>>Okay. We're back with Ari Basu, who is Cisco's resident philosopher, who also holds a master's in computer science. We're gonna have to unpack that a little bit and Najati chair he who's technical lead at Cisco. Welcome guys. Thanks for coming on the cube. >>Happy to be here. Thanks a >>Lot. All right, let's get into it. We want you to explain how Cisco validated the SBRI technology and the proof points that, that you have, that it actually works as advertised. So first Outre tell first, tell us about Cisco tech. What does Cisco tech do? >>So T is otherwise it's an acronym for technical assistance center is Cisco's support arm, the support organization, and, you know, the risk of sounding like I'm spotting a corporate line. The, the easiest way to summarize what tag does is provide world class support to Cisco customers. What that means is we have about 8,000 engineers worldwide, and any of our Cisco customers can either go on our web portal or call us to open a support request. And we get about 2.2 million of these support requests a year. And what these support requests are, are essentially the customer will describe something that they need done some networking goal that they have, that they wanna accomplish. And then it's tax job to make sure that that goal does get accomplished. Now, it could be something like they're having trouble with an existing network solution, and it's not working as expected, or it could be that they're integrating with a new solution. >>They're, you know, upgrading devices, maybe there's a hardware failure, anything really to do with networking support and, you know, the customer's network goals. If they open up a case for request for help, then tax job is to, is to respond and make sure the customer's, you know, questions and requirements are met about 44% of these support requests are usually trivial and, you know, can be solved within a call or within a day. But the rest of tax cases really involve getting into the network device, looking at logs. It's a very technical role. It's a very technical job. You're look you're, you need to be conversing with network solutions, their designs protocols, et cetera. >>Wow. So 56% non-trivial. And so I would imagine you spend a lot of time digging through through logs. Is that, is that true? Can you quantify that like, you know, every month, how much time you spend digging through logs and is that a pain point? >>Yeah, it's interesting. You asked that because when we started this on this journey to augment our support engineers workflow with zebra solution, one of the things that we did was we went out and asked our engineers what their experience was like doing log analysis. And the anecdotal evidence was that on average, an engineer will spend three out of their eight hours reviewing logs, either online or offline. So what that means is either with the customer live on a WebEx, they're going to be going over logs, network, state information, et cetera, or they're gonna do it offline, where the customer sends them the logs, it's attached to a, you know, a service request and they review it and try to figure out what's going on and provide the customer with information. So it's a very large chunk of our day. You know, I said 8,000 plus engineers, and so three hours a day, that's 24,000 man hours a day spent on long analysis. >>Now the struggle with logs or analyzing logs is there by out of necessity. Logs are very contr contr. They try to pack a lot of information in a very little space. And this is for performance reasons, storage reasons, et cetera, BEC, but the side effect of that is they're very esoteric. So they're hard to read if you're not conversant, if you're not the developer who wrote these logs or you or you, aren't doing code deep dives. And you're looking at where this logs getting printed and things like that, it may not be immediately obvious or even after a low while it may not be obvious what that log line means or how it correlates to whatever problem you're troubleshooting. So it requires tenure. It requires, you know, like I was saying before, it requires a lot of knowledge about the protocol what's expected because when you're doing log analysis, what you're really looking for is a needle in a haystack. You're looking for that one anomalous event, that single thing that tells you this shouldn't have happened. And this was a problem right now doing that kind of anomaly detection requires you to know what is normal. It requires, you know, what the baseline is. And that requires a very in-depth understanding of, you know, the state changes for that network solution or product. So it requires time, tenure and expertise to do well. And it takes a lot of time even when you have that kind of expertise. >>Wow. So thank you, archery. And Najati, that's, that's about, that's almost two days a week for, for a technical resource. That's that's not inexpensive. So what was Cisco looking for to sort of help with this and, and how'd you stumble upon zebra? >>Yeah, so, I mean, we have our internal automation system, which has been running more than a decade now. And what happens is when a customer attaches a log bundle or diagnostic bundle into the service request, we take that from the Sr we analyze it and we represent some kind of information. You know, it can be alert or some tables, some graph to the engineer, so they can, you know, troubleshoot this particular issue. This is an incredible system, but it comes with its own challenges around maintenance to keep it up to date and relevant with Cisco's new products or new version of the product, new defects, new issues, and all kind of things. And when I, what I mean with those challenges are, let's say Cisco comes up with a product today. We need to come together with those engineers. We need to figure out how this bundle works, how it's structured out. >>We need to select individual logs, which are relevant and then start modeling these logs and get some values out of those logs, using pars or some rag access to come to a level that we can consume the logs. And then people start writing rules on top of that abstraction. So people can say in this log, I'm seeing this value together with this other value in another log, maybe I'm hitting this particular defect. So that's how it works. And if you look at it, the abstraction, it can fail the next time. And the next release when the development or the engineer decides to change that log line, which you write that rag X, or we can come up with a new version, which we completely change the services or processes, then whatever you have wrote needs to be re written for that new service. And we see that a lot with products, like for instance, WebEx, where you have a very short release cycle that things can change maybe the next week with a new release. >>So whatever you are writing, especially for that abstraction and for those rules are maybe not relevant with that new release. With that being sake, we have a incredible rule creation process and governance process around it, which starts with maybe a defect. And then it takes it to a level where we have an automation in place. But if you look at it, this really ties to human bandwidth. And our engineers are really busy working on, you know, customer facing, working on issues daily and sometimes creating these rules or these pars are not their biggest priorities, so they can be delayed a bit. So we have this delay between a new issue being identified to a level where we have the automation to detect it next time that some customer faces it. So with all these questions and with all challenges in mind, we start looking into ways of actually how we can automate these automations. >>So these things that we are doing manually, how we can move it a bit further and automate. And we had actually a couple of things in mind that we were looking for and this being one of them being, this has to be product agnostic. Like if Cisco comes up with a product tomorrow, I should be able to take it logs without writing, you know, complex regs, pars, whatever, and deploy it into this system. So it can embrace our logs and make sense of it. And we wanted this platform to be unsupervised. So none of the engineers need to create rules, you know, label logs. This is bad. This is good. Or train the system like which requires a lot of computational power. And the other most important thing for us was we wanted this to be not noisy at all, because what happens with noises when your level of false PE positives really high your engineers start ignoring the good things between that noise. >>So they start the next time, you know, thinking that this thing will not be relevant. So we want something with a lot or less noise. And ultimately we wanted this new platform or new framework to be easily adaptable to our existing workflows. So this is where we started. We start looking into the, you know, first of all, internally, if we can build this thing and also start researching it, and we came up to Zeum actually Larry, one of the co co-founders of Zeum. We came upon his presentation where he clearly explained why this is different, how this works, and it immediately clicked in. And we said, okay, this is exactly what we were looking for. We dived deeper. We checked the block posts where SBRI guys really explained everything very clearly there, they are really open about it. And most importantly, there is a button in their system. >>So what happens usually with AI ML vendors is they have this button where you fill in your details and sales guys call you back. And, you know, we explain the system here. They were like, this is our trial system. We believe in the system, you can just sign up and try it yourself. And that's what we did. We took our, one of our Cisco live DNA center, wireless platforms. We start streaming logs out of it. And then we synthetically, you know, introduce errors, like we broke things. And then we realized that zebra was really catching the errors perfectly. And on top of that, it was really quiet unless you are really breaking something. And the other thing we realized was during that first trial is zebra was actually bringing a lot of context on top of the logs. During those failures, we work with couple of technical leaders and they said, okay, if this failure happens, I I'm expecting this individual log to be there. And we found out with zebra, apart from that individual log, there were a lot of other things which gives a bit more context around the root columns, which was great. And that's where we wanted to take it to the next level. Yeah. >>Okay. So, you know, a couple things to unpack there. I mean, you have the dart board behind you, which is kind of interesting, cuz a lot of times it's like throwing darts at the board to try to figure this stuff out. But to your other point, Cisco actually has some pretty rich tools with AppD and doing observability and you've made acquisitions like thousand eyes. And like you said, I'm, I'm presuming you gotta eat your own dog food or drink your own champagne. And so you've gotta be tools agnostic. And when I first heard about Z zebra, I was like, wait a minute. Really? I was kind of skeptical. I've heard this before. You're telling me all I need is plain text and, and a timestamp. And you got my problem solved. So, and I, I understand that you guys said, okay, let's run a POC. Let's see if we can cut that from, let's say two days a week down to one day, a week. In other words, 50%, let's see if we can automate 50% of the root cause analysis. And, and so you funded a POC. How, how did you test it? You, you put, you know, synthetic, you know, errors and problems in there, but how did you test that? It actually works Najati >>Yeah. So we, we wanted to take it to the next level, which is meaning that we wanted to back test is with existing SARS. And we decided, you know, we, we chose four different products from four different verticals, data center, security, collaboration, and enterprise networking. And we find out SARS where the engineer put some kind of log in the resolution summary. So they closed the case. And in the summary of the Sr, they put, I identified these log lines and they led me to the roots and we, we ingested those log bundles. And we, we tried to see if Zeum can surface that exact same log line in their analysis. So we initially did it with archery ourself and after 50 tests or so we were really happy with the results. I mean, almost most of them, we saw the log line that we were looking for, but that was not enough. >>And we brought it of course, to our management and they said, okay, let's, let's try this with real users because the log being there is one thing, but the engineer reaching to that log is another take. So we wanted to make sure that when we put it in front of our users, our engineers, they can actually come to that log themselves because, you know, we, we know this platform so we can, you know, make searches and find whatever we are looking for, but we wanted to do that. So we extended our pilots to some selected engineers and they tested with their own SRSS. Also do some back testing for some SARS, which are closed in the past or recently. And with, with a sample set of, I guess, close to 200 SARS, we find out like majority of the time, almost 95% of the time the engineer could find the log they were looking for in zebra analysis. >>Yeah. Okay. So you were looking for 50%, you got to 95%. And my understanding is you actually did it with four pretty well known Cisco products, WebEx client DNA center, identity services, engine ISE, and then, then UCS. Yes. Unified pursuit. So you use actual real data and, and that was kind of your proof proof point, but Ari. So that's sounds pretty impressive. And, and you've have you put this into production now and what have you found? >>Well, yes, we're, we've launched this with the four products that you mentioned. We're providing our tech engineers with the ability, whenever a, whenever a support bundle for that product gets attached to the support request. We are processing it, using sense and then providing that sense analysis to the tech engineer for their review. >>So are you seeing the results in production? I mean, are you actually able to, to, to reclaim that time that people are spending? I mean, it was literally almost two days a week down to, you know, a part of a day, is that what you're seeing in production and what are you able to do with that extra time and people getting their weekends back? Are you putting 'em on more strategic tasks? How are you handling that? >>Yeah. So, so what we're seeing is, and I can tell you from my own personal experience using this tool, that troubleshooting any one of the cases, I don't take more than 15 to 20 minutes to go through the zebra report. And I know within that time either what the root causes or I know that zebra doesn't have the information that I need to solve this particular case. So we've definitely seen, well, it's been very hard to measure exactly how much time we've saved per engineer, right? What we, again, anecdotally, what we've heard from our users is that out of those three hours that they were spending per day, we're definitely able to reclaim at least one of those hours and, and what, even more importantly, you know, what the kind of feedback that we've gotten in terms of, I think one statement that really summarizes how Zebra's impacted our workflow was from one of our users. >>And they said, well, you know, until you provide us with this tool, log analysis was a very black and white affair, but now it's become really colorful. And I mean, if you think about it, log analysis is indeed black and white. You're looking at it on a terminal screen where the background is black and the text is white, or you're looking at it as a text where the background is white and the text is black, but what's what they're really trying to say. Is there hardly any visual cues that help you navigate these logs, which are so esoteric, so dense, et cetera. But what XRM does is it provides a lot of color and context to the whole process. So now you're able to quickly get to, you know, using their word cloud, using their interactive histogram, using the summaries of every incident. You're very quickly able to summarize what might be happening and what you need to look into. >>Like, what are the important aspects of this particular log bundle that might be relevant to you? So we've definitely seen that a really great use case that kind of encapsulates all of this was very early on in our experiment. There was, there was this support request that had been escalated to the business unit or the development team. And the tech engineer had really, they, they had an intuition about what was going wrong because of their experience because of, you know, the symptoms that they'd seen. They kind of had an idea, but they weren't able to convince the development team because they weren't able to find any evidence to back up what they thought was happening. And we, it was entirely happenstance that I happened to pick up that case and did an analysis using Seebri. And then I sat down with the attack engineer and we were very quickly within 15 minutes, we were able to get down to the exact sequence of events that highlighted what the customer thought was happening, evidence of what the, so not the customer, what the attack engineer thought was the, was a root cause. It was a rude pause. And then we were able to share that evidence with our business unit and, you know, redirect their resources so that we could change down what the problem was. And that really has been, that that really shows you how that color and context helps in log analysis. >>Interesting. You know, we do a fair amount of work in the cube in the RPA space, the robotic process automation and the narrative in the press when our RPA first started taking off was, oh, it's, you know, machines replacing humans, or we're gonna lose jobs. And, and what actually happened was people were just eliminating mundane tasks and, and the, the employee's actually very happy about it. But my question to you is, was there ever a reticence amongst your team? Like, oh, wow, I'm gonna, I'm gonna lose my job if the machine's gonna replace me, or have you found that people were excited about this and what what's been the reaction amongst the team? >>Well, I think, you know, every automation and AI project has that immediate gut reaction of you're automating away our jobs and so forth. And there is initially there's a little bit of reticence, but I mean, it's like you said, once you start using the tool, you realize that it's not your job, that's getting automated away. It's just that your job's becoming a little easier to do, and it's faster and more efficient. And you're able to get more done in less time. That's really what we're trying to accomplish here at the end of the day, rim will identify these incidents. They'll do the correlation, et cetera. But if you don't understand what you're reading, then that information's useless to you. So you need the human, you need the network expert to actually look at these incidents, but what we are able to skin away or get rid of is all of the fat that's involved in our, you know, in our process, like without having to download the bundle, which, you know, when it's many gigabytes in size, and now we're working from home with the pandemic and everything, you're, you know, pulling massive amounts of logs from the corporate network onto your local device that takes time and then opening it up, loading it in a text editor that takes time. >>All of these things are we're trying to get rid of. And instead we're trying to make it easier and quicker for you to find what you're looking for. So it's like you said, you take away the mundane, you take away the, the difficulties and the slog, but you don't really take away the work, the work still needs to be done. >>Yeah. Great guys. Thanks so much. Appreciate you sharing your story. It's quite, quite fascinating. Really. Thank you for coming on. >>Thanks for having us. >>You're very welcome. Okay. In a moment, I'll be back to wrap up with some final thoughts. This is Dave Valante and you're watching the, >>So today we talked about the need, not only to gain end to end visibility, but why there's a need to automate the identification of root cause problems and doing so with modern technology and machine intelligence can dramatically speed up the process and identify the vast majority of issues right out of the box. If you will. And this technology, it can work with log bundles in batches, or with real time data, as long as there's plain text and a timestamp, it seems Zebra's technology will get you the outcome of automating root cause analysis with very high degrees of accuracy. Zebra is available on Preem or in the cloud. Now this is important for some companies on Preem because there's really some sensitive data inside logs that for compliance and governance reasons, companies have to keep inside their four walls. Now SBRI has a free trial. Of course they better, right? So check it out@zebra.com. You can book a live demo and sign up for a free trial. Thanks for watching this special presentation on the cube, the leader in enterprise and emerging tech coverage on Dave Valante and.

>>Good afternoon and welcome back to our audience here in Asia pacific This is Sandeep again uh from my home studio in Singapore, I hope you found the session to be insightful. I thought it was a key takeaway in terms of how you know the the world is going through a massive transformation, driven by underpinning the workload optimized solutions around up by round of security, 3 60 degree security. As Neil Mcdonald talked about underpinned by the scale, you know, whether you're on exa scale, compute public cloud or on the edge and that's kind of underpinning the digital transformation that our customers are going to go through. I have two special guests with me. Uh let me just quickly introduce them Santos restaurant martin who uh is the Managing director for intel in A P. K. And Dorinda Kapoor, Managing Director for HB Initial pacific So, good afternoon, both you gentlemen. >>Good afternoon. >>So Santos. My first question is to you, first of all, a comment, you know, the passion at which uh, pad Kill Singer talked through the four superpowers. That was amazing. You know, I could see that passion comes through the screen. You know, I think everybody in the audience could relate with that. We are like, you know, as you know, on the words of the launch, the gentle plus by power, but it's isolate processor from intel, what are you seeing and what do our customers should expect improvements, especially with regard to the business outcomes. >>Yeah, So first of all, thank you so much for having me in this session and, and as you said, Sandeep, I mean, you could really see how energized we are. And you heard that from pad as well. Uh, so we launched the third gen, intel, Xeon processors or isolate, you know about a couple of weeks ago and I'm sure, you know, there's lots of benefits that you get in these new products. But I thought what I'll do is I'll try and summarize them in three key buckets. The first one is about the performance benefits that these new products bring in. The 2nd 1 is the value of platforms and I think the last pieces about the partnerships and how it makes deployment really easy and simple for our customers. Let me start with the first one which is about performance and the and the big jump that we're staying. It's about a 46% performance, increased generation over generation. It's flexible, it's optimized performance from the edge to the cloud where you would see about 1.5 to 1.7 X improvements on key war clouds like the cloud five G I O D HPC and AI that are so critical all around us. It's probably the only data center processor that has built in A I acceleration that helps with faster analytics. It's got security optimist on intel SGX that basically gives you a secure on cliff when when sensitive data is getting transacted and it also has crypto acceleration that reduces any performance impact because of the pervasive encryption that we have all around us. Now The second key benefit is about platform and if you remember when we launch sky lake in 2017, we laid out a strategy that said that we are here to help customers >>move, >>store and process data. So it's not just the CPU that we announced with the third genitals, jOHn Announcements. We also announce products like the obtained persistent memory, 200 cds That gives you about a 32 higher memory bandwidth and six terabytes of memory capacity on stock. It the obtain S S D S, the intel internet, 800 cities adapter that gives you about 200 Gbps per port, which means you can move data much more faster and you have the intellectual X F P G s that gives you about a double the better fabric performance for what? Which means if there's key workloads that you want to go back and offloaded to a to a steak or a specific uh CPU then you have the F P G s that can really help you there Now. What does the platform do for our customers? It helps them build higher application and system level performance that they can all benefit from the last b which is the partnerships area is a critical one because we've had decades of experience of solution delivery with a broad ecosystem and with partners like HP and we build elements like the Intel select solution and the market ready solution that makes it so much more easier for our customers to deploy with Over 50 million Xeon scalable processes that is shipped around the world. A billion Xeon cores that are powering the cloud since 2013 customers have really a proven solution that they can work with. So in summary, I want you to remember the three key piece that can really >>help you be >>successful with these new products, the performance uplifted, you get generation over generation, the platform benefits. So it's not just the CPU but it's things around that that makes the system and the application work way better. And then the partnerships that give you peace of mind because you can go deploy proven solutions that you can go and implement in your organization and serve your customers better. >>Thanks. Thanks thanks and Tosha for clearly outlining, you know, the three PS and kind of really resonates well. Um, so let me just uh turn over you know, to Dorinda there in the hot, you know, there's a lot of new solutions, you're our new treaties that santos talked about security, you get a lot of performance benefits and yet our customers have to go through a massive amount of change from a digital transformation perspective in order that they take all the advantages in state competitive. We're using HP Iran addressing the needs for the challenges of our customers and how we really helping them accelerate their transformation journey. >>Yeah, sure. Sandeep, thanks a lot for the question. And you are right. Most of the businesses actually need to go uh digital transformation in order to stay relevant in the current times. And in fact actually COVID-19 has further accelerated the pace of digital transformation for uh most of our customers. And actually the digital transformation is all about delivering differentiated experiences and outcomes at the age by converting data collected from multiple different sources to insights and actions. So we actually an HP believe that enterprise of the future is going to be eight centric data driven and cloud enabled And with our strategy of providing H2 cloud platform and having a complete portfolio of uh software, networking computer and the storage solutions both at the age and court uh to of course collect, transmit secure, analyze and store data. I believe we are in the best position to help our customers start and execute on their transformation journey. Now reality is various enterprises are at different stages of their transformation journey. You know, uh we in HP are able to help our customers who are at the early stage or just starting the transformation journey to to help build their transformation broad maps with the help of our advisory teams and uh after that helped them to execute on the same with our professional services team. While for the customers who are already midway in the transformation journey, we have been helping them to differentiate themselves by delivering workload optimized solutions which provide latency, flexibility and performance. They need to turn data into insights and innovations to help their business. Now, speaking of the workload optimized solutions, HP has actually doubled down in this area with the help of our partners like Intel, which powers our latest Gentlemen plus platform. This brings more compute power, memory and storage capacity which our customers need as they process more data and solve more complex challenges within their business. >>Thank you. Thanks. And er in there I think that's really insightful. Hopefully you know our customer base, I will start joined in here, can hear that and take advantage of you know, how HP is helping you know, fast track the exploration. I come back to you something you don't like during the talk about expanding capacities and we saw news about you know Intel invest $20 billion dollars or so, something like that in terms of you know, adding capacities or manufacturing. So I'd like to hear from your perspective, you know how this investments which intel is putting is a kind of a game changer, how you're shaping the industry as we move forward. >>Yeah, I mean as we all know, I think there's accelerated demand for semiconductors across the world digitization especially in an environment that we're that we're going through has really made computing pervasive and it's it's becoming a foundation of every industry and our society, the world just needs more semiconductors. Intel is in a unique position to rise to that occasion and meet the growing demand for semiconductors given our advanced manufacturing scale that we have. So the intel foundry services and the that you mentioned is is part of the Intel's new I. D. M. Torrado strategy that Bad announced which is a differentiated winning formula that will really deliver the new era of innovation, manufacturing and product leadership. We will expand our manufacturing capacity as you mentioned with that 20 billion investments and building to fabs in Arizona. But there's more to come in the year ahead and these fans will support the expanding requirements of our current products and also provide committed capacity for our foundry customers. Our foundry customers will also be able to leverage our leading edge process, the treaty packaging technology, a world class I. P. Portfolio. So >>I'm really really >>excited. I think it's a truly exciting time for our industry. The world requires more semiconductors and Intel is stepping in to help build the same. >>Fantastic, fantastic. Thank you. Some potion is really heartening to know and we really cherish the long partnership, HP and Intel have together. I look forward that you know with this gentleman plus launch and the partnership going forward. You know, we have only motivation and work together. Really appreciate your taking the time and joining and thank you very much for joining us. >>Thank you. >>Thanks. >>Okay, so with that I will move on to our second segment and in white, another special guest and this is Pete Chambers who is the managing director for A N D N A P K. Good afternoon Pete. You can hear us Well >>I can. Thank you. Sandy, Great to be >>here. Good and thanks for joining me. Um I thought I just opened up, you know, like a comment around the 19 world Records uh, am D. N. H. We have together and it's a kind of a testament to the joint working model and relationship and the collaboration. And so again, really thank you for the partnership. We have any change. Uh, let me just quickly get to the first question. You know, when it comes to my mind listening over to what Antonio and Liza were discussing, you know, they're talking about there's a huge amount of flow of data. You know, the technology and the compute needs to be closer to where the data is being generated and how is A. M. D. You know, helping leverage some of those technologies to bring feature and benefits and driving outcome for customers here in asia. >>Yeah, as lisa mentioned, we're now in a high performance computing mega cycle driven by cloud computing, digital transformation five DNA. Which means that everyone needs and wants more computer IDC predicts that by 20 23/65 percent of the impact GDP will be digitized. So there's an inflection coming with digital transformation at the fall, businesses are ever increasingly looking for trusted partners like HP and HP and and to help them address and adapt to these complex emerging technologies while keeping their IT infrastructure highly efficient, you know, and is helping enable this transformation by bringing leadership performance such as high court densities, high PC and increased I. O. But at the same time offering the best efficiency and performance for what all third gen Epic. CPU support 100 and 28 lanes of superfast PC for connectivity to four terabytes of memory and multiple layers of security. You know, we've heard from our customers that security continues to be a key consideration, you know? And he continues to listen. And with third gen, Epic, we're providing a multitude of security features such as secure root of trust at the bios level which we work very closely with HP on secure encrypted virtualization, secure memory encryption and secure nested paging to really giving the customers confidence when designing Epic. We look very closely at the key workloads that our customers will be looking to enable. And we've designed Epic from the ground up to deliver superior experience. So high performance computing is growing in this region and our leadership per socket core density of up to 64 cause along with leading IO and high memory bandwidth provides a compelling solution to help solve customers most complex computational problems faster. New HP Apollo 6500 and 10 systems featuring third gen, Epic are also optimist for artificial intelligence capabilities to improve training and increased accuracy and results. And we also now support up to eight and instinct accelerators. In each of these systems, hyper converged infrastructure continues to gain momentum in today's modern data center and our superior core density helps deliver more VMS per CPU supported by a multitude of security virtualization features to provide peace of mind and works very closely with industry leaders in HD like HP but also Nutanix and VM ware to help simplify the customers infrastructure. And in recent times we've seen video. I have a resurgence as companies have looked to empower their remote employee remote employees. Third gen, Epic enables more video sessions per CPU providing a more cost optimized solution, simply put Epics higher core density per CPU means customers need fewer service. That means less space required, lower power and cooling expenditure and as a result, a tangibly lower total cost of ownership add to this the fact, as you mentioned that Andy Epic with HP of 19 world records across virtualization, energy efficiency, decision support, database workloads, etc. And service side java. And it all adds up to a very strong value proposition to encourage Cdos to embark on their next upgrade cycle with HP and Epic >>Interstate. Thank you Peter and really quite insightful. And I've just done that question over to Narendra Pete talked about great new technologies, new solution, new areas that are going to benefit from these technology enhancements at the same time. You know, if I'm a customer, I look at every time we talk about technology, you know, you need to invest and where is you know, the bigger concern for customers always wears this money will come from. So I want to uh, you know, uh, the if you share your insights, how is actually helping customers to be able to implement these technology solutions, giving them a financial flexibility so that they can drive business outcomes. >>Yes, and the very important point, you know, from how HP is able to help our customers from their transformation. Now, reality is that most of the traditional enterprises are being challenged by this new digital bond businesses who have no doubt of funding and very low expectation of profitability. But in reality, majority of the capital of these traditional enterprises has uh tied up in their existing businesses as they do need to keep current operations running while starting their digital transformation at the same time. This of course creates real challenges and funding their transformation. Now with HP, with our Green Lake Cloud services, we are able to help customers fund their transformation journey. Were instead of buying up front, customers pay only for what they consume as the scale. We are not only able to offer flexible consumption model for new investments but are also able to help our customers, you know, for monetize their capital, which is tied up in the old ICT infrastructure because we can buy back that old infrastructure and convert that into conception of frank. So while customers can continue to use those assets to run their current business and reality is HIV is the leader in the this as a service space and probably the only vendor to be able to offer as a service offering for all of our portfolio. Uh, if you look at the ideas prediction, 70 of the applications are not ready for public cloud and will continue to run in private environments in addition. And everybody talked about the beef for a I and you know, HPC as well as the edge and more and more workloads are actually moving to the edge where the public cloud will have for less and less a role to play. But when you look at the customers, they are more and more looking for a cloud, like business model for all the workloads, uh, that they're running outside the public cloud. Now, with our being like offering, we are able to take away all the complexity from customers, allowing them to run the workloads wherever they want. That means that the edge in the data center or in the cloud and consume in the way they want. In other words, we're able to provide cloud, like experience anytime, anywhere to our customers. And of course, all these Green Lake offerings are powered by our latest compute capabilities that HP has to offer. >>Thank you. Thank you, surrender. That's really, really, very insightful. I have a minute or two, so let me try to squeeze another question from your feet, you know, MD is just now introduced the third generation of epics and congratulations on that. How are you seeing that? Excellent. Helping you accelerate in this growth, in the impact? Uh, you know, the geography as as such. >>Sure, great question. And as I mentioned, you know, third gen Epic with me and and once again delivers industry leading solutions, bending the curve on performance efficiency and TCO helping more than ever to deliver along with HP the right technologies for today and tomorrow. You know, in the service space, it's not just about what you can offer today. You need to be able to predictably deliver innovation over the long term. And we are committed to doing just that, you know, and strategy is to focus on the customer. We continue to see strong growth both globally and in a pack in HPC cloud and Web tech manufacturing, Fc telco and public and government sectors are growth plan is focused on getting closer to our customers directly, engaging with HP and our partners and the end customer to help guide them on the best solution and assist them in solving their computing pain points cost effectively. A recent example of this is our partnership with palsy supercomputing center in Australia, where HP and M. D will be helping to provide some 200,000 cause across 1600 nodes and over 750 radio on instinct accelerators empowering scientists to solve today's most challenging problems. We have doubled ourselves and F8 teams in the region over the past year and will continue to invest in additional customer facing sales and technical people through 2021, you know, and has worked very closely with HP to co design and co developed the best technologies for our customers needs. We joined forces over seven years ago to prepare for the first generation of Epic at launch and you fast forward to today and it's great to see that HP now has a very broad range of Andy Epic servers spanning from the edge two extra scale. So we are truly excited about what we can offer the market in partnership with HP and feel that we offer a very strong foundation of differentiation for our channel partners to address their customers need to accelerate accelerate their digital transformation. Thank you. Sandy, >>thank you. Thanks Peter. And really it's been amazing partnering with the NDP here and thanks for your sponsorship on that. And together we want to work with you to create another 19 world records right from here in the issue. Absolutely. So with that we are coming to the end of the event. Really thanks for coming pete and to our audience here because the pig is being a great a couple of hours. I hope you all found these sessions very, very insightful. You heard from our worldwide experts as to where, you know, divorce, moving in terms of the transformation, what your hp is bringing to our compute workload optimized solutions which are going to go from regardless of what scale of computing you're using and wrapped around 3 60 security and then offer truly as a service experience. But before you drop off, I would like to request you to please scan the QR code you see on your screen and fill in the feedback form we have, you know, lucky draw for some $50 worth of vultures for the five lucky winners today. So please click up your phone and, you know, spend a minute or two and give us a feedback and thank you very much again for this wonderful day. And I wish everybody a great day. Thank you.

>>from around the globe. It's the Cube with digital coverage of enterprise data automation, an event Siri's brought to you by Iot. Tahoe, >>my buddy, We're back. And this is Dave Volante, and we're covering the whole notion of automating data in the Enterprise. And I'm really excited to have Paul Damico here. She's a senior vice president of enterprise data Architecture at Webster Bank. Good to see you. Thanks for coming on. >>Hi. Nice to see you, too. Yes. >>So let's let's start with Let's start with Webster Bank. You guys are kind of a regional. I think New York, New England, uh, leave headquartered out of Connecticut, but tell us a little bit about the bank. >>Yeah, Um, Webster Bank >>is regional Boston And that again, and New York, Um, very focused on in Westchester and Fairfield County. Um, they're a really highly rated saying regional bank for this area. They, um, hold, um, quite a few awards for the area for being supportive for the community and, um, are really moving forward. Technology lives. They really want to be a data driven bank, and they want to move into a more robust Bruce. >>Well, we got a lot to talk about. So data driven that is an interesting topic. And your role as data architect. The architecture is really senior vice president data architecture. So you got a big responsibility as it relates to It's kind of transitioning to this digital data driven bank. But tell us a little bit about your role in your organization, >>right? Um, currently, >>today we have, ah, a small group that is just working toward moving into a more futuristic, more data driven data warehouse. That's our first item. And then the other item is to drive new revenue by anticipating what customers do when they go to the bank or when they log into there to be able to give them the best offer. The only way to do that is you >>have uh huh. >>Timely, accurate, complete data on the customer and what's really a great value on off something to offer that or a new product or to help them continue to grow their savings or do and grow their investment. >>Okay. And I really want to get into that. But before we do and I know you're sort of part way through your journey, you got a lot of what they do. But I want to ask you about Cove. It how you guys you're handling that? I mean, you had the government coming down and small business loans and P p p. And huge volume of business and sort of data was at the heart of that. How did you manage through that? >>But we were extremely successful because we have a big, dedicated team that understands where their data is and was able to switch much faster than a larger bank to be able to offer. The TPP longs at to our customers within lightning speeds. And part of that was is we adapted to Salesforce very, for we've had salesforce in house for over 15 years. Um, you know, pretty much, uh, that was the driving vehicle to get our CPP is loans in on and then developing logic quickly. But it was a 24 7 development role in get the data moving, helping our customers fill out the forms. And a lot of that was manual. But it was a It was a large community effort. >>Well, think about that. Think about that too. Is the volume was probably much, much higher the volume of loans to small businesses that you're used to granting. But and then also, the initial guidelines were very opaque. You really didn't know what the rules were, but you were expected to enforce them. And then finally, you got more clarity. So you had to essentially code that logic into the system in real time, right? >>I wasn't >>directly involved, but part of my data movement Team Waas, and we had to change the logic overnight. So it was on a Friday night was released. We've pushed our first set of loans through and then the logic change, Um, from, you know, coming from the government and changed. And we had to re develop our our data movement piece is again and we design them and send them back. So it was It was definitely kind of scary, but we were completely successful. We hit a very high peak and I don't know the exact number, but it was in the thousands of loans from, you know, little loans to very large loans, and not one customer who buy it's not yet what they needed for. Um, you know, that was the right process and filled out the rate and pace. >>That's an amazing story and really great support for the region. New York, Connecticut, the Boston area. So that's that's fantastic. I want to get into the rest of your story. Now let's start with some of the business drivers in banking. I mean, obviously online. I mean, a lot of people have sort of joked that many of the older people who kind of shunned online banking would love to go into the branch and see their friendly teller had no choice, You know, during this pandemic to go to online. So that's obviously a big trend you mentioned. So you know the data driven data warehouse? I wanna understand that. But well, at the top level, what were some of what are some of the key business drivers there catalyzing your desire for change? >>Um, the ability to give the customer what they need at the time when they need it. And what I mean by that is that we have, um, customer interactions in multiple ways, right? >>And I want >>to be able for the customer, too. Walk into a bank, um, or online and see the same the same format and being able to have the same feel, the same look, and also to be able to offer them the next best offer for them. But they're you know, if they want looking for a new a mortgage or looking to refinance or look, you know, whatever it iss, um, that they have that data, we have the data and that they feel comfortable using it. And that's a untethered banker. Um, attitude is, you know, whatever my banker is holding and whatever the person is holding in their phone, that that is the same. And it's comfortable, so they don't feel that they've, you know, walked into the bank and they have to do a lot of different paperwork comparative filling out paperwork on, you know, just doing it on their phone. >>So you actually want the experience to be better. I mean, and it is in many cases now, you weren't able to do this with your existing against mainframe based Enterprise data warehouse. Is is that right? Maybe talk about that a little bit. >>Yeah, we were >>definitely able to do it with what we have today. The technology we're using, but one of the issues is that it's not timely, Um, and and you need a timely process to be able to get the customers to understand what's happening. Um, you want you need a timely process so we can enhance our risk management. We can apply for fraud issues and things like that. >>Yeah, so you're trying to get more real time in the traditional e g W. It's it's sort of a science project. There's a few experts that know how to get it. You consider line up. The demand is tremendous, and often times by the time you get the answer, you know it's outdated. So you're trying to address that problem. So So part of it is really the cycle time, the end end cycle, time that you're pressing. And then there's if I understand it, residual benefits that are pretty substantial from a revenue opportunity. Other other offers that you can you can make to the right customer, Um, that that you, you maybe know through your data. Is that right? >>Exactly. It's drive new customers, Teoh new opportunities. It's enhanced the risk, and it's to optimize the banking process and then obviously, to create new business. Um, and the only way we're going to be able to do that is that we have the ability to look at the data right when the customer walks in the door or right when they open up their app. And, um, by doing, creating more to New York time near real time data for the data warehouse team that's giving the lines of business the ability to to work on the next best offer for that customer. >>Paulo, we're inundated with data sources these days. Are there their data sources that you maybe maybe had access to before? But perhaps the backlog of ingesting and cleaning and cataloging and you know of analyzing. Maybe the backlog was so great that you couldn't perhaps tap some of those data sources. You see the potential to increase the data sources and hence the quality of the data, Or is that sort of premature? >>Oh, no. Um, >>exactly. Right. So right now we ingest a lot of flat files and from our mainframe type of Brennan system that we've had for quite a few years. But now that we're moving to the cloud and off Prem and on France, you know, moving off Prem into like an s three bucket. Where That data king, We can process that data and get that data faster by using real time tools to move that data into a place where, like, snowflake could utilize that data or we can give it out to our market. >>Okay, so we're >>about the way we do. We're in batch mode. Still, so we're doing 24 hours. >>Okay, So when I think about the data pipeline and the people involved, I mean, maybe you could talk a little bit about the organization. I mean, you've got I know you have data. Scientists or statisticians? I'm sure you do. Ah, you got data architects, data engineers, quality engineers, you know, developers, etcetera, etcetera. And oftentimes, practitioners like yourself will will stress about pay. The data's in silos of the data quality is not where we want it to be. We have to manually categorize the data. These are all sort of common data pipeline problems, if you will. Sometimes we use the term data ops, which is kind of a play on Dev Ops applied to the data pipeline. I did. You just sort of described your situation in that context. >>Yeah. Yes. So we have a very large data ops team and everyone that who is working on the data part of Webster's Bay has been there 13 14 years. So they get the data, they understand that they understand the lines of business. Um, so it's right now, um, we could we have data quality issues, just like everybody else does. We have. We have places in him where that gets clans, Um, and we're moving toward. And there was very much silo data. The data scientists are out in the lines of business right now, which is great, cause I think that's where data science belongs. We should give them on. And that's what we're working towards now is giving them more self service, giving them the ability to access the data, um, in a more robust way. And it's a single source of truth. So they're not pulling the data down into their own like tableau dashboards and then pushing the data back out. Um, so they're going to more not, I don't want to say a central repository, but a more of a robust repository that's controlled across multiple avenues where multiple lines of business can access. That said, how >>got it? Yes, and I think that one of the key things that I'm taking away from your last comment is the cultural aspects of this bite having the data. Scientists in the line of business, the line of lines of business, will feel ownership of that data as opposed to pointing fingers, criticizing the data quality they really own that that problem, as opposed to saying, Well, it's it's It's Paulus problem, >>right? Well, I have. My problem >>is, I have a date. Engineers, data architects, they database administrators, right, Um, and then data traditional data forwarding people. Um, and because some customers that I have that our business customers lines of business, they want to just subscribe to a report. They don't want to go out and do any data science work. Um, and we still have to provide that. So we still want to provide them some kind of regimen that they wake up in the morning and they open up their email. And there's the report that they just drive, um, which is great. And it works out really well. And one of the things is why we purchase I o waas. I would have the ability to give the lines of business the ability to do search within the data. And we read the data flows and data redundancy and things like that help me cleanup the data and also, um, to give it to the data. Analysts who say All right, they just asked me. They want this certain report, and it used to take Okay, well, we're gonna four weeks, we're going to go. We're gonna look at the data, and then we'll come back and tell you what we dio. But now with Iot Tahoe, they're able to look at the data and then, in one or two days of being able to go back and say, yes, we have data. This is where it is. This is where we found that this is the data flows that we've found also, which is that what I call it is the birth of a column. It's where the calm was created and where it went live as a teenager. And then it went to, you know, die very archive. Yeah, it's this, you know, cycle of life for a column. And Iot Tahoe helps us do that, and we do. Data lineage has done all the time. Um, and it's just takes a very long time. And that's why we're using something that has AI and machine learning. Um, it's it's accurate. It does it the same way over and over again. If an analyst leads, you're able to utilize talked something like, Oh, to be able to do that work for you. I get that. >>Yes. Oh, got it. So So a couple things there is in in, In researching Iot Tahoe, it seems like one of the strengths of their platform is the ability to visualize data the data structure and actually dig into it. But also see it, um, and that speeds things up and gives everybody additional confidence. And then the other pieces essentially infusing AI or machine intelligence into the data pipeline is really how you're attacking automation, right? And you're saying it's repeatable and and then that helps the data quality, and you have this virtuous cycle. Is there a firm that and add some color? Perhaps >>Exactly. Um, so you're able to let's say that I have I have seven cause lines of business that are asking me questions and one of the questions I'll ask me is. We want to know if this customer is okay to contact, right? And you know, there's different avenues, so you can go online to go. Do not contact me. You can go to the bank and you can say I don't want, um, email, but I'll take tests and I want, you know, phone calls. Um, all that information. So seven different lines of business asked me that question in different ways once said okay to contact the other one says, you know, customer one to pray All these, You know, um, and each project before I got there used to be siloed. So one customer would be 100 hours for them to do that and analytical work, and then another cut. Another analysts would do another 100 hours on the other project. Well, now I can do that all at once, and I can do those type of searches and say, Yes, we already have that documentation. Here it is. And this is where you can find where the customer has said, you know, you don't want I don't want to get access from you by email, or I've subscribed to get emails from you. >>Got it. Okay? Yeah. Okay. And then I want to come back to the cloud a little bit. So you you mentioned those three buckets? So you're moving to the Amazon cloud. At least I'm sure you're gonna get a hybrid situation there. You mentioned Snowflake. Um, you know what was sort of the decision to move to the cloud? Obviously, snowflake is cloud only. There's not an on Prem version there. So what precipitated that? >>Alright, So, from, um, I've been in >>the data I t Information field for the last 35 years. I started in the US Air Force and have moved on from since then. And, um, my experience with off brand waas with Snowflake was working with G McGee capital. And that's where I met up with the team from Iot to house as well. And so it's a proven. So there's a couple of things one is symptomatic of is worldwide. Now to move there, right, Two products, they have the on frame in the offering. Um, I've used the on Prem and off Prem. They're both great and it's very stable and I'm comfortable with other people are very comfortable with this. So we picked. That is our batch data movement. Um, we're moving to her, probably HBR. It's not a decision yet, but we're moving to HP are for real time data which has changed capture data, you know, moves it into the cloud. And then So you're envisioning this right now in Petrit, you're in the S three and you have all the data that you could possibly want. And that's Jason. All that everything is sitting in the S three to be able to move it through into snowflake and snowflake has proven cto have a stability. Um, you only need to learn in train your team with one thing. Um, aws has is completely stable at this 10.2. So all these avenues, if you think about it going through from, um, you know, this is your your data lake, which is I would consider your s three. And even though it's not a traditional data leg like you can touch it like a like a progressive or a dupe and into snowflake and then from snowflake into sandboxes. So your lines of business and your data scientists and just dive right in, Um, that makes a big, big win. and then using Iot. Ta ho! With the data automation and also their search engine, um, I have the ability to give the data scientists and eight analysts the the way of they don't need to talk to i t to get, um, accurate information or completely accurate information from the structure. And we'll be right there. >>Yes, so talking about, you know, snowflake and getting up to speed quickly. I know from talking to customers you get from zero to snowflake, you know, very fast. And then it sounds like the i o Ta ho is sort of the automation cloud for your data pipeline within the cloud. This is is that the right way to think about it? >>I think so. Um, right now I have I o ta >>ho attached to my >>on Prem. And, um, I >>want to attach it to my offering and eventually. So I'm using Iot Tahoe's data automation right now to bring in the data and to start analyzing the data close to make sure that I'm not missing anything and that I'm not bringing over redundant data. Um, the data warehouse that I'm working off is not a It's an on Prem. It's an Oracle database and its 15 years old. So it has extra data in it. It has, um, things that we don't need anymore. And Iot. Tahoe's helping me shake out that, um, extra data that does not need to be moved into my S three. So it's saving me money when I'm moving from offering on Prem. >>And so that was a challenge prior because you couldn't get the lines of business to agree what to delete or what was the issue there. >>Oh, it was more than that. Um, each line of business had their own structure within the warehouse, and then they were copying data between each other and duplicating the data and using that, uh so there might be that could be possibly three tables that have the same data in it. But it's used for different lines of business. And so I had we have identified using Iot Tahoe. I've identified over seven terabytes in the last, um, two months on data that is just been repetitive. Um, it just it's the same exact data just sitting in a different scheme. >>And and that's not >>easy to find. If you only understand one schema that's reporting for that line of business so that >>yeah, more bad news for the storage companies out there. Okay to follow. >>It's HCI. That's what that's what we were telling people you >>don't know and it's true, but you still would rather not waste it. You apply it to, you know, drive more revenue. And and so I guess Let's close on where you see this thing going again. I know you're sort of part way through the journey. May be you could sort of describe, you know, where you see the phase is going and really what you want to get out of this thing, You know, down the road Midterm. Longer term. What's your vision or your your data driven organization? >>Um, I want >>for the bankers to be able to walk around with on iPad in their hands and be able to access data for that customer really fast and be able to give them the best deal that they can get. I want Webster to be right there on top, with being able to add new customers and to be able to serve our existing customers who had bank accounts. Since you were 12 years old there and now our, you know, multi. Whatever. Um, I want them to be able to have the best experience with our our bankers, and >>that's awesome. I mean, that's really what I want is a banking customer. I want my bank to know who I am, anticipate my needs and create a great experience for me. And then let me go on with my life. And so that is a great story. Love your experience, your background and your knowledge. Can't thank you enough for coming on the Cube. >>No, thank you very much. And you guys have a great day. >>Alright, Take care. And thank you for watching everybody keep it right there. We'll take a short break and be right back. >>Yeah, yeah, yeah, yeah.

>>from around the globe. It's the Cube with digital coverage of enterprise data automation, an event Siri's brought to you by Iot. Tahoe Welcome everybody to Enterprise Data Automation. Ah co created digital program on the Cube with support from my hotel. So my name is Dave Volante. And today we're using the hashtag data automated. You know, organizations. They really struggle to get more value out of their data, time to data driven insights that drive cost savings or new revenue opportunities. They simply take too long. So today we're gonna talk about how organizations can streamline their data operations through automation, machine intelligence and really simplifying data migrations to the cloud. We'll be talking to technologists, visionaries, hands on practitioners and experts that are not just talking about streamlining their data pipelines. They're actually doing it. So keep it right there. We'll be back shortly with a J ahora who's the CEO of Iot Tahoe to kick off the program. You're watching the Cube, the leader in digital global coverage. We're right back right after this short break. Innovation impact influence. Welcome to the Cube disruptors. Developers and practitioners learn from the voices of leaders who share their personal insights from the hottest digital events around the globe. Enjoy the best this community has to offer on the Cube, your global leader. High tech digital coverage from around the globe. It's the Cube with digital coverage of enterprise, data, automation and event. Siri's brought to you by Iot. Tahoe. Okay, we're back. Welcome back to Data Automated. A J ahora is CEO of I O ta ho, JJ. Good to see how things in London >>Thanks doing well. Things in, well, customers that I speak to on day in, day out that we partner with, um, they're busy adapting their businesses to serve their customers. It's very much a game of ensuring the week and serve our customers to help their customers. Um, you know, the adaptation that's happening here is, um, trying to be more agile. Got to be more flexible. Um, a lot of pressure on data, a lot of demand on data and to deliver more value to the business, too. So that customers, >>as I said, we've been talking about data ops a lot. The idea being Dev Ops applied to the data pipeline, But talk about enterprise data automation. What is it to you. And how is it different from data off >>Dev Ops, you know, has been great for breaking down those silos between different roles functions and bring people together to collaborate. Andi, you know, we definitely see that those tools, those methodologies, those processes, that kind of thinking, um, lending itself to data with data is exciting. We look to do is build on top of that when data automation, it's the it's the nuts and bolts of the the algorithms, the models behind machine learning that the functions. That's where we investors, our r and d on bringing that in to build on top of the the methods, the ways of thinking that break down those silos on injecting that automation into the business processes that are going to drive a business to serve its customers. It's, um, a layer beyond Dev ops data ops. They can get to that point where well, I think about it is is the automation behind new dimension. We've come a long way in the last few years. Boy is, we started out with automating some of those simple, um, to codify, um, I have a high impact on organization across the data a cost effective way house. There's data related tasks that classify data on and a lot of our original pattern certain people value that were built up is is very much around that >>love to get into the tech a little bit in terms of how it works. And I think we have a graphic here that gets into that a little bit. So, guys, if you bring that up, >>sure. I mean right there in the middle that the heart of what we do it is, you know, the intellectual property now that we've built up over time that takes from Hacha genius data sources. Your Oracle Relational database. Short your mainframe. It's a lay and increasingly AP eyes and devices that produce data and that creates the ability to automatically discover that data. Classify that data after it's classified. Them have the ability to form relationships across those different source systems, silos, different lines of business. And once we've automated that that we can start to do some cool things that just puts of contact and meaning around that data. So it's moving it now from bringing data driven on increasingly where we have really smile, right people in our customer organizations you want I do some of those advanced knowledge tasks data scientists and ah, yeah, quants in some of the banks that we work with, the the onus is on, then, putting everything we've done there with automation, pacifying it, relationship, understanding that equality, the policies that you can apply to that data. I'm putting it in context once you've got the ability to power. Okay, a professional is using data, um, to be able to put that data and contacts and search across the entire enterprise estate. Then then they can start to do some exciting things and piece together the the tapestry that fabric across that different system could be crm air P system such as s AP and some of the newer brown databases that we work with. Snowflake is a great well, if I look back maybe five years ago, we had prevalence of daily technologies at the cutting edge. Those are converging to some of the cloud platforms that we work with Google and AWS and I think very much is, as you said it, those manual attempts to try and grasp. But it is such a complex challenges scale quickly runs out of steam because once, once you've got your hat, once you've got your fingers on the details Oh, um, what's what's in your data state? It's changed, You know, you've onboard a new customer. You signed up a new partner. Um, customer has, you know, adopted a new product that you just Lawrence and there that that slew of data keeps coming. So it's keeping pace with that. The only answer really is is some form of automation >>you're working with AWS. You're working with Google, You got red hat. IBM is as partners. What is attracting those folks to your ecosystem and give us your thoughts on the importance of ecosystem? >>That's fundamental. So, I mean, when I caimans where you tell here is the CEO of one of the, um, trends that I wanted us CIO to be part of was being open, having an open architecture allowed one thing that was close to my heart, which is as a CEO, um, a c i o where you go, a budget vision on and you've already made investments into your organization, and some of those are pretty long term bets. They should be going out 5 10 years, sometimes with the CRM system training up your people, getting everybody working together around a common business platform. What I wanted to ensure is that we could openly like it using AP eyes that were available, the love that some investment on the cost that has already gone into managing in organizations I t. But business users to before. So part of the reason why we've been able to be successful with, um, the partners like Google AWS and increasingly, a number of technology players. That red hat mongo DB is another one where we're doing a lot of good work with, um and snowflake here is, um Is those investments have been made by the organizations that are our customers, and we want to make sure we're adding to that. And they're leveraging the value that they've already committed to. >>Yeah, and maybe you could give us some examples of the r A y and the business impact. >>Yeah, I mean, the r a y David is is built upon on three things that I mentioned is a combination off. You're leveraging the existing investment with the existing estate, whether that's on Microsoft Azure or AWS or Google, IBM, and I'm putting that to work because, yeah, the customers that we work with have had made those choices. On top of that, it's, um, is ensuring that we have got the automation that is working right down to the level off data, a column level or the file level we don't do with meta data. It is being very specific to be at the most granular level. So as we've grown our processes and on the automation, gasification tagging, applying policies from across different compliance and regulatory needs that an organization has to the data, everything that then happens downstream from that is ready to serve a business outcome now without hoping out which run those processes within hours of getting started And, um, Bill that picture, visualize that picture and bring it to life. You know, the PR Oh, I that's off the bat with finding data that should have been deleted data that was copies off on and being able to allow the architect whether it's we're working on GCB or a migration to any other clouds such as AWS or a multi cloud landscape right off the map. >>A. J. Thanks so much for coming on the Cube and sharing your insights and your experience is great to have you. >>Thank you, David. Look who is smoking in >>now. We want to bring in the customer perspective. We have a great conversation with Paul Damico, senior vice president data architecture, Webster Bank. So keep it right there. >>Utah Data automated Improve efficiency, Drive down costs and make your enterprise data work for you. Yeah, we're on a mission to enable our customers to automate the management of data to realise maximum strategic and operational benefits. We envisage a world where data users consume accurate, up to date unified data distilled from many silos to deliver transformational outcomes, activate your data and avoid manual processing. Accelerate data projects by enabling non I t resources and data experts to consolidate categorize and master data. Automate your data operations Power digital transformations by automating a significant portion of data management through human guided machine learning. Yeah, get value from the start. Increase the velocity of business outcomes with complete accurate data curated automatically for data, visualization tours and analytic insights. Improve the security and quality of your data. Data automation improves security by reducing the number of individuals who have access to sensitive data, and it can improve quality. Many companies report double digit era reduction in data entry and other repetitive tasks. Trust the way data works for you. Data automation by our Tahoe learns as it works and can ornament business user behavior. It learns from exception handling and scales up or down is needed to prevent system or application overloads or crashes. It also allows for innate knowledge to be socialized rather than individualized. No longer will your companies struggle when the employee who knows how this report is done, retires or takes another job, the work continues on without the need for detailed information transfer. Continue supporting the digital shift. Perhaps most importantly, data automation allows companies to begin making moves towards a broader, more aspirational transformation, but on a small scale but is easy to implement and manage and delivers quick wins. Digital is the buzzword of the day, but many companies recognized that it is a complex strategy requires time and investment. Once you get started with data automation, the digital transformation initiated and leaders and employees alike become more eager to invest time and effort in a broader digital transformational agenda. Yeah, >>everybody, we're back. And this is Dave Volante, and we're covering the whole notion of automating data in the Enterprise. And I'm really excited to have Paul Damico here. She's a senior vice president of enterprise Data Architecture at Webster Bank. Good to see you. Thanks for coming on. >>Nice to see you too. Yes. >>So let's let's start with Let's start with Webster Bank. You guys are kind of a regional. I think New York, New England, uh, leave headquartered out of Connecticut, but tell us a little bit about the >>bank. Yeah, Webster Bank is regional, Boston. And that again in New York, Um, very focused on in Westchester and Fairfield County. Um, they're a really highly rated bank regional bank for this area. They, um, hold, um, quite a few awards for the area for being supportive for the community. And, um, are really moving forward. Technology lives. Currently, today we have, ah, a small group that is just working toward moving into a more futuristic, more data driven data warehouse. That's our first item. And then the other item is to drive new revenue by anticipating what customers do when they go to the bank or when they log into there to be able to give them the best offer. The only way to do that is you have timely, accurate, complete data on the customer and what's really a great value on off something to offer that >>at the top level, what were some of what are some of the key business drivers there catalyzing your desire for change >>the ability to give the customer what they need at the time when they need it? And what I mean by that is that we have, um, customer interactions and multiple weights, right? And I want to be able for the customer, too. Walk into a bank, um, or online and see the same the same format and being able to have the same feel, the same look and also to be able to offer them the next best offer for them. >>Part of it is really the cycle time, the end end cycle, time that you're pressing. And then there's if I understand it, residual benefits that are pretty substantial from a revenue opportunity >>exactly. It's drive new customers, Teoh new opportunities. It's enhanced the risk, and it's to optimize the banking process and then obviously, to create new business. Um, and the only way we're going to be able to do that is that we have the ability to look at the data right when the customer walks in the door or right when they open up their app. >>Do you see the potential to increase the data sources and hence the quality of the data? Or is that sort of premature? >>Oh, no. Um, exactly. Right. So right now we ingest a lot of flat files and from our mainframe type of runnin system that we've had for quite a few years. But now that we're moving to the cloud and off Prem and on France, you know, moving off Prem into, like, an s three bucket Where that data king, we can process that data and get that data faster by using real time tools to move that data into a place where, like, snowflake Good, um, utilize that data or we can give it out to our market. The data scientists are out in the lines of business right now, which is great, cause I think that's where data science belongs. We should give them on, and that's what we're working towards now is giving them more self service, giving them the ability to access the data in a more robust way. And it's a single source of truth. So they're not pulling the data down into their own like tableau dashboards and then pushing the data back out. I have eight engineers, data architects, they database administrators, right, um, and then data traditional data forwarding people, Um, and because some customers that I have that our business customers lines of business, they want to just subscribe to a report. They don't want to go out and do any data science work. Um, and we still have to provide that. So we still want to provide them some kind of read regiment that they wake up in the morning and they open up their email. And there's the report that they just drive, um, which is great. And it works out really well. And one of the things. This is why we purchase I o waas. I would have the ability to give the lines of business the ability to do search within the data, and we read the data flows and data redundancy and things like that and help me cleanup the data and also, um, to give it to the data. Analysts who say All right, they just asked me. They want this certain report and it used to take Okay, well, we're gonna four weeks, we're going to go. We're gonna look at the data, and then we'll come back and tell you what we dio. But now with Iot Tahoe, they're able to look at the data and then, in one or two days of being able to go back and say, Yes, we have data. This is where it is. This is where we found that this is the data flows that we've found also, which is what I call it is the birth of a column. It's where the calm was created and where it went live as a teenager. And then it went to, you know, die very archive. >>In researching Iot Tahoe, it seems like one of the strengths of their platform is the ability to visualize data the data structure, and actually dig into it. But also see it, um, and that speeds things up and gives everybody additional confidence. And then the other pieces essentially infusing ai or machine intelligence into the data pipeline is really how you're attacking automation, right? >>Exactly. So you're able to let's say that I have I have seven cause lines of business that are asking me questions. And one of the questions I'll ask me is, um, we want to know if this customer is okay to contact, right? And you know, there's different avenues so you can go online to go. Do not contact me. You can go to the bank And you could say, I don't want, um, email, but I'll take tests and I want, you know, phone calls. Um, all that information. So seven different lines of business asked me that question in different ways once said Okay to contact the other one says, You know, just for one to pray all these, you know, um, and each project before I got there used to be siloed. So one customer would be 100 hours for them to do that and analytical work, and then another cut. Another of analysts would do another 100 hours on the other project. Well, now I can do that all at once, and I can do those type of searches and say yes we already have that documentation. Here it is. And this is where you can find where the customer has said, You know, you don't want I don't want to get access from you by email, or I've subscribed to get emails from you. I'm using Iot typos eight automation right now to bring in the data and to start analyzing the data close to make sure that I'm not missing anything and that I'm not bringing over redundant data. Um, the data warehouse that I'm working off is not, um a It's an on prem. It's an oracle database. Um, and it's 15 years old, so it has extra data in it. It has, um, things that we don't need anymore. And Iot. Tahoe's helping me shake out that, um, extra data that does not need to be moved into my S three. So it's saving me money when I'm moving from offering on Prem. >>What's your vision or your your data driven organization? >>Um, I want for the bankers to be able to walk around with on iPad in their hands and be able to access data for that customer really fast and be able to give them the best deal that they can get. I want Webster to be right there on top, with being able to add new customers and to be able to serve our existing customers who had bank accounts. Since you were 12 years old there and now our, you know, multi. Whatever. Um, I want them to be able to have the best experience with our our bankers. >>That's really what I want is a banking customer. I want my bank to know who I am, anticipate my needs and create a great experience for me. And then let me go on with my life. And so that's a great story. Love your experience, your background and your knowledge. Can't thank you enough for coming on the Cube. >>No, thank you very much. And you guys have a great day. >>Next, we'll talk with Lester Waters, who's the CTO of Iot Toe cluster takes us through the key considerations of moving to the cloud. >>Yeah, right. The entire platform Automated data Discovery data Discovery is the first step to knowing your data auto discover data across any application on any infrastructure and identify all unknown data relationships across the entire siloed data landscape. smart data catalog. Know how everything is connected? Understand everything in context, regained ownership and trust in your data and maintain a single source of truth across cloud platforms, SAS applications, reference data and legacy systems and power business users to quickly discover and understand the data that matters to them with a smart data catalog continuously updated ensuring business teams always have access to the most trusted data available. Automated data mapping and linking automate the identification of unknown relationships within and across data silos throughout the organization. Build your business glossary automatically using in house common business terms, vocabulary and definitions. Discovered relationships appears connections or dependencies between data entities such as customer account, address invoice and these data entities have many discovery properties. At a granular level, data signals dashboards. Get up to date feeds on the health of your data for faster improved data management. See trends, view for history. Compare versions and get accurate and timely visual insights from across the organization. Automated data flows automatically captured every data flow to locate all the dependencies across systems. Visualize how they work together collectively and know who within your organization has access to data. Understand the source and destination for all your business data with comprehensive data lineage constructed automatically during with data discovery phase and continuously load results into the smart Data catalog. Active, geeky automated data quality assessments Powered by active geek You ensure data is fit for consumption that meets the needs of enterprise data users. Keep information about the current data quality state readily available faster Improved decision making Data policy. Governor Automate data governance End to end over the entire data lifecycle with automation, instant transparency and control Automate data policy assessments with glossaries, metadata and policies for sensitive data discovery that automatically tag link and annotate with metadata to provide enterprise wide search for all lines of business self service knowledge graph Digitize and search your enterprise knowledge. Turn multiple siloed data sources into machine Understandable knowledge from a single data canvas searching Explore data content across systems including GRP CRM billing systems, social media to fuel data pipelines >>Yeah, yeah, focusing on enterprise data automation. We're gonna talk about the journey to the cloud Remember, the hashtag is data automate and we're here with Leicester Waters. Who's the CTO of Iot Tahoe? Give us a little background CTO, You've got a deep, deep expertise in a lot of different areas. But what do we need to know? >>Well, David, I started my career basically at Microsoft, uh, where I started the information Security Cryptography group. They're the very 1st 1 that the company had, and that led to a career in information, security. And and, of course, as easy as you go along with information security data is the key element to be protected. Eso I always had my hands and data not naturally progressed into a roll out Iot talk was their CTO. >>What's the prescription for that automation journey and simplifying that migration to the cloud? >>Well, I think the first thing is understanding what you've got. So discover and cataloging your data and your applications. You know, I don't know what I have. I can't move it. I can't. I can't improve it. I can't build upon it. And I have to understand there's dependence. And so building that data catalog is the very first step What I got. Okay, >>so So we've done the audit. We know we've got what's what's next? Where do we go >>next? So the next thing is remediating that data you know, where do I have duplicate data? I may have often times in an organization. Uh, data will get duplicated. So somebody will take a snapshot of the data, you know, and then end up building a new application, which suddenly becomes dependent on that data. So it's not uncommon for an organization of 20 master instances of a customer, and you can see where that will go. And trying to keep all that stuff in sync becomes a nightmare all by itself. So you want to sort of understand where all your redundant data is? So when you go to the cloud, maybe you have an opportunity here to do you consolidate that that data, >>then what? You figure out what to get rid of our actually get rid of it. What's what's next? >>Yes, yes, that would be the next step. So figure out what you need. What, you don't need you Often times I've found that there's obsolete columns of data in your databases that you just don't need. Or maybe it's been superseded by another. You've got tables have been superseded by other tables in your database, so you got to kind of understand what's being used and what's not. And then from that, you can decide. I'm gonna leave this stuff behind or I'm gonna I'm gonna archive this stuff because I might need it for data retention where I'm just gonna delete it. You don't need it. All were >>plowing through your steps here. What's next on the >>journey? The next one is is in a nutshell. Preserve your data format. Don't. Don't, Don't. Don't boil the ocean here at music Cliche. You know, you you want to do a certain degree of lift and shift because you've got application dependencies on that data and the data format, the tables in which they sent the columns and the way they're named. So some degree, you are gonna be doing a lift and ship, but it's an intelligent lift and ship. The >>data lives in silos. So how do you kind of deal with that? Problem? Is that is that part of the journey? >>That's that's great pointed because you're right that the data silos happen because, you know, this business unit is start chartered with this task. Another business unit has this task and that's how you get those in stance creations of the same data occurring in multiple places. So you really want to is part of your cloud migration. You really want a plan where there's an opportunity to consolidate your data because that means it will be less to manage. Would be less data to secure, and it will be. It will have a smaller footprint, which means reduce costs. >>But maybe you could address data quality. Where does that fit in on the >>journey? That's that's a very important point, you know. First of all, you don't want to bring your legacy issues with U. S. As the point I made earlier. If you've got data quality issues, this is a good time to find those and and identify and remediate them. But that could be a laborious task, and you could probably accomplish. It will take a lot of work. So the opportunity used tools you and automate that process is really will help you find those outliers that >>what's next? I think we're through. I think I've counted six. What's the What's the lucky seven >>Lucky seven involved your business users. Really, When you think about it, you're your data is in silos, part of part of this migration to cloud as an opportunity to break down the silos. These silence that naturally occurs are the business. You, uh, you've got to break these cultural barriers that sometimes exists between business and say so. For example, I always advise there's an opportunity year to consolidate your sensitive data. Your P I. I personally identifiable information and and three different business units have the same source of truth From that, there's an opportunity to consolidate that into one. >>Well, great advice, Lester. Thanks so much. I mean, it's clear that the Cap Ex investments on data centers they're generally not a good investment for most companies. Lester really appreciate Lester Water CTO of Iot Tahoe. Let's watch this short video and we'll come right back. >>Use cases. Data migration. Accelerate digitization of business by providing automated data migration work flows that save time in achieving project milestones. Eradicate operational risk and minimize labor intensive manual processes that demand costly overhead data quality. You know the data swamp and re establish trust in the data to enable data signs and Data analytics data governance. Ensure that business and technology understand critical data elements and have control over the enterprise data landscape Data Analytics ENABLEMENT Data Discovery to enable data scientists and Data Analytics teams to identify the right data set through self service for business demands or analytical reporting that advanced too complex regulatory compliance. Government mandated data privacy requirements. GDP Our CCP, A, e, p, R HIPPA and Data Lake Management. Identify late contents cleanup manage ongoing activity. Data mapping and knowledge graph Creates BKG models on business enterprise data with automated mapping to a specific ontology enabling semantic search across all sources in the data estate data ops scale as a foundation to automate data management presences. >>Are you interested in test driving the i o ta ho platform Kickstart the benefits of data automation for your business through the Iot Labs program? Ah, flexible, scalable sandbox environment on the cloud of your choice with set up service and support provided by Iot. Top Click on the link and connect with the data engineer to learn more and see Iot Tahoe in action. Everybody, we're back. We're talking about enterprise data automation. The hashtag is data automated and we're going to really dig into data migrations, data migrations. They're risky, they're time consuming and they're expensive. Yousef con is here. He's the head of partnerships and alliances at I o ta ho coming again from London. Hey, good to see you, Seth. Thanks very much. >>Thank you. >>So let's set up the problem a little bit. And then I want to get into some of the data said that migration is a risky, time consuming, expensive. They're they're often times a blocker for organizations to really get value out of data. Why is that? >>I think I mean, all migrations have to start with knowing the facts about your data. Uh, and you can try and do this manually. But when you have an organization that may have been going for decades or longer, they will probably have a pretty large legacy data estate so that I have everything from on premise mainframes. They may have stuff which is probably in the cloud, but they probably have hundreds, if not thousands of applications and potentially hundreds of different data stores. >>So I want to dig into this migration and let's let's pull up graphic. It will talk about We'll talk about what a typical migration project looks like. So what you see, here it is. It's very detailed. I know it's a bit of an eye test, but let me call your attention to some of the key aspects of this, uh and then use if I want you to chime in. So at the top here, you see that area graph that's operational risk for a typical migration project, and you can see the timeline and the the milestones That Blue Bar is the time to test so you can see the second step. Data analysis. It's 24 weeks so very time consuming, and then let's not get dig into the stuff in the middle of the fine print. But there's some real good detail there, but go down the bottom. That's labor intensity in the in the bottom, and you can see hi is that sort of brown and and you could see a number of data analysis data staging data prep, the trial, the implementation post implementation fixtures, the transition to be a Blu, which I think is business as usual. >>The key thing is, when you don't understand your data upfront, it's very difficult to scope to set up a project because you go to business stakeholders and decision makers, and you say Okay, we want to migrate these data stores. We want to put them in the cloud most often, but actually, you probably don't know how much data is there. You don't necessarily know how many applications that relates to, you know, the relationships between the data. You don't know the flow of the basis of the direction in which the data is going between different data stores and tables. So you start from a position where you have pretty high risk and probably the area that risk you could be. Stack your project team of lots and lots of people to do the next phase, which is analysis. And so you set up a project which has got a pretty high cost. The big projects, more people, the heavy of governance, obviously on then there, then in the phase where they're trying to do lots and lots of manual analysis, um, manual processes, as we all know, on the layer of trying to relate data that's in different grocery stores relating individual tables and columns, very time consuming, expensive. If you're hiring in resource from consultants or systems integrators externally, you might need to buy or to use party tools. Aziz said earlier the people who understand some of those systems may have left a while ago. CEO even higher risks quite cost situation from the off on the same things that have developed through the project. Um, what are you doing with Ayatollah? Who is that? We're able to automate a lot of this process from the very beginning because we can do the initial data. Discovery run, for example, automatically you very quickly have an automated validator. A data met on the data flow has been generated automatically, much less time and effort and much less cars stopped. >>Yeah. And now let's bring up the the the same chart. But with a set of an automation injection in here and now. So you now see the sort of Cisco said accelerated by Iot, Tom. Okay, great. And we're gonna talk about this, but look, what happens to the operational risk. A dramatic reduction in that, That that graph and then look at the bars, the bars, those blue bars. You know, data analysis went from 24 weeks down to four weeks and then look at the labor intensity. The it was all these were high data analysis, data staging data prep trialling post implementation fixtures in transition to be a you all those went from high labor intensity. So we've now attacked that and gone to low labor intensity. Explain how that magic happened. >>I think that the example off a data catalog. So every large enterprise wants to have some kind of repository where they put all their understanding about their data in its price States catalog. If you like, imagine trying to do that manually, you need to go into every individual data store. You need a DB, a business analyst, reach data store. They need to do an extract of the data. But it on the table was individually they need to cross reference that with other data school, it stores and schemers and tables you probably with the mother of all Lock Excel spreadsheets. It would be a very, very difficult exercise to do. I mean, in fact, one of our reflections as we automate lots of data lots of these things is, um it accelerates the ability to water may, But in some cases, it also makes it possible for enterprise customers with legacy systems take banks, for example. There quite often end up staying on mainframe systems that they've had in place for decades. I'm not migrating away from them because they're not able to actually do the work of understanding the data, duplicating the data, deleting data isn't relevant and then confidently going forward to migrate. So they stay where they are with all the attendant problems assistance systems that are out of support. You know, you know, the biggest frustration for lots of them and the thing that they spend far too much time doing is trying to work out what the right data is on cleaning data, which really you don't want a highly paid thanks to scientists doing with their time. But if you sort out your data in the first place, get rid of duplication that sounds migrate to cloud store where things are really accessible. It's easy to build connections and to use native machine learning tools. You well, on the way up to the maturity card, you can start to use some of the more advanced applications >>massive opportunities not only for technology companies, but for those organizations that can apply technology for business. Advantage yourself, count. Thanks so much for coming on the Cube. Much appreciated. Yeah, yeah, yeah, yeah

(upbeat jazz music) >> Narrator: From our studios in the heart of Silicon Valley, Palo Alto, California, This is a Cube Conversation. >> Everyone welcome to this Cube Conversation here in Palo Alto, California. I'm John Furrier host of the Cube, here in the Cube Studios. We have Dom Wilde the CEO of SnapRoute, and Glenn Sullivan co-founder of SnapRoute hot startup. You guy are out there. Great to see you again, thanks of coming on. >> Good to see you. >> Appreciate it. >> Thanks. >> Your famous you got done at Apple, we talked about last time. You guys were in buildup mode, bringing your product to market. What is the update? You guys are now out there with traction. Dom give us the update. What's going on with the company? Quick update. >> Yeah, so if you remember we've built the sort of new generation of networking, targeted at the next generation of cloud around distributed compute networking. We have built Cloud Native microservices architecture from the ground up to reinvent networking. We now have the product out. We released the product back at the end of February of this year, 2019. So we're out with our initial POCs, we've got a couple of initial deals already done. And a couple customers of record and we deployed up and running with a lot of interest coming in. I think it's kind of one of the topics we want to talk about here is where is the interest coming from and where is this sort of new build out of networking, new build out of cloud happening. >> Yeah I want to get the detail on that traction but real quick what is the main motivator for some of these interest points? Obviously you got traction. What is the main traction points? >> So a couple things, number one, people need to be able to deploy apps faster. The network has always traditionally got in the way. It's been a inhibitor to the speed of business. So, number one, we enable people to deploy applications much faster because we're sort of integrating networking with the rest of the infrastructure operational model. We're also solving some of the problems around, or in fact, all of the problems around how do you keep your network compliant and security patched. And make it easier for operations teams to do those things and get security updates done really really quickly. So there's a whole bunch of operational problems that we're solving and then we're also looking at some of the issues around how do we have both a technology revolution in networking but also a economic revolution. Networking is just too expensive and always has been. So we've got quite a works of revolutionary model there in terms of bringing the cost of networking down significantly. >> Glenn, as the co-founder, as the baby starts to get out there and grow up, what's your perspective? Are you happy with things right now or how are things going on your end? >> Absolutely, the thing that I'm proudest of is the innovation that the team has been able to drive based on having folks that are real experts in Kubernetes, DevOps, and networking, all sitting in one room solving this problem of how you manage a distributed cloud using tool sets that are Cloud Native. That's really what I'm proudest of is the technology that we've been able to build and demonstrate to folks. Because nobody else can really do what we're doing with this mix of DevOps and Kubernetes, and Cloud Native engineering. Like general network protocol and systems people. >> You know it's always fun to interview the founders, and being an entrepreneur myself, sometimes where you get is not always where you thought you'd end up. But you guys always had a good line of sight on this Cloud Native shift in the modern infrastructure. >> Glenn: Right. >> You did work at Apple we talked about it in our last conversation. Really with obviously leading the win, they had pressure from the marketplace selling trillion dollar valuation company. But that was a early indicator. You guys had clear line of sight on this new modern architecture, kind of the cloud 2.0 we were talking about before we came on camera. This is now developing, right? So you guys are now in the market, you're riding that wave. It's a good wave to be on because certainly app developers are talking about microservices, or you talking about Kubernetes, talking about service meshes, stateful data. All these things are now part of the conversation but it's not siloed organizations doing it. So I want to dig into this topic of what is cloud 2.0. How do you guys define this cloud 2.0 and what is cloud 1.0? And then lets talk about cloud 2.0. >> Yeah, so cloud 1.0, huge success. The growth of the hyperscale vendors. You've got the success of Amazon, or Microsoft, Azure, and all of these guys. And that was all about the hyper-centralization of data, bringing all the desperate data centers that enterprises used to run and all that infrastructure into relatively a few locations. A few geographic locations and hyper-centralizing everything to support SaaS applications. Massively successful because really what cloud 1.0 did was it made infrastructure invisible. You could be an application developer and you didn't have to manage or understand infrastructure, you could just go and deploy your applications. So, the rise of SaaS with cloud 1.0. Cloud 2.0 is actually a evolution in our mind. It's not an alternative, it's actually an evolution that compliments what those vendors did with cloud 1.0. But it's actually... It's actually distributing data. So we pulled everything to central and now what we're seeing is that the applications themselves are developing such that we have new use cases. Things like enhanced reality and retail. We have massive sensor networks that are generating enormous amounts of data. We have self-driving cars where, you know, that need rapid response for safety things. And so what happens is you have to put compute closer to the devices that are generating that data. So you have to geographically now disperse and have edge compute and obviously the network that goes with that to support that. And you have to push that out into thousands of locations geographically. And so cloud 2.0 is this move of we've got this whole new class of cloud service providers and some regional telcos and things who are reinventing themselves, and saying, "Hey we can actually provide "the colos, we can provide the smaller locations "to host these edge compute capabilities." But what that creates is a huge networking problem. Distributed networking in massively distributed cases is a really big problem. What it does is it amplifies all of the problems that we coped with in networking for many years. I mean, Glenn, you can talk about this right? When you were at Apple one of the first realtime apps was Siri. >> Yeah, and I know it. Lets get back to the huge networking problem but I want to stay on the thread of cloud 2.0. Glenn, you were talking about that before we came on camera. He referenced that you worked for a time at Apple. Kind of a peak into the future around what cloud 2.0 was. Can you elaborate on this notion of realtime, latency, as an extension to the success of cloud 1.0? >> Right, so we saw this when we were deploying Siri. Siri was originally just a centralized application, just like every other centralized application. You know, iTunes. You buy a song, it doesn't really have to have that much data about you when you're buying that song. You go and you download it via the CDN and it gets it to you very quickly, and you're happy and everything's great. But Siri kind of changed that because now it has to know my voice, it has to know what questions I ask, it has to know things about me that are very personal. And it's also very latency sensitive, right? The quicker that it gets me a response the more likely I am to use it, the more data it gets about me the better the answers get. Everything about it drives that the data has to be close to the edge. So that means the network has to be a lot bigger than it was before. >> And this changes the architectural view. So just to summarize what you said is, iTunes needs to know a lot about the songs that it needs to deliver to. >> Glenn: Right. >> The network delivers it, okay easy. >> Glenn: Right. >> If you're clicking. But with the voice piece that kind of changed the paradigm a little bit because it had to be optimized and peaked for realtime, low latency, accuracy. Different problem set, than say, the iTunes. >> Glenn: Exactly. >> So they've networked together. >> Language specific, right? So, where is the user, what language are they speaking, how much data do we have to have for that language? It's all very very specific to the user. >> So cloud 2.0 is if I can piece this together is cloud 1.0 we get it, Amazon showcased there. It's kind of data, it's a data problem too. It's like AI, you seen the growth of AI validate that. It's about data personalization, Siri is a great example. Edge where you have data (chuckles) that needs to integrate into another application. So if cloud 1.0 is about making the infrastructure invisible, what is cloud 2.0 about? What's the main value proposition? >> To me it's about extracting the value from the data and personalizing it. It's about being able to provide more realtime services and applications while maintaining that infrastructure invisibility paradigm. That is still the big value of any cloud, any public cloud offering, is that I don't want to own the infrastructure, I don't want to know about it, I want to be able to use it and deploy applications. But it's the types of applications now and it's the value that the applications are delivering has changed. It's not just a standard SaaS application like Workday for instance, that is still a very static application-- >> John: It's a monolithic application, yeah. >> These are realtime apps, they're operating realtime. If you take an autonomous car, right? If I'm about to crash my car and the sensors are all going off, and it needs to brake and it needs to send information back and get a response. I want all that to happen in realtime, I don't want to sort of like have-- >> In any extraction layer of any layer of innovation 1.0, 2.0, as you're implying advancement. It's still an application developer opportunity, Glenn, right? >> Absolutely. >> Because at the end of the day the user expectations changed because of the experience that they're getting-- >> Yeah and it only gets worse right? Because the more network that I have the more distributed the network is, the harder it is to manage it. So if you don't take that network OS, the really really boring, not very exciting thing, and treat it the same way you always have. And try to take what you learned in the data center and apply to the edge, you lose the ability to really take advantage of all the things that we've learned from the Cloud Native era a from the public cloud 1.0, right? I mean just look at containers for instance, containers have taken over. But you still see this situation where most of the applications that are infrastructure based aren't actually containerized themselves. So how can they build upon what we've learned from pubic cloud 1.0 and take it to that next level, unless you start replacing the parts of the infrastructure with things that are containerized. >> This just is a side note, just going through my head right now. It's going to be a huge conflict between who leads the innovation in the future. >> Glenn: Absolutely. >> On premises or cloud. And that's going to be an interesting dynamic because you could argue that containerization and networking is a trend in mixed tense to be Cloud Native but now you got it on premises. It's going to be a dynamic we're going to have to watch. But you mentioned, Dom, about this huge networking problem that evolves out of cloud 2.0. >> Dom: Absolutely. >> What is that networking problem? And what specifically is a directionally correct solution for that problem? >> So I think the biggest problem is an operational one. In the cloud 1.0 era and even prior to that when we were in a hosted enterprise data centers, we've always built data centers and the applications running with them, with the assumption that there are physically expert resources there. That if something goes wrong, they can hands-on do something about it. With cloud 2.0 because it's so distributed, you can't have people everywhere. And one of the challenges that has always existed with networking technology and architecture is it is a very static thing. We set it, we forge it, we walk away, and try not to touch it again because it's pretty brittle. 'Cause we know that if we do touch it, it probably breaks and something goes wrong. And we see today a ton of outages, we were talking about a survey the other day that says the second biggest cause of outages in the cloud age is still the network. It's an operational problem whereby I want to be able to go and now touch these thousands of devices for... Usually I'm fixing a bug or I want to add a feature but more and more it's about security. It's more about security compliance, and I want to make sure that all my security updates are done. With a traditional network operating system, we call it The Monolith, all of the features are in big blob. You can turn them off but you can't remove them. So it's a big blob and all of those features are interdependent. When you have to do a security patch in a traditional model, what happens is that you actually are going to replace the blob. And so you're going to remove that and put a new blob in place. It's a rip and replace. >> And that's a hard enough operational problem all on it's own because when you do that you sort of down things and up things. So consequently-- >> And anyone who's done any location shifting on hardware knows it's a multi-day/week operation. >> It is but, ya know, and what people do is they overbuild the network, so they have two of everything. So it's when they down one, the other one stays up. When you're in thousands of geographic locations, that's really expensive to have two of everything. >> So the problem statement is essentially how do you have a functional robust network that can handle the kind of apps and IOT. Is that-- >> Yeah it is absolutely but as I said it's important to understand that you have this Monolith that is getting in the way of this robust network. What we've done is we've said, 'We'll apply Cloud Native technology in thinking.' Containerize the actual network operating system itself, not just the protocols, but the actually infrastructure services to the operating system. So if you have to security patch something or you have to fix something, you can replace an individual container and you don't touch anything else. So you maintain a known state for your network that devices is probably going to be way more reliable, and you don't have to interrupt any kind of service. So rather than downing and uping the thing you're just replacing a container. >> You guys built a service on top of the networks to make it manageable, make it more functional, is that-- >> We actually didn't build it. This is the beautiful part. If we built it then I would just be another network vendor that says, "Hey trust my propietary not-open solution. "I can do it better than everyone else." That would be what traditional vendors did with stuff like ISSU and things like that. We've actually just used Kubernetes to do that. So you've already trust Kubernetes, it came out of Google, everybody's adding to it, it's the best community project ever for distributed systems. So you don't have to trust that we've built the solution, you just trust in Kubernetes. So what we've done is we made the network native to that and then used that paradigm to do these updates and keep everything current. >> And the reason why you're getting traction is you're attractive to a network environment because you're not there to sell them more networking (laughs). >> Right. >> You're there to give them more network capability with Kubernetes. >> Yeah, well I mean-- Yeah we're attractive to a business for two reasons. We're attractive to the business because we enable you to move your business faster. You can deploy applications faster, more reliably, you can keep them up and running. So from a business perspective, we've taken away the pain of the network interrupting the business. From an operations perspective, from an IT operations network operations perspective, what we've done is we've made the network manageable. We've now, as you said, we've taken this paradigm and said what would've taken months of pretesting, and planning, and troubleshooting at two o'clock in the morning has now become a matter of seconds in order to replace a container. And has eased the burden operationally. And now those operational teams can do worthwhile work that is more meaningful than just testing a bunch of vendor fixes. >> Yeah, even though cloud 1.0 had networking in their computed storage, I think cloud 1.0 data would be about computing storage. cloud 2.0 is really about the network and all the data that's going around to help the app developers scale up their capability. >> Dom: Yeah, that's a great way to think about it. >> I was talking about the use cases. I think the next track that I'd love to dig in with you guys on is as you guys are pioneering this new modern approach, some of the use cases that you touch are probably also pretty modern. What specific use cases are you guys getting into or your customers are talking about. What are some of these cloud 2.0 use cases that you're seeing? >> Yeah, so one we already touched on was this sort of horizontally and generally was the security one. I mean security is everybody's business today. And it's a very very difficult networking problem, ya know, keeping things compliant. If you take for instance, recently Cisco announced that there was faulty vulnerabilities in their mainstream Nexus products. And that's not a terrible thing, it's normal course of business. And they put out the patches and the fixes and said, "Hey, here it is." But now when you think about the burden on any IT team. That comes out of the blue, they hadn't planned for it. Now they have to take the time to take a step back and what they have to do is say, well I've got this new code. I don't know what else was fixed or changed in there. So I now have to retest everything and retest all of my use cases, and I have to spend considerable time to do that to understand what else has changed. And then I have to have a plan to go out and deploy this. That's a hard enough problem in a centralized data center. Doing that across hundreds, if not thousands of geographically dispersed sights is a nightmare. But it's just, ya know, the new world we live in, this is going to happen more and more and more. And so being able to change that operational model to say actually this is trivial. And actually what you should be doing is doing these updates everyday to keep yourself compliant. >> Do the use cases Glenn, have certain characteristics? I mean, we're talking about latency and bandwidth that's a traditional networking kind of philosophy. Is there certain characteristics that these new use cases have? Is it latency and bandwidth, is there anything else? >> No it's mostly about bringing properties like CI/CD to networking, right? So the biggest thing we're seeing now is as people start to investigate disaggregated networking and new ways of doing things. They're not getting this free pass that they used to get for the network because the network isn't just an appliance anymore. When you had something that was from one of the three vendors you'd say, "Okay, that thing runs some version of Linux on it. "I don't know what it is. "Maybe it runs free SD in Juniper's case. "I don't understand what kernel it is, "I don't care just keep that thing up to date." But now it's like, "Oh I'm starting to "add more services to my network devices." Say in the remote sites I want to kickstart some servers with these network devices I install first, well that means that I have to start treating this thing like it's another server in my environment for my provisioning. That means that everything on that box has to be compliant just like it is in everything else. Lets not even get into personal credit card information, personal identifying information. Everything is becoming more and more heightened from a non-exemplary status. >> It's a surface area device, I mean it's part of the surface area. >> And if it's not inside a data center than it's even worse because you can't guarantee the physical security of that device as much as you could if it was inside a regular data center. >> So this is a new dynamic that's going on with the advent of security, regulatory issues, and also obviously the parameter being dismantled because of cloud. >> Glenn: Absolutely. >> Yeah, you also got specific use cases. There are multiple verticals and industries that are having these challenges. Retail is a good example, point-of-sale. Anywhere where you have the sort of a branch problem or mentality where you're running sophisticated applications, and by the way, people think of point-of-sale is not terribly sophisticated. It's incredibly sophisticated these days. Incredibly sophisticated. And there are thousands of these devices, hundreds of stores, thousands of devices, similar with healthcare. You know, again, distributed hospitals, medical centers, doctor's offices, etcetera. You have all running private mission critical data. I think one of the ones that we see coming is this kind of autonomous car thing. As we get IOT sensor networks, large amounts of data being aggregated from those. So there's lots of different use cases. We add on a lot of interest. And to be quite frankly, the challenge for us as a startup is keeping focused on just a few things today. But the number of things we're being asked to look at is just enormous. >> Well those tailwinds for you guys in terms of momentum, you have this cloud 2.0 trend. Which we talked about. But hybrid cloud and multi-cloud is essentially distributed cloud on edge? If you think about it. >> Yeah, yeah. >> And that's what most companies are going to do, they're going to keep there own premises and their going to treat it as either on their platform or an external remote location that's going to be everywhere, big surface area. So with that, what are some of the under the hood benefits of the OS? Can you go into more detail on that because I find that to be much more interesting to say the network architect or someone who's saying, "Hey you know what? "I got hybrid cloud right now. "I got Amazon, I know the future's coming on "to my front door step really fast. "I got to start architecting, I got to start hiring, "I got to start planning for distributed cloud "and distributed edge deployments." If not already doing it. So technical depth becomes an huge issue. I might try some things with my old gear or old stuff. They're in this mold, you know, a lot of people are in that mode. I'll do a little technical depth to learn but ultimately I got to build out this capability. What do you guys do for that? >> So the critical thing for us is that you have to standardize on an open non-proprietary orchestration layer, right? You can talk about containers and microservices all day long. We hear those terms all the time but what people really need to make sure that they focus on is that their orchestrator that managing those containers is open and non-proprietary. If you pull that from one of the current vendors it's going to be something that is network centric and it's going to be something that was developed by them for their use. Their basically saying here's another silo, keep feeding into it. Sure we give you API, sure we give you a way to programmatically configure the network but you're still doing it specifically to me. One of the smartest decisions we made besides just using Kubernetes as core infrastructure. We've also completely adapted their API structure. So if you already speak Kubernetes, if you understand how to configure network paradigms in Kubernetes, we just extend that. So now you can take somebody, who off the street might be a Cloud Native Kubernetes expert and say here's a little bit of networking, go to play the network, right? You just have to take the barrier down of what you have to teach them from this CLI and this API structure that's specific to this vendor, and then this CLI and this API structure. But the cool thing about what we're doing is we also don't leave the network engineers out in the cold, we've give them a fully Cloud Native network CLI that is just like everything else they're used to, but it's doing all this Cloud Native Kubernetes microservices containers stuff underneath to hide all that from them. So they don't have to learn it and that's powerful because we recognize because of our Ops experience, there's a lot of different people touching these boxes. Whether you put it in a ivory tower or not, you've got knocks that have to login and check 'em, you've got junior network admin, senior network engineers, architects. You've got Cloud Native folks, Kubernetes folks, everybody has to look at these boxes, so they all have to have ways that end of the switch, end of the routers that is native to what they understand. So that's very critical as to present data that makes sense to the audience. >> And also give them comfort to what they're used to like you said before. If they got whatever's running Linux on there, as long as it's operationally running, water's flowing through the pipes, your packets are moving through, their happy. >> Glenn: Right. >> But they got to have this new capability to please the people who need to touch the boxes and work with the network, and gives them some more capabilities. >> Right, it prevents you from building those silos which is really critical in the Cloud Native. And that's what public cloud 1.0 taught us, right? Is stop building these silos, these infrastructure silos and say okay, you look at AWS right now. There's AWS certified engineers, they're not network experts, they're not storage experts, their not compute experts, they're AWS experts. And you're going to see the same thing happen with Cloud Native. >> Cloud 3.0 is decimating the silos basically 'cause if this goes that next level, that's why horizontally scalable networks is the way to go, right? That's kind of what you were talking about about the use case. >> Yeah, I think all revolutionary ideas are all actually more transformational. Revolutions begin by taking something that is familiar and presenting it in a new way, and enabling somebody to do something different. So I think it's important as we approach this is to not just come in and go, 'Oh what you're doing is stupid, we have to replace it.' The answer is, what you're doing is obviously the right thing. But you've not been given the tools that enable you to take full advantage and achieve the full potential of the network as it relates to your business. >> And you guys know as well as we do is that the networking folks are, it's a high bar for them because you mentioned the security and the lockdown nature of networking. It's always been, you don't F with it because you think that thing is going to be, anyone who touches it, they need to be reviewed. So they're a hard customer to sell to. You got to align with their Ops mindset. >> I think the network operators have been, and Glenn, and our other co-founder have waxed theoretical about this. (laughter) But network operators have been forced to live in a world of no. Anytime the business comes to them and says, "Hey we need you to do X." The answer is no, because I know that if I touch my stuff it's going to break, or I'm limited in what I can do, or I can't achieve the timeframe that you're looking for. So the network has always been an inhibitor but the heroes of the moment are actually the network operations team. Because nobody knows that the network was an inhibitor. >> Well this is an interesting agile conversation we've been having this is our, here in our Cube Studios yesterday amongst our own team because we love agile content. Agile's different, agile is about getting to yes because iteration in a sense is about learning, right? So you have to say no, but you have to say no with the idea of getting to yes. Because the whole microservices is about figuring out through iteration and ultimately automation, what to tear down, what to. So I would see a trend where it's not the no Ops kind of guys, as they say, "No, no, no." It's no, don't mess with the current operational plumbing. >> Glenn: Right. >> But we got to get to yes for the new capabilities. So there's a shift in the Cloud Native. Your thoughts and reaction to that Glenn. >> Yeah, so it's basically like I set myself up so that I'm doing a whole drop the forklift with everything in there, like a crated replacement. Networking has always been this way. I'm not saying no to you, I'm just saying not right now. I do my maintenance three times a year on the third Sunday of the second month and the moon's in the right place, and I make sure that I've 50/60 changes. I've got 20 engineers on call, we do everything in order. We've got a rollback plan if something breaks. This is the problem. Network engineers don't do enough changes to build a muscle like the agile developers have seen or CI/CD developers have seen. Where it's like I do a little bit of changes everyday, if something breaks, I roll it back. I do a little bit of changes everyday, and if something breaks I roll it back. That's what we enable because you can do things without breaking the entire system, you can just replace a container, you can move on. In networking, the classic networking, you're stack modeling so many changes and so many new things that everything has to be a greenfield deployment. How many times have you heard that? Like, "Oh this thing would be perfect "for our Greenfield Data Center. "We're going to do everything different "in this Greenfield Data Center." And that doesn't work. >> You don't get a mulligan in network and you realize they say, look this is a good point, great conversation. I think that is a very good follow up topic because developing those muscles is an operational practice as well as understanding what you're building. You got to know what the outcome looks like, this is where we're starting to get into more of these agile apps. And you guys are at the front end of it, and I think this is a sea change, cloud 2.0. >> Yeah, it is. >> Quick plug for the company. Take the last minute to explain what you guys are up to, hiring, funding. What are you guys looking for? Give a quick plug for the company. >> Yeah, I mean, we're doing great. Always hiring, everybody always is if you're a cutting edge startup. We're always looking for great new talent. Yeah, we're moving forward with our next round of funding plans. We're looking at expanding the growth of the company or go to market. Doubling down on our engineering. We're just delivering now our Kubernetes fabric capabilities, so that's the next big functional release that we're actually already delivered the beta of. So taking Kubernetes and actually using it as a distributed fabric. So a lot of exciting things happening technology wise. A lot of customer engagements happening. So yeah, it's great. >> Glenn, what are you excited about now? Obviously Kubernetes, we know you're excited about. >> Oh yeah. >> But what's getting you excited. >> So the dual process that we have where we actually use, we're doing stuff in Kubernetes that nobody else is doing because we have a version that runs on the switch. And it manages all the containers local and then it also talks to a big controller. It's fixing that SDN issue, right? Where you have this SDN controller that manages everything in the data plane, and it controls my devices, and it uses open flow to do this. And it has a headless operation in case the controllers go away. Oh and if I need another controller, here's another one, so now I've got two controllers. It gets really messy, you got to buy a lot of gear to manage it. Now we're saying, 'Okay, you've got 'Kubernetes running local. 'You don't want to have a Kubernetes cluster, don't bother.' It just uses it autonomously. 'You want to manage it as a fabric like Dom says. 'Now you can use the Kubernetes fabric 'that you've already built. 'There are Kubernetes masters that 'you've already built for the applications.' And now we can start to really imbed some really neat operational stuff in there. Things that as a network engineer took me years of breaking stuff and then fixing it to learn, we can start putting those operational intelligence in the operating system itself to make it react to problems in the network and solve things before waking people up at three a.m.. >> This takes policy to a whole nother level. >> Absolutely. >> It's a whole nother intelligence layer. >> Yeah, if this is broken, do this, cut off the arm to save the rest of the animal. And don't wake people up and troubleshoot stuff, troubleshoot stuff during the day when everybody's there and happy and awake. >> Guys congratulations. SnapRoute, hot startup. Networking is the real area for cloud 2.0. You got realtime, you got data, you got to move packets from A to B, you got to store them, you got to move compute around, you need to (laughs) move stuff around the cloud to distribute to networks. Thanks for coming in. >> Thanks. >> Thank you. >> Appreciate it. >> Thanks for having us. >> I'm John Furrier for Cube Conversation here in Palo Alto which SnapRoute, thanks for watching. (upbeat jazz music)

>> Oh, from our studios in the heart of Silicon Valley. Palo ALTO, California.  This is a Cube Conversation. >> Hello Everyone. Welcome to this special cube conversation. I'm Childfree, host of The Cube, cofounder of Silicon Angle Media Inc and the Cube. We're here with Andy Cunningham, who is the president and founder of Cunning in collective and also the author of the book. Get to ah ha! Bestseller on line four categories on Amazon E book. Great book. I recommend all Andy. Welcome to the Cube. Great to see you. >> Hey, it's great to be here. Good to see you. You're a thought >> leader. Just what you've been. You've seen many ways of innovation. You've done so much in your career. >> Big, minimal experience. And >> we were all old here. We've no ageism issues here. It's silken angle, but But you've done so much on DH communications and PR. PR is part of communications. You've you've seen it all. You've done it all. And now you're helping cos I've got a great book out, which I recommend everyone should get getting toe really kind of breaks down thirty five years of experience into one book. That you had a talk about the book on your firm for stuff about Connie and collected quick pleasure. >> So Cunningham  >> Collective is a small marketing consultancy that focuses on positioning, which in my opinion, is the epicenter of marketing. If you dont position yourself well for success, you're never going to achieve success. So the >> book is >> about a framework for figuring out how to position yourself. And it's a framework I developed probably around seventeen years ago. But I've been using it over the last seventy years with clients, and I find that it's super successful, especially with technology companies, and because it's an actual step by step sort of framework. So the book tells you how to do it. And then there were six case studies at the back of the book that >> show >> Positioning in action. >> I want to get a book at some specific questions on the positioning, but I want to get your take on because you've seen many waves around PR public relations, which is corporate communications and communications in general. Over the years, where are we now? Because you're seeing you know, the media business change face. What's on the front page? Of all the news these days around how they sucked all the data in and fake news. All these things are happening Cos still need to get the word out. You know, New Channel's new realities take us through how you see the evolution of what the old way is in the new way are of communications. >> So PR was >> actually invented by a guy in the nineteen twenties named Eddie Bernays. And Eddie Bernays actually figured out that if you created a stunt like situation, you could get the journalist to cover it. He was very strategic about it. It sounds, sounds kind of, you know, loopy. But he was very strategic about it, and he actually invented the concept that he actually went to the phrase public relations, and he was modeling it after propaganda. That was the that was where he came up with that phrase. So it was like that for quite a long time until we got into an era of what I would call influence her marketing, you know, now we call it influencer marketing. But back in the you know when when there was a lot of investigative journalism going on, it was really just about who's who are the influencers that you need to influence in order to get them to say what you want them to say about your company or your product. So that was what my old boss, Regis McKenna called that, he said. She said, Journalism, if you're going to launch something into the marketplace, you need to get all the he said. And the she says to say what you want them to say before you actually say it yourself, because the journalists are gonna go back to those people and they're going to corroborate your story or not. So the idea was influenced the influencers. And then you can get your story that lasted for about probably thirty years, that era. Now we're in an era, then I call it's the era of content, marketing. And really, what happens today is you almost don't even need the journalists at all, because first of all, there aren't very many of them left. And second of all, there are so many channels available to ourselves as as communicators that if you build a digital footprint that has a great story and it that is compelling and consistent, and you keep saying the same key messages over and over again, you can build yourself a digital footprint that actually becomes starts to take over the word of mouth that we talked about earlier because we're the mouth is really what it's all about. But word of mouth hap and today because from results from a giant digital footprint about your story. >> I remember back in business school back in the day in the nineties when I got my MBA advertising class would break down. You need to copy strategy because, you know, reach media, print ads and radio really was the old school media and frequency was was a certain first radio print. You have time to read it so all the specs get laid out. Reaches reach, Right? So you broadcast cable or TV? The impression >> yeah, kind of digital brings >> everything kind of weaves it all together, but you mentioned frequency. Why is frequencies so important? Because is that because of the targeting, is that because there's not a lot of reaches more specialized? >> Well, it's still it's still the same reason. >> So there's a thing called the marketing rule of seven, and that means that a person needs to hear your message seven times before it. It seeps into their brain, and they actually either decide to do something about it or not do something about it. But that's what creates awareness seven times. So that still is true today as it was before. But now it's so much easier because now you don't have to buy ads to do it. You don't even have to pay a PR person to do it. You just fill your own social channels, your own website, your own blog's your own vlogs, your own video. You just fill up your own personal channels, however many there that you have with your own story. And then once it's out there as a digital footprint, then it's time to start talking to the journalism community, which is smaller than it used to be. But those who are left are pretty good. The Washington Post is pretty good. The New York Times is pretty good. So you call up the guy at The New York Times and you pitched him on your story, and instead of trying to spend a bunch of time pitching him, you just refer him back to someone of your channels. He Googles that he gets online, and he sees, Oh, my God, there's a giant story here because you've built the story. So you have so much more controlled today. We have so much more control over our stories. >> So the way to pitch, then based on what you're saying is to have the raw materials out there so they can make their story >> exactly. Put it together. We put it >> out there, and then the journalists just find it. It's like an Easter egg hunt. Look under that tree >> there. Well, here's a clip >> of an expert that's talking about something you might be interested in. This is the new model. Have the assets. Well, actually, we we love that came in what we do. But I want to get that to the book and the years of experience you have on this. But before we do that, I got to ask you when I was watching the Steve Jobs movie. You know, you're on the stage and you're part of that. >> You must get, well, an actress actress once you get your >> role. You were very instrumental, hectic days, people who know Steve and know the apple days. What >> did you >> learn from that? That's in the book from the Apple days. And how does and what has changed from the apple days. Now is there some things that are similar to the world's changed. But what are some of the key those key Learnings that that those magical moments. >> So my biggest >> key learning was ice. We spent about six months? Was Steve working on the messaging for the launch of the Macintosh, and we got it down to a Siri's of what I would I now call means that were just very, very. The computer for the rest of us was one of them, right? Everybody remembers that one small footprint was another one nobody remembers. Any more easy to use was another one. There was a Siri's of these things to explain the Macintosh. We then went through a process of educating one hundred journalists about about that and pumping them with those key messages at every juncture. Then we go to De Anza College and we did the big launch. We said those messages again and was a bunch of TV people around and everybody you know, everybody reported on it and I'm driving home in the car. After the show was over with, I turn on the radio and there's the messages that I had written, coming back at me over and over and again and change the station. Same thing over and over again. The Macintosh was launched today, and this is what everyone is saying. The same thing is, it was it gave me chills. It was like, Wow, this really works. And that lesson that I learned with Steve is the same lesson Eddie Bernays learned a hundred years ago. Its the same lesson Regis McKenna learned with influencing the influencers. And it's the same lesson people can learn today. You just you just get too. You get, too, ah ha! With a slightly different strategy. And today it's about building a big digital footprint before you ever talk to anybody. >> And I think this is key to the book of one of the things that you mentioned earlier. That's clearly in the book, and this is a lesson for the folks. Watching on and learn from this is that positioning is critical. Before the branding, the knee jerk reaction from most people. A new person Let's re branded system New Low goes out there. You're taking it a contrarian view on >> the sea >> or race on experience and success. Position first brand later or had second thoughts on that Wise wise is so important, specific successes you had. But what other reasons are important? >> Well, I got I learned this because >> the first part of my career I would I would get called in after somebody had already hired a branding firm and they re branded everything, Got a new new logo. New tagline, new color palette, all of this stuff and a few bits of copy that were really sexy and interesting. But they were finding it wasn't sticking. It wasn't making a difference in their in their sales, because, really, at the end of the day, we're all here to sell stuff, right? So I would come in and I would realize, Oh my God, you did all this first you didn't figure out your positioning strategy. Like what? Who are you in the market? And why do you matter? Those two questions are the two most important questions anybody can ask themselves. Is a market or a CEO? Who are you and why do you matter if you can't answer those questions? Doing a branding exercise is a waste of money. >> Talk about >> the conflict involved when you work for the client or when you have to get to this moment. This Ahamo sometimes is not a parent, sometimes is pretty clear. Sometimes you might think you're one, but you're really another. There's always maybe opinions about what, what people are in terms of a company internally amongst executives or the stakeholders. >> Yeah, how do you How do you figure it out? Is heroic >> golden rule or what's your What's your Tell them how to get to that moment of that self reflection >> is sure that sort of that's actually >> the key point of the book. It's it's based positioning. Really good positioning should be based on what your DNA as a company is, and the book tells you how to determine what is your DNA. But the the end of the day. They're three kinds of companies. There are product focus cos I happen to call them mechanics. There are customer focus, cos I call them mothers, and they're our concept, Focus Cos I call them missionaries. And interestingly, each of these types of companies do things entirely differently. They talk about different things and meetings. They hire different kinds of people. They train them differently. They measure success differently. They market themselves differently. There's actually, the DNA is reflected in there actions. So when I'm sitting around a workshop with a client, we have to determine Are they a mother? Are they a missionary or are there mechanic before we can actually figure out how to create marketing around them? So that's the biggest thing is there's some people over here. So we're a product company. These peoples, they know we're trying to change the world. And these people say, No, no, no, we're all about the customer and the discussion that you have around that is actually the where the ah ha moment comes When you decide okay, we really are a customer focus company doesn't mean the other two things go away. They just take a back seat to the marketing. So everybody has to agree that that's what they're going to move forward with. And that's what makes it. It's so much fun. It's like it's like doing and Myers Briggs test for a company. You know, everybody loves that, right? Oh, I'm in I n t j M e. And whatever the >> letters it was, I'm not that I'm really something else, >> but there's always confident. But >> you >> also mentioned the book that people can change, too. So you start out as something. Maybe a missionary evolve based upon the business changed. Talk about that, >> Yeah. So let's talk about Apple >> for a second cause that's the company that definitely was a missionary, and missionaries exist to change behavior on a fundamental level. And that was what Steve Jobs was all about, right? So when >> he was >> running the company even before he was running it, but he was a big influence, or there he basically was a missionary company. He was trying to change behavior, and that's what the Macintosh was all about. But after he passed away, he left the assets of the company in the hands of Tim Cook, who, by the way, is an amazing, amazing caretaker of those assets. I mean, he's grown them. He's turned them into it, turned the company into one of the world's most valuable companies. But unfortunately, he's not a missionary, and what he has done is he has kind of tried to keep the missionary thing going. But he hasn't been successful doing that. So what's happened is the market is turning Apple into a product focus company, and the leadership is not steering the company that direction they are trailing, so it's happening to ample, in other words. So you're going to start to see Apple focus more on Warren product over the years, which they which they have been. But they're starting to have some product issues, and I think that's the result of them, not it's tearing the company directly into this, >> finding that DNA and get filling the young count or hiring people toe >> exactly. Exactly. >> Just on that same point. Amazon is a company that is doing this to the market. So Amazon started as a product company, and now they've steered their steering themselves purposefully into a customer focus company. And if you go online and check out their new mission statement, it's to be Earth's most customer centric company. And this is the reason Jeff Bezos bought Zappos a number of years ago Wasn't because Jeff couldn't figure out how to sell shoes online. Of course he could. It was because he was buying that customer centric culture, So he's purposefully steering the company into the customer direction so >> you can change your DNA, >> but it ain't easy. >> I've any Jesse. Many times become a good friend on the Cube as well. He's the word customer so many times we can see the frequency, but they've been talking customer for a long time. So you say they were product company >> with his Amazon. Amazon lands >> on Web services. The missionary and a product focus because I think product would be. I think it's safe once >> I think early, early, early >> on meaning they started this customer transition probably five, six years ago, so but they were very much early on a product company, I think in bases his head. They were actually a missionary. But he never he never would go out and say that. What did he say about Amazon? Were online bookseller and oh, by the way, books are going so well now we're going to do music, and now we're going to, you know. And then >> it's product. >> It took about its product. It was product product product until he decided that he was going to eat the universe one bite at a time. And so, in order to be successful with that, he has to have a customer he feels he has to have customer relationships that are going to stick with him over the course of a lifetime. >> So you know a little about the Cube. What's the Cube? What are we? >> I think you're a missionary. I mean, you're trying to change >> behavior on a fundamental level, and, you know it's, um it's amazing what you've done. You know, we had this great conversation beforehand, and I learned about all the new things you're working on, and it's groundbreaking, groundbreaking stuff. >> Okay, Final question on the book is the funniest. Our craziest reaction you've had to it, either someone emailing You owe our ceremonial because it's pretty inspiring. You break it down free simply. But it's really a core fundamental practice. And I've read a lot of marketing books in my day. A lot of you know, these office come out. Process improvement. This is cuts to the chase. It's >> really thank you. Thank you. What's the big waves >> you heard or crazy? >> Well, I this is This is the >> most recent thing I can think of. I I ended up becoming number number one on Amazon's e book thing and four categories, just like two weeks ago, and I got Mohr social media coverage on that than >> anything else in my entire life with the most amazing >> thing that I've ever seen in all these. Congratulations. And, you >> know, they're they're categories. >> Not like this. Not like your New York Times best seller. It's like you're the best multi marketing, you know, book here, The best small business marketing book, those kinds of things. And it just was just blew up. It went viral. >> That's how it was all online. What made you write the book was That was the moment. When was the ah ha moment for you saying, You know what? I got to put the book together. Was it something that you had in mind? That you get this data collecting of institutional knowledge of the trade? When was the ah, ha moment for you to write the book? >> Well, I this framework that I developed here has been working for me really successfully for, like, seventeen years. And I just decided that wow, other people should know how to do this. You know, because when we charge when we hired when that when we hire when someone hires us, it's like one hundred fifty thousand dollars worth of worth of work to do what we do, they could do it for twenty two ninety nine or whatever the heck >> this thing costs these days. And you could occasionally you get a book out there to get an audio book as well. So s so I really wanted >> to spread the word about this framework in this methodology, cause I really believe that my, my inside my core of myself, that the epicentre of great marketing is positioning. And if you don't get that right, you will never succeed with any of the rest of it. So do >> the great folks. You have a great track record. I've seen personal your sex success of up close perambulations on that. Let's talk about cos now I want to get backto successful companies. He's a lot of conversation. I'd build a rocket ship. So you we live in Silicon Valley. There are rocket >> ships that there are, >> you know, go big or go home. Blitz Scaling his Reid, Hoffman would say, I endorse that one hundred percent think there's use cases clearly for blitz scaling. Other people have been throwing him under the bus saying that culture is not what we want and build a still stable business. And so the debate aside, there's two types of companies there's the Okay, I'm going to build this company. I might not know when they're when the growth's gonna be there. And then there's the big venture back category changer rocket ships. Can you talk about the success criteria in your mind of both companies around positioning approaches, things that you've seen in the past that work well, >> I think companies that understand who they are and why they matter are the ones that succeed. And it's also important that they have a good leader, a good, strong leader. But if you don't know who you are and why you matter, you can't build a new category. You can't even launch a new product. So I, >> you know, take a look at some of the companies that have done that. Well, Netflix has done that extremely well, right? Airbnb has done that extreme slack has done that really well. Microsoft is doing it really well again, right? They went through a downtime, and now you know their new CEO, Satya Nadella, is doing an unbelievable job with positioning. There's so much a product company, and he's not trying to make them into a customer. Companies trying to double down on the product so and Netflix is a is a missionary company there change behaviour on a fundamental >> of Microsoft's a great example because I think that's something into anything radical. In the product side, they looked at the tailwind of Cloud computing an A I and said, Let's throw the sails up there and let's let's get around behind it >> and grand source. >> And then they branded it. So they positioned themselves as a Claude company, and then they branded it. As as you're so >>On the tail winds concept of trends, Pat Gelsinger said that if you're not out in front of that next wave, you could be driftwood. Riding the waves are certainly a big part of jumping on a successful or tail wind some call it how important that have that positioning time to something that's trendy or something. >> Oh, that's a great question, because it's because the context in which you are actually putting something into the market is critical. So you have to really understand what are the waves that you want to ride and can ride. And don't try to be riding a wave that passed five years ago. Or that hasn't shown up yet. You might think there's a wave coming. That's the biggest danger of a lot of these high tech start ups is that they see a vision of something way down the line, and there's no way for them to ride today. And they launched their technology. But too early >> and to your point. If they don't have the positioning right, they won't be able to ride it. You >> know what they want. They won't be able to ride it. So if they if if they did a proper positioning exercise before that, they would realize that they're context in which they're doing this is not right for what they're saying. So have to pivot a little bit. These is where pivots come from, right? We have to pivot a little bit to make yourself relevant for the market today, and that's an important thing. >> Andy. Final question for the folks watching saying, I love the book. I'm gonna get it might have helped might need help and saying I need to call Andy and the team or figure it out. What are some of the tell signs that they're not getting it right or what? If some things when they need to call for help and howto people moved to the next level, some people might say, Hey, you know, we need help. We can't get concensus. The leader might not be strong enough to be a leadership transition. Could be a new wave that people have identified. Yeah. What? This is a tough challenge of self awareness. What is that? Some of the tell signs And how does >> > somebody actually make the change? It is a tough, and most CEOs are not into it enough of themselves to know to know those things. So what happens is they launch it and then they don't get traction. So the biggest reason why people call me is they're not getting traction. Now, the really the really smart ones do more analysis, like what you're talking about. Oh, there's something has changed in the context. So I better shift this or, you know, a competitors come up with something that sounds awful on awful lot like ours. Maybe we better get ahead of that. But that takes a really strategic CEO. And there are some of those out there, But not everyone is >> okay. So great book here. Getting toe, huh? Everyone great. It's a good thing I read. It. Came out the day. Volante. He's reading it. Thanks for coming out. Spend the time, John communications. Final word on the communications world. What's the message to folks out there? See, M O's out there and head of communications. What's the future look like for them? What should they do? Going forward to be successful? >> Well, the future of marketing is is really figuring out how to make word of mouth, you know, explode word of mouth, because that's why people buy things. You know, you told me I should check out this product or my book. He said, You told your friends I should check out the books, So he does. So it's all about word of mouth and starts with building a big digital footprint yourself and then going to the peak to the press side. >> Andy cutting him here in Palo Alto Studios. I'm John for with Keep conversations. Thanks for watching