seeing when people started deploying SD-WAN at like a larger scale. We started like, you know, customers coming to us and saying they needed connectivity into the cloud from the SD-WAN. They wanted to extend the SD-WAN fabric to the cloud. So we came up with an architecture, which was like later we started calling them Cloud onRamps, where we built, you know, a transit VPC and put like the virtual instances of SD-WAN appliances extended from there to the cloud. But before we knew, like it started becoming very complicated for the customers because it wasn't just connectivity, it also required, you know, other use cases. You had to instantiate or bring in security appliances in there. You had to secure all of that stuff. There were requirements for, you know, different regions. So you had to bring up the same thing in different regions. Then multiple clouds, what did you do? You had to replicate the same thing in multiple clouds. And now if there was was requirement between clouds, how were you going to do it? You had to route traffic from somewhere, and come up with all those routing controls and stuff. So, it was very complicated. >> Like spaghetti code, but on network. >> The games begin, in fact, one of our customers called it spaghetti mess. And so, that's where like we thought about where was the industry going and which direction the industry was going into? And we came up with the Alkira where what we are doing is building a common infrastructure across multiple clouds, across in, you know, on-prem locations, be it data centers or physical sites, branches sites, et cetera, with integrated security and network networking services inside. And, you know, nowadays, networking is not only about connectivity, you have to secure everything. So, security has to be built in. Redundancy, high availability, disaster recovery. So all of that needs to be built in. So that's like, you know, kind of a definition of like what we thought at that time, what is turning into supercloud now. >> Yeah. It's interesting too, you mentioned, you know, VPCs is not, configuration of loans a hassle. Nevermind the manual mistakes could be made, but as you decide to do something you got to, "Oh, we got to get these other things." A lot of the hyper scales and a lot of the alpha cloud players now, and cloud native folks, they're kind of in that mode of, "Wow, look at what we've built." Now, they're got to maintain, how do I refresh it? Like, how do I keep the talent? So they got this similar chaotic environment where it's like, okay, now they're already already through, so I think they're going to be okay. But then some people want to bypass it completely. So there's a lot of customers that we see out there that fit the makeup of, I'm cloud first, I've lifted and shifted, I move some stuff to the cloud. But I want to bypass all that learnings from all the people that are gone through the past three years. Can I just skip that and go to a multi-cloud or coherent infrastructure? What do you think about that? What's your view? >> So yeah, so if you look at these enterprises, you know, many of them just to find like the talent, which for one cloud as far as the IT staff is concerned, it's hard enough. And now, when you have multiple clouds, it's hard to find people the talent which is, you know, which has expertise across different clouds. So that's where we come into the picture. So our vision was always to simplify all of this stuff. And simplification, it cannot be just simplification because you cannot just automate the workflows of the cloud providers underneath. So you have to, you know, provide your full data plane on top of it, fed full control plane, management plane, policy and management on top of it. And coming back to like your question, so these nowadays, those people who are working on networking, you know, before it used to be like CLI. You used to learn about Cisco CLI or Juniper CLI, and you used to work on it. Nowadays, it's very different. So automation, programmability, all of that stuff is the key. So now, you know, Ops guys, the DevOps guys, so these are the people who are in high demand. >> So what do you think about the folks out there that are saying, okay, you got a lot of fragmentation. I got the stacks, I got a lot of stove pipes, if you will, out there on the stack. I got to learn this from Azure. Can you guys have with your product abstract the way that's so developers don't need to know the ins and outs of stack's, almost like a gateway, if you will, the old days. But like I'm a developer or team develop, why should I have to learn the management layer of Azure? >> That's exactly what we started, you know, out with to solve. So it's, what we have built is a platform and the platform sits inside the cloud. And customers are able to build their own network or a virtual network on top using that platform. So the platform has its own data plane, own control plane and management plane with a policy layer on top of it. So now, it's the platform which is sitting in different clouds, but from a customer's point of view, it's one way of doing networking. One way of instantiating or bringing in services or security services in the middle. Whether those are our security services or whether those are like services from our partners, like Palo Alto or Checkpoint or Cisco. >> So you guys brought the SD-WAN mojo and refactored it for the cloud it sounds like. >> No. >> No? (chuckles) >> We cannot said. >> All right, explain. >> It's way more than that. >> I mean, SD-WAN was wan. I mean, you're talking about wide area networks, talking about connected, so explain the difference. >> SD-WAN was primarily done for one major reason. MPLS was expensive, very strong SLAs, but very low speed. Internet, on the other hand, you sat at home and you could access your applications much faster. No SLA, very low cost, right? So we wanted to marry the two together so you could have a purely private infrastructure and a public infrastructure and secure both of them by creating a common secure fabric across all those environments. And then seamlessly tying it into your internal branch and data center and cloud network. So, it merely brought you to the edge of the cloud. It didn't do anything inside the cloud. Now, the major problem resides inside the clouds where you have to optimize the clouds themselves. Take a step back. How were the clouds built? Basically, the cloud providers went to the Ciscos and Junipers and the rest of the world, built the network in the data centers or across wide area infrastructure, and brought it all together and tried to create a virtualized layer on top of that. But there were many limitations of this underlying infrastructure that they had built. So number of routes per region, how inter region connectivity worked, or how many routes you could carry to the VPCs of V nets? That all those were becoming no common policy across, you know, these environments, no segmentation across these environments, right? So the networking constructs that the enterprise customers were used to as enterprise class carry class capabilities, they did not exist in the cloud. So what did the customer do? They ended up stitching it together all manually. And that's why Atif was alluding to earlier that it became a spaghetti mess for the customers. And then what happens is, as a result, day two operations, you know, troubleshooting, everything becomes a nightmare. So what do you do? You have to build an infrastructure inside the cloud. Cloud has enough raw capabilities to build the solutions inside there. Netflix's of the world. And many different companies have been born in the cloud and evolved from there. So why could we not take the raw capabilities of the clouds and build a network cloud or a supercloud on top of these clouds to optimize the whole infrastructure and seamlessly connecting it into the on-premise and remote user locations, right? So that's your, you know, hybrid multi-cloud solution. >> Well, great call out on the SD-WAN in common versus cloud. 'Cause I think this is important because you're building a network layer in the cloud that spans out so the customers don't have to get into the, there's a gap in the system that I'm used to, my operating environment, of having lockdown security and network. >> So yeah. So what you do is you use the raw capabilities like bandwidth or virtual machines, or you know, containers, or, you know, different types of serverless capabilities. And you bring it all together in a way to solve the networking problems, thereby creating a supercloud, which is an abstraction layer which hides all the complexity of the underlying clouds from the customer, right? And it provides a common infrastructure across all environments to that customer, right? That's the beauty of it. And it does it in a way that it looks like, if they have the networking knowledge, they can apply it to this new environment and carry it forward. One way of doing security across all clouds and hybrid environments. One way of doing routing. One way of doing large-scale network address translation. One way of doing IPAM services. So people are tired of doing individual things and individual clouds and on-premise locations, right? So now they're getting something common. >> You guys brought that, you brought all that to bear and flexible for the customer to essentially self-serve their network cloud. >> Yes, yeah. Is that the wave? >> And nowadays, from business perspective, agility is the key, right? You have to move at the pace of the business. If you don't, you are losing. >> So, would it be safe to say that you guys have a network supercloud? >> Absolutely, yeah. >> We, pretty much, yeah. Absolutely. >> What does that mean to our customer? What's in it for them? What's the benefit to the customer? I got a network supercloud, it connects, provides SLA, all the capabilities I need. What do they get? What's the end point for them? What's the end? >> Atif, maybe you can talk some examples. >> The IT infrastructure is all like distributed now, right? So you have applications running in data centers. You have applications running in one cloud. Other cloud, public clouds, enterprises are depending on so many SaaS applications. So now, these are, you can call these endpoints. So a supercloud or a network cloud, from our perspective, it's a cloud in the middle or a network in the middle, which provides connectivity from any endpoint to any endpoint. So, you are able to connect to the supercloud or network cloud in one way no matter where you are. So now, whichever cloud you are in, whichever cloud you need to connect to. And also, it's not just connecting to the cloud. So you need to do a lot of stuff, a lot of networking inside the cloud also. So now, as Amir was saying, every cloud has its own from a networking, you know, the concept perspective or the construct, they are different. There are limitations in there also. So this supercloud, which is sitting on top, basically, your platform is sitting into the cloud, but the supercloud is built on top of using your platform. So that abstracts all those complexities, all those limitations. So now your limitations are whatever the limitations of that platform are. So now your platform, that platform is in our control. So we can keep building it, we can keep scaling it horizontally. Because one of the things is that, you know, in this cloud era, one of the things is autoscaling these services. So why can't the network now autoscale also, just like your other services. >> Network autoscaling is a genius idea, and I think that's a killer. I want to ask the the follow on question because I think, first of all, I love what you guys are doing. So, I think it's a great example of this new innovation. It's not obvious until you see it, right? Geographical is huge. So, you know, single instance, global instances, multiple instances, you're seeing global. How do you guys look at that global equation? Because as companies expand their clouds into geos, and then ultimately, you know, it's obviously continent, region and locales. You're going to have geographic issues. So, this is an extension of your network cloud? >> Amir: It is the extension of the network cloud because if you look at this hyperscalers, they're sitting pretty much everywhere in the globe. So, wherever their regions are, the beauty of building a supercloud is that you can by definition, be available in those regions. It literally takes a day or two of testing for our stack to run in those regions, to make sure there are no nuances that we run into, you know, for that region. The moment we bring it up in that region, all customers can onboard into that solution. So literally, what used to take months or years to build a global infrastructure, now, you can configure it in 10 minutes basically, and bring it up in less than one hour. Since when did we see any solution- >> And by the way, >> that can come up with. >> when the edge comes out too, you're going to start to see more clouds get bolted on. >> Exactly. And you can expand to the edge of the network. That's why we call cloud the new edge, right? >> John: Yeah, it is. Now, I think you guys got a good solutions, network clouds, superclouds, good. So the question on the premise side, so I get the cloud play. It's very cool. You can expand out. It's a nice layer. I'm sure you manage the SLAs between latency and all kinds of things. Knowing when not to do things. Physics or physics. Okay. Now, you've got the on-premise. What's the on-premise equation look like? >> So on-premise, the kind of customers, we are working with large enterprises, mid-size enterprises. So they have on-prem networks, they have deployed, in many cases, they have deployed SD-WAN. In many cases, they have MPLS. They have data centers also. And a lot of these companies are, you know, moving the applications from the data center into the cloud. But we still have large enterprise- >> But for you guys, you can sit there too with non server or is it a box or what is it? >> It's a software stack, right? So, we are a software company. >> Okay, so no box. >> No box. >> Okay, got it. >> No box. >> It's even better. So, we can connect any, as I mentioned, any endpoint, whether it's data centers. So, what happens is usually these enterprises from the data centers- >> John: It's a cloud endpoint for you. >> Cloud endpoint for us. And they need highspeed connectivity into the cloud. And our network cloud is sitting inside the or supercloud is sitting inside the cloud. So we need highspeed connectivity from the data centers. This is like multi-gig type of connectivity. So we enable that connectivity as a service. And as Amir was saying, you are able to bring it up in minutes, pretty much. >> John: Well, you guys have a great handle on supercloud. I really appreciate you guys coming on. I have to ask you guys, since you have so much experience in the industry, multiple inflection points you've guys lived through and we're all old, and we can remember those glory days. What's the big deal going on right now? Because you can connect the dots and you can imagine, okay, like a Lambda function spinning up some connectivity. I need instant access to a new route, throw some, I need to send compute to an edge point for process data. A lot of these kind of ad hoc services are going to start flying around, which used to be manually configured as you guys remember. >> Amir: And that's been the problem, right? The shadow IT, that was the biggest problem in the enterprise environment. So that's what we are trying to get the customers away from. Cloud teams came in, individuals or small groups of people spun up instances in the cloud. It was completely disconnected from the on-premise environment or the existing IT environment that the customer had. So, how do you bring it together? And that's what we are trying to solve for, right? At a large scale, in a carrier cloud center (indistinct). >> What do you call that? Shift right or shift left? Shift left is in the cloud native world security. >> Amir: Yes. >> Networking and security, the two hottest areas. What are you shifting? Up or down? I mean, the network's moving up the stack. I mean, you're seeing the run times at Kubernetes later' >> Amir: Right, right. It's true we're end-to-end virtualization. So you have plumbing, which is the physical infrastructure. Then on top of that, now for the first time, you have true end-to-end virtualization, which the cloud-like constructs are providing to us. We tried to virtualize the routers, we try to virtualize instances at the server level. Now, we are bringing it all together in a truly end-to-end virtualized manner to connect any endpoint anywhere across the globe. Whether it's on-premise, home, multiple clouds, or SaaS type environments. >> Yeah. If you talk about the technical benefits beyond virtualizations, you kind of see in virtualization be abstracted away. So you got end-to-end virtualization, but you don't need to know virtualization to take advantage of it. >> Exactly. Exactly. >> What are some of the tech involved where, what's the trend around on top of virtual? What's the easy button for that? >> So there are many, many use cases from the customers and they're, you know, some of those use cases, they used to deliver out of their data centers before. So now, because you, know, it takes a long time to spend something up in the data center and stuff. So the trend is and what enterprises are looking for is agility. And to achieve that agility, they are moving those services or those use cases into the cloud. So another technical benefit of like something like a supercloud and what we are doing is we allow customers to, you know, move their services from existing data centers into the cloud as well. And I'll give you some examples. You know, these enterprises have, you know, tons of partners. They provide connectivity to their partners, to select resources. It used to happen inside the data center. You would bring in connectivity into the data center and apply like tons of ACLs and whatnot to make sure that you are able to only connect. And now those use cases are, they need to be enabled inside the cloud. And the customer's customers are also, it's not just coming from the on-prem, they're coming from the cloud as well. So, if they're coming from the cloud as well as from on-prem, so you need like an infrastructure like supercloud, which is sitting inside the cloud and is able to handle all these use cases. So all of these use cases have to be, so that requires like moving those services from the data center into the cloud or into the supercloud. So, they're, oh, as we started building this service over the last four years, we have come across so many use cases. And to deliver those use cases, you have to have a platform. So you have to have your own platform because otherwise you are depending on somebody else's, you know, capabilities. And every time their capabilities change, you have to change. >> John: I'm glad you brought up the platform 'cause I want to get your both reaction to this. So Bob Muglia just said on theCUBE here at Supercloud, that supercloud is a platform that provides programmatically consistent services hosted on heterogeneous cloud providers. So the question is, is supercloud a platform or an architecture in your view? >> That's an interesting view on things, you know? I mean, if you think of it, you have to design or architect a solution before we turn it into a platform. >> John: It's a trick question actually. >> So it's a, you know, so we look at it as that you have to have an architectural approach end to end, right? And then you build a solution based on that approach. So, I don't think that they are mutually exclusive. I think they go hand in hand. It's an architecture that you turn into a solution and provide that agility and high availability and disaster recovery capability that it built into that. >> It's interesting that these definitions might be actually redefined with this new configuration. >> Amir: Yes. >> Because architecture and platform used to mean something, like, aight here's a platform, you buy this platform. >> And then you architecture solution. >> Architect it via vendor. >> Right, right, right. >> Okay. And they have to deal with that architecture in the place of multiple superclouds. If you have too many stove pipes, then what's the purpose of supercloud? >> Right, right, right. And because, you know, historically, you built a router and you sold it to the customer. And the poor customer was supposed to install it all, you know, and interconnect all those things. And if you have 40, 50,000 router network, which we saw in our lifetime, 'cause there used to be many more branches when we were growing up in the networking industry, right? You had to create hierarchy and all kinds of things to figure out how to solve that problem. We are no longer living in that world anymore. You cannot deploy individual virtual instances. And that's what approach a lot of people are taking, which is a pure overly network. You cannot take that approach anymore. You have to evolve the architecture and then build the solution based on that architecture so that it becomes a platform which is readily available, highly scalable, and available. And at the same time, it's very, very easy to deploy. It's a SaaS type solution, right? >> So you're saying, do the architecture to get the solution for the platform that the customer has. >> Amir: Yes. >> They're not buying a platform, they end up with a platform- >> With the platform. >> as a result of Supercloud path. All right. So that's what's, so you mentioned, that's a great point. I want to double click on what you just said. 'Cause I like that what you said. What's the deployment strategy in your mind for supercloud? I'm an architect. I'm at an enterprise in the Midwest. I'm an insurance company, got some cloud action going on. I'm mostly on-premise. I've got the mandate to transform the company. We have apps. We'll be fully transformed in five years. What's my strategy? What do I do? >> Amir: The resources. >> What's the deployment strategy? Single global instance, code in every region, on every cloud? >> It needs to be a solution which is available as a SaaS service, right? So from the customer's perspective, they are onboarding into the supercloud. And then the supercloud is allowing them to do whatever they used to do, you know, historically and in the new world, right? That needs to come together. And that's what we have built is that, we have brought everything together in a way that what used to take months or years, and now taking an hour or two hours, and then people test it for a week or so and deploy it in production. >> I want to bring up something we were talking about before we were on camera about the TCP/IP, the OSI model. That was a concept that destroyed the proprietary narcissist. Work operating systems of the mini computers, which brought in an era of tech prosperity for generations. TCP/IP was kind of the magical moment that allowed for that kind of super networking connection. Inter networking is what's called as a category. It feels like something's going on here with supercloud. The way you describe it, it feels like there's this unification idea. Like the reality is we've got multiple stuff sitting around by default, you either clean it up or get rid of it, right? Or it's almost a, it's either a nuance, a new nuisance or chaos. >> Yeah. And we live in the new world now. We don't have the luxury of time. So we need to move as fast as possible to solve the business problems. And that's what we are running into. If we don't have automated solutions which scale, which solve our problems, then it's going to be a problem. And that's why SaaS is so important in today's world. Why should we have to deploy the network piecemeal? Why can't we have a solution? We solve our problem as we move forward and we accomplish what we need to accomplish and move forward. >> And we don't really need standards here, dude. It's not that we need a standards body if you have unification. >> So because things move so fast, there's no time to create a standards body. And that's why you see companies like ours popping up, which are trying to create a common infrastructure across all clouds. Otherwise if we vent the standardization path may take long. Eventually, we should be going in that direction. But we don't have the luxury of time. That's what I was trying to get to. >> Well, what's interesting is, is that to your point about standards and ratification, what ratifies a defacto anything? In the old days there was some technical bodies involved, but here, I think developers drive everything. So if you look at the developers and how they're voting with their code. They're instantly, organically defining everything as a collective intelligence. >> And just like you're putting out the paper and making it available, everybody's contributing to that. That's why you need to have APIs and terra form type constructs, which are available so that the customers can continue to improve upon that. And that's the Net DevOps, right? So that you need to have. >> What was once sacrilege, just sayin', in business school, back in the days when I got my business degree after my CS degree was, you know, no one wants to have a better mousetrap, a bad business model to have a better mouse trap. In this case, the better mouse trap, the better solution actually could be that thing. >> It is that thing. >> I mean, that can trigger, tips over the industry. >> And that that's where we are seeing our customers. You know, I mean, we have some publicly referenceable customers like Coke or Warner Music Group or, you know, multiple others and chart industries. The way we are solving the problem. They have some of the largest environments in the industry from the cloud perspective. And their whole network infrastructure is running on the Alkira infrastructure. And they're able to adopt new clouds within days rather than waiting for months to architect and then deploy and then figure out how to manage it and operate it. It's available as a service. >> John: And we've heard from your customer, Warner, they were just on the program. >> Amir: Yes. Okay, okay. >> So they're building a supercloud. So superclouds aren't just for tech companies. >> Amir: No. >> You guys build a supercloud for networking. >> Amir: It is. >> But people are building their own superclouds on top of all this new stuff. Talk about that dynamic. >> Healthcare providers, financials, high-tech companies, even startups. One of our startup customers, Tekion, right? They have these dealerships that they provide sales and support services to across the globe. And for them to be able to onboard those dealerships, it is 80% less time to production. That is real money, right? So, maybe Atif can give you a lot more examples of customers who are deploying. >> Talk about some of the customer activity. What are they like? Are they laggards, they innovators? Are they trying to hit the easy button? Are they coming in late or are you got some high customers? >> Actually most of our customers, all of our customers or customers in general. I don't think they have a choice but to move in this direction because, you know, the cloud has, like everything is quick now. So the cloud teams are moving faster in these enterprises. So now that they cannot afford the network nor to keep up pace with the cloud teams. So, they don't have a choice but to go with something similar where you can, you know, build your network on demand and bring up your network as quickly as possible to meet all those use cases. So, I'll give you an example. >> John: So the demand's high for what you guys do. >> Demand is very high because the cloud teams have- >> John: Yeah. They're going fast. >> They're going fast and there's no stopping. And then network teams, they have to keep up with them. And you cannot keep deploying, you know, networks the way you used to deploy back in the day. And as far as the use cases are concerned, there are so many use cases which our customers are using our platform for. One of the use cases, I'll give you an example of these financial customers. Some of the financial customers, they have their customers who they provide data, like stock exchanges, that provide like market data information to their customers out of data centers part. But now, their customers are moving into the cloud as well. So they need to come in from the cloud. So when they're coming in from the cloud, you cannot be giving them data from your data center because that takes time, and your hair pinning everything back. >> Moving data is like moving, moving money, someone said. >> Exactly. >> Exactly. And the other thing is like you have to optimize your traffic flows in the cloud as well because every time you leave the cloud, you get charged a lot. So, you don't want to leave the cloud unless you have to leave the cloud, your traffic. So, you have to come up or use a service which allows you to optimize all those traffic flows as well, you know? >> My final question to you guys, first of all, thanks for coming on Supercloud Program. Really appreciate it. Congratulations on your success. And you guys have a great positioning and I'm a big fan. And I have to ask, you guys are agile, nimble startup, smart on the cutting edge. Supercloud concept seems to resonate with people who are kind of on the front range of this major wave. While all the incumbents like Cisco, Microsoft, even AWS, they're like, I think they're looking at it, like what is that? I think it's coming up really fast, this trend. Because I know people talk about multi-cloud, I get that. But like, this whole supercloud is not just SaaS, it's more going on there. What do you think is going on between the folks who get it, supercloud, get the concept, and some are who are scratching their heads, whether it's the Ciscos or someone, like I don't get it. Why is supercloud important for the folks that aren't really seeing it? >> So first of all, I mean, the customers, what we saw about six months, 12 months ago, were a little slower to adopt the supercloud kind of concept. And there were leading edge customers who were coming and adopting it. Now, all of a sudden, over the last six to nine months, we've seen a flurry of customers coming in and they are from all disciplines or all very diverse set of customers. And they're starting to see the value of that because of the practical implications of what they're doing. You know, these shadow IT type environments are no longer working and there's a lot of pressure from the management to move faster. And then that's where they're coming in. And perhaps, Atif, if you can give a few examples of. >> Yeah. And I'll also just add to your point earlier about the network needing to be there 'cause the cloud teams are like, let's go faster. And the network's always been slow because, but now, it's been almost turbocharged. >> Atif: Yeah. Yeah, exactly. And as I said, like there was no choice here. You had to move in this industry. And the other thing I would add a little bit is now if you look at all these enterprises, most of their traffic is from, even from which is coming from the on-prem, it's going to the cloud SaaS applications or public clouds. And it's more than 50% of traffic, which is leaving your, you know, what you used to call, your network or the private network. So now it's like, you know, before it used to just connect sites to data centers and sites together. Now, it's a cloud as well as the SaaS application. So it's either internet bound or the public cloud bound. So now you have to build a network quickly, which caters to all these use cases. And that's where like something- >> And you guys, your solution to me is you eliminate all that work for the customer. Now, they can treat the cloud like a bag of Legos. And do their thing. Well, I oversimplify. Well, you know I'm talking about. >> Atif: Right, exactly. >> And to answer your question earlier about what about the big companies coming in and, you know, now they slow to adopt? And, you know, what normally happens is when Cisco came up, right? There used to be 16 different protocols suites. And then we finally settled on TCP/IP and DECnet or AppleTalk or X&S or, you know, you name it, right? Those companies did not adapt to the networking the way it was supposed to be done. And guess what happened, right? So if the companies in the networking space do not adopt this new concept or new way of doing things, I think some of them will become extinct over time. >> Well, I think the force and function too is the cloud teams as well. So you got two evolutions. You got architectural relevance. That's real as impact. >> It's very important. >> Cost, speed. >> And I look at it as a very similar disruption to what Cisco's the world, very early days did to, you know, bring the networking out, right? And it became the internet. But now we are going through the cloud. It's the cloud era, right? How does the cloud evolve over the next 10, 15, 20 years? Everything's is going to be offered as a service, right? So slowly data centers go away, the network becomes a plumbing thing. Very, you know, simple to deploy. And everything on top of that is virtualized in the cloud-like manners. >> And that makes the networks hardened and more secure. >> More secure. >> It's a great way to be secure. You remember the glory days, we'll go back 15 years. The Cisco conversation was, we got to move up to stack. All the manager would fight each other. Now, what does that actually mean? Stay where we are. Stay in your lane. This is kind of like the network's version of moving up the stack because not so much up the stack, but the cloud is everywhere. It's almost horizontally scaled. >> It's extending into the on-premise. It is already moving towards the edge, right? So, you will see a lot- >> So, programmability is a big program. So you guys are hitting programmability, compatibility, getting people into an environment they're comfortable operating. So the Ops people love it. >> Exactly. >> Spans the clouds to a level of SLA management. It might not be perfectly spanning applications, but you can actually know latencies between clouds, measure that. And then so you're basically managing your network now as the overall infrastructure. >> Right. And it needs to be a very intelligent infrastructure going forward, right? Because customers do not want to wait to be able to troubleshoot. They don't want to be able to wait to deploy something, right? So, it needs to be a level of automation. >> Okay. So the question for you guys both on we'll end on is what is the enablement that, because you guys are a disruptive enabler, right? You create this fabric. You're going to enable companies to do stuff. What are some of the things that you see and your customers might be seeing as things that they're going to do as a result of having this enablement? So what are some of those things? >> Amir: Atif, perhaps you can talk through the some of the customer experience on that. >> It's agility. And we are allowing these customers to move very, very quickly and build these networks which meet all these requirements inside the cloud. Because as Amir was saying, in the cloud era, networking is changing. And if you look at, you know, going back to your comment about the existing networking vendors. Some of them still think that, you know, just connecting to the cloud using some concepts like Cloud OnRamp is cloud networking, but it's changing now. >> John: 'Cause there's apps that are depending upon. >> Exactly. And it's all distributed. Like IT infrastructure, as I said earlier, is all distributed. And at the end of the day, you have to make sure that wherever your user is, wherever your app is, you are able to connect them securely. >> Historically, it used to be about building a router bigger and bigger and bigger and bigger, you know, and then interconnecting those routers. Now, it's all about horizontal scale. You don't need to build big, you need to scale it, right? And that's what cloud brings to the customer. >> It's a cultural change for Cisco and Juniper because they have to understand that they're still could be in the game and still win. >> Exactly. >> The question I have for you, what are your customers telling you that, what's some of the anecdotal, like, 'cause you guys have a good solution, is it, "Oh my god, you guys saved my butt." Or what are some of the commentary that you hear from the customers in terms of praise and and glory from your solution? >> Oh, some even say, when we do our demo and stuff, they say it's too hard to believe. >> Believe. >> Like, too hard. It's hard, you know, it's >> I dont believe you. They're skeptics. >> I don't believe you that because now you're able to bring up a global network within minutes. With networking services, like let's say you have APAC, you know, on-prem users, cloud also there, cloud here, users here, you can bring up a global network with full routed connectivity between all these endpoints with security services. You can bring up like a firewall from a third party or our services in the middle. This is a matter of minutes now. And this is all high speed connectivity with SLAs. Imagine like before connecting, you know, Singapore to U.S. East or Hong Kong to Frankfurt, you know, if you were putting your infrastructure in columns like E-connects, you would have to go, you know, figure out like, how am I going to- >> Seal line In, connect to it? Yeah. A lot of hassles, >> If you had to put like firewalls in the middle, segmentation, you had to, you know, isolate different entities. >> That's called heavy lifting. >> So what you're seeing is, you know, it's like customer comes in, there's a disbelief, can you really do that? And then they try it out, they go, "Wow, this works." Right? It's deployed in a small environment. And then all of a sudden they start taking off, right? And literally we have seen customers go from few thousand dollars a month or year type deployments to multi-million dollars a year type deployments in very, very short amount of time, in a few months. >> And you guys are pay as you go? >> Pay as you go. >> Pay as go usage cloud-based compatibility. >> Exactly. And it's amazing once they get to deploy the solution. >> What's the variable on the cost? >> On the cost? >> Is it traffic or is it. >> It's multiple different things. It's packaged into the overall solution. And as a matter of fact, we end up saving a lot of money to the customers. And not only in one way, in multiple different ways. And we do a complete TOI analysis for the customers. So it's bandwidth, it's number of connections, it's the amount of compute power that we are using. >> John: Similar things that they're used to. >> Just like the cloud constructs. Yeah. >> All right. Networking supercloud. Great. Congratulations. >> Thank you so much. >> Thanks for coming on Supercloud. >> Atif: Thank you. >> And looking forward to seeing more of the demand. Translate, instant networking. I'm sure it's going to be huge with the edge exploding. >> Oh yeah, yeah, yeah, yeah. >> Congratulations. >> Thank you so much. >> Thank you so much. >> Okay. So this is Supercloud 2 event here in Palo Alto. I'm John Furrier. The network Supercloud is here. Checkout Alkira. I'm John Furry, the host. Thanks for watching. (lively music)

(light music) >> Hi everybody. Welcome to this Cube Conversation. I'm really pleased to announce a collaboration with Rob Strechay. He's a guest cube analyst, and we'll be working together to extract the signal from the noise. Rob is a long-time product pro, working at a number of firms including AWS, HP, HPE, NetApp, Snowplow. I did a stint as an analyst at Enterprise Strategy Group. Rob, good to see you. Thanks for coming into our Marlboro Studios. >> Well, thank you for having me. It's always great to be here. >> I'm really excited about working with you. We've known each other for a long time. You've been in the Cube a bunch. You know, you're in between gigs, and I think we can have a lot of fun together. Covering events, covering trends. So. let's get into it. What's happening out there? We're sort of exited the isolation economy. Things were booming. Now, everybody's tapping the brakes. From your standpoint, what are you seeing out there? >> Yeah. I'm seeing that people are really looking how to get more out of their data. How they're bringing things together, how they're looking at the costs of Cloud, and understanding how are they building out their SaaS applications. And understanding that when they go in and actually start to use Cloud, it's not only just using the base services anymore. They're looking at, how do I use these platforms as a service? Some are easier than others, and they're trying to understand, how do I get more value out of that relationship with the Cloud? They're also consolidating the number of Clouds that they have, I would say to try to better optimize their spend, and getting better pricing for that matter. >> Are you seeing people unhook Clouds, or just reduce maybe certain Cloud activities and going maybe instead of 60/40 going 90/10? >> Correct. It's more like the 90/10 type of rule where they're starting to say, Hey I'm not going to get rid of Azure or AWS or Google. I'm going to move a portion of this over that I was using on this one service. Maybe I got a great two-year contract to start with on this platform as a service or a database as a service. I'm going to unhook from that and maybe go with an independent. Maybe with something like a Snowflake or a Databricks on top of another Cloud, so that I can consolidate down. But it also gives them more flexibility as well. >> In our last breaking analysis, Rob, we identified six factors that were reducing Cloud consumption. There were factors and customer tactics. And I want to get your take on this. So, some of the factors really, you got fewer mortgage originations. FinTech, obviously big Cloud user. Crypto, not as much activity there. Lower ad spending means less Cloud. And then one of 'em, which you kind of disagreed with was less, less analytics, you know, fewer... Less frequency of calculations. I'll come back to that. But then optimizing compute using Graviton or AMD instances moving to cheaper storage tiers. That of course makes sense. And then optimize pricing plans. Maybe going from On Demand, you know, to, you know, instead of pay by the drink, buy in volume. Okay. So, first of all, do those make sense to you with the exception? We'll come back and talk about the analytics piece. Is that what you're seeing from customers? >> Yeah, I think so. I think that was pretty much dead on with what I'm seeing from customers and the ones that I go out and talk to. A lot of times they're trying to really monetize their, you know, understand how their business utilizes these Clouds. And, where their spend is going in those Clouds. Can they use, you know, lower tiers of storage? Do they really need the best processors? Do they need to be using Intel or can they get away with AMD or Graviton 2 or 3? Or do they need to move in? And, I think when you look at all of these Clouds, they always have pricing curves that are arcs from the newest to the oldest stuff. And you can play games with that. And understanding how you can actually lower your costs by looking at maybe some of the older generation. Maybe your application was written 10 years ago. You don't necessarily have to be on the best, newest processor for that application per se. >> So last, I want to come back to this whole analytics piece. Last June, I think it was June, Dev Ittycheria, who's the-- I call him Dev. Spelled Dev, pronounced Dave. (chuckles softly) Same pronunciation, different spelling. Dev Ittycheria, CEO of Mongo, on the earnings call. He was getting, you know, hit. Things were starting to get a little less visible in terms of, you know, the outlook. And people were pushing him like... Because you're in the Cloud, is it easier to dial down? And he said, because we're the document database, we support transaction applications. We're less discretionary than say, analytics. Well on the Snowflake earnings call, that same month or the month after, they were all over Slootman and Scarpelli. Oh, the Mongo CEO said that they're less discretionary than analytics. And Snowflake was an interesting comment. They basically said, look, we're the Cloud. You can dial it up, you can dial it down, but the area under the curve over a period of time is going to be the same, because they get their customers to commit. What do you say? You disagreed with the notion that people are running their calculations less frequently. Is that because they're trying to do a better job of targeting customers in near real time? What are you seeing out there? >> Yeah, I think they're moving away from using people and more expensive marketing. Or, they're trying to figure out what's my Google ad spend, what's my Meta ad spend? And what they're trying to do is optimize that spend. So, what is the return on advertising, or the ROAS as they would say. And what they're looking to do is understand, okay, I have to collect these analytics that better understand where are these people coming from? How do they get to my site, to my store, to my whatever? And when they're using it, how do they they better move through that? What you're also seeing is that analytics is not only just for kind of the retail or financial services or things like that, but then they're also, you know, using that to make offers in those categories. When you move back to more, you know, take other companies that are building products and SaaS delivered products. They may actually go and use this analytics for making the product better. And one of the big reasons for that is maybe they're dialing back how many product managers they have. And they're looking to be more data driven about how they actually go and build the product out or enhance the product. So maybe they're, you know, an online video service and they want to understand why people are either using or not using the whiteboard inside the product. And they're collecting a lot of that product analytics in a big way so that they can go through that. And they're doing it in a constant manner. This first party type tracking within applications is growing rapidly by customers. >> So, let's talk about who wins in that. So, obviously the Cloud guys, AWS, Google and Azure. I want to come back and unpack that a little bit. Databricks and Snowflake, we reported on our last breaking analysis, it kind of on a collision course. You know, a couple years ago we were thinking, okay, AWS, Snowflake and Databricks, like perfect sandwich. And then of course they started to become more competitive. My sense is they still, you know, compliment each other in the field, right? But, you know, publicly, they've got bigger aspirations, they get big TAMs that they're going after. But it's interesting, the data shows that-- So, Snowflake was off the charts in terms of spending momentum and our EPR surveys. Our partner down in New York, they kind of came into line. They're both growing in terms of market presence. Databricks couldn't get to IPO. So, we don't have as much, you know, visibility on their financials. You know, Snowflake obviously highly transparent cause they're a public company. And then you got AWS, Google and Azure. And it seems like AWS appears to be more partner friendly. Microsoft, you know, depends on what market you're in. And Google wants to sell BigQuery. >> Yeah. >> So, what are you seeing in the public Cloud from a data platform perspective? >> Yeah. I think that was pretty astute in what you were talking about there, because I think of the three, Google is definitely I think a little bit behind in how they go to market with their partners. Azure's done a fantastic job of partnering with these companies to understand and even though they may have Synapse as their go-to and where they want people to go to do AI and ML. What they're looking at is, Hey, we're going to also be friendly with Snowflake. We're also going to be friendly with a Databricks. And I think that, Amazon has always been there because that's where the market has been for these developers. So, many, like Databricks' and the Snowflake's have gone there first because, you know, Databricks' case, they built out on top of S3 first. And going and using somebody's object layer other than AWS, was not as simple as you would think it would be. Moving between those. >> So, one of the financial meetups I said meetup, but the... It was either the CEO or the CFO. It was either Slootman or Scarpelli talking at, I don't know, Merrill Lynch or one of the other financial conferences said, I think it was probably their Q3 call. Snowflake said 80% of our business goes through Amazon. And he said to this audience, the next day we got a call from Microsoft. Hey, we got to do more. And, we know just from reading the financial statements that Snowflake is getting concessions from Amazon, they're buying in volume, they're renegotiating their contracts. Amazon gets it. You know, lower the price, people buy more. Long term, we're all going to make more money. Microsoft obviously wants to get into that game with Snowflake. They understand the momentum. They said Google, not so much. And I've had customers tell me that they wanted to use Google's AI with Snowflake, but they can't, they got to go to to BigQuery. So, honestly, I haven't like vetted that so. But, I think it's true. But nonetheless, it seems like Google's a little less friendly with the data platform providers. What do you think? >> Yeah, I would say so. I think this is a place that Google looks and wants to own. Is that now, are they doing the right things long term? I mean again, you know, you look at Google Analytics being you know, basically outlawed in five countries in the EU because of GDPR concerns, and compliance and governance of data. And I think people are looking at Google and BigQuery in general and saying, is it the best place for me to go? Is it going to be in the right places where I need it? Still, it's still one of the largest used databases out there just because it underpins a number of the Google services. So you almost get, like you were saying, forced into BigQuery sometimes, if you want to use the tech on top. >> You do strategy. >> Yeah. >> Right? You do strategy, you do messaging. Is it the right call by Google? I mean, it's not a-- I criticize Google sometimes. But, I'm not sure it's the wrong call to say, Hey, this is our ace in the hole. >> Yeah. >> We got to get people into BigQuery. Cause, first of all, BigQuery is a solid product. I mean it's Cloud native and it's, you know, by all, it gets high marks. So, why give the competition an advantage? Let's try to force people essentially into what is we think a great product and it is a great product. The flip side of that is, they're giving up some potential partner TAM and not treating the ecosystem as well as one of their major competitors. What do you do if you're in that position? >> Yeah, I think that that's a fantastic question. And the question I pose back to the companies I've worked with and worked for is, are you really looking to have vendor lock-in as your key differentiator to your service? And I think when you start to look at these companies that are moving away from BigQuery, moving to even, Databricks on top of GCS in Google, they're looking to say, okay, I can go there if I have to evacuate from GCP and go to another Cloud, I can stay on Databricks as a platform, for instance. So I think it's, people are looking at what platform as a service, database as a service they go and use. Because from a strategic perspective, they don't want that vendor locking. >> That's where Supercloud becomes interesting, right? Because, if I can run on Snowflake or Databricks, you know, across Clouds. Even Oracle, you know, they're getting into business with Microsoft. Let's talk about some of the Cloud players. So, the big three have reported. >> Right. >> We saw AWSs Cloud growth decelerated down to 20%, which is I think the lowest growth rate since they started to disclose public numbers. And they said they exited, sorry, they said January they grew at 15%. >> Yeah. >> Year on year. Now, they had some pretty tough compares. But nonetheless, 15%, wow. Azure, kind of mid thirties, and then Google, we had kind of low thirties. But, well behind in terms of size. And Google's losing probably almost $3 billion annually. But, that's not necessarily a bad thing by advocating and investing. What's happening with the Cloud? Is AWS just running into the law, large numbers? Do you think we can actually see a re-acceleration like we have in the past with AWS Cloud? Azure, we predicted is going to be 75% of AWS IAS revenues. You know, we try to estimate IAS. >> Yeah. >> Even though they don't share that with us. That's a huge milestone. You'd think-- There's some people who have, I think, Bob Evans predicted a while ago that Microsoft would surpass AWS in terms of size. You know, what do you think? >> Yeah, I think that Azure's going to keep to-- Keep growing at a pretty good clip. I think that for Azure, they still have really great account control, even though people like to hate Microsoft. The Microsoft sellers that are out there making those companies successful day after day have really done a good job of being in those accounts and helping people. I was recently over in the UK. And the UK market between AWS and Azure is pretty amazing, how much Azure there is. And it's growing within Europe in general. In the states, it's, you know, I think it's growing well. I think it's still growing, probably not as fast as it is outside the U.S. But, you go down to someplace like Australia, it's also Azure. You hear about Azure all the time. >> Why? Is that just because of the Microsoft's software state? It's just so convenient. >> I think it has to do with, you know, and you can go with the reasoning they don't break out, you know, Office 365 and all of that out of their numbers is because they have-- They're in all of these accounts because the office suite is so pervasive in there. So, they always have reasons to go back in and, oh by the way, you're on these old SQL licenses. Let us move you up here and we'll be able to-- We'll support you on the old version, you know, with security and all of these things. And be able to move you forward. So, they have a lot of, I guess you could say, levers to stay in those accounts and be interesting. At least as part of the Cloud estate. I think Amazon, you know, is hitting, you know, the large number. Laws of large numbers. But I think that they're also going through, and I think this was seen in the layoffs that they were making, that they're looking to understand and have profitability in more of those services that they have. You know, over 350 odd services that they have. And you know, as somebody who went there and helped to start yet a new one, while I was there. And finally, it went to beta back in September, you start to look at the fact that, that number of services, people, their own sellers don't even know all of their services. It's impossible to comprehend and sell that many things. So, I think what they're going through is really looking to rationalize a lot of what they're doing from a services perspective going forward. They're looking to focus on more profitable services and bringing those in. Because right now it's built like a layer cake where you have, you know, S3 EBS and EC2 on the bottom of the layer cake. And then maybe you have, you're using IAM, the authorization and authentication in there and you have all these different services. And then they call it EMR on top. And so, EMR has to pay for that entire layer cake just to go and compete against somebody like Mongo or something like that. So, you start to unwind the costs of that. Whereas Azure, went and they build basically ground up services for the most part. And Google kind of falls somewhere in between in how they build their-- They're a sort of layer cake type effect, but not as many layers I guess you could say. >> I feel like, you know, Amazon's trying to be a platform for the ecosystem. Yes, they have their own products and they're going to sell. And that's going to drive their profitability cause they don't have to split the pie. But, they're taking a piece of-- They're spinning the meter, as Ziyas Caravalo likes to say on every time Snowflake or Databricks or Mongo or Atlas is, you know, running on their system. They take a piece of the action. Now, Microsoft does that as well. But, you look at Microsoft and security, head-to-head competitors, for example, with a CrowdStrike or an Okta in identity. Whereas, it seems like at least for now, AWS is a more friendly place for the ecosystem. At the same time, you do a lot of business in Microsoft. >> Yeah. And I think that a lot of companies have always feared that Amazon would just throw, you know, bodies at it. And I think that people have come to the realization that a two pizza team, as Amazon would call it, is eight people. I think that's, you know, two slices per person. I'm a little bit fat, so I don't know if that's enough. But, you start to look at it and go, okay, if they're going to start out with eight engineers, if I'm a startup and they're part of my ecosystem, do I really fear them or should I really embrace them and try to partner closer with them? And I think the smart people and the smart companies are partnering with them because they're realizing, Amazon, unless they can see it to, you know, a hundred million, $500 million market, they're not going to throw eight to 16 people at a problem. I think when, you know, you could say, you could look at the elastic with OpenSearch and what they did there. And the licensing terms and the battle they went through. But they knew that Elastic had a huge market. Also, you had a number of ecosystem companies building on top of now OpenSearch, that are now domain on top of Amazon as well. So, I think Amazon's being pretty strategic in how they're doing it. I think some of the-- It'll be interesting. I think this year is a payout year for the cuts that they're making to some of the services internally to kind of, you know, how do we take the fat off some of those services that-- You know, you look at Alexa. I don't know how much revenue Alexa really generates for them. But it's a means to an end for a number of different other services and partners. >> What do you make of this ChatGPT? I mean, Microsoft obviously is playing that card. You want to, you want ChatGPT in the Cloud, come to Azure. Seems like AWS has to respond. And we know Google is, you know, sharpening its knives to come up with its response. >> Yeah, I mean Google just went and talked about Bard for the first time this week and they're in private preview or I guess they call it beta, but. Right at the moment to select, select AI users, which I have no idea what that means. But that's a very interesting way that they're marketing it out there. But, I think that Amazon will have to respond. I think they'll be more measured than say, what Google's doing with Bard and just throwing it out there to, hey, we're going into beta now. I think they'll look at it and see where do we go and how do we actually integrate this in? Because they do have a lot of components of AI and ML underneath the hood that other services use. And I think that, you know, they've learned from that. And I think that they've already done a good job. Especially for media and entertainment when you start to look at some of the ways that they use it for helping do graphics and helping to do drones. I think part of their buy of iRobot was the fact that iRobot was a big user of RoboMaker, which is using different models to train those robots to go around objects and things like that, so. >> Quick touch on Kubernetes, the whole DevOps World we just covered. The Cloud Native Foundation Security, CNCF. The security conference up in Seattle last week. First time they spun that out kind of like reinforced, you know, AWS spins out, reinforced from reinvent. Amsterdam's coming up soon, the CubeCon. What should we expect? What's hot in Cubeland? >> Yeah, I think, you know, Kubes, you're going to be looking at how OpenShift keeps growing and I think to that respect you get to see the momentum with people like Red Hat. You see others coming up and realizing how OpenShift has gone to market as being, like you were saying, partnering with those Clouds and really making it simple. I think the simplicity and the manageability of Kubernetes is going to be at the forefront. I think a lot of the investment is still going into, how do I bring observability and DevOps and AIOps and MLOps all together. And I think that's going to be a big place where people are going to be looking to see what comes out of CubeCon in Amsterdam. I think it's that manageability ease of use. >> Well Rob, I look forward to working with you on behalf of the whole Cube team. We're going to do more of these and go out to some shows extract the signal from the noise. Really appreciate you coming into our studio. >> Well, thank you for having me on. Really appreciate it. >> You're really welcome. All right, keep it right there, or thanks for watching. This is Dave Vellante for the Cube. And we'll see you next time. (light music)

(upbeat music) >> Hello and welcome back to theCUBE. We're here in Palo Alto, California. I'm your host, John Furrier with a special guest here in the studio. As part of our Cloud Native SecurityCon Coverage we had an opportunity to bring in Jon Turow who is the partner at Madrona Venture Partners formerly with AWS and to talk about machine learning, foundational models, and how the future of AI is going to be impacted by some of the innovation around what's going on in the industry. ChatGPT has taken the world by storm. A million downloads, fastest to the million downloads there. Before some were saying it's just a gimmick. Others saying it's a game changer. Jon's here to break it down, and great to have you on. Thanks for coming in. >> Thanks John. Glad to be here. >> Thanks for coming on. So first of all, I'm glad you're here. First of all, because two things. One, you were formerly with AWS, got a lot of experience running projects at AWS. Now a partner at Madrona, a great firm doing great deals, and they had this future at modern application kind of thesis. Now you are putting out some content recently around foundational models. You're deep into computer vision. You were the IoT general manager at AWS among other things, Greengrass. So you know a lot about data. You know a lot about some of this automation, some of the edge stuff. You've been in the middle of all these kind of areas that now seem to be the next wave coming. So I wanted to ask you what your thoughts are of how the machine learning and this new automation wave is coming in, this AI tools are coming out. Is it a platform? Is it going to be smarter? What feeds AI? What's your take on this whole foundational big movement into AI? What's your general reaction to all this? >> So, thanks, Jon, again for having me here. Really excited to talk about these things. AI has been coming for a long time. It's been kind of the next big thing. Always just over the horizon for quite some time. And we've seen really compelling applications in generations before and until now. Amazon and AWS have introduced a lot of them. My firm, Madrona Venture Group has invested in some of those early players as well. But what we're seeing now is something categorically different. That's really exciting and feels like a durable change. And I can try and explain what that is. We have these really large models that are useful in a general way. They can be applied to a lot of different tasks beyond the specific task that the designers envisioned. That makes them more flexible, that makes them more useful for building applications than what we've seen before. And so that, we can talk about the depths of it, but in a nutshell, that's why I think people are really excited. >> And I think one of the things that you wrote about that jumped out at me is that this seems to be this moment where there's been a multiple decades of nerds and computer scientists and programmers and data thinkers around waiting for AI to blossom. And it's like they're scratching that itch. Every year is going to be, and it's like the bottleneck's always been compute power. And we've seen other areas, genome sequencing, all kinds of high computation things where required high forms computing. But now there's no real bottleneck to compute. You got cloud. And so you're starting to see the emergence of a massive acceleration of where AI's been and where it needs to be going. Now, it's almost like it's got a reboot. It's almost a renaissance in the AI community with a whole nother macro environmental things happening. Cloud, younger generation, applications proliferate from mobile to cloud native. It's the perfect storm for this kind of moment to switch over. Am I overreading that? Is that right? >> You're right. And it's been cooking for a cycle or two. And let me try and explain why that is. We have cloud and AWS launch in whatever it was, 2006, and offered more compute to more people than really was possible before. Initially that was about taking existing applications and running them more easily in a bigger scale. But in that period of time what's also become possible is new kinds of computation that really weren't practical or even possible without that vast amount of compute. And so one result that came of that is something called the transformer AI model architecture. And Google came out with that, published a paper in 2017. And what that says is, with a transformer model you can actually train an arbitrarily large amount of data into a model, and see what happens. That's what Google demonstrated in 2017. The what happens is the really exciting part because when you do that, what you start to see, when models exceed a certain size that we had never really seen before all of a sudden they get what we call emerging capabilities of complex reasoning and reasoning outside a domain and reasoning with data. The kinds of things that people describe as spooky when they play with something like ChatGPT. That's the underlying term. We don't as an industry quite know why it happens or how it happens, but we can measure that it does. So cloud enables new kinds of math and science. New kinds of math and science allow new kinds of experimentation. And that experimentation has led to this new generation of models. >> So one of the debates we had on theCUBE at our Supercloud event last month was, what's the barriers to entry for say OpenAI, for instance? Obviously, I weighed in aggressively and said, "The barriers for getting into cloud are high because all the CapEx." And Howie Xu formerly VMware, now at ZScaler, he's an AI machine learning guy. He was like, "Well, you can spend $100 million and replicate it." I saw a quote that set up for 180,000 I can get this other package. What's the barriers to entry? Is ChatGPT or OpenAI, does it have sustainability? Is it easy to get into? What is the market like for AI? I mean, because a lot of entrepreneurs are jumping in. I mean, I just read a story today. San Francisco's got more inbound migration because of the AI action happening, Seattle's booming, Boston with MIT's been working on neural networks for generations. That's what we've found the answer. Get off the neural network, Boston jump on the AI bus. So there's total excitement for this. People are enthusiastic around this area. >> You can think of an iPhone versus Android tension that's happening today. In the iPhone world, there are proprietary models from OpenAI who you might consider as the leader. There's Cohere, there's AI21, there's Anthropic, Google's going to have their own, and a few others. These are proprietary models that developers can build on top of, get started really quickly. They're measured to have the highest accuracy and the highest performance today. That's the proprietary side. On the other side, there is an open source part of the world. These are a proliferation of model architectures that developers and practitioners can take off the shelf and train themselves. Typically found in Hugging face. What people seem to think is that the accuracy and performance of the open source models is something like 18 to 20 months behind the accuracy and performance of the proprietary models. But on the other hand, there's infinite flexibility for teams that are capable enough. So you're going to see teams choose sides based on whether they want speed or flexibility. >> That's interesting. And that brings up a point I was talking to a startup and the debate was, do you abstract away from the hardware and be software-defined or software-led on the AI side and let the hardware side just extremely accelerate on its own, 'cause it's flywheel? So again, back to proprietary, that's with hardware kind of bundled in, bolted on. Is it accelerator or is it bolted on or is it part of it? So to me, I think that the big struggle in understanding this is that which one will end up being right. I mean, is it a beta max versus VHS kind of thing going on? Or iPhone, Android, I mean iPhone makes a lot of sense, but if you're Apple, but is there an Apple moment in the machine learning? >> In proprietary models, here does seem to be a jump ball. That there's going to be a virtuous flywheel that emerges that, for example, all these excitement about ChatGPT. What's really exciting about it is it's really easy to use. The technology isn't so different from what we've seen before even from OpenAI. You mentioned a million users in a short period of time, all providing training data for OpenAI that makes their underlying models, their next generation even better. So it's not unreasonable to guess that there's going to be power laws that emerge on the proprietary side. What I think history has shown is that iPhone, Android, Windows, Linux, there seems to be gravity towards this yin and yang. And my guess, and what other people seem to think is going to be the case is that we're going to continue to see these two poles of AI. >> So let's get into the relationship with data because I've been emerging myself with ChatGPT, fascinated by the ease of use, yes, but also the fidelity of how you query it. And I felt like when I was doing writing SQL back in the eighties and nineties where SQL was emerging. You had to be really a guru at the SQL to get the answers you wanted. It seems like the querying into ChatGPT is a good thing if you know how to talk to it. Labeling whether your input is and it does a great job if you feed it right. If you ask a generic questions like Google. It's like a Google search. It gives you great format, sounds credible, but the facts are kind of wrong. >> That's right. >> That's where general consensus is coming on. So what does that mean? That means people are on one hand saying, "Ah, it's bullshit 'cause it's wrong." But I look at, I'm like, "Wow, that's that's compelling." 'Cause if you feed it the right data, so now we're in the data modeling here, so the role of data's going to be critical. Is there a data operating system emerging? Because if this thing continues to go the way it's going you can almost imagine as you would look at companies to invest in. Who's going to be right on this? What's going to scale? What's sustainable? What could build a durable company? It might not look what like what people think it is. I mean, I remember when Google started everyone thought it was the worst search engine because it wasn't a portal. But it was the best organic search on the planet became successful. So I'm trying to figure out like, okay, how do you read this? How do you read the tea leaves? >> Yeah. There are a few different ways that companies can differentiate themselves. Teams with galactic capabilities to take an open source model and then change the architecture and retrain and go down to the silicon. They can do things that might not have been possible for other teams to do. There's a company that that we're proud to be investors in called RunwayML that provides video accelerated, sorry, AI accelerated video editing capabilities. They were used in everything, everywhere all at once and some others. In order to build RunwayML, they needed a vision of what the future was going to look like and they needed to make deep contributions to the science that was going to enable all that. But not every team has those capabilities, maybe nor should they. So as far as how other teams are going to differentiate there's a couple of things that they can do. One is called prompt engineering where they shape on behalf of their own users exactly how the prompt to get fed to the underlying model. It's not clear whether that's going to be a durable problem or whether like Google, we consumers are going to start to get more intuitive about this. That's one. The second is what's called information retrieval. How can I get information about the world outside, information from a database or a data store or whatever service into these models so they can reason about them. And the third is, this is going to sound funny, but attribution. Just like you would do in a news report or an academic paper. If you can state where your facts are coming from, the downstream consumer or the human being who has to use that information actually is going to be able to make better sense of it and rely better on it. So that's prompt engineering, that's retrieval, and that's attribution. >> So that brings me to my next point I want to dig in on is the foundational model stack that you published. And I'll start by saying that with ChatGPT, if you take out the naysayers who are like throwing cold water on it about being a gimmick or whatever, and then you got the other side, I would call the alpha nerds who are like they can see, "Wow, this is amazing." This is truly NextGen. This isn't yesterday's chatbot nonsense. They're like, they're all over it. It's that everybody's using it right now in every vertical. I heard someone using it for security logs. I heard a data center, hardware vendor using it for pushing out appsec review updates. I mean, I've heard corner cases. We're using it for theCUBE to put our metadata in. So there's a horizontal use case of value. So to me that tells me it's a market there. So when you have horizontal scalability in the use case you're going to have a stack. So you publish this stack and it has an application at the top, applications like Jasper out there. You're seeing ChatGPT. But you go after the bottom, you got silicon, cloud, foundational model operations, the foundational models themselves, tooling, sources, actions. Where'd you get this from? How'd you put this together? Did you just work backwards from the startups or was there a thesis behind this? Could you share your thoughts behind this foundational model stack? >> Sure. Well, I'm a recovering product manager and my job that I think about as a product manager is who is my customer and what problem he wants to solve. And so to put myself in the mindset of an application developer and a founder who is actually my customer as a partner at Madrona, I think about what technology and resources does she need to be really powerful, to be able to take a brilliant idea, and actually bring that to life. And if you spend time with that community, which I do and I've met with hundreds of founders now who are trying to do exactly this, you can see that the stack is emerging. In fact, we first drew it in, not in January 2023, but October 2022. And if you look at the difference between the October '22 and January '23 stacks you're going to see that holes in the stack that we identified in October around tooling and around foundation model ops and the rest are organically starting to get filled because of how much demand from the developers at the top of the stack. >> If you look at the young generation coming out and even some of the analysts, I was just reading an analyst report on who's following the whole data stacks area, Databricks, Snowflake, there's variety of analytics, realtime AI, data's hot. There's a lot of engineers coming out that were either data scientists or I would call data platform engineering folks are becoming very key resources in this area. What's the skillset emerging and what's the mindset of that entrepreneur that sees the opportunity? How does these startups come together? Is there a pattern in the formation? Is there a pattern in the competency or proficiency around the talent behind these ventures? >> Yes. I would say there's two groups. The first is a very distinct pattern, John. For the past 10 years or a little more we've seen a pattern of democratization of ML where more and more people had access to this powerful science and technology. And since about 2017, with the rise of the transformer architecture in these foundation models, that pattern has reversed. All of a sudden what has become broader access is now shrinking to a pretty small group of scientists who can actually train and manipulate the architectures of these models themselves. So that's one. And what that means is the teams who can do that have huge ability to make the future happen in ways that other people don't have access to yet. That's one. The second is there is a broader population of people who by definition has even more collective imagination 'cause there's even more people who sees what should be possible and can use things like the proprietary models, like the OpenAI models that are available off the shelf and try to create something that maybe nobody has seen before. And when they do that, Jasper AI is a great example of that. Jasper AI is a company that creates marketing copy automatically with generative models such as GPT-3. They do that and it's really useful and it's almost fun for a marketer to use that. But there are going to be questions of how they can defend that against someone else who has access to the same technology. It's a different population of founders who has to find other sources of differentiation without being able to go all the way down to the the silicon and the science. >> Yeah, and it's going to be also opportunity recognition is one thing. Building a viable venture product market fit. You got competition. And so when things get crowded you got to have some differentiation. I think that's going to be the key. And that's where I was trying to figure out and I think data with scale I think are big ones. Where's the vulnerability in the stack in terms of gaps? Where's the white space? I shouldn't say vulnerability. I should say where's the opportunity, where's the white space in the stack that you see opportunities for entrepreneurs to attack? >> I would say there's two. At the application level, there is almost infinite opportunity, John, because almost every kind of application is about to be reimagined or disrupted with a new generation that takes advantage of this really powerful new technology. And so if there is a kind of application in almost any vertical, it's hard to rule something out. Almost any vertical that a founder wishes she had created the original app in, well, now it's her time. So that's one. The second is, if you look at the tooling layer that we discussed, tooling is a really powerful way that you can provide more flexibility to app developers to get more differentiation for themselves. And the tooling layer is still forming. This is the interface between the models themselves and the applications. Tools that help bring in data, as you mentioned, connect to external actions, bring context across multiple calls, chain together multiple models. These kinds of things, there's huge opportunity there. >> Well, Jon, I really appreciate you coming in. I had a couple more questions, but I will take a minute to read some of your bios for the audience and we'll get into, I won't embarrass you, but I want to set the context. You said you were recovering product manager, 10 plus years at AWS. Obviously, recovering from AWS, which is a whole nother dimension of recovering. In all seriousness, I talked to Andy Jassy around that time and Dr. Matt Wood and it was about that time when AI was just getting on the radar when they started. So you guys started seeing the wave coming in early on. So I remember at that time as Amazon was starting to grow significantly and even just stock price and overall growth. From a tech perspective, it was pretty clear what was coming, so you were there when this tsunami hit. >> Jon: That's right. >> And you had a front row seat building tech, you were led the product teams for Computer Vision AI, Textract, AI intelligence for document processing, recognition for image and video analysis. You wrote the business product plan for AWS IoT and Greengrass, which we've covered a lot in theCUBE, which extends out to the whole edge thing. So you know a lot about AI/ML, edge computing, IOT, messaging, which I call the law of small numbers that scale become big. This is a big new thing. So as a former AWS leader who's been there and at Madrona, what's your investment thesis as you start to peruse the landscape and talk to entrepreneurs as you got the stack? What's the big picture? What are you looking for? What's the thesis? How do you see this next five years emerging? >> Five years is a really long time given some of this science is only six months out. I'll start with some, no pun intended, some foundational things. And we can talk about some implications of the technology. The basics are the same as they've always been. We want, what I like to call customers with their hair on fire. So they have problems, so urgent they'll buy half a product. The joke is if your hair is on fire you might want a bucket of cold water, but you'll take a tennis racket and you'll beat yourself over the head to put the fire out. You want those customers 'cause they'll meet you more than halfway. And when you find them, you can obsess about them and you can get better every day. So we want customers with their hair on fire. We want founders who have empathy for those customers, understand what is going to be required to serve them really well, and have what I like to call founder-market fit to be able to build the products that those customers are going to need. >> And because that's a good strategy from an emerging, not yet fully baked out requirements definition. >> Jon: That's right. >> Enough where directionally they're leaning in, more than in, they're part of the product development process. >> That's right. And when you're doing early stage development, which is where I personally spend a lot of my time at the seed and A and a little bit beyond that stage often that's going to be what you have to go on because the future is going to be so complex that you can't see the curves beyond it. But if you have customers with their hair on fire and talented founders who have the capability to serve those customers, that's got me interested. >> So if I'm an entrepreneur, I walk in and say, "I have customers that have their hair on fire." What kind of checks do you write? What's the kind of the average you're seeing for seed and series? Probably seed, seed rounds and series As. >> It can depend. I have seen seed rounds of double digit million dollars. I have seen seed rounds much smaller than that. It really depends on what is going to be the right thing for these founders to prove out the hypothesis that they're testing that says, "Look, we have this customer with her hair on fire. We think we can build at least a tennis racket that she can use to start beating herself over the head and put the fire out. And then we're going to have something really interesting that we can scale up from there and we can make the future happen. >> So it sounds like your advice to founders is go out and find some customers, show them a product, don't obsess over full completion, get some sort of vibe on fit and go from there. >> Yeah, and I think by the time founders come to me they may not have a product, they may not have a deck, but if they have a customer with her hair on fire, then I'm really interested. >> Well, I always love the professional services angle on these markets. You go in and you get some business and you understand it. Walk away if you don't like it, but you see the hair on fire, then you go in product mode. >> That's right. >> All Right, Jon, thank you for coming on theCUBE. Really appreciate you stopping by the studio and good luck on your investments. Great to see you. >> You too. >> Thanks for coming on. >> Thank you, Jon. >> CUBE coverage here at Palo Alto. I'm John Furrier, your host. More coverage with CUBE Conversations after this break. (upbeat music)

(rousing music) >> Hello everyone. Welcome back to "theCUBE's" day one coverage of Cloud Native Security Con 23. This is going to be an exciting panel. I've got three great guests. I'm Lisa Martin, you know our esteemed analysts, John Furrier, and Dave Vellante well. And we're excited to welcome to "theCUBE" for the first time, Yves Sandfort, the CEO of Comdivision Group, who's coming to us from Germany. As you know, Cloud Native Security Con is a global event. Everyone welcome Yves, great to have you in particular. Welcome to "theCUBE." >> Great to be here. >> Thank you for inviting me. >> Yves, tell us a little bit, before we dig into really wanting to understand your perspectives on the event and get Dave and John's feedback as well, tell us a little bit about you. >> So yeah, talking about me, or talking about Comdivision real quick. We are in the business for over 27 years already. We started as a SaaS company, then became more like an architecture and, and Cloud Native company over the last few years. But what's interesting is, and I think that's, that's, that's really interesting when we look at our industry. It hasn't really, the requirements haven't really changed over the years. It's still security. We still have to figure out how we deal with security. We still have to figure out how we deal with compliance and everything else. And I think therefore, it's more and more important that we take these items more seriously. Also, based on the fact that when we look at it, how development and other things happen nowadays, it's, it's, everybody says it's like open source. It's great because everybody can look into the code. We, I think the last few years have shown us enough example that that's not necessarily solving all the issues, but it's also code and development has changed rapidly when we look at the Cloud Native approach, where it's far more about gluing the pieces together, versus the development pieces. When I was actually doing software development 25 years ago, and had to basically build my code because I didn't have that much internet access for it. So it has evolved, but even back then we had to deal with security and everything. >> Right. The focus on security is, is incredibly important, and the focus keeps growing as you mentioned. This is, guys, and I want to get your perspectives on this. We're going to start with John. This is the first time Cloud Native Security Con is its own event being extracted from, and amplified from KubeCon. John, I want to understand from your perspective, break down the event, what you see, what you've heard, and Cloud Native Security in general. What does this mean to companies? What does it mean to customers? Is this a reality? >> Well, I think that's the topic we want to discuss, and I think Yves background, you see the VMware certification, I love that. Because what VMware did with virtualization, was abstract that from server virtualization, kind of really changed the game on things, and you start to see Cloud Native kind of go that next level of how companies will be operating their business, not just digital transformation, as digital transformation goes to completion, it's total business transformation where IT is everywhere. And so you're starting to see the trends where, "Okay, that's happening." Now you're starting to see, that's Cloud Native Con, or KubeCon, AWS re:Invent, or whatever show, or whatever way you want to look at it. But in, in the past decade, past five years, security has always been front and center as almost a separate thing, and, in and of itself, but the same thing. So you're starting to see the breakout of security conversations around how to make things work. So a lot of operational conversations around what used to be DevOps makes infrastructure as code, and that was great, that fueled that. Then DevSecOps came. So the Cloud Native next level, is more application development at scale, developers driving the standards with developer first thinking, shifting left, I get all that. But down in the lower ends of the stack, you got real operational issues. DNS we've heard in the keynote, we heard about the Colonel, the Lennox Colonel. Things that need to be managed and taken care of at a security level. These are like, seem like in the weeds, but you're starting to see that happen. And the other thing that I think's real about Cloud Native Security Con that's going to be interesting to watch, is Amazon has pretty much canceled all their re:Invent like shows except for two; Re:Invent, which is their annual conference, and Re:Inforce, which is dedicated to securities. So Cloud Native, Linux, the Linux Foundation has now breaking out Cloud Native Con and KubeCon, and now Cloud Native Security Con. They can't call it KubeCon because it's not Kubernetes, but it's like security focus. I think this is the beginning of starting to see this new developer driving, developers driving the standards, and it has it implications, what used to be called IT ops, and that's like the VMwares of the world. You saw all the stuff that was not at developer focus, but more ops, becoming much more in the application. So I think, I think it's real. The question is where does it go? How fast does it develop? So to me, I think it's a real trend, and it's worthy of a breakout, but it's not yet clear of where the landing zone is for people to start doing it, how they get started, what are the best practices. Machine learning's going to be a big part of this. So to me it's totally cool, but I'm not yet seeing the beachhead. So that's kind of my take. >> Dave, our inventor and host of breaking analysis, what's your take? >> So when you, I think when you zoom out, there's some, there's a big macro change that's been going on. I think when you look back, let's say 10, 12 years ago, the, the need for speed far trumped the, the, the security aspect, the governance, the data privacy. It was like, "Yeah, the risks, they're not that great compared to our opportunity." That has completely changed because the risks are now so much higher. And so what's happening, I think there's a, there's a major effort amongst CIOs and CISOs to try to make security not a blocker because it use to be, it still is. "Okay, I got this great initiative." Eh, give it to the SecOps pros, and let them take it for a while before we can go to market. And so a huge challenge now is to simplify, automate, AI comes in, the whole supply chain security, so the, so the companies can not be facing so much friction. And that is non-trivial. I don't think we're anywhere close there, but I think the goal is by, within the next several years, we're going to be in a position, that security, we heard today, is, wasn't designed in to the initial internet protocols. It was bolted on. And so increasingly, the fundamental architecture of the internet, the Cloud, et cetera, is, is seeing designed in security, and, and that is an imperative, or else business is going to come to a grinding halt. >> Right. It's no longer, the bolt no longer works. Yves, what's your perspective on Cloud Native Security, where it stands today? What's in it for customers, whether we're talking about banks, or hospitals, or retailers, what do you think? >> I think when we, when we look at security in the, in the modern world, is we need to as, as Dave mentioned, we need to rethink how we apply it. Very often, security in the past has been always bolted on in the end. If we continue to do that, it'll become more and more difficult, because as companies evolve, and as companies want to bring products and software to market in a much faster and faster way, it's getting more and more difficult if we bolt on the security process at the end. It's like, developers build something and then someone checks security. That's not going to work any longer. Especially if we also consider now the changes in the industry. We had Stack Overflow over the last 10 years. If I would've had Stack Overflow 15, 20, what, 25 years ago when I was a developer, it would've changed a hell lot. Looking at it now, and looking at it what we had in the last few weeks, it's like where nearly all of my team members say is like finally I don't need any script kiddies anymore because I can't go to (indistinct) who writes the code for me. Which is on one end great, because it enables us to solve certain problems in a much higher pace. But the challenge with that is, if the people who just copy and past that code, don't understand the implications of that code, we have a much higher risk continuously. And what people thought was, is challenging with Stack Overflow. Imagine that something in one of these AI engines, is actually going ballistic, and it creates holes in nearly every one of these applications. And trust me, there will be enough developers who are going to use these tools to develop codes, the same as students in university are going to take this to write their essays and everything else. And so it's really important that every developer team basically has a security person within their team, and not a security at the end. So we build something, we check it, go through QA, and then it goes to security. Security needs to be at the forefront. And I think that's where we see Cloud Native Security Con, where we see AWS. I saw it during re:Invent already where they said is like, we have reinforced next year. I think this becomes more and more of a topic, and I think companies, as much as it is become a norm that you have a firewall and everything else, it needs to become a norm that when you are doing software development, and every development team needs to have a security person on that needs to be trained. >> I love that chat comment Dave, 'cause you and I were talking about this. And I think that is going to be the issue. Do we need security chat for the chat bot? And there's like a, like a recursive model there. The biases are built in. I think, and I think our interview with the Palo Alto Network's co-founder, Dave, when he talked about zero trust as a structured way to start things, but he was referencing that with Cloud, there's a chance to rethink or do a do-over in security. So, I think this is kind of to me, where this is all going. And I think you asked Pat Gelsinger what, year 2013, 2014, can, is security a do over? I think we're in that do over time. >> He said yes. >> He said yes. (laughing) He was right. But yeah, eight years later... But this is, how do you, zero trust gives you some structure, but how do you organize and redo security? Because to me, I think that's what's happening here. >> And John you heard, Zuk at Palo Alto Network said, "Yeah, the, the words security and architecture, they don't go together historically." And so it is a total, total retake. >> Well is that because there's too many tools out there and- >> Yeah. For sure. >> Yeah, well, first of all, a lot of hardware. And then yeah, a lot of tools. You even see IIOT and industry 40, you see IOT security coming up as another stove pipe, and that's not the right approach. And, and so- >> Well let me, let me ask you a question Dave, and Yves, if you don't mind. 'Cause I was just riffing on this yesterday about this. In the ML space, you're seeing the ML models, you're seeing proprietary models versus open source. Is security going to go down this proprietary security methods and open source? Because that's interesting, because the CNCF is run by the the Linux Foundation. So you can almost maybe see a model where there's more proprietary security methods than open source. Or is it, is that a non-issue? >> I would, I would, let me, if I, if I jump in here first, I think the last, especially last five or 10 years have clearly shown the, the whole and, and I invested early on in the, in the end 90s in several open source startups in the Bay area. So, I'm well behind the whole open source idea and, and mid (indistinct) and others back then several times. But the point is, I think what we have seen is open source is not in general, more secure or less secure, because code is too complex nowadays. You have millions of lines of code, and it's not that either one way or the other is going to solve it. The ways I think we are going to look at it is more is what's the role to market, because only because something is open source doesn't necessarily mean it's going to be available for everyone. And the same for proprietary source from that perspective, even though everybody mixes licensing and payments and all that all the time, but it doesn't necessarily have anything to do with it. But I think as we are going through it, and when we also look at the industry, security industry over the last 10 plus years has been primarily hardware focused. And a lot of these vendors have done a good business out of selling hardware boxes, putting software on top of it. Whereas in reality, those were still X86 standard boxes in the end. So it was not that we had specific security ethics or anything like that in there anymore. And so overall, the question of the market is going to change. And as we are looking into Cloud Native, think about someone like an AWS, do you really envision them to have a hardware box of every supplier in their data center, and that in every availability zone in every region? Same for Microsoft, same for Google, etc? So we need to have new ways on how we can apply security. And that applies both on the backend services, but also on the front end side. >> And if I, and if I could chime in, I think the, the good, I think the answer is, is, is no and yes. And what I mean by that is if you take, antivirus and known malware, I mean pretty much anybody today can, can solve that problem, it's the unknown malware. So I think the yes part of the answer is yes, it's, it's going to be proprietary, but in the sense we're going to use open source tooling, and then apply that in a proprietary way with, with specific algorithms and unique architectures that are going to solve problems. For example, XDR with, with unknown malware. So, and that's the, that's the hard part. As somebody said, I think this morning at the keynote, it's, it's all the stuff that, that the SecOps team couldn't find. That's the really hard part. >> (laughs) Well the question will be will, is the new IP, the ability to feed ChatGPT some magical spelled insertion query string that does the job, that's unique, that might be the new IP, the the question to ask. >> Well, that's what the hackers are going to do. And I, they're on offense. (John laughs) And the offense knows what play is coming. So, they're going to start. >> So guys, let's take this conversation up a level. I want to get your perspectives on what's in this for me as a customer? We know security is a board level conversation. We talk about this all the time. We also know that they're based on, I think David, was the conversations that you and I had, with Palo Alto Networks at Ignite in December. There's a, there's a lack of alignment between the executives and the board from a security perspective. When we talk about Cloud Native Security, we all talked about the value in that, what's in it for customers? I want to get your perspectives on should this be a board level conversation, and if so, how do you advise organizations, whether it is a hospital, or a bank, or an organization that is really affected by things like ransomware? How should they be thinking about this from an organizational perspective? >> Well, I'll start first, because we had this conversation during our Super Cloud event last month, and this comes up a lot. And this is, the CEO board level. Yes it is a board level conversation for security, as is application development as in terms of transforming their business to be competitive, not to be on the wrong side of history with this wave coming. So I think that's more of a management. But the issue is, they tell their people, "Go do it." And they're like, 'cause they get sold on the idea of, "Hey, won't you transform your business, and everything's going to be data driven, and machine learning's going to power your apps, get new customers, be profitable." "Oh, sign me up for that." When you have to implement this, it's really hard. And I think the core issue is, where are companies in their life cycle of the ability to execute and architect this thing properly as Dave said, Nick Zuk said, "You can't have architecture and security, you need platforms." So, I think the re-platforming, and the re-factoring of business is a big factor, and that's got to get down into the, the organizational shifts and the people to do it. So are there skills? Do I do a managed service? How do I architect it? Are there more services? Are there developers doing applications that are going to be more agile? So, this is not an easy thing. And to move a business from IT operations that is proven, to be positioned for this enablement, is just really difficult. And it's expensive. And if you screw it up, you could be, could be on the wrong side of things. So, to me, that's the big issue is, you sell the dream and then you got to implement it. And that's really difficult. >> Yves, give us your perspective on, based on John's comments, how do organizations shift so dramatically? There's a cultural element there as well, but there's also organizations that are, have competitive competitors in the rear view mirror, and there's time to waste. What are your thoughts on that? >> I think that's exactly the point. It's like, as an organization, you need to take the decision between the time, the risk, and all the other elements we have into this game. Because you can try to achieve 100% security, but that's exactly the same as trying to, to protect gold or anything else 100%. It's most likely not going to be from a risk perspective anyway sensible. And that's the same from a corporational perspective. When you look at building new internet services, or IOT services, or any kind of new shopping experience or whatever else, you need to balance out between the risks and the advantages out of it. And you also need to be accepting that you potentially on the way make mistakes, but then it's more important than ever that you are able to quickly fix any mistakes, and to adjust to anything what's happening in the market. Because as we are building all these new Cloud Native applications, and build up all these skill sets, one of the big scenarios is we are far more depending on individual building blocks. These building blocks come out of open source communities, which have a much different way. When we look back in software development, back then we had application servers from Oracle, Web Logic, whatsoever, they had a release cycles of every three to six months. As now we have to deal with open source, where sometimes release cycles are on a four week schedule, in between security patches. So you need to be much faster in adopting that, checking that, implementing that, getting things to work. So there is a security stretch from that perspective. There is a speech stretch on the other thing companies have to deal with, and on the other side it's always a measurement between the risk, and the security you can afford. Because reality is, you will not be 100% protected no matter what you do. So, you need to balance out what you as an organization can actually build on. But I think, coming back also to the point, it's on the bot level nowadays. It's like nearly every discussion we have with companies nowadays as they move into the Cloud, especially also here in Europe where for the last five years, it was always, it's like "It's data privacy." Data privacy is no longer, I mean, yes, for certain people, it's still the point, but for many more people it's like, "How protected is my data?" "What do we do in case of ransomware attack?" "What do we do in case of a denial of service?" All of these things become more vulnerable, where in the past you were discussing these things with a becking page, or, or like a stock exchange. They were, it's like, "What the hell is going to happen if we have a denial of service?" Now all of the sudden, this now affects nearly everyone in their storefronts and everything else, because everything is depending on it. >> Yeah, I think you're right on. You think about how cultural change occurs, it's bottom ups or, bottom up, top down or middle out. And what, what's happened with security is the people in the security team cared about it, they were the, everybody said, "Oh, it's their problem." And then it just did an end run to the board, kind of mid, early last decade. And then the board sort of pushed that down. And the line of business is realizing, "Holy cow. My business, my EBIT can be dramatically affected by this, so I care." Now it's this whole house, cultural team sport. I know it's sort of a, a cliche, but it, it's true. Everybody actually is beginning to care about security because the risks are now so high, and it's going to affect not only the bottom line of the company, the bottom line of the business, their job, it's, it's, it's virtually everywhere. It's a huge cultural shift that we're seeing. >> And that's a big challenge for organizations in any industry. And Yves, you talked about ransomware service. Every industry across the globe is vulnerable to this. But how can, maybe John, we'll start with you. How can Cloud Native Security help organizations if they're able to embrace it, operationally, culturally, dial down some of the vulnerabilities that just seem to keep growing? >> Well, I mean that's the big question. The breaches are, are critical. The governances also could be a way that anchors down growth. So I think the balance between the governance compliance piece of it is key, but making the developers faster and more productive is the key to me. And I think having the security paradigm where they're not blockers, as Dave said, is critical. So I love the whole shift left, but now that we have more data focused initiatives around how that, you can use data to understand the security issues, I think data and security are together, and I think there's a going to be a data operating system model emerging, where data and security will be almost one thing. And that will be set up by the security teams, and the data teams together. And that will feed guardrails into the developer environment. So the developer should feel no pain at all in doing this. So I think the best practice will end up being what we're seeing with supply chain, security, with making sure code's verified. And you're going to see the container, security side completely address has been, and KubeCon, we just, I asked Scott Johnson, the CEO of Docker, and I asked him directly, "Are you guys all tight on container security?" He said, yes, but other people are suggesting that's not true. There's a lot of issues with the container security. So, there's all kinds of areas where there's holes. So Cloud Native is cool on one hand, and very relevant, but if it's not shored up, it's going to be a problem. But I, so I think that's where the action will be, at the developer pipeline, in the containers, and the data. So, that will be very relevant, and if companies nail that, they'll be faster, they'll have better apps, and that'll be the differentiator. And again, if they don't on this next wave, they're going to be driftwood. >> Dave, how do they prevent becoming driftwood? >> Well, I think Cloud has had a huge impact. And a Cloud's by no means a panacea, but let's face it, it's dramatically improved a lot of companies security posture. Now there's still that shared responsibility. Even though an S3 bucket is encrypted, it's still your responsibility to make sure that it doesn't get decrypted by somebody who has access to it. So there are things like that, but to Yve's earlier point, that can be, that's done through software now, it's done through best practices. Those best practices can be shared. So the way you, you don't become driftwood, is you start to, you step back, rethink that security architecture as we were talking about earlier, take advantage of the Cloud, take advantage of Cloud Native, and all the, the rapid pace of innovation that's occurring there, and you don't use, it's called before, The audit is the last line of defense. That's no longer a check box item. "Oh yeah, we're in compliance." It's, this is a business imperative, and because we're going to reduce our expected loss and reduce our business risk. That's part of the business case today. >> Yeah. >> It's a huge, critically important part of the business case. Yves, question for you. If you're in an elevator with a CEO, a CFO, and a CISO, and they're talking about security and Cloud Native Security, what's your value proposition to them on a, on a say a 32nd elevator ride? >> Difficult story. I think at the moment, the most important part is, we need to get people to work together, and we need to train people to work more much better together. I think that's the overall most important part for all of these solutions, because in the end, security is always a person issue. If, we can have the best tools in the industry, as long as we don't get all of these teams to work together, then we have a problem. If the security team is always seen as the end of the solution to fix everything, that's not going to work because they always are the bad guys in the game. And so we need to bring the teams together. And once we have the teams work together, I think we have a far better track on, on maintaining security. >> John and Dave, I want to get your perspectives on what Yves just said. In all the experience that the two of you have as industry analysts here on "theCUBE," Wikibon, Siliconangle Media. How do you advise organizations to get those teams together? As Eve said, that alignment is critical, but John, we'll start with you, then Dave go to you. What's your advice for organizations that need to align those teams and really don't have a lot of time to wait to do it? >> (chuckling) That's a great question. I think, I think that's everyone pays hundreds of thousands of millions of dollars to get that advice from these consultants, organizations out there doing the transformations. But I think it comes down to personnel and commitment. I think if there's a C-level commitment to the effort, you'll see the institutional structure change. So you can see really getting behind it with their, with their wallet and their, and their support of either getting more personnel to support and assist, or manage services, or giving the power to the teams to execute and doing it in a way that, that's, that's well known and best practices. Start small, build out the pilots, build the platform, and then start getting it right. And I think that's the key. Not the magic wand, the old model of rolling out stuff in, in six month cycles. It's really, get the proof points, double down and change the culture, but also execute and have real metrics. And changing the architecture, like having more penetration tests as a service. Doing pen tests is like a joke now. So that doesn't make any sense. You got to have that built in almost every day, and every minute. So, these kinds of new techniques have to be implemented and have to be tried. So that's why these communities are growing. That's why I like what open source has been doing, and I like the open source as the place to have these conversations, because that's where the action will be for new stuff. And I think people will implement open source like they did before, but with different ways, better testing, better supply chain on the software side, verifying code. So, I see open source actually getting a tailwind from this, not a headwind. So, I'm bullish on the open source piece here on, on all levels, machine learning- >> Lisa, my answer is intramural sports. And it's 'cause I think it's cultural. And what I mean by that, is you take your your best and brightest security, and this is what frankly, a lot of CISOs do, an examples is Lena Smart, MongoDB. Take your best and brightest security pros, make them captains of the intramural teams, and pair them up with pods of individuals across the organization, which is most people who don't know anything about security, and put them together, so that they can, they, so that the folks that understand security can, can realize how little people know, what, what, what, how, what the worst practices that are out there in the reverse, how they can cross pollinate. And they do that on a regular basis, I know at Mongo and other companies. And that kind of cultural assimilation is a starting point for how you get security awareness up to your question around making it a team sport. >> Absolutely critical. Yves, I want to kind of wrap things with you. We've got a couple of minutes left. When you're really looking at the Cloud Native community, the growth of it, we talked about earlier in the program, Cloud Native Security Con being now extracted and elevated out of KubeCon, what are your thoughts on the groundswell that this community is generating around Cloud Native Security, the benefits that organizations will achieve from it? >> I think overall, when we have these securities conferences, or these security arms a bit spread out and separated out of the main conference, it helps to a certain degree, because especially in the security space, when you look at at other like black hat or white hat conferences and things like that in the past, although they were not focused on Cloud Native, a lot of these security folks didn't feel well taken care of in any of the other conferences because they were always these, it's like they are always blocking us, they're always making us problems, and all these kinds of things. Now that we really take the Cloud Native piece and the security piece together, or like AWS does it with re:Inforce, I think we will see more and more that people understand is that security is a permanent topic we need to cover, but we need to bring different people together, because security also has compliance and a lot of other components in there. So we will see at these conferences moving forward, also a different audience. It's not going to be only the Cloud Native developers. And if I see some of these security audiences, I can't really imagine them to really be at KubeCon because there is too much other things going on. And you couldn't really see much of that at re:Invent because re:Invent by itself has become a complete monster of a conference. It covers too many topics. And so having this very, very important security piece separated, also gives the opportunity, I think, that we can bring in the security people, but also have the type of board level discussions potentially, between the leaders of the industry, to also discuss on how we can evolve, how we can make things better, and how, how we can actually, yeah, evolve our industry for it. Because let's face it, that threat is not going to go away. It's, it's a business. And one of the last security conferences I was on, on the ransomware part, it was one of the topics someone said is like, "Look, currently on average, it takes a hacker group roughly around they said 15 to 20 K to break into a company, and they on average make 100K. It's a business, let's face it. And it's a business we don't like. And ethically, it's no discussion that this is not good, but that's something which is happening. People are making money with it. And as long as that's going to go on, and we have enough countries where these people can hide, it's going to stay and survive. And so, with that being said, it's important for us to really build an industry around this. But I also think it's good that we have separate conferences. In the past we had more the RSA conference, which tried to cover all of these areas. But that is not really fitting Cloud Native and everything else. So I think it's good that we have these new opportunities, the Cloud Native one, but also what AWS brings up for someone. >> Yves, you just nailed it. It just comes down to simple math. It's a fraction. Revenue over cost. And if you could increase the hacker's cost, increase the denominator, their ROI will go down. And that is the game. >> Great point, Dave. What I'm hearing guys, and we can talk about technology for days and days. I know all of you. But there's, there's a big component that, that the elevation of Cloud Native Security, on its own as standalone is critical, as is the people component. You guys all talked about that. We talked about the cultural change necessary for that. Hopefully what we're seeing with Cloud Native Security Con 23, this first event is going to give us more insight over the next couple of days, and the next months or so, as to how this elevation, and how the people can come together to really help organizations from a math perspective as, as Dave talked about, really dial down the risks there, understand more of the vulnerabilities so that ransomware as a service is not as lucrative as it is today. Guys, so much appreciate your time, really breaking down Cloud Native Security, the value in it from different perspectives, and what your thoughts are on where it's going. Thanks so much for your time. >> All right. Thanks. >> Thanks, Lisa. >> Thank you. >> Thanks, Yves. >> All right. For my guests, I'm Lisa Martin. You're watching theCUBE's day one coverage of Cloud Native Security Con 23. Thanks for watching. (rousing music)

(upbeat music) >> Hello, this is Dave Valente with theCUBE, and one of the most gratifying aspects of my role as a host of "theCUBE TV" is I get to cover a wide range of topics. And quite often, we're able to bring to our program a level of expertise that allows us to more deeply explore and unpack some of the topics that we cover throughout the year. And one of our favorite topics, of course, is data. Now, in 2021, after being in isolation for the better part of two years, a group of industry analysts met up at AWS re:Invent and started a collaboration to look at the trends in data and predict what some likely outcomes will be for the coming year. And it resulted in a very popular session that we had last year focused on the future of data management. And I'm very excited and pleased to tell you that the 2023 edition of that predictions episode is back, and with me are five outstanding market analyst, Sanjeev Mohan of SanjMo, Tony Baer of dbInsight, Carl Olofson from IDC, Dave Menninger from Ventana Research, and Doug Henschen, VP and Principal Analyst at Constellation Research. Now, what is it that we're calling you, guys? A data pack like the rat pack? No, no, no, no, that's not it. It's the data crowd, the data crowd, and the crowd includes some of the best minds in the data analyst community. They'll discuss how data management is evolving and what listeners should prepare for in 2023. Guys, welcome back. Great to see you. >> Good to be here. >> Thank you. >> Thanks, Dave. (Tony and Dave faintly speaks) >> All right, before we get into 2023 predictions, we thought it'd be good to do a look back at how we did in 2022 and give a transparent assessment of those predictions. So, let's get right into it. We're going to bring these up here, the predictions from 2022, they're color-coded red, yellow, and green to signify the degree of accuracy. And I'm pleased to report there's no red. Well, maybe some of you will want to debate that grading system. But as always, we want to be open, so you can decide for yourselves. So, we're going to ask each analyst to review their 2022 prediction and explain their rating and what evidence they have that led them to their conclusion. So, Sanjeev, please kick it off. Your prediction was data governance becomes key. I know that's going to knock you guys over, but elaborate, because you had more detail when you double click on that. >> Yeah, absolutely. Thank you so much, Dave, for having us on the show today. And we self-graded ourselves. I could have very easily made my prediction from last year green, but I mentioned why I left it as yellow. I totally fully believe that data governance was in a renaissance in 2022. And why do I say that? You have to look no further than AWS launching its own data catalog called DataZone. Before that, mid-year, we saw Unity Catalog from Databricks went GA. So, overall, I saw there was tremendous movement. When you see these big players launching a new data catalog, you know that they want to be in this space. And this space is highly critical to everything that I feel we will talk about in today's call. Also, if you look at established players, I spoke at Collibra's conference, data.world, work closely with Alation, Informatica, a bunch of other companies, they all added tremendous new capabilities. So, it did become key. The reason I left it as yellow is because I had made a prediction that Collibra would go IPO, and it did not. And I don't think anyone is going IPO right now. The market is really, really down, the funding in VC IPO market. But other than that, data governance had a banner year in 2022. >> Yeah. Well, thank you for that. And of course, you saw data clean rooms being announced at AWS re:Invent, so more evidence. And I like how the fact that you included in your predictions some things that were binary, so you dinged yourself there. So, good job. Okay, Tony Baer, you're up next. Data mesh hits reality check. As you see here, you've given yourself a bright green thumbs up. (Tony laughing) Okay. Let's hear why you feel that was the case. What do you mean by reality check? >> Okay. Thanks, Dave, for having us back again. This is something I just wrote and just tried to get away from, and this just a topic just won't go away. I did speak with a number of folks, early adopters and non-adopters during the year. And I did find that basically that it pretty much validated what I was expecting, which was that there was a lot more, this has now become a front burner issue. And if I had any doubt in my mind, the evidence I would point to is what was originally intended to be a throwaway post on LinkedIn, which I just quickly scribbled down the night before leaving for re:Invent. I was packing at the time, and for some reason, I was doing Google search on data mesh. And I happened to have tripped across this ridiculous article, I will not say where, because it doesn't deserve any publicity, about the eight (Dave laughing) best data mesh software companies of 2022. (Tony laughing) One of my predictions was that you'd see data mesh washing. And I just quickly just hopped on that maybe three sentences and wrote it at about a couple minutes saying this is hogwash, essentially. (laughs) And that just reun... And then, I left for re:Invent. And the next night, when I got into my Vegas hotel room, I clicked on my computer. I saw a 15,000 hits on that post, which was the most hits of any single post I put all year. And the responses were wildly pro and con. So, it pretty much validates my expectation in that data mesh really did hit a lot more scrutiny over this past year. >> Yeah, thank you for that. I remember that article. I remember rolling my eyes when I saw it, and then I recently, (Tony laughing) I talked to Walmart and they actually invoked Martin Fowler and they said that they're working through their data mesh. So, it takes a really lot of thought, and it really, as we've talked about, is really as much an organizational construct. You're not buying data mesh >> Bingo. >> to your point. Okay. Thank you, Tony. Carl Olofson, here we go. You've graded yourself a yellow in the prediction of graph databases. Take off. Please elaborate. >> Yeah, sure. So, I realized in looking at the prediction that it seemed to imply that graph databases could be a major factor in the data world in 2022, which obviously didn't become the case. It was an error on my part in that I should have said it in the right context. It's really a three to five-year time period that graph databases will really become significant, because they still need accepted methodologies that can be applied in a business context as well as proper tools in order for people to be able to use them seriously. But I stand by the idea that it is taking off, because for one thing, Neo4j, which is the leading independent graph database provider, had a very good year. And also, we're seeing interesting developments in terms of things like AWS with Neptune and with Oracle providing graph support in Oracle database this past year. Those things are, as I said, growing gradually. There are other companies like TigerGraph and so forth, that deserve watching as well. But as far as becoming mainstream, it's going to be a few years before we get all the elements together to make that happen. Like any new technology, you have to create an environment in which ordinary people without a whole ton of technical training can actually apply the technology to solve business problems. >> Yeah, thank you for that. These specialized databases, graph databases, time series databases, you see them embedded into mainstream data platforms, but there's a place for these specialized databases, I would suspect we're going to see new types of databases emerge with all this cloud sprawl that we have and maybe to the edge. >> Well, part of it is that it's not as specialized as you might think it. You can apply graphs to great many workloads and use cases. It's just that people have yet to fully explore and discover what those are. >> Yeah. >> And so, it's going to be a process. (laughs) >> All right, Dave Menninger, streaming data permeates the landscape. You gave yourself a yellow. Why? >> Well, I couldn't think of a appropriate combination of yellow and green. Maybe I should have used chartreuse, (Dave laughing) but I was probably a little hard on myself making it yellow. This is another type of specialized data processing like Carl was talking about graph databases is a stream processing, and nearly every data platform offers streaming capabilities now. Often, it's based on Kafka. If you look at Confluent, their revenues have grown at more than 50%, continue to grow at more than 50% a year. They're expected to do more than half a billion dollars in revenue this year. But the thing that hasn't happened yet, and to be honest, they didn't necessarily expect it to happen in one year, is that streaming hasn't become the default way in which we deal with data. It's still a sidecar to data at rest. And I do expect that we'll continue to see streaming become more and more mainstream. I do expect perhaps in the five-year timeframe that we will first deal with data as streaming and then at rest, but the worlds are starting to merge. And we even see some vendors bringing products to market, such as K2View, Hazelcast, and RisingWave Labs. So, in addition to all those core data platform vendors adding these capabilities, there are new vendors approaching this market as well. >> I like the tough grading system, and it's not trivial. And when you talk to practitioners doing this stuff, there's still some complications in the data pipeline. And so, but I think, you're right, it probably was a yellow plus. Doug Henschen, data lakehouses will emerge as dominant. When you talk to people about lakehouses, practitioners, they all use that term. They certainly use the term data lake, but now, they're using lakehouse more and more. What's your thoughts on here? Why the green? What's your evidence there? >> Well, I think, I was accurate. I spoke about it specifically as something that vendors would be pursuing. And we saw yet more lakehouse advocacy in 2022. Google introduced its BigLake service alongside BigQuery. Salesforce introduced Genie, which is really a lakehouse architecture. And it was a safe prediction to say vendors are going to be pursuing this in that AWS, Cloudera, Databricks, Microsoft, Oracle, SAP, Salesforce now, IBM, all advocate this idea of a single platform for all of your data. Now, the trend was also supported in 2023, in that we saw a big embrace of Apache Iceberg in 2022. That's a structured table format. It's used with these lakehouse platforms. It's open, so it ensures portability and it also ensures performance. And that's a structured table that helps with the warehouse side performance. But among those announcements, Snowflake, Google, Cloud Era, SAP, Salesforce, IBM, all embraced Iceberg. But keep in mind, again, I'm talking about this as something that vendors are pursuing as their approach. So, they're advocating end users. It's very cutting edge. I'd say the top, leading edge, 5% of of companies have really embraced the lakehouse. I think, we're now seeing the fast followers, the next 20 to 25% of firms embracing this idea and embracing a lakehouse architecture. I recall Christian Kleinerman at the big Snowflake event last summer, making the announcement about Iceberg, and he asked for a show of hands for any of you in the audience at the keynote, have you heard of Iceberg? And just a smattering of hands went up. So, the vendors are ahead of the curve. They're pushing this trend, and we're now seeing a little bit more mainstream uptake. >> Good. Doug, I was there. It was you, me, and I think, two other hands were up. That was just humorous. (Doug laughing) All right, well, so I liked the fact that we had some yellow and some green. When you think about these things, there's the prediction itself. Did it come true or not? There are the sub predictions that you guys make, and of course, the degree of difficulty. So, thank you for that open assessment. All right, let's get into the 2023 predictions. Let's bring up the predictions. Sanjeev, you're going first. You've got a prediction around unified metadata. What's the prediction, please? >> So, my prediction is that metadata space is currently a mess. It needs to get unified. There are too many use cases of metadata, which are being addressed by disparate systems. For example, data quality has become really big in the last couple of years, data observability, the whole catalog space is actually, people don't like to use the word data catalog anymore, because data catalog sounds like it's a catalog, a museum, if you may, of metadata that you go and admire. So, what I'm saying is that in 2023, we will see that metadata will become the driving force behind things like data ops, things like orchestration of tasks using metadata, not rules. Not saying that if this fails, then do this, if this succeeds, go do that. But it's like getting to the metadata level, and then making a decision as to what to orchestrate, what to automate, how to do data quality check, data observability. So, this space is starting to gel, and I see there'll be more maturation in the metadata space. Even security privacy, some of these topics, which are handled separately. And I'm just talking about data security and data privacy. I'm not talking about infrastructure security. These also need to merge into a unified metadata management piece with some knowledge graph, semantic layer on top, so you can do analytics on it. So, it's no longer something that sits on the side, it's limited in its scope. It is actually the very engine, the very glue that is going to connect data producers and consumers. >> Great. Thank you for that. Doug. Doug Henschen, any thoughts on what Sanjeev just said? Do you agree? Do you disagree? >> Well, I agree with many aspects of what he says. I think, there's a huge opportunity for consolidation and streamlining of these as aspects of governance. Last year, Sanjeev, you said something like, we'll see more people using catalogs than BI. And I have to disagree. I don't think this is a category that's headed for mainstream adoption. It's a behind the scenes activity for the wonky few, or better yet, companies want machine learning and automation to take care of these messy details. We've seen these waves of management technologies, some of the latest data observability, customer data platform, but they failed to sweep away all the earlier investments in data quality and master data management. So, yes, I hope the latest tech offers, glimmers that there's going to be a better, cleaner way of addressing these things. But to my mind, the business leaders, including the CIO, only want to spend as much time and effort and money and resources on these sorts of things to avoid getting breached, ending up in headlines, getting fired or going to jail. So, vendors bring on the ML and AI smarts and the automation of these sorts of activities. >> So, if I may say something, the reason why we have this dichotomy between data catalog and the BI vendors is because data catalogs are very soon, not going to be standalone products, in my opinion. They're going to get embedded. So, when you use a BI tool, you'll actually use the catalog to find out what is it that you want to do, whether you are looking for data or you're looking for an existing dashboard. So, the catalog becomes embedded into the BI tool. >> Hey, Dave Menninger, sometimes you have some data in your back pocket. Do you have any stats (chuckles) on this topic? >> No, I'm glad you asked, because I'm going to... Now, data catalogs are something that's interesting. Sanjeev made a statement that data catalogs are falling out of favor. I don't care what you call them. They're valuable to organizations. Our research shows that organizations that have adequate data catalog technologies are three times more likely to express satisfaction with their analytics for just the reasons that Sanjeev was talking about. You can find what you want, you know you're getting the right information, you know whether or not it's trusted. So, those are good things. So, we expect to see the capabilities, whether it's embedded or separate. We expect to see those capabilities continue to permeate the market. >> And a lot of those catalogs are driven now by machine learning and things. So, they're learning from those patterns of usage by people when people use the data. (airy laughs) >> All right. Okay. Thank you, guys. All right. Let's move on to the next one. Tony Bear, let's bring up the predictions. You got something in here about the modern data stack. We need to rethink it. Is the modern data stack getting long at the tooth? Is it not so modern anymore? >> I think, in a way, it's got almost too modern. It's gotten too, I don't know if it's being long in the tooth, but it is getting long. The modern data stack, it's traditionally been defined as basically you have the data platform, which would be the operational database and the data warehouse. And in between, you have all the tools that are necessary to essentially get that data from the operational realm or the streaming realm for that matter into basically the data warehouse, or as we might be seeing more and more, the data lakehouse. And I think, what's important here is that, or I think, we have seen a lot of progress, and this would be in the cloud, is with the SaaS services. And especially you see that in the modern data stack, which is like all these players, not just the MongoDBs or the Oracles or the Amazons have their database platforms. You see they have the Informatica's, and all the other players there in Fivetrans have their own SaaS services. And within those SaaS services, you get a certain degree of simplicity, which is it takes all the housekeeping off the shoulders of the customers. That's a good thing. The problem is that what we're getting to unfortunately is what I would call lots of islands of simplicity, which means that it leads it (Dave laughing) to the customer to have to integrate or put all that stuff together. It's a complex tool chain. And so, what we really need to think about here, we have too many pieces. And going back to the discussion of catalogs, it's like we have so many catalogs out there, which one do we use? 'Cause chances are of most organizations do not rely on a single catalog at this point. What I'm calling on all the data providers or all the SaaS service providers, is to literally get it together and essentially make this modern data stack less of a stack, make it more of a blending of an end-to-end solution. And that can come in a number of different ways. Part of it is that we're data platform providers have been adding services that are adjacent. And there's some very good examples of this. We've seen progress over the past year or so. For instance, MongoDB integrating search. It's a very common, I guess, sort of tool that basically, that the applications that are developed on MongoDB use, so MongoDB then built it into the database rather than requiring an extra elastic search or open search stack. Amazon just... AWS just did the zero-ETL, which is a first step towards simplifying the process from going from Aurora to Redshift. You've seen same thing with Google, BigQuery integrating basically streaming pipelines. And you're seeing also a lot of movement in database machine learning. So, there's some good moves in this direction. I expect to see more than this year. Part of it's from basically the SaaS platform is adding some functionality. But I also see more importantly, because you're never going to get... This is like asking your data team and your developers, herding cats to standardizing the same tool. In most organizations, that is not going to happen. So, take a look at the most popular combinations of tools and start to come up with some pre-built integrations and pre-built orchestrations, and offer some promotional pricing, maybe not quite two for, but in other words, get two products for the price of two services or for the price of one and a half. I see a lot of potential for this. And it's to me, if the class was to simplify things, this is the next logical step and I expect to see more of this here. >> Yeah, and you see in Oracle, MySQL heat wave, yet another example of eliminating that ETL. Carl Olofson, today, if you think about the data stack and the application stack, they're largely separate. Do you have any thoughts on how that's going to play out? Does that play into this prediction? What do you think? >> Well, I think, that the... I really like Tony's phrase, islands of simplification. It really says (Tony chuckles) what's going on here, which is that all these different vendors you ask about, about how these stacks work. All these different vendors have their own stack vision. And you can... One application group is going to use one, and another application group is going to use another. And some people will say, let's go to, like you go to a Informatica conference and they say, we should be the center of your universe, but you can't connect everything in your universe to Informatica, so you need to use other things. So, the challenge is how do we make those things work together? As Tony has said, and I totally agree, we're never going to get to the point where people standardize on one organizing system. So, the alternative is to have metadata that can be shared amongst those systems and protocols that allow those systems to coordinate their operations. This is standard stuff. It's not easy. But the motive for the vendors is that they can become more active critical players in the enterprise. And of course, the motive for the customer is that things will run better and more completely. So, I've been looking at this in terms of two kinds of metadata. One is the meaning metadata, which says what data can be put together. The other is the operational metadata, which says basically where did it come from? Who created it? What's its current state? What's the security level? Et cetera, et cetera, et cetera. The good news is the operational stuff can actually be done automatically, whereas the meaning stuff requires some human intervention. And as we've already heard from, was it Doug, I think, people are disinclined to put a lot of definition into meaning metadata. So, that may be the harder one, but coordination is key. This problem has been with us forever, but with the addition of new data sources, with streaming data with data in different formats, the whole thing has, it's been like what a customer of mine used to say, "I understand your product can make my system run faster, but right now I just feel I'm putting my problems on roller skates. (chuckles) I don't need that to accelerate what's already not working." >> Excellent. Okay, Carl, let's stay with you. I remember in the early days of the big data movement, Hadoop movement, NoSQL was the big thing. And I remember Amr Awadallah said to us in theCUBE that SQL is the killer app for big data. So, your prediction here, if we bring that up is SQL is back. Please elaborate. >> Yeah. So, of course, some people would say, well, it never left. Actually, that's probably closer to true, but in the perception of the marketplace, there's been all this noise about alternative ways of storing, retrieving data, whether it's in key value stores or document databases and so forth. We're getting a lot of messaging that for a while had persuaded people that, oh, we're not going to do analytics in SQL anymore. We're going to use Spark for everything, except that only a handful of people know how to use Spark. Oh, well, that's a problem. Well, how about, and for ordinary conventional business analytics, Spark is like an over-engineered solution to the problem. SQL works just great. What's happened in the past couple years, and what's going to continue to happen is that SQL is insinuating itself into everything we're seeing. We're seeing all the major data lake providers offering SQL support, whether it's Databricks or... And of course, Snowflake is loving this, because that is what they do, and their success is certainly points to the success of SQL, even MongoDB. And we were all, I think, at the MongoDB conference where on one day, we hear SQL is dead. They're not teaching SQL in schools anymore, and this kind of thing. And then, a couple days later at the same conference, they announced we're adding a new analytic capability-based on SQL. But didn't you just say SQL is dead? So, the reality is that SQL is better understood than most other methods of certainly of retrieving and finding data in a data collection, no matter whether it happens to be relational or non-relational. And even in systems that are very non-relational, such as graph and document databases, their query languages are being built or extended to resemble SQL, because SQL is something people understand. >> Now, you remember when we were in high school and you had had to take the... Your debating in the class and you were forced to take one side and defend it. So, I was was at a Vertica conference one time up on stage with Curt Monash, and I had to take the NoSQL, the world is changing paradigm shift. And so just to be controversial, I said to him, Curt Monash, I said, who really needs acid compliance anyway? Tony Baer. And so, (chuckles) of course, his head exploded, but what are your thoughts (guests laughing) on all this? >> Well, my first thought is congratulations, Dave, for surviving being up on stage with Curt Monash. >> Amen. (group laughing) >> I definitely would concur with Carl. We actually are definitely seeing a SQL renaissance and if there's any proof of the pudding here, I see lakehouse is being icing on the cake. As Doug had predicted last year, now, (clears throat) for the record, I think, Doug was about a year ahead of time in his predictions that this year is really the year that I see (clears throat) the lakehouse ecosystems really firming up. You saw the first shots last year. But anyway, on this, data lakes will not go away. I've actually, I'm on the home stretch of doing a market, a landscape on the lakehouse. And lakehouse will not replace data lakes in terms of that. There is the need for those, data scientists who do know Python, who knows Spark, to go in there and basically do their thing without all the restrictions or the constraints of a pre-built, pre-designed table structure. I get that. Same thing for developing models. But on the other hand, there is huge need. Basically, (clears throat) maybe MongoDB was saying that we're not teaching SQL anymore. Well, maybe we have an oversupply of SQL developers. Well, I'm being facetious there, but there is a huge skills based in SQL. Analytics have been built on SQL. They came with lakehouse and why this really helps to fuel a SQL revival is that the core need in the data lake, what brought on the lakehouse was not so much SQL, it was a need for acid. And what was the best way to do it? It was through a relational table structure. So, the whole idea of acid in the lakehouse was not to turn it into a transaction database, but to make the data trusted, secure, and more granularly governed, where you could govern down to column and row level, which you really could not do in a data lake or a file system. So, while lakehouse can be queried in a manner, you can go in there with Python or whatever, it's built on a relational table structure. And so, for that end, for those types of data lakes, it becomes the end state. You cannot bypass that table structure as I learned the hard way during my research. So, the bottom line I'd say here is that lakehouse is proof that we're starting to see the revenge of the SQL nerds. (Dave chuckles) >> Excellent. Okay, let's bring up back up the predictions. Dave Menninger, this one's really thought-provoking and interesting. We're hearing things like data as code, new data applications, machines actually generating plans with no human involvement. And your prediction is the definition of data is expanding. What do you mean by that? >> So, I think, for too long, we've thought about data as the, I would say facts that we collect the readings off of devices and things like that, but data on its own is really insufficient. Organizations need to manipulate that data and examine derivatives of the data to really understand what's happening in their organization, why has it happened, and to project what might happen in the future. And my comment is that these data derivatives need to be supported and managed just like the data needs to be managed. We can't treat this as entirely separate. Think about all the governance discussions we've had. Think about the metadata discussions we've had. If you separate these things, now you've got more moving parts. We're talking about simplicity and simplifying the stack. So, if these things are treated separately, it creates much more complexity. I also think it creates a little bit of a myopic view on the part of the IT organizations that are acquiring these technologies. They need to think more broadly. So, for instance, metrics. Metric stores are becoming much more common part of the tooling that's part of a data platform. Similarly, feature stores are gaining traction. So, those are designed to promote the reuse and consistency across the AI and ML initiatives. The elements that are used in developing an AI or ML model. And let me go back to metrics and just clarify what I mean by that. So, any type of formula involving the data points. I'm distinguishing metrics from features that are used in AI and ML models. And the data platforms themselves are increasingly managing the models as an element of data. So, just like figuring out how to calculate a metric. Well, if you're going to have the features associated with an AI and ML model, you probably need to be managing the model that's associated with those features. The other element where I see expansion is around external data. Organizations for decades have been focused on the data that they generate within their own organization. We see more and more of these platforms acquiring and publishing data to external third-party sources, whether they're within some sort of a partner ecosystem or whether it's a commercial distribution of that information. And our research shows that when organizations use external data, they derive even more benefits from the various analyses that they're conducting. And the last great frontier in my opinion on this expanding world of data is the world of driver-based planning. Very few of the major data platform providers provide these capabilities today. These are the types of things you would do in a spreadsheet. And we all know the issues associated with spreadsheets. They're hard to govern, they're error-prone. And so, if we can take that type of analysis, collecting the occupancy of a rental property, the projected rise in rental rates, the fluctuations perhaps in occupancy, the interest rates associated with financing that property, we can project forward. And that's a very common thing to do. What the income might look like from that property income, the expenses, we can plan and purchase things appropriately. So, I think, we need this broader purview and I'm beginning to see some of those things happen. And the evidence today I would say, is more focused around the metric stores and the feature stores starting to see vendors offer those capabilities. And we're starting to see the ML ops elements of managing the AI and ML models find their way closer to the data platforms as well. >> Very interesting. When I hear metrics, I think of KPIs, I think of data apps, orchestrate people and places and things to optimize around a set of KPIs. It sounds like a metadata challenge more... Somebody once predicted they'll have more metadata than data. Carl, what are your thoughts on this prediction? >> Yeah, I think that what Dave is describing as data derivatives is in a way, another word for what I was calling operational metadata, which not about the data itself, but how it's used, where it came from, what the rules are governing it, and that kind of thing. If you have a rich enough set of those things, then not only can you do a model of how well your vacation property rental may do in terms of income, but also how well your application that's measuring that is doing for you. In other words, how many times have I used it, how much data have I used and what is the relationship between the data that I've used and the benefits that I've derived from using it? Well, we don't have ways of doing that. What's interesting to me is that folks in the content world are way ahead of us here, because they have always tracked their content using these kinds of attributes. Where did it come from? When was it created, when was it modified? Who modified it? And so on and so forth. We need to do more of that with the structure data that we have, so that we can track what it's used. And also, it tells us how well we're doing with it. Is it really benefiting us? Are we being efficient? Are there improvements in processes that we need to consider? Because maybe data gets created and then it isn't used or it gets used, but it gets altered in some way that actually misleads people. (laughs) So, we need the mechanisms to be able to do that. So, I would say that that's... And I'd say that it's true that we need that stuff. I think, that starting to expand is probably the right way to put it. It's going to be expanding for some time. I think, we're still a distance from having all that stuff really working together. >> Maybe we should say it's gestating. (Dave and Carl laughing) >> Sorry, if I may- >> Sanjeev, yeah, I was going to say this... Sanjeev, please comment. This sounds to me like it supports Zhamak Dehghani's principles, but please. >> Absolutely. So, whether we call it data mesh or not, I'm not getting into that conversation, (Dave chuckles) but data (audio breaking) (Tony laughing) everything that I'm hearing what Dave is saying, Carl, this is the year when data products will start to take off. I'm not saying they'll become mainstream. They may take a couple of years to become so, but this is data products, all this thing about vacation rentals and how is it doing, that data is coming from different sources. I'm packaging it into our data product. And to Carl's point, there's a whole operational metadata associated with it. The idea is for organizations to see things like developer productivity, how many releases am I doing of this? What data products are most popular? I'm actually in right now in the process of formulating this concept that just like we had data catalogs, we are very soon going to be requiring data products catalog. So, I can discover these data products. I'm not just creating data products left, right, and center. I need to know, do they already exist? What is the usage? If no one is using a data product, maybe I want to retire and save cost. But this is a data product. Now, there's a associated thing that is also getting debated quite a bit called data contracts. And a data contract to me is literally just formalization of all these aspects of a product. How do you use it? What is the SLA on it, what is the quality that I am prescribing? So, data product, in my opinion, shifts the conversation to the consumers or to the business people. Up to this point when, Dave, you're talking about data and all of data discovery curation is a very data producer-centric. So, I think, we'll see a shift more into the consumer space. >> Yeah. Dave, can I just jump in there just very quickly there, which is that what Sanjeev has been saying there, this is really central to what Zhamak has been talking about. It's basically about making, one, data products are about the lifecycle management of data. Metadata is just elemental to that. And essentially, one of the things that she calls for is making data products discoverable. That's exactly what Sanjeev was talking about. >> By the way, did everyone just no notice how Sanjeev just snuck in another prediction there? So, we've got- >> Yeah. (group laughing) >> But you- >> Can we also say that he snuck in, I think, the term that we'll remember today, which is metadata museums. >> Yeah, but- >> Yeah. >> And also comment to, Tony, to your last year's prediction, you're really talking about it's not something that you're going to buy from a vendor. >> No. >> It's very specific >> Mm-hmm. >> to an organization, their own data product. So, touche on that one. Okay, last prediction. Let's bring them up. Doug Henschen, BI analytics is headed to embedding. What does that mean? >> Well, we all know that conventional BI dashboarding reporting is really commoditized from a vendor perspective. It never enjoyed truly mainstream adoption. Always that 25% of employees are really using these things. I'm seeing rising interest in embedding concise analytics at the point of decision or better still, using analytics as triggers for automation and workflows, and not even necessitating human interaction with visualizations, for example, if we have confidence in the analytics. So, leading companies are pushing for next generation applications, part of this low-code, no-code movement we've seen. And they want to build that decision support right into the app. So, the analytic is right there. Leading enterprise apps vendors, Salesforce, SAP, Microsoft, Oracle, they're all building smart apps with the analytics predictions, even recommendations built into these applications. And I think, the progressive BI analytics vendors are supporting this idea of driving insight to action, not necessarily necessitating humans interacting with it if there's confidence. So, we want prediction, we want embedding, we want automation. This low-code, no-code development movement is very important to bringing the analytics to where people are doing their work. We got to move beyond the, what I call swivel chair integration, between where people do their work and going off to separate reports and dashboards, and having to interpret and analyze before you can go back and do take action. >> And Dave Menninger, today, if you want, analytics or you want to absorb what's happening in the business, you typically got to go ask an expert, and then wait. So, what are your thoughts on Doug's prediction? >> I'm in total agreement with Doug. I'm going to say that collectively... So, how did we get here? I'm going to say collectively as an industry, we made a mistake. We made BI and analytics separate from the operational systems. Now, okay, it wasn't really a mistake. We were limited by the technology available at the time. Decades ago, we had to separate these two systems, so that the analytics didn't impact the operations. You don't want the operations preventing you from being able to do a transaction. But we've gone beyond that now. We can bring these two systems and worlds together and organizations recognize that need to change. As Doug said, the majority of the workforce and the majority of organizations doesn't have access to analytics. That's wrong. (chuckles) We've got to change that. And one of the ways that's going to change is with embedded analytics. 2/3 of organizations recognize that embedded analytics are important and it even ranks higher in importance than AI and ML in those organizations. So, it's interesting. This is a really important topic to the organizations that are consuming these technologies. The good news is it works. Organizations that have embraced embedded analytics are more comfortable with self-service than those that have not, as opposed to turning somebody loose, in the wild with the data. They're given a guided path to the data. And the research shows that 65% of organizations that have adopted embedded analytics are comfortable with self-service compared with just 40% of organizations that are turning people loose in an ad hoc way with the data. So, totally behind Doug's predictions. >> Can I just break in with something here, a comment on what Dave said about what Doug said, which (laughs) is that I totally agree with what you said about embedded analytics. And at IDC, we made a prediction in our future intelligence, future of intelligence service three years ago that this was going to happen. And the thing that we're waiting for is for developers to build... You have to write the applications to work that way. It just doesn't happen automagically. Developers have to write applications that reference analytic data and apply it while they're running. And that could involve simple things like complex queries against the live data, which is through something that I've been calling analytic transaction processing. Or it could be through something more sophisticated that involves AI operations as Doug has been suggesting, where the result is enacted pretty much automatically unless the scores are too low and you need to have a human being look at it. So, I think that that is definitely something we've been watching for. I'm not sure how soon it will come, because it seems to take a long time for people to change their thinking. But I think, as Dave was saying, once they do and they apply these principles in their application development, the rewards are great. >> Yeah, this is very much, I would say, very consistent with what we were talking about, I was talking about before, about basically rethinking the modern data stack and going into more of an end-to-end solution solution. I think, that what we're talking about clearly here is operational analytics. There'll still be a need for your data scientists to go offline just in their data lakes to do all that very exploratory and that deep modeling. But clearly, it just makes sense to bring operational analytics into where people work into their workspace and further flatten that modern data stack. >> But with all this metadata and all this intelligence, we're talking about injecting AI into applications, it does seem like we're entering a new era of not only data, but new era of apps. Today, most applications are about filling forms out or codifying processes and require a human input. And it seems like there's enough data now and enough intelligence in the system that the system can actually pull data from, whether it's the transaction system, e-commerce, the supply chain, ERP, and actually do something with that data without human involvement, present it to humans. Do you guys see this as a new frontier? >> I think, that's certainly- >> Very much so, but it's going to take a while, as Carl said. You have to design it, you have to get the prediction into the system, you have to get the analytics at the point of decision has to be relevant to that decision point. >> And I also recall basically a lot of the ERP vendors back like 10 years ago, we're promising that. And the fact that we're still looking at the promises shows just how difficult, how much of a challenge it is to get to what Doug's saying. >> One element that could be applied in this case is (indistinct) architecture. If applications are developed that are event-driven rather than following the script or sequence that some programmer or designer had preconceived, then you'll have much more flexible applications. You can inject decisions at various points using this technology much more easily. It's a completely different way of writing applications. And it actually involves a lot more data, which is why we should all like it. (laughs) But in the end (Tony laughing) it's more stable, it's easier to manage, easier to maintain, and it's actually more efficient, which is the result of an MIT study from about 10 years ago, and still, we are not seeing this come to fruition in most business applications. >> And do you think it's going to require a new type of data platform database? Today, data's all far-flung. We see that's all over the clouds and at the edge. Today, you cache- >> We need a super cloud. >> You cache that data, you're throwing into memory. I mentioned, MySQL heat wave. There are other examples where it's a brute force approach, but maybe we need new ways of laying data out on disk and new database architectures, and just when we thought we had it all figured out. >> Well, without referring to disk, which to my mind, is almost like talking about cave painting. I think, that (Dave laughing) all the things that have been mentioned by all of us today are elements of what I'm talking about. In other words, the whole improvement of the data mesh, the improvement of metadata across the board and improvement of the ability to track data and judge its freshness the way we judge the freshness of a melon or something like that, to determine whether we can still use it. Is it still good? That kind of thing. Bringing together data from multiple sources dynamically and real-time requires all the things we've been talking about. All the predictions that we've talked about today add up to elements that can make this happen. >> Well, guys, it's always tremendous to get these wonderful minds together and get your insights, and I love how it shapes the outcome here of the predictions, and let's see how we did. We're going to leave it there. I want to thank Sanjeev, Tony, Carl, David, and Doug. Really appreciate the collaboration and thought that you guys put into these sessions. Really, thank you. >> Thank you. >> Thanks, Dave. >> Thank you for having us. >> Thanks. >> Thank you. >> All right, this is Dave Valente for theCUBE, signing off for now. Follow these guys on social media. Look for coverage on siliconangle.com, theCUBE.net. Thank you for watching. (upbeat music)

>>From the Cube Studios in Palo Alto in Boston, bringing you data-driven insights from the cube and E T R. This is breaking analysis with Dave Valante. >>Making technology predictions in 2022 was tricky business, especially if you were projecting the performance of markets or identifying I P O prospects and making binary forecast on data AI and the macro spending climate and other related topics in enterprise tech 2022, of course was characterized by a seesaw economy where central banks were restructuring their balance sheets. The war on Ukraine fueled inflation supply chains were a mess. And the unintended consequences of of forced march to digital and the acceleration still being sorted out. Hello and welcome to this week's weekly on Cube Insights powered by E T R. In this breaking analysis, we continue our annual tradition of transparently grading last year's enterprise tech predictions. And you may or may not agree with our self grading system, but look, we're gonna give you the data and you can draw your own conclusions and tell you what, tell us what you think. >>All right, let's get right to it. So our first prediction was tech spending increases by 8% in 2022. And as we exited 2021 CIOs, they were optimistic about their digital transformation plans. You know, they rushed to make changes to their business and were eager to sharpen their focus and continue to iterate on their digital business models and plug the holes that they, the, in the learnings that they had. And so we predicted that 8% rise in enterprise tech spending, which looked pretty good until Ukraine and the Fed decided that, you know, had to rush and make up for lost time. We kind of nailed the momentum in the energy sector, but we can't give ourselves too much credit for that layup. And as of October, Gartner had it spending growing at just over 5%. I think it was 5.1%. So we're gonna take a C plus on this one and, and move on. >>Our next prediction was basically kind of a slow ground ball. The second base, if I have to be honest, but we felt it was important to highlight that security would remain front and center as the number one priority for organizations in 2022. As is our tradition, you know, we try to up the degree of difficulty by specifically identifying companies that are gonna benefit from these trends. So we highlighted some possible I P O candidates, which of course didn't pan out. S NQ was on our radar. The company had just had to do another raise and they recently took a valuation hit and it was a down round. They raised 196 million. So good chunk of cash, but, but not the i p O that we had predicted Aqua Securities focus on containers and cloud native. That was a trendy call and we thought maybe an M SS P or multiple managed security service providers like Arctic Wolf would I p o, but no way that was happening in the crummy market. >>Nonetheless, we think these types of companies, they're still faring well as the talent shortage in security remains really acute, particularly in the sort of mid-size and small businesses that often don't have a sock Lacework laid off 20% of its workforce in 2022. And CO C e o Dave Hatfield left the company. So that I p o didn't, didn't happen. It was probably too early for Lacework. Anyway, meanwhile you got Netscope, which we've cited as strong in the E T R data as particularly in the emerging technology survey. And then, you know, I lumia holding its own, you know, we never liked that 7 billion price tag that Okta paid for auth zero, but we loved the TAM expansion strategy to target developers beyond sort of Okta's enterprise strength. But we gotta take some points off of the failure thus far of, of Okta to really nail the integration and the go to market model with azero and build, you know, bring that into the, the, the core Okta. >>So the focus on endpoint security that was a winner in 2022 is CrowdStrike led that charge with others holding their own, not the least of which was Palo Alto Networks as it continued to expand beyond its core network security and firewall business, you know, through acquisition. So overall we're gonna give ourselves an A minus for this relatively easy call, but again, we had some specifics associated with it to make it a little tougher. And of course we're watching ve very closely this this coming year in 2023. The vendor consolidation trend. You know, according to a recent Palo Alto network survey with 1300 SecOps pros on average organizations have more than 30 tools to manage security tools. So this is a logical way to optimize cost consolidating vendors and consolidating redundant vendors. The E T R data shows that's clearly a trend that's on the upswing. >>Now moving on, a big theme of 2020 and 2021 of course was remote work and hybrid work and new ways to work and return to work. So we predicted in 2022 that hybrid work models would become the dominant protocol, which clearly is the case. We predicted that about 33% of the workforce would come back to the office in 2022 in September. The E T R data showed that figure was at 29%, but organizations expected that 32% would be in the office, you know, pretty much full-time by year end. That hasn't quite happened, but we were pretty close with the projection, so we're gonna take an A minus on this one. Now, supply chain disruption was another big theme that we felt would carry through 2022. And sure that sounds like another easy one, but as is our tradition, again we try to put some binary metrics around our predictions to put some meat in the bone, so to speak, and and allow us than you to say, okay, did it come true or not? >>So we had some data that we presented last year and supply chain issues impacting hardware spend. We said at the time, you can see this on the left hand side of this chart, the PC laptop demand would remain above pre covid levels, which would reverse a decade of year on year declines, which I think started in around 2011, 2012. Now, while demand is down this year pretty substantially relative to 2021, I D C has worldwide unit shipments for PCs at just over 300 million for 22. If you go back to 2019 and you're looking at around let's say 260 million units shipped globally, you know, roughly, so, you know, pretty good call there. Definitely much higher than pre covid levels. But so what you might be asking why the B, well, we projected that 30% of customers would replace security appliances with cloud-based services and that more than a third would replace their internal data center server and storage hardware with cloud services like 30 and 40% respectively. >>And we don't have explicit survey data on exactly these metrics, but anecdotally we see this happening in earnest. And we do have some data that we're showing here on cloud adoption from ET R'S October survey where the midpoint of workloads running in the cloud is around 34% and forecast, as you can see, to grow steadily over the next three years. So this, well look, this is not, we understand it's not a one-to-one correlation with our prediction, but it's a pretty good bet that we were right, but we gotta take some points off, we think for the lack of unequivocal proof. Cause again, we always strive to make our predictions in ways that can be measured as accurate or not. Is it binary? Did it happen, did it not? Kind of like an O K R and you know, we strive to provide data as proof and in this case it's a bit fuzzy. >>We have to admit that although we're pretty comfortable that the prediction was accurate. And look, when you make an hard forecast, sometimes you gotta pay the price. All right, next, we said in 2022 that the big four cloud players would generate 167 billion in IS and PaaS revenue combining for 38% market growth. And our current forecasts are shown here with a comparison to our January, 2022 figures. So coming into this year now where we are today, so currently we expect 162 billion in total revenue and a 33% growth rate. Still very healthy, but not on our mark. So we think a w s is gonna miss our predictions by about a billion dollars, not, you know, not bad for an 80 billion company. So they're not gonna hit that expectation though of getting really close to a hundred billion run rate. We thought they'd exit the year, you know, closer to, you know, 25 billion a quarter and we don't think they're gonna get there. >>Look, we pretty much nailed Azure even though our prediction W was was correct about g Google Cloud platform surpassing Alibaba, Alibaba, we way overestimated the performance of both of those companies. So we're gonna give ourselves a C plus here and we think, yeah, you might think it's a little bit harsh, we could argue for a B minus to the professor, but the misses on GCP and Alibaba we think warrant a a self penalty on this one. All right, let's move on to our prediction about Supercloud. We said it becomes a thing in 2022 and we think by many accounts it has, despite the naysayers, we're seeing clear evidence that the concept of a layer of value add that sits above and across clouds is taking shape. And on this slide we showed just some of the pickup in the industry. I mean one of the most interesting is CloudFlare, the biggest supercloud antagonist. >>Charles Fitzgerald even predicted that no vendor would ever use the term in their marketing. And that would be proof if that happened that Supercloud was a thing and he said it would never happen. Well CloudFlare has, and they launched their version of Supercloud at their developer week. Chris Miller of the register put out a Supercloud block diagram, something else that Charles Fitzgerald was, it was was pushing us for, which is rightly so, it was a good call on his part. And Chris Miller actually came up with one that's pretty good at David Linthicum also has produced a a a A block diagram, kind of similar, David uses the term metacloud and he uses the term supercloud kind of interchangeably to describe that trend. And so we we're aligned on that front. Brian Gracely has covered the concept on the popular cloud podcast. Berkeley launched the Sky computing initiative. >>You read through that white paper and many of the concepts highlighted in the Supercloud 3.0 community developed definition align with that. Walmart launched a platform with many of the supercloud salient attributes. So did Goldman Sachs, so did Capital One, so did nasdaq. So you know, sorry you can hate the term, but very clearly the evidence is gathering for the super cloud storm. We're gonna take an a plus on this one. Sorry, haters. Alright, let's talk about data mesh in our 21 predictions posts. We said that in the 2020s, 75% of large organizations are gonna re-architect their big data platforms. So kind of a decade long prediction. We don't like to do that always, but sometimes it's warranted. And because it was a longer term prediction, we, at the time in, in coming into 22 when we were evaluating our 21 predictions, we took a grade of incomplete because the sort of decade long or majority of the decade better part of the decade prediction. >>So last year, earlier this year, we said our number seven prediction was data mesh gains momentum in 22. But it's largely confined and narrow data problems with limited scope as you can see here with some of the key bullets. So there's a lot of discussion in the data community about data mesh and while there are an increasing number of examples, JP Morgan Chase, Intuit, H S P C, HelloFresh, and others that are completely rearchitecting parts of their data platform completely rearchitecting entire data platforms is non-trivial. There are organizational challenges, there're data, data ownership, debates, technical considerations, and in particular two of the four fundamental data mesh principles that the, the need for a self-service infrastructure and federated computational governance are challenging. Look, democratizing data and facilitating data sharing creates conflicts with regulatory requirements around data privacy. As such many organizations are being really selective with their data mesh implementations and hence our prediction of narrowing the scope of data mesh initiatives. >>I think that was right on J P M C is a good example of this, where you got a single group within a, within a division narrowly implementing the data mesh architecture. They're using a w s, they're using data lakes, they're using Amazon Glue, creating a catalog and a variety of other techniques to meet their objectives. They kind of automating data quality and it was pretty well thought out and interesting approach and I think it's gonna be made easier by some of the announcements that Amazon made at the recent, you know, reinvent, particularly trying to eliminate ET t l, better connections between Aurora and Redshift and, and, and better data sharing the data clean room. So a lot of that is gonna help. Of course, snowflake has been on this for a while now. Many other companies are facing, you know, limitations as we said here and this slide with their Hadoop data platforms. They need to do new, some new thinking around that to scale. HelloFresh is a really good example of this. Look, the bottom line is that organizations want to get more value from data and having a centralized, highly specialized teams that own the data problem, it's been a barrier and a blocker to success. The data mesh starts with organizational considerations as described in great detail by Ash Nair of Warner Brothers. So take a listen to this clip. >>Yeah, so when people think of Warner Brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of H B O, you think of t n t, you think of C N N. We have 30 plus brands in our portfolio and each have their own needs. So the, the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against, as an example, HBO if Game of Thrones is going on. >>So it's often the case that data mesh is in the eyes of the implementer. And while a company's implementation may not strictly adhere to Jamma Dani's vision of data mesh, and that's okay, the goal is to use data more effectively. And despite Gartner's attempts to deposition data mesh in favor of the somewhat confusing or frankly far more confusing data fabric concept that they stole from NetApp data mesh is taking hold in organizations globally today. So we're gonna take a B on this one. The prediction is shaping up the way we envision, but as we previously reported, it's gonna take some time. The better part of a decade in our view, new standards have to emerge to make this vision become reality and they'll come in the form of both open and de facto approaches. Okay, our eighth prediction last year focused on the face off between Snowflake and Databricks. >>And we realized this popular topic, and maybe one that's getting a little overplayed, but these are two companies that initially, you know, looked like they were shaping up as partners and they, by the way, they are still partnering in the field. But you go back a couple years ago, the idea of using an AW w s infrastructure, Databricks machine intelligence and applying that on top of Snowflake as a facile data warehouse, still very viable. But both of these companies, they have much larger ambitions. They got big total available markets to chase and large valuations that they have to justify. So what's happening is, as we've previously reported, each of these companies is moving toward the other firm's core domain and they're building out an ecosystem that'll be critical for their future. So as part of that effort, we said each is gonna become aggressive investors and maybe start doing some m and a and they have in various companies. >>And on this chart that we produced last year, we studied some of the companies that were targets and we've added some recent investments of both Snowflake and Databricks. As you can see, they've both, for example, invested in elation snowflake's, put money into Lacework, the Secur security firm, ThoughtSpot, which is trying to democratize data with ai. Collibra is a governance platform and you can see Databricks investments in data transformation with D B T labs, Matillion doing simplified business intelligence hunters. So that's, you know, they're security investment and so forth. So other than our thought that we'd see Databricks I p o last year, this prediction been pretty spot on. So we'll give ourselves an A on that one. Now observability has been a hot topic and we've been covering it for a while with our friends at E T R, particularly Eric Bradley. Our number nine prediction last year was basically that if you're not cloud native and observability, you are gonna be in big trouble. >>So everything guys gotta go cloud native. And that's clearly been the case. Splunk, the big player in the space has been transitioning to the cloud, hasn't always been pretty, as we reported, Datadog real momentum, the elk stack, that's open source model. You got new entrants that we've cited before, like observe, honeycomb, chaos search and others that we've, we've reported on, they're all born in the cloud. So we're gonna take another a on this one, admittedly, yeah, it's a re reasonably easy call, but you gotta have a few of those in the mix. Okay, our last prediction, our number 10 was around events. Something the cube knows a little bit about. We said that a new category of events would emerge as hybrid and that for the most part is happened. So that's gonna be the mainstay is what we said. That pure play virtual events are gonna give way to hi hybrid. >>And the narrative is that virtual only events are, you know, they're good for quick hits, but lousy replacements for in-person events. And you know that said, organizations of all shapes and sizes, they learn how to create better virtual content and support remote audiences during the pandemic. So when we set at pure play is gonna give way to hybrid, we said we, we i we implied or specific or specified that the physical event that v i p experience is going defined. That overall experience and those v i p events would create a little fomo, fear of, of missing out in a virtual component would overlay that serves an audience 10 x the size of the physical. We saw that really two really good examples. Red Hat Summit in Boston, small event, couple thousand people served tens of thousands, you know, online. Second was Google Cloud next v i p event in, in New York City. >>Everything else was, was, was, was virtual. You know, even examples of our prediction of metaverse like immersion have popped up and, and and, and you know, other companies are doing roadshow as we predicted like a lot of companies are doing it. You're seeing that as a major trend where organizations are going with their sales teams out into the regions and doing a little belly to belly action as opposed to the big giant event. That's a definitely a, a trend that we're seeing. So in reviewing this prediction, the grade we gave ourselves is, you know, maybe a bit unfair, it should be, you could argue for a higher grade, but the, but the organization still haven't figured it out. They have hybrid experiences but they generally do a really poor job of leveraging the afterglow and of event of an event. It still tends to be one and done, let's move on to the next event or the next city. >>Let the sales team pick up the pieces if they were paying attention. So because of that, we're only taking a B plus on this one. Okay, so that's the review of last year's predictions. You know, overall if you average out our grade on the 10 predictions that come out to a b plus, I dunno why we can't seem to get that elusive a, but we're gonna keep trying our friends at E T R and we are starting to look at the data for 2023 from the surveys and all the work that we've done on the cube and our, our analysis and we're gonna put together our predictions. We've had literally hundreds of inbounds from PR pros pitching us. We've got this huge thick folder that we've started to review with our yellow highlighter. And our plan is to review it this month, take a look at all the data, get some ideas from the inbounds and then the e t R of January surveys in the field. >>It's probably got a little over a thousand responses right now. You know, they'll get up to, you know, 1400 or so. And once we've digested all that, we're gonna go back and publish our predictions for 2023 sometime in January. So stay tuned for that. All right, we're gonna leave it there for today. You wanna thank Alex Myerson who's on production and he manages the podcast, Ken Schiffman as well out of our, our Boston studio. I gotta really heartfelt thank you to Kristen Martin and Cheryl Knight and their team. They helped get the word out on social and in our newsletters. Rob Ho is our editor in chief over at Silicon Angle who does some great editing for us. Thank you all. Remember all these podcasts are available or all these episodes are available is podcasts. Wherever you listen, just all you do Search Breaking analysis podcast, really getting some great traction there. Appreciate you guys subscribing. I published each week on wikibon.com, silicon angle.com or you can email me directly at david dot valante silicon angle.com or dm me Dante, or you can comment on my LinkedIn post. And please check out ETR AI for the very best survey data in the enterprise tech business. Some awesome stuff in there. This is Dante for the Cube Insights powered by etr. Thanks for watching and we'll see you next time on breaking analysis.

(corporate electronic xylophone jingle intro) >> Good afternoon and welcome back to our very last segment of Tuesday's live broadcast here on theCUBE from AWS re:Invent in fabulous Las Vegas, Nevada. My name is Savannah Peterson and I am joined here by the brilliant Paul Gillin. Paul, end of our first day. You holding up, are you still feeling overwhelmed with fire hose... >> Savannah, yet my feet are killing me. (savannah laughs) >> Yeah, we've done so much walking in these chairs. >> 14,000 steps already today. It's not even dinner time. >> Hey, well, at least you've earned your dinner, Paul. I love that. I love that. I'm very excited about our next guests. We have Krishna and Sowmya joining us from Tata Consultancy Services. Now, I was impressed when I was doing my background research on you all. The Tata Group has locations in 150 different spots, 46 different countries. You have over 600,000 employees on the team. We are talking about absolutely massive scale here but, today we're going to be focused specifically on the Tata Consultancy Services. Sowmya, can you tell me what you all do? What is that team specifically in charge of? >> Yeah, TCS, first of all, thank you very much for inviting us. >> Savannah: Our pleasure. >> Maybe the last session but, we'll make it very lively. >> Savannah: It's going to be the best session. That's the best part of the day. >> Yes, that's the attitude. From a company standpoint, we are a 50 plus year old company. Part of the Tata group. We focus on IT services. We are categorized as industry verticals and we have horizontal services where AWS is one of the horizontal services that we have. And, when I talk about TCS, we focus a lot more on growth and transformation of our customers. That is one of the key objectives of the current company's growth, I would say. So, that is TCS in a nutshell. >> Extraordinarily important topic to be focused on right now. Growth, transformation, pretty much the core topics of the show. I know you're on the hospitality and transportation side of the business, which is very exciting. And, we're going to dig into that a little bit more. Krishna, you're overseeing the world. Tell us a little bit more about your role within the whole ecosystem. >> Yeah, thank you for the opportunity. Great meeting all of you. It's been awesome experience here. re:Invent is coming back, catching up, right? 50,000 people compared to 25,000 last year. So, great to see and meet all of you. Coming to my role, I am responsible for AWS Business Unit within TCS. That means I am responsible for anything that happens on cloud, on AWS. It's a Full Stack unit. I have the global responsibility. That's whether it's a applications, data, infrastructure, transformation that happens, as well as OT at the edge. So, that's my responsibility. >> Savannah: Well, I love talking about the edge. One of my favorite. >> Transformation is a theme of what you do. We heard that the pandemic accelerated digital transformation initiatives at many companies. How did you see the pandemic affecting your business, affecting the customers you were working with? >> Pandemic definitely kind of accelerated a lot of cloud adoption, right? A lot of companies initially focused on resiliency, coming back to handling the pandemic, the situation. But, it also drove a lot of innovation in the business models. They had to think on their feet, re-look at their business models, change the channels and that continued. Pandemic is thankfully gone by but, the transformation actually continued. The way that we actually see on cloud, especially transformation, it has evolved. What we call as Cloud 2.0. Now, cloud is actually more focused on future-proofing the businesses. And, the initial days it was more about future-proofing the technology and technology architecture. But, it has evolved to future-proofing businesses. That means implementing new business models, bringing in agility, measuring the business value. And, that's where we see a significant traction. >> So, it's not about technology then. It's not about infrastructure. >> It is about technology but, really delivering business value. It's about, how can I improve the customer experience? >> Well, can you give us a couple of examples of companies you work with that embody this idea? >> I can imagine in the travel and hospitality zone. Probably few communities more sensitive than when someone's having a disruption or frustration within that process. And, perhaps few time periods less chaotic than the last few years. Tell us about your experience and what you've seen. >> Absolutely. To answer your question, first of all, coming out of pandemic, right? Many customers in the travel and hospitality industry where legacy, did not modernize for the last decade or so because, there have been many ups and downs in the industry. So, during pandemic, post-pandemic, one of the the way they wanted to rebound was, can we do the transformation? First of all, cloud as a technology adoption, but, beyond that, how do customers derive value, business value? That is one of the key aspects of the old transformation. And, if you take, I can give a couple of examples. Avis Car Rental, they had monolith mainframe applications and, that was there for almost couple of decades, right? But, over a period of time, they were not able to have the availability of those applications. There were many outages. As a result, businesses could not do the bookings. Like OTAs, customers could not do the bookings, the application was not available most of the time. And, it's all legacy, right? So, that is where we all came in, TCS. How do we first of all, simplify the complexity of the landscape? That is one. Then, second is, modernize the legacy application. That's the second thing. Third is, how do you scale it? Because, everyone wants to go faster, right? How do you scale it? That is where we partnered with AWS as well, to bring in some specific solutions. One example for Avis', their Rent Shop. Because, of the lack of availability, because, it's monolith application and legacy application. It was not available. So, as a result, we partnered and we brought in our contextual knowledge of the car rental industry to kind of transform, move it to cloud. And, today, as a result of it, Avis was able to save millions of dollars from a MIB standpoint. Second, in terms of availability, that was 99.9% availability. As a result, they had a pick in their business revenue as well. So, this is one of the ways that its helped. The second example I want to quote is, United Airlines. Here again, we've been present for a long time. We have a deep industry knowledge of the airline industry. So, we brought in our airline contextual knowledge and the United landscape to bring in a TCS's solution that we developed. It's called the Aviana. It's an intelligent operations solution for the airline industry, which we have developed. It's on AWS as well, that is being implemented in United. As a result, the ground staff, they have to take decisions on the moment when there is a irregular operation. That could be flight delays, as a result, customers connections will be lost. >> Savannah: Baggage. >> Baggage, right? Baggage delays. >> So many variables. The complexity... >> exactly >> in this matrix is wild. >> So, leveraging the Aviana solution, the ground staff were able to take decisions based on exceptions. They were able to take decisions quickly so that, they improved the customer experience. I think that was one of the key successes for United in the recent times. So, those two are the examples that I would call where customers have the right business value. So, cloud was not just for technology. They all are deriving a lot of business value as well. I would say. >> How important do you think it is for companies facing these unique challenges and scaling to work with partners like TCS? And, I'm sure you would say very important, but, tell me a little bit more why it's so important and those core benefits that they're going to get. Krishna, let's start off with you. Yeah, let me take again the AWS cloud transformation, right? TCS has formed AWS Business Unit two years back. So, we are a covid baby in a way. We have been working with the AWS for more than a decade but, we formed a dedicated Full-Stack Unit to drive cloud transformation on AWS. In these last two years, we've grown three X and customers we have added 400 new customers we have added. >> Nicely done. Just want to see you there. That's huge. Especially during these times. Congratulations. >> So, it's basically about the scale that we bring in. What we have done as a differentiation is, if you look at the entire cloud journey, right from taking a decision which cloud is, right, all the way to the cloud migration modernization and running operations. So, we have built complete platform. AML based platforms, where we have taken our delivery wisdom and codified it onto these platforms. So, we support around thousand plus customers on AWS in varying capacity. All of that knowledge is codified and, that is what we bring to the table, to the customers. And, so, customers obviously appreciate that value that best practices that are coming. And, coupled with that, the industry knowledge that we have on banking, life sciences, healthcare, automotive. So, it's partly the IT, it is the industry transformation as well. Because, we are working on connected cars, for example, in automotive. We are working on accelerated drug development platforms. We're working on complete banks as a platform that we have. TCS has built on AWS. So, 400 customers are there. It's the complete banking and insurance platform. So, this is the combination of the technical expertize that is digitized using platforms, as well as the industry knowledge, is the reason why customers work with us on the cloud transformation. >> So, we're seeing you talk about the vertical industry knowledge. AWS also has its own vertical industry plays. How do you, I guess, coordinate with them or, do you compete with them or, do you stay out of each other's way? >> No, we actually collaborate aggressively. >> Savannah: I like that (laughs) >> Right, so, it's not.. >> Savannah: With vigor. >> With vigor. TCS supports approximately 14 verticals. With AWS, we went with the focused industry play. We said we look at financial services, travel, transportation, hospitality, healthcare, life sciences and automotive, to start with. And, we have Go Big plans with AWS. very focused. The collaboration is actually at the industry solutions because, AWS is a great platform, ever evolving, keeps you on on your toes to really adapt it. But, that is always going on, the collaboration. But, the industry, I'm actually glad AWS last year took a pivot on focusing on industries. Now, we talk the same language when we go in front of a board or a CEO or COO. Present it. We are talking about the future of the industry not just the future of the technology. So, it's a win-win. >> You are also developing products on top of AWS that are not industry verticals, that build on the platform. What kinds of products are those? >> For cloud transformation, for example, consulting. We have a product called Cloud Counsell. We have a decision engine on the data side. We have something called Cloud Foundation, Mason. CloudMason. It's just the foundation, right? And, entire migration and modernization factory. And, the last one on cloud operations is actually Cloud Exponence. So, these are time tested. You have Fortune 500 customers using this regularly actively leveraging that. And, these are all AWS in a well architecture framework certified. So, they work well and they're designed to work on cloud, not only in the native environment, but, also legacy environment. Because, enterprises is not just only native, cloud-native. There is a lot of legacy. Sowmya spoke about the mainframe model... >> So much legacy, we were talking about it. >> So, you have to have a combination of solutions. So, the platforms that we're building, the products we're building, work in both the environments. >> Yeah, and that agility and ability to help customers navigate that prioritization. I mean, there's so many options. We talk about how many new companies there are every year. New solutions. Our adoption of technology is accelerating. As, McKinsey said, we went through 10 years of technological evolution and workplace evolution over the first six months of the pandemic. So, really everything's moving at unprecedented velocity unlike ever before. We have a new game here on theCUBE specifically for this show. And, we are challenging our guests, prompting our guests, to give us a 30 second sizzly sound bite with your hot take on the most important themes of this year's show. Think of it as a thought leadership moment. Opportunity to plug if you really want it. Krishna, you've just given me the nod. I'm going to start with you first and then we'll then we'll pass it along, yeah >> Sure. I think on thought leadership, the way that on cloud, business value is the focus, not the technology. Technology is important, but business value is the focus. And, the way that I see it evolving is with quantum computing coming out more and more, becoming relevant, and Edge is actually becoming quite active as well. All this while on cloud, we focused on business value at the centralized place at the corporate. But, I think the real value of cloud is when you deliver the results, business results, where the customers consume it, that is at the edge. I think that's basically the combination of centralized and the edge is where the real value of cloud is, right. And, I also loud, I know you said 30 seconds but, give me 30 more seconds. >> I like your answer right now. So, I'm going to give you a little more time. Yeah, thank you. >> You've earned more time. (laughs) >> So, I like the way Adam said in the keynote, if you look at it broadly, I categorizes two things. There are a lot of offerings that are becoming comprehensive, like AWS Connect, bringing in workforce management into it, making it a complete end to end product. Similarly, Security Lake, all bringing in the entire security and compliance under one, similarly data. So, there are lot of things that he announced where it is an end to end comprehensiveness of the thing. But, what I love about is, what Amazon is known for, supply chain. So, they rolled out AWS Supply Chain offering. Walk Out technology. So, the Amazon proposition is actually being brought to AWS as a core proposition. I think that's very futuristic and I think we can see more and more customers, enterprise customers, adopting AWS more to drive transformation >> Badly needed right now. Supply chain resiliency. >> Supply chain really having its moment the last two years. File under two words. No one knew, many of us did who worked in it before this. And, here we are, soon as we lost our toilet paper, everyone's freaked out. I love that you talked about business value and also that the end customer is on the edge and, everyone kind of forgets we are essentially the edge device. This is the edge device, it's all around us. And, all the technology that we're all using that you're even talking about is built right inside here from my airlines app to my car rentals to all of it. All right Sowmya, give us your 30 second hot take, roughly. >> Taking the cue from Krishna, right? Today, things are available on AWS Marketplace. So, tomorrow, somebody wants to start an airline, they just have to come and plug and play the apps that are available in the marketplace. Especially your supply chain. The Amazon is known for that. And, a small and medium business they want to start something, right, a .com. It's very easy. So, that's something that we are all looking for. The future is going to be very, very bright and great for the businesses, is what I would say because, most of it could be plug and play with all the solutions. >> Paul: It's already been built. >> On the cloud, so, we are looking forward to it. The second thing I would talk about is, we have to take it to scale. How more and more people can leverage AWS, right? The talent is very important and, that is where partners like us focus on re-scaling our talent. We have 600,000 people, right? We are not just... >> 600,000 people! That's basically as many people live in the San Francisco Bay area for contexts for our listeners. It's how many people work for Walmart? >> It's 1.2 million in Walmart? >> Is it really? >> It is, yes, yes. That's work for Walmart, sidebar. >> So from that standpoint, as the company, we are focusing on re-skilling, up-skilling our talent in order to work AWS cloud and so on, so, that they can go and support our customers. That is something that is very important and that's going to be the future as well. Bring it to scale, go faster. >> I love that you just touched on the fact that you essentially have to practice what you preach because, you've got to think about those 600,000 people in a 100 locations across 40 plus different countries. I love it. Sowmya, I'm going to close on that note. The future is bright, just like your fabulous blazer. >> Thank you so much. Krishna, Sowmya, thank you so much for being here with us. We can't wait to see what happens next, who you help next, and how Tata continues to transform. Thank all of you for tuning in today. A full jam packed day of coverage live here from Las Vegas, Nevada. We are at AWS re:Invent with Paul Gillin. I'm Savannah Peterson. We're theCUBE, the leader in High-Tech Coverage. (corporate electronic xylophone jingle outro)

(bright upbeat music) >> Well, hello everybody. John Walls is here on "the CUBE". Great to have you with us as we continue our series here at the AWS Executive Summit sponsored by Accenture. And today we're talking about public service and not just a little slice of public service but probably the largest public sector offering in the UK and for with us or with us. Now to talk about that is Lianne Anderton, who is in with the Intelligent Automation Garage Delivery Lead at the UK Department of Work and Pension. Lianne, good to see you today. Thanks for joining us here on "the CUBE". >> Hi, thanks for having me. >> And also with this us is Amar Narayan, who is a Manager Director at Accenture the AWS Business Group for the Lead in Health and Public Sector, also UK and Ireland. And Amar, I think, you and Lianne, are in the same location, Newcastle, I believe in the UK, is that right? >> Yeah, absolutely. Yep, yeah, we're, here in the northeast of UK. >> Well, thank you for being with us. I appreciate the time. Lianne, let's talk about what you do, the Department of Work and Pension, the famous DWP in England. You have influence or certainly touchpoints with a huge amount of the British population. In what respects, what are you doing for the working class in England and what does technology have to do with all that? >> Sure, so for the Department for Work and Pensions I think the pensions bit is fairly self explanatory so anybody who is over state pension age within the UK. for the work part of that we also deal with people of working age. So, these are people who are either in employment and need additional help through various benefits we offer in the UK. Those people who are out of work. And we also deal with health related benefits as well. And we are currently serving over 20 million claimants every year at this moment in time. So, we're aware of a huge part of the UK government. >> All right, so say that number again. How many? >> 20 million claimants every year. >> Million with an M, right? >> Yeah. >> So, and that's individuals. And so how many transactions, if you will, how many do you think you process in a month? How, much traffic basically, are you seeing? >> An extraordinary amount? I'm not even, I don't think I even know that number. (Lianne laughing) >> Mind blowing, right? So, it's- >> A huge, huge amount. >> Mind blowing. >> Yeah, so, basically the we kind of keep the country going. So, you know, if the department for Work and Pensions kind of didn't exist anymore then actually it would cause an infinite number of problems in society. We, kind of help and support the people who need that. And, yeah, so we play a really vital role in kind of you know, social care and kind of public service. >> So, what was your journey to Accenture then? What, eventually led you to them? What problem were you having and how have you collaborated to solve that? >> So, in terms of how we work with Accenture. So, we had in around 2017 DWP was looking at a projected number of transactions growing by about 210 million which was, you know, an extraordinary amount. And, you know, I think as we've kind of covered everything that we do is on a massive scale. So, we as DWP as an organization we had absolutely no idea how we were going to be able to handle such a massive increase in the transactions. And actually, you know, after kind of various kind of paths and ideas of how we were going to do that, automation, was actually the answer. But the problem that we have with that is that we have, like many governments around the world, we have really older legacy systems. So, each of these benefits that we deal with are on legacy systems. So, whatever we were going to develop had to, you know, connect to all of these, it had to ingest and then process all of these pieces of data some of which, you know, given the fact that a lot of these systems have a lot of manual input you have data issues there that you have to solve and whatever we did, you know, as we've talked about in terms of volumes has to scale instantly as well. So, it has to be able to scale up and down to meet demand and, you know, and that down scaling is also equally as important. So yeah, you've got to be able to scale up to meet the volumes but also you've got to be able to downscale when when it's not needed. But we had nothing that was like that kind of helped us to meet that demand. So, we built our own automation platform, The Intelligent Automation Garage and we did that with Accenture. >> So Amar, I'd like you to chime in here then. So, you're looking at this client who has this massive footprint and obviously vital services, right? So, that's paramount that you have to keep that in mind and the legacy systems that Lianne was just talking about. So, now you're trying to get 'em in the next gen but also respecting that they have a serious investment already in a lot of technology. How do you approach that kind of problem solving, those dynamics and how in this case did you get them to automation as the solution? >> Sure, so I think I think one of the interesting things, yeah as Lianne has sort of described it, right? It's effectively like, you know the department has to have be running all of the time, right? They can't, you know, they can't effectively stop and then do a bunch of IT transformation, you know it's effectively like, you know, changing the wheels of a jumbo jet whilst it's taking off, right? And you've got to do all of that all in one go. But what I think we really, really liked about the situation that we were in and the client relationship we had was that we knew we had to it wasn't just a technology play, we couldn't just go, "All right, let's just put some new technology in." What we also needed to do was really sort of create a culture, an innovation culture, and go, "Well how do we think about the problems that we currently have and how do we think about solving them differently and in collaboration, right?" So, not just the, "Let's just outsource a bunch of technology for to, you know, to Accenture and build a bunch of stuff." So, we very carefully thought about, well actually, the unique situation that they're in the demands that the citizens have on the services that the department provide. And as Lianne mentioned, that technology didn't exist. So, we fundamentally looked at this in a different way. So, we worked really closely with the department. We said, Look, actually what we ultimately need is the equivalent of a virtual workforce. Something where if you already, you know all of a sudden had a hundred thousand pension claims that needed to be processed in a week that you could click your fingers and, you know in a physical world you'd have another building all of your kits, a whole bunch of trained staff that would be able to process that work. And if in the following week you didn't need that you no longer needed that building that stuff or the machinery. And we wanted to replicate that in the virtual world. So, we started designing a platform we utilized and focused on using AWS because it had the scalability. And we thought about, how were we going to connect something as new as AWS to all of these legacy systems. How are we going to make that work in the modern world? How are we going to integrate it? How we going to make sure it's secure? And frankly, we're really honest with the client we said, "Look, this hasn't been done before. Like, nowhere in Accenture has done it. No one's done it in the industry. We've got some smart people, I think we can do it." And, we've prototyped and we've built and we were able to prove that we can do that. And that in itself just created an environment of solving tricky problems and being innovative but most importantly not doing sort of proof of concepts that didn't go anywhere but building something that actually scaled. And I think that was really the real the start of what was has been the Garage. >> So, And Lianne, you mentioned this and you just referred to it Amar, about The Garage, right? The Intelligent Automation Garage. What exactly is it? I mean, we talked about it, what the needs are all this and that, but Lianne, I'll let you jump in first and Amar, certainly compliment her remarks, but what is the IAG, what's the... >> So, you know, I think exactly what kind of Amar, has said from a from a kind of a development point of view I think it started off, you know, really, really small. And the idea is that this is DWP, intelligent automation center of excellence. So, you know, it's aims are that, you know, it makes sure that it scopes out kind of the problems that DWP are are facing properly. So, we really understand what the crux of the problem is. In large organizations It's very easy, I think to think you understand what the problem is where actually, you know, it is really about kind of delving into what that is. And actually we have a dedicated design team that really kind of get under the bonnet of what these issues really are. It then kind of architects what the solutions need to look like using as Amar said, all the exciting new technology that we kind of have available to us. That kind of sensible solution as to what that should look like. We then build that sensible solution and we then, you know as part of that, we make sure that it scales to demand. So, something that might start out with, I dunno, you know a few hundred claimants or kind of cases going through it can quite often, you know, once that's that's been successful scale really, really quickly because as you know, we have 20 million claimants that come through us every year. So, these types of things can grow and expand but also a really key function of what we do is that we have a fully supported in-house service as well. So, all of those automations that we build are then maintained and you know, so any changes that kind of needed to be need to be made to them, we have all that and we have that control and we have our kind of arms wrapped around all of those. But also what that allows us to do is it allows us to be very kind of self-sufficient in making sure that we are as sufficient, sorry, as efficient as possible. And what I mean by that is looking at, you know as new technologies come around and they can allow us to do things more effectively. So, it allows us to kind of almost do that that kind of continuous improvement ourselves. So, that's a huge part of what we do as well. And you know, I think from a size point of view I said this started off really small as in the idea was this was a kind of center of excellence but actually as automation, I think as Amar alluded to is kind of really started to embed in DWP culture what we've started to kind of see is the a massive expansion in the types of of work that people want us to do and the volume of work that we are doing. So, I think we're currently running at around around a hundred people at the moment and I think, you know we started off with a scrum, a couple of scrum teams under Amar, so yeah, it's really grown. But you know, I think this is here to stay within DWP. >> Yeah, well when we talk about automation, you know virtual and robotics and all this I like to kind of keep the human element in mind here too. And Amar, maybe you can touch on that in certain terms of the human factors in this equation. 'Cause people think about, you know, robots it means different things to different people. In your mind, how does automation intersect with the human element here and in terms of the kinds of things Lianne wants to do down the road, you know, is a road for people basically? >> Oh yeah, absolutely. I think fundamentally what the department does is support people and therefore the solutions that we designed and built had to factor that in mind right? We were trying to best support and provide the best service we possibly can. And not only do we need to support the citizens that it supports. The department itself is a big organization, right? We're up to, we're talking between sort of 70 and 80,000 employees. So, how do we embed automation but also make the lives of the, of the DWP agents better as well? And that's what we thought about. So we said, "Well look, we think we can design solutions that do both." So, a lot of our automations go through a design process and we work closely with our operations team and we go, well actually, you know in processing and benefit, there are some aspects of that processing that benefit that are copy and paste, right? It doesn't require much thought around it, but it just requires capturing data and there's elements of that solution or that process that requires actual thought and understanding and really empathy around going, "Well how do I best support this citizen?" And what we tended to do is we took all of the things that were sort of laborious and took a lot of time and would slow down the overall process and we automated those and then we really focused on making sure that the elements that required the human, the human input was made as user friendly and centric as we possibly could. So, if there's a really complex case that needs to be processed, we were able to present the information in a really digestible and understandable way for the agents so that they could make a informed and sensible decision based around a citizen. And what that enabled us to do is essentially meet the demands of the volumes and the peaks that came in but also maintain the quality and if not improve, you know the accuracy of the claims processing that we had. >> So, how do you know, and maybe Lianne, you can address this. How do you know that it's successful on both sides of that equation? And, 'cause Amar raised a very good point. You have 70 to 80,000 employees that you're trying to make their work life much more efficient, much simpler and hopefully make them better at their jobs at the end of the day. But you're also taking care of 20 million clients on the, your side too. So, how do you, what's your measurement for success and what kind of like raw feedback do you get that says, "Okay, this has worked for both of our client bases, both our citizens and our employees?" >> Yeah, so we can look at this both from a a quantitative and a qualitative point of view as well. So, I think from a let take the kind figures first. So we are really hot on making sure that whatever automations we put in place we are there to measure how that automation is working what it's kind of doing and the impact that it's having from an operational point of view. So I think, you know, I think the proof of the fact that the Intelligent Automation Garage is working is that, you know, in the, in its lifetime, we've processed over 20 million items and cases so far. We have 65 scaled and transitioned automations and we've saved over 2 million operational hours. I was going to say that again that's 2 million operational hours. And what that allows us to do as an organization those 2 million hours have allowed us to rather than people as Amar, said, cutting and pasting and doing work that that is essentially very time consuming and repetitive. That 2 million hours we've been able to use on actual decision making. So, the stuff that you need as sentient human being to make judgment calls on and you know and kind of make those decisions that's what it's allowed us as an organization to do. And then I think from a quality point of view I think the feedback that we have from our operational teams is, you know is equally as as great. So, we have that kind of feedback from, you know all the way up from to the director level about, you know how it's kind of like I said that freeing up that time but actually making the operational, you know they don't have an easy job and it's making that an awful lot easier on a day to day basis. It has a real day to day impact. But also, you know, there are other things that kind of the knock on effects in terms of accuracy. So for example, robot will do is exactly as it's told it doesn't make any mistakes, it doesn't have sick days, you know, it does what it says on the tin and actually that kind of impact. So, it's not necessarily, you know, counting your numbers it's the fact that then doesn't generate a call from a customer that kind of says, "Well you, I think you've got this wrong." So, it's all that kind of, these kind of ripple effects that go out. I think is how we measure the fact that A, the garage is working and b, it's delivering the value that we needed to deliver. >> Robots, probably ask better questions too so yeah... (Lianne laughing) So, real quick, just real quick before you head out. So, the big challenge next, eureka, this works, right? Amar, you put together this fantastic system it's in great practice at the DWP, now what do we do? So, it's just in 30 seconds, Amar, maybe if you can look at, be the headlights down the road here for DWP and say, "This is where I think we can jump to next." >> Yeah, so I think, what we've been able to prove as I say is that is scaled innovation and having the return and the value that it creates is here to stay, right? So, I think the next things for us are a continuous expand the stuff that we're doing. Keeping hold of that culture, right? That culture of constantly solving difficult problems and being able to innovate and scale them. So, we are now doing a lot more automations across the department, you know, across different benefits across the digital agenda. I think we're also now becoming almost a bit of the fabric of enabling some of the digital transformation that big organizations look at, right? So moving to a world where you can have a venture driven architectures and being able to sort of scale that. I also think the natural sort of expansion of the team and the type of work that we're going to do is probably also going to expand into sort of the analytics side of it and understanding and seeing how we can take the data from the cases that we're processing to overall have a smoother journey across for our citizens. But it's looking, you know, the future's looking bright. I think we've got a number of different backlogs of items to work on. >> Well, you've got a great story to tell and thank you for sharing it with us here on "the CUBE", talking about DWP, the Department of Work and Pensions in the UK and the great work that Accenture's doing to make 20 million lives plus, a lot simpler for our friends in England. You've been watching ""the CUBE"" the AWS Executive Summit sponsored by Accenture. (bright upbeat music)

>>Prior to the pandemic, organizations were largely optimized for efficiency as the best path to bottom line profits. Many CIOs tell the cube privately that they were caught off guard by the degree to which their businesses required greater resiliency beyond their somewhat cumbersome disaster recovery processes. And the lack of that business resilience has actually cost firms because they were unable to respond to changing market forces. And certainly we've seen this dynamic with supply chain challenges and there's a little doubt. We're also seeing it in the area of cybersecurity generally, and data recovery. Specifically. Over the past 30 plus months, the rapid adoption of cloud to support remote workers and build in business resilience had the unintended consequences of expanding attack vectors, which brought an escalation of risk from cybercrime. Well, security in the public clouds is certainly world class. The result of multi-cloud has brought with it multiple shared responsibility models, multiple ways of implementing security policies across clouds and on-prem. >>And at the end of the day, more, not less complexity, but there's a positive side to this story. The good news is that public policy industry collaboration and technology innovation is moving fast to accelerate data protection and cybersecurity strategies with a focus on modernizing infrastructure, securing the digital supply chain, and very importantly, simplifying the integration of data protection and cybersecurity. Today there's heightened awareness that the world of data protection is not only an adjacency to, but it's becoming a fundamental component of cybersecurity strategies. In particular, in order to build more resilience into a business, data protection, people, technologies, and processes must be more tightly coordinated with security operations. Hello and welcome to the future of Multi-Cloud Data Protection Made Possible by Dell in collaboration with the Cube. My name is Dave Ante and I'll be your host today. In this segment, we welcome into the cube, two senior executives from Dell who will share details on new technology announcements that directly address these challenges. >>Jeff Boudreau is the president and general manager of Dell's Infrastructure Solutions Group, isg, and he's gonna share his perspectives on the market and the challenges he's hearing from customers. And we're gonna ask Jeff to double click on the messages that Dell is putting into the marketplace and give us his detailed point of view on what it means for customers. Now, Jeff is gonna be joined by Travis Vhi. Travis is the senior Vice President of product management for ISG at Dell Technologies, and he's gonna give us details on the products that are being announced today and go into the hard news. Now, we're also gonna challenge our guests to explain why Dell's approach is unique and different in the marketplace. Thanks for being with us. Let's get right into it. We're here with Jeff Padre and Travis Behill. We're gonna dig into the details about Dell's big data protection announcement. Guys, good to see you. Thanks >>For coming in. Good to see you. Thank you for having us. >>You're very welcome. Right. Let's start off, Jeff, with the high level, you know, I'd like to talk about the customer, what challenges they're facing. You're talking to customers all the time, What are they telling you? >>Sure. As you know, we do, we spend a lot of time with our customers, specifically listening, learning, understanding their use cases, their pain points within their specific environments. They tell us a lot. Notice no surprise to any of us, that data is a key theme that they talk about. It's one of their most important, important assets. They need to extract more value from that data to fuel their business models, their innovation engines, their competitive edge. So they need to make sure that that data is accessible, it's secure in its recoverable, especially in today's world with the increased cyber attacks. >>Okay. So maybe we could get into some of those, those challenges. I mean, when, when you talk about things like data sprawl, what do you mean by that? What should people know? Sure. >>So for those big three themes, I'd say, you know, you have data sprawl, which is the big one, which is all about the massive amounts of data. It's the growth of that data, which is growing at an unprecedented rates. It's the gravity of that data and the reality of the multi-cloud sprawl. So stuff is just everywhere, right? Which increases that service a tax base for cyber criminals. >>And by gravity you mean the data's there and people don't wanna move it. >>It's everywhere, right? And so when it lands someplace, I think edge, core or cloud, it's there and that's, it's something we have to help our customers with. >>Okay, so just it's nuanced cuz complexity has other layers. What are those >>Layers? Sure. When we talk to our customers, they tell us complexity is one of their big themes. And specifically it's around data complexity. We talked about that growth and gravity of the data. We talk about multi-cloud complexity and we talk about multi-cloud sprawl. So multiple vendors, multiple contracts, multiple tool chains, and none of those work together in this, you know, multi-cloud world. Then that drives their security complexity. So we talk about that increased attack surface, but this really drives a lot of operational complexity for their teams. Think about we're lack consistency through everything. So people, process, tools, all that stuff, which is really wasting time and money for our customers. >>So how does that affect the cyber strategies and the, I mean, I've often said the ciso now they have this shared responsibility model, they have to do that across multiple clouds. Every cloud has its own security policies and, and frameworks and syntax. So maybe you could double click on your perspective on that. >>Sure. I'd say the big, you know, the big challenge customers have seen, it's really inadequate cyber resiliency. And specifically they're feeling, feeling very exposed. And today as the world with cyber tax being more and more sophisticated, if something goes wrong, it is a real challenge for them to get back up and running quickly. And that's why this is such a, a big topic for CEOs and businesses around the world. >>You know, it's funny, I said this in my open, I, I think that prior to the pandemic businesses were optimized for efficiency and now they're like, wow, we have to actually put some headroom into the system to be more resilient. You know, I you hearing >>That? Yeah, we absolutely are. I mean, the customers really, they're asking us for help, right? It's one of the big things we're learning and hearing from them. And it's really about three things, one's about simplifying it, two, it is really helping them to extract more value from their data. And then the third big, big piece is ensuring their data is protected and recoverable regardless of where it is going back to that data gravity and that very, you know, the multi-cloud world just recently, I don't know if you've seen it, but the global data protected, excuse me, the global data protection index gdp. >>I, Yes. Jesus. Not to be confused with gdpr, >>Actually that was released today and confirms everything we just talked about around customer challenges, but also it highlights an importance of having a very cyber, a robust cyber resilient data protection strategy. >>Yeah, I haven't seen the latest, but I, I want to dig into it. I think this, you've done this many, many years in a row. I like to look at the, the, the time series and see how things have changed. All right. At, at a high level, Jeff, can you kind of address why Dell and from your point of view is best suited? >>Sure. So we believe there's a better way or a better approach on how to handle this. We think Dell is uniquely positioned to help our customers as a one stop shop, if you will, for that cyber resilient multi-cloud data protection solution and needs. We take a modern, a simple and resilient approach. >>What does that mean? What, what do you mean by modern? >>Sure. So modern, we talk about our software defined architecture, right? It's really designed to meet the needs not only of today, but really into the future. And we protect data across any cloud and any workload. So we have a proven track record doing this today. We have more than 1700 customers that trust us to protect them more than 14 exabytes of their data in the cloud today. >>Okay, so you said modern, simple and resilient. What, what do you mean by simple? Sure. >>We wanna provide simplicity everywhere, going back to helping with the complexity challenge, and that's from deployment to consumption to management and support. So our offers will deploy in minutes. They are easy to operate and use, and we support flexible consumption models for whatever customer may desire. So traditional subscription or as a service. >>And when you, when you talk about resilient, I mean, I, I put forth that premise, but it's hard because people say, Well, that's gonna gonna cost us more. Well, it may, but you're gonna also reduce your, your risk. So what's your point of view on resilience? >>Yeah, I think it's, it's something all customers need. So we're gonna be providing a comprehensive and resilient portfolio of cyber solutions that are secured by design. We have some ver some unique capabilities and a combination of things like built in amenability, physical and logical isolation. We have intelligence built in with AI par recovery. And just one, I guess fun fact for everybody is we have our cyber vault is the only solution in the industry that is endorsed by Sheltered Harbor that meets all the needs of the financial sector. >>So it's interesting when you think about the, the NIST framework for cybersecurity, it's all about about layers. You're sort of bringing that now to, to data protection, correct? Yeah. All right. In a minute we're gonna come back with Travis and dig into the news. We're gonna take a short break. Keep it right there. Okay. We're back with Jeff and Travis Vhi to dig deeper into the news. Guys, again, good to see you. Travis, if you could, maybe you, before we get into the news, can you set the business context for us? What's going on out there? >>Yeah, thanks for that question, Dave. To set a little bit of the context, when you look at the data protection market, Dell has been a leader in providing solutions to customers for going on nearly two decades now. We have tens of thousands of people using our appliances. We have multiple thousands of people using our latest modern simple power protect data managers software. And as Jeff mentioned, we have, you know, 1700 customers protecting 14 exabytes of data in the public clouds today. And that foundation gives us a unique vantage point. We talked to a lot of customers and they're really telling us three things. They want simple solutions, they want us to help them modernize and they want us to add as the highest priority, maintain that high degree of resiliency that they expect from our data protection solutions. So tho that's the backdrop to the news today. And, and as we go through the news, I think you'll, you'll agree that each of these announcements deliver on those pillars. And in particular today we're announcing the Power Protect data manager appliance. We are announcing power protect cyber recovery enhancements, and we are announcing enhancements to our Apex data storage >>Services. Okay, so three pieces. Let's, let's dig to that. It's interesting appliance, everybody wants software, but then you talk to customers and they're like, Well, we actually want appliances because we just wanna put it in and it works, right? It performs great. So, so what do we need to know about the appliance? What's the news there? Well, >>You know, part of the reason I gave you some of those stats to begin with is that we have this strong foundation of, of experience, but also intellectual property components that we've taken that have been battle tested in the market. And we've put them together in a new simple integrated appliance that really combines the best of the target appliance capabilities we have with that modern simple software. And we've integrated it from the, you know, sort of taking all of those pieces, putting them together in a simple, easy to use and easy to scale interface for customers. >>So the premise that I've been putting forth for, you know, months now, probably well, well over a year, is that, that that data protection is becoming an extension of your, your cybersecurity strategies. So I'm interested in your perspective on cyber recovery, you specific news that you have there. >>Yeah, you know, we, we are, in addition to simplifying things via the, the appliance, we are providing solutions for customers no matter where they're deploying. And cyber recovery, especially when it comes to cloud deployments, is an increasing area of interest and deployment that we see with our customers. So what we're announcing today is that we're expanding our cyber recovery services to be available in Google Cloud with this announcement. It means we're available in all three of the major clouds and it really provides customers the flexibility to secure their data no matter if they're running, you know, on premises in a colo at the edge in the public cloud. And the other nice thing about this, this announcement is that you have the ability to use Google Cloud as a cyber recovery vault that really allows customers to isolate critical data and they can recover that critical data from the vault back to on premises or from that vault back to running their cyber cyber protection or their data protection solutions in the public cloud. >>I always invoke my, my favorite Matt Baker here. It's not a zero sum game, but this is a perfect example where there's opportunities for a company like Dell to partner with the public cloud provider. You've got capabilities that don't exist there. You've got the on-prem capabilities. We can talk about edge all day, but that's a different topic. Okay, so my, my other question Travis, is how does this all fit into Apex? We hear a lot about Apex as a service, it's sort of the new hot thing. What's happening there? What's the news around Apex? >>Yeah, we, we've seen incredible momentum with our Apex solutions since we introduced data protection options into them earlier this year. And we're really building on that momentum with this announcement being, you know, providing solutions that allow customers to consume flexibly. And so what we're announcing specifically is that we're expanding Apex data storage services to include a data protection option. And it's like with all Apex offers, it's a pay as you go solution really streamlines the process of customers purchasing, deploying, maintaining and managing their backup software. All a customer really needs to do is, you know, specify their base capacity, they specify their performance tier, they tell us do they want a a one year term or a three year term and we take it from there. We, we get them up and running so they can start deploying and consuming flexibly. And it's, as with many of our Apex solutions, it's a simple user experience all exposed through a unified Apex console. >>Okay. So it's you keeping it simple, like I think large, medium, small, you know, we hear a lot about t-shirt sizes. I I'm a big fan of that cuz you guys should be smart enough to figure out, you know, based on my workload, what I, what I need, how different is this? I wonder if you guys could, could, could address this. Jeff, maybe you can, >>You can start. Sure. I'll start and then pitch me, you know, Travis, you you jump in when I screw up here. So, awesome. So first I'd say we offer innovative multi-cloud data protection solutions. We provide that deliver performance, efficiency and scale that our customers demand and require. We support as Travis and all the major public clouds. We have a broad ecosystem of workload support and I guess the, the great news is we're up to 80% more cost effective than any of the competition. >>80%. 80%, That's a big number, right? Travis, what's your point of view on this? Yeah, >>I, I think number one, end to end data protection. We, we are that one stop shop that I talked about. Whether it's a simplified appliance, whether it's deployed in the cloud, whether it's at the edge, whether it's integrated appliances, target appliances, software, we have solutions that span the gamut as a service. I mentioned the Apex solution as well. So really we can, we can provide solutions that help support customers and protect them, any workload, any cloud, anywhere that data lives edge core to cloud. The other thing that we hear as a, as a, a big differentiator for Dell and, and Jeff touched on on this a little bit earlier, is our intelligent cyber resiliency. We have a unique combination in, in the market where we can offer immutability or protection against deletion as, as sort of that first line of defense. But we can also offer a second level of defense, which is isolation, talking, talking about data vaults or cyber vaults and cyber recovery. And the, at more importantly, the intelligence that goes around that vault. It can look at detecting cyber attacks, it can help customers speed time to recovery and really provides AI and ML to help early diagnosis of a cyber attack and fast recovery should a cyber attack occur. And, and you know, if you look at customer adoption of that solution specifically in the clouds, we have over 1300 customers utilizing power protect cyber recovery. >>So I think it's fair to say that your, I mean your portfolio has obvious been a big differentiator whenever I talk to, you know, your finance team, Michael Dell, et cetera, that end to end capability that that, that your ability to manage throughout the supply chain. We actually just did a a, an event recently with you guys where you went into what you're doing to make infrastructure trusted. And so my take on that is you, in a lot of respects, you're shifting, you know, the client's burden to your r and d now they have a lot of work to do, so it's, it's not like they can go home and just relax, but, but that's a key part of the partnership that I see. Jeff, I wonder if you could give us the, the, the final thoughts. >>Sure. Dell has a long history of being a trusted partner with it, right? So we have unmatched capabilities. Going back to your point, we have the broadest portfolio, we have, you know, we're a leader in every category that we participate in. We have a broad deep breadth of portfolio. We have scale, we have innovation that is just unmatched within data protection itself. We have the trusted market leader, no, if and or buts, we're number one for both data protection software in appliances per idc and we would just name for the 17th consecutive time the leader in the, the Gartner Magic Quadrant. So bottom line is customers can count on Dell. >>Yeah, and I think again, we're seeing the evolution of, of data protection. It's not like the last 10 years, it's really becoming an adjacency and really a key component of your cyber strategy. I think those two parts of the organization are coming together. So guys, really appreciate your time. Thanks for Thank you sir. Thanks Travis. Good to see you. All right, in a moment I'm gonna come right back and summarize what we learned today, what actions you can take for your business. You're watching the future of multi-cloud data protection made possible by Dell and collaboration with the cube, your leader in enterprise and emerging tech coverage right back >>In our data driven world. Protecting data has never been more critical to guard against everything from cyber incidents to unplanned outages. You need a cyber resilient, multi-cloud data protection strategy. >>It's not a matter of if you're gonna get hacked, it's a matter of when. And I wanna know that I can recover and continue to recover each day. >>It is important to have a cyber security and a cyber resiliency plan in place because the threat of cyber attack are imminent. >>Power protects. Data manager from Dell Technologies helps deliver the data protection and security confidence you would expect from a trusted partner and market leader. >>We chose Power Protect Data Manager because we've been a strategic partner with Dell Technologies for roughly 20 years now. Our partnership with Dell Technologies has provided us with the ability to scale and grow as we've transitioned from 10 billion in assets to 20 billion. >>With Power Protect Data Manager, you can enjoy exceptional ease of use to increase your efficiency and reduce costs. >>Got installed it by myself, learned it by myself with very intuitive >>While restoring a machine with Power Protect Data Manager is fast. We can fully manage Power Protect through the center. We can recover a whole machine in seconds. >>Data Manager offers innovation such as Transparent snapshots to simplify virtual machine backups and it goes beyond backup and restore to provide valuable insights and to protected data workloads and VMs. >>In our previous environment, it would take anywhere from three to six hours at night to do a single backup of each vm. Now we're backing up hourly and it takes two to three seconds with the transparent snapshots. >>With Power Protects Data Manager, you get the peace of mind knowing that your data is safe and available whenever you need it. >>Data is extremely important. We can't afford to lose any data. We need things just to work. >>Start your journey to modern data protection with Dell Power Protect Data manager. Visit dell.com/power Protect Data Manager. >>We put forth the premise in our introduction that the worlds of data protection in cybersecurity must be more integrated. We said that data recovery strategies have to be built into security practices and procedures and by default this should include modern hardware and software. Now in addition to reviewing some of the challenges that customers face, which have been pretty well documented, we heard about new products that Dell Technologies is bringing to the marketplace that specifically address these customer concerns. There were three that we talked about today. First, the Power Protect Data Manager Appliance, which is an integrated system taking advantage of Dell's history in data protection, but adding new capabilities. And I want to come back to that in the moment. Second is Dell's Power Protect cyber recovery for Google Cloud platform. This rounds out the big three public cloud providers for Dell, which joins AWS and and Azure support. >>Now finally, Dell has made its target backup appliances available in Apex. You might recall earlier this year we saw the introduction from Dell of Apex backup services and then in May at Dell Technologies world, we heard about the introduction of Apex Cyber Recovery Services. And today Dell is making its most popular backup appliances available and Apex. Now I wanna come back to the Power Protect data manager appliance because it's a new integrated appliance. And I asked Dell off camera really what is so special about these new systems and what's really different from the competition because look, everyone offers some kind of integrated appliance. So I heard a number of items, Dell talked about simplicity and efficiency and containers and Kubernetes. So I kind of kept pushing and got to what I think is the heart of the matter in two really important areas. One is simplicity. >>Dell claims that customers can deploy the system in half the time relative to the competition. So we're talking minutes to deploy and of course that's gonna lead to much simpler management. And the second real difference I heard was backup and restore performance for VMware workloads. In particular, Dell has developed transparent snapshot capabilities to fundamentally change the way VMs are protected, which leads to faster backup and restores with less impact on virtual infrastructure. Dell believes this new development is unique in the market and claims that in its benchmarks the new appliance was able to back up 500 virtual machines in 47% less time compared to a leading competitor. Now this is based on Dell benchmarks, so hopefully these are things that you can explore in more detail with Dell to see if and how they apply to your business. So if you want more information, go to the data protectionPage@dell.com. You can find that at dell.com/data protection. And all the content here and other videos are available on demand@thecube.net. Check out our series on the blueprint for trusted infrastructure, it's related and has some additional information. And go to silicon angle.com for all the news and analysis related to these and other announcements. This is Dave Valante. Thanks for watching the future of multi-cloud protection made possible by Dell in collaboration with the Cube, your leader in enterprise and emerging tech coverage.

(upbeat music) >> Welcome to the Cube's special program series Women of the Cloud brought to you by AWS. I'm your host Lisa Martin. Very pleased to welcome Megan Hayes to the program now. Megan is the COO of Blackstone's Technology Group. Megan, great to have you on the program. Thanks for joining me today. >> Thanks for having me. >> Tell me a little bit about your background, a little bit about your role as COO. >> Yeah, definitely, so I joined Blackstone about six months ago. And, I serve as our chief operating officer of Blackstone Tech and Innovations. And, really what that means is I look at all of our aspects of operational excellence. I run our agile practice. I look at our labor strategies, what we do with workforce, our finance function and what we do with our communications strategy, training and really just everything that centers around continuous improvement and how we really drive culture across technology. So, it's pretty dynamic role. It really looks to serve across how we enable our entire tech organization, and it's just a lot of fun. >> That's good. Fun is good. Sounds like it's a very comprehensive role as well. I was looking at you on LinkedIn. You've got background in physics and engineering. Talk to me a little bit about your path to getting to the COO level. >> Yeah. I mean, it has been a non-linear path for sure. I think a big part of it has been just jumping at opportunities that have had lots of different aspects of technology, product, data, anything that I've had a passion in has, you know, revolved around technology. And so, I think being a good COO is about asking really good questions, being thoughtful and looking for opportunities where organizations can keep driving excellence, keep getting better. One of the things that I think is really important about being a good COO is just thinking innovatively about, you know, what's next and what's on the horizon in terms of, you know, the future of technology and how you can enable that within an organization. And, you know, one like ours, we're always kind of looking for what's next. So, you know, I think for me it's been about taking those next opportunities, about really continuing to drive that across finance. So, I've had positions across various industries within the financial services sector. And, have really had a great opportunity to be here at Blackstone, so I'm very excited. >> Sounds like you have a really diverse background. And, I always think those non-linear paths, mine wasn't linear either. And, I talk to very few people whose career paths are linear, but then, you get so much thought diversity, because you bring in different perspectives. I can imagine that's a huge advantage to the COO role at Blackstone. I'd love to get your advice on how would you advise the audience watching this who may be looking to grow their careers in tech? What are some of the key recommendations that you would deliver to them? >> Yeah, I think about it in two ways. I mean, the first thing for me is about asking for feedback. One of the things that has really come to me throughout my career has been people that are willing to give those tough messages to really push you to grow. So, one of the things that I have been challenged with is exploring, you know, what do I need to get better at? Where do I really need to lean in to growing those skills? And, part of that is it comes in messages from others where they're willing to, you know, reach out to you and give you that feedback proactively. But, sometimes you have to seek it out. And, you have to really, you know, ask peers, mentors, people that you have seen grow in their own careers, people that you admire and you wonder how they got there themselves. And, just really seeking out feedback, asking what you can do to grow yourself. And, not being scared of hearing those tough messages and leaning into it. So, you know, I think that is the first part of it. And, really fostering and cultivating a sense of not being fearful of hearing those messages of feedback. So, thing one for me is go after that. It's never a bad time in your career to really listen for feedback and to think about what you can do to keep getting better. And so, I would encourage anybody to really think about ways that they can continue to grow and continue to cultivate new skills and new competencies and look at those opportunities to get better. And then, the second thing is, you know, don't be scared of taking on opportunities or roles where you don't have, you know, every last skill. I think, predominantly, along with women, we try to see ourselves as being perfect in every aspect of a job opportunity. We really want to know that we're going to be great. And, oftentimes, where you see the most growth is when you don't have some of those skills where maybe you have, you know, 40 or 50% of what's on that job spec and the rest of it is well, you know, I'll learn as I go. And, that is where the growth is. So, don't be scared of being really interested in a job opportunity where you have part of what's on that job rec, and you don't have all the rest of it. And, lean into that, and let a little bit of that fear, a little bit of the anxiety drive you to keep getting better and cultivating those skills in that new opportunity. I've been fortunate to have senior leaders and other mentors in my career see things in me that I couldn't see and encouraged me to take opportunities that I didn't think I could be successful at, and I've gone after those things and then seen the growth, you know, in hindsight. So, I would really encourage people to look at those opportunities where it's a little bit scary and then go for it, because that really cultivates a whole new set of skills. And, when you look back on those, you know, challenges and those opportunities that you jump at, you're like, wow, you know, I would do that again. And, you keep doing that, and you find yourself going. You know, now I know so many more things than when I started, so the combination of feedback and taking risks, I think, really helps elevate someone's career over, you know, the course of decades. So, those are two things that I think could be just incredibly powerful throughout a person's career. >> Absolutely. I couldn't agree with your advice more. You know, I think it's open your ears, open your mind, open your heart, but also to your point, I was doing some research a few months ago for a Women in Tech segment, and I saw that women will apply, and you talked about this, for a job say on LinkedIn if they meet, they won't apply unless they meet 100% of the job requirements, whereas men will apply if they meet only 40. And, your point is you don't have to meet all the job requirements. Very few people do. Take the risk, and to your point, there's so many new skills and new growth areas that you can unlock for yourself by just, you know, taking the risk, maybe it's a little bit scary, but I always think a goal isn't worth having if it doesn't give you some butterflies in your stomach. >> Exactly. Exactly. And, I think so many of those butterflies, it's the adrenaline. It's the motivator. And, it is the thing that will push you to take the training course, to read the book, to reach out to a friend, to seek out help. And, that is how we continue to get better. It's, you know, education at its finest. It's upscaling at its finest. And, I don't think that, you know, being perfect at something is necessarily the best thing. It is the imperfect, you know, nature of learning and education that actually makes us, you know, better at what we do and more introspective about where the failures are that we can continue to grow. So, you know, I think that we should be a bit fearless when it comes to our own growth in our careers. >> Yes. I love that, becoming more fearless. There's so many positive things that can come from it as a result. Talk to me now about some of the successes you've had where you've really helped solve problems for customers or even for Blackstone related to the cloud. >> Yeah, I mean, one of the biggest things for us has been, you know, the journey of migration. So, when you go from going off of your data centers and migrating to the cloud, that is a tremendous effort for any company and a, you know, especially one as we look to do it here at Blackstone. So, we've migrated over 240 applications to the cloud. We've shut down our four data centers. That's a tremendous effort that the team went through. And, you know, a huge success story, I think, across all of the teams that had to come together to do that. The biggest benefit truly was the cultural one though. The ways that the teams collaborated. The ways that they came together, how they had to work across the different leaders, across the different teams and figure out how to get things done. And so, that culture of collaboration that was really instilled across the day to day teams that were so used to working together. And then, all of a sudden so many teams across different executives had to come together and figure out how to get the work done. That has been instilled now, I think in the ways that we work to date. And then, the upscaling and the leverage and the opportunities where they could focus on things that created business opportunities for our, you know, consumers and our businesses across the company. And, working on those higher leverage opportunities has been phenomenal. And so, those are the things, those carry forward lessons that we've been able to take with us and are here today. And so, as we've been, you know, planning for the years ahead, what the tech strategy and how do we look at things? You know, those cultural benefits have been massive successes, I think, for our team. And, you know, something that have really been instilled now in the culture of our, you know, our tech work force. >> It sounds like you've really positively impacted the ethos of the company. And, I think, you know, projects like what you talked about migrating 240 apps, being able to shut down four data centers, I don't think you can do that without cultural change at the same time, and that cultural transformation is really the fuel that has to be there for digital transformation to be successful. I kind of see them like this, like they're linked. And, it sounds like you guys have done a great job at Blackstone about really, really driving that cultural change for the better for permanent, but also for the better across all lines of business. >> Yeah, and I think it opens up, you know, for people what they can do. They start to see the momentum build. They start to collaborate together. They start to share their expertise with one another. And, I think they see those linkages getting created and they realize, okay, I've been doing this day to day, you know, before the migration took place. And now that there's this forcing function and the teams come, you know, they come together in a big room. They're trying to figure out large scale plans. They're trying to understand how to get things done, how to leverage the latest and greatest technologies, how to make things more modern and reach those huge milestones. All of sudden it enables this creativity and this uplift that they haven't seen from, you know, prior ways of working, and I think that just creates a huge amount of ways of thinking about how we're going to work going forward. And, the creativity just starts to flow for the future. And, it creates a lot of leverage and uplift going forward. And so, you know, that culture that just starts to get instilled and ingrained also then becomes a huge recruiting, you know, lever for us. And then, also a retention lever for us, because people start to work in a more, you know, modern stack and a more modern way of being. >> It's momentum, right. I mean, absolutely it's momentum that's driving that flywheel. I want to switch topics a little bit and get your perspectives on diversity. We talk about it so often in technology, in every industry, but there are still some challenges there. I'd love to get your perspectives on some of the things that you've seen. And, what are some of the improvements that you think can be made where diversity is concerned? >> Yeah, I mean, diversity is still a huge challenge in so many facets of the pipeline as I, you know, think about it. And, as I've experienced it and the recruiting aspects, the retention aspects and even the elevation of great talent. So, in technology, specifically, it's still a challenge to really ensure that we can get into, you know, minority and lower income communities to ensure that there's great access to training and to folks that really want to participate in this kind of a field. It's expensive. It has a high barrier to entry, and to really get out there and teach people these great and innovative technologies is difficult. And, you know, from my perspective, the essence of technology is diversity, because it's about how you look at the future. It's about how you innovate. It's about how you see things with a different perspective. And so, it's vital that we have people from all different walks of life, from all different experiences, you know, having these skills, and can see the world through, you know, blends that they experience the world from. And so, it's not just the beginning of the pipeline ensuring the people have access, but also when they have a passion for it, and when we recruit them, and when we give them opportunities to have a career and flourish that we can also make sure that it's a place where they can flourish, be promoted and then be retained but also elevate and feel a sense of community that is inclusive and that represents them and that they have, you know, the encouragement to continue to like have amazing, amazing careers where their perspectives are valued, and their experiences are valued. And so, you know, I think the entire pipeline is something where there is still so much work to do. I think for my own experience, you know, it can be challenging. It can be challenging to be unique with your own perspective and with your own experience kind of walking through a tech world whether it's as a woman or whether it's as, you know, a person of color, or whether it's as a person with a different sexual orientation and any person that has any intersectionality. And so, there's all these different ways that we look at the world and our experiences. And, technology, again, is this essence of diversity and development. So, I think that we still have a lot of challenges in how we build the pipeline and retain the pipeline. And, for me, I always try to think of like how do you use your own experiences to cultivate that in others, to mentor and to build programming that allows your organization to bring in the best and the brightest and to really give back? Because, that is- >> Yeah. >> Really the best way to do that. >> It really is. And, I like you you talk about, you know, the diversity pipeline, and we think of filling the front end, but to your point, it's really got to be across the life cycle of that, from a retention perspective, attraction talent, retaining them, making sure people feel that they're included, that their voice matters, that their thought diversity impact matters. Last question for you as we wrap up here. Just what are some of the things that are next on the horizon that you see in terms of cloud? How do you see your role evolving? >> Yeah, I mean, I think really the biggest thing from a cloud perspective really is the talent and the diversity. I think that the more that we cultivate skills, upscale our engineering population and continue to look for ways to modernize our applications that inherently will attract new talent, new ways of thinking, new skills that are coming out of all sorts of organizations, all sorts of colleges, all sorts of institutions that will attract the right kind of talent that we want here at Blackstone. It's really exciting to think about what we can do for our companies that we invest in, for our businesses here at Blackstone, for our engineers and the value that we can bring, you know, across the communities that we work within. And so, I think that we really just want to continue to track that talent and drive that value, really, across the organization. >> Awesome. Megan, it's been such a pleasure having you on the program talking about your role as COO of Blackstone's Technology Group, the great successes that you've had where cloud is concerned, your thoughts and recommendations on diversity and what you see as next. We really appreciate your time and your insights. >> Thank you for having me. It's been great. >> Oh, our pleasure. For Megan Hayes, I'm Lisa Martin. You're watching this Cube special program series, Women at the Cloud brought to you by AWS. Thanks for watching. (upbeat music)

(bright upbeat music) >> Well, hello everybody. John Walls is here on "the CUBE". Great to have you with us as we continue our series here at the AWS Executive Summit sponsored by Accenture. And today we're talking about public service and not just a little slice of public service but probably the largest public sector offering in the UK and for with us or with us. Now to talk about that is Lianne Anderton, who is in with the Intelligent Automation Garage Delivery Lead at the UK Department of Work and Pension. Lianne, good to see you today. Thanks for joining us here on "the CUBE". >> Hi, thanks for having me. >> And also with this us is Amar Narayan, who is a Manager Director at Accenture the AWS Business Group for the Lead in Health and Public Sector, also UK and Ireland. And Amar, I think, you and Lianne, are in the same location, Newcastle, I believe in the UK, is that right? >> Yeah, absolutely. Yep, yeah, we're, here in the northeast of UK. >> Well, thank you for being with us. I appreciate the time. Lianne, let's talk about what you do, the Department of Work and Pension, the famous DWP in England. You have influence or certainly touchpoints with a huge amount of the British population. In what respects, what are you doing for the working class in England and what does technology have to do with all that? >> Sure, so for the Department for Work and Pensions I think the pensions bit is fairly self explanatory so anybody who is over state pension age within the UK. for the work part of that we also deal with people of working age. So, these are people who are either in employment and need additional help through various benefits we offer in the UK. Those people who are out of work. And we also deal with health related benefits as well. And we are currently serving over 20 million claimants every year at this moment in time. So, we're aware of a huge part of the UK government. >> All right, so say that number again. How many? >> 20 million claimants every year. >> Million with an M, right? >> Yeah. >> So, and that's individuals. And so how many transactions, if you will, how many do you think you process in a month? How, much traffic basically, are you seeing? >> An extraordinary amount? I'm not even, I don't think I even know that number. (Lianne laughing) >> Mind blowing, right? So, it's- >> A huge, huge amount. >> Mind blowing. >> Yeah, so, basically the we kind of keep the country going. So, you know, if the department for Work and Pensions kind of didn't exist anymore then actually it would cause an infinite number of problems in society. We, kind of help and support the people who need that. And, yeah, so we play a really vital role in kind of you know, social care and kind of public service. >> So, what was your journey to Accenture then? What, eventually led you to them? What problem were you having and how have you collaborated to solve that? >> So, in terms of how we work with Accenture. So, we had in around 2017 DWP was looking at a projected number of transactions growing by about 210 million which was, you know, an extraordinary amount. And, you know, I think as we've kind of covered everything that we do is on a massive scale. So, we as DWP as an organization we had absolutely no idea how we were going to be able to handle such a massive increase in the transactions. And actually, you know, after kind of various kind of paths and ideas of how we were going to do that, automation, was actually the answer. But the problem that we have with that is that we have, like many governments around the world, we have really older legacy systems. So, each of these benefits that we deal with are on legacy systems. So, whatever we were going to develop had to, you know, connect to all of these, it had to ingest and then process all of these pieces of data some of which, you know, given the fact that a lot of these systems have a lot of manual input you have data issues there that you have to solve and whatever we did, you know, as we've talked about in terms of volumes has to scale instantly as well. So, it has to be able to scale up and down to meet demand and, you know, and that down scaling is also equally as important. So yeah, you've got to be able to scale up to meet the volumes but also you've got to be able to downscale when when it's not needed. But we had nothing that was like that kind of helped us to meet that demand. So, we built our own automation platform, The Intelligent Automation Garage and we did that with Accenture. >> So Amar, I'd like you to chime in here then. So, you're looking at this client who has this massive footprint and obviously vital services, right? So, that's paramount that you have to keep that in mind and the legacy systems that Lianne was just talking about. So, now you're trying to get 'em in the next gen but also respecting that they have a serious investment already in a lot of technology. How do you approach that kind of problem solving, those dynamics and how in this case did you get them to automation as the solution? >> Sure, so I think I think one of the interesting things, yeah as Lianne has sort of described it, right? It's effectively like, you know the department has to have be running all of the time, right? They can't, you know, they can't effectively stop and then do a bunch of IT transformation, you know it's effectively like, you know, changing the wheels of a jumbo jet whilst it's taking off, right? And you've got to do all of that all in one go. But what I think we really, really liked about the situation that we were in and the client relationship we had was that we knew we had to it wasn't just a technology play, we couldn't just go, "All right, let's just put some new technology in." What we also needed to do was really sort of create a culture, an innovation culture, and go, "Well how do we think about the problems that we currently have and how do we think about solving them differently and in collaboration, right?" So, not just the, "Let's just outsource a bunch of technology for to, you know, to Accenture and build a bunch of stuff." So, we very carefully thought about, well actually, the unique situation that they're in the demands that the citizens have on the services that the department provide. And as Lianne mentioned, that technology didn't exist. So, we fundamentally looked at this in a different way. So, we worked really closely with the department. We said, Look, actually what we ultimately need is the equivalent of a virtual workforce. Something where if you already, you know all of a sudden had a hundred thousand pension claims that needed to be processed in a week that you could click your fingers and, you know in a physical world you'd have another building all of your kits, a whole bunch of trained staff that would be able to process that work. And if in the following week you didn't need that you no longer needed that building that stuff or the machinery. And we wanted to replicate that in the virtual world. So, we started designing a platform we utilized and focused on using AWS because it had the scalability. And we thought about, how were we going to connect something as new as AWS to all of these legacy systems. How are we going to make that work in the modern world? How are we going to integrate it? How we going to make sure it's secure? And frankly, we're really honest with the client we said, "Look, this hasn't been done before. Like, nowhere in Accenture has done it. No one's done it in the industry. We've got some smart people, I think we can do it." And, we've prototyped and we've built and we were able to prove that we can do that. And that in itself just created an environment of solving tricky problems and being innovative but most importantly not doing sort of proof of concepts that didn't go anywhere but building something that actually scaled. And I think that was really the real the start of what was has been the Garage. >> So, And Lianne, you mentioned this and you just referred to it Amar, about The Garage, right? The Intelligent Automation Garage. What exactly is it? I mean, we talked about it, what the needs are all this and that, but Lianne, I'll let you jump in first and Amar, certainly compliment her remarks, but what is the IAG, what's the... >> So, you know, I think exactly what kind of Amar, has said from a from a kind of a development point of view I think it started off, you know, really, really small. And the idea is that this is DWP, intelligent automation center of excellence. So, you know, it's aims are that, you know, it makes sure that it scopes out kind of the problems that DWP are are facing properly. So, we really understand what the crux of the problem is. In large organizations It's very easy, I think to think you understand what the problem is where actually, you know, it is really about kind of delving into what that is. And actually we have a dedicated design team that really kind of get under the bonnet of what these issues really are. It then kind of architects what the solutions need to look like using as Amar said, all the exciting new technology that we kind of have available to us. That kind of sensible solution as to what that should look like. We then build that sensible solution and we then, you know as part of that, we make sure that it scales to demand. So, something that might start out with, I dunno, you know a few hundred claimants or kind of cases going through it can quite often, you know, once that's that's been successful scale really, really quickly because as you know, we have 20 million claimants that come through us every year. So, these types of things can grow and expand but also a really key function of what we do is that we have a fully supported in-house service as well. So, all of those automations that we build are then maintained and you know, so any changes that kind of needed to be need to be made to them, we have all that and we have that control and we have our kind of arms wrapped around all of those. But also what that allows us to do is it allows us to be very kind of self-sufficient in making sure that we are as sufficient, sorry, as efficient as possible. And what I mean by that is looking at, you know as new technologies come around and they can allow us to do things more effectively. So, it allows us to kind of almost do that that kind of continuous improvement ourselves. So, that's a huge part of what we do as well. And you know, I think from a size point of view I said this started off really small as in the idea was this was a kind of center of excellence but actually as automation, I think as Amar alluded to is kind of really started to embed in DWP culture what we've started to kind of see is the a massive expansion in the types of of work that people want us to do and the volume of work that we are doing. So, I think we're currently running at around around a hundred people at the moment and I think, you know we started off with a scrum, a couple of scrum teams under Amar, so yeah, it's really grown. But you know, I think this is here to stay within DWP. >> Yeah, well when we talk about automation, you know virtual and robotics and all this I like to kind of keep the human element in mind here too. And Amar, maybe you can touch on that in certain terms of the human factors in this equation. 'Cause people think about, you know, robots it means different things to different people. In your mind, how does automation intersect with the human element here and in terms of the kinds of things Lianne wants to do down the road, you know, is a road for people basically? >> Oh yeah, absolutely. I think fundamentally what the department does is support people and therefore the solutions that we designed and built had to factor that in mind right? We were trying to best support and provide the best service we possibly can. And not only do we need to support the citizens that it supports. The department itself is a big organization, right? We're up to, we're talking between sort of 70 and 80,000 employees. So, how do we embed automation but also make the lives of the, of the DWP agents better as well? And that's what we thought about. So we said, "Well look, we think we can design solutions that do both." So, a lot of our automations go through a design process and we work closely with our operations team and we go, well actually, you know in processing and benefit, there are some aspects of that processing that benefit that are copy and paste, right? It doesn't require much thought around it, but it just requires capturing data and there's elements of that solution or that process that requires actual thought and understanding and really empathy around going, "Well how do I best support this citizen?" And what we tended to do is we took all of the things that were sort of laborious and took a lot of time and would slow down the overall process and we automated those and then we really focused on making sure that the elements that required the human, the human input was made as user friendly and centric as we possibly could. So, if there's a really complex case that needs to be processed, we were able to present the information in a really digestible and understandable way for the agents so that they could make a informed and sensible decision based around a citizen. And what that enabled us to do is essentially meet the demands of the volumes and the peaks that came in but also maintain the quality and if not improve, you know the accuracy of the claims processing that we had. >> So, how do you know, and maybe Lianne, you can address this. How do you know that it's successful on both sides of that equation? And, 'cause Amar raised a very good point. You have 70 to 80,000 employees that you're trying to make their work life much more efficient, much simpler and hopefully make them better at their jobs at the end of the day. But you're also taking care of 20 million clients on the, your side too. So, how do you, what's your measurement for success and what kind of like raw feedback do you get that says, "Okay, this has worked for both of our client bases, both our citizens and our employees?" >> Yeah, so we can look at this both from a a quantitative and a qualitative point of view as well. So, I think from a let take the kind figures first. So we are really hot on making sure that whatever automations we put in place we are there to measure how that automation is working what it's kind of doing and the impact that it's having from an operational point of view. So I think, you know, I think the proof of the fact that the Intelligent Automation Garage is working is that, you know, in the, in its lifetime, we've processed over 20 million items and cases so far. We have 65 scaled and transitioned automations and we've saved over 2 million operational hours. I was going to say that again that's 2 million operational hours. And what that allows us to do as an organization those 2 million hours have allowed us to rather than people as Amar, said, cutting and pasting and doing work that that is essentially very time consuming and repetitive. That 2 million hours we've been able to use on actual decision making. So, the stuff that you need as sentient human being to make judgment calls on and you know and kind of make those decisions that's what it's allowed us as an organization to do. And then I think from a quality point of view I think the feedback that we have from our operational teams is, you know is equally as as great. So, we have that kind of feedback from, you know all the way up from to the director level about, you know how it's kind of like I said that freeing up that time but actually making the operational, you know they don't have an easy job and it's making that an awful lot easier on a day to day basis. It has a real day to day impact. But also, you know, there are other things that kind of the knock on effects in terms of accuracy. So for example, robot will do is exactly as it's told it doesn't make any mistakes, it doesn't have sick days, you know, it does what it says on the tin and actually that kind of impact. So, it's not necessarily, you know, counting your numbers it's the fact that then doesn't generate a call from a customer that kind of says, "Well you, I think you've got this wrong." So, it's all that kind of, these kind of ripple effects that go out. I think is how we measure the fact that A, the garage is working and b, it's delivering the value that we needed to deliver. >> Robots, probably ask better questions too so yeah... (Lianne laughing) So, real quick, just real quick before you head out. So, the big challenge next, eureka, this works, right? Amar, you put together this fantastic system it's in great practice at the DWP, now what do we do? So, it's just in 30 seconds, Amar, maybe if you can look at, be the headlights down the road here for DWP and say, "This is where I think we can jump to next." >> Yeah, so I think, what we've been able to prove as I say is that is scaled innovation and having the return and the value that it creates is here to stay, right? So, I think the next things for us are a continuous expand the stuff that we're doing. Keeping hold of that culture, right? That culture of constantly solving difficult problems and being able to innovate and scale them. So, we are now doing a lot more automations across the department, you know, across different benefits across the digital agenda. I think we're also now becoming almost a bit of the fabric of enabling some of the digital transformation that big organizations look at, right? So moving to a world where you can have a venture driven architectures and being able to sort of scale that. I also think the natural sort of expansion of the team and the type of work that we're going to do is probably also going to expand into sort of the analytics side of it and understanding and seeing how we can take the data from the cases that we're processing to overall have a smoother journey across for our citizens. But it's looking, you know, the future's looking bright. I think we've got a number of different backlogs of items to work on. >> Well, you've got a great story to tell and thank you for sharing it with us here on "the CUBE", talking about DWP, the Department of Work and Pensions in the UK and the great work that Accenture's doing to make 20 million lives plus, a lot simpler for our friends in England. You've been watching ""the CUBE"" the AWS Executive Summit sponsored by Accenture. (bright upbeat music)