(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)

(upbeat instrumental) >> Hello and welcome to this CUBE conversation. I'm John Furrier, host of theCUBE, here in Palo Alto, California. Got a great conversation with Robert Nishihara who's the co-founder and CEO of Anyscale. Robert, great to have you on this CUBE conversation. It's great to see you. We did your first Ray Summit a couple years ago and congratulations on your venture. Great to have you on. >> Thank you. Thanks for inviting me. >> So you're first time CEO out of Berkeley in Data. You got the Databricks is coming out of there. You got a bunch of activity coming from Berkeley. It's like a, it really is kind of like where a lot of innovations going on data. Anyscale has been one of those startups that has risen out of that scene. Right? You look at the success of what the Data lakes are now. Now you've got the generative AI. This has been a really interesting innovation market. This new wave is coming. Tell us what's going on with Anyscale right now, as you guys are gearing up and getting some growth. What's happening with the company? >> Yeah, well one of the most exciting things that's been happening in computing recently, is the rise of AI and the excitement about AI, and the potential for AI to really transform every industry. Now of course, one of the of the biggest challenges to actually making that happen is that doing AI, that AI is incredibly computationally intensive, right? To actually succeed with AI to actually get value out of AI. You're typically not just running it on your laptop, you're often running it and scaling it across thousands of machines, or hundreds of machines or GPUs, and to, so organizations and companies and businesses that do AI often end up building a large infrastructure team to manage the distributed systems, the computing to actually scale these applications. And that's a, that's a, a huge software engineering lift, right? And so, one of the goals for Anyscale is really to make that easy. To get to the point where, developers and teams and companies can succeed with AI. Can build these scalable AI applications, without really you know, without a huge investment in infrastructure with a lot of, without a lot of expertise in infrastructure, where really all they need to know is how to program on their laptop, how to program in Python. And if you have that, then that's really all you need to succeed with AI. So that's what we've been focused on. We're building Ray, which is an open source project that's been starting to get adopted by tons of companies, to actually train these models, to deploy these models, to do inference with these models, you know, to ingest and pre-process their data. And our goals, you know, here with the company are really to make Ray successful. To grow the Ray community, and then to build a great product around it and simplify the development and deployment, and productionization of machine learning for, for all these businesses. >> It's a great trend. Everyone wants developer productivity seeing that, clearly right now. And plus, developers are voting literally on what standards become. As you look at how the market is open source driven, a lot of that I love the model, love the Ray project love the, love the Anyscale value proposition. How big are you guys now, and how is that value proposition of Ray and Anyscale and foundational models coming together? Because it seems like you guys are in a perfect storm situation where you guys could get a real tailwind and draft off the the mega trend that everyone's getting excited. The new toy is ChatGPT. So you got to look at that and say, hey, I mean, come on, you guys did all the heavy lifting. >> Absolutely. >> You know how many people you are, and what's the what's the proposition for you guys these days? >> You know our company's about a hundred people, that a bit larger than that. Ray's been going really quickly. It's been, you know, companies using, like OpenAI uses Ray to train their models, like ChatGPT. Companies like Uber run all their deep learning you know, and classical machine learning on top of Ray. Companies like Shopify, Spotify, Netflix, Cruise, Lyft, Instacart, you know, Bike Dance. A lot of these companies are investing heavily in Ray for their machine learning infrastructure. And I think it's gotten to the point where, if you're one of these, you know type of businesses, and you're looking to revamp your machine learning infrastructure. If you're looking to enable new capabilities, you know make your teams more productive, increase, speed up the experimentation cycle, you know make it more performance, like build, you know, run applications that are more scalable, run them faster, run them in a more cost efficient way. All of these types of companies are at least evaluating Ray and Ray is an increasingly common choice there. I think if they're not using Ray, if many of these companies that end up not using Ray, they often end up building their own infrastructure. So Ray has been, the growth there has been incredibly exciting over the, you know we had our first in-person Ray Summit just back in August, and planning the next one for, for coming September. And so when you asked about the value proposition, I think there's there's really two main things, when people choose to go with Ray and Anyscale. One reason is about moving faster, right? It's about developer productivity, it's about speeding up the experimentation cycle, easily getting their models in production. You know, we hear many companies say that they, you know they, once they prototype a model, once they develop a model, it's another eight weeks, or 12 weeks to actually get that model in production. And that's a reason they talk to us. We hear companies say that, you know they've been training their models and, and doing inference on a single machine, and they've been sort of scaling vertically, like using bigger and bigger machines. But they, you know, you can only do that for so long, and at some point you need to go beyond a single machine and that's when they start talking to us. Right? So one of the main value propositions is around moving faster. I think probably the phrase I hear the most is, companies saying that they don't want their machine learning people to have to spend all their time configuring infrastructure. All this is about productivity. >> Yeah. >> The other. >> It's the big brains in the company. That are being used to do remedial tasks that should be automated right? I mean that's. >> Yeah, and I mean, it's hard stuff, right? It's also not these people's area of expertise, and or where they're adding the most value. So all of this is around developer productivity, moving faster, getting to market faster. The other big value prop and the reason people choose Ray and choose Anyscale, is around just providing superior infrastructure. This is really, can we scale more? You know, can we run it faster, right? Can we run it in a more cost effective way? We hear people saying that they're not getting good GPU utilization with the existing tools they're using, or they can't scale beyond a certain point, or you know they don't have a way to efficiently use spot instances to save costs, right? Or their clusters, you know can't auto scale up and down fast enough, right? These are all the kinds of things that Ray and Anyscale, where Ray and Anyscale add value and solve these kinds of problems. >> You know, you bring up great points. Auto scaling concept, early days, it was easy getting more compute. Now it's complicated. They're built into more integrated apps in the cloud. And you mentioned those companies that you're working with, that's impressive. Those are like the big hardcore, I call them hardcore. They have a good technical teams. And as the wave starts to move from these companies that were hyper scaling up all the time, the mainstream are just developers, right? So you need an interface in, so I see the dots connecting with you guys and I want to get your reaction. Is that how you see it? That you got the alphas out there kind of kicking butt, building their own stuff, alpha developers and infrastructure. But mainstream just wants programmability. They want that heavy lifting taken care of for them. Is that kind of how you guys see it? I mean, take us through that. Because to get crossover to be democratized, the automation's got to be there. And for developer productivity to be in, it's got to be coding and programmability. >> That's right. Ultimately for AI to really be successful, and really you know, transform every industry in the way we think it has the potential to. It has to be easier to use, right? And that is, and being easier to use, there's many dimensions to that. But an important one is that as a developer to do AI, you shouldn't have to be an expert in distributed systems. You shouldn't have to be an expert in infrastructure. If you do have to be, that's going to really limit the number of people who can do this, right? And I think there are so many, all of the companies we talk to, they don't want to be in the business of building and managing infrastructure. It's not that they can't do it. But it's going to slow them down, right? They want to allocate their time and their energy toward building their product, right? To building a better product, getting their product to market faster. And if we can take the infrastructure work off of the critical path for them, that's going to speed them up, it's going to simplify their lives. And I think that is critical for really enabling all of these companies to succeed with AI. >> Talk about the customers you guys are talking to right now, and how that translates over. Because I think you hit a good thread there. Data infrastructure is critical. Managed services are coming online, open sources continuing to grow. You have these people building their own, and then if they abandon it or don't scale it properly, there's kind of consequences. 'Cause it's a system you mentioned, it's a distributed system architecture. It's not as easy as standing up a monolithic app these days. So when you guys go to the marketplace and talk to customers, put the customers in buckets. So you got the ones that are kind of leaning in, that are pretty peaked, probably working with you now, open source. And then what's the customer profile look like as you go mainstream? Are they looking to manage service, looking for more architectural system, architecture approach? What's the, Anyscale progression? How do you engage with your customers? What are they telling you? >> Yeah, so many of these companies, yes, they're looking for managed infrastructure 'cause they want to move faster, right? Now the kind of these profiles of these different customers, they're three main workloads that companies run on Anyscale, run with Ray. It's training related workloads, and it is serving and deployment related workloads, like actually deploying your models, and it's batch processing, batch inference related workloads. Like imagine you want to do computer vision on tons and tons of, of images or videos, or you want to do natural language processing on millions of documents or audio, or speech or things like that, right? So the, I would say the, there's a pretty large variety of use cases, but the most common you know, we see tons of people working with computer vision data, you know, computer vision problems, natural language processing problems. And it's across many different industries. We work with companies doing drug discovery, companies doing you know, gaming or e-commerce, right? Companies doing robotics or agriculture. So there's a huge variety of the types of industries that can benefit from AI, and can really get a lot of value out of AI. And, but the, but the problems are the same problems that they all want to solve. It's like how do you make your team move faster, you know succeed with AI, be more productive, speed up the experimentation, and also how do you do this in a more performant way, in a faster, cheaper, in a more cost efficient, more scalable way. >> It's almost like the cloud game is coming back to AI and these foundational models, because I was just on a podcast, we recorded our weekly podcast, and I was just riffing with Dave Vellante, my co-host on this, were like, hey, in the early days of Amazon, if you want to build an app, you just, you have to build a data center, and then you go to now you go to the cloud, cloud's easier, pay a little money, penny's on the dollar, you get your app up and running. Cloud computing is born. With foundation models in generative AI. The old model was hard, heavy lifting, expensive, build out, before you get to do anything, as you mentioned time. So I got to think that you're pretty much in a good position with this foundational model trend in generative AI because I just looked at the foundation map, foundation models, map of the ecosystem. You're starting to see layers of, you got the tooling, you got platform, you got cloud. It's filling out really quickly. So why is Anyscale important to this new trend? How do you talk to people when they ask you, you know what does ChatGPT mean for Anyscale? And how does the financial foundational model growth, fit into your plan? >> Well, foundational models are hugely important for the industry broadly. Because you're going to have these really powerful models that are trained that you know, have been trained on tremendous amounts of data. tremendous amounts of computes, and that are useful out of the box, right? That people can start to use, and query, and get value out of, without necessarily training these huge models themselves. Now Ray fits in and Anyscale fit in, in a number of places. First of all, they're useful for creating these foundation models. Companies like OpenAI, you know, use Ray for this purpose. Companies like Cohere use Ray for these purposes. You know, IBM. If you look at, there's of course also open source versions like GPTJ, you know, created using Ray. So a lot of these large language models, large foundation models benefit from training on top of Ray. And, but of course for every company training and creating these huge foundation models, you're going to have many more that are fine tuning these models with their own data. That are deploying and serving these models for their own applications, that are building other application and business logic around these models. And that's where Ray also really shines, because Ray you know, is, can provide common infrastructure for all of these workloads. The training, the fine tuning, the serving, the data ingest and pre-processing, right? The hyper parameter tuning, the and and so on. And so where the reason Ray and Anyscale are important here, is that, again, foundation models are large, foundation models are compute intensive, doing you know, using both creating and using these foundation models requires tremendous amounts of compute. And there there's a big infrastructure lift to make that happen. So either you are using Ray and Anyscale to do this, or you are building the infrastructure and managing the infrastructure yourself. Which you can do, but it's, it's hard. >> Good luck with that. I always say good luck with that. I mean, I think if you really need to do, build that hardened foundation, you got to go all the way. And I think this, this idea of composability is interesting. How is Ray working with OpenAI for instance? Take, take us through that. Because I think you're going to see a lot of people talking about, okay I got trained models, but I'm going to have not one, I'm going to have many. There's big debate that OpenAI is going to be the mother of all LLMs, but now, but really people are also saying that to be many more, either purpose-built or specific. The fusion and these things come together there's like a blending of data, and that seems to be a value proposition. How does Ray help these guys get their models up? Can you take, take us through what Ray's doing for say OpenAI and others, and how do you see the models interacting with each other? >> Yeah, great question. So where, where OpenAI uses Ray right now, is for the training workloads. Training both to create ChatGPT and models like that. There's both a supervised learning component, where you're pre-training this model on doing supervised pre-training with example data. There's also a reinforcement learning component, where you are fine-tuning the model and continuing to train the model, but based on human feedback, based on input from humans saying that, you know this response to this question is better than this other response to this question, right? And so Ray provides the infrastructure for scaling the training across many, many GPUs, many many machines, and really running that in an efficient you know, performance fault tolerant way, right? And so, you know, open, this is not the first version of OpenAI's infrastructure, right? They've gone through iterations where they did start with building the infrastructure themselves. They were using tools like MPI. But at some point, you know, given the complexity, given the scale of what they're trying to do, you hit a wall with MPI and that's going to happen with a lot of other companies in this space. And at that point you don't have many other options other than to use Ray or to build your own infrastructure. >> That's awesome. And then your vision on this data interaction, because the old days monolithic models were very rigid. You couldn't really interface with them. But we're kind of seeing this future of data fusion, data interaction, data blending at large scale. What's your vision? How do you, what's your vision of where this goes? Because if this goes the way people think. You can have this data chemistry kind of thing going on where people are integrating all kinds of data with each other at large scale. So you need infrastructure, intelligence, reasoning, a lot of code. Is this something that you see? What's your vision in all this? Take us through. >> AI is going to be used everywhere right? It's, we see this as a technology that's going to be ubiquitous, and is going to transform every business. I mean, imagine you make a product, maybe you were making a tool like Photoshop or, or whatever the, you know, tool is. The way that people are going to use your tool, is not by investing, you know, hundreds of hours into learning all of the different, you know specific buttons they need to press and workflows they need to go through it. They're going to talk to it, right? They're going to say, ask it to do the thing they want it to do right? And it's going to do it. And if it, if it doesn't know what it's want, what it's, what's being asked of it. It's going to ask clarifying questions, right? And then you're going to clarify, and you're going to have a conversation. And this is going to make many many many kinds of tools and technology and products easier to use, and lower the barrier to entry. And so, and this, you know, many companies fit into this category of trying to build products that, and trying to make them easier to use, this is just one kind of way it can, one kind of way that AI will will be used. But I think it's, it's something that's pretty ubiquitous. >> Yeah. It'll be efficient, it'll be efficiency up and down the stack, and will change the productivity equation completely. You just highlighted one, I don't want to fill out forms, just stand up my environment for me. And then start coding away. Okay well this is great stuff. Final word for the folks out there watching, obviously new kind of skill set for hiring. You guys got engineers, give a plug for the company, for Anyscale. What are you looking for? What are you guys working on? Give a, take the last minute to put a plug in for the company. >> Yeah well if you're interested in AI and if you think AI is really going to be transformative, and really be useful for all these different industries. We are trying to provide the infrastructure to enable that to happen, right? So I think there's the potential here, to really solve an important problem, to get to the point where developers don't need to think about infrastructure, don't need to think about distributed systems. All they think about is their application logic, and what they want their application to do. And I think if we can achieve that, you know we can be the foundation or the platform that enables all of these other companies to succeed with AI. So that's where we're going. I think something like this has to happen if AI is going to achieve its potential, we're looking for, we're hiring across the board, you know, great engineers, on the go-to-market side, product managers, you know people who want to really, you know, make this happen. >> Awesome well congratulations. I know you got some good funding behind you. You're in a good spot. I think this is happening. I think generative AI and foundation models is going to be the next big inflection point, as big as the pc inter-networking, internet and smartphones. This is a whole nother application framework, a whole nother set of things. So this is the ground floor. Robert, you're, you and your team are right there. Well done. >> Thank you so much. >> All right. Thanks for coming on this CUBE conversation. I'm John Furrier with theCUBE. Breaking down a conversation around AI and scaling up in this new next major inflection point. This next wave is foundational models, generative AI. And thanks to ChatGPT, the whole world's now knowing about it. So it really is changing the game and Anyscale is right there, one of the hot startups, that is in good position to ride this next wave. Thanks for watching. (upbeat instrumental)

(soft electronic music) >> Hey everyone, welcome to this CUBE conversation as part of the AWS Startup Showcase, season three, episode one, featuring Astronomer. I'm your host, Lisa Martin. I'm in the CUBE's Palo Alto Studios, and today excited to be joined by a couple of guests, a couple of co-founders from Astronomer. Viraj Parekh is with us, as is Paola Peraza-Calderon. Thanks guys so much for joining us. Excited to dig into Astronomer. >> Thank you so much for having us. >> Yeah, thanks for having us. >> Yeah, and we're going to be talking about the role of data orchestration. Paola, let's go ahead and start with you. Give the audience that understanding, that context about Astronomer and what it is that you guys do. >> Mm-hmm. Yeah, absolutely. So, Astronomer is a, you know, we're a technology and software company for modern data orchestration, as you said, and we're the driving force behind Apache Airflow. The Open Source Workflow Management tool that's since been adopted by thousands and thousands of users, and we'll dig into this a little bit more. But, by data orchestration, we mean data pipeline, so generally speaking, getting data from one place to another, transforming it, running it on a schedule, and overall just building a central system that tangibly connects your entire ecosystem of data services, right. So what, that's Redshift, Snowflake, DVT, et cetera. And so tangibly, we build, we at Astronomer here build products powered by Apache Airflow for data teams and for data practitioners, so that they don't have to. So, we sell to data engineers, data scientists, data admins, and we really spend our time doing three things. So, the first is that we build Astro, our flagship cloud service that we'll talk more on. But here, we're really building experiences that make it easier for data practitioners to author, run, and scale their data pipeline footprint on the cloud. And then, we also contribute to Apache Airflow as an open source project and community. So, we cultivate the community of humans, and we also put out open source developer tools that actually make it easier for individual data practitioners to be productive in their day-to-day jobs, whether or not they actually use our product and and pay us money or not. And then of course, we also have professional services and education and all of these things around our commercial products that enable folks to use our products and use Airflow as effectively as possible. So yeah, super, super happy with everything we've done and hopefully that gives you an idea of where we're starting. >> Awesome, so when you're talking with those, Paola, those data engineers, those data scientists, how do you define data orchestration and what does it mean to them? >> Yeah, yeah, it's a good question. So, you know, if you Google data orchestration you're going to get something about an automated process for organizing silo data and making it accessible for processing and analysis. But, to your question, what does that actually mean, you know? So, if you look at it from a customer's perspective, we can share a little bit about how we at Astronomer actually do data orchestration ourselves and the problems that it solves for us. So, as many other companies out in the world do, we at Astronomer need to monitor how our own customers use our products, right? And so, we have a weekly meeting, for example, that goes through a dashboard and a dashboarding tool called Sigma where we see the number of monthly customers and how they're engaging with our product. But, to actually do that, you know, we have to use data from our application database, for example, that has behavioral data on what they're actually doing in our product. We also have data from third party API tools, like Salesforce and HubSpot, and other ways in which our customer, we actually engage with our customers and their behavior. And so, our data team internally at Astronomer uses a bunch of tools to transform and use that data, right? So, we use FiveTran, for example, to ingest. We use Snowflake as our data warehouse. We use other tools for data transformations. And even, if we at Astronomer don't do this, you can imagine a data team also using tools like, Monte Carlo for data quality, or Hightouch for Reverse ETL, or things like that. And, I think the point here is that data teams, you know, that are building data-driven organizations have a plethora of tooling to both ingest the right data and come up with the right interfaces to transform and actually, interact with that data. And so, that movement and sort of synchronization of data across your ecosystem is exactly what data orchestration is responsible for. Historically, I think, and Raj will talk more about this, historically, schedulers like KRON and Oozie or Control-M have taken a role here, but we think that Apache Airflow has sort of risen over the past few years as the defacto industry standard for writing data pipelines that do tasks, that do data jobs that interact with that ecosystem of tools in your organization. And so, beyond that sort of data pipeline unit, I think where we see it is that data acquisition is not only writing those data pipelines that move your data, but it's also all the things around it, right, so, CI/CD tool and Secrets Management, et cetera. So, a long-winded answer here, but I think that's how we talk about it here at Astronomer and how we're building our products. >> Excellent. Great context, Paola. Thank you. Viraj, let's bring you into the conversation. Every company these days has to be a data company, right? They've got to be a software company- >> Mm-hmm. >> whether it's my bank or my grocery store. So, how are companies actually doing data orchestration today, Viraj? >> Yeah, it's a great question. So, I think one thing to think about is like, on one hand, you know, data orchestration is kind of a new category that we're helping define, but on the other hand, it's something that companies have been doing forever, right? You need to get data moving to use it, you know. You've got it all in place, aggregate it, cleaning it, et cetera. So, when you look at what companies out there are doing, right. Sometimes, if you're a more kind of born in the cloud company, as we say, you'll adopt all these cloud native tooling things your cloud provider gives you. If you're a bank or another sort of institution like that, you know, you're probably juggling an even wider variety of tools. You're thinking about a cloud migration. You might have things like Kron running in one place, Uzi running somewhere else, Informatics running somewhere else, while you're also trying to move all your workloads to the cloud. So, there's quite a large spectrum of what the current state is for companies. And then, kind of like Paola was saying, Apache Airflow started in 2014, and it was actually started by Airbnb, and they put out this blog post that was like, "Hey here's how we use Apache Airflow to orchestrate our data across all their sources." And really since then, right, it's almost been a decade since then, Airflow emerged as the open source standard, and there's companies of all sorts using it. And, it's really used to tie all these tools together, especially as that number of tools increases, companies move to hybrid cloud, hybrid multi-cloud strategies, and so on and so forth. But you know, what we found is that if you go to any company, especially a larger one and you say like, "Hey, how are you doing data orchestration?" They'll probably say something like, "Well, I have five data teams, so I have eight different ways I do data orchestration." Right. This idea of data orchestration's been there but the right way to do it, kind of all the abstractions you need, the way your teams need to work together, and so on and so forth, hasn't really emerged just yet, right? It's such a quick moving space that companies have to combine what they were doing before with what their new business initiatives are today. So, you know, what we really believe here at Astronomer is Airflow is the core of how you solve data orchestration for any sort of use case, but it's not everything. You know, it needs a little more. And, that's really where our commercial product, Astro comes in, where we've built, not only the most tried and tested airflow experience out there. We do employ a majority of the Airflow Core Committers, right? So, we're kind of really deep in the project. We've also built the right things around developer tooling, observability, and reliability for customers to really rely on Astro as the heart of the way they do data orchestration, and kind of think of it as the foundational layer that helps tie together all the different tools, practices and teams large companies have to do today. >> That foundational layer is absolutely critical. You've both mentioned open source software. Paola, I want to go back to you, and just give the audience an understanding of how open source really plays into Astronomer's mission as a company, and into the technologies like Astro. >> Mm-hmm. Yeah, absolutely. I mean, we, so we at Astronomers started using Airflow and actually building our products because Airflow is open source and we were our own customers at the beginning of our company journey. And, I think the open source community is at the core of everything we do. You know, without that open source community and culture, I think, you know, we have less of a business, and so, we're super invested in continuing to cultivate and grow that. And, I think there's a couple sort of concrete ways in which we do this that personally make me really excited to do my own job. You know, for one, we do things like we organize meetups and we sponsor the Airflow Summit and there's these sort of baseline community efforts that I think are really important and that reminds you, hey, there just humans trying to do their jobs and learn and use both our technology and things that are out there and contribute to it. So, making it easier to contribute to Airflow, for example, is another one of our efforts. As Viraj mentioned, we also employ, you know, engineers internally who are on our team whose full-time job is to make the open source project better. Again, regardless of whether or not you're a customer of ours or not, we want to make sure that we continue to cultivate the Airflow project in and of itself. And, we're also building developer tooling that might not be a part of the Apache Open Source project, but is still open source. So, we have repositories in our own sort of GitHub organization, for example, with tools that individual data practitioners, again customers are not, can use to make them be more productive in their day-to-day jobs with Airflow writing Dags for the most common use cases out there. The last thing I'll say is how important I think we've found it to build sort of educational resources and documentation and best practices. Airflow can be complex. It's been around for a long time. There's a lot of really, really rich feature sets. And so, how do we enable folks to actually use those? And that comes in, you know, things like webinars, and best practices, and courses and curriculum that are free and accessible and open to the community are just some of the ways in which I think we're continuing to invest in that open source community over the next year and beyond. >> That's awesome. It sounds like open source is really core, not only to the mission, but really to the heart of the organization. Viraj, I want to go back to you and really try to understand how does Astronomer fit into the wider modern data stack and ecosystem? Like what does that look like for customers? >> Yeah, yeah. So, both in the open source and with our commercial customers, right? Folks everywhere are trying to tie together a huge variety of tools in order to start making sense of their data. And you know, I kind of think of it almost like as like a pyramid, right? At the base level, you need things like data reliability, data, sorry, data freshness, data availability, and so on and so forth, right? You just need your data to be there. (coughs) I'm sorry. You just need your data to be there, and you need to make it predictable when it's going to be there. You need to make sure it's kind of correct at the highest level, some quality checks, and so on and so forth. And oftentimes, that kind of takes the case of ELT or ETL use cases, right? Taking data from somewhere and moving it somewhere else, usually into some sort of analytics destination. And, that's really what businesses can do to just power the core parts of getting insights into how their business is going, right? How much revenue did I had? What's in my pipeline, salesforce, and so on and so forth. Once that kind of base foundation is there and people can get the data they need, how they need it, it really opens up a lot for what customers can do. You know, I think one of the trendier things out there right now is MLOps, and how do companies actually put machine learning into production? Well, when you think about it you kind of have to squint at it, right? Like, machine learning pipelines are really just any other data pipeline. They just have a certain set of needs that might not not be applicable to ELT pipelines. And, when you kind of have a common layer to tie together all the ways data can move through your organization, that's really what we're trying to make it so companies can do. And, that happens in financial services where, you know, we have some customers who take app data coming from their mobile apps, and actually run it through their fraud detection services to make sure that all the activity is not fraudulent. We have customers that will run sports betting models on our platform where they'll take data from a bunch of public APIs around different sporting events that are happening, transform all of that in a way their data scientist can build models with it, and then actually bet on sports based on that output. You know, one of my favorite use cases I like to talk about that we saw in the open source is we had there was one company whose their business was to deliver blood transfusions via drone into remote parts of the world. And, it was really cool because they took all this data from all sorts of places, right? Kind of orchestrated all the aggregation and cleaning and analysis that happened had to happen via airflow and the end product would be a drone being shot out into a real remote part of the world to actually give somebody blood who needed it there. Because it turns out for certain parts of the world, the easiest way to deliver blood to them is via drone and not via some other, some other thing. So, these kind of, all the things people do with the modern data stack is absolutely incredible, right? Like you were saying, every company's trying to be a data-driven company. What really energizes me is knowing that like, for all those best, super great tools out there that power a business, we get to be the connective tissue, or the, almost like the electricity that kind of ropes them all together and makes so people can actually do what they need to do. >> Right. Phenomenal use cases that you just described, Raj. I mean, just the variety alone of what you guys are able to do and impact is so cool. So Paola, when you're with those data engineers, those data scientists, and customer conversations, what's your pitch? Why use Astro? >> Mm-hmm. Yeah, yeah, it's a good question. And honestly, to piggyback off of Viraj, there's so many. I think what keeps me so energized is how mission critical both our product and data orchestration is, and those use cases really are incredible and we work with customers of all shapes and sizes. But, to answer your question, right, so why use Astra? Why use our commercial products? There's so many people using open source, why pay for something more than that? So, you know, the baseline for our business really is that Airflow has grown exponentially over the last five years, and like we said has become an industry standard that we're confident there's a huge opportunity for us as a company and as a team. But, we also strongly believe that being great at running Airflow, you know, doesn't make you a successful company at what you do. What makes you a successful company at what you do is building great products and solving problems and solving pin points of your own customers, right? And, that differentiating value isn't being amazing at running Airflow. That should be our job. And so, we want to abstract those customers from meaning to do things like manage Kubernetes infrastructure that you need to run Airflow, and then hiring someone full-time to go do that. Which can be hard, but again doesn't add differentiating value to your team, or to your product, or to your customers. So, folks to get away from managing that infrastructure sort of a base, a base layer. Folks who are looking for differentiating features that make their team more productive and allows them to spend less time tweaking Airflow configurations and more time working with the data that they're getting from their business. For help, getting, staying up with Airflow releases. There's a ton of, we've actually been pretty quick to come out with new Airflow features and releases, and actually just keeping up with that feature set and working strategically with a partner to help you make the most out of those feature sets is a key part of it. And, really it's, especially if you're an organization who currently is committed to using Airflow, you likely have a lot of Airflow environments across your organization. And, being able to see those Airflow environments in a single place and being able to enable your data practitioners to create Airflow environments with a click of a button, and then use, for example, our command line to develop your Airflow Dags locally and push them up to our product, and use all of the sort of testing and monitoring and observability that we have on top of our product is such a key. It sounds so simple, especially if you use Airflow, but really those things are, you know, baseline value props that we have for the customers that continue to be excited to work with us. And of course, I think we can go beyond that and there's, we have ambitions to add whole, a whole bunch of features and expand into different types of personas. >> Right? >> But really our main value prop is for companies who are committed to Airflow and want to abstract themselves and make use of some of the differentiating features that we now have at Astronomer. >> Got it. Awesome. >> Thank you. One thing, one thing I'll add to that, Paola, and I think you did a good job of saying is because every company's trying to be a data company, companies are at different parts of their journey along that, right? And we want to meet customers where they are, and take them through it to where they want to go. So, on one end you have folks who are like, "Hey, we're just building a data team here. We have a new initiative. We heard about Airflow. How do you help us out?" On the farther end, you know, we have some customers that have been using Airflow for five plus years and they're like, "Hey, this is awesome. We have 10 more teams we want to bring on. How can you help with this? How can we do more stuff in the open source with you? How can we tell our story together?" And, it's all about kind of taking this vast community of data users everywhere, seeing where they're at, and saying like, "Hey, Astro and Airflow can take you to the next place that you want to go." >> Which is incredibly- >> Mm-hmm. >> and you bring up a great point, Viraj, that every company is somewhere in a different place on that journey. And it's, and it's complex. But it sounds to me like a lot of what you're doing is really stripping away a lot of the complexity, really enabling folks to use their data as quickly as possible, so that it's relevant and they can serve up, you know, the right products and services to whoever wants what. Really incredibly important. We're almost out of time, but I'd love to get both of your perspectives on what's next for Astronomer. You give us a a great overview of what the company's doing, the value in it for customers. Paola, from your lens as one of the co-founders, what's next? >> Yeah, I mean, I think we'll continue to, I think cultivate in that open source community. I think we'll continue to build products that are open sourced as part of our ecosystem. I also think that we'll continue to build products that actually make Airflow, and getting started with Airflow, more accessible. So, sort of lowering that barrier to entry to our products, whether that's price wise or infrastructure requirement wise. I think making it easier for folks to get started and get their hands on our product is super important for us this year. And really it's about, I think, you know, for us, it's really about focused execution this year and all of the sort of core principles that we've been talking about. And continuing to invest in all of the things around our product that again, enable teams to use Airflow more effectively and efficiently. >> And that efficiency piece is, everybody needs that. Last question, Viraj, for you. What do you see in terms of the next year for Astronomer and for your role? >> Yeah, you know, I think Paola did a really good job of laying it out. So it's, it's really hard to disagree with her on anything, right? I think executing is definitely the most important thing. My own personal bias on that is I think more than ever it's important to really galvanize the community around airflow. So, we're going to be focusing on that a lot. We want to make it easier for our users to get get our product into their hands, be that open source users or commercial users. And last, but certainly not least, is we're also really excited about Data Lineage and this other open source project in our umbrella called Open Lineage to make it so that there's a standard way for users to get lineage out of different systems that they use. When we think about what's in store for data lineage and needing to audit the way automated decisions are being made. You know, I think that's just such an important thing that companies are really just starting with, and I don't think there's a solution that's emerged that kind of ties it all together. So, we think that as we kind of grow the role of Airflow, right, we can also make it so that we're helping solve, we're helping customers solve their lineage problems all in Astro, which is our kind of the best of both worlds for us. >> Awesome. I can definitely feel and hear the enthusiasm and the passion that you both bring to Astronomer, to your customers, to your team. I love it. We could keep talking more and more, so you're going to have to come back. (laughing) Viraj, Paola, thank you so much for joining me today on this showcase conversation. We really appreciate your insights and all the context that you provided about Astronomer. >> Thank you so much for having us. >> My pleasure. For my guests, I'm Lisa Martin. You're watching this Cube conversation. (soft electronic music)

(upbeat music beginning) >> From the Cube Studios in Palo Alto and Boston, bringing you data-driven insights from the Cube and ETR, this is "Breaking Analysis" with Dave Vellante. >> Making predictions about the future of enterprise tech is more challenging if you strive to lay down forecasts that are measurable. In other words, if you make a prediction, you should be able to look back a year later and say, with some degree of certainty, whether the prediction came true or not, with evidence to back that up. Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this breaking analysis, we aim to do just that, with predictions about the macro IT spending environment, cost optimization, security, lots to talk about there, generative AI, cloud, and of course supercloud, blockchain adoption, data platforms, including commentary on Databricks, snowflake, and other key players, automation, events, and we may even have some bonus predictions around quantum computing, and perhaps some other areas. To make all this happen, we welcome back, for the third year in a row, my colleague and friend Eric Bradley from ETR. Eric, thanks for all you do for the community, and thanks for being part of this program. Again. >> I wouldn't miss it for the world. I always enjoy this one. Dave, good to see you. >> Yeah, so let me bring up this next slide and show you, actually come back to me if you would. I got to show the audience this. These are the inbounds that we got from PR firms starting in October around predictions. They know we do prediction posts. And so they'll send literally thousands and thousands of predictions from hundreds of experts in the industry, technologists, consultants, et cetera. And if you bring up the slide I can show you sort of the pattern that developed here. 40% of these thousands of predictions were from cyber. You had AI and data. If you combine those, it's still not close to cyber. Cost optimization was a big thing. Of course, cloud, some on DevOps, and software. Digital... Digital transformation got, you know, some lip service and SaaS. And then there was other, it's kind of around 2%. So quite remarkable, when you think about the focus on cyber, Eric. >> Yeah, there's two reasons why I think it makes sense, though. One, the cybersecurity companies have a lot of cash, so therefore the PR firms might be working a little bit harder for them than some of their other clients. (laughs) And then secondly, as you know, for multiple years now, when we do our macro survey, we ask, "What's your number one spending priority?" And again, it's security. It just isn't going anywhere. It just stays at the top. So I'm actually not that surprised by that little pie chart there, but I was shocked that SaaS was only 5%. You know, going back 10 years ago, that would've been the only thing anyone was talking about. >> Yeah. So true. All right, let's get into it. First prediction, we always start with kind of tech spending. Number one is tech spending increases between four and 5%. ETR has currently got it at 4.6% coming into 2023. This has been a consistently downward trend all year. We started, you know, much, much higher as we've been reporting. Bottom line is the fed is still in control. They're going to ease up on tightening, is the expectation, they're going to shoot for a soft landing. But you know, my feeling is this slingshot economy is going to continue, and it's going to continue to confound, whether it's supply chains or spending. The, the interesting thing about the ETR data, Eric, and I want you to comment on this, the largest companies are the most aggressive to cut. They're laying off, smaller firms are spending faster. They're actually growing at a much larger, faster rate as are companies in EMEA. And that's a surprise. That's outpacing the US and APAC. Chime in on this, Eric. >> Yeah, I was surprised on all of that. First on the higher level spending, we are definitely seeing it coming down, but the interesting thing here is headlines are making it worse. The huge research shop recently said 0% growth. We're coming in at 4.6%. And just so everyone knows, this is not us guessing, we asked 1,525 IT decision-makers what their budget growth will be, and they came in at 4.6%. Now there's a huge disparity, as you mentioned. The Fortune 500, global 2000, barely at 2% growth, but small, it's at 7%. So we're at a situation right now where the smaller companies are still playing a little bit of catch up on digital transformation, and they're spending money. The largest companies that have the most to lose from a recession are being more trepidatious, obviously. So they're playing a "Wait and see." And I hope we don't talk ourselves into a recession. Certainly the headlines and some of their research shops are helping it along. But another interesting comment here is, you know, energy and utilities used to be called an orphan and widow stock group, right? They are spending more than anyone, more than financials insurance, more than retail consumer. So right now it's being driven by mid, small, and energy and utilities. They're all spending like gangbusters, like nothing's happening. And it's the rest of everyone else that's being very cautious. >> Yeah, so very unpredictable right now. All right, let's go to number two. Cost optimization remains a major theme in 2023. We've been reporting on this. You've, we've shown a chart here. What's the primary method that your organization plans to use? You asked this question of those individuals that cited that they were going to reduce their spend and- >> Mhm. >> consolidating redundant vendors, you know, still leads the way, you know, far behind, cloud optimization is second, but it, but cloud continues to outpace legacy on-prem spending, no doubt. Somebody, it was, the guy's name was Alexander Feiglstorfer from Storyblok, sent in a prediction, said "All in one becomes extinct." Now, generally I would say I disagree with that because, you know, as we know over the years, suites tend to win out over, you know, individual, you know, point products. But I think what's going to happen is all in one is going to remain the norm for these larger companies that are cutting back. They want to consolidate redundant vendors, and the smaller companies are going to stick with that best of breed and be more aggressive and try to compete more effectively. What's your take on that? >> Yeah, I'm seeing much more consolidation in vendors, but also consolidation in functionality. We're seeing people building out new functionality, whether it's, we're going to talk about this later, so I don't want to steal too much of our thunder right now, but data and security also, we're seeing a functionality creep. So I think there's further consolidation happening here. I think niche solutions are going to be less likely, and platform solutions are going to be more likely in a spending environment where you want to reduce your vendors. You want to have one bill to pay, not 10. Another thing on this slide, real quick if I can before I move on, is we had a bunch of people write in and some of the answer options that aren't on this graph but did get cited a lot, unfortunately, is the obvious reduction in staff, hiring freezes, and delaying hardware, were three of the top write-ins. And another one was offshore outsourcing. So in addition to what we're seeing here, there were a lot of write-in options, and I just thought it would be important to state that, but essentially the cost optimization is by and far the highest one, and it's growing. So it's actually increased in our citations over the last year. >> And yeah, specifically consolidating redundant vendors. And so I actually thank you for bringing that other up, 'cause I had asked you, Eric, is there any evidence that repatriation is going on and we don't see it in the numbers, we don't see it even in the other, there was, I think very little or no mention of cloud repatriation, even though it might be happening in this in a smattering. >> Not a single mention, not one single mention. I went through it for you. Yep. Not one write-in. >> All right, let's move on. Number three, security leads M&A in 2023. Now you might say, "Oh, well that's a layup," but let me set this up Eric, because I didn't really do a great job with the slide. I hid the, what you've done, because you basically took, this is from the emerging technology survey with 1,181 responses from November. And what we did is we took Palo Alto and looked at the overlap in Palo Alto Networks accounts with these vendors that were showing on this chart. And Eric, I'm going to ask you to explain why we put a circle around OneTrust, but let me just set it up, and then have you comment on the slide and take, give us more detail. We're seeing private company valuations are off, you know, 10 to 40%. We saw a sneak, do a down round, but pretty good actually only down 12%. We've seen much higher down rounds. Palo Alto Networks we think is going to get busy. Again, they're an inquisitive company, they've been sort of quiet lately, and we think CrowdStrike, Cisco, Microsoft, Zscaler, we're predicting all of those will make some acquisitions and we're thinking that the targets are somewhere in this mess of security taxonomy. Other thing we're predicting AI meets cyber big time in 2023, we're going to probably going to see some acquisitions of those companies that are leaning into AI. We've seen some of that with Palo Alto. And then, you know, your comment to me, Eric, was "The RSA conference is going to be insane, hopping mad, "crazy this April," (Eric laughing) but give us your take on this data, and why the red circle around OneTrust? Take us back to that slide if you would, Alex. >> Sure. There's a few things here. First, let me explain what we're looking at. So because we separate the public companies and the private companies into two separate surveys, this allows us the ability to cross-reference that data. So what we're doing here is in our public survey, the tesis, everyone who cited some spending with Palo Alto, meaning they're a Palo Alto customer, we then cross-reference that with the private tech companies. Who also are they spending with? So what you're seeing here is an overlap. These companies that we have circled are doing the best in Palo Alto's accounts. Now, Palo Alto went and bought Twistlock a few years ago, which this data slide predicted, to be quite honest. And so I don't know if they necessarily are going to go after Snyk. Snyk, sorry. They already have something in that space. What they do need, however, is more on the authentication space. So I'm looking at OneTrust, with a 45% overlap in their overall net sentiment. That is a company that's already existing in their accounts and could be very synergistic to them. BeyondTrust as well, authentication identity. This is something that Palo needs to do to move more down that zero trust path. Now why did I pick Palo first? Because usually they're very inquisitive. They've been a little quiet lately. Secondly, if you look at the backdrop in the markets, the IPO freeze isn't going to last forever. Sooner or later, the IPO markets are going to open up, and some of these private companies are going to tap into public equity. In the meantime, however, cash funding on the private side is drying up. If they need another round, they're not going to get it, and they're certainly not going to get it at the valuations they were getting. So we're seeing valuations maybe come down where they're a touch more attractive, and Palo knows this isn't going to last forever. Cisco knows that, CrowdStrike, Zscaler, all these companies that are trying to make a push to become that vendor that you're consolidating in, around, they have a chance now, they have a window where they need to go make some acquisitions. And that's why I believe leading up to RSA, we're going to see some movement. I think it's going to pretty, a really exciting time in security right now. >> Awesome. Thank you. Great explanation. All right, let's go on the next one. Number four is, it relates to security. Let's stay there. Zero trust moves from hype to reality in 2023. Now again, you might say, "Oh yeah, that's a layup." A lot of these inbounds that we got are very, you know, kind of self-serving, but we always try to put some meat in the bone. So first thing we do is we pull out some commentary from, Eric, your roundtable, your insights roundtable. And we have a CISO from a global hospitality firm says, "For me that's the highest priority." He's talking about zero trust because it's the best ROI, it's the most forward-looking, and it enables a lot of the business transformation activities that we want to do. CISOs tell me that they actually can drive forward transformation projects that have zero trust, and because they can accelerate them, because they don't have to go through the hurdle of, you know, getting, making sure that it's secure. Second comment, zero trust closes that last mile where once you're authenticated, they open up the resource to you in a zero trust way. That's a CISO of a, and a managing director of a cyber risk services enterprise. Your thoughts on this? >> I can be here all day, so I'm going to try to be quick on this one. This is not a fluff piece on this one. There's a couple of other reasons this is happening. One, the board finally gets it. Zero trust at first was just a marketing hype term. Now the board understands it, and that's why CISOs are able to push through it. And what they finally did was redefine what it means. Zero trust simply means moving away from hardware security, moving towards software-defined security, with authentication as its base. The board finally gets that, and now they understand that this is necessary and it's being moved forward. The other reason it's happening now is hybrid work is here to stay. We weren't really sure at first, large companies were still trying to push people back to the office, and it's going to happen. The pendulum will swing back, but hybrid work's not going anywhere. By basically on our own data, we're seeing that 69% of companies expect remote and hybrid to be permanent, with only 30% permanent in office. Zero trust works for a hybrid environment. So all of that is the reason why this is happening right now. And going back to our previous prediction, this is why we're picking Palo, this is why we're picking Zscaler to make these acquisitions. Palo Alto needs to be better on the authentication side, and so does Zscaler. They're both fantastic on zero trust network access, but they need the authentication software defined aspect, and that's why we think this is going to happen. One last thing, in that CISO round table, I also had somebody say, "Listen, Zscaler is incredible. "They're doing incredibly well pervading the enterprise, "but their pricing's getting a little high," and they actually think Palo Alto is well-suited to start taking some of that share, if Palo can make one move. >> Yeah, Palo Alto's consolidation story is very strong. Here's my question and challenge. Do you and me, so I'm always hardcore about, okay, you've got to have evidence. I want to look back at these things a year from now and say, "Did we get it right? Yes or no?" If we got it wrong, we'll tell you we got it wrong. So how are we going to measure this? I'd say a couple things, and you can chime in. One is just the number of vendors talking about it. That's, but the marketing always leads the reality. So the second part of that is we got to get evidence from the buying community. Can you help us with that? >> (laughs) Luckily, that's what I do. I have a data company that asks thousands of IT decision-makers what they're adopting and what they're increasing spend on, as well as what they're decreasing spend on and what they're replacing. So I have snapshots in time over the last 11 years where I can go ahead and compare and contrast whether this adoption is happening or not. So come back to me in 12 months and I'll let you know. >> Now, you know, I will. Okay, let's bring up the next one. Number five, generative AI hits where the Metaverse missed. Of course everybody's talking about ChatGPT, we just wrote last week in a breaking analysis with John Furrier and Sarjeet Joha our take on that. We think 2023 does mark a pivot point as natural language processing really infiltrates enterprise tech just as Amazon turned the data center into an API. We think going forward, you're going to be interacting with technology through natural language, through English commands or other, you know, foreign language commands, and investors are lining up, all the VCs are getting excited about creating something competitive to ChatGPT, according to (indistinct) a hundred million dollars gets you a seat at the table, gets you into the game. (laughing) That's before you have to start doing promotion. But he thinks that's what it takes to actually create a clone or something equivalent. We've seen stuff from, you know, the head of Facebook's, you know, AI saying, "Oh, it's really not that sophisticated, ChatGPT, "it's kind of like IBM Watson, it's great engineering, "but you know, we've got more advanced technology." We know Google's working on some really interesting stuff. But here's the thing. ETR just launched this survey for the February survey. It's in the field now. We circle open AI in this category. They weren't even in the survey, Eric, last quarter. So 52% of the ETR survey respondents indicated a positive sentiment toward open AI. I added up all the sort of different bars, we could double click on that. And then I got this inbound from Scott Stevenson of Deep Graham. He said "AI is recession-proof." I don't know if that's the case, but it's a good quote. So bring this back up and take us through this. Explain this chart for us, if you would. >> First of all, I like Scott's quote better than the Facebook one. I think that's some sour grapes. Meta just spent an insane amount of money on the Metaverse and that's a dud. Microsoft just spent money on open AI and it is hot, undoubtedly hot. We've only been in the field with our current ETS survey for a week. So my caveat is it's preliminary data, but I don't care if it's preliminary data. (laughing) We're getting a sneak peek here at what is the number one net sentiment and mindshare leader in the entire machine-learning AI sector within a week. It's beating Data- >> 600. 600 in. >> It's beating Databricks. And we all know Databricks is a huge established enterprise company, not only in machine-learning AI, but it's in the top 10 in the entire survey. We have over 400 vendors in this survey. It's number eight overall, already. In a week. This is not hype. This is real. And I could go on the NLP stuff for a while. Not only here are we seeing it in open AI and machine-learning and AI, but we're seeing NLP in security. It's huge in email security. It's completely transforming that area. It's one of the reasons I thought Palo might take Abnormal out. They're doing such a great job with NLP in this email side, and also in the data prep tools. NLP is going to take out data prep tools. If we have time, I'll discuss that later. But yeah, this is, to me this is a no-brainer, and we're already seeing it in the data. >> Yeah, John Furrier called, you know, the ChatGPT introduction. He said it reminded him of the Netscape moment, when we all first saw Netscape Navigator and went, "Wow, it really could be transformative." All right, number six, the cloud expands to supercloud as edge computing accelerates and CloudFlare is a big winner in 2023. We've reported obviously on cloud, multi-cloud, supercloud and CloudFlare, basically saying what multi-cloud should have been. We pulled this quote from Atif Kahn, who is the founder and CTO of Alkira, thanks, one of the inbounds, thank you. "In 2023, highly distributed IT environments "will become more the norm "as organizations increasingly deploy hybrid cloud, "multi-cloud and edge settings..." Eric, from one of your round tables, "If my sources from edge computing are coming "from the cloud, that means I have my workloads "running in the cloud. "There is no one better than CloudFlare," That's a senior director of IT architecture at a huge financial firm. And then your analysis shows CloudFlare really growing in pervasion, that sort of market presence in the dataset, dramatically, to near 20%, leading, I think you had told me that they're even ahead of Google Cloud in terms of momentum right now. >> That was probably the biggest shock to me in our January 2023 tesis, which covers the public companies in the cloud computing sector. CloudFlare has now overtaken GCP in overall spending, and I was shocked by that. It's already extremely pervasive in networking, of course, for the edge networking side, and also in security. This is the number one leader in SaaSi, web access firewall, DDoS, bot protection, by your definition of supercloud, which we just did a couple of weeks ago, and I really enjoyed that by the way Dave, I think CloudFlare is the one that fits your definition best, because it's bringing all of these aspects together, and most importantly, it's cloud agnostic. It does not need to rely on Azure or AWS to do this. It has its own cloud. So I just think it's, when we look at your definition of supercloud, CloudFlare is the poster child. >> You know, what's interesting about that too, is a lot of people are poo-pooing CloudFlare, "Ah, it's, you know, really kind of not that sophisticated." "You don't have as many tools," but to your point, you're can have those tools in the cloud, Cloudflare's doing serverless on steroids, trying to keep things really simple, doing a phenomenal job at, you know, various locations around the world. And they're definitely one to watch. Somebody put them on my radar (laughing) a while ago and said, "Dave, you got to do a breaking analysis on CloudFlare." And so I want to thank that person. I can't really name them, 'cause they work inside of a giant hyperscaler. But- (Eric laughing) (Dave chuckling) >> Real quickly, if I can from a competitive perspective too, who else is there? They've already taken share from Akamai, and Fastly is their really only other direct comp, and they're not there. And these guys are in poll position and they're the only game in town right now. I just, I don't see it slowing down. >> I thought one of your comments from your roundtable I was reading, one of the folks said, you know, CloudFlare, if my workloads are in the cloud, they are, you know, dominant, they said not as strong with on-prem. And so Akamai is doing better there. I'm like, "Okay, where would you want to be?" (laughing) >> Yeah, which one of those two would you rather be? >> Right? Anyway, all right, let's move on. Number seven, blockchain continues to look for a home in the enterprise, but devs will slowly begin to adopt in 2023. You know, blockchains have got a lot of buzz, obviously crypto is, you know, the killer app for blockchain. Senior IT architect in financial services from your, one of your insight roundtables said quote, "For enterprises to adopt a new technology, "there have to be proven turnkey solutions. "My experience in talking with my peers are, "blockchain is still an open-source component "where you have to build around it." Now I want to thank Ravi Mayuram, who's the CTO of Couchbase sent in, you know, one of the predictions, he said, "DevOps will adopt blockchain, specifically Ethereum." And he referenced actually in his email to me, Solidity, which is the programming language for Ethereum, "will be in every DevOps pro's playbook, "mirroring the boom in machine-learning. "Newer programming languages like Solidity "will enter the toolkits of devs." His point there, you know, Solidity for those of you don't know, you know, Bitcoin is not programmable. Solidity, you know, came out and that was their whole shtick, and they've been improving that, and so forth. But it, Eric, it's true, it really hasn't found its home despite, you know, the potential for smart contracts. IBM's pushing it, VMware has had announcements, and others, really hasn't found its way in the enterprise yet. >> Yeah, and I got to be honest, I don't think it's going to, either. So when we did our top trends series, this was basically chosen as an anti-prediction, I would guess, that it just continues to not gain hold. And the reason why was that first comment, right? It's very much a niche solution that requires a ton of custom work around it. You can't just plug and play it. And at the end of the day, let's be very real what this technology is, it's a database ledger, and we already have database ledgers in the enterprise. So why is this a priority to move to a different database ledger? It's going to be very niche cases. I like the CTO comment from Couchbase about it being adopted by DevOps. I agree with that, but it has to be a DevOps in a very specific use case, and a very sophisticated use case in financial services, most likely. And that's not across the entire enterprise. So I just think it's still going to struggle to get its foothold for a little bit longer, if ever. >> Great, thanks. Okay, let's move on. Number eight, AWS Databricks, Google Snowflake lead the data charge with Microsoft. Keeping it simple. So let's unpack this a little bit. This is the shared accounts peer position for, I pulled data platforms in for analytics, machine-learning and AI and database. So I could grab all these accounts or these vendors and see how they compare in those three sectors. Analytics, machine-learning and database. Snowflake and Databricks, you know, they're on a crash course, as you and I have talked about. They're battling to be the single source of truth in analytics. They're, there's going to be a big focus. They're already started. It's going to be accelerated in 2023 on open formats. Iceberg, Python, you know, they're all the rage. We heard about Iceberg at Snowflake Summit, last summer or last June. Not a lot of people had heard of it, but of course the Databricks crowd, who knows it well. A lot of other open source tooling. There's a company called DBT Labs, which you're going to talk about in a minute. George Gilbert put them on our radar. We just had Tristan Handy, the CEO of DBT labs, on at supercloud last week. They are a new disruptor in data that's, they're essentially making, they're API-ifying, if you will, KPIs inside the data warehouse and dramatically simplifying that whole data pipeline. So really, you know, the ETL guys should be shaking in their boots with them. Coming back to the slide. Google really remains focused on BigQuery adoption. Customers have complained to me that they would like to use Snowflake with Google's AI tools, but they're being forced to go to BigQuery. I got to ask Google about that. AWS continues to stitch together its bespoke data stores, that's gone down that "Right tool for the right job" path. David Foyer two years ago said, "AWS absolutely is going to have to solve that problem." We saw them start to do it in, at Reinvent, bringing together NoETL between Aurora and Redshift, and really trying to simplify those worlds. There's going to be more of that. And then Microsoft, they're just making it cheap and easy to use their stuff, you know, despite some of the complaints that we hear in the community, you know, about things like Cosmos, but Eric, your take? >> Yeah, my concern here is that Snowflake and Databricks are fighting each other, and it's allowing AWS and Microsoft to kind of catch up against them, and I don't know if that's the right move for either of those two companies individually, Azure and AWS are building out functionality. Are they as good? No they're not. The other thing to remember too is that AWS and Azure get paid anyway, because both Databricks and Snowflake run on top of 'em. So (laughing) they're basically collecting their toll, while these two fight it out with each other, and they build out functionality. I think they need to stop focusing on each other, a little bit, and think about the overall strategy. Now for Databricks, we know they came out first as a machine-learning AI tool. They were known better for that spot, and now they're really trying to play catch-up on that data storage compute spot, and inversely for Snowflake, they were killing it with the compute separation from storage, and now they're trying to get into the MLAI spot. I actually wouldn't be surprised to see them make some sort of acquisition. Frank Slootman has been a little bit quiet, in my opinion there. The other thing to mention is your comment about DBT Labs. If we look at our emerging technology survey, last survey when this came out, DBT labs, number one leader in that data integration space, I'm going to just pull it up real quickly. It looks like they had a 33% overall net sentiment to lead data analytics integration. So they are clearly growing, it's fourth straight survey consecutively that they've grown. The other name we're seeing there a little bit is Cribl, but DBT labs is by far the number one player in this space. >> All right. Okay, cool. Moving on, let's go to number nine. With Automation mixer resurgence in 2023, we're showing again data. The x axis is overlap or presence in the dataset, and the vertical axis is shared net score. Net score is a measure of spending momentum. As always, you've seen UI path and Microsoft Power Automate up until the right, that red line, that 40% line is generally considered elevated. UI path is really separating, creating some distance from Automation Anywhere, they, you know, previous quarters they were much closer. Microsoft Power Automate came on the scene in a big way, they loom large with this "Good enough" approach. I will say this, I, somebody sent me a results of a (indistinct) survey, which showed UiPath actually had more mentions than Power Automate, which was surprising, but I think that's not been the case in the ETR data set. We're definitely seeing a shift from back office to front soft office kind of workloads. Having said that, software testing is emerging as a mainstream use case, we're seeing ML and AI become embedded in end-to-end automations, and low-code is serving the line of business. And so this, we think, is going to increasingly have appeal to organizations in the coming year, who want to automate as much as possible and not necessarily, we've seen a lot of layoffs in tech, and people... You're going to have to fill the gaps with automation. That's a trend that's going to continue. >> Yep, agreed. At first that comment about Microsoft Power Automate having less citations than UiPath, that's shocking to me. I'm looking at my chart right here where Microsoft Power Automate was cited by over 60% of our entire survey takers, and UiPath at around 38%. Now don't get me wrong, 38% pervasion's fantastic, but you know you're not going to beat an entrenched Microsoft. So I don't really know where that comment came from. So UiPath, looking at it alone, it's doing incredibly well. It had a huge rebound in its net score this last survey. It had dropped going through the back half of 2022, but we saw a big spike in the last one. So it's got a net score of over 55%. A lot of people citing adoption and increasing. So that's really what you want to see for a name like this. The problem is that just Microsoft is doing its playbook. At the end of the day, I'm going to do a POC, why am I going to pay more for UiPath, or even take on another separate bill, when we know everyone's consolidating vendors, if my license already includes Microsoft Power Automate? It might not be perfect, it might not be as good, but what I'm hearing all the time is it's good enough, and I really don't want another invoice. >> Right. So how does UiPath, you know, and Automation Anywhere, how do they compete with that? Well, the way they compete with it is they got to have a better product. They got a product that's 10 times better. You know, they- >> Right. >> they're not going to compete based on where the lowest cost, Microsoft's got that locked up, or where the easiest to, you know, Microsoft basically give it away for free, and that's their playbook. So that's, you know, up to UiPath. UiPath brought on Rob Ensslin, I've interviewed him. Very, very capable individual, is now Co-CEO. So he's kind of bringing that adult supervision in, and really tightening up the go to market. So, you know, we know this company has been a rocket ship, and so getting some control on that and really getting focused like a laser, you know, could be good things ahead there for that company. Okay. >> One of the problems, if I could real quick Dave, is what the use cases are. When we first came out with RPA, everyone was super excited about like, "No, UiPath is going to be great for super powerful "projects, use cases." That's not what RPA is being used for. As you mentioned, it's being used for mundane tasks, so it's not automating complex things, which I think UiPath was built for. So if you were going to get UiPath, and choose that over Microsoft, it's going to be 'cause you're doing it for more powerful use case, where it is better. But the problem is that's not where the enterprise is using it. The enterprise are using this for base rote tasks, and simply, Microsoft Power Automate can do that. >> Yeah, it's interesting. I've had people on theCube that are both Microsoft Power Automate customers and UiPath customers, and I've asked them, "Well you know, "how do you differentiate between the two?" And they've said to me, "Look, our users and personal productivity users, "they like Power Automate, "they can use it themselves, and you know, "it doesn't take a lot of, you know, support on our end." The flip side is you could do that with UiPath, but like you said, there's more of a focus now on end-to-end enterprise automation and building out those capabilities. So it's increasingly a value play, and that's going to be obviously the challenge going forward. Okay, my last one, and then I think you've got some bonus ones. Number 10, hybrid events are the new category. Look it, if I can get a thousand inbounds that are largely self-serving, I can do my own here, 'cause we're in the events business. (Eric chuckling) Here's the prediction though, and this is a trend we're seeing, the number of physical events is going to dramatically increase. That might surprise people, but most of the big giant events are going to get smaller. The exception is AWS with Reinvent, I think Snowflake's going to continue to grow. So there are examples of physical events that are growing, but generally, most of the big ones are getting smaller, and there's going to be many more smaller intimate regional events and road shows. These micro-events, they're going to be stitched together. Digital is becoming a first class citizen, so people really got to get their digital acts together, and brands are prioritizing earned media, and they're beginning to build their own news networks, going direct to their customers. And so that's a trend we see, and I, you know, we're right in the middle of it, Eric, so you know we're going to, you mentioned RSA, I think that's perhaps going to be one of those crazy ones that continues to grow. It's shrunk, and then it, you know, 'cause last year- >> Yeah, it did shrink. >> right, it was the last one before the pandemic, and then they sort of made another run at it last year. It was smaller but it was very vibrant, and I think this year's going to be huge. Global World Congress is another one, we're going to be there end of Feb. That's obviously a big big show, but in general, the brands and the technology vendors, even Oracle is going to scale down. I don't know about Salesforce. We'll see. You had a couple of bonus predictions. Quantum and maybe some others? Bring us home. >> Yeah, sure. I got a few more. I think we touched upon one, but I definitely think the data prep tools are facing extinction, unfortunately, you know, the Talons Informatica is some of those names. The problem there is that the BI tools are kind of including data prep into it already. You know, an example of that is Tableau Prep Builder, and then in addition, Advanced NLP is being worked in as well. ThoughtSpot, Intelius, both often say that as their selling point, Tableau has Ask Data, Click has Insight Bot, so you don't have to really be intelligent on data prep anymore. A regular business user can just self-query, using either the search bar, or even just speaking into what it needs, and these tools are kind of doing the data prep for it. I don't think that's a, you know, an out in left field type of prediction, but it's the time is nigh. The other one I would also state is that I think knowledge graphs are going to break through this year. Neo4j in our survey is growing in pervasion in Mindshare. So more and more people are citing it, AWS Neptune's getting its act together, and we're seeing that spending intentions are growing there. Tiger Graph is also growing in our survey sample. I just think that the time is now for knowledge graphs to break through, and if I had to do one more, I'd say real-time streaming analytics moves from the very, very rich big enterprises to downstream, to more people are actually going to be moving towards real-time streaming, again, because the data prep tools and the data pipelines have gotten easier to use, and I think the ROI on real-time streaming is obviously there. So those are three that didn't make the cut, but I thought deserved an honorable mention. >> Yeah, I'm glad you did. Several weeks ago, we did an analyst prediction roundtable, if you will, a cube session power panel with a number of data analysts and that, you know, streaming, real-time streaming was top of mind. So glad you brought that up. Eric, as always, thank you very much. I appreciate the time you put in beforehand. I know it's been crazy, because you guys are wrapping up, you know, the last quarter survey in- >> Been a nuts three weeks for us. (laughing) >> job. I love the fact that you're doing, you know, the ETS survey now, I think it's quarterly now, right? Is that right? >> Yep. >> Yep. So that's phenomenal. >> Four times a year. I'll be happy to jump on with you when we get that done. I know you were really impressed with that last time. >> It's unbelievable. This is so much data at ETR. Okay. Hey, that's a wrap. Thanks again. >> Take care Dave. Good seeing you. >> All right, many thanks to our team here, Alex Myerson as production, he manages the podcast force. Ken Schiffman as well is a critical component of our East Coast studio. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hoof is our editor-in-chief. He's at siliconangle.com. He's just a great editing for us. Thank you all. Remember all these episodes that are available as podcasts, wherever you listen, podcast is doing great. Just search "Breaking analysis podcast." Really appreciate you guys listening. I publish each week on wikibon.com and siliconangle.com, or you can email me directly if you want to get in touch, david.vellante@siliconangle.com. That's how I got all these. I really appreciate it. I went through every single one with a yellow highlighter. It took some time, (laughing) but I appreciate it. You could DM me at dvellante, or comment on our LinkedIn post and please check out etr.ai. Its data is amazing. Best survey data in the enterprise tech business. This is Dave Vellante for theCube Insights, powered by ETR. Thanks for watching, and we'll see you next time on "Breaking Analysis." (upbeat music beginning) (upbeat music ending)

(vibrant electronic music) >> Welcome back to SuperCloud2. You know, this event, and the Super Cloud initiative in general, it's an open industry-wide collaboration. Last August at SuperCloud22, we really honed in on the definition, which of course we've published. And there's this shared doc, which folks are still adding to and refining, in fact, just recently, Dr. Nelu Mihai added some critical points that really advanced some of the community's initial principles, and today at SuperCloud2, we're digging further into the topic with input from real world practitioners, and we're exploring that intersection of data, data mesh, and cloud, and importantly, the realities and challenges of deploying technology to drive new business capability, and I'm pleased to welcome Ash Naseer to the program. He's a Senior Director of Data Engineering at Warner Bros. Discovery. Ash, great to see you again, thanks so much for taking time with us. >> It's great to be back, these conversations are always very fun. >> I was so excited when we met last spring, I guess, so before we get started I wanted to play a clip from that conversation, it was June, it was at the Snowflake Summit in Las Vegas. And it's a comment that you made about your company but also data mesh. Guys, roll the clip. >> Yeah, so, when people think of Warner Bros., you always think of the movie studio. But we're more than that, right, I mean, you think of HBO, you think of TNT, you think of CNN. We have 30 plus brands in our portfolio, and each have their own needs. So the idea of a data mesh really helps us because what we can do is we can federate access across the company, so that 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 bump up against, as an example, HBO, if Game of Thrones is goin' on. >> So-- Okay, so that's pretty interesting, so you've got these sort of different groups that have different data requirements inside of your organization. Now data mesh, it's a relatively new concept, so you're kind of ahead of the curve. So Ash, my question is, when you think about getting value from data, and how that's changed over the past decade, you've had pre-Hadoop, Hadoop, what do you see that's changed, now you got the cloud coming in, what's changed? What had to be sort of fixed? What's working now, and where do you see it going? >> Yeah, so I feel like in the last decade, we've gone through quite a maturity curve. I actually like to say that we're in the golden age of data, because the tools and technology in the data space, particularly and then broadly in the cloud, they allow us to do things that we couldn't do way back when, like you suggested, back in the Hadoop era or even before that. So there's certainly a lot of maturity, and a lot of technology that has come about. So in terms of the good, bad, and ugly, so let me kind of start with the good, right? In terms of bringing value from the data, I really feel like we're in this place where the folks that are charged with unlocking that value from the data, they're actually spending the majority of their time actually doing that. And what do I mean by that? If you think about it, 10 years ago, the data scientist was the person that was going to sort of solve all of the data problems in a company. But what happened was, companies asked these data scientists to come in and do a multitude of things. And what these data scientists found out was, they were spending most of their time on, really, data wrangling, and less on actually getting the value out of the data. And in the last decade or so, I feel like we've made the shift, and we realize that data engineering, data management, data governance, those are as important practices as data science, which is sort of getting the value out of the data. And so what that has done is, it has freed up the data scientist and the business analyst and the data analyst, and the BI expert, to really focus on how to get value out of the data, and spend less time wrangling data. So I really think that that's the good. In terms of the bad, I feel like, there's a lot of legacy data platforms out there, and I feel like there's going to be a time where we'll be in that hybrid mode. And then the ugly, I feel like, with all the data and all the technology, creates another problem of itself. Because most companies don't have arms around their data, and making sure that they know who's using the data, what they're using for, and how can the company leverage the collective intelligence. That is a bigger problem to solve today than 10 years ago. And that's where technologies like the data mesh come in. >> Yeah, so when I think of data mesh, and I say, you're an early practitioner of data mesh, you mentioned legacy technology, so the concept of data mesh is inclusive. In theory anyway, you're supposed to be including the legacy technologies. Whether it's a data lake or data warehouse or Oracle or Snowflake or whatever it is. And when you think about Jamak Dagani's principles, it's domain-centric ownership, data as product. And that creates challenges around self-serve infrastructure and automated governance, and then when you start to combine these different technologies. You got legacy, you got cloud. Everything's different. And so you have to figure out how to deal with that, so my question is, how have you dealt with that, and what role has the cloud played in solving those problems, in particular, that self-serve infrastructure, and that automated governance, and where are we in terms of solving that problem from a practitioner's standpoint? >> Yeah, I always like to say that data is a team sport, and we should sort of think of it as such, and that's, I feel like, the key of the data mesh concept, is treating it as a team sport. A lot of people ask me, they're like, "Oh hey, Ash, I've heard about this thing called data mesh. "Where can I buy one?" or, "what's the technology that I use to get a data mesh? And the reality is that there isn't one technology, you can't really buy a data mesh. It's really a way of life, it's how organizations decide to approach data, like I said, back to a team sport analogy, making sure that everyone has the seat on the table, making sure that we embrace the fact that we have a lot of data, we have a lot of data problems to solve. And the way we'll be successful is to make everyone inclusive. You know, you think about the old days, Data silos or shadow IT, some might call it. That's been around for decades. And what hasn't changed was this notion that, hey, everything needs to be sort of managed centrally. But with the cloud and with the technologies that we have today, we have the right technology and the tooling to democratize that data, and democratize not only just the access, but also sort of building building blocks and sort of taking building blocks which are relevant to your product or your business. And adding to the overall data mesh. We've got all that technology. The challenge is for us to really embrace it, and make sure that we implement it from an organizational standpoint. >> So, thinking about super cloud, there's a layer that lives above the clouds and adds value. And you think about your brands you got 30 brands, you mentioned shadow IT. If, let's say, one of those brands, HBO or TNT, whatever. They want to go, "Hey, we really like Google's analytics tools," and they maybe go off and build something, I don't know if that's even allowed, maybe it's not. But then you build this data mesh. My question is around multi-cloud, cross cloud, super cloud if you will. Is that a advantage for you as a practitioner, or does that just make things more complicated? >> I really love the idea of a multi-cloud. I think it's great, I think that it should have been the norm, not the exception, I feel like people talk about it as if it's the exception. That should have been the case. I will say, though, I feel like multi-cloud should evolve organically, so back to your point about some of these different brands, and, you know, different brands or different business units. Or even in a merger and acquisitions situation, where two different companies or multiple different companies come together with different technology stacks. You know, I feel like that's an organic evolution, and making sure that we use the concepts and the technologies around the multi-cloud to bring everyone together. That's where we need to be, and again, it talks to the fact that each of those business units and each of those groups have their own unique needs, and we need to make sure that we embrace that and we enable that, rather than stifling everything. Now where I have a little bit of a challenge with the multi-cloud is when technology leaders try to build it by design. So there's a notion there that, "Hey, you need to sort of diversify "and don't put all your eggs in one basket." And so we need to have this multi-cloud thing. I feel like that is just sort of creating more complexity where it doesn't need to be, we can all sort of simplify our lives, but where it evolves organically, absolutely, I think that's the right way to go. >> But, so Ash, if it evolves organically don't you need some kind of cloud interpreter, to create a common experience across clouds, does that exist today? What are your thoughts on that? >> There is a lot of technology that exists today, and that helps go between these different clouds, a lot of these sort of cloud agnostic technologies that you talked about, the Snowflakes and the Databricks and so forth of the world, they operate in multiple clouds, they operate in multiple regions, within a given cloud and multiple clouds. So they span all of that, and they have the tools and technology, so, I feel like the tooling is there. There does need to be more of an evolution around the tooling and I think the market's need are going to dictate that, I feel like the market is there, they're asking for it, so, there's definitely going to be that evolution, but the technology is there, I think just making sure that we embrace that and we sort of embrace that as a challenge and not try to sort of shut all of that down and box everything into one. >> What's the biggest challenge, is it governance or security? Or is it more like you're saying, adoption, cultural? >> I think it's a combination of cultural as well as governance. And so, the cultural side I've talked about, right, just making sure that we give these different teams a seat at the table, and they actually bring that technology into the mix. And we use the modern tools and technologies to make sure that everybody sort of plays nice together. That is definitely, we have ways to go there. But then, in terms of governance, that is another big problem that most companies are just starting to wrestle with. Because like I said, I mean, the data silos and shadow IT, that's been around there, right? The only difference is that we're now sort of bringing everything together in a cloud environment, the collective organization has access to that. And now we just realized, oh we have quite a data problem at our hands, so how do we sort of organize this data, make sure that the quality is there, the trust is there. When people look at that data, a lot of those questions are now coming to the forefront because everything is sort of so transparent with the cloud, right? And so I feel like, again, putting in the right processes, and the right tooling to address that is going to be critical in the next years to come. >> Is sharing data across clouds, something that is valuable to you, or even within a single cloud, being able to share data. And my question is, not just within your organization, but even outside your organization, is that something that has sort of hit your radar or is it mature or is that something that really would add value to your business? >> Data sharing is huge, and again, this is another one of those things which isn't new. You know, I remember back in the '90s, when we had to share data externally, with our partners or our vendors, they used to physically send us stacks of these tapes, or physical media on some truck. And we've evolved since then, right, I mean, it went from that to sharing files online and so forth. But data sharing as a concept and as a concept which is now very frictionless, through these different technologies that we have today, that is very new. And that is something, like I said, it's always been going on. But that needs to be really embraced more as well. We as a company heavily leverage data sharing between our own different brands and business units, that helps us make that data mesh, so that when CNN, as an example, builds their own data model based on election data and the kinds of data that they need, compare that with other data in the rest of the company, sports, entertainment, and so forth and so on. Everyone has their unique data, but that data sharing capability brings it together wherever there is a need. So you think about having a Tiger Woods documentary, as an example, on HBO Max and making sure that you reach the audiences that are interested in golf and interested in sports and so forth, right? That all comes through the magic of data sharing, so, it's really critical, internally, for us. And then externally as well, because just understanding how our products are doing on our partners' networks and different distribution channels, that's important, and then just understanding how our consumers are consuming it off properties, right, I mean, we have brands that transcend just the screen, right? We have a lot of physical merchandise that you can buy in the store. So again, understanding who's buying the Batman action figures after the Batman movie was released, that's another critical insight. So it all gets enabled through data sharing, and something we rely heavily on. >> So I wanted to get your perspective on this. So I feel like the nirvana of data mesh is if I want to use Google BigQuery, an Oracle database, or a Microsoft database, or Snowflake, Databricks, Amazon, whatever. That that's a node on the mesh. And in the perfect world, you can share that data, it can be governed, I don't think we're quite there today, so. But within a platform, maybe it's within Google or within Amazon or within Snowflake or Databricks. If you're in that world, maybe even Oracle. You actually can do some levels of data sharing, maybe greater with some than others. Do you mandate as an organization that you have to use this particular data platform, or are you saying "Hey, we are architecting a data mesh for the future "where we believe the technology will support that," or maybe you've invented some technology that supports that today, can you help us understand that? >> Yeah, I always feel like mandate is a strong area, and it breeds the shadow IT and the data silos. So we don't mandate, we do make sure that there's a consistent set of governance rules, policies, and tooling that's there, so that everyone is on the same page. However, at the same time our focus is really operating in a federated way, that's been our solution, right? Is to make sure that we work within a common set of tooling, which may be different technologies, which in some cases may be different clouds. Although we're not that multi-cloud. So what we're trying to do is making sure that everyone who has that technology already built, as long as it sort of follows certain standards, it's modern, it has the capabilities that will eventually allow us to be successful and eventually allow for that data sharing, amongst those different nodes, as you put it. As long as that's the case, and as long as there's a governance layer, a master governance layer, where we know where all that data is and who has access to what and we can sort of be really confident about the quality of the data, as long as that case, our approach to that is really that federated approach. >> Sorry, did I hear you correctly, you're not multi-cloud today? >> Yeah, that's correct. There are certain spots where we use that, but by and large, we rely on a particular cloud, and that's just been, like I said, it's been the evolution, it was our evolution. We decided early on to focus on a single cloud, and that's the direction we've been going in. >> So, do you want to go to a multi-cloud, or, you mentioned organic before, if a business unit wants to go there, as long as they're adhering to those standards that you put out, maybe recommendations, that that's okay? I guess my question is, does that bring benefit to your business that you'd like to tap, or do you feel like it's not necessary? >> I'll go back to the point of, if it happens organically, we're going to be open about it. Obviously we'll have to look at every situations, not all clouds are created equal as well, so there's a number of different considerations. But by and large, when it happens organically, the key is time to value, right? How do you quickly bring those technologies in, as long as you could share the data, they're interconnected, they're secured, they're governed, we are confident on the quality, as long as those principles are met, we could definitely go in that direction. But by and large, we're sort of evolving in a singular direction, but even within a singular cloud, we're a global company. And we have audiences around the world, so making sure that even within a single cloud, those different regions interoperate as one, that's a bigger challenge that we're having to solve as well. >> Last question is kind of to the future of data and cloud and how it's going to evolve, do you see a day when companies like yours are increasingly going to be offering data, their software, services, and becoming more of a technology company, sort of pointing your tooling and your proprietary knowledge at the external world, as an opportunity, as a business opportunity? >> That's a very interesting concept, and I know companies have done that, and some of them have been extremely successful, I mean, Amazon is the biggest example that comes to mind, right-- >> Yeah. >> When they launched AWS, something that they had that expertise they had internally, and they offered it to the world as a product. But by and large, I think it's going to be far and few between, especially, it's going to be focused on companies that have technology as their DNA, or almost like in the technology sector, building technology. Most other companies have different markets that they are addressing. And in my opinion, a lot of these companies, what they're trying to do is really focus on the problems that we can solve for ourselves, I think there are more problems than we have people and expertise. So my guess is that most large companies, they're going to focus on solving their own problems. A few, like I said, more tech-focused companies, that would want to be in that business, would probably branch out, but by and large, I think companies will continue to focus on serving their customers and serving their own business. >> Alright, Ash, we're going to leave it there, Ash Naseer. Thank you so much for your perspectives, it was great to see you, I'm sure we'll see you face-to-face later on this year. >> This is great, thank you for having me. >> Ah, you're welcome, alright. Keep it right there for more great content from SuperCloud2. We'll be right back. (gentle percussive music)

(upbeat music) >> Welcome back to Supercloud 2, an open collaborative where we explore the future of cloud and data. Now, you might recall last August at the inaugural Supercloud event we validated the technical feasibility and tried to further define the essential technical characteristics, and of course the deployment models of so-called supercloud. That is, sets of services that leverage the underlying primitives of hyperscale clouds, but are creating new value on top of those clouds for organizations at scale. So we're talking about capabilities that fundamentally weren't practical or even possible prior to the ascendancy of the public clouds. And so today at Supercloud 2, we're digging further into the topic with input from real-world practitioners. And we're exploring the intersection of data and cloud, And importantly, the realities and challenges of deploying technology for a new business capability. I'm pleased to have with me in our studios, west of Boston, Veronika Durgin, who's the head of data at Saks. Veronika, welcome. Great to see you. Thanks for coming on. >> Thank you so much. Thank you for having me. So excited to be here. >> And so we have to say upfront, you're here, these are your opinions. You're not representing Saks in any way. So we appreciate you sharing your depth of knowledge with us. >> Thank you, Dave. Yeah, I've been doing data for a while. I try not to say how long anymore. It's been a while. But yeah, thank you for having me. >> Yeah, you're welcome. I mean, one of the highlights of this past year for me was hanging out at the airport with you after the Snowflake Summit. And we were just chatting about sort of data mesh, and you were saying, "Yeah, but." There was a yeah, but. You were saying there's some practical realities of actually implementing these things. So I want to get into some of that. And I guess starting from a perspective of how data has changed, you've seen a lot of the waves. I mean, even if we go back to pre-Hadoop, you know, that would shove everything into an Oracle database, or, you know, Hadoop was going to save our data lives. And the cloud came along and, you know, that was kind of a disruptive force. And, you know, now we see things like, whether it's Snowflake or Databricks or these other platforms on top of the clouds. How have you observed the change in data and the evolution over time? >> Yeah, so I started as a DBA in the data center, kind of like, you know, growing up trying to manage whatever, you know, physical limitations a server could give us. So we had to be very careful of what we put in our database because we were limited. We, you know, purchased that piece of hardware, and we had to use it for the next, I don't know, three to five years. So it was only, you know, we focused on only the most important critical things. We couldn't keep too much data. We had to be super efficient. We couldn't add additional functionality. And then Hadoop came along, which is like, great, we can dump all the data there, but then we couldn't get data out of it. So it was like, okay, great. Doesn't help either. And then the cloud came along, which was incredible. I was probably the most excited person. I'm lying, but I was super excited because I no longer had to worry about what I can actually put in my database. Now I have that, you know, scalability and flexibility with the cloud. So okay, great, that data's there, and I can also easily get it out of it, which is really incredible. >> Well, but so, I'm inferring from what you're saying with Hadoop, it was like, okay, no schema on write. And then you got to try to make sense out of it. But so what changed with the cloud? What was different? >> So I'll tell a funny story. I actually successfully avoided Hadoop. The only time- >> Congratulations. >> (laughs) I know, I'm like super proud of it. I don't know how that happened, but the only time I worked for a company that had Hadoop, all I remember is that they were running jobs that were taking over 24 hours to get data out of it. And they were realizing that, you know, dumping data without any structure into this massive thing that required, you know, really skilled engineers wasn't really helpful. So what changed, and I'm kind of thinking of like, kind of like how Snowflake started, right? They were marketing themselves as a data warehouse. For me, moving from SQL Server to Snowflake was a non-event. It was comfortable, I knew what it was, I knew how to get data out of it. And I think that's the important part, right? Cloud, this like, kind of like, vague, high-level thing, magical, but the reality is cloud is the same as what we had on prem. So it's comfortable there. It's not scary. You don't need super new additional skills to use it. >> But you're saying what's different is the scale. So you can throw resources at it. You don't have to worry about depreciating your hardware over three to five years. Hey, I have an asset that I have to take advantage of. Is that the big difference? >> Absolutely. Actually, from kind of like operational perspective, which it's funny. Like, I don't have to worry about it. I use what I need when I need it. And not to take this completely in the opposite direction, people stop thinking about using things in a very smart way, right? You like, scale and you walk away. And then, you know, the cool thing about cloud is it's scalable, but you also should not use it when you don't need it. >> So what about this idea of multicloud. You know, supercloud sort of tries to go beyond multicloud. it's like multicloud by accident. And now, you know, whether it's M&A or, you know, some Skunkworks is do, hey, I like Google's tools, so I'm going to use Google. And then people like you are called on to, hey, how do we clean up this mess? And you know, you and I, at the airport, we were talking about data mesh. And I love the concept. Like, doesn't matter if it's a data lake or a data warehouse or a data hub or an S3 bucket. It's just a node on the mesh. But then, of course, you've got to govern it. You've got to give people self-serve. But this multicloud is a reality. So from your perspective, from a practitioner's perspective, what are the advantages of multicloud? We talk about the disadvantages all the time. Kind of get that, but what are the advantages? >> So I think the first thing when I think multicloud, I actually think high-availability disaster recovery. And maybe it's just how I grew up in the data center, right? We were always worried that if something happened in one area, we want to make sure that we can bring business up very quickly. So to me that's kind of like where multicloud comes to mind because, you know, you put your data, your applications, let's pick on AWS for a second and, you know, US East in AWS, which is the busiest kind of like area that they have. If it goes down, for my business to continue, I would probably want to move it to, say, Azure, hypothetically speaking, again, or Google, whatever that is. So to me, and probably again based on my background, disaster recovery high availability comes to mind as multicloud first, but now the other part of it is that there are, you know, companies and tools and applications that are being built in, you know, pick your cloud. How do we talk to each other? And more importantly, how do we data share? You know, I work with data. You know, this is what I do. So if, you know, I want to get data from a company that's using, say, Google, how do we share it in a smooth way where it doesn't have to be this crazy, I don't know, SFTP file moving. So that's where I think supercloud comes to me in my mind, is like practical applications. How do we create that mesh, that network that we can easily share data with each other? >> So you kind of answered my next question, is do you see use cases going beyond H? I mean, the HADR was, remember, that was the original cloud use case. That and bursting, you know, for, you know, Thanksgiving or, you know, for Black Friday. So you see an opportunity to go beyond that with practical use cases. >> Absolutely. I think, you know, we're getting to a world where every company is a data company. We all collect a lot of data. We want to use it for whatever that is. It doesn't necessarily mean sell it, but use it to our competitive advantage. So how do we do it in a very smooth, easy way, which opens additional opportunities for companies? >> You mentioned data sharing. And that's obviously, you know, I met you at Snowflake Summit. That's a big thing of Snowflake's. And of course, you've got Databricks trying to do similar things with open technology. What do you see as the trade-offs there? Because Snowflake, you got to come into their party, you're in their world, and you're kind of locked into that world. Now they're trying to open up. You know, and of course, Databricks, they don't know our world is wide open. Well, we know what that means, you know. The governance. And so now you're seeing, you saw Amazon come out with data clean rooms, which was, you know, that was a good idea that Snowflake had several years before. It's good. It's good validation. So how do you think about the trade-offs between kind of openness and freedom versus control? Is the latter just far more important? >> I'll tell you it depends, right? It's kind of like- >> Could be insulting to that. >> Yeah, I know. It depends because I don't know the answer. It depends, I think, because on the use case and application, ultimately every company wants to make money. That's the beauty of our like, capitalistic economy, right? We're driven 'cause we want to make money. But from the use, you know, how do I sell a product to somebody who's in Google if I am in AWS, right? It's like, we're limiting ourselves if we just do one cloud. But again, it's difficult because at the same time, every cloud provider wants for you to be locked in their cloud, which is why probably, you know, whoever has now data sharing because they want you to stay within their ecosystem. But then again, like, companies are limited. You know, there are applications that are starting to be built on top of clouds. How do we ensure that, you know, I can use that application regardless what cloud, you know, my company is using or I just happen to like. >> You know, and it's true they want you to stay in their ecosystem 'cause they'll make more money. But as well, you think about Apple, right? Does Apple do it 'cause they can make more money? Yes, but it's also they have more control, right? Am I correct that technically it's going to be easier to govern that data if it's all the sort of same standard, right? >> Absolutely. 100%. I didn't answer that question. You have to govern and you have to control. And honestly, it's like it's not like a nice-to-have anymore. There are compliances. There are legal compliances around data. Everybody at some point wants to ensure that, you know, and as a person, quite honestly, you know, not to be, you know, I don't like when my data's used when I don't know how. Like, it's a little creepy, right? So we have to come up with standards around that. But then I also go back in the day. EDI, right? Electronic data interchange. That was figured out. There was standards. Companies were sending data to each other. It was pretty standard. So I don't know. Like, we'll get there. >> Yeah, so I was going to ask you, do you see a day where open standards actually emerge to enable that? And then isn't that the great disruptor to sort of kind of the proprietary stack? >> I think so. I think for us to smoothly exchange data across, you know, various systems, various applications, we'll have to agree to have standards. >> From a developer perspective, you know, back to the sort of supercloud concept, one of the the components of the essential characteristics is you've got this PaaS layer that provides consistency across clouds, and it has unique attributes specific to the purpose of that supercloud. So in the instance of Snowflake, it's data sharing. In the case of, you know, VMware, it might be, you know, infrastructure or self-serve infrastructure that's consistent. From a developer perspective, what do you hear from developers in terms of what they want? Are we close to getting that across clouds? >> I think developers always want freedom and ability to engineer. And oftentimes it's not, (laughs) you know, just as an engineer, I always want to build something, and it's not always for the, to use a specific, you know, it's something I want to do versus what is actually applicable. I think we'll land there, but not because we are, you know, out of the kindness of our own hearts. I think as a necessity we will have to agree to standards, and that that'll like, move the needle. Yeah. >> What are the limitations that you see of cloud and this notion of, you know, even cross cloud, right? I mean, this one cloud can't do it all. You know, but what do you see as the limitations of clouds? >> I mean, it's funny, I always think, you know, again, kind of probably my background, I grew up in the data center. We were physically limited by space, right? That there's like, you can only put, you know, so many servers in the rack and, you know, so many racks in the data center, and then you run out space. Earth has a limited space, right? And we have so many data centers, and everybody's collecting a lot of data that we actually want to use. We're not just collecting for the sake of collecting it anymore. We truly can't take advantage of it because servers have enough power, right, to crank through it. We will run enough space. So how do we balance that? How do we balance that data across all the various data centers? And I know I'm like, kind of maybe talking crazy, but until we figure out how to build a data center on the Moon, right, like, we will have to figure out how to take advantage of all the compute capacity that we have across the world. >> And where does latency fit in? I mean, is it as much of a problem as people sort of think it is? Maybe it depends too. It depends on the use case. But do multiple clouds help solve that problem? Because, you know, even AWS, $80 billion company, they're huge, but they're not everywhere. You know, they're doing local zones, they're doing outposts, which is, you know, less functional than their full cloud. So maybe I would choose to go to another cloud. And if I could have that common experience, that's an advantage, isn't it? >> 100%, absolutely. And potentially there's some maybe pricing tiers, right? So we're talking about latency. And again, it depends on your situation. You know, if you have some sort of medical equipment that is very latency sensitive, you want to make sure that data lives there. But versus, you know, I browse on a website. If the website takes a second versus two seconds to load, do I care? Not exactly. Like, I don't notice that. So we can reshuffle that in a smart way. And I keep thinking of ways. If we have ways for data where it kind of like, oh, you are stuck in traffic, go this way. You know, reshuffle you through that data center. You know, maybe your data will live there. So I think it's totally possible. I know, it's a little crazy. >> No, I like it, though. But remember when you first found ways, you're like, "Oh, this is awesome." And then now it's like- >> And it's like crowdsourcing, right? Like, it's smart. Like, okay, maybe, you know, going to pick on US East for Amazon for a little bit, their oldest, but also busiest data center that, you know, periodically goes down. >> But then you lose your competitive advantage 'cause now it's like traffic socialism. >> Yeah, I know. >> Right? It happened the other day where everybody's going this way up. There's all the Wazers taking. >> And also again, compliance, right? Every country is going down the path of where, you know, data needs to reside within that country. So it's not as like, socialist or democratic as we wish for it to be. >> Well, that's a great point. I mean, when you just think about the clouds, the limitation, now you go out to the edge. I mean, everybody talks about the edge in IoT. Do you actually think that there's like a whole new stove pipe that's going to get created. And does that concern you, or do you think it actually is going to be, you know, connective tissue with all these clouds? >> I honestly don't know. I live in a practical world of like, how does it help me right now? How does it, you know, help me in the next five years? And mind you, in five years, things can change a lot. Because if you think back five years ago, things weren't as they are right now. I mean, I really hope that somebody out there challenges things 'cause, you know, the whole cloud promise was crazy. It was insane. Like, who came up with it? Why would I do that, right? And now I can't imagine the world without it. >> Yeah, I mean a lot of it is same wine, new bottle. You know, but a lot of it is different, right? I mean, technology keeps moving us forward, doesn't it? >> Absolutely. >> Veronika, it was great to have you. Thank you so much for your perspectives. If there was one thing that the industry could do for your data life that would make your world better, what would it be? >> I think standards for like data sharing, data marketplace. I would love, love, love nothing else to have some agreed upon standards. >> I had one other question for you, actually. I forgot to ask you this. 'Cause you were saying every company's a data company. Every company's a software company. We're already seeing it, but how prevalent do you think it will be that companies, you've seen some of it in financial services, but companies begin to now take their own data, their own tooling, their own software, which they've developed internally, and point that to the outside world? Kind of do what AWS did. You know, working backwards from the customer and saying, "Hey, we did this for ourselves. We can now do this for the rest of the world." Do you see that as a real trend, or is that Dave's pie in the sky? >> I think it's a real trend. Every company's trying to reinvent themselves and come up with new products. And every company is a data company. Every company collects data, and they're trying to figure out what to do with it. And again, it's not necessarily to sell it. Like, you don't have to sell data to monetize it. You can use it with your partners. You can exchange data. You know, you can create products. Capital One I think created a product for Snowflake pricing. I don't recall, but it just, you know, they built it for themselves, and they decided to kind of like, monetize on it. And I'm absolutely 100% on board with that. I think it's an amazing idea. >> Yeah, Goldman is another example. Nasdaq is basically taking their exchange stack and selling it around the world. And the cloud is available to do that. You don't have to build your own data center. >> Absolutely. Or for good, right? Like, we're talking about, again, we live in a capitalist country, but use data for good. We're collecting data. We're, you know, analyzing it, we're aggregating it. How can we use it for greater good for the planet? >> Veronika, thanks so much for coming to our Marlborough studios. Always a pleasure talking to you. >> Thank you so much for having me. >> You're really welcome. All right, stay tuned for more great content. From Supercloud 2, this is Dave Vellante. We'll be right back. (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.

(intro music) >> Good afternoon guys and gals. Welcome back to The Strip, Las Vegas. It's "theCUBE" live day four of our coverage of "AWS re:Invent". Lisa Martin, Dave Vellante. Dave, we've had some awesome conversations the last four days. I can't believe how many people are still here. The AWS ecosystem seems stronger than ever. >> Yeah, last year we really noted the ecosystem, you know, coming out of the isolation economy 'cause everybody had this old pent up demand to get together and the ecosystem, even last year, we were like, "Wow." This year's like 10x wow. >> It really is 10x wow, it feels that way. We're going to have a 10x wow conversation next. We're bringing back DataStax to "theCUBE". Please welcome Thomas Bean, it's CMO. Thomas welcome to "theCUBE". >> Thanks, thanks a lot, thanks for having me. >> Great to have you, talk to us about what's going on at DataStax, it's been a little while since we talked to you guys. >> Indeed, so DataStax, we are the realtime data company and we've always been involved in technology such as "Apache Cassandra". We actually created to support and take this, this great technology to the market. And now we're taking it, combining it with other technologies such as "Apache Pulse" for streaming to provide a realtime data cloud. Which helps our users, our customers build applications faster and help them scale without limits. So it's all about mobilizing all of this information that is going to drive the application going to create the awesome experience, when you have a customer waiting behind their mobile phone, when you need a decision to take place immediately to, that's the kind of data that we, that we provide in the cloud on any cloud, but especially with, with AWS and providing the performance that technologies like "Apache Cassandra" are known for but also with market leading unit economics. So really empowering customers to operate at speed and scale. >> Speaking of customers, nobody wants less data slower. And one of the things I think we learned in the in the pan, during the pandemic was that access to realtime data isn't nice to have anymore for any business. It is table stakes, it's competitive advantage. There's somebody right behind in the rear view mirror ready to take over. How has the business model of DataStax maybe evolved in the last couple of years with the fact that realtime data is so critical? >> Realtime data has been around for some time but it used to be really niches. You needed a lot of, a lot of people a lot of funding actually to, to implement these, these applications. So we've adapted to really democratize it, made super easy to access. Not only to start developing but also scaling. So this is why we've taken these great technologies made them serverless cloud native on the cloud so that developers could really start easily and scale. So that be on project products could be taken to the, to the market. And in terms of customers, the patterns is we've seen enterprise customers, you were talking about the pandemic, the Home Depot as an example was able to deliver curbside pickup delivery in 30 days because they were already using DataStax and could adapt their business model with a real time application that combines you were just driving by and you would get the delivery of what exactly you ordered without having to go into the the store. So they shifted their whole business model. But we also see a real strong trend about customer experiences and increasingly a lot of tech companies coming because scale means success to them and building on, on our, on our stack to, to build our applications. >> So Lisa, it's interesting. DataStax and "theCUBE" were started the same year, 2010, and that's when it was the beginning of the ascendancy of the big data era. But of course back then there was, I mean very little cloud. I mean most of it was on-prem. And so data stacks had, you know, had obviously you mentioned a number of things that you had to do to become cloud friendly. >> Thomas: Yes. >> You know, a lot of companies didn't make it, make it through. You guys just raised a bunch of dough as well last summer. And so that's been quite a transformation both architecturally, you know, bringing the customers through. I presume part of that was because you had such a great open source community, but also you have a unique value problem. Maybe you could sort of describe that a little. >> Absolutely, so the, I'll start with the open source community where we see a lot of traction at the, at the moment. We were always very involved with, with the "Apache Cassandra". But what we're seeing right now with "Apache Cassandra" is, is a lot of traction, gaining momentum. We actually, we, the open source community just won an award, did an AMA, had a, a vote from their readers about the top open source projects and "Apache Cassandra" and "Apache Pulse" are part of the top three, which is, which is great. We also run a, in collaboration with the Apache Project, the, a series of events around the, around the globe called "Cassandra Days" where we had tremendous attendance. We, some of them, we had to change venue twice because there were more people coming. A lot of students, a lot of the big users of Cassandra like Apple, Netflix who spoke at these, at these events. So we see this momentum actually picking up and that's why we're also super excited that the Linux Foundation is running the Cassandra Summit in in March in San Jose. Super happy to bring that even back with the rest of the, of the community and we have big announcements to come. "Apache Cassandra" will, will see its next version with major advances such as the support of asset transactions, which is going to make it even more suitable to more use cases. So we're bringing that scale to more applications. So a lot of momentum in terms of, in terms of the, the open source projects. And to your point about the value proposition we take this great momentum to which we contribute a lot. It's not only about taking, it's about giving as well. >> Dave: Big committers, I mean... >> Exactly big contributors. And we also have a lot of expertise, we worked with all of the members of the community, many of them being our customers. So going to the cloud, indeed there was architectural work making Cassandra cloud native putting it on Kubernetes, having the right APIs for developers to, to easily develop on top of it. But also becoming a cloud company, building customer success, our own platform engineering. We, it's interesting because actually we became like our partners in a community. We now operate Cassandra in the cloud so that all of our customers can benefit from all the power of Cassandra but really efficiently, super rapidly, and also with a, the leading unit economies as I mentioned. >> How will the, the asset compliance affect your, you know, new markets, new use cases, you know, expand your TAM, can you explain that? >> I think it will, more applications will be able to tap into the power of, of "NoSQL". Today we see a lot on the customer experience as IOT, gaming platform, a lot of SaaS companies. But now with the ability to have transactions at the database level, we can, beyond providing information, we can go even deeper into the logic of the, of the application. So it makes Cassandra and therefore Astra which is our cloud service an even more suitable database we can address, address more even in terms of the transaction that the application itself will, will support. >> What are some of the business benefits that Cassandra delivers to customers in terms of business outcomes helping businesses really transform? >> So Cassandra brings skill when you have millions of customers, when you have million of data points to go through to serve each of the customers. One of my favorite example is Priceline, who runs entirely on our cloud service. You may see one offer, but it's actually everything they know about you and everything they have to offer matched while you are refreshing your page. This is the kind of power that Cassandra provide. But the thing to say about "Apache Cassandra", it used to be also a database that was a bit hard to manage and hard to develop with. This is why as part of the cloud, we wanted to change these aspects, provide developers the API they like and need and what the application need. Making it super simple to operate and, and, and super affordable, also cost effective to, to run. So the the value to your point, it's time to market. You go faster, you don't have to worry when you choose the right database you're not going to, going to have to change horse in the middle of the river, like sixth month down the line. And you know, you have the guarantee that you're going to get the performance and also the best, the best TCO which matters a lot. I think your previous person talking was addressing it. That's also important especially in the, in a current context. >> As a managed service, you're saying, that's the enabler there, right? >> Thomas: Exactly. >> Dave: That is the model today. I mean, you have to really provide that for customers. They don't want to mess with, you know, all the plumbing, right? I mean... >> Absolutely, I don't think people want to manage databases anymore, we do that very well. We take SLAs and such and even at the developer level what they want is an API so they get all the power. All of of this powered by Cassandra, but now they get it as a, and it's as simple as using as, as an API. >> How about the ecosystem? You mentioned the show in in San Jose in March and the Linux Foundation is, is hosting that, is that correct? >> Yes, absolutely. >> And what is it, Cassandra? >> Cassandra Summit. >> Dave: Cassandra Summit >> Yep. >> What's the ecosystem like today in Cassandra, can you just sort of describe that? >> Around Cassandra, you have actually the big hyperscalers. You have also a few other companies that are supporting Cassandra like technologies. And what's interesting, and that's been a, a something we've worked on but also the "Apache Project" has worked on. Working on a lot of the adjacent technologies, the data pipelines, all of the DevOps solutions to make sure that you can actually put Cassandra as part of your way to build these products and, and build these, these applications. So the, the ecosystem keeps on, keeps on growing and actually the, the Cassandra community keeps on opening the database so that it's, it's really easy to have it connect to the rest of the, the rest environment. And we benefit from all of this in our Astra cloud service. >> So things like machine learning, governance tools that's what you would expect in the ecosystem forming around it, right? So we'll see that in March. >> Machine learning is especially a very interesting use case. We see more and more of it. We recently did a, a nice video with one of our customers called Unifour who does exactly this using also our abstract cloud service. What they provide is they analyze videos of sales calls and they help actually the sellers telling them, "Okay here's what happened here was the customer sentiment". Because they have proof that the better the sentiment is, the shorter the sell cycle is going to be. So they teach the, the sellers on how to say the right things, how to control the thing. This is machine learning applied on video. Cassandra provides I think 200 data points per second that feeds this machine learning. And we see more and more of these use cases, realtime use cases. It happens on the fly when you are on your phone, when you have a, a fraud maybe to detect and to prevent. So it is going to be more and more and we see more and more of these integration at the open source level with technologies like even "Feast" project like "Apache Feast". But also in the, in, in the partners that we're working with integrating our Cassandra and our cloud service with. >> Where are customer conversations these days, given that every company has to be a data company. They have to be able to, to democratize data, allow access to it deep into the, into the organizations. Not just IT or the data organization anymore. But are you finding that the conversations are rising up the, up the stack? Is this, is this a a C-suite priority? Is this a board level conversation? >> So that's an excellent question. We actually ran a survey this summer called "The State of the Database" where we, we asked these tech leaders, okay what's top of mind for you? And real time actually was, was really one of the top priorities. And they explained for the one that who call themselves digital leaders that for 71% of them they could correlate directly the use of realtime data, the quality of their experience or their decision making with revenue. And that's really where the discussion is. And I think it's something we can relate to as users. We don't want the, I mean if the Starbucks apps take seconds to to respond there will be a riot over there. So that's, that's something we can feel. But it really, now it's tangible in, in business terms and now then they take a look at their data strategy, are we equipped? Very often they will see, yeah, we have pockets of realtime data, but we're not really able to leverage it. >> Lisa: Yeah. >> For ML use cases, et cetera. So that's a big trend that we're seeing on one end. On the other end, what we're seeing, and it's one of the things we discussed a lot at the event is that yeah cost is important. Growth at all, at all cost does not exist. So we see a lot of push on moving a lot of the workloads to the cloud to make them scale but at the best the best cost. And we also see some organizations where like, okay let's not let a good crisis go to waste and let's accelerate our innovation not at all costs. So that we see also a lot of new projects being being pushed but reasonable, starting small and, and growing and all of this fueled by, by realtime data, so interesting. >> The other big topic amongst the, the customer community is security. >> Yep. >> I presume it's coming up a lot. What's the conversation like with DataStax? >> That's a topic we've been working on intensely since the creation of Astra less than two years ago. And we keep on reinforcing as any, any cloud provider not only our own abilities in terms of making sure that customers can manage their own keys, et cetera. But also integrating to the rest of the, of the ecosystem when some, a lot of our customers are running on AWS, how do we integrate with PrivateLink and such? We fit exactly into their security environment on AWS and they use exactly the same management tool. Because this is also what used to cost a lot in the cloud services. How much do you have to do to wire them and, and manage. And there are indeed compliance and governance challenges. So that's why making sure that it's fully connected that they have full transparency on what's happening is, is a big part of the evolution. It's always, security is always something you're working on but it's, it's a major topic for us. >> Yep, we talk about that on pretty much every event. Security, which we could dive into, but we're out of time. Last question for you. >> Thomas: Yes. >> We're talking before we went live, we're both big Formula One fans. Say DataStax has the opportunity to sponsor a team and you get the whole side pod to, to put like a phrase about DataStax on the side pod of this F1 car. (laughter) Like a billboard, what does it say? >> Billboard, because an F1 car goes pretty fast, it will be hard to, be hard to read but, "Twice the performance at half the cost, try Astra a cloud service." >> Drop the mike. Awesome, Thomas, thanks so much for joining us. >> Thank for having me. >> Pleasure having you guys on the program. For our guest, Thomas Bean and Dave Vellante, I'm Lisa Martin and you're watching "theCUBE" live from day four of our coverage. "theCUBE", the leader in live tech coverage. (outro music)

(upbeat music) >> Hello, everyone. Welcome to theCUBE's coverage here at AWS re:Invent 2022. This is the Executive Summit with Accenture. I'm John Furrier, your host of theCUBE with two great guests coming on today, really talking about the future, the role of humans. Radically human is going to be the topic. Paul Daugherty, the group Chief Executive Technology and CTO at Accenture. And Jim Wilson, Global Managing Director of Thought Leadership and Technology Research, Accenture. Gentlemen, thank you for coming on theCUBE for this conversation around your new hit book, "Radically Human." >> Thanks, John. It's great to be with you and great to be present at re:Invent. >> We've been following you guys for many, many years now, over a decade. You always have the finger on the pulse. I mean, and as these waves come in, it's really important to understand impact. And more than ever, we're in this, I call it the systems thinking, revolution is going on now where things have consequences and machines are now accelerating their role. Developers are becoming the front lines of running companies, seeing a massive shift. This new technology is transforming the business and shaping our future as as humans. And so I love the book, very, very strong content, really right on point. What was the motivation for the book? And congratulations, but I noticed you got the structure, part one and part two, this book seems to be packing a big punch. What was the motivation, and what was some of the background in putting the book together? >> That's a great question, John. And I'll start, and then, Jim, my co-author and colleague and partner on the book can join in too. If you step back from the book itself, we'd written a first book called "Human + Machine", which focused a lot on artificial intelligence and talked about the potential and future of artificial intelligence to create a more human future for us with the human plus machine pairing. And then when we started working on the next book, it was the COVID era. COVID came on line as we were writing the book. And that was causing really an interesting time in technology for a lot of companies. I mean, think back to what you were doing. Once COVID hit, every company became more dependent on technology. Technology was the lifeline. And so Jim and I got interested in what the impacts of that were on companies, and what was different from the first research we had done around our first book. And what we found, which was super interesting, is that pre-pandemic, the leading companies, the digital leaders that were applying cloud data, AI, and related technologies faster, we're outperforming others by a factor of 2x. And that was before the pandemic. After the pandemic, we redid the research and the gap widened into 5x. And I think that's played a lot into our book. And we talk about that in the opening of our book. And the message there is exactly what you said is technology is not just the lifeline from the pandemic, but now technology is the heart and soul of how companies are driving innovation, how they're responding to global crises around inflation, energy, supply chain crisis because of the war in Ukraine, et cetera. And companies need the technology more than ever. And that's what we're writing about in "Radically Human." And we're taking a step beyond our previous book to talk about what we believe is next. And it's really cloud, data and AI, and the metaverse that signal out as three trends that are really driving transformative change for companies. In the first part of the book, to your question on the structure, talks about the roadmap to that. We talked about the ideas framework, five areas where you need to change your thinking, flip your assumptions on how to apply technology. And then the second part of the book talks about the differentiators that we believe are going to set companies apart as they look to implement this technology and transform their companies for the future. >> Jim, weigh in on this flipping the script, flipping the assumptions. >> You used a really important word there and that is systems. I think when we think about artificial intelligence, and when Paul and I have now talking to companies, a lot of executives think of AI as a point solution. They don't think about AI in terms of taking a systems approach. So we were trying to address that. All right, if you're going to build a roadmap, a technology roadmap for applying intelligent technologies like artificial intelligence, how do you take a holistic systematic view? And that's really the focus of the first section of the book. And then as Paul mentioned, how do you take those systems and really differentiate it using your talent, focusing on trust, experiences and sustainability? >> I like how it reads. It's almost like a masterclass book because you set the table. It's like, 'cause people right now are like in the mode of what's going on around me? I've been living through three years of COVID. We're coming out the other side. The world looks radically different. Humans are much more important. Automation's great, but people are finding out that the human's key, but people are trying to figure out where am I today. So I think the first part really to me hits home. Like, here's the current situation and then part two is here's how you can get better. And it's not just about machines, machines, machines and automation, automation, automation. We're seeing examples where the role of the human, the person in society, whether it's individually or as part of a group, are really now key assets in that kind of this new workforce or this new production system or society. >> Yeah. And just to take a couple examples from the book and highlight that, I think you're exactly right. And that's where "Radically Human", the title came from. And what's happening with technology is that technology itself is becoming more human like in its capability. When you think about the power of the transformer technologies and other things that we're reading about a lot. And the whole hypothesis or premise of the book I should say, is that the more human like the technology is, the more radically human or the more radical the human potential improvement is, the bigger the opportunity. It's pairing the two together rather than, as you said, just looking at the automation or the machine side of it. That's really the radical leap. And one thing Jim and I talked about in context of the book is companies really often haven't been radical enough in applying technology to really get to dramatic gains that they can get. Just a couple examples from the ideas framework, the I in IDEAS. The ideas framework is the first part of the book. The five areas to flip your assumptions. The I stands for intelligence and we're talking about more human and less artificial in terms of the intelligence techniques. Things like common sense learning and other techniques that allow you to develop more powerful ways of engaging people, engaging humans in the systems that we build using the kind of systems thinking that Jim mentioned. And things like emotional AI, common sense AI, new techniques in addition to machine, the big data driven machine learning techniques, which are essential to vision and solving big problems like that. So that's just an example of how you bring it together and enable that human potential. >> I love the idea, go ahead Jim. >> I was going to say we've been used to adapting to technology, and contorting our fingers to keyboards and so on for a long time. And now we're starting to see that technology is in fact beginning to adapt to us and become more natural in many instances. One point that we make is now in the human technology nexus, in fact, the human is in the ascended. That's one of the big ideas that we try to put out there in this book. >> I love the idea of flipping the script, flipping the assumptions, but ideas framework is interesting. I for intelligence, D for data, E for expertise, A for architecture, S for strategy. Notice the strategies last. Normally in the old school days, it's like, hey, strategy first and execution. Really interesting how you guys put that together. It feels like business is becoming agile and iterative and how it's going to be forming. Can you guys, I mean that's my opinion, but I think observing how developers becoming much more part of the app. I mean, if you take digital transformation to its conclusion, the application is the company, It's not a department serving the business, it is the business, therefore developers are running the business, so to speak. This is really radical. I mean, this is how I'm seeing it. What's your reaction to that? Do you see similar parallels to this transformation if you take it down to a conclusion and strategy is just what you do after you get the outcomes you need? What's your reaction to that? >> Yeah, I think one of the most lasting elements of the book might be that chapter on strategy in my opinion, because you need to think about it differently. The old way of doing strategy is dead. You can't do it the way you used to do it. And that's what we tried to lay out with the S in IDEAS, the strategy. The subtitle that chapter is we're all technology companies now. And if you're a technology driven company, the way you need to think about and every company is becoming, that's what I hear when I talk to these suites and CEOs and boards, is everybody's recognizing the essential role that technology plays and therefore they need to master technology. Well, you need to think about strategy differently then because of the pace of technology innovation. And so you need to throw out the old way of doing it. We suggest three new archetypes of how to do strategy that I think are really important. It's about continuous strategy in all cases. An example is one of the techniques we talk about, forever beta, which is, think about a Tesla or companies that it's never quite done. They're always improving and the product is designed to be connected and improving. So it changes along the product and the strategy along how you deploy it to consumers changes as you go. And that's an example of a very different approach to strategy that we believe is essential to consider as you look at the future. Yeah, those multi-month strategy sessions might play out over two or three quarters of going away. And strategy and execution are becoming almost simultaneous these days as Paul was saying. >> It's interesting because that's the trend you're seeing with more data, more automation, but the human plays a much critical role. And just aside on the Tesla example, is well documented. I think I wrote about in a post just this week that during the model three, Elon wanted full automation and had to actually go off scripts and get to humans back in charge 'cause it wasn't working properly. Now they have a balance. But that brings up to part two, which I like, which is this human piece of it. We always talk about skills gaps, there's not enough people to do this, that and the other thing. And talent was a big part of that second half, trust, talent, experiences. That's more of the person's role, either individually as part of a collective group. Is talent the scarce resource now where that's the goal, that's the key 'cause it all could point to that in a way. Skills gap points to, hey, humans are valuable. In fact the value's going up if it's properly architected. What's your reaction to that, guys? Because I think that's something that is not, kind of nuanced point, but it's a feature, not a bug maybe, I don't know. What's your thoughts? >> Yeah, go ahead Jim. >> I was going to say it, we're dramatically underestimating the amount of focus we need to put on talent. That's why we start off that second part of the book, really zooming in on talent. I think you might think that for every hundred dollars that you put into a technology initiative, you might put 50 or 75 into re-skilling initiatives to really compliment that. But what we're seeing is companies need to be much more revolutionary in their focus on talent. We saw economic analysis recently that pointed out that for every $1 you spend on technology, you are likely going to need to spend about $9 on intangible human capital. That means on talent, on getting the best talent, on re-skilling and on changing processes and work tasks. So there's a lot of work that needs to be done. Really that's human focus. It's not just about adopting the technology. Certainly the technology's critical, but we're underestimating the amount of focus that needs to go into the talent factors. >> That's a huge point. >> And I think some of the elements of talent that become really critical that we talked about in the book are becoming a talent creator. We believe the successful companies of the future are going to be able not just to post a job opening and hire people in because there's not going to be enough. And a lot of the jobs that companies are creating don't exist 'cause the technology changing so fast. So the companies that succeed are going to know how to create talent, bring in people, apprentices and such, and shape to tale as they go. We're doing a significant amount of that in our own company. They're going to be learning based organizations where you'll differentiate, you'll get the best employees if you provide better learning environments because that's what employees want. And then democratizing access to technology. Things like Amazon's Honeycode is an example, low-code/no-code development to spread development to wider pools of people. Those types of things are really critical going forward to really unlock the talent potential. And really what you end up with is, yeah, the human talent's important, but it's magnified and multiplied by the power of people, giving them in essence superpowers in using technology in new ways. >> I think you nailed it, that's super important. That point about the force multiplier when you put things in combination, whether it's group constructs, two pizza teams flexing, leveraging the talent. I mean, this is a new configuration. You guys are nailing it there. I love that piece. And I think groups and collectives you're going to start to see a lot more of that. But again, with talent comes trust when you start to have these ephemeral and or forming groups that are forming production systems or experiences. So trust comes up a lot. You guys see the metaverse as an important part there. Obviously metaverse is a pretext to the virtual world where we're going to start to create these group experiences and create new force multipliers. How does the metaverse play into this new radically human world, and what does it mean for the future of business? >> Yeah, I think the metaverse is radically misunderstood to use the word title when we're not with the title of our book. And we believe that the metaverse does have real big potential, massive potential, and I think it'll transform the way we think about digital more so than we've changed our thinking on digital in the last 10 years. So that's the potential of the metaverse. And it's not just about the consumer things, it's about metaverse and the enterprise. It's about the new products you create using distributed ledger and other technologies. And it's about the industrial metaverse of how you bring digital twins and augmented workers online in different ways. And so I believe that it has tremendous potential. We write about that in the book and it really takes radically human to another level. And one way to think about this is cloud is really becoming the operating system of business. You have to build your enterprise around the cloud as you go forward. That's going to shape the way you do business. AI becomes the insight and intelligence in how you work, infused with the human talent and such as we said. And the metaverse then reshapes the experience layers. So you have cloud, AI building on top of this metaverse providing a new way to generate experiences for employees, citizens, consumers, et cetera. And that's the way it unfolds, but trust becomes more important because just as AI raises new questions around trust, every technology raises new questions around trust. The metaverse raises a whole new set of questions. And in the book we outline a five-part framework or five essential parts of the framework around how you establish trust as you implement these new technologies. >> Yeah, we're seeing that about three quarters of companies are really trying to figure out trust, certainly with issues like the metaverse more broadly across their IT so they're focusing on security and privacy, transparency, especially when you're talking about AI systems, explainability. One of the more surprising things that we learned when doing the book, when we were doing the research is that we saw that increasingly consumers and employees want systems to be informed by a sense of humanity. So one company that we've been looking at that's been developing autonomous vehicles, self-driving car systems, they're actually training the system by emulating human behavior. So turning the cameras on test drivers to see how they learn and then training the AI using that sense of humanity 'cause other drivers on the road find human behavior more trustworthy. And similarly, that system is also using explainable AI to actually show which human behaviors that AI system is learning from. Some really interesting innovations happening in that trust space. John. >> Jim, I think you bring up a great point that's worth talking more about. Because you're talking about how human behaviors are being put into the design of new things like machines or software. And we're living in this era of cloud scale, which is compressing this transformation timeline and we've been calling it supercloud, some call it multi-cloud, but it's really a new thing happening where you're seeing an acceleration of the transformation. We think it's going to happen much faster in the next five to 10 years. And so that means these new things are emerging, not just, hey, I'm running a virtual event with chat and some video. It's group behavior, it's groups convening, talking, getting things done, debating, doing things differently. And so this idea of humans informing design decisions or software with low-code/no-code, this completely changes strategy. I mean this is a big point of the book. >> Yeah, no, I go back to one of the, the E in the IDEAS framework is expertise. And we talk about from machine learning to machine teaching, which is exactly that. Machine learning is maybe humans tag data and stuff and feed into algorithms. Machine teaching is how do you really leverage the human expertise in the systems that you develop with AI. One of the examples we give is one of the large consumer platforms that uses human designers to give the system a sense of aesthetic design and product design. A very difficult thing, especially with changing fashion interest and everything else to encode in algorithms and to even have AI do, even if you have fast amounts of data, but with the right human insight and human expertise injected in, you can create amazing new capability that responds to consumers in a much more powerful way. And that's an example of what you just said, John, bringing the two together. >> Well, yeah, it's interesting. I want to to get your thoughts as we get wrap up here soon. How do you apply all these human-centric technologies to the future of business? As you guys talk to leaders in the enterprise of their businesses, as they look at the horizon, they see the the future. They got to start thinking about things like generative AI and how they can bring some of these technologies to the table. We were talking about if open source continues to grow the way it's going, there might not be any code to write, it just writes itself at some point. So you got supply chain issues with security. These are new things you guys are hitting in the book where these are new dynamics, new power dynamics in how things get built. So if you're a business owner and leader, this is a new opportunity, a challenge certainly that is an opportunity. How do you apply all this stuff for business? >> I'll go first then Jim can add in. But the first thing I think starts with recognizing the role that technology does play and investing accordingly in it. So the right technology talent, rethinking the way you do strategy as we talked about earlier and recognizing how you need to build a foundation. That's why the fact you're at re:Invent is so important because companies are, again, rebuilding that operating system of their business in the cloud. And you need that as the foundation to go forward, to do, to build the other types of capabilities. And then I think it's developing those talent systems as well. Do you have the right talent brand? Are you attracting the right employees? Are you developing them in the right way so that you have the right future talent going forward? And then you marry the two together and that's what gives you the radically human formula. >> Yeah. When we were developing that first part of the book, Paul and I did quite a bit of research, and Paul kind of alluded to that research earlier, but one of the things that we saw in really the first year of the pandemic was that there was a lot of first time adoption of intelligent technologies like artificial intelligence. One statistic is that 70% of companies that had never tried AI before went ahead and tried it during the pandemic. So first time adoption rates were way up, but the thing is companies were not trying to do it themselves and to necessarily build an AI department. They were partnering and it's really important to find a partner, often a cloud partner as a way to get started, start small scale, and then scale up doing experiments. So that was one of the key insights that we had. You don't need to do it all yourself. >> If you see the transformation of just AWS, we're here at re:Invent, since we've been covering the events since 2013, every year there's been a thematic thing. It was startups, enterprise, now builders, and now change your company. This year it's continuing that same thing where you're starting to see new things happen. It's not just lift and shift and running a SaaS application on the cloud. People are are changing and refactoring and replatforming categorical applications in for this new era. And we're calling it supercloud, superservices, superapps, 'cause they're different. They're doing different things in leveraging large scale CapEx, large scale talent pools, or talent pools in certain ways. So this is real, something's happening here and we've been talking about it a lot lately. So I have to ask you guys, how does a company know if they're radical enough? Like what is radical? How can I put a pin in that? It's like take a temperature or we like radical enough, what some tell signs can you guys share for companies that are really leaning into this new next inflection point because there are new things happening? How do you know if you're you're pushing the envelope radical enough to take advantage? >> Yeah, I think one. >> You can go ahead, Paul. >> Yeah, I was going to say one of the tests is the impact on your business. You have to start by looking at all this in the context of your business, and is it really taking you to another level? You said it perfectly, John, it used to be we used to talk about migration and workloads to the cloud and things like that. That's still something you need to do. But now our focus with a lot of our customers is on how do you innovate and grow your business in the cloud? What's the platform that you're using for your new digital products and services you're offering to your consumers. I mean it is the business and I think that's the test whether you're being radical enough is on the one hand, are you really using the technology to drive differentiation and real growth and change in your business? And are you equipping people, your human talent with the capabilities they need to perform in very different ways? And those are the two tests that I would give. >> Totally agree. >> Interesting enough, we love this topic and you guys, again, the book is spot on. Very packs of big punch on content, but very relevant in today. And I think one of the things we're looking at is that people who do things differently take advantage of some of these radical approaches like IDEAS, your framework, and understand where they are and what's available and what's coming around the corner. They stand out in the pack or create new business opportunities because the CapEx is taken care of. Now you got your cloud, I mean you're building clouds on top of clouds or something's happening. I think you see it, look at like companies like Snowflake, it's a data warehouse on the cloud. What does that mean? They didn't build a cloud, they used Amazon. So you're starting to see these new things pop up. >> Yeah and that's a good example. And it sounds like a simple thing, data warehouse in the cloud, but the new business capability that a technology like that allows and the portability of being able to connect and use data across cloud environments and such is tremendously powerful. And I think that's why, you talk about companies doing things differently, that's why it's great, again, that you're at re:Invent. If you look at the index of our book, you'll see AWS mentioned a number of times 'cause we tell a lot of customer company stories about how they're leveraging AWS capabilities in cloud and AI to really do transformative things in their business. And I think that's what it's all about. >> Yeah, and one of the things too in the book, it's great 'cause it has the systems thinking, it's got really relevant information, but you guys have seen the movie before. I think one of the wild cards in this era is global. We're global economy, you've got regions, you've got data sovereignty, you're seeing all kinds of new things emerging. Thoughts on the global impact 'cause you take your book and you overlay that to business, like you got to operate all over the world as a human issue, as a geography issue. What's your guys take on the global impact? >> Well that's why you got to think about cloud as one technology. We talked about in the book and cloud is, I think a lot of people think, well, clouds, it's almost old news. Maybe it's been around for a while. As you said, you've been going to re:Invent since 2013. Cloud is really just getting started. And it's 'cause the reasons you said, when you look at what you need to do around sovereign cloud capability if you're in Europe. For many companies it's about multi-cloud capabilities that you need to deploy differently in different regions. And they need to, in some cases for good reason, they have hybrid cloud capability that they match on their own. And then there's the edge capability which comes into play in different ways. And so the architecture becomes very complex and we talk the A in IDEAS is architecture. We talk about all this and how you need to move from the old conception of architecture, which was more static and just modularity was the key thing you thought about. It's more the idea of a living system, of living architecture that's expanding and is what's much more dynamic. And I think that's the way you need to think about it as you manage in a global environment today with the pace of technology advancement. >> Yeah, the innovation is here. It's not stopping. How do you create some defacto standards while not stunting the innovation is going to be a big discussion as these new flipped assumptions start to generate more activity. It's going to be very interesting to watch. Gentlemen, thank you so much for spending the time here on theCUBE as we break down your new book, "Radically Human" and how business leads can flip the script on their business assumptions and put ideas and access to work. This is a big part of the cloud show at re:Invent. Thanks so much for sharing and congratulations on a great book. >> Thanks, John. And just one point I'd add is that one of the things we do talk about in talent is the need to reskill talent. People who need to be relevant in the rapidly changing future. And that's one area where I think we all as institutions, as communities and individuals need to do more is to help those that need to reskilling. And the final point I mentioned is that we've mentioned at the end of the book that all proceeds from the book are being donated to NGOs and nonprofits that are focused on reskilling those who need a skill refresh in light of the radically human change in technology that's happening. >> Great. Buy the book. Proceeds go to a great cause and it's a very relevant book. If you're in the middle of this big wave that's coming. this is a great book. There's a guidepost and also give you some great ideas to reset, reflip the scripts, refactor, replatform. Guys, thanks for coming on and sharing. I really appreciate it. Again, congratulations. >> Thanks, John. >> Thanks, John. Great discussion. >> You're watching theCUBE here covering the executive forum here at AWS re:Invent '22. I'm John Furrier, you're host with Accenture. Thanks for watching. (gentle music)

(techno music) >> Welcome back to the Cube. I'm John Walls. We continue our coverage here at AWS reInvent 22. We're in the Venetian in Las Vegas, wrapping up our day one coverage here in the executive summit sponsored by Accenture and with me to talk about Accenture, couple of guys who are no strangers at all to the Cube. In fact, I think we got to give you like alumni passes or something. (Chris and Merim laugh) We got to come up with something like that. Um, Merim Becirovic is with us. Uh, Merim's a global IT at Accenture. And Chris Wegmann, who's already been on once today, as a matter of fact. >> Yeah (indistinct) >> So we're going to start charging you rent, Chris. (Chris and Merim laugh) Uh, global technology and practice lead with the AWS business group at Accenture. Good, glad to have you both back and, um, you're welcome to the Cube any time, by the way. >> So don't be scared. >> Thanks, great to be back. Let's talk about >> Sure. >> What, what you folks have been up to. So, um, you are, as we were talking earlier, you are where a lot of your clients would like to be. You, you've begun this transformation. You have fully migrated to the cloud, you've learned, right? >> Yes. You've hit all the bumps along the way. So talk about your journey. >> Yeah. >> And then how you think that experience could be translated to what your clients are going through. >> Yeah, so I'll, I'll hit it from the lessons learned and working together with our business group partners. We, so Accenture's journey to the cloud is complete. We have finished that journey, and as part of that journey, we have migrated all of the services it takes to run Accenture to the public cloud. So now that's done. That was complete. But now we are this, now it is this cloud continuum living in the cloud. And the, now, the thing we talk about, and I'd love to have Chris, you know, shine a little bit more, is we have built our digital core in a cloud, now. We're no longer dependent on data centers. And that has given us tremendous flexibility around how to enable the business as it has grown significantly since we started this journey a few years back. >> Yeah, you know, Merim, like you talk about, right? We talk about our client, we've talked to our clients about building this digital core, right? And, and we've been through that as Accenture, as a global IT organization, you know. Supporting well over 720,000 people. >> Yeah. >> Right? That growth over the last year has been tremendous. Right? So, without the strong digital core built on cloud, right? We couldn't do that, right? We couldn't add that number of people, right? We couldn't make the, the, the changes were needed during, uh, Covid to bring people home, working from home. You know, whether it being uh, the way we changed our business model or things like that, um, you know that was all enabled by cloud. It couldn't be done without that. And, you know, also the variable in our business, right? Is very tied now to our cloud consumption, right? So, you know, it goes up, it goes down, right? We've, you know, Merim and his team have completely built their, their their core with those, with those concepts uh, in mind. >> Yeah, I mean, you're talking about, you know, 700, 800,000 employees and how many countries did you say? >> 130 different countries, at least. >> 130 different countries. So, I mean, no small task, obviously, uh, to get everything done. When did you start? >> So our cloud journey, effectively, we started in 2015. And we were done, kind of right before Covid around 2019. We took a pause for a couple of different things but we could have probably done that faster. And if we were, if I was to do it again now, today we could probably do it in two to three years, flat. With everything that we've learned so far. >> So what's the application, then, to your clients' experiences that, I mean, been there, done that, right? >> You can, exactly right. I mean, you know, we always say that we want to be our best credential, right? And Merim and his team are our best credential in this space. Um, so, you know, a lot of our customers, you know, struggle making that commitment. A lot of 'em are past that struggle, now. They're committed, they're going. Uh, but I talk to a lot of my customers about, you know, do I, do I migrate? Do I modernize? You know, how do I do it? And, and it was interesting with Accenture, right? It, it started out very much as a migration program. >> Yeah. >> Right, so, we made the decision, Merim and his team made the decision to do a migration and now a modernization, right? And, and that's proven very effective. Uh, it, it's, it's, it's proven, you know, uh, we got that core in place, right? We were able to build off of that versus, you know, spending- it would've taken a lot more time just to start with a modernization approach. >> Yeah. Where, where do you draw the line between the two, between migration and modernization, then? Because just by migrating alone, you are modernizing, you know, some of your operations, so you're getting up to speed. But, but how do you draw that line and then how do you get people to jump over it? >> So I, I'll hit it from how our lessons learned. So, when we first started and we did the migrations it was literally lift and shift. And it was a lot of argument about lift and shift isn't worth it. But we found out it was, because it wasn't just about moving the work loads and keeping it like a data center. It was moving the work loads and then optimizing because everything in the cloud was significantly faster. So then I didn't have to consume all the services the same way I did in the data center. I can actually consume them smaller. But also as time went by, what we learned is, hey, now these services are working here. Which ones are actually costing us more money to run? And not that they were costing more than the data center, but it's relative to the cloud which ones cost more in the cloud? Then we looked at that and said, okay how do we want to modernize those? And then we modernized as container capabilities started the evolving, got much more mature. We shifted a lot of workloads to containers. But otherwise, the other principle we push very hard is big consumption of Lambda and uh, serverless capabilities on Amazon. So we have refactored multiple applications to give us that capability to say we no longer need the IAS capabilities, those servers, those VM's, and we run on, on serverless capability. And what's great about that is, now I don't have a server to patch, to scan, to remediate, to upgrade. I've moved away from that capability. And the teams can focus more on building the business capabilities the business wants. Um, like we did to our pricing team. I don't know if you knew this one, Chris, but all the pricing capability has been redone to be cloud native on, on AWS. >> And how, how do you deal with the folks that, that still kind of have a foot in the on-prem world that, um, that they're just not ready to give it up? You know, they, they like the control, they like the self-management. >> Yeah. >> They, they want to be in charge. >> Well, yeah. I mean, a lot of, a lot of our customers, it's, there's a reason why they need on-prem still. And there is on-prem, let's be clear. I mean, it, it is a hybrid cloud world for most of our, our customers, right? Whether they got manufacturing, whether they've got, you know, datas that are, you know, SCADA systems or, or operational IT systems that have to be close to their, their execution or to their, to their factories and things like that. So that's going to happen. I think everyone, and I shouldn't say everyone, but you know, most of our customers know they need to get there, right? And are somewhere on their journey, right? Very few have not started at all. Uh, but it's about acceleration, right? And I, I do think, um, we're going to see more and more acceleration. We saw it with Covid, right? >> Mm-hm. >> And then, you know, obviously I think we're going to see it again, right? With you know, kind of what's going on with the economy and stuff like that. It, it's, you know, it's a great way to push that change through. >> Right. >> And I, I'm really excited, to be honest what I'm really excited about, if I look at what Merim and his team's doing, is they're just leveraging that digital core and truly taking the investments that the hyper scaler's are making, the AWS's are making, and leveraging 'em. So we're not making that investment, right? We're a capital white company, right? So we don't like making good capital investments, right? And we're taking advantage of the capital investments. And we couldn't do that of the, of the hyper scales. We couldn't do that without being there. Right? >> Right. >> We just couldn't do it. >> And maybe, John, if I can build on that. >> Sure. >> Like, one of, one of the things for me when I think about the cloud is, I'm not alone. You know, because when you're in a data center when you're running a data center, you're kind of on an island. And on that island, if you've got security issues, if you got stuff you're dealing with with attackers, you know, you're, you're kind of on an island and you're alone. Whereas in this world, I am where all the investment is, where all the security capabilities are being built, and I have partners that are there with us that help us when these situations come up. So for me, I'm very uh, grateful that we pushed very hard in the beginning to get here. But I wouldn't have it any other way. For us. >> So like, do you- do you want to live outside the fort? >> Yeah. >> No >> No. (laughs) >> You're exactly right. >> Yeah. >> I don't want to live outside the fort. >> Right. >> There are a lot of bad guys out there right now. >> Yeah. >> All right, so, the journey is over. >> Right. You can unpack your bags and get comfortable, right? (Merim laughs) >> No. >> Hardly. >> No. >> So, so what is the, what has this done in terms of setting you up for your future plans? And, and >> So I'll talk about a couple different things and maybe you can build on it, Chris, from what you're seeing, like for us, we, we got very good at, I hate the concept of just FinOps but it's the way of being in the cloud. It's different than running a data center and uh, the way we think about building services, consuming services, allocating services, provisioning services. There's just so much more flexibility there that we can completely fine tune the service that we want to provide. That helps us from when we think about 360 degree value, as we talk to our clients, for ourselves to say it also helps just simply on the sustainability agenda, right, because now, as Amazon builds their capabilities to be more sustainable, those SKUs are available to us, we can naturally consume those SKUs much more effectively. Um, and then uh, the next thing to me, what I'm, what I'm especially excited about is all the stuff we're doing around network. So, you know, pre-Covid, 95% of our traffic was just straight to the internet because we had already finished the journey. So now what do you need a wide area network for anymore? >> Right. >> If you're not routing traffic between data centers what do you need it for? So, we have been working with, with AWS especially, like building these cloud land type capabilities and consuming it. So think of consuming, uh, network same way as you do the cloud. So I'm excited about that one. >> Yeah. That, that, I'm super excited about that, right? Because you know, network's at the core of everything you do, right? And there's always a lot of concern, hey, when I go to the cloud, my network costs are going to go up, right? Um, but I think we've proven, right? >> Yes. >> Being able, that those costs can come down, right? And we can have a better experience, uh, deal with the ebbs and flows of our business whether it's people working from home, people working in the office, you know, or at the client sites. We, we've, you know, we've got that cloud-based backbone that we support. You know, I, I mean Merim, I agree a hundred percent. I think you and your team have done a great job of cost management, cloud cost management, optimization, right? You didn't stop, right? >> No. >> You didn't lo- you didn't just live after the migration on VMs. Right? You know, you went serverless, you went, you know, containerization. >> Yep. >> Uh, and that's kept our cloud bill going down. >> Yes. >> Right. Versus going up, right? >> Yes. >> And I hear from a lot of customers concerned about cloud costs and that type of stuff, but you've proven right, >> Yes. >> That you can keep it flat, if not going down because you're using those last minutes. Sustainability is the other thing that I truly am, I, I love, right? Is, you know, we're all trying to become a more sustainable, sustainable organization. We're trying to help our clients become more sustainable organizations. And you know, you know, your ability to take on Gravitant processors, right? Which use less power. >> Yes. >> Right? Overnight, right? >> Yes. >> Or, hey, I'm using a, you know a, uh, serverless lambda, whatever, right? And I'm not running that server. >> Right. >> You know, so, you're able to show that sustainability gains, um, you know, very quickly. Which you could not do, right? You know, in just doing cloud basic migrations. >> Well, I tell you what I think is impressive, is that you put your money where your mouth is, right? >> Yep. (laughs) >> Is that, that it's, and, and if I'm going to be a client, not to, you know, give you guys a pat on the back, you don't need it. You're doing great without me. But I'd say you've been there, you've done that. And, and so I can learn from you. You understand my pain. >> Yes. >> You understand my reservations, my challenges and uh, you could be my, my headlights here. (Merim laughs) >> So, I think great approach. Kudos to you and certainly wish you both success and to your fourth and fifth appearances on the Cube. (Merim and Chris laugh) Um, we have slots tomorrow if you're arou- available. So, maybe we'll fill it up >> There you go. >> and bring it back again. >> Awesome. >> Guys, thanks for being here. >> Sure. >> It was very nice. >> Appreciate the time. >> All right. >> That's great. >> I've been talking, uh, about Accenture. This is the, of course, executive summit being sponsored by Accenture here at AWS reInvent 22. I'm John Walls. You're watching the Cube, the leader in tech coverage.

(bright music) >> Welcome back to Vegas, guys and girls. We're pleased that you're watching theCUBE. We know you've been with us. This is our fourth day. We know you've been with us since day one. Why wouldn't you be? Lisa Martin, here. As I mentioned, day four of theCUBE's coverage of AWS re:Invent. There are north of 55,000 people that have been at this event this week. We're hearing hundreds of thousands online. It really feels like old times, which is awesome. We're pleased to welcome back a gentleman from Dataiku who's actually new to theCUBE but Dataiku is not. Jed Dougherty is here, the VP of Platform Strategy. Thanks to joining me today, Jed. >> Oh, I'm so happy to be here. >> Talk a little bit, for anybody that isn't familiar with Dataiku, tell the audience a little bit about the technology, what you guys do. >> Dataiku is an end-to-end data science machine learning platform. We take everything from data ingestion, piplining of that data, bringing it all together, something that's useful for building models, deploying those models and then managing your ML ops workflow. So, really all the way across. And we sit on top of, basically, tons of different AWS stack as well as lots of the partners that are here today. >> Okay, got it. >> Snowflake, Databricks, all that. >> Got it, so one of the things that, it was funny, I think it was Adam's keynote Tuesday morning. I didn't time it, I watched it, but one of my guests said to me earlier this week that Adam spent exactly 52 minutes talking about data. >> Yeah. >> 52 minutes. Obviously, we can't come to an event like this without talking about data. Every company these days has to be a data company. Whether it's my grocery store or a retailer, a hospital, and so- >> Jed: It is the lifeblood of every modern company. >> It is, but you have to be able to access it. You have to be able to harness it, access it, derive insights from it, and be able to act on that faster than the competitors that are waiting, like, right back here. One of the things Adam Selipsky talked about with our boss, John Furrier, who's the co-CEO of theCUBE, they had a sit-down about a week before re:Invent. John always gets a preview of the show and Adam said, you know, he thinks the role of data analyst is going to go away. Or at least the term, because with data democratization that needs to happen. Putting data in the hands of all the business users, that every business user, whether you're in technology or marketing or ops or finance, it's going to have to analyze data to do their jobs. >> Could not agree more. >> Are you hearing that from customers? >> 100% >> Yeah. >> I was just at the CTO Summit of Bank of America two weeks ago out in California, and they told, their CTO had a statistic, 60,000 technologists in Bank of America, all asking data-type questions. You can have the best team of data scientists in the world, and they do. They have some of the best data scientists in the world there. And this team of data scientists could answer any one of the questions that those 60,000 people might have but they can't answer all of them, right? You need those people to be able to answer their own questions. I don't know if the term data analysts are going away. I think, yeah, everybody's just going to have to become a bit more of one. Just like how Excel taught everybody how to use the spreadsheet, in the future, in the next five, 10 years, the democratization of AI means that tools like Dataiku and other data science tools are going to teach everybody how to analyze data. >> Talk about Dataiku as a facilitator of that, of that democratization. Giving, like the citizen technologist who might be in finance, the ability to do that. >> So, a lot of data science tools are aimed at your hardcore coder, right? Somebody who wants to be sitting at a notebook writing (indistinct) or something like that and running models on some big fancy Spark server. Dataiku is still going to be running models on some big fancy Spark server but we're really obfuscating the challenge of writing code away from the user. So we target low code, no code, and high code users all working together in a collaborative platform. So we really do, we believe that there is always going to be a place for data scientists. That role is not going away. You will always need hardcore coders to take on those moonshot very challenging topics. But for every day AI, anybody should be able to do this and it should be open to anybody. >> Right. >> Jed: Really aim to facilitate that. >> I would love to hear some feedback, you know, this is day four of the show as I was saying, and day four is packed. I mean, this is energy-level-wise, guys, it is the same as it was when we started here on Friday night. But I'd love to hear, Jed, from your perspective some of the customer conversations that you've had, what are some of the challenges? They're coming to you saying, "Jed, Dataiku, help us eradicate these challenges so we can transform our business." >> What I'm hearing from customers and partners and AWS here is, over and over, we don't want to buy tools anymore. We want to buy solutions. We want a vertical solution that's pre-built for our industry. And we want it to be, not necessarily click and run out of the box, but we want a template that we can build off of quickly. And I've heard that customers are also looking to understand how tools can be packaged together. You got how many booths are here? 1000 booths? >> Yes, easily. >> You have 1000 different products being talked about, right behind us. Customers need to know which of these products are friends with each other and how they fit together so that they are making sure that when they purchase a set, a suite of tools to do their jobs, it's all going to work naturally together. So, being able, I think this is a really vital concept for GSIs as well. GSIs needs to understand how to package sets of tools together to deliver a full solution to clients. People don't want to be, you know, I think 10 years ago, five years ago, AWS was in the business of selling servers in the cloud. But basically what you do is, you would buy an EC two instance and you install whatever software you wanted on it. I don't know that they're in that business still but customers don't want to buy servers from AWS anymore. They want to buy solutions. >> Right. >> Rent, whatever. >> Yeah. (chuckles) >> That is the big repeated message that I've heard here. >> So you brought up a good point that there are probably 1000 booths here. You could be here every day and not get to see everything that's going on. Plus this show was going on across the strip. We're only getting a fraction of the people that are here. But with that said, to your point, there are so many tools out there. Customers are looking for solutions. One of the things that we say about theCUBE is, we extract the signal from the noise. How does Dataiku get past the noise? How do you get up the stack to really impact customers so they understand the value that you're delivering? >> I think that Data science and ML sound like a very complicated topic but our value prop is relatively simple. And we appeal both to your end users who are excited to learn about how data science works and how they can leverage these tools in their day-to-day jobs, as well as appealing to IT. IT, right now, at major organizations they want to be able to build a full stack that makes sense. And the big choices they're making right now are around infrastructure. Where am I going to run my compute? So, they're choosing between Snowflake or Databricks or a native AWS compute solution, right? And so they make this big choice around compute and then they realize, "Oh, how many of our users across our organization are actually able to leverage this big compute choice?" Oh, maybe 100, maybe 200. That's not incredibly useful for what we've just decided to completely stand behind. Dataiku, all of a sudden, opens that up to 1000s of users across your organization. So it makes IT feel empowered by being able to help more people. And it makes users feel empowered by being able to use a great tool and start answering their own questions. >> And where are your customer conversations these days? As we look at AI and ML, emerging technologies, so many customers and companies, knowing we have to go in this direction. We have to have AI to speed the business. Are you seeing more of the conversations are still in IT or are they actually going up the stack? >> (chuckles) It's a great question. When you're going into large organizations, there's two sales motions, right? There's convincing the business users that this is a great thing and then convincing IT that it's not going to be too painful. You always have to go to both places. IT doesn't want to take on a boondoggler, or there's an albatross, I don't remember the word, but, something that they're going to have to deal with for the next 10 years and then eventually dismantle and pull apart. I think a lot of IT got very scared about big data platforms and solutions because of Hadoop. To be honest, Hadoop was incredibly powerful but maybe not as mature of technology as IT would've liked it to be. From a maintenance and administration standpoint. So yes, you will always have to sell to IT and help IT feel comfortable with the platform. But no, the conversations that I want to have are the use case conversations with a Chief Data Officer, Chief Revenue Officer, Chief Marketing Officer. That's who I really want to convince that this is going to be a worthwhile opportunity. >> And what are some of the key, sorry. What are some of the key use cases that Dataiku is tackling in the market these days? >> So we work a lot. Two of the biggest organizations, or verticals, that I work with personally are finance and pharmaceuticals. In finance, we are closely embedded with wealth management organizations. So, a lot of that is around customer entertainment, churn, relatively obvious, simple concepts but ones where it's worth a lot of money. In pharma, we work both on the supply side. So, doing supply chain optimization, ensuring the right drugs get to the right places at the right time. As well as on the business and marketing side. So, ensuring that your ad spend is correctly distributed across different advertising platforms. >> So if you're working with a financial organization, I want to understand from a consumer, from the end user's perspective, although obviously this technology impacts the end user who's trying to do a transaction. What's in it for me? And I don't know as the end user that Dataiku is under the hood. >> You'd never know. >> Which is good. I shouldn't have to worry about the technology. >> Jed: You shouldn't have to worry about that at all. >> What's in it for the end user customer? What are they gaining from this? >> So, from a very end user perspective, if you think about when you logged onto maybe your Bank of America, your Chase app, five or 10 years ago, maybe you didn't even have it on your phone five years ago. Or when you logged into your account online. We do 95% of our banking online right now, right? I go into a physical location, what? I don't know, once every six months or something? Get a cashier's check? I don't know. The experience that you're getting and the amount of information you're getting back about your spending habits, where your money is going, what your credit score is, all of these things are being driven by these big data organizations inside the banks. Also, any type, this is a little creepier, but any type of promotional emails or the types of things that you get feedback on when you use your credit card and the offers that you get through that, are all being personalized to you through the information that these banks are collecting about your spending habits. >> Yeah, but we want that as a consumer, we want the personalized. >> Yeah, of course. We want it to be magic slash not creepy. (laughs) >> Right, I want them to recommend the best card for me. >> Right. >> The next best thing. >> It's good for me, it's good for them. >> Don't serve me up something that I've already bought. That always bugs me when I'm like, I already bought that. >> I get that all the time. I'm like, yeah, I have that card already. It's in my wallet. Why are you telling me? >> We only have a couple of minutes left Jed, but talk to me about from a platform strategy perspective, what's next for Dataiku and AWS? >> So we are making a matrix transition right now and it's core to our platform. For a long time, the way that we've installed Dataiku is, we help our customers install it on their AWS account so it runs inside their tenant. This is very comfortable for, for example, large banking clients, pharma clients that have personally identifiable information, all that kind of thing. They own everything. However, as we were talking about before, we're really moving from providing a tool to providing solutions. And part of that is obviously a move to SaaS. So two years ago we released a SaaS offering. We've been expanding it more and more to, this year, we want to be pushing SaaS first. So Dataiku online should be the first option when new customers move on. And that is a huge platform shift. It means making sure that we have the right security in place. It means making sure that we have the right scaling in place, that we have 24-7 support. All this has been a big challenge. A big fascinating challenge, actually, to put together. >> Awesome. Last question for you. Say you get a brand new DeLorean, I hear they're coming back, and you want to put, you really, really want to put a bumper sticker on it, 'cause why not? And it's about Dataiku and it's like a sizzle reel kind of thing. >> A sizzle real, alright. >> Yeah. What does it say? >> Extraordinary people, everyday AI. >> Wow. Drop the mic, Jed. That was awesome. Thank you so much for coming on the program. We really appreciate the update on Dataiku. What you guys are doing for customers, your specialization and solutions for verticals. Awesome stuff, we'll have to have you back. >> Thank you so much. >> Alright, my pleasure. >> Bye-Bye. >> For my guest, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (bright music)

(mellow music) >> Oh, welcome back to theCUBE. We continue our coverage here at AWS re:Invent 22. We're in the Venetian in Las Vegas, and this place is hopping. I'm tell you what. It is a nearly standing room only that exhibit floor is jam packed, and it's been great to be along for the ride here on Accenture's sponsorship at the Executive summit as well. We'll talk about Pfizer today, you know them quite well, one of the largest biopharmaceutical companies in the world but their tech footprint is impressive, to say the least. And to talk more about that is Wes Barnes, senior Director of Pfizer's Digital Hosting Solutions. Wes, good to see you, sir. >> Good to meet you, John. >> And Jon Harrison, the North American lead for Infrastructure and engineering at Accenture. Jon, good to see you as well. >> Good to see you as well. >> Thanks for joining us. >> Happy to be here. >> Alright, so let's jump in. Pfizer, we make drugs, right? >> Pharmaceuticals. >> Yes. >> Among the most preeminent, as I said biopharms in the world. But your tech capabilities and your tech focus as we were talking about earlier, has changed dramatically in the 18 years that you've been there. >> Yep. >> Now, talk about that evolution a little bit to where you were and what you have to be now. >> Yeah, yeah. It's interesting. When I started at Pfizer, IT was an enabling function. It was akin to HR or our facilities function. And over the past couple years, it's dramatically changed. Where Digital now is really at the center of everything we do across Pfizer. You know it really is a core strategic element of our business. >> Yeah. And those elements that you were talking about, just in terms of whether it's research, whether it's your patients, I don't want to go through the laundry lists the litany of things, but the touch points with data and what you need it to do for you in terms of you know, computations, what you, the list is long. It's pretty impressive. >> Yeah, yeah, for sure yeah. >> I mean, shed some light on that for us. >> We cannot release a medicine without the use of technology. And if you think about research now, a huge component of our research is computational chemistry. Manufacturing medicines now is a practice in using data and analytics and predictive machine learning and analytics capabilities to help us determine how to best you know, apply the capabilities to deliver the outcomes that we need. The way in which we connect with patients and payers now is wholly digital. So it's an entirely different way of operating than it was 10 years ago. >> And the past three years, pretty remarkable in many respects, to say the least, I would think, I mean, John, you've seen what Pfizer's been up to, talk about maybe just this, the recent past and all that has happened and what they've been able to do. >> Yeah, I mean, what is so exciting to me about working with a company like Pfizer and working in life sciences more broadly is the impact that they make on patients around the world world, right? I mean, think about those past three years and Pfizer stepped up and met the moment for all of us, right? And as we talk a little bit about the role that we played together with Pfizer with AWS in their journey to the cloud, it's so motivating for myself personally it's so motivating for every single person on the team that we ask to spend nights and weekends migrating things to the cloud, creating new capabilities, knowing that at the end of the day, the work that they're doing is making the world a healthier place. >> Yeah, we talk so much about modernization now, right? And it's, but it kind of means different things to different people depending on where you're coming into the game, right? If you've been smart and been planning all along then this is not a dramatic shift in some cases though, for others it is. Right? >> Yeah. >> Traumatic in some cases for some people. >> For sure. >> For Pfizer, I mean talk about how do you see modernization and what does it mean to your operations? >> Following our success of the COVID program of 2021, I mean it became evident to us that, you know we needed to maintain a new pace of innovation and in fact try to find ways to accelerate that pace of innovation. And as I said earlier everything we do at Pfizer is centered around digital. But despite that, and despite 10 years of consolidating infrastructure and moving towards modern technology, last year, only 10% of Pfizer's infrastructure was in the native public cloud. So we had a problem to solve. In fact, I remember, you know, we had to build up our clinical systems to support the volume of work that we were doing for COVID-19 vaccine. We were rolling things into our data center to build up the capacity to achieve what we needed to achieve. Moving to the public cloud became more imperative to try to achieve the scale and the modern capabilities that we need. >> And so where did you come into play here with this? Because obviously as a partner you're right alongside for the ride but you saw these inherent challenges that they had and how did Accenture answer the bell there? >> Well, so look, I mean we saw Pfizer react to the pandemic. We saw them seize the moment. We talked together about how IT needed to move quicker and quicker towards the cloud to unlock capabilities that would serve Pfizer's business well into the future. And together we laid out some pretty ambitious goals. I mean, really moving at a velocity in a pace that I think for both Accenture and AWS surpassed the velocity and pace that we've done anywhere else. >> Yeah, right, yeah. >> So we've set out on an ambitious plan together. You know, I was kind of reflecting about some of the successes, what went well what didn't in preparation for re:Invent. And you know, many of the folks that'll listen to this will remember the old days of moving data centers when you'd have a war room you'd have a conference bridge open the whole time. Someone would be running around the tile floor in the data center, do a task, call back up to the bridge and say, what do I do next, right? Then when I think about what we did together at Pfizer in moving towards the public cloud, I mean, we had weekends most weekends where we were running a wave with 10,000 plus discrete activities. >> Yeah. >> Wow. >> Right, so that old model doesn't scale. >> Right. >> And we really anchored, >> You have a very crowded data center with a lot of people running into each other. >> You'd have a whole lot of people running around. But we really anchored to an Accenture capability that we call myNav Migrate. I know you guys have talked about it here before so I won't go into that. But what we found is that we approached this problem of velocity not as a technical problem to solve for but as a loading and optimization problem of resources. Right, thought about it just a little bit different way and made sure that we could programmatically control command and control of the program in a way that people didn't have to wait around all Saturday afternoon to be notified that their next activity was ready, right? They could go out, they could live their day and they could get a notification from the platform that says, hey it's about your turn. Right, they could claim it they could do it, they could finish it, and that was really important to us. I mean, to be able to control the program in that type of way at scale. >> Yeah, by the way, the reason we went as fast it was a deliberate choice and you'll talk to plenty of folks who have a five year journey to the public cloud. And the reason we wanted to move as fast as we did and Jon talked about some of it, we wanted to get the capabilities to the business as quickly as we could. The pace of innovation was such that we had to offer native cloud capabilities we had to offer quickly. We also knew that by compressing the time it took to get to the cloud, we could focus the organization get it done as economically as possible but then lift all boats with the tide and move the organization forward in terms of the skills and the capabilities that we need to deliver modern outcomes. >> So, you know, we talk about impacts internally, obviously with your processes, but beyond that, not just scientists not just chemists, but to your, I mean, millions of customers, right? We're talking, you know, globally here. What kind of impacts can you see that directly relate to them, and benefits that they're receiving by this massive technical move you've made? >> Pfizer's mission is breakthroughs that change patient lives. I mean, the work that we do the work that everybody does within Pfizer is about delivering therapies that, you know provide health outcomes that make people live longer, live healthier lives. For us, modernizing our infrastructure means that we can enable the work of scientists to find novel therapies faster or find things that perhaps couldn't have been found any other way without some of the modern technologies that we're bringing to bear. Saving money within infrastructure and IT is treasure that we can pour back into the important areas of research or development or manufacturing. We're also able to, you know, offer an ecosystem and a capability in which we connect with patients differently through digital mechanisms. And modern cloud enables that, you know, using modern digital experiences and customer experience, and patient experience platforms means that we can use wearable devices and mobile technologies and connect to people in different ways and offer solutions that just didn't exist a couple years ago. >> And so, I mean, you're talking about IoT stuff too, right? >> 100%. >> It's way out on the edge and personal mobile, in a mobile environment. And so challenges in terms of you know, data governance and compliance and security, all these things, right? They come into play because it's personal health information. So how, as you've taken them, you know to this public cloud environment how much of a factor are those considerations? Because, you know, this is not just a product a service, it's a live human being. >> Yeah. I mean, you start with that, you think about it through the process and you think about it afterwards, right, I mean, that has to be a core factor in every stage of the program, and it was. >> So in, in terms of where you are now, then, okay, it's not over. >> It's never over. >> I mean, you know, as good as you are today and as fast as you are and as accurate and as efficient. >> Yeah. >> Got to get better, right? You got to stay competitive. >> Yeah. >> So where do you find that? Because, you know, with powers being what they are with speed and what it is how much more is there to squeeze out of this rock? >> There's a lot more to squeeze out of the rock. If you think about what we've done over the past year it's about creating sort of a new minimum viable product for infrastructure. So we've sort of raised the bar and created an environment upon which we can continue to innovate that innovation is going to continue sort of forever at this point. You know, the next focus for us is how to identify the business processes that deliver the greatest value ultimately to our patients. And use the modern platform that we've just built to improve those processes to deliver things faster, deliver new capabilities. Pfizer is making a huge investment in digital medicines therapies that are delivered through smart devices through wearables using, as I said technology that didn't exist before. That wouldn't be possible without the platform that we've built. So over the past year, we've come a long way but I think that we've effectively set the table for all of the things that are yet to come. >> So, Jon, how do you then, as you've learned a lot about life science or, and certainly Pfizer with what they're up to, how do you then apply, you know, what you know about their world to what you know about the tech world and make it actionable for growth to make it actionable for, for future expansion? >> Yeah, I mean, we start by doing it together, right? I think that's a really important part. Accenture brings a wealth of knowledge, both industry experience and expertise, technology experience and expertise. We work together with our clients like Pfizer with our partners like AWS to bring the best across that power of three to meet clients where they're at to understand where they want to go, and then create a bespoke approach that meets their business needs. And that's effectively what we're doing now, right? I mean, if you think about the phase that we've just went through, I mean, a couple of fast facts here no pun intended, right? 7,800 server instances across 11 operating system versions 7,500 databases across 20 database versions, right? 4,700 applications, 350,000 migration activities managed across an eight month period. >> In eight months. >> Yeah. But that's not the goal, right? The goal is now to take, to Wes' point that platform that's been developed and leverage that to the benefit of the business ultimately to the benefit of the patient. >> You know, why them, we have we've talked a lot about Pfizer, but why Accenture? What, what, what's, 'cause it's got to be a two way street, right? >> We've had a long partnership with Accenture. Accenture supports a huge component of our application environment at Pfizer and has for quite a long time. Look, we didn't make it easy on them. We put them up against a large number of world class SIs. But look, Accenture brought, you know, sort of what I think of as the trifecta here. They brought the technical capabilities and knowledge of the AWS environment. They brought the ability to really understand the business outcomes that we were trying to achieve and a program leadership capability that, you know I think is world class. And Jon talked about myNav, you know, we recognized that doing what we were trying to do in the time that we were doing it required new machinery, new analytics and data capabilities that just didn't exist. Automation didn't exist. Some people experience capabilities that would allow us to interface with application owners and users at a velocity and a pace and a scale that just hasn't been seen before at Pfizer. Accenture brought all three of those things together and I think they did a great job helping us get to where we need to be. >> When you hear Jon rattle through the stats like he just did, right? We talk about all, I mean, not that I'm going to ask you to pat yourself on the back but do you ever, >> He should. >> Does it blow your mind a little bit, honestly that you're talking about that magnitude of activity in that compressed period of time? That's extraordinary. >> It's 75% of our global IT footprint now in the public cloud, which is fantastic. I mean, look, I think the timing was right. I think Pfizer is in a little bit of a unique position coming off of COVID. We are incredibly motivated to keep the pace up, I mean across all lines of business. So, you know what we found is a really willing leadership team, executive leadership team, digital leadership team to endorse a change of this magnitude. >> Well, it's a great success story. It's beyond impressive. So congratulations to both you on that front and certainly you wish you continued success down the road as well. >> Thank you. >> Thank you gentlemen. >> Thank you. >> Good job. >> Pfizer, and boy, you talk about a job well done. Just spectacular. All right, you are watching our coverage here on theCUBE, we're at the AWS re:Invent 22 show. This is Executive Summit sponsored by Accenture and you're watching theCUBE the leader in high tech coverage.

(soft music) >> We're in Las Vegas at The Venetian for our continued coverage here of re:Invent '22, AWS's big show going on. Great success off to a wonderful start. We're in the Executive Summit sponsored by Accenture. And we're going to talk about public health and the cloud, how those have come together in the great state of North Carolina. Charles Carter is going to help us do that. He's assistant secretary for technology services with the state of North Carolina's Department of Health and Human Services. Charles, good to see you. Thanks for joining us here on "theCUBE". >> Thank you very much for having me. >> Yeah, thanks for making the time. So first off, let's talk about what you do on the homefront before what you're doing here and where you're going. But in terms of kind of what your plan has been, what your journey has been from a cloud perspective and how you've implemented that and where you are right now in your journey. >> Sure, so we started. When I got there, we didn't have a cloud footprint at all. There was a- >> Host: Which was how long ago? >> I got there in 2016, so about six years. >> Host: Six, seven years, yeah. >> Yeah, five, six years. So anyways, we started off with our first module within our Medicaid expansion. And that was the first time that we went into the cloud. We worked with AWS to do our encounter processing system. And it was an incredible success. I think the ease of use was really kind of something that people weren't quite ready for. But it was really exciting to see that. And the scalability, to be able to turn that on and cover the entirety of North Carolina was awesome. So once we saw that and get a little taste of it, then we really wanted to start implementing it throughout DHHS. And we marshaled in a cloud-only cloud-first strategy where you had to actually get an exemption not to go to the cloud. And that was a first for our state. So that was really kind of the what launched us. But then COVID hit. And once COVID came in, that took us to a new level. COVID forced us to build technologies that enabled a better treatment, a better care, a better response from our team. And so we were able to stand up platforms in 48 hours. We were able to stand up COVID vaccine management systems in six weeks. And none of that would've been possible without the cloud. >> So forced your hand in a way because all of a sudden you've got this extraordinarily remote workforce, right, and people trying to- And you're doing different tasks that were totally unexpected, right, prior to that. What kind of a shock to the system was that from I get from an IT perspective? >> Yeah, so from a state government perspective, for example, you never hear you have all the money you need and you have to do it quickly. It just doesn't work like that. But this was a rare moment in time where you had this critical need. The entire country and our state population was kind of on edge. How do we move through this? How do we factor our lives into this new integration? What is this virus? Is it spreading in my county, in my city, my zip code? Where is it? And that kind of desperation really kind of focused everybody in on build me technologies that can get me the data that I need to make good healthcare decisions, good clinical decisions. And so that was our challenge. Cloud enabled it because it can scale so quickly. We can set up things, we can exchange data. We can move data around a lot easier. And the security is better from our perspective. So that COVID experience really kind of pushed us, you know, if you will, out the door. And we're never going back because it's just too good. >> Yeah, was that the aha moment then in a way because you had to do so much so fast and before capabilities that maybe you didn't have or maybe hadn't tapped? >> Yeah, yeah. >> I mean what was the accelerant there? Was COVID that big, or was it somebody who had to make a decision to say, this is where we're going with this, somebody in your shoes or somebody with whom you work? >> Yeah, no, I mean cloud at the end of the day, we knew that in order to do what we needed to do we couldn't do it on-prem. It wasn't an option. So if we wanted to build these capabilities, if we wanted to bring in technologies that really brought data to our key, our governor, our secretary, to make good decisions on behalf of our residents in North Carolina, then we were going to have to build things quickly. And the only way you can do that is in the cloud. So it was when they came back and said, "We need these things," there's only one answer. That's a good thing about technology. It's pretty binary, so it was either go with what we had, which wasn't adequate, or build to what we knew we could do and pretty short order. And because of that, we were able to actually usher in a huge expansion of cloud footprint within DHHS. And now we've actually been able to implement it in other departments simply because of our expertise. And that's been a huge asset for the state of North Carolina as a whole. >> So what's your measuring stick then for value in terms of identifying benefit? 'Cause it's not really about cost. This is about service, I assume, right? >> Right. >> So, you know, how do you quantify the values and the benefits that you're deriving from this migration over to the cloud? >> So from our perspective, it hits several different areas. I mean, you can start in security. We know that if we're in the cloud the tools that can manage and give us visibility in the cloud are 10 times better than an on-prem environment. And so if we can take a lot of these legacy systems and move them to the cloud, we'll be in a better security posture. So we have that piece of it. The other part of it is the data aspect of it, being able to- We're 33 divisions strong, right? We have a large footprint. We have a lot of siloed data elements. And cloud allows us to start integrating those data sets in a much more usable fashion so that we can see that if Charles Carter's in one area in division, a specific division with DHHS, is he somewhere else? And if he is somewhere else, then how do we provide a better clinical care for that individual? And those are conversations that we can't really have if we don't move to the cloud. So those types of- And of course there's always the OKRs, the actual measurements that you apply to things that we're doing. But at the end of the day, can we get the requirements from our business partners, bring those requirements to bear in technology, and really enable the indoctrination of these requirements throughout our clinical and healthcare kills? >> What about they're always pillars here, right? Governance, huge pillar, security, huge pillar, especially in your world, right? >> Yeah. >> So making that move over to the cloud and still recognizing that these are essentials that you have to have in place, I wouldn't say adjustments, but what kind of, I guess, recognition have you had toward that and making sure that you're still very true to those principles that are vital in the terms of public health? >> It is a great question because our secretary at the time and our governor, Roy Cooper, were very focused on enabling transparency. We had to be very transparent with what we were doing because the residents in North Carolina were just really kind of, "What's going on?" It was a scary time for a lot of us. So transparency was a key element towards our success. And in order to do that, you've got to have proper security. You got to have proper governance. You've got to have proper builds within technology that really enable that kind of visibility. One of the things that we did very early on was we set up a governance structure for our cloud environments so that as we wanted to and stand up an easy-to environment or we wanted to do some sort of work within a cloud or stand up in a different environment, we were able actually to set up a framework for how do you introduce that. Are you doing it correctly? Do you have the proper security on it? Do you have the funding for it? Like all the steps that you need to really kind of build into the scaffolding around a lot of these efforts we had to put in place and pretty quickly to get them going. But once we did that, the acceptance and the adoption of it was just tremendous. I mean, it was a light on for all of our business partners 'cause they understood I can either build on-prem, in which case I won't be able to get what I want in any kind of reasonable time period. Or I can build on cloud. And I can have it in some cases in 48 hours. >> Right, tomorrow. >> Yeah, exactly. >> You know, it was a huge difference. >> So where are you there? I mean, this is just not like a really big old lift and shift and we're all done and this is great. Cloud's taken care of all of our needs. Where are you in terms of the journey that you're undertaking? And then ultimately where do you want to go, like how far? What kind of goals have you set for yourself for the next two, three years down the road? >> Yeah, so this is an exciting part because we have actually- Like I mentioned earlier, we are a cloud-first cloud-only strategy, right? There's no reasons for us to be on-prem. It's just a matter of us kind of sunsetting legacy systems and bringing on cloud performance. We hope to be a 60% of our applications, which we have over 400 applications. So it's pretty large footprint. But we're wanting to migrate all of that to the cloud by 2025. So if we can achieve that, I think we'll be well on our way. And the momentum will carry forward for us to do that. We've actually had to do a reorganization of our whole IT structure. I think this is an important part to maintain that momentum because we've reorganized our staff, reorganized ourselves so that we can focus more on how do you adopt cloud, how do you bring in platforms which are all cloud-based, how do you use data within those systems? And that has allowed us to kind of think differently about our responsibilities, who's accountable for what, and to kind of keep those, that momentum going. So we've got some big projects that are on right now. Some of them are lift and shift, like you mentioned. We have a project with kind of a clumsy, monolithic system. It's called (indistinct). We're trying to migrate that to the cloud. We're in the process of doing that. And it's an excellent demonstration of capability once we pull that off. And then of course any new procurement that we put out there no one's making anything for on-prem anymore. Everyone's making their SaaS products for cloud-based experiences. Or if we're going to build or just use integrators then we'll build that in house. But all of it's based on cloud. >> And you mentioned SaaS. How much of this stuff are you doing on your own? And how much are you doing through managed services? >> Yeah, so like I mentioned, we have over 400 applications. So we had a pretty large footprint, right? >> Big, it's huge, right. >> So we're only who we are, and we can only build so much. So we're kind of taking- We did a application rationalization effort, which kind of identified some threats to our systems. Like maybe they're older things, FoxPro, kind of older languages that we're using. And in some cases we got people who are retiring. And there's not many people who can support that anymore. So how do we take those and migrate them to the cloud, either put them on a Salesforce or ServiceNow or Microsoft Dynamics platform and really kind of upgrade those systems? So we're in the process of kind of analyzing those elements. But yeah, that's kind of the exciting launch, if you will, of kind of taking the existing visibility of our applications and then applying it to what we're capable of with the cloud. >> And if you had advice that you could give to your colleagues who are in public health or just in public, the public sector- And your resources, they're finite. This is kind of what you have to deal with. And yet you have needs, and you're trying to stay current. You've got talent challenges, right? You've got rev or spending challenges. So if you could sit down your colleagues in a room and say, "Okay, this has been our experience. Here's what I would keep an eye out for," what kind of headlights would you beat for them? >> Yeah, so I think the biggest aha that I'd like to share with my contemporaries out there is that you've got a great ability to lower your costs, to excite your own personnel because they want to work on the new stuff. We've actually set up a whole professional development pathway within our organization to start getting people certified on AWS, certified on other platforms, to get them ready to start working in those environments. And so all of that work that we're been doing is coming together and allowing us to maintain the momentum. So what I'd recommend to people is, A, look at your own individual staff. I don't think you need to go outside to find the talent. I think you can train the talent that you have interior. I think you've got to aggressively pursue modernization because modernization enables a lot more. It's less expensive. It enables quicker adoption of business requirements and modern business requirements. And then lastly, focus on your data sharing because what you're going to find in the platforms and in the clouds is that there is a lot more opportunities for data integrations and conjoining disparate data sources. So if you can do those elements, you'll find that your capabilities on the business side are much more, much greater on the other end. >> Don't be scared, right, jump in? (laughing) >> Definitely don't be scared. Don't be, the water's warm. (host laughing) Come on in, you're fine, you're fine. (laughing) >> No little toe dipping in there. You're going to dive into the deep end, let her rip. >> Exactly, just go right in, just go right in. >> Well, it sounds like you've done that with great success. >> I'm very happy with it. >> Congratulations on that. And wish you success down the road. >> Thank you very much, I appreciate it. >> Yeah, thank you, Charles. All right, back with more. You are watching theCUBE here in Las Vegas. theCUBE of course the leader, as you know, in tech coverage. (soft music)

>>Now welcome back to the Cube as we continue our coverage here. AWS Reinvent 2022, going out here at the Venetian in Las Vegas. Tens of thousands of attendees. That exhibit Hall is full. Let me tell you, it's been something else. Well, here in the executive summit, sponsored by Accenture. Accenture rather. We're gonna talk about Baptist Health, what's going on with that organization down in South Florida with me. To do that, I have Tony Abro, who's the SVP and Chief Digital and Information Officer. I have Ashley Lane, the managing director of the Accenture Healthcare Practice, and on the far end Poop Kumar, who is the VP and cto Baptist Health Florida won and all. Welcome. Thank you. First off, let's just talk about Baptist Health, the size of your footprint. One and a half million patient visits a year, not a small number. >>That was probably last year's number, but okay. >>Right. But not a small number about your footprint and, and what, I guess the client base basically that you guys are serving in it. >>Absolutely. So we are the largest organization in South Florida system provider and the 11 hospitals soon to be 12, as you said, it's probably about 1.8 million by now. People were, were, were supporting a lot of other units and you know, we're focusing on the four southern counties of South Florida. Okay. >>So got day Broward. Broward, yep. Down that way. Got it. So now let's get to your migration or your cloud transformation. As we're talking about a lot this week, what's been your, I guess, overarching goal, you know, as you worked with Accenture and, and developed a game plan going forward, you know, what was on the front end of that? What was the motivation to say this is the direction we're going to go and this is how we're gonna get there? >>Perfect. So Baptist started a digital transformation initiative before I came about three years ago. The board, the executive steering committee, decided that this is gonna be very important for us to support us, to help our patients and, and consumers. So I was brought in for that digital transformation. And by the way, digital transformation is kind of an umbrella. It's really business transformation with technology, digital technologies. So that's, that's basically where we started in terms of consumer focused and, and, and patient focus. And digital is a big word that really encompasses a lot of things. Cloud is one of, of course. And, you know, AI and ML and all the things that we are here for this, this event, you know, and, and we've started that journey about two years ago. And obviously cloud is very important. AWS is our main cloud provider and clearly in AWS or any club providers is not just the infrastructure they're providing, it's the whole ecosystem that provides us back value into, into our transformation. And then somebody, I think Adam this morning at the keynote said, this is a team sport. So with this big transformation, we need all the help and that we can get to mines and, and, and hands. And that's where Accenture has been invaluable over the last two years. >>Yeah, so as a team sport then depu, you, you've got external stakeholders, otherwise we talked about patience, right? Internal, right. You've, you've got a whole different set of constituents there, basically, but it takes that team, right? You all have to work together. What kind of conversations or what kind of actions, I guess have you had with different departments and what different of sectors of, of the healthcare business as Baptist Health sees it in order to bring them along too, because this is, you know, kind of a shocking turn for them too, right? And how they're gonna be doing business >>Mostly from an end user perspective. This is something that they don't care much about where the infrastructure is hosted or how the services are provided from that perspective. As long as the capabilities function in a better way, they are seemingly not worried about where the hosting is. So what we focus on is in terms of how it's going to be a better experience for, from them, from, from their perspective, right? How is it going to be better responsiveness, availability, or stability overall? So that's been the mode of communication from that perspective. Other than that, from a, from a hosting and service perspective, the clientele doesn't care as much as the infrastructure or the security or the, the technology and digital teams themselves. >>But you know, some of us are resistant to change, right? We're, we're just, we are old dogs. We don't like new tricks and, and change can be a little daunting sometimes. So even though it is about my ease of use and my efficiency and why I can then save my time on so and so forth, if I'm used to doing something a certain way, and that's worked fine for me and here comes Tony and Depo and here comes a, >>They're troublemaker >>And they're stir my pot. Yeah. So, so how do you, the work, you were giving advice maybe to somebody watching this and say, okay, you've got internal, I wouldn't say battles, but discussions to be held. How did you navigate through that? >>Yeah, no, absolutely. And Baptist has been a very well run system, very successful for 60 something odd years. Clearly that conversation did come, why should we change? But you always start with, this is what we think is gonna happen in the future. These are the changes that very likely will happen in the future. One is the consumer expectations are the consumer expectations in terms of their ability to have access to information, get access to care, being control of the process and their, their health and well-being. Everything else that happens in the market. And so you start with the, with that, and that's where clearly there are, there are a lot of signs that point to quite a lot of change in the ecosystem. And therefore, from there, the conversation is how do we now meet that challenge, so to speak, that we all face in, in, in healthcare. >>And then from there, you kind of designed the, a vision of where we want to be in terms of that digital transformation and how do we get there. And then once that is well explained and evangelized, and that's part of our jobs with the help of our colleagues who have, have been doing this with others, then is the, what I call a tell end show. We're gonna say, okay, in this, in this road, we're gonna start with this. It's a small thing and we're gonna show you how it works in terms of, in terms of the process, right? And then as, as you go along and you deliver some things, people understand more, they're on board more and they're ready for for more. So it's iterative from small to larger. >>The proof is always in the place, right? If you can show somebody, so actually I, I obviously we know about Accenture's role, but in terms of almost, almost what Tony was just saying, that you have to show people that it works. How, how do you interface with a client? And when you're talking about these new approaches and you're suggesting changes and, and making these maybe rather dramatic proposals, you know, to how they do things internally, from Accenture's perspective, how do you make it happen? How, how do you bring the client along in this case, batches >>Down? Well, in this case, with Tony and Depu, I mean, they have been on this journey already at another client, right? So they came to Baptist where they had done a similar journey previously. And so it wasn't really about convincing >>Also with Accenture's >>Health, also with Accenture's Health, correct. But it wasn't about telling Tony Dupe, how do we do this? Or anything like that. Cuz they were by far the experts and have, you know, the experience behind it. Well, it's really like, how do we make sure that we're providing the right, right team, the right skills to match, you know, what they wanted to do and their aspirations. So we had brought the, the healthcare knowledge along with the AWS knowledge and the architects and you know, we said that we gotta, you know, let's look at the roadmap and let's make sure that we have the right team and moving at the right pace and, you know, testing everything out and working with all the different vendors in the provider world specifically, there's a lot of different vendors and applications that are, you know, that are provided to them. It's not a lot of custom activity, you know, applications or anything like that. So it was a lot of, you know, working with other third party that we really had to align with them and with Baptist to make sure that, you know, we were moving together at speed. >>Yeah, we've heard about transformation quite a bit. Tony, you brought it up a little bit ago, depu, just, if you had to define transformation in this case, I mean, how big of a, of a, of a change is that? I mean, how, how would you describe it when you say we're gonna transform our, you know, our healthcare business? I mean, I think there are a lot of things that come to my mind, but, but how do you define it and, and when you're, when you're talking to the folks with whom you've got to bring along on this journey? >>So there's the transformation umbrella and compos two or three things. As Tony said, there is this big digital transformation that everybody's talking about. Then there is this technology transformation that powers the digital transformation and business transformation. That's the outcome of the digital transformation. So I think we, we started focusing on all three areas to get the right digital experience for the consumers. We have to transform the way we operate healthcare in its current state or, or in the existing state. It's a lot of manual processes, a lot of antiquated processes, so to speak. So we had to go and reassess some of that and work with the respective business stakeholders to streamline those because in, it's not about putting a digital solution out there with the anti cured processes because the outcome is not what you expect when you do that. So from that perspective, it has been a heavy lifting in terms of how we transform the operations or the processes that facilitates some of the outcomes. >>How do you know it's working >>Well? So I I, to add to what Deep was saying is I think we are fortunate and that, you know, there are a lot of folks inside Baptist who have been wanting this and they're instrumental to this. So this is not a two man plus, you know, show is really a, you know, a, a team sport. Again, that same. So in, in that, that in terms of how do we know it works well when, when we define what we want to do, there is some level of precision along the way. In those iterations, what is it that we want to do next, right? So whatever we introduce, let's say a, a proper fluid check in for a patient into a, for an appointment, we measure that and then we measure the next one, and then we kind of zoom out and we look at the, the journey and say, is this better? >>Is this better for the consumer? Do they like it better? We measure that and it's better for the operations in terms of, but this is the interesting thing is it's always a balance of how much you can change. We want to improve the consumer experience, but as deeply said, there's lot to be changed in, in the operations, how much you do at the same time. And that's where we have to do the prioritization. But you know, the, the interesting thing is that a lot of times, especially on the self servicing for consumers, there are a lot of benefits for the operations as well. And that's, that's where we're in, we're in it together and we measure. Yeah, >>Don't gimme too much control though. I don't, I'm gonna leave the hard lifting for you. >>Absolutely, absolutely right. Thank you. >>So, and, and just real quick, Ashley, maybe you can shine some light on this, about the relationship, about, about next steps, about, you know, you, you're on this, this path and things are going well and, and you've got expansion plans, you want, you know, bring in other services, other systems. Where do you want to take 'em in the big picture in terms of capabilities? >>Well, I, I mean, they've been doing a fantastic job just being one of the first to actually say, Hey, we're gonna go and make an investment in the cloud and digital transformation. And so it's really looking at like, what are the next problems that we need to solve, whether it's patient care diagnosis or how we're doing research or, you know, the next kind of realm of, of how we're gonna use data and to improve patient care. So I think it's, you know, we're getting the foundation, the basics and everything kind of laid out right now. And then it's really, it's like what's the next thing and how can we really improve the patient care and the access that they have. >>Well, it sure sounds like you have a winning accommodation, so I I keep the team together. >>Absolutely. >>Teamwork makes the dream >>Work. Absolutely. It is, as you know. So there's a certain amount of, if you look at the healthcare industry as a whole, and not, not just Baptist, Baptist is, you know, fourth for thinking, but entire industry, there's a lot of catching up to do compared to whatever else is doing, whatever else the consumers are expecting of, of an entity, right? But then once we catch up, there's a lot of other things that we were gonna have to move on, innovate for, for problems that we maybe we don't know we have will have right now. So plenty of work to do. Right. >>Which is job security for everybody, right? >>Yes. >>Listen, thanks for sharing the story. Yeah, yeah. Continued success. I wish you that and I appreciate the time and expertise here today. Thank you. Thanks for being with us. Thank you. Thank you. We'll be back with more. You're watching the Cube here. It's the Executive Summit sponsored by Accenture. And the cube, as I love to remind you, is the leader in tech coverage.

(steady music) >> Oh, welcome back here on theCUBE. I'm John Walls, we're in The Venetian and day one of a jam-packed three days here at AWS re:Invent '22. This is the Executive Summit sponsored by Accenture, and it is Merck time. And I mean, it is loaded with Merck time. We have quite the panel here, in fact. First threesome of the day, by the way. I see you guys have really loaded up nicely. Ron Kim is with us, the SVP and CTO of Merck. Ron, good to see you, sir. >> Thanks, John. >> Also, Fran Gaetens, who's the VP of Technology Infrastructure, Operations, and Experience, which I want to hear more about. Love that job title, Fran. >> Thanks, John. >> And Sean Finnerty, VP of Cloud and Infrastructure Technology. Again, everybody here from Merck. So fellows, thanks for being with us. >> Thanks, John. >> Appreciate the time. >> Yeah. >> So let's just talk about Merck, first off in general in terms of what's happening with the cloud. And Ron, I'll let you jump onto that first. I realize this talk of journey, right? >> Mm-hm. >> It's different for everybody, different slices depending upon where you are, where you start, where you need to finish. Where are you right now in terms of what you're doing with the cloud? >> Yeah, John, we've been on this journey for about two years, have done some great work and achieve some great results in proving we could move to the cloud, moving to the cloud at scale, achieving really measurable financial and operational results. Where we're focusing now going forward is transforming the business. And as you know, our business is saving and improving lives. And so when we talk about moving things to the cloud, it's much more than just moving servers or things like that. It's really contributing towards our business that saves and improves lives. So for our work that we work on together moving into the cloud, the stakes are high, but we think the opportunity's great. And the way to seize that opportunity is what we're doing now, is our BlueSky program and working with AWS and Accenture on it. >> Yeah, so two years, you're two years in. It's like nascent stage still, right? I mean, and it never ends, (laughs) frankly. >> Yeah. >> But talk about that progression and was it, you know, baby steps, was it diving in? I mean, how do you decide, you know, the batting order basically here about how you're going to get things going? >> The early parts of the the two-year journey so far, we're really starting small, primarily driven by a central team. And we did that consciously to get momentum, build the foundation, prove again we could move things to the cloud with success, we could start to scale. And then as that journey went on, now instead of just relying on the central team, we're starting to get the rest of the company involved. So this is not just this team doing the cloud journey. It's the whole company, and that's an ongoing journey, getting all the different stakeholders involved and things like that. But I think that's where we are on the journey now, is look, let's lock arms with everybody in the company. So it's a Merck-wide cloud transformation, not just the BlueSky team. >> Right, and of course, as you know, the C-suite's got to be behind all this. And we hear about how that it's now being driven in some cases, you know, these kind of transformations, whether it's from CEO level down the CTO and CIO and what have you. Fran, the experience part of your job. I just want to get to that real quick. So you know, how do you define that? >> Yeah. First of all, I'm delighted you asked. >> Okay. (laughs) >> And the focus on experience that my team's accountable for transcends, you know, our cloud journey. We have held for the last three years within my organization a priority that's focused on improving the experience that colleagues in our company have with workplace technology and services. And so I'd come into this role at the time and thought carefully, you know, about how to best title our organization in a way that would draw curiosity or inquisition. >> Sure. >> A very creative colleague that we have an opportunity to work with in our company suggested the term, and I loved it and ran with it. And today, it's, you know, still something that we spend a significant portion of cycles focused on. >> Well, it's a very clear signal, right? And a reminder as well that ultimately the experience whether it's your internal stakeholders or external, your customers, right, that you're delivering a very pleasant and efficient, and hopefully you said life-saving >> Yeah. >> experience as well. And I think that'd be a pretty good reminder for your team, isn't it? >> It is. >> "Hey, we're all part of the experience here." >> Yeah. >> Yeah. Right, so Sean, let's talk about some of the things that we've discussed here, branching out within Merck. >> Yep. >> You know, and making it a company effort, not just an IT effort. Right, now all of a sudden, you're into everybody's business and everybody is sharing this. I mean, is there buying that's necessary here? I mean, how do you bring that bunch along? You've all lived it, you know it. They're experiencing it for the first time. >> Yeah, it's a great question and it's one we get quite a bit walking the halls here at re:Invent. We're very lucky in that we do have, you mentioned earlier, top-down support, right? So when we're talking about moving to the cloud, we're not just running around the halls of the technology, you know, cubes of all the people that are sitting there at computers banging away every day. We're meeting with the CEO and a significant portion of the executive team, talking about how does our cloud journey underpin our business transformation aspirations? How do we speed up scientific research? How do we do clinical trials more effectively? How do we manufacture medicine more effectively, more reliably? Those are all underpinned by this technology transformation that we're embarking on sort of from the bottoms up, and meeting in the middle with the top-down strategic imperative to transform the business by leveraging technology. So that clear and unambiguous support coming from the C-suite at our company allows us to prioritize very aggressively and point at that mission to say, "Hey, we're not just here to talk about moving a server or two. We're here to talk about how we transform scientific research and discovery in the interest of our patients and delivering medicine more effectively, more quickly." So it's really, really interesting. >> Yeah, and so being on one side of that, you know, obviously you're dealing with people, whether chemists, scientists, whomever doing computational chemistry whatever it might be. They know their business and you're trying to integrate these new capabilities into their business, right? How do you do that? I mean, how do you know what they need and how do they tell you what they need when they don't know what you have? (laughs) >> That's quite a question. >> Yeah. I got there. >> Yeah, I mean, my initial thought is, you know, there has to be a compelling value to anybody getting impacted by this. And that's what we all work to do. So whether it's faster, less lead time, reducing cycle times, more reliability, innovation, I mean, there has to be something in it for them, and the work we're doing crosses that whole spectrum. So some of the efforts we have, "Hey, this is a cost-savings effort, this is for agility, this is for speed." So you know, it can't be just we're just doing this for the sake of moving of the cloud. There has some business value in it. And you know, Sean and the team have done a great job on kind of putting the rigor behind how do we describe that value so people then say, "Is that value really there or not? And does it really add up?" And I think that's been one of the keys to our success, is the work that Sean and members of his team have been doing is there's a pretty rigorous way we track our progress. And we've involved finance from day one in that. So having their buy in, you know, gives the whole set of results a lot of credibility. >> But tell me about that, Sean, about in terms of identifying value and quantifying it, in terms that a bottom line can orient to that. >> Yeah, absolutely. I mean, I've been at the cloud migration game myself personally for years, right? I got into this game back in 2011. The challenge of those programs has always been articulating the value associated with migrating stuff. It's easy to say, "I'm going to take a server. I'm going to move it from here to here. Then that difference is X, point at that." That's easy, everyone can understand that. But the labor efficiencies and the business value and the business transformation that comes with moving a capability from on-prem or from another hosting service to the cloud and transforming how we deliver, manage, operate, and scale those solutions, that's really where the power of this comes from, is business value tied to discrete actions, moving systems with a plan from one point to another point. And then being able to clearly articulate the value by implementing, as Ron mentioned, models we've created. So we've created actually financial calculation models to put dollars and cents next to labor efficiencies, time liberated, you know, the ability to deliver with higher velocity, higher quality, higher reliability. Those now have dollar values associated with them, which we're able to take, apply to our portfolio, and look for those opportunities that jump out as, "Hey, you know, that one's worth a million bucks. Let's prioritize that one. The ones that maybe have lower value or less business impact, you know, let's put those to the side and get to those later." So we can constantly demonstrate that not only are we raising our ability to deliver for our patients, but we're also delivering value back to the corporation to invest in other things that need focus and attention. >> Yeah, so talk about AWS and Accenture a little bit about, I mean, obviously big players with this. I'm assuming that interaction, maybe Fran, you know, talk about the partnership and again, how they have helped you get to the point that where you currently reside. >> Yeah, our partnership with both firms has been longstanding. That said, you know, what's changed in a market way happened a couple years ago when we originated this cloud acceleration program that we called BlueSky. We worked directly with Accenture to develop a comprehensive business case that, you know, fundamentally lined out the detail of our intention, how we would prosecute this work, and you know, among other things be crystal clear about the value at stake and how we would capture and realize that over time. So you know, through that lens, it's really taken a village with parties from all three firms, you know, to come together, prosecute this important work, but likewise, as I like to say, keep score, you know, in the context of value because ultimately, it's the one thing that we can talk about unambiguously with the program in the context of measurable results. >> Because of the work you do, obviously, you know, invaluable in many respects. But just the thought about cloud, and I know governance, security, compliance, all these things are critical. You know, how do those weigh in, in terms of considerations you have to make? And especially going forward as you develop new ideas, new things, ideas you're trying to bring to market, >> Yeah. >> I mean, how much does that play and the cloud and what exposures there might be? >> Yeah, it plays in quite a bit. And no matter what type of work we do, cloud or on-premise, I mean, security is of utmost importance. That's how we operate. Now what's interesting is when we think about in AWS, you know, AWS has the ability, they have the the scale and the learnings from multiple clients, right? So rather than a single company like us trying to figure out security on our own, we can benefit from what are all the lessons that they've learned that they bake back into their platform. So that's been a great benefit. But regardless of our partner, we'll always be very, a lot of scrutiny about security no matter what. And that's how we should operate. But the benefits of the platform within AWS, I mean, there's a lot of security intelligence built in from their experience, so that's- >> If I can add to that- >> Sure. >> Yeah. To build on prior remarks that Sean had articulated, this migration to the cloud, right, happens to be a catalyst for a broader transformation. One where we're fundamentally changing our ways of working. Ways that consider, you know, topics like security, compliance, documentation, regulatory requirements. And choosing to bake those in to these solutions from the onset rather than consider them as an appendage or an afterthought. So you know, the cloud is a really important part of this. You know, there's no mistake about it, but it's also a powerful catalyst for something that's broader. >> Tremendously more efficient, right? With our thinking and how we're going to plan and how we're going to execute. >> Yeah, and to build on that even more, we view it as an opportunity to raise the bar on our compliance, security, and regulatory readiness game. As we're touching applications across our portfolio, rearchitecting, leaning in on things like the well-architected framework and other things that AWS and Accenture bring to bear. We set the bar higher when we move things from where they are today to a new destination and introduce automation so that that uplift of control does not come at the cost of additional time or labor. It's simply we're raising the bar in ourselves. We're using this transformational opportunity to implement that change. Our customers are along for the ride and reap the benefits of the fact that, you know, we've raised the bar on ourselves, basically. >> Well, you said two years, so the first steps, and I'm sure the next ones are going to be just as successful. I really appreciate the time. Thanks for sharing that and for bringing so much expertise at the table. >> Thanks, John. >> Appreciate that, good to have you guys with us. >> Thanks. >> Talking about Merck and their cloud transformation. Love that word, we've been talking a lot about it this week. You're watching theCUBE, of course, here at the Executive Summit sponsored by Accenture. And theCUBE being, of course, the leader at tech coverage. (calm music)

(relaxing intro music) >> Welcome back to theCUBE's Coverage here live at reinvent 2022. We're here at the Executive Summit upstairs with the Accenture Set three sets broadcasting live four days with theCUBE. I'm John Furrier your host, with two great guests, cube alumnis, back Tanuja Randery, managing director Amazon web service for Europe middle East and Africa, known as EMEA. Welcome back to the Cube. >> Thank you. >> Great to see you. And Karthik Narain, who's the Accenture first cloud lead. Great to see you back again. >> Thank you. >> Thanks for coming back on. All right, so business transformation is all about digital transformation taken to its conclusion. When companies transform, they are now a digital business. Technologies powering value proposition, data security all in the keynotes higher level service at industry specific solutions. The dynamics of the industry are changing radically in front of our eyes for for the better. Karthik, what's your position on this as Accenture looks at this, we've covered all your successes during the pandemic with AWS. What, what do you guys see out there now as this next layer of power dynamics in the industry take place? >> I think cloud is getting interesting and I think there's a general trend towards specialization that's happening in the world of cloud. And cloud is also moving from a general purpose technology backbone to providing specific industry capabilities for every customer within various industries. But the industry cloud is not a new term. It has been used in the past and it's been used in the past in various degrees, whether that's building horizontal solutions, certain specialized SaaS software or providing capabilities that are horizontal for certain industries. But we see the evolution of industry cloud a little differently and a lot more dynamic, which is we see this as a marketplace where ecosystem of capabilities are going to come together to interact with a common data platform data backbone, data model with workflows that'll come together and integrate all of this stuff and help clients reinvent their industry with newer capabilities, but at the same time use the power of democratized innovation that's already there within that industry. So that's the kind of change we are seeing where customers in their strategy are going to implement industry cloud as one of the tenants as they go through their strategy. >> Yeah, and I see in my notes, fit for purposes is a buzzword people are talking about right size in the cloud and then just building on that. And what's interesting, Tanuja I want to get your thoughts because in the US we're one country, so yeah, integrating is kind of within services. You have purview over countries and these regions it's global impact. This is now a global environment. So it's not just the US North America, it's Latin America it's EMEA, this is another variable in the cross connecting of these fit for purpose. What's your view of the these industry specific solutions? >> Yeah, no and thanks Karthik 'cause I'm a hundred percent aligned. You know, I mean, you know this better than me, John, but 90% of workloads have not yet moved to the cloud. And the only way that we think that's going to happen is by bringing together business and IT. So what does that mean? It means starting with business use cases whether that's digital banking or smart connected factories or frankly if it's predictive maintenance or connected beds. But how do we take those use cases leverage them to really drive outcomes with the technology behind them? I think that's the key unlock that we have to get to. And very specifically, and Adam talked about this a lot today, but data, data is the single unifier for all of business and IT coming together to drive value, right? However, the issue is there's a ton of it, (John Furrier chuckling) right? In fact, fun fact if you put all the data that's going to be created over the next five years, which is more than the last 30 years, on a one terabyte little floppy, disk drive, remember those? Well that's going to be 15 round trips to the moon (John Furrier chuckling) and back. That's how much data it is. So our perspective is you got to unify, single data lake, you got to modernize with AI and ML, and then you're going to have to drive innovation on that. Now, I'll give you one tiny example if I may which I love Ryanair, big airline, 150 million passengers. They are also the largest supplier of ham and cheese sandwiches in the air. And catering at that scale is really difficult, right? If you have too much food wastage, sustainability issues, too little customers are really unhappy. So we work with them leveraging AWS cloud and AI ML to build a panini predictor. And in essence, it's taking the data they've got, data we've got, and actually giving them the opportunity to have just the right number of paninis. >> I love the lock and and the key is data to unlock the value. We heard that in the keynote. Karthik, you guys have been working together with AWS and a lot of successes. We've covered some of those on the cube. As you look at these industry solutions they're not the obvious big problems. They're like businesses, you know it could be the pizza shop it could be the dentist office, it could be any business any industry specific carries over. What is the key to unlock it? Is it the data? Is it the solution? What's that key? >> I think, you know the easier answer is all of the about, but like Tanuja said it all starts by bringing the data together and this is a funny thing. It's not creating new data. This data is there within enterprises. Our clients have these data the industries have the data, but for ages these data has been trapped in functional silos and organizations have been doing analytics within those functions. It's about bringing the data together whether that's a single data warehouse or a data mesh. Those are architectural considerations. But it's about bringing cross-functional data together as step one. Step two, is about utilizing the power of cloud for democratized innovation. It's no longer about one company trying to reinvent the wheel, or create a a new wheel within their enterprise. It's about looking around through the power of cloud marketplace to see if there's a solution that is already existing can we use that? Or if I've created something within my company can I use that as a service for others to use? So, the number one thing is using the power of democratized innovation. Second thing is how do you standardize and digitize functions that does not need to be reinvented every single time so that, you know, your organization can do it or you could use that or take that from elsewhere. And the third element is using the power of the platform economy or platforms to find new avenues of revenue opportunity, customer engagement and experiences. So these are all the things that differentiates organization, but all of this is underpinned by a unified data model that helps, you know, use all the (indistinct) there. >> Tanuja, you have mentioned earlier that not everyone has their journey of the cloud looks the same and certainly in the US and EMEA you have different countries and different areas. >> Yep. >> Their journeys are different. Some want speed and fees, some will roll their own. I mean data brick CEO, when I interviewed them that last week, they started database on a credit card swiped it and they didn't want any support. Amazon's knocking on their door saying, "you want support?" "No, we got it covered." Obviously they're from Berkeley and they're nerds, and they're cool. They can roll their own, but not everyone can. >> Yeah. >> And so you have a mix of customer profiles. How do you view that and what's your strategy? How do you get them over productive seeing that business value? What's that transformation look like? >> Yeah, John, you're absolutely right. So you've got those who are born in cloud, they're very savvy, they know exactly what they need. However, what I do find increasingly, even with these digital native customers, is they're also starting to talk business use cases. So they're talking about, "okay how do I take my platform and build a whole bunch of new services on top of that platform?" So, we still have to work with them on this business use case dimension for the next curve of growth that they want to drive. Currently with the global macroeconomic factors obviously they're also very concerned about profitability and costs. So that's one model. In the enterprise space, you have differences. >> Yeah. >> Right, You have the sort of very, very, very savvy enterprises, right? Who know exactly what they're looking for. But for them then it's about how do I lean into sustainability? In fact, we did a survey, and 77% of users that we surveyed said that they could accelerate their sustainably goals by using cloud. So in many cases they haven't cracked that and we can help them do that. So it's really about horses for courses there. And then, then with some other companies, they've done a lot of the basic infrastructure modernization. However, what they haven't been able to yet do is figure out how they're going to actually become a tech company. So I keep getting asked, can I become a tech company? How do I do that? Right? And then finally there are companies which don't have the skills. So if I go to the SMB segment, they don't always have the skills or the resources. And there using scalable market platforms like AWS marketplace, >> Yeah. >> Allows them to get access to solutions without having to have all the capabilities. So it really is- >> This is where partner network really kind of comes in. >> Absolutely. >> Huge value. Having that channel of solution providers I use that term specifically 'cause you're providing the solution for those folks. >> Yeah. Exact- >> And then the folks at the enterprise, we had a quote on the analyst segment earlier on our Cube, "spend more, save more." >> Yeah. >> That's the cloud equations, >> Yeah. because you're going to get it on sustainability you're going to save it on, you're going to save on cost recovery for revenue, time to revenue. So the cloud is the answer for a lot of enterprises out of the recession. >> Absolutely, and in fact, we need to lean in now you heard Adam say this, right? I mean the cost savings potential alone from on-prem to cloud is between 40 and 60 percent. Just that. But I don't think that's it John. >> The bell tightening he said is reigning some right size. Okay, but then also do more, he didn't say that, but analysts are generally saying, if you spend right on the cloud, you'll save more. That's a general thesis. >> Yeah. >> Do you agree with that? >> I absolutely think so. And by the way, usage is, people use it differently as they get smarter. We're constantly working with our customers by the way though, to continuously cost optimize. So you heard about our Graviton3 instances for example. We're using that to constantly optimize, but at the same time, what are the workloads that you haven't yet brought over to the cloud? (John Furrier chuckling) And so supply chain is a great idea. Our health cloud initiative. So we worked with Accenture on the Accenture Health Insights platform, which runs on AWS as an example or the Goldman Sachs one last year, if you remember. >> I do >> The financial cloud. So those, those are some of the things that I think make it easier for people to consume cloud and reimagine their businesses. >> It's funny, I was talking with Adam and we had a little debate about what an ISV is and I talked to the CEO of Mongo. They don't see themselves on the ISV. As they grew up on the cloud, they become platforms, they have their own ISVs and data bricks and Snowflake and others are developing that dynamic. But there's still ISVs out there. So there's a dynamic of growth going on and the need for partners and our belief is that the ecosystem is going to start doubling in size we believe, because of the demand for purpose built or so out of the box. I hate to use that word "out of the box", but you know turnkey solutions that you can buy another one if it breaks. But use the building blocks if you want to build the foundation. That is more durable, more customizable. Do that if you can. >> Well, >> but- >> we've got a phenomenal, >> shall we talk about this? >> Yeah, go get into- >> So, we've built a five year vision together, Accenture and us. which is called Velocity and you'll be much better in describing it, but I'll give you the simple version of Velocity which is taking AWS powered industry solutions and bringing it to market faster, more repeatable and at lower cost. And so think about vertical solutions sitting on a horizontal accelerator platform able to be deployed making transformation less complex. >> Yeah. >> Karthik, weight in on this, because I've talked to you about this before. We've said years ago the horizontal scalability of the cloud's a beautiful thing but verticals where the ML works great too. Now you got ML in all aspects of it. Horizontal verticals here now. >> Yeah, Yeah, absolutely. Again, the power of this kind of platform that we are launching, by the way we're launching tomorrow we are very excited about it, is, create a platform- >> What are you launching tomorrow? Hold on, I got news out there. What's launching? >> We are going to launch a giant platform, which will help clients accelerate their journey to industry cloud. So that's going to happen tomorrow. So what this platform would provide is that this is going to provide the horizontal capabilities that will help clients bootstrap their launch into cloud. And once they get into cloud, they would be able to build industry solutions on this. The way I imagine this is create the chassis that you need for your industry and then add the cartridges, industry cartridges, which are going to be solutions that are going to be built on top of it. And we are going to do this across various industries starting from, you know, healthcare, life sciences to energy to, you know, public services and so on and so forth >> You're going to create a channel machine. A channel creation machine, you're going to allow people to build their own solutions on top of that platform. And that's launching tomorrow. Make sure we get the news on that. >> Exactly. And- >> Ah, No, >> Sorry, and we genuinely believe the power of industry cloud, if you think about it in the past to create a solution one had to be an ISV to create a solution. What cloud is providing for industry today in the concept of industry clouds, this, industry companies are creating industry solution. The best example is, along with, you know, AWS and Accenture, Ecopetrol, which is a leader in the energy industry, has created a platform, you know called Water Intelligence and Management platform. And through this platform, they are attacking the audacious goal of water sustainability, which is going to be a huge problem for humanity that everybody needs to solve. As part of this platform, the goal is to reduce, you know, fresh water usage by 66% or zero, you know, you know, impact to, you know, groundwater is going to be the goal or ambition of Ecopetrol. So all of this is possible because industry players want to jump to the bandwagon because they have all the toolkit of of the cloud that's available with which they could build a software platform with which they can power their entire industry. >> And make money and have a good business. You guys are doing great. Final word, partnership. Where's it go next? You're doing great. Put a plugin for the Accenture AWS partnership. >> Well, I mean we have a phenomenal relationship and partnership, which is amazing. We really believe in the power of three which is the GSI, the ISV, and us together. And I have to go back to the thing I keep focused on 90% of workloads not in cloud. I think together we can enable those companies to come into the cloud. Very importantly, start to innovate launch new products and refuel the economy. So I think- >> We'll have to check on that >> Very, very optimistic. >> We'll have to check on that number. >> That seems a little- >> You got to check on that number. >> 90 seems a little bit amazing. >> 90% of workloads. >> That sounds, maybe, I'd be surprised. Maybe a little bit lower than that. Maybe. We'll see. >> We got to start turning it. >> It's still a lot. >> (laughs) It's still a lot. >> A lot more. Still first, still early days. Thanks so much for the conversation Karthik great to see you again Tanuja, thanks for your time. >> Thank you, John. >> Congratulations, on your success. Okay, this is theCube up here in the executive summit. You're watching theCube, the leader in high tech coverage, we'll be right back with more coverage here, and the Accenture set after the short break. (calm outro music)