>>Good morning and welcome back to the Cube where we are excited to be broadcasting live all week from Detroit to Michigan at Cuban slash cloud Native con. Depending on who you're asking, Lisa, it's day two things are buzzing. How are you feeling? >>Good, excited. Ready for day two, ready to have more great conversations to see how this community is expanding, how it's evolving, and how it's really supporting it itself. >>Yeah, Yeah. This is a very supportive community. Something we talked a lot about. And speaking of community, we've got some very bold and brave folks over here. We've got this CTO and the head of product from Storm Forge, and they are on a mission to automate Kubernetes. Now automatic and Kubernetes are not words that go in the same sentence very often, so please welcome Patrick and Yasmin. Thank you both for being here. Hello. How you doing? >>Thanks for having us. >>Thanks for having us. >>Talk about what you guys are doing. Cause as you said, Kubernetes auto spelling is anything but auto. >>Yeah. >>The, what are some of the challenges? How do you help >>Eliminate this? Yeah, so the mission at Storm Forge is primarily automatic resource configuration and optimization essentially. So we started as a machine learning company first. And it's kind of an interesting story cuz we're one of those startups that has pivoted a few times. And so we were running our machine learning workloads. Most >>Have, I think, >>Right? Yeah. Yeah. We were, we started out running our machine learning workloads and moving them into Kubernetes. And then we weren't quite sure how to correctly adjust and size our containers. And so our ML team, we've got three PhDs and applied mathematics. They said, Well, hang on, we could write an algorithm for that. And so they did. And then, Oh, I love this. Yeah. And then we said, Well holy cow, that's actually really useful. I wonder if other people would like that. And that's kind of where we got our start. >>You solved your own problem and then you built a business >>Around it. Yeah, exactly. >>That is fantastic. Is, is that driving product development at Storm Forge still? That kind of attitude? >>I mean that kind of attitude definitely drives product development, but we're, you know, balancing that with what the users are, the challenges that they have, especially at large scale. We deal with a lot of large enterprises and for us as a startup, we can relate to the problems that come with Kubernetes when you're trying to scale it. But when you're talking about the scale of some of these larger enterprises, it's just a different mentality. So we're trying to balance that of how we take that input into how we build our product. Talk >>About that, like the, the end user input and how you're taking that in, because of course it's only going to be a, you know, more of a symbiotic relationship when that customer feedback is taken and >>Acted on. Yeah, totally. And for us, because we use machine learning, it's a lot of building confidence with our users. So making sure that they understand how we look at the data, how we come up with the recommendations, and actually deploy those changes in their environment. There's a lot of trust that needs to be built there. So being able to go back to our users and say, Okay, we're presenting you this type of data, give us your feedback and building it alongside them has helped a lot in these >>Relationships. Absolutely. You said the word trust, and that's something that we talk about at every >>Show. I was gonna jump on that too. It's >>Not, Yeah, it's not a buzzword. It's not, It shouldn't be. Yeah. It really should be, I wanna say lived and breathed, but that's probably grammatically incorrect. >>We're not a gram show. It's okay darling. Yeah, thank >>You. It should be truly embodied. >>Yeah. And I, I think it's, it's not even unique to just what we do, but across tech in general, right? Like when I talk about SRE and building SRE teams, one of the things I mentioned is you have to build that trust first. And with machine learning, I think it can be really difficult too for a couple different reasons. Like one, it tends to be a black box if it's actually true machine learning. Totally. Which ours is. But the other piece that we run into. Yeah. And the other piece we run into though is, is what I was an executive at United Health Group before I joined Storm Forge. And I would get companies that would come to me and try to sell me machine learning and I would kind of look at it and say, Well no, that's just a basic decision tree. Or like, that's a super basic whole winter forecast, right? Like that's not actually machine learning. And that's one of the things that we actually find ourselves kind of battling a little bit when we talk about what we do in building that trust. >>Talk a little bit about the latest release as you guys had a very active September. Here we are. And towards the, I think end of October. Yeah. What are some of the, the new things that have come out? New integrations, new partnerships. Give us a scoop on that. >>Yeah, well I guess I'll start and then I'll probably hand it over to you. But like the, the big thing for us is we talked about automating Kubernetes in the very beginning, right? Like Kubernetes has got a vpa it's >>A wild sentence anyway. Yeah, yeah. >>It it >>Has. We're not gonna get over at the whole show. Yeah. >>It as a VPA built in, it has an HPA built in and, and when you look at the data and even when you read the documentation from Google, it explicitly says never the two should meet. Right. Because you'll end up thrashing and they'll fight each other. Well the big release we just announced is with our machine learning, we can now do both. And so we vertically scale your pods to the correct up. Yeah. >>Follow status. I love that. >>Yeah, we can, we can scale your pods to the correct size and still allow you to enable the HPA and we'll make recommendations for your scaling points and your thresholds on the HPA as well so that they can work together to really truly maximize your efficiency that without sacrificing your performance and your reliability of the applications that you're running. That >>Sounds like a massive differentiator for >>Storm launch, which I would say it is. Yeah. I think as far as I know, we're the first in the industry that can do this. Yeah. >>And >>From very singularity vibes too. You know, the machines are learning, teaching themselves and doing it all automatically. Yep. Gets me very >>Excited. >>Yeah, absolutely. And from a customer demand perspective, what's the feedback been? Yeah, it's been a few >>Weeks. Yeah, it's been really great actually. And a lot of why we went down this path was user driven because they're doing horizontal scale and they want to be able to vertically size as they're scaling. So if you put yourself in the shoes of someone that's configuring Kubernetes, you're usually guessing on what you're setting your CPU requests and limits do. But horizontal scale makes sense. You're either adding more things or removing more things. And so once they actually are scaled out as a large environment and they have to rethink, how am I gonna resize this now? It's just not possible. It's so many thousands of settings across all the different environments and you're only thinking about CPU memory, You're not thinking about a lot of things. It's just, but once you scale that out, it's a big challenge. So they came to us and said, Okay, you're doing, cuz we were doing vertical scaling before and now we enable vertical and horizontal. And so they came to us and said, I love what you're doing about right sizing, but we wanna be able to do this while also horizontally scaling. And so the way that our software works is we give you the recommendations for what the setting should be and then allow Kubernetes to continue to add and remove replicas as needed. So it's not like we're going in and making changes to Kubernetes, but we make changes to the configuration settings so that it's the most optimal from a resource perspective. >>Efficiency has been a real big theme of the show. Yeah. And it's clear that that's a focus for you. Everyone here wants to do more faster Of course. And innovation, that's the thing to do that sometimes we need partners. You just announced an integration with Datadog. Tell us about that. Yeah, >>Absolutely. Yeah. So the way our platform works is we need data of course, right? So they're, they're a great partner for us and we use them both as an input and an output. So we pull in metrics from Datadog to provide recommendations and we'll actually display all those within the Datadog portal. Cause we have a lot of users that are like, Look, Datadog's my single pane of glass and I hate using that word, but they get all their insights there. They can see their recommendations and then actually go deploy those. Whether they wanna automatically have the recommendations deployed or go in and actually push a button. >>So give me an example of a customer that is using the, the new release and some of the business outcomes they're achieving. I imagine one of the things that you're enabling is just closing that ES skills gap. But from a business level perspective, how are they gaining like competitive advantages to be able to get products to market faster, for example? >>Yeah, so one of the customers that was actually part of our press release and launch and spoke about us at a webinar, they are a SaaS product and deal with really bursty workloads. And so their cloud costs have been growing 40% year over year. And their platform engineering team is basically enabled to provide the automation for developers and in their environment, but also to reduce those costs. So they want to, it's that trade off of resiliency and cost performance. And so they came to us and said, Look, we know we're over provisioned, but we don't know how to tackle that problem without throwing tons of humans at the problem. And so we worked with them and just on a single app found 60% savings and we're working now to kind of deploy that across their entire production workload. But that allows them to then go back and get more out of the, the budget that they already have and they can kind of reallocate that in other areas, >>Right? So there can be chop line and bottom >>Line impact. Yeah. And I, I think there's some really direct impact to the carbon emissions of an organization as well. That's a good point. When you can reduce your compute consumption by 60%. >>I love this. We haven't talked about this at all during the show. Yeah. And I'm really glad that you brought this up. All of the things that power this use energy. Yeah. >>What is it like seven to 8% of all electricity in the world is consumed by data centers. Like it's crazy. Yeah. Yeah. And so like that's wild. Yeah. Yeah. So being able to make a reduction in impact there too, especially with organizations that are trying to sign green pledges and everything else. >>It's hard. Yeah. ESG initiatives are huge. >>Absolut, >>It's >>A whole lot. A lot of companies have ESG initiatives where they can't even go out and do an RFP with a business, Right. If they don't have an actual active starting, impactful ESG program. Yes. Yeah. >>And the RFPs that we have to fill out, we have to tell them how they'll help. >>Yeah. Yes. It's so, yeah, I mean I was really struck when I looked on your website and I saw 54% average cost reduction for Yeah. For your cloud operations. I hadn't even thought about it from a power perspective. Yeah. I mean, imagine if we cut that to 3% of the world's power grid. That is just, that is very compelling. Speaking of compelling and exciting future things, talk to us about what's next? What's got you pumped for 2023 and and what lies >>Ahead? Oh man. Well that seems like a product conversation for sure. >>Well, we're super excited about extending what we do to other platforms, other metrics. So we optimize a lot right now around CPU and memory, but we can also give people insights into, you know, limiting kills, limiting CPU throttling, so extending the metrics. And when you look at hba and horizontal scale today, most of it is done with cpu, but there are some organizations out there that are scaling on custom metrics. So being able to take in more data to provide more recommendations and kind of extend what we can do from an optimization standpoint. >>That's, yeah, that's cool. And what house you most excited on the show floor? Anything? Anything that you've seen? Any keynotes? >>There's, Well, I haven't had a lot of time to go to the keynotes unfortunately, but it's, >>Well, I'm shock you've been busy or something, right? Much your time here. >>I can't imagine why. But no, there's, it's really interesting to see all the vendors that are popping up around Kubernetes focus specifically with security is always something that's really interesting to me. And automating CICD and how they continue to dive into that automation devs, SEC ops continues to be a big thing for a lot of organizations. Yeah. Yeah. >>I I do, I think it's interesting when we marry, Were you guys here last year? >>I was not here. >>No. So at, at the smaller version of this in Los Angeles. Yeah. I, I was really struck because there was still a conversation of whether or not we were all in on Kubernetes as, as kind of a community and a society this year. And I'm curious if you feel this way too. Everyone feels committed. Yeah. Yeah. I I I feel like there's no question that Kubernetes is the tool that we are gonna be using. >>Yeah. I I think so. And I think a lot of that is actually being unlocked by some of these vendors that are being partners and helping people get the most outta Kubernetes, you know, especially at the larger enterprise organizations. Like they want to do it, but the skills gap is a very real problem. Right. And so figuring out, like Jasmine talked about figuring out how do we, you know, optimize or set up the correct settings without throwing thousands of humans at it. Never mind the fact you'll never find a thousand people that wanna do that all day every day. >>I was gonna, It's a fold endeavor for those >>People study, right? Yeah. And, and being able to close some of those gaps, whether it's optimization, security, DevOps, C I C D. As we get more of those partners like I just talked about on the floor, then you see more and more enterprises being more open to leaning into Kubernetes a little bit. >>Yeah. Yeah. We've seen, we've had some great conversations the last day and, and today as well with organizations that are history companies like Ford Motor Companies for >>Example. Yeah. Right. >>Just right behind us. One of their EVs and, and it's, they're becoming technology companies that happen to do cars or home >>Here. I had a nice job with 'em this morning. Yes. With that storyline, honestly. >>Yes. That when we now have such a different lens into these organizations, how they're using technologies, advanced technologies, Kubernetes, et cetera, to really become data companies. Yeah. Because they have to be, well the consumers on the other end expect a Home Depot or a Ford or whomever or your bank Yeah. To know who you are. I want the information right here whenever I need it so I can do the transaction I need and I want you to also deliver me information that is relevant to me. Yeah. Because there, there's no patience anymore. Yeah. >>And we partner with a lot of big FinTech companies and it's, it's very much that. It's like how do we continue to optimize? But then as they look at transitioning off of older organizations and capabilities, whether that's, they have a physical data center that's racked to the gills and they can't do anything about that, so they wanna move to cloud or they're just dipping their toe into even private cloud with Kubernetes in their own instances. A lot of it is how do we do this right? Like how do we lean in and, Yeah. >>Yeah. Well I think you said it really well that the debate seems to be over in terms of do we go in on Kubernetes? That that was a theme that I think we felt that yesterday, even on on day one of the keynotes. The community seems to be just craving more. I think that was another thing that we felt yesterday was all of the contributors and the collaborators, people want to be able to help drive this community forward because it's, it's a flywheel of symbiosis for all of the vendors here. The maintainers and, and really businesses in any industry can benefit. >>Yeah. It's super validating. I mean if you just look at the floor, there's like 20 different booths that talk about cost reporting for Kubernetes. So not only have people moved, but now they're dealing with those challenges at scale. And I think for us it's very validating because there's so many vendors that are looking into the reporting of this and showing you the problem that you have. And then where we can help is, okay, now you know, you have a problem, here's how we can fix it for you. >>Yeah. Yeah. That, that sort of dealing with challenges at scale that you set, I think that's also what we're hearing. Yeah. And seeing and feeling on the show floor. >>Yeah, absolutely. >>What can folks see and, and touch and feel in your booth? >>We have some demos there you can play around with the product. We're giving away a Lego set so we've let >>Gotta gets >>Are right now we're gonna have to get some Lego, We do a swag segment at the end of the day every day. Now we've >>Some cool socks. >>Yep. Socks are hot. Let's, let's actually talk about scale internally as our closing question. What's going on at Storm Forge? If someone's watching right now, they're excited. Are you hiring? We are hiring. Yeah. How can they stalk you? What's the >>School? Absolutely. So you can check us out on Storm forge.io. We're certainly hiring across the engineering organization. We're hiring across the UX a product organization. We're dealing, like I said, we've got some really big customers that we're, we're working through with some really fun challenges. And we're looking to continue to build on what we do and do new innovative things like especially cuz like I said, we are a machine learning organization first. And so for me it's like how do I collect all the data that I can and then let's find out what's interesting in there that we can help people with. Whether that's cpu, memory, custom metrics, like as said, preventing kills, driving availability, reliability, What can we do to, to kind of make a little bit more transparent the stuff that's going on underneath the covers in Kubernetes for the decision makers in these organizations. >>Yes. Transparency is a goal of >>Many. >>Yeah, absolutely. Well, and you mentioned fun. If this conversation is any representation, it would be very fun to be working on both of your teams. We, we have a lot of fun Ya. Patrick, thank you so much for joining. Thanks for having us, Lisa, As usual, thanks for being here with me. My pleasure. And thank you to all of you for turning into the Cubes live show from Detroit. My name's Savannah Peterson and we'll be back in a few.

>>Hi, buddy. Welcome back to the keeps coverage of AWS 2021. Re-invent you're watching the cube and I'm really excited. We're going to go outside the storage box. I like to say with my lawn Thompson Bukovac, who's the vice-president of block and object storage and Wayne Duso was a VP of storage edge and data governance guys. Great to see you again, we saw you at storage day, the 15 year anniversary of AWS, of course, the first product service ever. So awesome to be here. Isn't it. Wow. >>So much energy in the room. It's so great to see customers learning from each other, learning from AWS, learning from the things that you're observing as well. >>A lot of companies decided not to do physical events. I think you guys are on the right side of history. We're going to show you, you weren't exactly positive. How many people are going to show up. Everybody showed. I mean, it's packed house here, so >>Number 10. Yeah. >>All right. So let's get right into it. Uh, news of the week. >>So much to say, when you want to kick this off, >>We had a, we had a great set of announcements that Milan, uh, talked about yesterday, uh, in her talk and, and a couple of them in the file space, specifically a new, uh, member of the FSX family. And if you remember that the FSA, Amazon FSX is, uh, for customers who want to run fully managed versions of third party and open source file systems on AWS. And so yesterday we announced a new member it's FSX for open ZFS. >>Okay, cool. And there's more, >>Well, there's more, I mean, one of the great things about the new match file service world and CFS is it's powered by gravity. >>It is taught by Gravatar and all of the capabilities that AWS brings in terms of networking, storage, and compute, uh, to our customers. >>So this is really important. I want the audience to understand this. So I I've talked on the cube about how a large proportion let's call it. 30% of the CPU cycles are kind of wasted really on things like offloads, and we could be much more efficient, so graviton much more efficient, lower power and better price performance, lower cost. Amazon is now on a new curve, uh, cycles are faster for processors, and you can take advantage of that in storage it's storage users, compute >>That's right? In fact, you have that big launch as well for luster, with gravity. >>We did in fact, uh, so with, with, uh, Yasmin of open CFS, we also announced the next gen Lustre offering. And both of these offerings, uh, provide a five X improvement in performance. For example, now with luster, uh, customers can drive up to one terabyte per second of throughput, which is simply amazing. And with open CFS, right out of, right out of the box at GA a million IOPS at sub-millisecond latencies taking advantage of gravitas, taking advantage of our storage and networking capabilities. >>Well, I guess it's for HPC workloads, but what's the difference between these days HPC, big data, data intensive, a lot of AI stuff, >>All right. You to just, there's a lot of intersection between all of those different types of workloads they have, as you said, and you know, it all, it all depends on it all matters. And this is the reason why having the suite of capabilities that the, if you would, the members of the family is so important to our guests. >>We've talked a lot about, it's really can't think about traditional storage as a traditional storage anymore. And certainly your world's not a box. It's really a data platform, but maybe you could give us your point of view on that. >>Yeah, I think, you know, if, if we look, if we take a step back and we think about how does AWS do storage? Uh, we think along multiple dimensions, we have the dimension that Wayne's talking about, where you bring together the power of compute and storage for these managed file services that are so popular. You and I talked about, um, NetApp ONTAP. Uh, we went into some detail on that with you as well, and that's been enormously popular. And so that whole dimension of these managed file services is all about where is the customer today and how can we help them get to the cloud? But then you think about the other things that we're also imagining, and we're, re-imagining how customers want to grow those applications and scale them. And so a great example here at reinvent is let's just take the concept of archive. >>So many people, when they think about archive, they think about taking that piece of data and putting it away on tape, putting it away in a closet somewhere, never pulling it out. We don't think about archive like that archive just happens to be data that you just aren't using at the moment, but when you need it, you need it right away. And that's why we built a new storage class that we launched just yesterday, Dave, and it's called glacier instead of retrieval, it has retrieval and milliseconds, just like an Esri storage class has the same pricing of four tenths of a cent as glacier archive. >>So what's interesting at the analyst event today, Adam got a question about, and somebody was poking at him, you know, analysts can be snarky sometimes about, you know, price, declines and so forth. And he said, you know, one of the, one of the things that's not always shown up and we don't always get credit for lowering prices, but we might lower costs. And there's the archive and deep archive is an example of that. Maybe you could explain that point of view. >>Yeah. The way we look at it is that our customers, when they talk to us about the cost of storage, they talked to us about the total cost of the storage, and it's not just storing the data, it's retrieving it and using it. And so we have done an amazing amount across all the portfolio around reducing costs. We have glacier answer retrieval, which is 68% cheaper than standard infrequent access. That's a big cost reduction. We have EBS snapshots archive, which we introduced yesterday, 75% cheaper to archive a snapshot. And these are the types of that just transform the total cost. And in some cases we just eliminate costs. And so the glacier storage class, all bulk retrievals of data from the glacier storage class five to 12 hours, it's now free of charge. If you don't even have to think about, we didn't even reduce it. We just eliminated the cost of that data retrieval >>And additive to what Milan said around, uh, archiving. If you look at what we've done throughout the entire year, you know, a interesting statistic that was brought up yesterday is over the course of 2021, between our respective teams, we've launched over 105 capabilities for our customers throughout this year. And in some of them, for instance, on the file side for EFS, we launched one zone which reduced, uh, customer costs by 47%. Uh, you can now achieve on EFS, uh, cost of roughly 4.30 cents per gigabyte month on, uh, FSX, we've reduced costs up to 92%, uh, on Lustre and FSX for windows and with the introduction of ONTAP and open CFS, we continue those forward, including customers ability to compress and Dedoose against those costs. So they ended up seeing a considerable savings, even over what our standard low prices are. >>100 plus, what can I call them releases? And how can you categorize those? Are they features of eight? Do they fall into, >>Because they range for major services, like what we've launched with open ZFS to major features and really 95 of those were launched before re-invent. And so really what you have between the different teams that work in storage is you have this relentless drive to improve all the storage platforms. And we do it all across the course of the year, all across the course of the year. And in some cases, the benefit shows up at no cost at all to a customer. >>Uh, how, how did this, it seems like you're on an accelerated pace, a S3 EBS, and then like hundreds of services. I guess the question is how come it took so long and how is it accelerating now? Is it just like, there was so much focus on compute before you had to get that in place, or, but now it's just rapidly accessing, >>I I'll tell you, Dave, we took the time to count this year. And so we came to you with this number of 106, uh, that acceleration has been in place for many years. We just didn't take the time to couch. Correct. So this has been happening for years and years. Wayne and I have been with AWS for, for a long time now for 10 plus years. And really that velocity that we're talking about right now that has been happening every single year, which is where you have storage today. And I got to tell you, innovation is in our DNA and we are not going to stop now >>So 10 years. Okay. So it was really, the first five years was kind of slow. And then >>I think that's true at all. I don't think that try, you know, if you, if you look at, uh, the services that we have, we have the most complete portfolio of any cloud provider when it comes to storage and data. And so over the years, we've added to the foundation, which is S3 and the foundation, which is EBS. We've come out with a number of storage services in the, in the file space. Now you have an entire suite of persistent data stores within AWS and the teams behind those that are able to accelerate that pace. Just to give you an example, when I joined 10 years ago, AWS launched within that year, roughly a hundred and twenty, a hundred and twenty eight services or features our teams together this year have launched almost that many, just in those in, just in this space. So AWS continues to accelerate the storage teams continue to accelerate. And as my line said, we just started counting >>The thing. And if you think about those first five years, that was laying the baseline to launch us three, to launch EBS, to get that foundation in place, get lifecycle policies in place. But really, I think you're just going to see an even faster acceleration that number's going up. >>No, I that's what I'm saying. It does appear that way. And you had to build a team and put teams in place. And so that's, you know, part of the equation. But again, I come back to, it's not even, I don't even think of it as storage anymore. It's it's data. People are data lake is here to stay. You might not like the term. We always use the joke about a data ocean, but data lake is here to say 200,000 data lakes. Now we heard Adam talk about, uh, this morning. I think it was Adam. No, it was Swami. Do you want a thousand data lakes in your customer base now? And people are adding value to that data in new ways, injecting machine intelligence, you know, SageMaker is a big piece of that. Tying it in. I know a lot of customers are using glue as catalogs and which I'm like, wow, is glue a catalog or, I mean, it's just so flexible. So what are you seeing customers do with that base of data now and driving new business value? Because I've said last decade plus has been about it transformation. And now we're seeing business transformation. Maybe you could talk about that a little bit. >>Well, the base of every data lake is going to be as three yesterday has over 200 trillion objects. Now, Dave, and if you think about that, if you took every person on the planet, each of those people would have 26,000 S3 objects. It's gotten that big. And you know, if you think about the base of data with 200 trillion plus objects, really the opportunity for innovation is limitless. And you know, a great example for that is it's not just business value. It's really the new customer experiences that our customers are inventing the NFL. Uh, they, you know, they have that application called digital athlete where, you know, they started off with 10,000 labeled images or up to 20,000 labeled images now. And they're all using it to drive machine learning models that help predict and support the players on the field when they start to see things unfold that might cause injury. That is a brand new experience. And it's only possible with vast amounts of data >>Additive to when my line said, we're, we're in you talk about business transformation. We are in the age of data and we represent storage services. But what we really represent is what our customers hold one of their most valuable assets, which is their data. And that set of data is only growing. And the ability to use that data, to leverage that data for value, whether it's ML training, whether it's analytics, that's only accelerated, this is the feedback we get from our customers. This is where these features and new capabilities come from. So that's, what's really accelerating our pace >>Guys. I wish we had more time. I'd have to have you back because we're on a tight clock here, but, um, so great to see you both especially live. I hope we get to do more of this in 2022. I'm an optimist. Okay. And keep it right there, everybody. This is Dave Volante for the cube you're leader in live tech coverage, right back.

>> Viewers of our breaking analysis series know that we've been following the developments in cybersecurity for a number of years and of course, throughout the pandemic. Focusing on the permanent shifts that we see in cyber from remote work, distributed computing and technology advancements. We've reported how the adversaries are highly capable they're well-funded and they're motivated. And how they're constantly upping their game on defenders, island hopping, stealthily living off the land, planting self forming malware at various points in the digital supply chain, offering advanced ransomware as a service of the dark web to any disreputable individual with or without a high school diploma that may have access to a server and is brazen enough to steal from their company. We've also shared this chart from Optiv many, many times, it's a taxonomy of the cybersecurity landscape, and it is meant to make your eyes bleed, ask any CSO and they'll tell you they're drowning in fragmented tooling, technical debt, and their number one challenge is lack of talent. Not that their people aren't capable, they are, but CSOs just don't have enough of them. They can't hire fast enough or they can't retain qualified people with the talent war that's going on. Or they can't train people fast enough, or they just don't have the budget. Hello everyone, this is Dave Vellante and welcome to this video exclusive with Nick Schneider, president and CEO of Arctic Wolf Networks, Nick, so good to see you. Thanks for coming on the cube. >> Thanks for having me, Dave. >> That's our pleasure. So Arctic Wolf networks, let's talk about the company, the problem, you heard my little narrative upfront. What are you guys all about? >> Yeah, so at its core, we're a cybersecurity technology company. You know, it's our belief that we've really pioneered the first full scale cloud native security operations platform and at its core, what we're trying to do as a business is make security operations something that's fast, easy and economical for really a company of any size and scale to implement with really two key components, one we're agnostic to the technology and the landscape of the technology that they have already implemented within their environment, and two, we can feather into really any organization, regardless of the skill set they have from a cybersecurity standpoint in house. And really the problem that we're setting out to solve, I think you illustrated well at the beginning of the show here is that it's our belief that the cybersecurity industry in a sense has failed the end user or failed the customer by throwing, you know, a myriad of different tools at them. And it's really, you know, our mission here as a company to end cyber risk. And it's our belief that through the cloud native platform that we've bought in the cybersecurity security operations cloud that we've built, that we can deliver the outcomes that have been promised over time to these customers, which at the end of the day, is really just to be safe and have their customer and have their business protected. >> So you guys are the experts. You can kind of provide a white glove service that essentially plugs in to my business. Is that right? And how easy is that to do, what do I have to do to, to set it up? How complicated is that for me, the customer? >> Yeah, so it's, it's very straightforward. We can implement our security operations platform, you know, in as short as a week and generally speaking, you know, about a month and we plug in really to the infrastructure that the customer has in place. And for some of our customers, that's very little and for some of our customers, most of our customers, that's quite a bit of technology. And the beauty of the way that we've built the platform is that we're really agnostic to that tech. So, we can take feeds from kind of any technology that are in place, that helps to augment the platform that we've built. And then we feather in kind of the technologies that we've built within the platform, into their existing infrastructure. And at the end of the day, what we're trying to do is give the customer visibility, you know, into the tools that they have, the gaps that they might have as a result of the tools, you know, in some cases, the duplication of efforts that they have, you know, between these tools and then deliver a security outcome or a protection that maybe they haven't otherwise felt as a business. And then outside of kind of the technology platform, we add what we call our concierge security team as a layer to the deliverable that we give to the customer. And why that's important is that not all customers are created equal and with regard to the skillset that they have in house, in that that concierge security platform allows us to kind of work with a customer at any kind of, you know, point along their security journey, regardless of the in-house technology talent that they have. >> Now, so I got to ask you, our largest footprint for the cube is in the heart of Silicon Valley. We love the valley, but I also love stories of high growth companies that are outside of Silicon Valley. You guys are in the Midwest in Minnesota, it's got some Compellent DNA in there. And I remember my, so my business friends, Phil Soren, and Larry Yasmin, you know them, Phil used to tell me, Dave, this is actually an advantage for us to be in the middle of the part of the country. There's a talent war going on, which back then was a lot less than it is today, even. So how do you see that? Are there advantages to you and being in that part of the country, or does it not matter because you're so distributed around the world? >> Yeah, I mean, I would follow a similar tune to Phil, right. I, you know, obviously worked at Compellent early and, you know, historically I've worked at other Minneapolis based technology companies and the reality is there's a really strong technology ecosystem in Minneapolis. And a lot of the, of the talent, you know, is not just in sales and marketing or just on the technical side, but it's in building high growth technology companies kind of from the ground up into, you know, large scale. And now we've seen not only the fortune 500 kind of base that we have here in Minneapolis, but also a growing contingency of larger technology companies using Minneapolis as at least, you know, one of the spokes against their hub, if not the hub themselves. And clearly my pedigree in history was out of Minneapolis based tech, you know, and I've moved to other locations throughout the country, but as we started to build out, you know, Arctic Wolf and what we wanted Artic Wolf's culture to look like, and as we started to lay out the foundation for what we wanted our growth to look like, it became very clear to myself, you know, our chairman and co-founder Brian Nesmith, that Minneapolis would be a great home for us as Arctic Wolf. And then we would continue to invest in some of the locations that we have, you know, both across the country and now across the globe. >> So there are a lot of companies that are doing managed security services, but if I got it right, you guys specifically target smaller and midsize companies, is that correct? And why is that? >> Yeah, so I would say that that would be correct as of a few years ago, the dynamic has changed quite a bit. And I think it's a result of the dynamic of the market. First and foremost, we are a technology company. We have this concierge layer on top, which is really what the customers are looking for, but it's all powered by the platform. So the platform kind of allows us to do what we've done as a business, into both small organizations, which is, you know, where we probably got our start, but over the last few years, we've seen tremendous growth up market, you know, so for example, we as a business have grown, you know, over a hundred percent now for eight years in a row and now on a much larger denominator, but our upmarket business is growing at four to 500%. And I think that's a result of really two things. I think, A, customers of that size and scale have realized that cyber security and cybersecurity operations as a problem is something that's really hard to accomplish in-house regardless of your size and complexity. And then two, I think what happened over the past year, year and a half is that we saw a lot of organizations move from a centralized I.T or a centralized, you know, security function where they could all operate within an office and all operate in a centralized environment, all of a sudden becoming very disparate in their geography. And that led to a lot more interest in what we did with larger customers, because we could deploy a security operation effectively, remotely in a really short amount of time. And we could do it more effectively and economically than, than they could do on their own. And then we also solve for a component of the human aspect of what a security operation means, right. And what I mean by that is these larger organizations can take their highly skilled cybersecurity talent and focus them on the strategic initiatives within the company. Whereas a lot of the security work or risk is in kind of the day to day, right? The dieting that takes place within an organization. And that's where a lot of the breaches take place is in making sure that you're actually paying attention to, you know, the alerts that you're getting and paying attention to the telemetry and the tools that you've made investments in. And we augment that portion of a cybersecurity operation really, really well for larger organizations and for smaller organizations, we are that security operation. So it's kind of dependent on the way in which they're set up. >> Okay. So it's a mix of both well augmenting, and basically you take the whole thing and so, so your ideal customer profile, your ICP is anybody with a security problem. I mean, that's everybody, well, maybe you could describe paint a picture of your perfect customer, if you would. >> Yeah, so, and you, I know you said that somewhat jokingly, but it, but it is true. We have customers of all sizes, you know, so I, I bet our smallest customer is under 10 employees. Our largest customer is over 50,000 employees. We have customers in every vertical of the market, you know, mostly centralized in healthcare, financial organizations, manufacturing, but, you know, the largest swath of customers by industry would probably not top 10%. So, we service really any account that's looking to develop and invest in a security operation and has the support of their organization and the support of their board and their leadership teams to make that investment. And then where we, where we fall within the account is really dependent on the way in which their current operation is set up. And certainly, you know, the massive organizations that have, you know, 50 people within their cybersecurity team, and they have a hundred different tools. They're probably not the best target for us, but if they have security awareness, if they have a security as a top need or a top priority within their business, and they're looking for a way to build out a true security operation within their account, whether that be wholesale through a third-party or in part through a third-party, we're a perfect fit for all those accounts, which makes our addressable market massive. >> Yeah, so what's unique about you guys, I mean, this may be not the right analogy, but you're kind of like the easy button for cyber. I mean, there's nothing easy about cyber., I get that, but you, you do make it easy, especially for companies that don't have any cyber expertise to engage and get up to speed fast, and certainly be more protected. That's one aspect of your uniqueness. The other is, I think, is your tech stack. I'm hearing, you've got a platform. I know you're focused on network detection and fast response. Maybe you could talk a little bit about what's unique about Arctic Wolf. >> Yeah, so the platform itself is really what we founded the company on. So we spent the first few years of our organization in really building out this cloud scale, multitenant cloud, native platform, understanding that the volume of data and the amount of sophistication that we would need to deliver the security operation in the long run was going to be massive. So the platforms really kind of, you know, set on a few different founding principles. One, the platform needs to work for any organization regardless of their size, regardless of their underlying tech and regardless of the skill set within their account. And that's really important. A lot of the tools in the market today require certain things of the, of the customer. And it's our premise, regardless of the customer that we won't require anything from the customers themselves. It's up to them to tell us which portions of the experience they want to own, verse Artic Wolf owning. The second would be that we need to be able to ingest a vast amount of data, and we need to be able to make intelligent decisions with that data, in a short amount of time. And as we've built out our machine learning and our AR algorithms, what we've been able to do is leverage a tool set that allows us to ingest. I think we're up to now 1.5 approaching 2 trillion observations a week, right. Which might equate to a few hundred alerts within our SOC on a per customer basis. But we're only bringing one or two things to a customer on a weekly basis that really need attention. And that's all about the platform kind of curating, cultivating the vast amount of data that we've brought into it. And then, how do we explain and how do we sell that platform with this concierge later into the customer base is also important. And we've done that through what we call modules. So we kind of founded the company on MDR managed detection and response, but we are not a managed detection and response company. It's one of our modules. We've then added manage risk, which competes kind of in the vulnerability management space. We've added a SAS and IAS monitoring, which is really cloud security. We've added what we call log search, which is really our first foray into collaboration. And then we just recently launched a quarter ago, what we call managed security awareness training, which is, you know, training the human aspect of the company on the threats of cybersecurity. And we actually just announced another acquisition in the managed security space today with habituate, which is going to give us, you know, kind of a Hollywood style approach to content within managed awareness training. But tying all those together is very unique in the market. So generally speaking, you'll see a company focused on a specific attack surface, or a specific threat. And what we're trying to say is, look, you're not a hundred percent protected as a business, or you don't have a robust security operation unless you're bringing together all aspects of cybersecurity under one umbrella. And that's really our goal as a company. >> Okay. So you got all these different modules and you may not want to go here cause you're in the cyber business and you're, you're prudently secretive, but, but I'm interested in kind of what's underneath. I presume you're using best of breed tooling underneath, but unlike, you know, the hosting company of the past or those, you know, a big, you know, integrator who could do this, but they've got one of everything and it's sort of, kind of a mess. You're building a scalable business, but you're not, you're not developing, you know, best of breed, identity access products for the marketplace. You're I presume you're buying those in integrating them and working through whatever APIs and making it all work across your stack. Can you talk a little bit about your tech stack? >> Yeah, so the technology stack has been built from the ground up by Arctic Wolf. So certainly we're using, you know, various technologies or open source technologies from within the ecosystem, but the technology and the platform itself is Arctic Wolf. So we're not beholden to any third parties for what we deliver to the customer. And that makes us very nimble in a few areas. One, it makes us very nimble in the way that we price the solution to the customer, which for us is a very predictable model. And then two, it allows us to be nimble with customer needs as to what they want from us, both of the existing modules that we have, but also additional modules or, you know, additional solutions that we might bring to the market. So a lot of vendors that have historically kind of lived within the MDR space and certainly vendors that have lived in the managed, you know, the MSSP or MSP space, which we are certainly not, they're generally leveraging third-party technologies. They're generally buying and implementing or white labeling third-party technologies. And then they're layering kind of a services component on top. And we are not doing that. We've built the technology ourselves and don't get me wrong. That was a massive investment in both time and resources. But I think in the end, what it'll allow us to do is be very nimble with the market and most importantly, be very nimble with the customer's requirements and requests. >> Right. Okay. So let's talk about your market opportunity. I mean, the cyberspace, I mean, I got it well over a hundred billion, I don't know, maybe it's 110, 120 billion. That's kind of your tan, you may be not serving that entire market today. Although you said you started in small and mid-size, you're targeting now your enterprise, your higher end businesses growing, you talked about, I think you said a hundred percent growth, like eight quarters in a row. And so there's no shortage of opportunity for you. How do you think about your total available market? Maybe you could add some color to that. >> Yeah. Yeah. So it's been eight years of a hundred percent growth. >> Eight years, not eight quarter, I apologize. >> It's been going really well for us. And it's a reflection on the market itself and the approach we're taking. So in our view, security operations is really the opportunity to unify all these disparate markets in cybersecurity. And, when I walk into a customer account, if I had to use two words to describe how they're feeling, one would be confused, the other would be frustrated. Sometimes they're both. Sometimes they're only one, but generally speaking, one of those two words comes out of their mouth. And the reason for it is at the end of the day, they just want to be protected. They want the outcome. And all of these disparate markets are promising the same outcome, but they're just promising it on the endpoint or just on the network or just in cloud or just an IOT or just an OT, or just in fill in the blank. And it's our view that it's our opportunity as a company to really fill that void for the customer, which is to unify all of these different technologies and spaces into one security operation. And sometimes that means that we're delivering our own end point. And sometimes that means that we're leveraging an end point or an end point solution that the customer has in house. And we're ingesting that data into our platform and we're making sense of it to the end user. But when you put that market together, you know, it's a hundred, I think Gartner's recent numbers there are 150 plus billion dollar market. And in 2021, I think it's growing at, you know, 12 to 15%. And it's our view that we can service the majority of that market, you know, I think on a conservative measure, you know, 90 to a hundred billion is the, is the Tam that we're addressing. And we're now starting to go, not only scaling out from the number of products for the markets that we service, and you can see that through managed security awareness training, but also the geographies we service, the segments of the market we service, specialization within verticals. And, for us, that is the opportunity at the end here. >> I wonder if you could help us squint through some of the data you hear in the industry, some of the trends you see in the press, certainly this came up in the, in the solar winds hack. We were seeing, I mentioned upfront, the adversaries are very capable. They're able to get in, live off the land, live stealthily, they're island hopping into the supply chain. You know, oftentimes you don't know, more than often, you don't know they're there, I've heard stats like, and we look at the solar winds hack, we saw that it was, you know, 300 days or over a year that they were inside the company. And you've heard, you know, average statistics from, you know, whatever that it's hundreds of days are those, are you able to compress those? Can you talk about that a little bit in terms of where you see your customers and how you're helping them, you know, respond? >> Yeah, so at the end of the day, you know, cybersecurity, the industry is really about limiting the volume of incidents within a customer account and then limiting the impact. And what you're talking about is the impact. And the impact as these threat actors have become, you know, more sophisticated is larger as they're in the environment for a longer period of time. So the faster you can get to an attack or the faster you can detect an attack, the better off you'll be as a business. And that is the core of what we do as a company. And, and certainly, you know, managed detection response or MDR, our first offering was all about that. It's all about detecting early and responding early to a threat so that you can get anything that has gotten through your perimeter defenses out of your systems, as fast as humanly possible. And then we feathered in, you know, manage risk, which is more about the front end. So how do we make sure that we have everything configured properly? How do we make sure that we, you know, fill any holes that are in the current environment so that we don't even get to a point where we have to manage the time with which an attack has had to live within your environment? So, it's all about kind of those two things, reduce the frequency and reduce the impact. And we're, we're focused on both, both the, kind of the proactive measures, which would be more on the front end and then the reactive measures, which is what do you do and how can you act as quickly as possible within your environment to ensure that, you know, they're not getting into the crown jewels of the business. >> We've seen lately where the, the attackers have. I mean, it's really insidious, right Nick, they, they will exfiltrate, they'll get in they'll exfiltrate stealthily and they'll be ready to attack from a ransomware standpoint. And then they, you know, maybe they're hitting the bank and they're scouring to see what the Chief Information Officer is going to invest in. And they're actually making trades ahead of that. They're making more money, you know, snooping than from the ransomware. And then when the company realizes and they respond, then they get them in a headlock and say, okay, now, now that you're going to stop us from making all this money through exfiltration, we're going to hit you with ransomware. So it's just, it's a really awful situation. So my point being that, or we've said, organizations have to be stealthy in their response. Have you seen that as a trend? Am I overstating that? >> No, no. I mean, customers are, you know, good news, bad news customers are very aware of the threats in particular ransomware, data exfiltration and all the other trends in the market. And I think they become more sophisticated in the way in which they respond. And I think as a result, we've seen both changes in the way customers kind of set up their environment technologically, but we've also seen a pretty dramatic shift recently with the way in which they view insurance and the way in which, you know, carriers, view insurance, and how that plays a role in, you know, cybersecurity in their cybersecurity operation. And for a lot of customers, I think recent trends are that the carriers are struggling to, you know, make money on their cyber books. And the reason for that is because they need to make sure that the customer's environment is truly secure, or they're kind of flying blind on what their book looks like. And we've started to see that both on the end-user side, we've seen that through the carriers themselves, and that also has played an integral role in the way in which the customer views risk. And I think that dynamics changing. And I think what the result of that will be is that customers are going to be looking more and more towards how they solve this problem by alleviating risk in-house, as opposed to transferring some of that risk to an insurance carrier or a third party. And what I hope that means for customers is that they'll have the proper investment. They'll have the proper tooling, they'll have the proper operations around how to react and how to respond in the quickest possible manner, which at the end of the day, the faster you can react to an incident, the smaller the impact will be and the smaller of a financial burden it will be. And they'll do that through vendors like Arctic Wolf, you know, tools that are best of breed within their infrastructure. And then a really well thought out plan about how to respond to anything that, that you know, happens within their environment. >> Yeah. I mean, if I'm an insurance company, I give a discount to somebody who's got an alarm in their house and they use it. Maybe I'll give a discount if they're working with a company like Arctic Wolf. >> Exactly. >> What percent, do you have a census to what percent of enterprises actually have a SOC? >> Yeah, we actually did a, some homework here and there's kind of two stats that jump out. And these are through a few different surveys through very well-known organizations in the cybersecurity market. But one is that last year, which would have been, you know, 2020, about 60% of organizations said that they suffered some semblance of a breach, 60%, you know, think about how many tools and how much money these organizations are investing in protecting their businesses. And over half are suffering some semblance of a breach. When those same customers are asked whether or not they felt like they have a security operation, over 99% answered no. >> Wow. >> Right. So they have a bunch of tools they're investing a ton of money, but at the end of the day, when asked, hey, do you feel like you have an operation that can protect your business? Their answer is no. And that's really the void we're trying to. >> And you and I both know that 60%, okay. But then the other 40%, they've been hacked. They just don't know it. So, all right. Let's wrap with the sub stats on the company. I think you've raised nearly half a million, half a billion dollars to date $500 million to date. So that's, I can infer from that some pretty lofty numbers, but where are you in funding with that kind of growth? I got to believe IPO is and you and your future. What can you tell, what metrics can you share? What can you tell us about where you want to take this thing? >> Yeah, so I'll give you a few metrics on the platform and a few metrics on the company. So the platform itself, you know, we're observing over 1.5 trillion observations a week, we have 10,000 plus sensors in the field. You know, we're ingesting coming from a, you know, Compellent infrastructure guy. You know, we're in ingesting over a petabyte and a half a data week. I would have loved to have been that sales guy in the glory days, you know, but the platforms, you know, operating at massive scale, we've grown the business eight years in a row, over a hundred percent. We've talked about that. Our subscription gross margins are very software-like. We have over 2000 customers. You know, our customers are really happy with an NPS score, you know, approaching 70, you know, over a million licensed users. So we're, we're doing very, very well as a business. And as a result, we've raised money to invest in that growth, which is to the tune of about a half a billion dollars and our path here, and we've stated this publicly now is that, you know, next summer give or take a quarter is really the timeframe that we're marching towards for an IPO. If I'm being honest, given the metrics that we have as a business, we could be a publicly traded company today, especially with the way the market's operating in the valuations of some of the businesses that have gone out. There might be some, even some pressure to do so, but we want to make sure that we are ready to go from a systems and an operation standpoint to not just be, you know, a flashing the pan awesome IPO, but a company that's really kind of the backbone of cybersecurity for years to come. >> Well, obviously a hot space. What we've been covering for a couple of years now, Okta, CrowdStrike, Zscaler, we've seen what's happened in the action in the market there. I mean, what are your comps? I mean, I know, I think dark trace is getting ready to go. I don't think they've gone yet. I know Sentinel One went out. How should we think about you? You're not an Okta or I don't think well, CrowdStrike, but you know, those are pure play product companies. How should we think about you guys? >> Yeah, I mean, companies that were on a similar trajectory as us at our size, Sentinel One's a very good example. And you can kind of look across all the core business metrics on that. And clearly those will all be public here in under a year. CrowdStrike's a great example. If you go, you know, reel back the tape to when they were, you know, our size we're right in line with them Zscaler, Okta, you know, I joke with our board and investors and our CFO, that the number of companies that we benchmark ourselves against is starting to become a very small number, given you know, our growth at the scale that we're at. >> Well, that's an awesome story, Nick. We're really excited that you could make some time to come on the Cube and we want to follow your progress. Welcome you back anytime. Really appreciate your time. >> Yeah. Great. Thanks for having me, Dave, and looking forward to continuing the conversation at some point. >> Excellent and thank you for watching everybody. This is Dave Vellante for the Cube and we'll see you next time.

>>Hi, everyone. Welcome to ASML day. My name is Yasmin Joffey. I'm the director of systems engineering for ASML at HPE. Today. We're here and joined by my colleague, Don wake, who is a technical marketing engineer who will talk to us about the date and the life of an it administrator through the lens of ASML container platform. We'll be answering your questions real time. So if you have any questions, please feel free to put your questions in the chat, and we should have some time at the end for some live Q and a. Don wants to go ahead and kick us off. >>All right. Thanks a lot, Yasir. Yeah, my name is Don wake. I'm the tech marketing guy and welcome to asthma all day, day in the life of an it admin and happy St. Patrick's day. At the same time, I hope you're wearing green virtual pinch. If you're not wearing green, don't have to look that up if you don't know what I'm scouting. So we're just going to go through some quick things. Talk about discussion of modern business. It needs to kind of set the stage and go right into a demo. Um, so what is the need here that we're trying to fulfill with, uh, ASML container platform? It's, it's all rooted in analytics. Um, modern businesses are driven by data. Um, they are also application centric and the separation of applications and data has never been more important or, or the relationship between the two applications are very data hungry. >>These days, they consume data in all new ways. The applications themselves are, are virtualized, containerized, and distributed everywhere, and optimizing every decision and every application is, is become a huge problem to tackle for every enterprise. Um, so we look at, um, for example, data science, um, as one big use case here, um, and it's, it's really a team sport and I'm today wearing the hat of perhaps, you know, operations team, maybe software engineer, guy working on, you know, continuous integration, continuous development integration with source control, and I'm supporting these data scientists, data analysts. And I also have some resource control. I can decide whether or not the data science team gets a, a particular cluster of compute and storage so that they can do their work. So this is the solution that I've been given as an it admin, and that is the ASML container platform. >>And just walking through this real quick, at the top, I'm trying to, as wherever possible, not get involved in these guys' lives. So the data engineers, scientists, app developers, dev ops guys, they all have particular needs and they can access their resources and spin up clusters, or just do work with the Jupiter notebook or run spark or Kafka or any of the, you know, popular analytics platforms by just getting in points that we can provide to them web URLs and their self service. But in the backend, I can then as the it guy makes sure the Kubernetes clusters are up and running, I can assign particular access to particular roles. I can make sure the data's well protected and I can connect them. I can import clusters from public clouds. I can, uh, you know, put my like clusters on premise if I want to. >>And I can do all this through this centralized control plane. So today I'm just going to show you I'm supporting some data scientists. So one of our very own guys is actually doing a demo right now as well, called the a day in the life of the data scientist. And he's on the opposite side, not caring about all the stuff I'm doing in the backend and he's training models and registering the models and working with data, uh, inside his, you know, Jupiter notebook, running inferences, running postman scripts. And so I'm in the background here, making sure that he's got access to his cluster storage protected, make sure it's, um, you know, his training models are up, he's got service endpoints, connecting him to, um, you know, his source control and making sure he's got access to all that stuff. So he's got like a taxi ride prediction model that he's working on and he has a Jupiter notebook and models. So why don't we, um, get hands on and I'll just jump right over it. >>It was no container platform. So this is a web UI. So this is the interface into the container platform. Our centralized control plane, I'm using my active directory credentials to log in here. >>And >>When I log in, I've also been assigned a particular role, uh, with regard to how much of the resources I can access. Now, in my case, I'm a site admin you can see right up here in the upper right hand, I'm a site admin and I have access to lots and lots of resources. And the one I'm going to be focusing on today is a Kubernetes cluster. Um, so I have a cluster I can go in here and let's say, um, we have a new data scientists come on board one. I can give him his own resources so he can do whatever he wants, use some GPU's and not affect other clusters. Um, so we have all these other clusters already created here. You can see here that, um, this is a very busy, um, you know, production system. They've got some dev clusters over here. >>I see here, we have a production cluster. So he needs to produce something for data scientists to use. It has to be well protected and, and not be treated like a development resource. So under his production cluster, I decided to create a new Kubernetes cluster. And literally I just push a button, create Kubernetes cluster once I've done that. And I'll just show you some of the screens and this is a live environment. So this is, I could actually do it all my hosts are used up right now, but I wouldn't be able to go in here and give it a name, just select, um, some hosts to use as the primary master controller and some workers answer a few more questions. And then once that's done, I have now created a special, a whole nother Kubernetes cluster, um, that I could also create tenants from. >>So tenants are really Kubernetes. Uh namespaces so in addition to taking hosts and Kubernetes clusters, I can also go to that, uh, to existing clusters and now carve out a namespace from that. So I look at some of the clusters that were already created and, um, let's see, we've got, um, we've got this year is an example of a tenant that I could have created from that production cluster. And to do that here in the namespace, I just hit create and similar to how you create a cluster. You can now carve down from a given cluster and we'll say the production cluster and give it a name and a description. I can even tell it, I want this specific one to be an AI ML project, um, which really is our ML ops license. So at the end of the day, I can say, okay, I'm going to create an ML ops tenant from that cluster that I created. >>And so I've already created it here for this demo. And I'm going to just go into that Kubernetes namespace now that we also call it tenant. I mean, it's like, multitenancy the name essentially means we're carving out resources so that somebody can be isolated from another environment. First thing I typically do. Um, and at this point I could also give access to this tenant and only this tenant to my data scientist. So the first thing I typically do is I go in here and you can actually assign users right here. So right now it's just me. But if I want it to, for example, give this, um, to Terry, I could go in here and find another user and assign him from this lead, from this list, as long as he's got the proper credentials here. So you can see here, all these other users have active directory credentials, and they, uh, when we created the cluster itself, we also made sure it integrated with our active directory, so that only authorized users can get in there. >>Let's say the first thing I want to do is make sure when I do Jupiter notebook work, or when Terry does, I'm going to connect him up straight up to the get hub repository. So he gives me a link to get hub and says, Hey man, this is all of my cluster work that I've been doing. I've got my source control there. My scripts, my Python notebooks, my Jupiter notebooks. So when I create that, I simply give him, you know, he gives me his, I create a configuration. I say, okay, here's a, here's a get repo. Here's the link to it. I can use a token, here's his username. And I can now put in that token. So this is actually a private repo and using a token, you know, standard get interface. And then the cool thing after that, you can go in here and actually copy the authorization secret. >>And this gets into the Kubernetes world. Um, you know, if you want to make sure you have secure integration with things like your source control or perhaps your active directory, that's all maintained in secrets. So you can take that secret. And when I then create his notebook, I can put that secret right in here in this, uh, launch Yammel. And I say, Hey, connect this Jupiter notebook up with this secret so he can log in. And when I've launched this Jupiter notebook cluster, this is actually now, uh, within my, my, uh, Kubernetes tenant. It is now really a pod. And if I want to, I can go right into a terminal for that, uh, Kubernetes tenant and say, coop CTL, these are standard, you know, CNCF certified Kubernetes get pods. And when I do this, it'll tell me all of the active pods and within those positive containers that I'm running. >>So I'm running quite a few pods and containers here in this, uh, artificial intelligence machine learning, um, tenant. So that's kind of cool. Also, if I wanted to, I could go straight and I can download the config for Kubernetes, uh, control. Uh well, and then I can do something like this, where on my own system where I'm more comfortable, perhaps coop CTL get pods. So this is running on my laptop and I just had to do a coop CTL refresh and give the IP address and authorization, um, information in order to connect from my laptop to that end point. So from a CIC D perspective from, you know, an it admin guides, he usually wants to use tools right on his, uh, desktop. So here am I back in my web browser, I'm also here on the dashboard of this, uh, Kubernetes, um, tenant, and I can see how it's doing. >>It looks like it's kind of busy here. I can focus specifically on a pod if I want to. I happen to know this pod is my Jupiter notebook pod. So aren't, I show how, you know, I could enable my data scientists by just giving him the, uh, URL or what we call a notebook service end points or notebook end point. And just by clicking on this URL or copying it, copying, you know, it's a link, uh, and then emailing it to them and say, okay, here's your, uh, you know, here's your duper notebook. And I say, Hey, just log in with your credentials. I've already logged in. Um, and so then he's got his Jupiter notebook here and you can see that he's connected to his GitHub repo directly. He's got all of the files that he needs to run his data science project and within here, and this is really in the data science realm, data scientists realm. >>He can see that he can have access to centralized storage and he can copy the files from his GitHub repo to that centralized storage. And, you know, these, these commands, um, are kind of cool. They're a little Jupiter magic commands, and we've got some of our own that showed that attachment to the cluster. Um, but you can see here if you run these commands, they're actually looking at the shared project repository managed by the container platform. So, you know, just to show you that again, I'll go back to the container platform. And in fact, the data scientist, uh, could do the same thing. Attitude put a notebook back to platform. So here's this project repository. So this is other big point. So now putting on my storage admin hat, you know, I've got this shared, um, storage, um, volume that is managed for me by the ESMO data fabric. >>Um, in, in here, you can see that the data scientist, um, from his get repo is able to through Jupiter notebook directly, uh, copy his code. He was able to run as Jupiter notebook and create this XG boost, uh, model. So this file can then be registered in this AIML tenant. So he can go in here and register his model. So this is, you know, this is really where the data scientist guy can self-service kick off his notebooks, even get a deployment end point so that he can then inference his cluster. So here again, another URL that you could then take this and put it into like a postman rest URL and get answers. Um, but let's say he wants to, um, he's been doing all this work and I want to make sure that his, uh, data's protected, uh, how about creating a mirror. >>So if I want to create a mirror of that data, now I go back to this other, uh, and this is the, the, uh, data fabric embedded in a very special cluster called the Picasso cluster. And it's a version of the ASML data fabric that allows you to launch what was formerly called Matt bar as a Kubernetes cluster. And when you create this special cluster, every other cluster that you create is automatically, uh, gets things like that. Tenant storage. I showed you to create a shared workspace, and it's automatically managed by this, uh, data fabric. Uh, and you're even given an end point to go into the data fabric and then use all of the awesome features of ASML data fabric. So here I can just log in here. And now I'm at the, uh, data fabric, web UI to do some data protection and mirroring. >>So >>Let's go over here. Let's say I want to, uh, create a mirror of that tenant. So I forgot to note what the name of my tenant was. I'm going to go back to my tenant, the name of the volume that I'm playing with here. So in my AIML tenant, I'm going to go to my source, control my project repository that I want to protect. And I see that the ESMO data fabric has created 10 and 30 as a volume. So I'll go back to my, um, data fabric here, and I'm going to look for 10 and 30. And if I want to, I can go into tenant 30, >>Okay. >>Down here, I can look at the usage. I can look at all of the, you know, I've used very little of the, uh, allocated storage that I want, but let's, uh, you know what, let's go ahead and create a volume to mirror that one. So very simple web UI that has said create volume. I go in here and I say, I want to do a, a tenant 30 mirror. And I say, mirror the mirror volume. Um, I want to use my Picasso cluster. I want to use tenant 30. So now that's actually looking up in the data fabric, um, database there's 10 and 30 K. So it knows exactly which one I want to use. I can go in here and I can say, you know, ext HCP, tenant, 30 mirror, you know, I can give it whatever name I want and this path here. >>And that's a whole nother, uh, demo is this could be in Tokyo. This could be mirrored to all kinds of places all over the world, because this is truly a global name, split namespace, which is a huge differentiator for us in this case, I'm creating a local mirror and that can go down here and, um, I can add, uh, audit and encryptions. I can do, um, access control. I can, you know, change permissions, you know, so full service, um, interactivity here. And of course this is using the web UI, but there's also rest API interfaces as well. So that is pretty much the, the brunt of what I wanted to show you in the demo. Um, so we got hands on and I'm just going to throw this up real quick and then come back to Yasser. See if he's got any questions he has received from anybody watching, if you have any new questions. >>Yeah. We've got a few questions. Um, we can, uh, just take some time to go, hopefully answer a few. Um, so it, it does look like you can integrate or incorporate your existing get hub, uh, to be able to, um, extract, uh, shared code or repositories. Correct? >>Yeah. So we have that built in and can either be, um, get hub or bit bucket it's, you know, pretty standard interface. So just like you can go into any given, get hub and do a clone of a, of a repo, pull it into your local environment. We integrated that directly into the gooey so that you can, uh, say to your, um, AIML tenant, uh, to your Jupiter notebook. You know, here's, here's my GitHub repo. When you open up my notebook, just connect me straight up. So it saves you some, some steps there because Jupiter notebook is designed to be integrated with get hub. So we have get hub integrated in as well or bit bucket. Right. >>Um, another question around the file system, um, has the map, our file system that was carried over, been modified in any way to run on top of Kubernetes. >>So yeah, I would say that the map, our file system data fabric, what I showed here is the Kubernetes version of it. So it gives you a lot of the same features, but if you need, um, perhaps run it on bare metal, maybe you have performance, um, concerns, um, you know, you can, uh, you can also deploy it as a separate bare metal instance of data fabric, but this is just one way that you can, uh, use it integrated directly into Kubernetes depends really the needs of, of the, uh, the user and that a fabric has a lot of different capabilities, but this is, um, it has a lot of the core file system capabilities where you can do snapshots and mirrors, and it it's of course, striped across multiple, um, multiple disks and nodes. And, uh, you know, Matt BARR data fabric has been around for years. It's, uh, and it's designed for integration with these, uh, analytic type workloads. >>Great. Um, you showed us how you can manage, um, Kubernetes clusters through the ASML container platform you buy. Um, but the question is, can you, uh, control who accesses, which tenant, I guess, namespace that you created, um, and also can you restrict or, uh, inject resource limitations for each individual namespace through the UI? >>Oh yeah. So that's, that's a great question. Yes. To both of those. So, um, as a site admin, I had lots of authority to create clusters, to go into any cluster I wanted, but typically for like the data scientist example I used, I would give him, I would create a user for him. And there's a couple of ways you can create users. Um, and it's all role-based access control. So I could create a local user and have container platform authenticate him, or I can say integrate directly with, uh, active directory or LDAP, and then even including which groups he has access to. And then in the user interface for the site admin, I could say he gets access to this tenant and only this tenant. Um, another thing you asked about is his limitations. So when you create the tenant to prevent that noisy neighbor problem, you can, um, go in and create quotas. >>So I didn't show the process of actually creating a Quentin, a tenant, but integral to that, um, flow is okay, I've defined which cluster I want to use. I defined how much memory I want to use. So there's a quota right there. You could say, Hey, how many CPU's am I taking from this pool? And that's one of the cool things about the platform is that it abstracts all that away. You don't have to really know exactly which host, um, you know, you can create the cluster and select specific hosts, but once you've created the cluster, it's not just a big pool of resources. So you can say Bob, over here, um, he's only going to get 50 of the a hundred CPU's available and he's only going to get X amount of gigabytes of memory. And he's only going to get this much storage that he can consume. So you can then safely hand off something and know they're not going to take all the resources, especially the GPU's where those will be expensive. And you want to make sure that one person doesn't hog all the resources. And so that absolutely quotas are built in there. >>Fantastic. Well, we, I think we are out of time. Um, we have, uh, a list of other questions that we will absolutely reach out and, um, get all your questions answered, uh, for those of you who ask questions in the chat. Um, Don, thank you very much. Thanks everyone else for joining Don, will this recording be made available for those who couldn't make it today? >>I believe so. Honestly, I'm not sure what the process is, but, um, yeah, it's being recorded so they must've done that for a reason. >>Fantastic. Well, Don, thank you very much for your time and thank everyone else for joining. Thank you.

>>Yeah, yeah. >>Welcome back to our final session in cultivating a data fluent culture track earlier today, we heard from experts like Valerie from the Data Lodge who shared best practices that you can apply to build that data flew into culture in your organization and tips on how to become the next analyst of the future from Yasmin at Comcast and Steve at all Terex. Then we heard from a captivating session with Cindy Hausen and Ruhollah Benjamin, professor at Princeton, on how now is our chance to change the patterns of injustice that we see have been woven into the fabric of society. If you do not have a chance to see today's content, I highly recommend that you check it out on demand. There's a lot of great information that you could start applying today. Now I'm excited to introduce our next session, which will take a look at how the democratization of data is powering digital transformation in the insurance industry. We have two prestigious guests joining us today. First Jim Bramblett, managing director of North America insurance practice, lead at its center. Throughout Jim's career, he's been focused on large scale transformation from large to midsize insurance carriers. His direct experience with clients has traditionally been in the intersection of technology, platform transformation and operating remodel redesign. We also have Michael cast Onus, executive VP and chief operating officer at DNA. He's responsible for all information technology, analytics and operating functions across the organization. Michael has led major initiatives to launch digital programs and incorporating modern AP I architectures ER, which was primarily deployed in the cloud. Jim, please take it away. >>Great. Thanks, Paula E thought we'd cover a few things today around around data. This is some of the trends we see in data within the insurance sector. And then I'll hand it over to Michael Teoh, take you through his story. You know, I think at the macro level, as we think about data and we think about data in the context of the insurance sector, it's interesting because the entire history of the insurance sector has been built on data and yet, at the same time, the entire future of it relies on that same data or similar similar themes for data. But but different. Right? So we think about the history, what has existed in an insurance companies. Four walls was often very enough, very enough to compete, right? So if you think about your customer data, claims, data, CRM, data, digital data, all all the data that was yeah, contained within the four walls of your company was enough to compete on. And you're able to do that for hundreds of years. But as we we think about now as we think about the future and the ability to kind of compete on data, this data comes from many more places just than inside your four walls. It comes from every device, every human, every vehicle, every property, every every digital interaction. Um in upon this data is what we believe insurers need to pivot to. To compete right. They need to be able to consume this data at scale. They need to be able to turn through this data to drive analytics, and they serve up insights based on those analytics really at the desktop of insurance professionals. And by the way, that has to be in the natural transition of national transaction. Of that employees work day. So an underwriter at a desktop claim him on the desktop, the sales associate of desktop. Those insights need to be served up at that point in time when most relevant. And you know. So if we think about how insurance companies are leveraging data, we see this really on kind of three horizons and starting from the left hand side of the page here, this is really brilliant basics. So how my leveraging core core data and core applied intelligence to monetize your existing strategy? And I think this brilliant based, brilliant basics concept is where most of most of my clients, at least within insurance are are today. You know, how are we leveraging data in the most effective way and putting it in the hands of business decision makers to make decisions largely through reporting and some applied intelligence? Um, Horizon two. We see, you know, definitely other industries blazing a trail here, and this is really about How do we integrate ecosystems and partners Now? I think within insurance, you know, we've had data providers forever, right? Whether it's NPR data, credit data risk data, you know, data aggregators and data providers have been a critical part of the insurance sector for for decades. I think what's different about this this ecosystem and partnership model is that it's much more Oneto one and it's much more, you know, kind of. How do we integrate more tightly and how do we become more embedded in each other's transactions? I think that we see some emergence of this, um, in insurance with automotive manufacturers with building management systems. But I think in the grand scheme of things, this is really very, very nascent for us as a sector. And I think the third horizon is is, you know, how do we fundamentally think about data differently to drive new business models? And I, you know, I don't know that we haven't ensure here in North America that's really doing this at any sort of scale. We certainly see pilots and proofs of concepts. We see some carriers in Europe farther down this path, but it's really it's really very new for us. A Z Think about these three horizons for insurance. So you know what's what's behind all this and what's behind. You know, the next powering of digital transformation and and we think at the end of the exercise, its data data will be the next engine that powers digital transformation. So in this exhibit, you know we see the three horizons across the top. You know, data is activated and activating digital transformation. And this, you know, this purple 3rd, 3rd road here is we think some of the foundational building blocks required to kind of get this right. But I think what's most important about about this this purple third bar here is the far right box, which is business adoption. Because you can build this infrastructure, you can have. You know, this great scalable cloud capability. Um, you can create a bunch of applications and intelligence, but unless it's adopted by the business, unless it's democratized, unless those insights and decisions air served up in the natural course of business, you're gonna have trouble really driving value. So that way, I think this is a really interesting time for data. We think this is kind of the next horizon to power the next age of digital transformation for insurance companies. With that brief prelude, I am, I'm honored. Thio, turn it over to Michael Stone Is the Cielo at CNN Insurance? >>Thanks, Jim, for that intro and very exciting Thio be here is part of part of beyond when I think a digital transformation within the context of insurance, actually look at it through the lens of competing in an era of near perfect information. So in order to be able to deliver all of the potential value that we talked about with regard to data and changing ecosystem and changing demands, the question becomes, How do you actually harness the information that's available to everybody to fundamentally change the business? So if you'll indulge me a bit here, let me tell you just a little bit more for those that don't know about insurance, what it really is. And I use a very long run on sentence to do that. It's a business model where capital is placed against risk in the form of products and associated services sold the customers through channels two companies to generate a return. Now, this sounds like a lot of other businesses in across multiple industries that were there watching today. But the difference within insurance is that every major word in that long run on sentence is changing sources of capital that we could draw on to be able to underwrite risk of going away. The nature of risk itself is changing from the perspective of policies that live six months to a year, the policies that could last six minutes. The products that we're creating are changing every day for our ability to actually put a satellite up in the air or ensure against the next pandemic. Our customers are not just companies or individuals, but they could be governments completely different entities than we would have been in sharing in the past and channels were changing. We sell direct, we sell through brokers and products are actually being embedded in other products. So you may buy something and not even know that insurance is a part of it. And what's most interesting here is the last word which is around return In the old world. Insurance was a cash flow business in which we could bring the premium in and get a level of interest income and being able to use that money to be able thio buffer the underwriting results that we would have. But those returns or dramatically reduced because of the interest income scenario, So we have to generate a higher rate of return. So what do we need to do? Is an insurance company in through this digital transformation to be able to get there? Well, fundamentally, we need to rethink how we're using information, and this is where thought spot and the cloud coming for us. We have two basic problems that we're looking to solve with information. The first one is information veracity. Do we believe it? When we get it? Can we actually trust it? Do we know what it means when we say that this is a policy in force or this is a new customer where this is the amount of attention or rate that we're going to get? Do we actually believe in that piece of data? The second is information velocity. Can we get it fast enough to be able to capitalize upon it? So in other words, we're We're working in a situation where the feedback loop is closing quickly and it's operating at a speed that we've never worked in before. So if we can't solve veracity and velocity, then we're never going to be able to get to where we need to go. So when we think of something like hot spot, what do we use it for? We use it to be able to put it in the hands of our business years so that they could ask the key questions about how the business is running. How much profit of my generating this month? What brokers do I need to talk? Thio. What is my rate retention? Look like what? The trends that I'm seeing. And we're using that mechanism not just to present nice visualizations, but to enable that really quick, dynamic question and answer and social, socially enabled search, which completely puts us in a different position of being able to respond to the market conditions. In addition, we're using it for pattern recognition. Were using it for artificial intelligence. We're gonna be capitalizing on the social aspect of of search that's that's enabled through thought spot and also connecting it into our advanced machine learning models and other capabilities that we currently have. But without it solving the two fundamental problems of veracity and velocity, we would be handicapped. So let me give you some advice about if I were in your position and you don't need to be in sleepy old industry like insurance to be able to do this, I'll leave you with three things. The first one is picking water holes so What are the things that you really want to be good at? What are the pieces of information that you really need to know more about? I mean, in insurance, its customers, it's businesses, locations, it's behavior. There are only a few water also really understand and pick those water holes that you're going to be really good at. The second is stand on the shoulders of giants. You know, in the world of technology, there's often a philosophy that says, Well, I can build it something better than somebody else create if I have it in house. But I'm happy to stand on the shoulders of giants like Thought Spot and Google and others to be able to create this capability because guess what? They're gonna out innovate any of the internal shops all day and every day. So don't be afraid. Thio. Stand side by side on the shoulders of giants as part of your journey. Unless you've got to build these organizations not just the technology for rapid experimentation and learning, because guess what? The moment you deliver insight, it begs another question, which also could change the business process, which could change the business model and If your organization the broader organization of business technology, analytics, customer service operations, etcetera is not built in a way that could be dynamic and flexible based on where the market is or is going, then you're gonna miss out on the opportunity. So again, I'm proud to be part of the fast black community. Really love the technology. And if if you look too, have the same kind of issues with your given industry about how you can actually speed up decision making, deliver insights and deliver this kind of search and recommended to use it. And with that, let's go to some questions. >>Awesome. Thank you so much, Michael and Jim for that in depth perspective and those tangible takeaways for our audience. We have a few minutes left and would love to ask a few questions. So here's the first one for Michael Michael. What are some of the most important things that you know now that you didn't know before you started this process? I think one of >>the things that's a great question. I think one of the things that really struck me is that, you know, traditional thinking would be very use case centric or pain point centric Show me, uh, this particular model or a particular question you want me to answer that can build your own analytics to do that or show me a deficiency in the system and I can go and develop a quick head that will do well, then you know, wallpaper over that particular issue. But what we've really learned is the foundation matters. So when we think about building things is building the things that are below the waterline, the pipes and plumbing about how you move data around how the engines work and how it all connects together gives you the above the waterline features that you could deliver to. You know, your employees into your customers much faster chasing use cases across the top above the waterline and ignoring what's below the water line to me. Is it really, uh, easy recipe too quick? Get your way to nothing. So again, focus on the foundation bill below the water line and then iterated above the water line that z what the lessons we've learned. It has been very effective for us. >>I think that's a very great advice for all those watching today on. But Here's one for Jim. Jim. What skills would you say are required for teams to truly adopt this digital transformation process? >>Yeah, well, I think that's a really good question, and I think I'd start with it's It's never one. Well, our experience has shown us number a one person show, right? So So we think to kind of drive some of the value that that that Michael spoke about. We really looked across disciplinary teams, which is a an amalgamation of skills and and team members, right? So if you think about the data science skills required, just kinda under under understand how toe toe work with data and drive insights, Sometimes that's high end analytic skills. Um, where you gonna find value? So some value architectural skills Thio really articulate, you know, Is this gonna move the needle for my business? I think there's a couple of critical critical components of this team. One is, you know, the operation. Whatever. That operation maybe has to be embedded, right, because they designed this is gonna look at a piece of data that seems interesting in the business Leader is going to say that that actually means nothing to me in my operation. So and then I think the last the last type of skill would be would be a data translator. Um, sitting between sometimes the technology in the business so that this amalgamation of skills is important. You know, something that Michael talked about briefly that I think is critical is You know, once you deliver insight, it leads to 10 more questions. So just in a intellectual curiosity and an understanding of, you know, if I find something here, here, the implications downstream from my business are really important. So in an environment of experimenting and learning thes thes cross discipline teams, we have found to be most effective. And I think we thought spot, you know, the platform is wired to support that type of analysis and wired to support that type of teaming. >>Definitely. I think that's though there's some really great skills. That's for people to keep in mind while they are going through this process. Okay, Michael, we have another question for you. What are some of the key changes you've had to make in your environment to make this digital transformation happen? >>That's a great question. I think if you look at our environment. We've got a mixture of, you know, space agent Stone age. We've got old legacy systems. We have all sorts of different storage. We have, you know, smatterings of things that were in cloud. The first thing that we needed to do was make a strong commitment to the cloud. So Google is our partner for for the cloud platform on unabashedly. The second thing that we needed to dio was really rethink the interplay between analytics systems in operational systems. So traditionally, you've got a large data warehouses that sit out over here that, you know, we've got some kind of extract and low that occurs, and we've got transactional operational systems that run the business, and we're thinking about them very differently from the perspective of bringing them together. How Doe I actually take advantage of data emotion that's in the cloud. So then I can actually serve up analytics, and I can also change business process as it's happening for the people that are transacting business. And in the meantime, I can also serve the multiple masters of total cost and consumption. So again, I didn't applications are two ships that pass in the night and never be in the world of Sienna. When you look at them is very much interrelated, especially as we want to get our analytics right. We want to get our A i m all right, and we want to get operational systems right By capturing that dated motion force across that architecture er that was an important point. Commit to the cloud, rethink the way we think analytics systems, work and operational systems work and then move them in tandem, as opposed to doing one without the other one in the vacuum. >>That's that's great advice, Michael. I think it's very important those key elements you just hit one question that we have final question we have for Jim. Jim, how do you see your client sustain the benefits that they've gained through this process? >>Yeah, it's a really good question. Um, you know, I think about some of the major themes around around beyond right, data fluency is one of them, right? And as I think about fluency, you only attain fluency through using the language every single day. They were day, week, over week, month over month. So you know, I think that applies to this. This problem too. You know, we see a lot of clients have to change probably two things at the same time. Number one is mindset, and number two is is structure. So if you want to turn these data projects from projects into processes, right, so so move away from spinning up teams, getting getting results and winding down. You wanna move away from that Teoh process, which is this is just the way working for these teams. Um, you have to change the mindset and often times you have to marry that with orb structure change. So So I'm gonna spin up these teams, but this team is going to deliver a set of insights on day. Then we're gonna be continuous improvement teams that that persist over time. So I think this shifting from project teams to persistent teams coupled with mindset coupled with with or structure changed, you know, a lot of times has to be in place for a period of time to get to get the fluency and achieve the fluency that that most organizations need. >>Thanks, Jim, for that well thought out answer. It really goes to show that the transformation process really varies when it comes to organizations, but I think this is a great way to close out today's track. I like to think Jim, Michael, as well as all the experts that you heard earlier today for sharing. There's best practice as to how you all can start transforming your organization's by building a data fluent culture, Um, and really empowering your employees to understand what data means and how to take actions with it. As we wrap up and get ready for the next session, I'd like to leave you all with just a couple of things. Number one if you miss anything or would like to watch any of the other tracks. Don't worry. We have everything available after this event on demand number two. If you want to ask more questions from the experts that you heard earlier today, you have a chance to do so. At the Meet The Experts Roundtable, make sure to attend the one for track four in cultivating a data fluent culture. Now, as we get ready for the product roadmap, go take a sip of water. This is something you do not want to miss. If you love what you heard yesterday, you're gonna like what you hear today. I hear there's some type of Indiana Jones theme to it all, so I won't say anything else, but I'll see you there.

>>Yeah, yeah. >>Hello and welcome back. I hope you're ready for our next session. Become the analyst of the future. We'll hear the customer's perspective about their increasingly strategic role and the potential career growth that comes with it. Joining us today are Nate Weaver, director of product marketing at Thought Spot. Yasmin Natasa, senior director of national sales strategy and insights over at Comcast and Steve Would Ledge VP of customer and partner initiatives. Oughta Terex. We're so happy to have you all here today. I'll hand things over to meet to kick things off. >>Yeah, thanks, Paula. I'd like to start with a personal story that might resonate with our audience, says an analyst. Early in my career, I was the intermediary between the business and what we called I t right. Basically database administrators. I was responsible for understanding business logic gathering requirements, Ringling data building dashboards for executives and, in my case, 100 plus sales reps. Every request that came through the business intelligence team. We owned everything, right? Indexing databases for speed, S s. I s packages for data transfer maintaining Department of Data Lakes all out cubes, etcetera. We were busy. Now we were constantly building or updating something. The worst part is an analyst, If you ask the business, every request took too long. It was slow. Well, from an analyst perspective, it was slow because it's a complex process with many moving parts. So as an analyst fresh out of grad school often felt overeducated, sometimes underappreciated, like a report writer, we were constantly overwhelmed by never ending ad hoc request, even though we had hundreds of reports and robust dashboards that would answer 90% of the questions. If the end user had an analytical foundation like I did right, if they knew where to look and how to navigate dimensions and hierarchies, etcetera. So anyway, point is, we had to build everything through this complex and slow, um, process. So for the first decade of my career, I had this gut feeling there had to be a better way, and today we're going to talk about how thought SWAT and all tricks are empowering the analysts of the future by reimagining the entire data pipeline. This paradigm shift allows businesses and data teams thio, connect, transform, model and, most importantly, automate what used to be this terribly complex data analysis process. With that, I'd like to hand it over to Steve to describe the all tricks analytic process automation platform and how they help analysts create more robust data sets that enable non technical end users toe ask and answer their own questions, but also more sophisticated business questions. Using Search and AI Analytics in Thoughts Fire Steve over to you. >>Thanks for that really relevant example. Nate and Hi, everyone. I'm Steve. Will it have been in the market for about 20 years, and then Data Analytics and I can completely I can completely appreciate what they was talking about. And what I think is unique about all tricks is how we not only bring people to the data for a self service environment, but I think what's often missed in analytics is the automation and figure out. What is the business process that needs to be repeated and connecting the dots between the date of the process and the people To speed up those insights, uh, to not only give people to self service, access to information, to do data prep and blending, but more advanced analytics, and then driving that into the business in terms of outcomes. And I'll show you what that looks like when you talk about the analytic process automation platform on the next slide. What we've done is we've created this end to end workflow where data is on the left, outcomes around the right and within the ultras environment, we unify data prep and blend analytics, data science and process automation. In this continuous process, so is analysis or an end user. I can go ahead and grab whatever data is made available to me by i t. You have got 80 plus different inputs and a p i s that we connect to. You have this drag and drop environment where you conjoined the data together, apply filters, do some descriptive analytics, even do things like grab text documents and do sentiments analysis through that with text, mining and natural language processing. As people get more used to the platform and want to do more advanced analytics and process automation, we also have things like assisted machine learning and predictive analytics out of the box directly within it as well and typically within organizations. These would be different departments and different tools doing this and we try to bring all this together in one system. So there's 260 different automation building blocks again and drag a drop environment. And then those outcomes could be published into a place where thoughts about visualizes that makes it accessible to the business users to do additional search based B I and analytics directly from their browser. And it's not just the insights that you would get from thought spot, but a lot of automation is also driving unattended, unattended or automated actions within operational systems. If you take an example of one of our customers that's in the telecommunications world, they drive customer insights around likeliness to turn or next best offers, and they deliver that within a salesforce applications. So when you walk into a retail store for your cell phone provider, they will know more about you in terms of what services you might be interested in. And if you're not happy at the time and things like that. So it's about how do we connect all those components within the business process? And what this looks like is on this screen and I won't go through in detail, but it's ah, dragon drop environment, where everything from the input data, whether it's cloud on Prem or even a local file that you might have for a spreadsheet. Uh, I t wants to have this environment where it's governed, and there's sort of components that you're allowed to have access to so that you could do that data crept and blending and not just data within your organization, but also then being able to blend in third party demographic data or firm a graphic information from different third party data providers that we have joined that data together and then do more advanced analytics on it. So you could have a predictive score or something like that being applied and blending that with other information about your customer and then sharing those insights through thought spots and more and more users throughout the organization. And bring that to life. In addition to you, as we know, is gonna talk about her experience of Comcast. Given the world that we're in right now, uh, hospital care and the ability to have enough staff and and take care of all of our people is a really important thing. So one of our customers, a large healthcare network in the South was using all tricks to give not only analyst with the organization, but even nurses were being trained on how to use all tricks and do things like improve observation. Wait time eso that when you come in, the nurse was actually using all tricks to look at the different time stamps out of ethic and create a process for the understands. What are all the causes for weight in three observation room and identify outliers of people that are trying to come in for a certain type of care that may wait much longer than on average. And they're actually able to reduce their wait time by 22%. And the outliers were reduced by about 50% because they did a better job of staffing. And overall staffing is a big issue if you can imagine trying to have a predictive idea of how many staff you need in the different medical facilities around the network, they were bringing in data around the attrition of healthcare workers, the volume of patient load, the scheduled holidays that people have and being able to predict 4 to 6 months out. What are the staff that they need to prepare toe have on on site and ready so they could take care of the patients as they're coming in. In this case, they used in our module within all tricks to do that, planning to give HR and finance a view of what's required, and they could do a drop, a drop down by department and understand between physicians, nurses and different facilities. What is the predicted need in terms of staffing within that organization? So you go to the next slide done, you know, aside from technology, the number one thing for the analysts of the future is being able to focus on higher value business initiatives. So it's not just giving those analysts the ability to do this self service dragon drop data prep and blend and analytics, but also what are the the common problems that we've solved as a community? We have 150,000 people in the alter its community. We've been in business for over 23 years, so you could go toe this gallery and not only get things like the thought spot tools that we have to connect so you can do direct query through T Q l and pushed it into thought spot in Falcon memory and other things. But look at things like the example here is the healthcare District, where we have some of our third party partners that have built out templates and solutions around predictive staffing and tracking the complicating conditions around Cove. It as an example on different KPs that you might have in healthcare, environment and retail, you know, over 150 different solution templates, tens of thousands of different posts across different industries, custom return and other problems that we can solve, and bringing that to the community that help up level, that collective knowledge, that we have this business analyst to solve business problems and not just move data, and then finally, you know, as part of that community, part of my role in all tricks is not only working with partners like thought spot, but I also share our C suite advisory board, which we just happen to have this morning, as a matter of fact, and the number one thing we heard and discussed at that customer advisory board is a round up Skilling, particularly in this virtual world where you can't do in classroom learning how do we game if I and give additional skills to our staff so that they can digitize and automate more and more analytic processes in their organization? I won't go through all this, but we do have learning paths for both beginners. A swell as advanced people that want to get more into the data science world. And we've also given back to our community. There's an initiative called Adapt where we've essentially donated 125 hours of free training free access to our products. Within the first two weeks, we've had over 9000 people participate in that get certified across 100 different companies and then get jobs in this new world where they've got additional skills now around analytics. So I encourage you to check that out, learn what all tricks could do for you in up Skilling your journey becoming that analysts of the future And thanks for having me today thoughts fun looking forward to the rest of conversation with the Azmin. >>Yeah, thanks. I'm gonna jump in real quick here because you just mentioned something that again as an analyst, is incredibly important. That's, you know, empowering Mia's an analyst to answer those more sophisticated business questions. There's a few things that you touched on that would be my personal top three. Right? Is an analyst. You talked about data cleansing because everyone has data quality problems enhancing the data sets. I came from a supply chain analytics background. So things like using Dun and Bradstreet in your examples at risk profiles to my supplier data and, of course, predictive analytics, like creating a forecast to estimate future demand. These are things that I think is an analyst. I could truly provide additional value. I'd like to show you a quick example, if I may, of the type of ad hoc request that I would often get from the business. And it's fairly complex, but with a combination of all tricks and thought spots very easy to answer. Crest. The request would look something like this. I'd like to see my spend this year versus last year to date. Uh, maybe look at that monthly for Onley, my area of responsibility. But I only want to focus on my top five suppliers from this year, right? And that's like an end statement. I saw that in one of your slides and so in thoughts about that's answering or asking a simple question, you're getting the answer in maybe 30 seconds. And that's because behind the scenes, the last part is answering those complexities for you. And if I were to have to write this out in sequel is an analyst, it could take me upwards, maybe oven our because I've got to get into the right environment in the database and think about the filters and the time stamps, and there's a lot going on. So again, thoughts about removes that curiosity tax, which when becoming the analysts of the future again, if I don't have to focus on the small details that allows me to focus on higher value business initiatives, right. And I want to empower the business users to ask and answer their own questions. That does come with up Skilling, the business users as well, by improving data fluency through education and to expand on this idea. I wanna invite Yasmin from Comcast to kind of tell her personal story. A zit relates to analysts of the future inside Comcast. >>Well, thank you for having me. It's such a pleasure. And Steve, thank you so much for starting and setting the groundwork for this amazing conversation. You hit the nail on the head. I mean, data is a Trojan horse off analytics, and our ability to generate that inside is eyes busy is anchored on how well we can understand the data on get the data clean It and tools, like all tricks, are definitely at the forefront off ability to accelerate the I'll speak to incite, which is what hot spot brings to the table. Eso My story with Thought spot started about a year and a half ago as I'm part of the Sales Analytics team that Comcast all group is officially named, uh, compensation strategy and insight. We are part of the Consumer Service, uh, Consumer Service expected Consumer Service group in the cell of Residential Sales Organization, and we were created to provide insight to the Comcast sells channel leaders Thio make sure that they have database insight to drive sales performance, increased revenue. We When we started the function, we were really doing a lot of data wrangling, right? It wasn't just a self performance. It waas understanding who are customers were pulling a data on productivity. Uh, so we were going into HR systems are really going doing the E T l process, but manually sometimes. And we took a pause at one point because we realized that we're spending a good 70% of our time just doing that and maybe 5% of our time storytelling. Now our strength was the storytelling. And so you see how that balance wasn't really there. And eso Jim, my leader pause. It pulls the challenge of Is there a better way of doing this on DSO? We scan the industry, and that's how we came across that spot. And the first time I saw the tool, I fell in love. There's not a way for me to describe it. I fell in love because I love the I love the the innovation that it brought in terms of removing the middleman off, having to create all these layers between the data and me. I want to touch the data. I want to feel it, and I want to ask questions directly to it, and that's what that's what does for us. So when we launched when we launch thoughts about for our team, we immediately saw the difference in our ability to provide our stakeholders with better answers faster. And the combination of the two makes us actually quite dangerous right on. But it has been It has been a great great journey altogether are inter plantation was done on the cloud because at the time, uh, the the we had access to AWS account and I love to be at the edge of technology, So I figured it would be a good excuse for me to learn more about cloud technology on its been things. Video has been a great journey. Um, my, my background, uh, into analytics comes from science. And so, for me, uh, you know, we are really just stretching the surface off. What is possible in terms off the how well remind data to answer business questions on Do you know, tools like thought spot in combination with technologies. Like all trades, eyes really are really the way to go about it. And the up skilling, um the up skilling off the analysts that comes with it is really, really, really exciting because people who love data want to be able to, um want to be efficient about how they spend time with data. Andi and that's what? That's what I spend a lot of my Korea I'd Comcast and before Comcast doing so It gives me a lot of ah, a lot of pleasure to, um to bring that to my organization and to walk with colleagues outside off. We didn't Comcast to do so The way we the way we use stops, that's what we did not seem is varies. One of the things that I'm really excited about is integrating it with all the tools that we have in our analytics portfolio, and and I think about it as the over the top strategy. Right. Uh, group, like many other groups, wouldn't Comcast and with our organizations also used to be I tools. And it is not, um, you choose on a mutually exclusive strategies, right? Eso In our world, we build decision making, uh, decision making tools from the analysis that we generate. When we have the read out with the cells channel leaders, we we talk about the insight, and invariably there's some components off those insight that they want to see on a regular basis. That becomes a reporting activity. We're not in a reporting team. We partner with reporting team for them to think that input and and and put it on and create a regular cadence for it. Uh, the over the top strategy for me is, um, are working with the reporting team to then embed the link to talk spot within the report so that the questions that can be answered by the reports left dashboard are answered within the dashboard. But we make sure that we replicate the data source that feeds that report into thought spot so that the additional questions can then be insert in that spot. It and it works really well because it creates a great collaboration with our partners on the on the reporting side of the house on it also helps of our end the end users do the cell service in along the analytic spectrum, right? You go to the report when you can, when all you need is dropped down the filters and when the questions become more sophisticated, you still have a platform in the place to go to ask the questions directly and do things that are a bit funk here, like, you know, use for like you because you don't know what you're looking for. But you know that there's there's something there to find. >>Yeah, so yeah, I mean, a quick question. Our think would be on this year's analytics meet Cloud open for everyone and your experience. What does that mean to you? Including in the context of the thought spot community inside Comcast? >>Oh yes, it's the Comcast community. The passport commedia Comcast is very vibrant. My peers are actually our colleagues, who I have in my analytics village prior to us getting on board with hot spot and has been a great experience for us. So have thoughts, but as an additional kind of topic Thio to connect on. So my team was the second at Comcast to implement that spot. The first waas, the product team led by Skylar, and he did his instance on Prem. Um, he the way that he brings his data is, is through a sequel server. When I came what, as I mentioned earlier, I went on the cloud because, as I mentioned earlier, I like to be on the edge of technology and at the time thought spot was moving towards towards the cloud. So I wanted to be part of that wave. There's Ah, mobile team has a new instance that is on the cloud thing. The of the compliance team uses all tricks, right? And the S O that that community to me is really how the intellectual capital that we're building, uh, using thought spot is really, really growing on by what happens to me. And the power of being on the cloud is that if we are all using the same tool, right and we are all kind of bringing our data together, um, we are collaborating in ways that make the answer to the business questions that the C suite is asking much better, much richer. They don't always come to us at the same time, right? Each function has his own analytics group, Andi. Sometimes if we are not careful, we're working silo. But the community allows us to know about what each other are working on. And the fact that we're using the same tool creates a common language that translates into opportunities for collaboration, which will translate into, as I mentioned earlier, richer better on what comprehensive answers to the business. So analyst Nick the cloud means better, better business and better business answers and and better experiences for customers at the end of the day, so I'm all for it. >>That's great. Yeah. Comcast is obviously a very large enterprise. Lots of data sources, lots of data movement. It's cool to hear that you have a bit of a hybrid architecture, er thought spot both on premise. Stand in the cloud and you did bring up one other thing that I think is an important question for Steve. Most people may just think of all tricks as an E T l tool, but I know customers like Comcast use it for way more than just that. Can you expand upon the differences between what people think of a detail tool and what all tricks is today? >>Yeah, I think of E. T L tools as sort of production class source to target mapping with transformations and data pipelines that air typically built by I t. To service, you know, major areas within the business, and that's super valuable. One doesn't go away, and in all tricks can provide some of that. But really, it's about the end user empowerment. So going back to some of guys means examples where you know there may be some new information that you receive from a third party or even a spreadsheet that you develop something on. You wanna start to play around that information so you can think of all the tricks as a data lab or data science workbench, in fact, that you know, we're in the Gartner Magic Quadrant for data science and machine learning platforms. Because a lot of that innovation is gonna happen at the individual level we're trying to solve. And over time, you might want to take that learning and then have I t production eyes it within another system. But you know, there's this trade off between the agility that end users need and sort of the governance that I t needs to bring. So we work best in a environment where you have that in user autonomy. You could do E tail workloads, data prep and Glenn bringing your own information on then work with i t. To get that into the right server based environment to scale out in the thought spot and other applications that you develop new insights for the business. So I see it is ah, two sides of the same coin. In many ways, a home. And >>with that we're gonna hand it back over to a Paula. >>Thank you, Nate, Yasmin and Steve for the insights into the journey of the analyst of the future. Next up in a couple minutes, is our third session of today with Ruhollah Benjamin, professor of African American Studies at Princeton University, and our chief data strategy officer, Cindy House, in do a couple of jumping jacks or grab a glass of water and don't miss out on the next important discussion about diversity and data.