(upbeat music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase AI/ML Top Startups Building Foundation Model Infrastructure. This is season three, episode one of our ongoing series covering exciting startups from the AWS ecosystem to talk about data and analytics. I'm your host, Lisa Martin and today we're excited to be joined by two guests from Astronomer. Steven Hillion joins us, it's Chief Data Officer and Jeff Fletcher, it's director of ML. They're here to talk about machine learning and data orchestration. Guys, thank you so much for joining us today. >> Thank you. >> It's great to be here. >> Before we get into machine learning let's give the audience an overview of Astronomer. Talk about what that is, Steven. Talk about what you mean by data orchestration. >> Yeah, let's start with Astronomer. We're the Airflow company basically. The commercial developer behind the open-source project, Apache Airflow. I don't know if you've heard of Airflow. It's sort of de-facto standard these days for orchestrating data pipelines, data engineering pipelines, and as we'll talk about later, machine learning pipelines. It's really is the de-facto standard. I think we're up to about 12 million downloads a month. That's actually as a open-source project. I think at this point it's more popular by some measures than Slack. Airflow was created by Airbnb some years ago to manage all of their data pipelines and manage all of their workflows and now it powers the data ecosystem for organizations as diverse as Electronic Arts, Conde Nast is one of our big customers, a big user of Airflow. And also not to mention the biggest banks on Wall Street use Airflow and Astronomer to power the flow of data throughout their organizations. >> Talk about that a little bit more, Steven, in terms of the business impact. You mentioned some great customer names there. What is the business impact or outcomes that a data orchestration strategy enables businesses to achieve? >> Yeah, I mean, at the heart of it is quite simply, scheduling and managing data pipelines. And so if you have some enormous retailer who's managing the flow of information throughout their organization they may literally have thousands or even tens of thousands of data pipelines that need to execute every day to do things as simple as delivering metrics for the executives to consume at the end of the day, to producing on a weekly basis new machine learning models that can be used to drive product recommendations. One of our customers, for example, is a British food delivery service. And you get those recommendations in your application that says, "Well, maybe you want to have samosas with your curry." That sort of thing is powered by machine learning models that they train on a regular basis to reflect changing conditions in the market. And those are produced through Airflow and through the Astronomer platform, which is essentially a managed platform for running airflow. So at its simplest it really is just scheduling and managing those workflows. But that's easier said than done of course. I mean if you have 10 thousands of those things then you need to make sure that they all run that they all have sufficient compute resources. If things fail, how do you track those down across those 10,000 workflows? How easy is it for an average data scientist or data engineer to contribute their code, their Python notebooks or their SQL code into a production environment? And then you've got reproducibility, governance, auditing, like managing data flows across an organization which we think of as orchestrating them is much more than just scheduling. It becomes really complicated pretty quickly. >> I imagine there's a fair amount of complexity there. Jeff, let's bring you into the conversation. Talk a little bit about Astronomer through your lens, data orchestration and how it applies to MLOps. >> So I come from a machine learning background and for me the interesting part is that machine learning requires the expansion into orchestration. A lot of the same things that you're using to go and develop and build pipelines in a standard data orchestration space applies equally well in a machine learning orchestration space. What you're doing is you're moving data between different locations, between different tools, and then tasking different types of tools to act on that data. So extending it made logical sense from a implementation perspective. And a lot of my focus at Astronomer is really to explain how Airflow can be used well in a machine learning context. It is being used well, it is being used a lot by the customers that we have and also by users of the open source version. But it's really being able to explain to people why it's a natural extension for it and how well it fits into that. And a lot of it is also extending some of the infrastructure capabilities that Astronomer provides to those customers for them to be able to run some of the more platform specific requirements that come with doing machine learning pipelines. >> Let's get into some of the things that make Astronomer unique. Jeff, sticking with you, when you're in customer conversations, what are some of the key differentiators that you articulate to customers? >> So a lot of it is that we are not specific to one cloud provider. So we have the ability to operate across all of the big cloud providers. I know, I'm certain we have the best developers that understand how best practices implementations for data orchestration works. So we spend a lot of time talking to not just the business outcomes and the business users of the product, but also also for the technical people, how to help them better implement things that they may have come across on a Stack Overflow article or not necessarily just grown with how the product has migrated. So it's the ability to run it wherever you need to run it and also our ability to help you, the customer, better implement and understand those workflows that I think are two of the primary differentiators that we have. >> Lisa: Got it. >> I'll add another one if you don't mind. >> You can go ahead, Steven. >> Is lineage and dependencies between workflows. One thing we've done is to augment core Airflow with Lineage services. So using the Open Lineage framework, another open source framework for tracking datasets as they move from one workflow to another one, team to another, one data source to another is a really key component of what we do and we bundle that within the service so that as a developer or as a production engineer, you really don't have to worry about lineage, it just happens. Jeff, may show us some of this later that you can actually see as data flows from source through to a data warehouse out through a Python notebook to produce a predictive model or a dashboard. Can you see how those data products relate to each other? And when something goes wrong, figure out what upstream maybe caused the problem, or if you're about to change something, figure out what the impact is going to be on the rest of the organization. So Lineage is a big deal for us. >> Got it. >> And just to add on to that, the other thing to think about is that traditional Airflow is actually a complicated implementation. It required quite a lot of time spent understanding or was almost a bespoke language that you needed to be able to develop in two write these DAGs, which is like fundamental pipelines. So part of what we are focusing on is tooling that makes it more accessible to say a data analyst or a data scientist who doesn't have or really needs to gain the necessary background in how the semantics of Airflow DAGs works to still be able to get the benefit of what Airflow can do. So there is new features and capabilities built into the astronomer cloud platform that effectively obfuscates and removes the need to understand some of the deep work that goes on. But you can still do it, you still have that capability, but we are expanding it to be able to have orchestrated and repeatable processes accessible to more teams within the business. >> In terms of accessibility to more teams in the business. You talked about data scientists, data analysts, developers. Steven, I want to talk to you, as the chief data officer, are you having more and more conversations with that role and how is it emerging and evolving within your customer base? >> Hmm. That's a good question, and it is evolving because I think if you look historically at the way that Airflow has been used it's often from the ground up. You have individual data engineers or maybe single data engineering teams who adopt Airflow 'cause it's very popular. Lots of people know how to use it and they bring it into an organization and say, "Hey, let's use this to run our data pipelines." But then increasingly as you turn from pure workflow management and job scheduling to the larger topic of orchestration you realize it gets pretty complicated, you want to have coordination across teams, and you want to have standardization for the way that you manage your data pipelines. And so having a managed service for Airflow that exists in the cloud is easy to spin up as you expand usage across the organization. And thinking long term about that in the context of orchestration that's where I think the chief data officer or the head of analytics tends to get involved because they really want to think of this as a strategic investment that they're making. Not just per team individual Airflow deployments, but a network of data orchestrators. >> That network is key. Every company these days has to be a data company. We talk about companies being data driven. It's a common word, but it's true. It's whether it is a grocer or a bank or a hospital, they've got to be data companies. So talk to me a little bit about Astronomer's business model. How is this available? How do customers get their hands on it? >> Jeff, go ahead. >> Yeah, yeah. So we have a managed cloud service and we have two modes of operation. One, you can bring your own cloud infrastructure. So you can say here is an account in say, AWS or Azure and we can go and deploy the necessary infrastructure into that, or alternatively we can host everything for you. So it becomes a full SaaS offering. But we then provide a platform that connects at the backend to your internal IDP process. So however you are authenticating users to make sure that the correct people are accessing the services that they need with role-based access control. From there we are deploying through Kubernetes, the different services and capabilities into either your cloud account or into an account that we host. And from there Airflow does what Airflow does, which is its ability to then reach to different data systems and data platforms and to then run the orchestration. We make sure we do it securely, we have all the necessary compliance certifications required for GDPR in Europe and HIPAA based out of the US, and a whole bunch host of others. So it is a secure platform that can run in a place that you need it to run, but it is a managed Airflow that includes a lot of the extra capabilities like the cloud developer environment and the open lineage services to enhance the overall airflow experience. >> Enhance the overall experience. So Steven, going back to you, if I'm a Conde Nast or another organization, what are some of the key business outcomes that I can expect? As one of the things I think we've learned during the pandemic is access to realtime data is no longer a nice to have for organizations. It's really an imperative. It's that demanding consumer that wants to have that personalized, customized, instant access to a product or a service. So if I'm a Conde Nast or I'm one of your customers, what can I expect my business to be able to achieve as a result of data orchestration? >> Yeah, I think in a nutshell it's about providing a reliable, scalable, and easy to use service for developing and running data workflows. And talking of demanding customers, I mean, I'm actually a customer myself, as you mentioned, I'm the head of data for Astronomer. You won't be surprised to hear that we actually use Astronomer and Airflow to run all of our data pipelines. And so I can actually talk about my experience. When I started I was of course familiar with Airflow, but it always seemed a little bit unapproachable to me if I was introducing that to a new team of data scientists. They don't necessarily want to have to think about learning something new. But I think because of the layers that Astronomer has provided with our Astro service around Airflow it was pretty easy for me to get up and running. Of course I've got an incentive for doing that. I work for the Airflow company, but we went from about, at the beginning of last year, about 500 data tasks that we were running on a daily basis to about 15,000 every day. We run something like a million data operations every month within my team. And so as one outcome, just the ability to spin up new production workflows essentially in a single day you go from an idea in the morning to a new dashboard or a new model in the afternoon, that's really the business outcome is just removing that friction to operationalizing your machine learning and data workflows. >> And I imagine too, oh, go ahead, Jeff. >> Yeah, I think to add to that, one of the things that becomes part of the business cycle is a repeatable capabilities for things like reporting, for things like new machine learning models. And the impediment that has existed is that it's difficult to take that from a team that's an analyst team who then provide that or a data science team that then provide that to the data engineering team who have to work the workflow all the way through. What we're trying to unlock is the ability for those teams to directly get access to scheduling and orchestrating capabilities so that a business analyst can have a new report for C-suite execs that needs to be done once a week, but the time to repeatability for that report is much shorter. So it is then immediately in the hands of the person that needs to see it. It doesn't have to go into a long list of to-dos for a data engineering team that's already overworked that they eventually get it to it in a month's time. So that is also a part of it is that the realizing, orchestration I think is fairly well and a lot of people get the benefit of being able to orchestrate things within a business, but it's having more people be able to do it and shorten the time that that repeatability is there is one of the main benefits from good managed orchestration. >> So a lot of workforce productivity improvements in what you're doing to simplify things, giving more people access to data to be able to make those faster decisions, which ultimately helps the end user on the other end to get that product or the service that they're expecting like that. Jeff, I understand you have a demo that you can share so we can kind of dig into this. >> Yeah, let me take you through a quick look of how the whole thing works. So our starting point is our cloud infrastructure. This is the login. You go to the portal. You can see there's a a bunch of workspaces that are available. Workspaces are like individual places for people to operate in. I'm not going to delve into all the deep technical details here, but starting point for a lot of our data science customers is we have what we call our Cloud IDE, which is a web-based development environment for writing and building out DAGs without actually having to know how the underpinnings of Airflow work. This is an internal one, something that we use. You have a notebook-like interface that lets you write python code and SQL code and a bunch of specific bespoke type of blocks if you want. They all get pulled together and create a workflow. So this is a workflow, which gets compiled to something that looks like a complicated set of Python code, which is the DAG. I then have a CICD process pipeline where I commit this through to my GitHub repo. So this comes to a repo here, which is where these DAGs that I created in the previous step exist. I can then go and say, all right, I want to see how those particular DAGs have been running. We then get to the actual Airflow part. So this is the managed Airflow component. So we add the ability for teams to fairly easily bring up an Airflow instance and write code inside our notebook-like environment to get it into that instance. So you can see it's been running. That same process that we built here that graph ends up here inside this, but you don't need to know how the fundamentals of Airflow work in order to get this going. Then we can run one of these, it runs in the background and we can manage how it goes. And from there, every time this runs, it's emitting to a process underneath, which is the open lineage service, which is the lineage integration that allows me to come in here and have a look and see this was that actual, that same graph that we built, but now it's the historic version. So I know where things started, where things are going, and how it ran. And then I can also do a comparison. So if I want to see how this particular run worked compared to one historically, I can grab one from a previous date and it will show me the comparison between the two. So that combination of managed Airflow, getting Airflow up and running very quickly, but the Cloud IDE that lets you write code and know how to get something into a repeatable format get that into Airflow and have that attached to the lineage process adds what is a complete end-to-end orchestration process for any business looking to get the benefit from orchestration. >> Outstanding. Thank you so much Jeff for digging into that. So one of my last questions, Steven is for you. This is exciting. There's a lot that you guys are enabling organizations to achieve here to really become data-driven companies. So where can folks go to get their hands on this? >> Yeah, just go to astronomer.io and we have plenty of resources. If you're new to Airflow, you can read our documentation, our guides to getting started. We have a CLI that you can download that is really I think the easiest way to get started with Airflow. But you can actually sign up for a trial. You can sign up for a guided trial where our teams, we have a team of experts, really the world experts on getting Airflow up and running. And they'll take you through that trial and allow you to actually kick the tires and see how this works with your data. And I think you'll see pretty quickly that it's very easy to get started with Airflow, whether you're doing that from the command line or doing that in our cloud service. And all of that is available on our website >> astronomer.io. Jeff, last question for you. What are you excited about? There's so much going on here. What are some of the things, maybe you can give us a sneak peek coming down the road here that prospects and existing customers should be excited about? >> I think a lot of the development around the data awareness components, so one of the things that's traditionally been complicated with orchestration is you leave your data in the place that you're operating on and we're starting to have more data processing capability being built into Airflow. And from a Astronomer perspective, we are adding more capabilities around working with larger datasets, doing bigger data manipulation with inside the Airflow process itself. And that lends itself to better machine learning implementation. So as we start to grow and as we start to get better in the machine learning context, well, in the data awareness context, it unlocks a lot more capability to do and implement proper machine learning pipelines. >> Awesome guys. Exciting stuff. Thank you so much for talking to me about Astronomer, machine learning, data orchestration, and really the value in it for your customers. Steve and Jeff, we appreciate your time. >> Thank you. >> My pleasure, thanks. >> And we thank you for watching. This is season three, episode one of our ongoing series covering exciting startups from the AWS ecosystem. I'm your host, Lisa Martin. You're watching theCUBE, the leader in live tech coverage. (upbeat music)

(upbeat music) >> Commentator: From our studios in the heart of Silicon Valley, Palo Alto, California, this is a Cube conversation. >> Welcome to this special Cube conversation here in Palo Alto, California. I'm John Furrier, host of the Cube. We're here with Fletcher Previn, who's the CIO of IBM, part of a series we're calling a new brand of tech leaders, where we profile leaders in technology and business, where there's innovation and a changing of the guard of approaches and results. Fletcher, thanks for joining me today. >> Thanks for having me. >> So we were talking before you came on camera, you have an interesting background. You kind of went to an arts school, got into entertainment as an intern, Conan O'Brien... >> David Letterman. >> David Letterman. You were fast track to be a comedian and get into the business of entertainment, (laughter) and you ended up as the CIO. How does that happen? Tell us the story. >> The comedy's better in tech. (laughter) >> These days, certainly, watching the Senate hearings, it's phenomenal. >> Well, yeah. As you said, I thought I very well might go into entertainment, that's kind of more of the family business. And I spent a lot of time on movie sets and worked as a production assistant on a couple movies, and then was an intern at the David Letterman Show and Conon during college. But I did always have this strong other thread of really loving technology and being drawn into it. First family computer was a Commodore 64, but my first real computer for me was the original Mac 128K. And I knew something was awry when I was working at the Letterman Show, I was kind of more interested in the phone system than who the guest that night was. And so, when I graduated, I just accepted it. Why keep fighting this? I'm going to go out to the West coast and start my career in tech. >> That's interesting, you know you always gravitate towards what your affinity is, and I think a lot of people look at today's work environment as an environment where there's so many shifts and new kind of waves. To me, we've always said on theCube, you know, this wave that we're living on, tech wave, is kind of a combination of main frame, mini computers, localary networks and PCs all kind of rolled up in one. Because there's so many different touchpoints that's changing things. You know, you don't need to be a coder to be successful in cyber security, you can be a policy person. Lot of societal changes with self-driving cars, which side of the street do they drive on? All these new things are happening. And so it's really putting the pressure on digital, and the notion of data, IT has become a central part of it. You're the CIO at IBM, how do you look at that world? Because now, being a technologist, we'll get to the idea of it in a minute, but as a technologist, as someone who's the Chief Information Officer, when you look at the world today, you look at the wave we're on, what does that wave of technology mean to you? >> Yeah, well I think as you said, there is no part of our modern life that is not touched, and hopefully augmented in some way, by technology. And so, you know, that's the answer to the question why am I at IBM. Because the kinds of businesses that IBM is involved in, the kinds of enabling technology that it provides, really underlies a lot of the critical infrastructure and systems for our modern way of life. And so, being able to be at a company that has a narrative position in what our collective future looks like is what drives me. >> Yeah, a lot of the application developers, you guys have a huge portfolio of applications. You got cloud computer, you got on-premise, you got IOT, a lot of things, AI changing. You're changing the nature of application development, but also the role of data. At IBM as the CIO, what is your strategy in looking at all these changes? And how do you implement it with IBM? What is specifically your strategy? >> Well, certainly our strategy is there will be no part of the IT portfolio that is not augmented with IBM technology, and in particular AI. And an AI strategy is a data strategy, for us to be able to really collect, organize, harness the power of that data and then leverage it in innovative ways to be a more effective and efficient business. More broadly though, in terms of what is my strategy to deliver IT services to a huge company of IT professionals, it's to lead with design. And there's a lot underneath that, but one of the first changes that I made when I became CIO of IBM was adding, as a direct report to myself, a person responsible for design and user experience. And IBM's got a huge focus on design thinking and leading with the user experience, but for us to be successful, we got to create an environment where successful, excuse me, where talented people want to work. And that requires us to have empathy, and engineer from the user in instead of IT out. >> And making service is a big part, because we've got consumption, people consuming IT. >> Yeah, exactly. The barrier to entry for people to make decisions about what they use or don't use is very different. I think people coming to the business 10 years ago, very different set of expectations, even 5 or 3 years ago. And so, it's got to be carrot, it can't be stick. People just won't do something because you tell them to do it, they have to perceive that this is making their work life better in some way. >> Culture's a huge thing, I want to get your thoughts on engineering for excellence, this is something that you believe in. What's your view on that? What does that mean? What does engineering for excellence mean for you as CIO? >> Yeah, well we spend a lot of time thinking of IT as the driver of culture change. And when people say we're a culture that values engineering excellence, what does that really mean? And it means that we recognize and reward people who are really passionate about what they do for a living, deep subject matter experts in their field. You know, I sometimes get asked, what are you looking for when you're hiring people? And I'm looking to hire people who are kind, passionate about what they do for a living, and believe in our purpose as a company. And if we surround ourselves with those kind of people, we will be successful in whatever kind of problem we happen to be trying to solve at that moment. >> What are some of the guiding principles in an organization that's engineered for excellence? What are some of the guiding principles that you hire and push forth through your organization? >> Yeah, well as I said, we are trying to attract and retain, and ultimately reward the people who are deeply passionate about what they do and believe in our collective purpose. And so I think the era of the generalist is probably a bygone era. I'm looking to attract people that are doing in their spare time and in their hobbies and at home the same thing that we're paying them to do at work, because they love it and they feel fulfilled by it. >> And the roles are changing too. Talk about the skill gap, this is a big talk track we hear at every event we go to and exec we talk to. The new brand of tech leaders have to address the skill gap because there's more job openings in jobs that don't have a degree requirement. Meaning, the job doesn't have a certificate or a diploma because it's new. Whether it's cyber security, or data science, new kinds of roles and the skill gaps there. Talk about that, that challenge, that opportunity. >> Yeah, well these new and emerging fields, AI, blockchain, cloud, or otherwise, you're right. A lot of those are new, and there are not well established four-year degrees around those kinds of professions. And so, IBM is very heavily involved in what we call the P Tech, or the Pathway to Technology Program, where people can have a successful career in technology without having a traditional four-year college degree. But more broadly, yeah, there is a gap. A gap between the demand and the supply for people in these fields, and so the best protection all of us have against obsolescence is continuous upscaling and education. And that happens organically if you're passionate about what you do, because you're eating, breathing and sleeping the area that you work in. >> Yeah, and sometimes learning on the job too is key, and getting content on the internet, people can self-learn and apply that. Talk about how your organization's structured for learning. How do you retain the best talent? What are some of the strategies you deploy to keep people motivated, keep them informed, and keep them engaged with a good assignment? >> Yeah, well that is a challenge in any large organization, and IBM is 350,000 plus people in 170 countries. And so the era of us being able to get everybody together in a town hall meeting is long gone. And so, how we communicate and get everybody on the same page around mission alignment, what is our strategy, and what skills do you need, and how do you stay informed and educated. That's an ongoing challenge. I think, ultimately, we try to attract people with our purpose as a company. It's an employer of purpose, the kinds of work IBM's involved in attracts people that are mission driven. And then, there is a tremendous focus on providing distance based self-paced learning, online learning, in person learning, badgen programs, the P Tech Program that I mentioned. And to make sure that a person who is motivated and wants to grow their skills, that they have all the vehicles to do that. But I think the other thing I spend a lot of time focused on is, does everybody in this organization have a good understanding of what our purpose as a company is, and how what they do contributes to that purpose, and can they map back really clearly I'm not just a widget in a machine doing something and I have no idea what the impact of it is. I see that what I'm doing contributes to our collective success. >> People want to work for a mission driven company, that's a new data point we've been seeing. >> Fletcher: Yes. >> Talk about the outcome of focus. You know, you hear digital transformation being kicked around, I think it's happening now more than ever, obviously been hyped up. But now you're starting to see companies really digging in. You guys are going through a digital transformation over many years. You supply technology for companies that are transforming digitally. The notion of business outcomes becomes a big part of that. How have you evolved your organization, from an outcome standpoint, that's new and different from the old ways? Can you give an example and talk about that? Old way of doing things and the new way of doing things. How do you talk about technology for business outcomes in a new way? >> Well, ultimately it's a business problem that you're solving. And so there has to be a business driver behind any project that we engage in. And having good discipline around... organizations tend to die of indigestion, not starvation, and getting really disciplined about what we say no to, in some cases is more important than what we agree to do. And it's much harder to stop work than it is to start work in a large organization. And so we've really leveraged Agile as a new way of working to say, "We have a well-defined methodology for "one funnel of work, that gets prioritized "in partnership with the business in a transparent way." where we say, "You submit this many units of demand, "we have this much unit of supply, let's go through "the story definitions, backlog grooming, "future presentations, retrospectives, the mechanics "of working in an Agile way, to be really disciplined "about everyone's on the same page about what "we are going to do and what we're not going to do." >> Yeah, that's a great point. We hear this all the time. Certainly, it's looking valid here, where I'm located. The notion of Agile, fail fast, the lean startup. You know, I never bought into the fail fast thing. No one wants to fail, but in the spirit of learning Agile, failure is a part of the process. So getting to yes is what people want to get to, but you can't say yes to everything. IT has failed in that area. You can't say yes to everything. So you got to say no. >> Yep. >> You got to also get to what you don't want to do. So knowing what is not the right way to go is where Agile kicks in. So Agile, you want to get to a fail point and know what not to do, at the same time you got to say no to all the requests that you possibly could do. >> Yes. >> Is kind of the formula. Talk about that dynamic. Because this is where Agile translates or DevOps translates into business. It's the same kind of concept applied to organizations, process, and people. >> Yeah, so I think in terms of how do we have good discipline around what we do and don't do. It's very important that people understand what their role in the company is and what their lane is and what their mission is. And if we say no to something, it's not an indictment that that piece of work is not valuable. It just may not be something that is aligned to our mission or something that we're supposed to do. And I think those things can get blurry if you don't have really well defined Agile frameworks and ways of working and everybody on the same page. And so all kinds of things can sound like a good idea potentially, but if it's ultimately not really what we're supposed to be doing, that's what creates friction, right? >> I'd love to get your thoughts just as a person in tech who's got a lot of responsibility in IBM. But you talk about IBM, from an IBM capacity or as a person, but we have a lot of conversations here in the Cube, from Netflix to IBM, to practitioners in the field around the role of data. [Fletcher] Mhmm. Everyone wants to be data driven, so there's no debate there. Data driven is a good approach to take on things. >> Fletcher: Yes. >> But how you look at data depends on what you're lookin' at. You can correlate data and you've got causation. So a lot of conversation's been around don't get too caught up in the data for data's sake. Because if you look at just correlation, you might not know what's causing something. So most data scientists love correlation because it's numbers, they're there, you can look at all those correlations, but not understand the cause of something. Can you talk about how you view this? Because this has become an important part of decision making with data. >> Yeah, for sure. And AI very closely related to having a good data and data governance and taxonomy strategy. To really be able to harness the insights from all that data, you got to have a good data governance strategy behind it. But behind every piece of data is a business process. And so ultimately, being able to really map back and understand which business processes are generating this data is sort of the methodology for trying to put your arms around all the massive amounts of data that are being collected. And I think our old strategy was, we'll have a data lake and we'll just dump everything into it. The advent of AI sort of requires a different data strategy and says we need to have a good governance process around this and have a data platform, not a data lake. That we can then build automation against, run AI against it, and be a business that makes better, more informed decisions based on that data, and then help our customers do the same thing. >> And this has certainly come up a lot in AI around bias and contextual relevance, I think it's a big part of what's behind the data. >> Yeah, right. And you need to have explainability and transparency into the recommendations that AI is making. You know, if it's a black box, that's an issue. If AI came back and told you, "I think you should make "your product more expensive." Your first question would be why? And if you can't answer that... And so, AI's autonomous driving is a good example of that. Where you put a human being in the seat and he or she drives the car, and the system compares the inputs that they would make versus what the human is doing, and can explain why they had variances. But if it's just a complete mystery, that's not going to work. >> Yeah, the contextual why is a great question. I want to get your thoughts on security. [Fletcher] Mhmm. But you had made a comment earlier around the general purpose, IT person is kind of a thing of the past. Meaning that specialism and or variety and diversity of skills are always going to be out there. >> Fletcher: Yeah. >> With security, no one company has the same security makeup. Because their posture and or their organization structures are different because their organization mission is different. No one company is the same. >> Right. >> It's kind of like we as individuals, our DNA, everyone's different. So that means that security's not always the same in every company. As the CIO of IBM, you guys are a large multi-national, you're obviously huge. >> Other companies might have different approaches. How do you see security playing out? Because in some cases, CIOs manage security, in some cases the CSO is bolted out separately. >> Fletcher: Right. >> Either way, we know security's a board issue, as is IT. What's your view on security and the role of security within an organization. >> Security's a huge focus for us, it consumes a large amount of my time. And as much as we worry about our data, we really worry about customer data. And the kinds of threats that we're seeing are evolving rapidly, and as an industry statement I would say the advantage continues to go to bad guys, not good guys. Red is easier than blue. And so this really becomes an exercise in do we understand our networks and the systems that underlie those networks better than the people who are trying to break into it? And in particular, some of the more Apex predator, advanced nation state activities. In terms of the organizational construct of CSO, and where it fits in the company, we've had different models. Where we're at today is that the CSO is a peer of mine, and we work very closely together. And the CSO really, for the most part, defines risk and understands what is the attack surface and threat profile of any particular area. And then anything operational falls to the IT department. And so, in our environment, you know IBM's 350,000 plus employees, the IT department that I lead is about 12,000 people. And so, we have to work very closely together on very different threat profiles of general back office workers, people building commercial software, researchers building quantum computers, people doing outsourced IT. All of them have very different security profiles, and we have to be able to meet those requirements for each of those segments. >> We could do a whole hour just on security, one of my favorite topics. But you guys do have large surface area. >> Fletcher: Yes. >> You got a large employee base, diverse virtual workforce and offices. >> Mhmm. >> You got applications. I mean this is a really complicated security framework you guys have. Well, not framework, but just in terms of challenge, opportunity. >> It's a large surface area, hopefully the framework is not complicated, but it does require vigilance and focus. And so as an example, I am a customer of IBM's Xforce and managed security services. IBM's a market leader in the security services business and they're my kind of perimeter defense on some of these things. But no, you're right. It's something that we can't take our focus off of. >> You know, I had a conversation recently with General Keith Alexander, formally the original commander of cyber command, now he's CEO of a startup, doing a private version of NSA. Signaling is huge in security. >> Fletcher: Yep. >> And I know one of your hobbies is to study kind of the general national security thing as a techie. >> Fletcher: Mhmm. >> The enterprises, they're private organizations. You know, the government's job is to protect IBM. But you guys have to protect yourselves. So you have a new world now where there's a private, public partnerships going on where signaling is super important. Where's the data coming, so real time, and sometimes systems can slow that down for the sake of protecting. But at the same time, you need real time. Not just for security, but in business. Retail, to users. So real time's become a big part of it. What's your thoughts on the notion of real time and security? >> It's huge. Our capacity to detect, respond, and remediate a threat in real time, or as near real time as we can, is the name of the game. You're exactly right, the partnership between governments and public sector and private sector, I think is evolving in a positive way. Where we're beginning to see, as an industry statement, more of these kind of advanced nation state type tactics even being used outside of governments. And so that requires a different kind of response. And then we've got to kind of move forward from an environment where things that are publicly available get enriched and analyzed in some way and then become classified and we can't have access to it. And so the kind of information sharing between companies and governments is really helpful in being able to detect threat on the internet in a real time way. And by the way, if you think we got threats now, when you get to AI and then eventually quantum, threat in the future is not going to be about getting you to click on a link in your email that you think is a legitimate email and install some piece of malware. It's going to be about injecting the minimum amount of data required to teach a system something incorrect or different. So you think of image classification in autonomous driving, with a very small piece of data you can teach it that a stop sign is a yield sign. And that's a fairly benign kind of use case, a simple one, but now imagine financial systems, healthcare systems. So that is leading to quantum resistance cryptography, which is how long do you need to retain data and then what is your encryption strategy around it. >> You know it's interesting. The cost of malware injection can be applied to anything with this. So, I got to ask you, 'cause you guys are leading a lot in quantum, Baba Giano and I have had many conversations. You guys got a great group over there, you got power, amazing stuff happening in quantum. Quantum does change security. What specifically should people know about when they hear quantum. Good for security? Potentially harmful for security? It's an opportunity in both ways. You have a quantum computer, you can crack things much faster. The notion of passwords pretty much goes away. So I need multi-factorial authentication. I mean the whole world's changing with quantum. What's your view? >> Well, like all technology, it can be leveraged for good or less good, and it's a reflection of what the human beings who are using that technology intend to do with it. At IBM, we are working on both sides of that issue. We are developing quantum computers, and then on the other side of it developing encryption methodologies that are quantum resistant or quantum proof. So things like lattice cryptography, where you can mathematically prove you can hide keys in N number of layers such that even a quantum computer can't decrypt it. And so then, how long do you really need to keep that data? If it's two or three years, maybe quantum resistant cryptography is less of an issue for you. If you are the social security administration and you got to keep data for the next 50 years, you got to start investing now in what does the quantum future look like and what are the implications to me from a data and encryption perspective? >> Quantam's super exciting. Fletcher, thanks for coming on and sharing your insight, final question for you. As a person in the tech industry, you've had a chance to see the waves, you got a big one coming up from quantum cloud to AI. What are you most excited about? What should people be paying attention to? In terms of the macro trends. Not necessarily just IBM, just your personal view. To be a new brand of tech leader, what are some of the things that people should pay attention to? And what are you excited about? >> Well, what I'm excited about is what all of this technology is going to bring to bear on our lives. I mean, autonomous driving is going to be life changing for people. The insights that AI will drive. And think about how much time all of us spend doing menial, non-value added tasks at work and in our personal lives. And those things we won't have to worry about as much, with RPA and AI and all kinds of technologies. And I think that will free us to be more creative and be more fulfilled, and I feel very optimistic about the future. In terms of the second part of your question, what advice would I have for tech leaders, I think it's do what you're passionate about. I spend a lot of time focused on trying to create an environment where I think talented people want to work, and that means understanding our purpose, communicating that purpose well. And as I say, kind, passionate about what you do, and believe in the company's purpose. >> Yeah, that's interesting. You mentioned tech for good is always an underbelly in every new trend. And if you look at what happened with, say Facebook, I mean we were talking in 2012 around how data could be weaponized. That was years before so called election or other things meddling. >> Fletcher: Yeah. >> I think there's a community obligation, from sharing data for security risks to seeing the good as a vision, but also identifying bad actors that are going to weaponize the good first. Right? You always have those kind of early adopters. Might not be the best characters. So there's kind of a community has to come together and be faster to identify those. >> Yeah, I do think all of us as leaders have an obligation to understand that risk, and then make decisions around, we as the designers of these systems have to make sure that we're engineering them with fairness and without bias. And then, are the people that we're consuming technology from, are the people creating that technology, are their business models compatible with people who are consuming that technology? And making decisions around who is an ethical, trustworthy partner that I want to be in business with to develop the future. >> Fletcher, thanks for coming on. CIO of IBM here inside theCube, as part of this special program, new brand of tech leaders. I'm John Furrier, thanks for watching. (upbeat music)

>>Good afternoon everyone, and thank you so much for joining us. My name is Savannah Peterson, joined by my co-host Paul for the afternoon. Very excited. Oh, Savannah. Hello. I'm, I'm pumped for this. This is our first bit together. Exactly. >>It's gonna be fun. Yes. We have a great guest to kick off with. >>We absolutely do. We're at Supercomputing 2022 today, and very excited to talk to our next guest. We're gonna be talking about data at scale and data that really matters to us joining us. Kelly Gayer, thank you so much for being here and you are with tech. Tell everyone what TAC is. >>Tech is the Texas Advanced Computing Center at the University of Texas at Austin. And thank you so much for having me here. >>It is wonderful to have you. Your smile's contagious. And one of the themes that's come up a lot with all of our guests, and we just talked about it, is how good it is to be back in person, how good it is to be around our hardware, community tech. You did some very interesting research during the pandemic. Can you tell us about that? >>I can. I did. So when we realized sort of mid-March, we realized that, that this was really not normal times and the pandemic was statement. Yes. That pandemic was really gonna touch everyone. I think a lot of us at the center and me personally, we dropped everything to plug in and that's what we do. So UT's tagline is what starts here changes the world and tax tagline is powering discoveries that change the world. So we're all about impact, but I plugged in with the research group there at UT Austin, Dr. Lauren Myers, who's an epidemiologist, and just we figured out how to plug in and compute so that we could predict the spread of, of Covid 19. >>And you did that through the use of mobility data, cell phone signals. Tell us more about what exactly you were choreographing. >>Yeah, so that was really interesting. Safe graph during the pandemic made their mobility data. Typically it was used for marketing purposes to know who was going into Walmart. The offenses >>For advertising. >>Absolutely, yeah. They made all of their mobility data available for free to people who were doing research and plugging in trying to understand Covid. 19, I picked that data up and we used it as a proxy for human behavior. So we knew we had some idea, we got weekly mobility updates, but it was really mobility all day long, you know, anonymized. I didn't know who they were by cell phones across the US by census block group or zip code if we wanted to look at it that way. And we could see how people were moving around. We knew what their neighbor, their home neighborhoods were. We knew how they were traveling or not traveling. We knew where people were congregating, and we could get some idea of, of how people were behaving. Were they really, were they really locking down or were they moving in their neighborhoods or were they going outside of their neighborhoods? >>What a, what a fascinating window into our pandemic lives. So now that you were able to do this for this pandemic, as we look forward, what have you learned? How quickly could we forecast? What's the prognosis? >>Yeah, so we, we learned a tremendous amount. I think during the pandemic we were reacting, we were really trying. It was a, it was an interesting time as a scientist, we were reacting to things almost as if the earth was moving underneath us every single day. So it was something new every day. And I've told people since I've, I haven't, I haven't worked that hard since I was a graduate student. So it was really daylight to dark 24 7 for a long period of time because it was so important. And we knew, we, we knew we were, we were being a part of history and affecting something that was gonna make a difference for a really long time. And, and I think what we've learned is that indeed there is a lot of data being collected that we can use for good. We can really understand if we get organized and we get set up, we can use this data as a means of perhaps predicting our next pandemic or our next outbreak of whatever. It is almost like using it as a canary in the coal mine. There's a lot in human behavior we can use, given >>All the politicization of, of this last pandemic, knowing what we know now, making us better prepared in theory for the next one. How confident are you that at least in the US we will respond proactively and, and effectively when the next one comes around? >>Yeah, I mean, that's a, that's a great question and, and I certainly understand why you ask. I think in my experience as a scientist, certainly at tech, the more transparent you are with what you do and the more you explain things. Again, during the pandemic, things were shifting so rapidly we were reacting and doing the best that we could. And I think one thing we did right was we admitted where we felt uncertain. And that's important. You have to really be transparent to the general public. I, I don't know how well people are gonna react. I think if we have time to prepare, to communicate and always be really transparent about it. I think those are three factors that go into really increasing people's trust. >>I think you nailed it. And, and especially during times of chaos and disaster, you don't know who to trust or what to believe. And it sounds like, you know, providing a transparent source of truth is, is so critical. How do you protect the sensitive data that you're working with? I know it's a top priority for you and the team. >>It is, it is. And we, we've adopted the medical mantra, do no harm. So we have, we feel a great responsibility there. There's, you know, two things that you have to really keep in mind when you've got sensitive data. One is the physical protection of it. And so that's, that's governed by rule, federal rules, hipaa, ferpa, whatever, whatever kind of data that you have. So we certainly focus on the physical protection of it, but there's also sort of the ethical protection of it. What, what is the quote? There's lies, damn lies and statistics. >>Yes. Twain. >>Yeah. So you, you really have to be responsible with what you're doing with the data, how you're portraying the results. And again, I think it comes back to transparency is is basically if people are gonna reproduce what I did, I have to be really transparent with what I did. >>I, yeah, I think that's super important. And one of the themes with, with HPC that we've been talking about a lot too is, you know, do people trust ai? Do they trust all the data that's going into these systems? And I love that you just talked about the storytelling aspect of that, because there is a duty, it's not, you can cut data kind of however you want. I mean, I come from marketing background and we can massage it to, to do whatever we want. So in addition to being the deputy director at Tech, you are also the DEI officer. And diversity I know is important to you probably both as an individual, but also in the work that you're doing. Talk to us about that. >>Yeah, I mean, I, I very passionate about diversity, equity and inclusion in a sense of belongingness. I think that's one of the key aspects of it. Core >>Of community too. >>I got a computer science degree back in the eighties. I was akin to a unicorn in a, in an engineering computer science department. And, but I was really lucky in a couple of respects. I had a, I had a father that was into science that told me I could do anything I, I wanted to set my mind to do. So that was my whole life, was really having that support system. >>He was cheers to dad. >>Yeah. Oh yeah. And my mom as well, actually, you know, they were educators. I grew up, you know, in that respect, very, very privileged, but it was still really hard to make it. And I couldn't have told you back in that time why I made it and, and others didn't, why they dropped out. But I made it a mission probably back, gosh, maybe 10, 15 years ago, that I was really gonna do all that I could to change the needle. And it turns out that there are a number of things that you can do grassroots. There are certainly best practices. There are rules and there are things that you really, you know, best practices to follow to make people feel more included in an organization, to feel like they belong it, shared mission. But there are also clever things that you can do with programming to really engage students, to meet people and students where they are interested and where they are engaged. And I think that's what, that's what we've done over, you know, the course of our programming over the course of about maybe since 2016. We have built a lot of programming ATAC that really focuses on that as well, because I'm determined the needle is gonna change before it's all said and done. It just really has to. >>So what, what progress have you made and what goals have you set in this area? >>Yeah, that, that's a great question. So, you know, at first I was a little bit reluctant to set concrete goals because I really didn't know what we could accomplish. I really wasn't sure what grassroots efforts was gonna be able to, you're >>So honest, you can tell how transparent you are with the data as well. That's >>Great. Yeah, I mean, if I really, most of the successful work that I've done is both a scientist and in the education and outreach space is really trust relationships. If I break that trust, I'm done. I'm no longer effective. So yeah, I am really transparent about it. But, but what we did was, you know, the first thing we did was we counted, you know, to the extent that we could, what does the current picture look like? Let's be honest about it. Start where we are. Yep. It was not a pretty picture. I mean, we knew that anecdotally it was not gonna be a great picture, but we put it out there and we leaned into it. We said, this is what it is. We, you know, I hesitated to say we're gonna look 10% better next year because I'm, I'm gonna be honest, I don't always know we're gonna do our best. >>The things that I think we did really well was that we stopped to take time to talk and find out what people were interested in. It's almost like being present and listening. My grandmother had a saying, you have two errors in one mouth for a reason, just respect the ratio. Oh, I love that. Yeah. And I think it's just been building relationships, building trust, really focusing on making a difference, making it a priority. And I think now what we're doing is we've been successful in pockets of people in the center and we are, we are getting everybody on board. There's, there's something everyone can do, >>But the problem you're addressing doesn't begin in college. It begins much, much, that's right. And there's been a lot of talk about STEM education, particularly for girls, how they're pushed out of the system early on. Also for, for people of color. Do you see meaningful progress being made there now after years of, of lip service? >>I do. I do. But it is, again, grassroots. We do have a, a, a researcher who was a former teacher at the center, Carol Fletcher, who is doing research and for CS for all we know that the workforce, so if you work from the current workforce, her projected workforce backwards, we know that digital skills of some kind are gonna be needed. We also know we have a, a, a shortage. There's debate on how large that shortage is, but about roughly about 1 million unmet jobs was projected in 2020. It hasn't gotten a lot better. We can work that problem backwards. So what we do there is a little, like a scatter shot approach. We know that people come in all forms, all shapes, all sizes. They get interested for all different kinds of reasons. We expanded our set of pathways so that we can get them where they can get on to the path all the way back K through 12, that's Carol's work. Rosie Gomez at the center is doing sort of the undergraduate space. We've got Don Hunter that does it, middle school, high school space. So we are working all parts of the problem. I am pretty passionate about what we consider opportunity youth people who never had the opportunity to go to college. Is there a way that we can skill them and get, get them engaged in some aspect and perhaps get them into this workforce. >>I love that you're starting off so young. So give us an example of one of those programs. What are you talking to kindergartners about when it comes to CS education? >>You know, I mean, gaming. Yes. Right. It's what everybody can wrap their head around. So most kids have had some sort of gaming device. You talk in the context, in the context of something they understand. I'm not gonna talk to them about high performance computing. It, it would go right over their heads. And I think, yeah, you know, I, I'll go back to something that you said Paul, about, you know, girls were pushed out. I don't know that girls are being pushed out. I think girls aren't interested and things that are being presented and I think they, I >>Think you're generous. >>Yeah. I mean, I was a young girl and I don't know why I stayed. Well, I do know why I stayed with it because I had a father that saw something in me and I had people at critical points in my life that saw something in me that I didn't see. But I think if we ch, if we change the way we teach it, maybe in your words they don't get pushed out or they, or they won't lose interest. There's, there's some sort of computing in everything we do. Well, >>Absolutely. There's also the bro culture, which begins at a very early >>Age. Yeah, that's a different problem. Yeah. That's just having boys in the classroom. Absolutely. You got >>It. That's a whole nother case. >>That's a whole other thing. >>Last question for you, when we are sitting here, well actually I've got, it's two parter, let's put it that way. Is there a tool or something you wish you could flick a magic wand that would make your job easier? Where you, you know, is there, can you identify the, the linchpin in the DEI challenge? Or is it all still prototyping and iterating to figure out the best fit? >>Yeah, that is a, that's a wonderful question. I can tell you what I get frustrated with is that, that >>Counts >>Is that I, I feel like a lot of people don't fully understand the level of effort and engagement it takes to do something meaningful. The >>Commitment to a program, >>The commitment to a program. Totally agree. It's, there is no one and done. No. And in fact, if I do that, I will lose them forever. They'll be, they will, they will be lost in the space forever. Rather. The engagement is really sort of time intensive. It's relationship intensive, but there's a lot of follow up too. And the, the amount of funding that goes into this space really is not, it, it, it's not equal to the amount of time and effort that it really takes. And I think, you know, I think what you work in this space, you realize that what you gain is, is really more of, it's, it really feels good to make a difference in somebody's life, but it's really hard to do on a shoer budget. So if I could kind of wave a magic wand, yes, I would increase understanding. I would get people to understand that it's all of our responsibility. Yes, everybody is needed to make the difference and I would increase the funding that goes to the programs. >>I think that's awesome, Kelly, thank you for that. You all heard that. More funding for diversity, equity, and inclusion. Please Paul, thank you for a fantastic interview, Kelly. Hopefully everyone is now inspired to check out tac perhaps become a, a Longhorn, hook 'em and, and come deal with some of the most important data that we have going through our systems and predicting the future of our pandemics. Ladies and gentlemen, thank you for joining us online. We are here in Dallas, Texas at Supercomputing. My name is Savannah Peterson and I look forward to seeing you for our next segment.

>> Live from Las Vegas, it's theCUBE. Covering VMworld 2018. Brought to you by VMware and its ecosystem partners. >> Welcome inside the VM Village at VMworld 2018 where we have a nice, big set. Double set of theCUBE. I'm Stu Miniman, joined with my co-host John Troyer and wait, Keith Townsend? >> Did you mess up the intro? >> Oh my gosh. (Keith chuckling) Luckily, the great thing about VMworld is it's got a great community. Remember a couple of years ago, had a couple of my staff that weren't going to be here and I'm like oh my gosh, what do we do? So I reached out to community members. John Troyer, Keith Townsend. I said hey, guys, how'd you like to do some CUBE stuff? Keith did a whole bunch of CUBE with us for a couple of years and something happened. You decided to go and take a real job? >> Evidently, you can't live off borrowed time for too long. It catches up with you. But VMware, obviously, world-class organization. I've been on the other side interview folks on here so I've gotten a good window in to the org over the past couple of years, thanks to theCUBE. >> Yeah, well, Keith, look, first of all, thank you for all the time you did. We call you the once and future guest host of theCUBE. (both laughing) So we have not seen the end of Keith Townsend, the CTO Advisor. You're now a solutions architect, though, at VMware. If people want, go read Keith's blog. Great resource to the community as to looking at jobs. Keith didn't apply to VMware once or twice, it was one of those you keep trying and eventually you found a pretty sweet job. >> Yeah. >> Maybe give us a little insight as to what brought you, what excited you to come join VMware? You've know the community, been a vExpert. Been a watcher and a partner and a customer of VMware. What's it like being inside, wearing that logo? >> I've said on theCUBE, a couple of times, VMware moves at the speed of the CIO. You can take that one of two different ways. You can say VMware is really slow organization, or they go right where the CIO needs them to go. The thing the intrigued me about VMware all the time is that no company is better positioned to walk through digital transformation than VMware. As seen by the announcements this morning. VMware is struggling through, we're struggling through to find our way through what it is that the right combination of partnerships, technologies, people, process to help companies transition to this new digital age and that is an exciting thing to be a part of. >> Definitely interesting times. I'm sure there's a number of companies that would say hi, Microsoft, Amazon, and the like, that we think we're pretty well positioned to lead companies to where you need to go. But definitely interesting stuff in the keynote. That maturation of cloud and networking. Put your CTO Advisor hat on there. How're they doing? >> This is where I got, I tweeted it out earlier that man, I got to be careful, because some of the stuff that I want to tweet I'm like, oh, I can't say that as a VMware employee. But I can say definitely, I was surprised at the RDS announcement and people love the VMware ESXi on ARM. Two amazing announcements, but what really excited me was the RDS announcement. On theCUBE, I've pushed Chris Wolf, I've pushed Lee Caswell, all of these GMs, these BU GMs, about when is the innovation going to come out of VMware again? Let's not just get V1 updates. Why should somebody upgrade from vSphere 5.5 to 6.7? Give us a compelling reason. I think this morning we heard some really compelling stuff. RDS on vSphere is, I can't overstate how disruptive of an innovation that is. >> That could be really interesting. I like what you said in the beginning about the digital transformation. I think we also heard this morning the word digital foundation a lot, which is, again, one of my goals here for this show, Stu and Keith, is to pin down what does VMware do? What does it do? And it's not quite fair, because it has quite a wide portfolio but it seems to me, Keith, that it feels like the early days when I was there. You had to work with a whole set of OEMs in the hypervisor and some of the same things are happening with a whole bunch of clouds and working as a neutral Switzerland or partners with all them. But I was actually wanting to pivot over a little bit over to you as a communicator and as a member of the community. You were a customer. You worked for a large pharmaceutical company and ran a lot of billion dollars worth of stuff. You chose to become a communicator and an explainer and to be part of the learning process and buying process as an independent. Now back on the vendor side. Is there anything in that journey you've learned about 2018 about how people learn and how IT people figure this stuff. How do I even know where to go or what to buy or even what to consider? Any insights into that? >> So John, that's a really great question. I went on a run this morning, the vFit Run. We do it every year at VMworld and I was with VMUG CEO, Brad Tompkins. And we actually talked about this. vSphere admins want all the vSphere content that they can consume. In reality, they need to transition from just being focused on vSphere, vSphere, vSphere, and VXLAN and NSX to this broader picture. Pat on stage this morning talked through PKS, which is Kubernetes, he talked a little bit of serverless. I mean, from a CEO of a software company, that was a lot to consume just on the stage this morning. So you can be a deer in the headlights and think, what should I focus on? I think the thing to focus on, one of my peers gave a talk, well two of my peers, Craig Fletcher, who brought me into VMware, and Joseph Griffith, gave a talk today on culture. And this is about culture. The culture to learn and grow. You don't necessarily have to learn a specific technology, but you should most definitely have the attitude that if the CXO comes to me and asks me about X business process, I need to know a high level answer to that and how do I get there? Simple, simple steps is learn your business processes. I'll throw just one out there. Order to cash. Every organization has some process from when they either request money, they place an order, and how they eventually get paid. If you learn that process, the technology bits I think fall in place. >> Yeah it's an interesting point. I've talked to some of the users here, and they were a little bit overwhelmed this morning. I don't think there's anybody at this show, that if you put them in front of the CEO of their company, and said, okay tell me everything VMware's doing. (Keith laughing) Nobody can explain that. Nobody inside VMware nobody out. There's too much. Part of the answer I get all the time, is how do I keep up? Look, you're not going to keep up on everything. You need to have, I think the role you're in now Keith, is part of helping customers understand what are the things they need to understand, what are the steps they can be taking in the areas they need to learn and the things they can lean on you and your partners to get there. Is that a fair statement? >> Yeah I did a podcast with Brian Gracely maybe about a year, a year and half ago and we talked about this very topic. At the highest level, you just need, from a CIO perspective, CIO, CTO, and if you don't have a CTO, that's probably step one. But from a CIO perspective, you need someone who can just think about big picture, how the moving parts work. And then you need people to go deep and different areas. I talked to a financial services senior VP and he was talking through how he needed today a Pivotal guy But tomorrow that Pivotal guy would not need to be a Pivotal guy but a Kubernetes guy specifically. And how that guy would morph into something else so he's structured in his organization. So that he can, hey today, this guy or gal knows this technology stack but more important, they know systems and they can adjust and learn the technology that they need to learn to be effective. Because even as an analyst, near the end of the CTO Advisor as a full time opportunity, I thought about focusing all on VMware, because the company's that big now. Pat on stage said one of the things they learned from AWS, is how to add features every quarter. Stu, if I told you five years ago VMware would add a feature every quarter, the culture just isn't there, until now. >> Yeah, so, Keith, that's a really interesting point. That pace of change, because most people when you talk about vSphere upgrades, it was oh wow. It came out every year, every year and a half or so like that >> That's too fast >> I'm usually a couple generations behind. Every quarter there's no way I'm going to do that. We still have a bit of an impedance mismatch. When I go use the cloud, some of the base things happen under line. But other things I still need to choose or there's automation that will help me. How do we help CIOs, IT businesses to get to this more fluid, dynamic, upgradeable environment compared to the oh wait I need to consciously think about when do I upgrade, when do I move, how do I make those changes? >> So we have to get out of this mindset that IT is in this constant ops mode. Whether it's vSphere and the announcements that were made today or any other platform. We add no value by engineering upgrades. Putting time into designing and testing the upgrade from vSphere 6.7 to vSphere 6.7 update 1 really doesn't add value at the end of the day. VMware made critical announcements about the path to having VMware manage that. VMware cloud on AWS is a great example but the technologies are out there where we're no longer consuming our OSes. There's Linux distributions, there's Windows 10 will be the last version of Windows desktop ever and we'll get those updates directly from Microsoft. So we need to get out of the mindset that we add value as executives to managing upgrades and move our organizations where we're consuming these things as the black boxes they should be. >> Alright, so Keith, last question. What's surprised you so much, so far inside of VMware? >> You know what? I'm going to give an honest, raw answer to that, Stu. I'm not used to competing against my friends. (Stu laughing) It's one of those things, you know what, you got to make money, you got to win deals but both me and you have made a lot of friends, and John, we've made a lot of friends in this community. And you run into situations where you're pitting your technology against someone you just had dinner with last night or the week before at the last conference. And you've known for years and they're actually your friend. And keeping that competitive nature but at the same time maintaining your friendship, that's been surprisingly interesting. >> Alright, well hey, Keith, pleasure to catch up with you, as always, you're always welcome on our program in one of these seats. And yeah, absolutely, what I love about this community is that I see lots of people that are friends that are fierce competitors but they're grabbing out, hanging out at parties, taking selfies together, doing stuff like that. So, community, definitely key themes. Keith, thank you for being our community guest for today. Day one of three days live wall-to-wall coverage here in Las Vegas, VMworld 2018. For John Troyer, the CTO Advisor Keith Townsend, I'm Stu Miniman, thank you for watching theCUBE. (techno music)

>> Hi, this is George Gilbert. We have an extra-special guest today on our CUBEcast, Aman Naimat, Senior Vice President and CTO of Demandbase started with a five-person startup, Spiderbook. Almost like a reverse IPO, Demandbase bought Spiderbook, but it sounds like Spiderbook took over Demandbase. So Aman, welcome. >> Thank you, excited to be here. Always good to see you. >> So, um, Demandbase is a Next Gen CRM program. Let's talk about, just to set some context. >> Yes. >> For those who aren't intimately familiar with traditional CRM, what problems do they solve? And how did they start, and how did they evolve? >> Right, that's a really good question. So, for the audience, CRM really started as a contact manager, right? And it was replicating what a salesperson did in their own private notebook, writing contact phone numbers in an electronic version of it, right? So you had products that were really built for salespeople on an individual basis. But it slowly evolved, particularly with Siebel, into more of a different twist. It evolved into more of a management tool or reporting tool because Tom Siebel was himself a sales manager, ran a sales team at Oracle. And so, it actually turned from an individual-focused product to an organization management reporting product. And I've been building this stuff since I was 19. And so, it's interesting that, you know, the products today, we're going, actually pivoting back into products that help salespeople or help individual marketers and add value and not just focus on management reporting. >> That's an interesting perspective. So it's more now empowering as opposed to, sort of, reporting. >> Right, and I think some of it is cultural influence. You know, over the last decade, we have seen consumer apps actually take a much more, sort of predominant position rather than in the traditional, earlier in the 80s and 90s, the advanced applications were corporate applications, your large computers and companies. But over the last year, as consumer technology has taken off, and actually, I would argue has advanced more than even enterprise technology, so in essence, that's influencing the business. >> So, even ERP was a system of record, which is the state of the enterprise. And this is much more an organizational productivity tool. >> Right. >> So, tell us now, the mental leap, the conceptual leap that Demandbase made in terms of trying to solve a different problem. >> Right, so, you know, Demandbase started on the premise or around marketing automation and marketing application which was around identifying who you are. As we move towards more digital transaction and Web was becoming the predominant way of doing business, as people say that's 70 to 80 percent of all businesses start using online digital research, there was no way to know it, right? The majority of the Internet is this dark, unknown place. You don't know who's on your website, right? >> You're referring to the anonymity. >> Exactly. >> And not knowing who is interacting with you until very late. >> Exactly, and you can't do anything intelligent if you don't know somebody, right? So if you didn't know me, you couldn't really ask. What will you do? You'll ask me stupid questions around the weather. And really, as humans, I can only communicate if you know somebody. So the sort of innovation behind Demandbase was, and it still continues to be to actually bring around and identify who you're talking to, be it online on your website and now even off your website. And that allows you to have a much more sort of personalized conversation. Because ultimately in marketing and perhaps even in sales, it comes down to having a personal conversation. So that's really what, which if you could have a billion people who could talk to every person coming to your website in a personalized manner, that would be fantastic. But that's just not possible. >> So, how do you identify a person before they even get to a vendor's website so that you can start on a personalized level? >> Right, so Demandbase has been building this for a long time, but really, it's a hard problem. And it's harder now than ever before because of security and privacy, lots of hackers out there. People are actually trying to hide, or at least prevent this from leaking out. So, eight, nine years ago, we could buy registries or reverse DNS. But now with ISBs, and we are behind probably Comcast or Level 3. So how do you even know who this IP address is even registered to? So about eight years ago, we started mapping IP addresses, 'cause that's how you browse the Internet, to companies that they work at, right? But it turned out that was no longer effective. So we have built over the last eight years proprietary methods that know how companies relate to the IP addresses that they have. But we have gone to doing partnerships. So when you log into certain websites, we partner with them to identify you if you self-identify at Forbes.com, for example. So when you log in, we do a deal. And we have hundreds of partners and data providers. But now, the state of the art where we are is we are now looking at behavioral signals to identify who you are. >> In other words, not just touch points with partners where they collect an identity. >> Right. >> You have a signature of behavior. >> That's right. >> It's really interesting that humans are very unique. And based on what they're reading online and what they're reading about, you can actually identify a person and certainly identify enough things about them to know that this is an executive at Tesla who's interested in IOT manufacturing. >> Ah, so you don't need to resolve down to the name level. >> No. >> You need to know sort of the profile. >> Persona, exactly. >> The persona. >> The persona, and that's enough for marketing. So if I knew that this is a C-level supply chain executive from Tesla who lives in Palo Alto and has interests in these areas or problems, that's enough for Siemens to then have an intelligent conversation to this person, even if they're anonymous on their website or if they call on the phone or anything else. >> So, okay, tell us the next step. Once you have a persona, is it Demandbase that helps them put together a personalized? >> Profile. >> Profile, and lead it through the conversation? >> Yeah, so earlier, well, not earlier, but very recently, rebuilding this technology was just a very hard problem. To identify now hundreds of millions of people, I think around 700 are businesspeople globally which is majority of the business world. But we realize that in AI, making recommendations or giving you data in advanced analytics is just not good enough because you need a way to actually take action and have a personalized conversation because there are 100 thousand people on your website. Making recommendations, it's just overwhelming for humans to get that much data. So the better sort of idea now that we're working on is just take the action. So if somebody from Tesla visits your website, and they are an executive who will buy your product, take them to the right application. If they go back and leave your website, then display them the right message in a personalized ad. So it's all about taking actions. And then obviously, whenever possible, guiding humans towards a personalized conversation that will maximize your relationship. >> So, it sounds like sometimes it's anticipating and recommending a next best action. >> Yeah. >> And sometimes, it's your program taking the next best action. >> That's right, because it's just not possible to scale people to take actions. I mean, we have 30, 40 sales reps in Demandbase. We can't handle the volume. And it's difficult to create that personalized letter, right? So we make recommendations, but we've found that it's just too overwhelming. >> Ah, so in other words, when you're talking about recommendations, you're talking about recommendations for Demandbase for? >> Or our clients, employees, or salespeople, right? >> Okay. >> But whenever possible, we are looking to now build systems that in essence are in autopilot mode, and they take the action. They drive themselves. >> Give us some examples of the actions. >> That's right, so some actions could be if you know that a qualified person came to your website, notify the salesperson and open a chat window saying, "This is an executive. "This is similar to a person who will buy "a product from you. "They're looking for this thing. "Do you want to connect with a salesperson?" And obviously, only the people that will buy from you. Or, the action could be, send them an email automatically based on something they will be interested in, and in essence, have a conversation. Right? So it's all about conversation. An ad or an email or a person are just ways of having a conversation, different channels. >> So, it sounds like there was an intermediate marketing automation generation. >> Right. >> After traditional CRM which was reporting. >> Right, that's true. >> Where it was basically, it didn't work until you registered on the website. >> That's right. >> And then, they could email you. They could call you. The inside sales reps. >> That's right. >> You know, if you took a demo, >> That's right. >> you had to put an idea in there. >> And that's still, you know, so when Demandbase came around, that was the predominant between the CRM we were talking about. >> George: Right. >> There was a gap. There was a generation which started to be marketing. It was all about form fills. >> George: Yeah. >> And it was all about nurturing, but I think that's just spam. And today, their effectiveness is close to nothing. >> Because it's basically email or outbound calls. >> Yeah, it's email spam. Do you know we all have email boxes filled with this stuff? And why doesn't it work? Because, not only because it's becoming ineffective and that's one reason. Because they don't know me, right? And it boils down to if the email was really good and it related to what you're looking for or who you are, then it will be effective. But spam, or generic email is just not effective. So it's to some extent, we lost the intimacy. And with the new generation of what we call account-based marketing, we are trying to build intimacy at scale. >> Okay, so tell us more. Tell us first the philosophy behind account-based marketing and then the mechanics of how you do it. >> Sure, really, account-based marketing is nothing new. So if you walk into a corporation, they have these really sophisticated salespeople who understand their clients, and they focus on one-on-one, and it's very effective. So if you had Google as a client or Tesla as a client, and you are Siemens, you have two people working and keeping that relationship working 'cause you make millions of dollars. But that's not a scalable model. It's certainly not scalable for startups here to work with or to scale your organization, be more effective. So really, the idea behind account-based marketing is to scale that same efficacy, that same personalized conversation but at higher volume, right? And maximize, and the only way to really do that is using artificial intelligence. Because in essence, we are trying to replicate human behavior, human knowledge at scale. Right? And to be able to harvest and know what somebody who knows about pharma would know. >> So give me an example of, let's stay in pharma for a sec. >> Sure. >> And what are the decision points where based on what a customer does or responds to, you determine the next step or Demandbase determines what next step to take? >> Right. >> What are some of those options? Like a decision tree maybe? >> You can think of it, it's quite faddish in our industry now. It's reinforcement learning which is what Google used in the Go system. >> George: Yeah, AlphaGo. >> AlphaGo, right, and we were inspired by that. And in essence, what we are trying to do is predict not only what will keep you going but where you will win. So we give rewards at each point. And the ultimate goal is to convert you to a customer. So it looks at all your possible futures, and then it figures out in what possible futures you will be a customer. And then it works backwards to figure out where it should take you next. >> Wow, okay, so this is very different from >> They play six months ahead. So it's a planning system. >> Okay. >> Cause your sales cycles are six months ahead. >> So help us understand the difference between the traditional statistical machine learning that is a little more mainstream now. >> Sure. >> Then the deep learning, the neural nets, and then reinforcement learning. >> Right. >> Where are the sweet spots? What are the sweet spots for the problems they solve? >> Yeah, I mean, you know, there's a lot of fad and things out there. In my opinion, you can achieve a lot and solve real-world problems with simpler machine learning algorithms. In fact, for the data science team that I run, I always say, "Start with like the most simplest algorithm." Because if the data is there and you have the intuition, you can get to a 60% F-score or quality with the most naive implementation. >> George: 60% meaning? >> Like accuracy of the model. >> Confidence. >> Confidence. Sure, how good the model is, how precise it is. >> Okay. >> And sure, then you can make it better by using more advanced algorithms. The reinforcement learning, the interesting thing is that its ability to plan ahead. Most machine learning can only make a decision. They are classifiers of sorts, right? They say, is this good or bad? Or, is this blue? Or, is this a cat or not? They're mostly Boolean in nature or you can simulate that in multi-class classifiers. But reinforcement learning allows you to sort of plan ahead. And in CRM or as humans, we're always planning ahead. You know, a really good salesperson knows that for this stage opportunity or this person in pharma, I need to invite them to the dinner 'cause their friends are coming and they know that last year when they did that, then in the future, that person converted. Right, if they go to the next stage and they, so it plans ahead the possible futures and figures out what to do next. >> So, for those who are familiar with the term AB testing. >> Sure. >> And who are familiar with the notion that most machine learning models have to be trained on data where the answer exists, and they test it out, train it on one set of data >> Sure. >> Where they know the answers, then they hold some back and test it and see if it works. So, how does reinforcement learning change that? >> I mean, it's still testing on supervised models to know. It can be used to derive. You still need data to understand what the reward function would be. Right? And you still need to have historical data to understand what you should give it. And sure, have humans influence it as well, right? At some point, we always need data. Right? If you don't have the data, you're nowhere. And if you don't have, but it also turns out that most of the times, there is a way to either derive the data from some unsupervised method or have a proxy for the data that you really need. >> So pick a key feature in Demandbase and then where you can derive the data you need to make a decision, just as an example. >> Yeah, that's a really good question. We derive datas all the time, right? So, let me use something quite, quite interesting that I wish more companies and people used is the Internet data, right? The Internet today is the largest source of human knowledge, and it actually know more than you could imagine. And even simple queries, so we use the Bing API a lot. And to know, so one of the simple problems we ran into many years ago, and that's when we realized how we should be using Internet data which in academia has been used but not as used as it should be. So you know, you can buy APIs from Bing. And I wish Google would give their API, but they don't. So, that's our next best choice. We wanted to understand who people are. So there's their common names, right? So, George Gilbert is a common name or Alan Fletcher who's my co-founder. And, you know, is that a common name? And if you search that, just that name, you get that name in various contexts. Or co-occurring with other words, you can see that there are many Alan Fletchers, right? Or if you get, versus if you type in my name, Aman Naimat, you will always find the same kind of context. So you will know it's one person or it's a unique name. >> So, it sounds to me that reinforcement learning is online learning where you're using context. It's not perfectly labeled data. >> Right. I think there is no perfectly labeled data. So there's a misunderstanding of data scientists coming out of perfectly labeled data courses from Stanford, or whatever machine learning program. And we realized very quickly that the world doesn't have any perfect labeled data. We think we are going to crowdsource that data. And it turns out, we've tried it multiple times, and after a year, we realized that it's just a waste of time. You can't get, you know, 20 cents or 25 cents per item worker somewhere in wherever to hat and label data of any quality to you. So, it's much more effective to, and we were a startup, so we didn't have money like Google to pay. And even if you had the money, it generally never works out. We find it more effective to bootstrap or reuse unsupervised models to actually create data. >> Help us. Elaborate on that, the unsupervised and the bootstrapping where maybe it's sort of like a lawnmower where you give it that first. >> That's right. >> You know, tug. >> I mean, we've used it extensively. So let me give you an example. Let's say you wanted to create a list of cities, right? Or a list of the classic example actually was a paper written by Sergey Brin. I think he was trying to figure out the names of all authors in the world, and this is 1988. And basically if you search on Google, the term "has written the book," just the term "has written the book," these are called patterns, or hearse patterns, I think. Then you can imagine that it's also always preceded by a name of a person who's an author. So, "George Gilbert has written the book," and then the name of the book, right? Or "William Shakespeare has written the book X." And you seed it with William Shakespeare, and you get some books. Or you put Shakespeare and you get some authors, right? And then, you use it to learn other patterns that also co-occurred between William Shakespeare and the book. >> George: Ah. >> And then you learn more patterns and you use it to extract more authors. >> And in the case of Demandbase, that's how you go from learning, starting bootstrapping within, say, pharma terminology. >> Yes. >> And learning the rest of pharma terminology. >> And then, using generic terminology to enter an industry, and then learning terminology that we ourselves don't understand yet it means. For example, I always used this example where if we read a sentence like "Takeda has in-licensed "a molecule from Roche," it may mean nothing to us, but it means that they're partnered and bought a product, in pharma lingo. So we use it to learn new language. And it's a common technique. We use it extensively, both. So it goes down to, while we do use highly sophisticated algorithms for some problems, I think most problems can be solved with simple models and thinking through how to apply domain expertise and data intuition and having the data to do it. >> Okay, let's pause on that point and come back to it. >> Sure. >> Because that sounds like a rich vein to explore. So this is George Gilbert on the ground at Demandbase. We'll be right back in a few minutes.