(soft music) >> Welcome back to Chicago guys and gals. Lisa Martin here with John Furrier. We have been covering Ansible Fest '22 for the last two days. This is our show wrap. We're going to leave you with some great insights into the things that we were able to dissect over the last two days. John, this has been an action packed two days. A lot of excitement, a lot of momentum. Good to be back in person. >> It's great to be back in person. It was the first time for you to do Ansible Fest. >> Yes. >> My first one was 2019 in person. That's the last time they had an event in person. So again, it's a very chill environment here, but it's content packed, great active loyal community and is growing. It's changing. Ansible now owned by Red Hat, and now Red Hat owned by IBM. Kind of see some game changing kind of movements here on the chess board, so to speak, in the industry. Ansible has always been a great product. It started in open source. It evolved configuration management configuring servers, networks. You know, really the nuts and bolts of IT. And became a fan favorite mainly because it was built by the fans and I think that never stopped. And I think you started to see an opportunity for Ansible to be not only just a, I won't say niche product or niche kind of use case to being the overall capabilities for large scale enterprise system architectures, system management. So it's very interesting. I mean I find it fascinating how, how it stays relevant and cool and continues to power through a massive shift >> A massive shift. They've done a great job though since the inception and through the acquisition of being still community first. You know, we talked a lot yesterday and today about helping organizations become automation first that Ansible has really stayed true to its roots in being community first, community driven and really that community flywheel was something that was very obvious the last couple of days. >> Yeah, I mean the community thing is is is their production system. I mean if you look at Red Hat, their open source, Ansible started open source, good that they're together. But what people may or may not know about Ansible is that they build their product from the community. So the community actually makes the suggestions. Ansible's just in listening modes. So when you have a system that's that efficient where you have direct working backwards from the customer like that, it's very efficient. Now, as a product manager you might want to worry about scope creep, but at the end of the day they do a good job of democratizing that process. So again, very strong product production system with open source, very relevant, solves the right problems. But this year the big story to me is the cultural shift of Ansible's relevance. And I think with multicloud on the horizon, operations is the new kind of developer kind of ground. DevOps has been around for a while. That's now shifted up to the developer themselves, the cloud native developer. But at cloud scale and hybrid computing, it's about the operations. It's about the data and the security. All of it's about the data. So to me there's a new ops configuration operating model that you're seeing people use, SRE and DevOps. That's the new culture, and the persona's changing. The operator of a large scale enterprise is going to be a lot different than it was past five, 10 years. So major cultural shift, and I think this community's going to step up to that position and fill that role. >> They seem to be having a lot of success meeting people where they are, meeting the demographics, delivering on how their community wants to work, how they want to collaborate. But yesterday you talked about operations. We talked a lot about Ops as code. Talk about what does that mean from your perspective, and what did you hear from our guests on the program with respect that being viable? >> Well great, that's a great point. Ops as code is the kind of their next layer of progression. Infrastructure is code. Configuration is code. Operations is code. To me that means running the company as software. So software influencing how operators, usually hardware in the past. Now it's infrastructure and software going to run things. So ops as code's, the next progression in how people are going to manage it. And I think most people think of that as enterprises get larger, when they hear words like SRE, which stands for Site Reliable Engineer. That came out of Google, and Google had all these servers that ran the search engine and at scale. And so one person managed boatload of servers and that was efficient. It was like a multiple 10x engineer, they used to call it. So that that was unique to Google but not everyone's Google. So it became language or parlance for someone who's running infrastructure but not everyone's that scale. So scale is a big issue. Ops as code is about scale and having that program ability as an operator. That's what Ops as code is. And that to me is a sign of where the scale meets the automation. Large scale is hard to do. Automating at large scale is even harder. So that's where Ansible fits in with their new automation platform. And you're seeing new things like signing code, making sure it's trusted and verified. So that's the software supply chain issue. So they're getting into the world where software, open source, automation are all happening at scale. So to me that's a huge concept of Ops as code. It's going to be very relevant, kind of the next gen positioning. >> Let's switch gears and talk about the partner ecosystem. We had Stefanie Chiras on yesterday, one of our longtime theCUBE alumni, talking about what they're doing with AWS in the marketplace. What was your take on that, and what's the "what's in it for me" for both Red Hat, Ansible and AWS? >> Yeah, so the big news on the automation platform was one. The other big news I thought was really, I won't say watered down, but it seems small but it's not. It's the Amazon Web Services relationship with Red Hat, now Ansible, where Ansible's now a product in AWS's marketplace. AWS marketplace is kind of hanging around. It's a catalog right now. It's not the most advanced technical system in the world, and it does over 2 billion plus revenue transactions. So even if it's just sitting there as a large marketplace, that's already doing massive amounts of disruption in the procurement, how software is bought. So we interviewed them in the past, and they're innovating on that. They're going to make that a real great platform. But the fact that Ansible's in the marketplace means that their sales are going to go up, number one. Number two, that means customers can consume it simply by clicking a button on their Amazon bill. That means they don't have to do anything. It's like getting a PO for free. It's like, hey, I'm going to buy Ansible, click, click, click. And then by the way, draw that down from their commitment to AWS. So that means Amazon's going into business with Ansible, and that is a huge revenue thing for Ansible, but also an operational efficiency thing that gives them more of an advantage over the competition. >> Talk what's in it for me as a customer. At Red Hat Summit a few months ago they announced similar partnership with Azure. Now we're talking about AWS. Customers are living in this hybrid cloud world, often by default. We're going to see that proliferate. What do you think this means for customers in terms of being able to- >> In the marketplace deal or Ansible? >> Yeah, the marketplace deal, but also what Red Hat and Ansible are doing with the hyperscalers to enable customers to live successfully in the hyper hybrid cloud world. >> It's just in the roots of the company. They give them the choice to consume the product on clouds that they like. So we're seeing a lot of clients that have standardized on AWS with their dev teams but also have productivity software on Azure. So you have the large enterprises, they sit on both clouds. So you know, Ansible, the customer wants to use Ansible anyway, they want that to happen. So it's a natural thing for them to work anywhere. I call that the Switzerland strategy. They'll play with all the clouds. Even though the clouds are fighting against each other, and they have to to differentiate, there's still going to be some common services. I think Ansible fits this shim layer between clouds but also a bolt on. Now that's a really a double win for them. They can bolt on to the cloud, Azure and bolt on to AWS and Google, and also be a shim layer technically in clouds as well. So there's two technical advantages to that strategy >> Can Ansible be a facilitator of hybrid cloud infrastructure for organizations, or a catalyst? >> I think it's going to be a gateway on ramp or gateway to multicloud or supercloud, as we call it, because Ansible's in that configuration layer. So you know, it's interesting to hear the IBM research story, which we're going to get to in a second around how they're doing the AI for Ansible with that wisdom project. But the idea of configuring stuff on the fly is really a concept that's needed for multicloud 'Cause programs don't want to have to configure anything. (he laughs) So standing up an application to run on Azure that's on AWS that spans both clouds, you're going to need to have that automation, and I think this is an opportunity whether they can get it or not, we'll see. I think Red Hat is probably angling on that hard, and I can see them kind of going there and some of the commentary kind of connects the dots for that. >> Let's dig into some of news that came out today. You just alluded to this. IBM research, we had on with Red Hat. Talk about what they call project wisdom, the value in that, what it also means for for Red Hat and IBM working together very synergistically. >> I mean, I think the project wisdom is an interesting dynamic because you got the confluence of the organic community of Ansible partnering with a research institution of IBM research. And I think that combination of practitioners and research groups is going to map itself out to academic and then you're going to see this kind of collaboration going forward. So I think it's a very nuanced story, but the impact to me is very clear that this is the new power brokers in the tech industry, because researchers have a lot of muscle in terms of deep research in the academic area, and the practitioners are the ones who are actually doing it. So when you bring those two forces together, that pretty much trumps any kind of standards bodies or anything else. So I think that's a huge signaling benefit to Ansible and Red Hat. I think that's an influence of Red Hat being bought by IBM. But the project itself is really amazing. It's taking AI and bringing it to Ansible, so you can do automated configurations. So for people who don't know how to code they can actually just automate stuff and know the process. I don't need to be a coder, I can just use the AI to do that. That's a low code, no code dynamic. That kind of helps with skill gaps, because I need to hire someone to do that. Today if I want to automate something, and I don't know how to code, I've got to get someone who codes. Here I can just do it and automate it. So if that continues to progress the way they want it to, that could literally be a game changer, 'cause now you have software configuring machines and that's pretty badass in my opinion. So that thought that was pretty cool. And again it's just an evolution of how AI is becoming more relevant. And I think it's directionally correct, and we'll see how it goes. >> And they also talked about we're nearing an inflection point in AI. You agree? >> Yeah I think AI is at an inflection point because it just falls short on the scale side. You see it with chatbots, NLP. You see what Amazon's doing. They're building these models. I think we're one step away from model scaling. I think the building the models is going to be one of these things where you're going to start to see marketplace and models and you start to composability of AI. That's where it's going to get very interesting to see which cloud is the best AI scale. So I think AI at scale's coming, and that's going to be something to watch really closely. >> Something exciting. Another thing that was big news today was the event driven Ansible. Talk about that, and that's something they've been working on in conjunction with the community for quite a while. They were very proud of that release and what that's going to enable organizations to do. >> Well I think that's more meat on the bone on the AI side 'cause in the big trend right now is MLAI ops. You hear that a lot. Oh, data ops or AI ops. What event driven automation does is allows you to take things that are going on in your world, infrastructure, triggers, alarms, notifications, data pipelining flows, things that go on in the plumbing of infrastructure. are being monitored and observed. So when events happen they trigger events. You want to stream something, you send a trigger and things happen. So these are called events. Events are wide ranging number of events. Kafka streaming for data. You got anything that produces data is an event. So harnessing that data into a pipeline is huge. So doing that at scale, that's where I think that product's a home run, and I think that's going to be a very valuable product, 'cause once you understand what the event triggers are, you then can automate that, and no humans involved. So that will save a lot of time for people in the the higher pay grade of MLAI ops automate some of that low level plumbing. They move their skill set to something more valuable or more impactful. >> And we talked about, speaking of impact, we talked about a lot of the business impact that organizations across industries are going to be able to likely achieve by using that. >> Yeah, I mean I think that you're going to see the community fill the gap on that. I mean the big part about all this is that their community builds the product and they have the the playbooks and they're shareable and they're reusable. So we produce content as a media company. They'd talk about content as is playbooks and documentation for people to use. So reuse and and reusing these playbooks is a huge part of it. So as they build up these catalogs and these playbooks and rules, it gets better by the community. So it's going to be interesting to see the adoption. That's going to be a big tell sign for what's going to happen. >> Yep, we get definitely are going to be watching that space. And the last thing, we got to talk to a couple of customers. We talked to Wells Fargo who says "We are a tech company that does banking," which I loved. We got to talk with Rockwell Automation. What are some of your takeaways from how the customers are leveraging Ansible and the technology to drive their businesses forward to meet demanding customers where they are? >> I think you're seeing the script flipping a little bit here, where the folks that used to use Ansible for configuration are flipping to be on the front edge of the innovation strategy where what process to automate is going to drive the profitability and scale. Cause you're talking about things like skill gaps, workflows. These are business constructs and people These are assets so they have economic value. So before it was just, IT serve the business, configure some servers, do some stuff. When you start getting into automation where you have expertise around what this means, that's economic value. So I think you're going to see the personas change significantly in this community where they're on the front lines, kind of like developers are. That's why ops as code is to me a developer kind of vibe. That's going to completely change how operations runs in IT. And I think that's going to be a very interesting cultural shift. And some will make it, some won't. That's going to be a big thing. Some people say, I'm going to retire. I'm old school storage server person, or no, I'm the new guard. I'm going to be the new team. I'm going be on the right side of history here. So they're clearly going down that right path in my opinion. >> What's your overall summary in the last minute of what this event delivered the last couple of days in terms of really talking about the transformation of enterprises and industries through automation? >> I think the big takeaway from me in listening and reading the tea leaves was the Ansible company and staff and the community together. It was really a call for arms. Like, hey, we've had it right from the beginning. We're on the right wave and the wave's getting bigger. So expand your scope, uplevel your skills. They're on the right side of history. And I think the message was engage more. Bring more people in because it is open source, and if they are on that track, you're going to see more of hey, we got it right, let's continue. So they got platform release. They got the key products coming out after years of work. So you know, they're doing their work. And the message I heard was, it's bigger than we thought. So I think that's interesting. We'll see what that means. We're going to unpack that after the event in series of showcases. But yeah, it was very positive, I thought. Very positive. >> Yeah, I think there was definitely some surprises in there for them. John, thank you so much. It's been a pleasure co-hosting with you the last couple of days, really uncovering what Ansible is doing, what they're enabling customers in every industry to achieve. >> Been fun. >> Yes. All right for my co-host, John Furrier, I'm Lisa Martin. You've been watching theCUBE's coverage of Ansible Fest 2022 live from Chicago. We hope you take good care and we'll see you soon.

(melodic music) >> Good afternoon. Welcome back to Chicago. Lisa Martin here with John Furrier. Day one of our coverage of Ansible Fest 2022. John, it's great to be back in person. People are excited to be here. >> Yeah. We've had some great conversations with folks from Ansible and the community and the partner side. >> Yeah. One of the things I always love talking about John, is talking with organizations that have been around for a long time that maybe history, maybe around nearly a hundred years, how are they embracing technology to modernize? Yeah, we got a great segment here with the financial services leader, end user of Ansible. So it's be great segment. >> Absolutely. Please welcome Noor Shadid to the program, the senior SVP, excuse me, senior technology manager at Wells Fargo. Noor it's great to have you on theCUBE. Thank you for joining us. >> Of course. Happy to be here. >> Thanks. >> Talk a little bit about technology at Wells Fargo. I was mentioning to you I've been a longtime customer and I've seen the bank evolve incredibly so in the years I've been with it. But... >> Yeah. >> ...talk about Wells Fargo was a technology-driven company. >> Yeah. So I like to consider Wells, right? Being in a financial institution company. So I consider us a technology company that does banking as a customer, right? Like we were talking about. There's so much that we've been able to release over the couple of years, right? I mean, decades worth of automation and technology has been coming out, but lately, right? The way we provide for our customers, how fast at scale, what we're doing for our customers, it's been, it's been significant, right? And I think our goal is always how can we enhance the process for our customers and how can we provide them the next best thing? And I think technology has really allowed us to evolve with our customers. >> The customers. We are so demanding these days. Right? I think one of the things that short supplied in the last two years was patience and tolerance. >> Yes. >> People. And I don't think that's going to rubber band back? >> Yeah. No, I don't think so. >> So how, talk to us about how Wells is using automation to really drive innovation and, surprise and delight those customers on a minute by minute basis. >> Yeah. And so, you know, if you think about banking, we've been able, with automation, we've been able to bring banking into the 21st century. You do not have to go to a branch to manage your money anymore. You do not have to go, you know, go to deposit your check inside of a branch. You can do it through your mobile app, right? That's driven by automation and innovation, right? And, you know, we have all of these back ends tools working for us to help get us to this next generation of, of banking. We can instantly send money to each other. We don't have to worry about, I need to go and figure out how I'm going to get money to this person and I need to wait, you know, X amount of days. You, you have the ability and you have, you feel safe being able to manage your money at the organization. And so automation has really allowed us to get to this place where we can constantly enhance and provide features and reliability to our customers. >> It's interesting you mentioned that you guys are a technology can have it do banking reminds me of the old iPhone analogy. It's a computer that happens to make phone calls. >> Yeah. >> So like, this is the similar mindset. How do you guys keep up? >> Yeah. >> With the technology? >> So it's tough, right? Because there's so much that comes out. And I think the only thing that's constant in technology is change, right? Because it's constantly evolving. But what we do is we, integrate very well with these new tools. We do proof of concepts where we try to, you know, what's on the market, what's hot, how can we involve, like, how can we involve these new tools in our processes? How can we provide a better end result for our customers by bringing in these new tools? So we have a lot of different teams that bring, you know, their jobs are to like, do these proof of concepts and help us build and evolve our own strategies, right? So it keeps us, it keeps us on our toes and I think it keeps, you know, all these new things that are coming out in the market. We're a part of it. We want to evolve with those, what the latest and greatest is. And it's, it's been working right as customers of financial services and us managing our money through, you know, through banks. It's been great. >> So the business is the application. >> Yes. >> And how do you guys make that happen when it comes down to getting the teams aligned? What's the culture like? Explain. >> Yeah. So at Wells we have evolved so much over the, over the last few years. The culture right now is we want to make changes. You know, we are making changes. We want to drive through innovation. We want to be able to provide our, you know, it's a developer centric approach right now, right? We want to push to the next and the greatest. And so everybody is excited and everybody's adapting to all of what's happening in the environment right now. So it's been great because we are able to use all of these new features and tools and things that we were just talking about by allowing our developers to do that work and allowing people to learn these new skills and be able to apply them in their jobs, which is now creating this, you know, a better result for our customers because we're releasing at such a faster pace. And at scale. >> Talk about how, you talked about multiple groups in the organization really investing in innovative technology. How do you get buy-in? What's that sort of pyramid like up to the top level? >> Yeah. >> Because to your point, you're making changes very quickly and consumers demand it. >> Yep. >> You can do everything from home these days. >> Yep. >> You don't have to go into a branch. >> Yeah, yeah. >> Which has changed dramatically in the last it's. >> Powerful few years. Yeah. >> But how, what's that buy-in conversation like from our leadership? >> Yeah. If you don't have leadership buy-in, it's very difficult to make those changes happen. But we at Wells have such a strong support from our leadership to be a part of the change and be, you know, constantly evolve and get better. So the way we work, cause we're such a large organization, you know, we bring in our business, you know, our business teams and we talk to them about what is it that's best going to better our customers. How do we also not just support external but internal, right? How do we provide these automated tools or processes for people to want to do this next work and, and do these, you know, these new releases for our customers. And so we bring in our business partners and, and we bring in our leadership and, our stakeholders and we kind of present to them, you know, this is what we're trying to do. This is the return that you'll get. This is what our customers will also receive. And this is, you know, this is how we keep evolving with that. >> How has the automation culture changed? Because big discussion here is reuse, teamwork, I call it multiplayer kind of organizations where people are working together. 'Cause that's a big theme of automation. >> Yeah. >> Reuse, leverage. >> Yep. >> Can you explain how you guys look at that? >> Yeah. It's changed the way that we do banking because we're eliminating a lot of the repetitive tasks in the toil because we have partners that are developing these, you know, services. So specifically with Ansible, we have these playbooks, rather than having every customer write the same playbook but with their own little, you know, flavor to it, we're able to create these generic patterns that customers can just consume simply by just going into a tool, filling out you know, filling out that playbook template, credentials, or whatever it is that they need and executing it. They don't have to worry about developing something from scratch. And it also allows our customers to feel safe because they don't have to have those skills out the box to be able to use these automation tools, right? They can use what's already been written and executed. >> So that make things go faster with the benefits or what? Speed? >> Faster stability, right? We're now speed, stability, scalability, because we're now able to use this at scale. It's not just individual teams trying to do this within small spaces. We're able to reliable, right? Automation allows us to be reliable internally and for our customers. Because you're not asking, there's no human intervention when you're automating, right? You have these opportunities now for people to just, it's one click, you know, one click solution or you're, you're end to end. You got self-healing involved. It's really driving the way that we do our work today. >> So automation sounds like it's really fueling the internal employee experience at Wells... >> Yes. >> ...as well as the customer experience. And those two things are like this to me. They're inextricably linked. >> A hundred percent because if you need it, they need to be together, right? You want your internal to also be happy because they want to be able to develop these solutions and provide these automation opportunities for our teams, right? And so with the customers, they're constantly seeing these great features come out, right? We can, you know, with AIML today, we're now able to detect fraud significantly. What we would've, what we could've done a couple years ago. And, and developers are excited to be able to do that, right? To be able to learn all these new tools and new technologies. >> What's interesting Wells is you guys are like an edge application. Obviously everyone's got banking in their hand. FinTech obviously money's involved. So there's people interested in getting that money. >> Yeah. >> Security hackers or whatnot. So when you got speed and you got the consistency, I get that. As you look at securing the app, that becomes a big part of what, what's the conversations like there? >> Yeah. >> 'Cause that's the number one concern. And it's an Edge app. I got my mobile, I got my desktop. >> Yeah. >> Everything's in the cloud on premise. >> Yeah. And, and I think for us, security is number one. You know, we want to make sure that we are providing the best for our customers and that they feel safe. Banking, whatever financial service you're working with, you want to feel like you can trust that your money with those services. Right? So what we do is we make sure that our security partners are with us from day one. They're a part of the process. They're automating their pieces as well. We don't want to rely on humans to do a lot of the manual work and do the checking and the logging. You want it to be through automation and new tools, right? You want it to be done through trusted services. You don't, you know, security is right there with us. They're part of our technology organization. They are in the technology org. So they're the ones that are helping us get to that next generation to provide, you know, more secure processes and services for customers. >> And that's key for trust. >> Yes. >> And trust is critical to reduce churn and to, you know, increase the customer lifetime value. But, but people, I mean, especially with the amount of generations that are alive today in banking, you need to be able to deliver that trust intrinsically to any customer. >> Yes, a hundred percent. And you want to be able to not only trust the service but yourself that you can do it. You know, when you go into your app and you make a payment, or when you go in and you want to send, you know, you want to send money to a different, you know, a different bank account, you want to be able to know that what you just did is secure and is where you plan to send it. And so being able to create that environment and provide those services is, is everything right for our customers. >> What are some of the state-of-the-art kind of techniques or trade craft around building apps? 'Cause I mean, basically you're digitally transformed. I mean, you guys are technology first. >> Yeah. >> The app is the company. >> Yeah. >> That's, that's the bank. How do you stay current? What's some of the state of the art things that you guys do that wasn't around just a few years ago? >> Yeah, I mean, right now just using, we're using tools like Terraform and Ansible. We're making sure that those two are hand in hand working well together. So when we work on provisioning, when we, during provisioning where it's all, you know, it's automated, fully end to end, you know, AI ops, right? Being able to detect reoccurring issues that are happening. So if you have a incident we want to learn from that incident and we want to be able to create, you know, incident tickets without having to rely on a human to find that, you know, that problem that was occurring and self-healing, right? All of this is starting to evolve and bringing in the, the proper alerting tools, bringing in the pro, you know, the right automation tools to allow that self-healing to work. That's, you know, these are things that we didn't have, you know, year, decade ago. This is all coming out now as we're starting to progress and, and really take innovation and, you know, automation itself.... >> What's the North star internally when you guys say, hey, you know, down five years down the road, bridge to the future, we're transforming, we've continued to innovate. Scale is a big deal. Data, data sovereignty, all these things are coming up. And what's the internal conversation like when you talk about a future state? >> Yeah, I think right now we're on our cloud transformation journey, right? We're moving right now. We have workloads into our two CSPs or public cloud. Also providing a better service for infrastructure and being able to provide services internally at a faster space, right? So moving into the public cloud, making sure everything's virtualized, moving away from hard, you know, physical hardware or physical servers. That's kind of the journey that we're on right now. Right? Also, machine learning. We want to be able to rely on these, you know, bots. We want to be able to rely on, on things learning from what we're doing so that we don't make the same mistakes again. >> Where would you say the most value or the highest ROI that you've gotten from automation today? Where is that in the organization? >> There's so much, but what I mean because of all of the work that we're doing, there's a lot that I could list, but what I will say is that the ability to allow self-healing in our environments without causing issues is a very big return. Automating failovers, right? I think a lot of our financial institutions have made that a priority where they want to make sure that their applications are active, active and also that when things do go wrong, there is something in place to make sure that that incident actually doesn't, you know, take down any problems. I think it's just also investing in people. Right now, the market is hot and we want to make sure that people feel like they're being able to contribute, they're using the latest and greatest tools. They're able to upskill within our own environments at the firm. And I think our organization does an amazing job of prioritizing people. And so we see the return because we're prioritizing people. And I think, you know, a lot of institutions are trying, you know, people first, people first. But I can say that at Wells, because we are actually driving this, we're allowing, you know, we're enforcing that. We want our engineers to get the certifications. We're providing, you know, vouchers so that people can get those clouds certifications. It's when you do that and you put people first, everything kind of comes together. And I think, you know, a lot of what we see in our industry, it's not really the technology that's the problem, it's process because you're so, you know, we're working at large scales. Our environments are massive. So, you know, my three years at Wells have seen a significant amount of change that has really driven us to be.... >> On that point better. How about changing of the roles? IT, I mean, back in the day, IT serves the business, you know, IT is the business now, right? As, as you've been pointing out. What does the roles change of as automation scales in, is it the operator? I mean, we know what's going on with dev's devs are doing more IT in the CICD pipe lining. >> Yep. >> So we see that velocity check, good cloud native development. What's the op scene look like? It seems to be a multi-tool role. >> Yeah. >> Where the versatility of the skill set... >> Yep. >> ...is the quick learner. >> Yep, able to adapt. >> And yeah, what's your view on this new persona that's emerging from this new opportunity? >> Yeah, and I think it's a great question because if you think about where we're going, and even the term DevOps, right? It means so many things to different people. But literally when you think about what DevOps is allowing our developers and our operations to work together on one team, it's allowing, you know, our operation engineers aren't, you know, years ago, ops engineers were not doing the development work. They were relying on somebody to do the development work and they were just supporting making sure our systems were always available, right? Our engineers are ops are now doing the development work. They're able to contribute and to get, they're writing their own playbooks. They're able to take them into production and ensure that they're, being used correctly. We are change driven execution organization. Everything is driven through change and allowing our ops engineers or production score engineers to write their own playbooks, right? And they know what's happening in the environment. It's powerful. >> Yeah. You're seeing DevOps become a job title. >> Yeah (laughs). >> Used to be like a function of philosophy... >> Yeah, yeah. >> ... and then SRE's... >> SRE's. >> SRE are like how many servers do you have? I don't know, a cloud, what's next? (all laugh) >> What's next? Yeah, I think with SREs it's, you know, it's important that if you have site reliability engineers, you're working towards, you know, those non-functional requirements... >> Yeah. >> ...making sure that you're handling those key components that are required to ensure that our systems, our applications and our integrations, you know, are up there and they're meeting the standards that we set for those other faults. >> And, and I think Red Hat Ansible nailed it here because infrastructure is code. We get that infrastructure has configuration as code, but OPS says code really is that SRE outcome. SRE also came from the Google background, but that means infrastructure's just doing, it's thing. >> Yes. >> The ops is automated. >> Yes. >> That's an interesting concept. >> Yeah, because it's not, you know, it's still new, right? A lot of organizations used to see, and they probably still see operations as being the, you know, their role is just to make sure that the lights are on and they have specific access so they, you know, they're not touching code, but the people that are doing the work and know the environment should really be the ones under creating the content for it. So yeah, I mean it's crazy what's happening now. >> So I got an analogy that's going to be banking analogy, but for tech, you know, back in the automation, Oh, going to put my job out of business, ATMs are going to put the teller out of business as more tellers now than there are before the ATMs. So that metaphor applies into tech where people are like, "What am I auto? What's automating away? Is it my job?" And so actually people know it's not. >> Yeah. >> But what does that free up? So if you assume, if you believe that's good, you say, okay, all the grunt work and the low level on differentiated heavy lifting gets automated away. >> Yeah. >> Great. What does that free up the talent to do? >> Yeah, so when you, and that's great that you bring it up because I think people fear, you know, of automation, especially people that weren't doing automation in the past and now their roles are now they're able to automate those roles out. They're fearful that they don't have a space, a role anymore. But that's not the case at all. What we prioritize is now that those new engineers have this new skill set, apply them. Start using it to be a part of this transformation, right? We're moving from, we went from physical to virtual to now, you know, we're moving into the public, moving into the cloud, right? And that, that transformation, you need people who are ramping up their skill sets, you know, being a part of one of the tools that I own is terraform at Wells that, you know, right now our priority is we're trying to ramp up the organization to learn terraform, right? We want people to learn, you know, this new syntax, this new, you know, HCL and it's, you know, people have been automating some of the stuff that they're doing in their day to day and now trying to learn something new so that they can contribute to this new transformation. >> So new functionality, higher value services? >> Yes, yeah. >> It brings tremendous opportunity for those folks involved in automation. >> Yes. >> or on so many levels. >> Yep. >> Last question, Noor for you is what, you know, as we are rounding out calendar year 2022, entering into 2023, that patience is, that we talked about is still not coming back. What's next for Wells as a technology company that does banking? >> I mean, you name it, we're working on it, because we want to be able to deliver the best for our customers. And I think right now, you know, our digital transformation strategy and, and moving into the public cloud and getting our applications re-architected so that we are moving into microservice driven apps, right? We're moving these workloads into the public cloud in a seamless way. We're not lifting and shifting so that we're not causing more problems into the environment. Right. And I think our, our, our goal is right, Like I was saying earlier, people and evolving with the technology that's coming out. We're not, you know, we are a part of the change and we are happy to be a part of that change and making those changes happen. >> People first. >> Awesome, awesome stuff. >> Automation first sounds outstanding and I will never look at Wells Fargo as a bank again. >> Yeah. (laughter) >> Perfect. Perfect. >> Yeah, that's awesome. >> It's been such a pleasure having you on the program, talking about how transformative Wells has been and continues to be. >> Yeah. >> We appreciate your insights and your time. >> Thank you. >> Thank you so much. It was lovely being her. Pleasure here. Thank you guys. >> For our guest and John Furrier, I'm Lisa Martin. You've been watching theCUBE all day, I'm sure, live from Chicago at Ansible Fest 2022. We hope you have a wonderful rest of your day and John and I will see you tomorrow morning.

(upbeat music) >> Hello, and welcome to this Cube conversation. I'm Dave Nicholson, and this is part of our continuing coverage of Google Cloud Next 2021. We have a very interesting subject to discuss. I have two special guests from Google to join me in a conversation about the financial services space. I'm joined by Yolande Piazza, vice president of financial services sales for Google Cloud and Zac Mauf, managing director for global financial services solutions for Google Cloud. Yolande and Zac, welcome to the Cube. >> Thank you for having us. Looking forward to it. >> Well it's great to have you here. You know, financial services is really an interesting area when you talk about cloud because I'm sure you both remember a time, not that long ago, when we could ask a financial services organization, what their plans for cloud or what their cloud strategy was, and they would give a one word answer and that answer was, never. (laughing) So Zac, let's start out with you, what has changed? Are you and Yolande going to tell us that in fact, financial services organizations are leveraging cloud now? >> Yeah, it's a very exciting time to be in the cloud space, in financial services, because you're exactly right David. People are starting to make the transition to cloud in a real way. And a lot has gone into that, as you know, it's a highly regulated space and so there were a lot of legitimate reasons around getting kind of the regulatory frameworks in place and making sure that the risk and compliance pieces were addressed. But then there was also, as you know, technology is a major backbone for financial services. And so there's also this question of, how do we transition? And a lot of work and time has gone into moving workloads, thinking about like, what is the sort of the right migration strategy for you to get from the current situation to a more cloud native world. And to your point, we're really early, we're really early, but we're very excited and we've been investing heavily on our side to get those foundational pieces in place. But we also realized that we have to think about what are the business cases, that we want to build on top of cloud. It's not just a kind of IT modernization, which is a big part of the story, but the other part of the story is once you get all of this, technology onto the cloud platform, there are things that you can do that you couldn't do in on-prem situations. And a lot of that for us is around the data, AI and ML space. And we really see that being the way to really unlock huge amounts of value. Both of them require massive amounts of compute and breaking down all of these silos that have really developed over time within financial institutions. And really moving to the cloud is the way to unlock a lot of that. So we're really excited about a lot of those use cases that are starting to come to life now. >> Yeah. So I want to dig a little deeper on some of that Zac, but before we do, Yolande make this real for us. Give me some examples of actual real-life financial services organizations and what they're doing with Google Cloud now. >> Yeah, absolutely. And I think we're really proud to be able to announce, a number of new partnerships across the industry. You think about Wells Fargo, you think about Scotia Bank, you think about what we're doing with HSBC. They really are starting to bring to life and recognized that it's not just internally, you have to look at that transformation to cloud, it's really, how do you use this platform to help you go on the journey with your customers? I think a move to a multi-cloud common approach for our customers and our clients, is exactly what we need to be focused on. And the other- >> Hold on, hold on, Yolande. I'm sorry. Did the Google person just say multi-cloud? Because multi- cloud doesn't sound like, only Google Cloud to me. Can you- >> No, and I think Wells, absolutely, and I think Wells announced it's taking a multi-cloud approach to its digital infrastructure strategy, leveraging both Google Cloud and Microsoft Azure. And the reason being is they've openly communicated that a locked in and preparatory systems, isn't the way to go for them. They want that open flexibility. They want the ability to be able to move workloads across the different industries. And I think it's well known that this aligns completely with our principles and at Google we've always said that we support open multi and hybrid cloud strategies because we believe our customers should be able to run what they want, where they want it. And that was exactly the philosophy that that Wells took. So, and if you look at what they were trying to do is they're looking to be able to serve their customers in a different way. I think that it's true now that customers are looking for personalized services, instant gratification, the ability to interact, where they want and when they want. So we're walking with the Wells teams to really bring to life through AI, our complex AI and data solutions to really enable them to move at speed and serve their customers in a rapidly changing world. >> So Yolande, part of the move to cloud includes the fact that we're all human beings and perception can become reality. Issues like security, which are always at the forefront of someone's mind in financial services space, there is the perception, and then there is the reality. Walk us through today where perception is in the financial services space. And then Zac, I'm going to go back to you to tell us what's the reality. And is there a disconnect? Because often technology in this space has been ahead of people's comfort level for rational reasons. So Yolande, can you talk about from a perception perspective where people are. >> So I have to tell you, we are having conversations with both the incumbents and traditional organizations, as well as, the uprising, the fintechs, and the neobanks around how does technology really unlock and unleash a new business model. So we're talking about things like how does technology and help them grow that organization. How does it take out costs in that organization? How do you use all cloud platform to think about managing risks, whether that's operational, whether it's reputational, industry or regulatory type risk? And then how do we enable our partners and our customers to be able to move at speed? So all of those conversations are now on the table. And I think a big shift from when Zac and I both were sitting on the other side of the table in those financial services industries is a recognition that this couldn't and shouldn't be done alone, that it's going to require a partnership, it's going to require, really shifting to put technology at the forefront. And I think when you talk about perception, I would say a couple of years ago, I think it was more of a perception that they were really technology companies. And I think now we're really starting to see the shifts that these are technology companies serving their customers in a banking environment. >> So Zac, can you give us some- Yeah. Yeah. Zac, can you give us some examples of how that plays out from a solutions perspective? What are some of the things that you and Yolande are having conversations with these folks in? >> Yeah. - I mean, absolutely. I think there's three major trends that we're seeing, where I think we can bring the power of sort of the Google ecosystem to really change business models and change how things are done. The first is really this massive change that's been happening for like over 10 years now, but it's really this change in customers, expecting financial institutions to meet them where they are. And that started with information being delivered to them through mobile devices and online banking. And then it went to payments, and now it's going into lending and it's going into insurance. But it changes the way that financial services companies need to operate because now they need to figure out how to deliver everything digitally, embedded into the experience that their customers are having in all of these digital ecosystems. So there's lot that we're doing in that space. The second is really around modernizing the technology environment. There is still a massive amount of paper in these organizations. Most of it has been transferred to digital paper, but the workflows and the processes that are still needing to be streamlined. And there's a lot that we can do with our AI model and technology to be able to basically take unstructured data and create structured data. Thank Google Photos, you can now search for your photo library and find, pictures of you on bridges. The same thing we can now do with documents and routine interactions with chat bot. People are expecting 24/7 service. And a lot of people want to be able to interact through chat versus through voice. And the final part of this that we're seeing a lot of use cases in is in the kind of risk and regulatory space. Coming out of the financial crisis, there was this need to massively upgrade everybody's data capabilities and control and risk environments, because so much it was very manual, and a lot of the data to do a lot of the risk and control work was kind of glued together. So everybody went off and built data lakes and figured out that that was actually a really difficult challenge and they quickly became data swamps. And so really how do you unlock the value of those things? Those three use cases, and there's lots of things underneath those, are areas that we're working with customers on. And it's, like you said, it's really exciting because the perception has changed. The perception has changed that now cloud is the sort of future, and everybody is kind of now realized they have to figure out how to engage. And I think a lot of the partnership things that Yolande was talking about is absolutely true. They're looking for a strategic relationship versus a vendor relationship, and those are really exciting changes for us. >> So I just imagined a scenario where a Dave, Zac, and Yolande are at the cloud pub talking after hours over a few pints, and Dave says, "Wow, you know, 75%, 80% of IT is still on-premises." And Yolande looks at me and says, "On-premises? We're dealing with on-paper still." Such as the life of a financial services expert in this space. So Yolande, what would you consider sort of the final frontier or at least the next frontier in cloud meets financial services? What are the challenges that we have yet to overcome? I just mentioned, the large amount of stuff that's still on premises, the friction associated with legacy applications and infrastructure. That's one whole thing. But is there one thing that in a calendar year, 2022, if you guys could solve this for the financial services industry, what would it be? And if I'm putting you on the spot, so be it. >> No, no. I'm not going to hold it to just one thing. I think the shift, I think the shift to personalization and how does the power of, you know, AI and machine learning really start to change and get into way more predictive technologies. As I mentioned, customers want to be a segmentation of one. They don't want to be forced fit into the traditional banking ecosystems. There's a reason that customers have on average 14 different financial services apps on their phones. Yep. Less than three to 5% of their screen time is actually spent on them. It's because something is missing in that environment. There's a reason that you could go to any social media site and in no time at all, be able to pull up over 200 different communities of people trying to find out financial services information in layman's terms that is relevant to them. So the ability and where we're really doubling down is on this personalization. Being way more predictive, understanding where a customer is on their journey and being able to meet them at that point, whether that's the bright offers, whether that's recognizing, to Zac's point, that they've come in on one channel but they now want to switch to another channel. And how do they not have to start again every time? So these are some of the basics things, so we really doubled down on how do we start to solve in those areas. I think also the shift, I think in many cases, especially in the risk space, it's been very much what I would call, a people process technology approach, start to imagine what happens if you turn that around and think about how technology can help you be more predictive internally in your business and create better outcomes. So I think there's so many areas of opportunities, and what's really exciting is we're not restricted, we're having conversations that are titled, the art of the possible, or the future of, or help us come in and reinvent. So I think you're going to see a lot of shift probably in the next 12 to 18 months, I would say, and the capabilities and the ability to service the customer differently and meet them on their journey. >> Well, it sounds like the life of a cloud financial services person is much more pleasurable than back when it consisted of primarily running into brick walls constantly. This conversation five or 10 years ago would have been more like, please trust us, please. Just give us a shot. >> I think Zac and I both reminisce that we couldn't have joined at a more exciting time. It's the locker or whatever you want to call it, but it is a completely different world and the conversations are fun and refreshing, and you can really start to see how we have the ability to partner to change the landscape, across all of the different financial services industries. And I think that's what keeps Zac and I going every day. >> And you said earlier that you alluded to the idea that you used to be on the other side of the table, in other words, in the financial services industry on the customer side. So you pick the right time to come across. >> Without a doubt, without a doubt. Yes. >> Well, with that, I want to thank both of you for joining me today. This is really fascinating. Financial services is something that touches all of us individually in our daily lives. It's something that everyone can relate to at some level. And it also represents, that tip of the spear, the cutting edge of cloud. So very interesting. Thank you both again, pleasure to meet you both. Next time, hopefully it will be in-person and we can compare our steps that we've taken during the conference. With that I'll sign off. This has been a fantastic Cube conversation, part of our continuing coverage of Google Cloud Next 2021. I'm Dave Nicholson, Thanks again for joining us. >> Thank you. (upbeat music)

(upbeat music) >> Greetings from Los Angeles, Lisa Martin here with Dave Nicholson. We are on day three of the caves wall-to-wall coverage of KubeCon CloudNativeCon North America 21. We're pleased to welcome Mark Hinkle to the program, the co-founder and CEO of TriggerMesh. Mark welcome. >> Thank you, It's nice to be here. >> Lisa: Love the name. Very interesting TriggerMesh. Talk to us about what TriggerMesh does and what, when you were founded and what some of the gaps were that you saw in the market. >> Yeah, so TriggerMesh actually the Genesis of the name is in, cloud event, driven architecture. You trigger workloads. So that's the trigger and trigger mesh, and then mesh, we mesh services together, so cloud, so that's why we're called TriggerMesh. So we're a cloud native open source integration platform. And the idea is that, the number of cloud services are proliferating. You still have stuff in your data center that you can't decommission and just wholesale lift and shift to the cloud. So we wanted to provide a platform to create workflows from the data center, to the cloud, from cloud to cloud and not, and use all the cloud native design principles, but not leave your past behind. So that's, what we do. We're, very, we were cloud, we are cloud operators and developers, and we wanted the experience to be very similar to the way that DevOps folks are doing infrastructure code and deploying that we want to make it easy to do integration as code. So we follow the same design patterns, use the same domain languages, some of those tools like Hashi corpse, Terraform, and that that's what we do and how we go about doing it. >> Lisa: And when were you guys founded? >> September, 2018. >> Oh so your young, your three years young. >> Three years it's feels like 21 >> I bet. >> And startup years it's a lot has happened, but yeah, we my co-founder and I were former early cloud folks. We were at cloud.com worked through the OpenStack years and the CloudStack, and we just saw the pattern of, abstraction coming about. So first you abstract the hardware, then you abstract the operating system. And now at with the Kubernetes container, you know, evolution, you're abstracting it up to the application layer and we want it to be able to provide tooling that lets you take full advantage of that. >> Dave: So being founded in 2018, what's your perception of that? The shift that happened during the pandemic in terms of the drive towards cloud adoption and the demands for services like you provide? >> Mark: Yeah, I think it's a mixed blessing. So we, people became more remote. They needed to enable digital transformation. Biggest thing, I think that that for us is, you know, you don't go to the bank anymore. And the banking industry is doing, you know, exponentially more remote, online transactions than in person. And it's very important. So we decided that financial services is where we were going to start with first because they have a lot of legacy architecture. They have a lot of need to move to the cloud to have better digital experiences. And we wanted to enable them to, you know, keep their mainframes online while they were still doing cutting edge, you know, mobile applications, that kind of thing. >> Lisa: And of course the legacy institutions like the BFA's the Wells Fargo, they're competing with the fintechs who are much more nimble, much more agile and able to sort of disrupt the financial services industry. Was that part of also your decision to start in financial services? >> It was a little bit of luck because we started with our network and it turned out the, you know, we saw, we started talking to our friends early on, cause we're a startup and said, this is what we're going to do. And where it really resonated was PNC bank was our, one of our first customers. You know, another financial regulatory company was another one, a couple of banks in Europe. And we, you know, as we started talking about what we were doing, that we just gravitated there because they had the, the biggest need, even though everybody has the need, their businesses are, you know, critically tied to digital transformation. >> So starting with financial services. >> It's, it's counter intuitive, isn't it? >> It was counterintuitive, but it lends credibility to any other industry vertical that you're going to approach. >> Yeah, yeah it does. It's a, it's a great, they're going to be our hardest customers and they have more at stake than a lot of like transactions are millions and millions of dollars per hour for these folks. So they don't want to play around, they, they have no tolerance for failure. So it's a good start, but it's sort of like taking up jogging and running a marathon in your first week. It's very very grilling in that sense, but it really has made us a lot better and gave us a lot of insight into the kinds of things we need to do from not just functionality, but security and that kind of thing. >> Where are you finding these customers with respect to adoption of Kubernetes? Are they leading? Are they knowing we've got to get there eventually from an infrastructure perspective? >> So the interesting thing is Kubernetes is a platform for us to deliver on, so we, we don't require you to be a Kubernetes expert we offer it as a SaaS, but what happens is that the Kubernetes folks are the ones that we end up really engaging with earlier on. And I think that we find that they're in this phase of they're containerizing their apps, that's the first step. And then they're putting them on Kubernetes and then their next step is a security and integration path. So once she, I think they call it and this is my buzzword of the show day two operations, right? So they, they get to day two and then they have a security and an integration concern before they go live. So they want to be able to make sure that they don't increase their attack face. And then they also want to make sure that this newly deployed containerized infrastructure is as well integrated as the previous, you know, virtualized or even, you know, on the server infrastructure that they had before. >> So TriggerMesh, doesn't solely work in the containerized world, you're, you're sort of you're bridging the divide. >> Mark: Yes. >> What percentage of the workloads that you're seeing are the result of modernization migration, as opposed to standing up net new application environments in Kubernetes? Do you have a sense for that? >> I think we live in a lot in the brown field. So, you know, folks that have an existing project that they're trying to bridge to it versus the Greenfield kind of, you know, the, the huge wins that you saw in the early cloud days of the Netflix and the Twitter's Dwayne scale. Now we're talking to the enterprises who have, you know, they have existing concerns. So I would say that it's, it's mostly people that are, you know, very few net new projects, unless it's a modernization and they're getting ready to decommission an old one, which is. >> Dave: So Brownfield financial services. You just said, you know, let's just, let's just go after that. >> You know, yeah. I mean, we had this dart forward and we put up buzzwords, but no, it was, it was actually just, and you know, we're still finding our way as far as early on where we're open source folks. And we did not open source from day one, which is very weird when everybody's new, your identity is, you know, I worked, I was the VP of marketing for Linux foundation and no JS and all these open source projects. And my co-founder and I are Apache committers. And our project wasn't open yet because we had to get to the point where it could be open and people could be productive in the use and contribution. And we had to staff up engineers. And now I think this week we open-sourced our entire platform. And I think that's going to open up, you know, that's where we started because it was not necessarily the lowest hanging fruit, but the profitable, less profitable, lowest hanging fruit was financial services. Now we are letting our code out into the wild. And I think it'll be interesting to see what comes back. >> So you just announced that this week TriggerMesh integration platform as an open source project here at KubeCon, what's been some of the feedback? >> It's all been positive. I haven't heard anything negative. We did it, so we're very, very, there's a very, the culture around open source is very tough. It's very critical if you don't do it right. So I think we did a good job, we used enough, we used a OSI approved. They've been sourced, licensed the Apache software, a V2 license. We hired someone who was well-respected in the DevREL world from a chef who understands the DevOps sort of culture methodologies. We staffed up our engineers who are going to be helping the free and open source users. So they're successful and we're betting that that will yield business results down the road. >> Lisa: And what are the two I see on your website, two primary use cases that you guys support. Can you dig into details on that? >> So the first one is sort of a workflow automation and a really simple example of that is you have a, something that happens in one cloud. So for example, you take a picture on your phone and you upload it and it goes to Amazon and there is a service that wants to identify what's in that picture. And once you put it on the line and the internship parlance, you could kick off a workflow from TensorFlow, which is artificial intelligence to identify the picture. And there isn't a good way for clouds to communicate from one to the other, without writing custom blue, which is really what, what we're helping to get rid of is there's a lot of blue written to put together cloud native applications. So that's a workflow, you know, triggering a server less function is the workflow. The other thing is actually breaking up data gravity. So I have a warehouse of data, in my data center, and I want to start replicating some portion of that. As it changes to a database as a service, we can based on an event flow, which is passive. We're not, we're not making, having a conversation like you would with an API where there's an event stream. That's like drinking from the fire hose and TriggerMesh is the nozzle. And we can direct that data to a DBaaS. We can direct that data to snowflake. We can direct that data to a cloud-based data lake on Microsoft Azure, or we can split it up, so some events could go to Splunk and all of the events can go to your data lake or some of those, those things can be used to trigger workloads on other systems. And that event driven architecture is really the design pattern of the individual clouds. We're just making it multi-cloud and on-prem. >> Lisa: Do you have a favorite customer example that you think really articulates that the value of that use case? >> Mark: Yeah I think a PNC is probably our, well for the, for the data flow one, I would say we have a regular to Oracle and one of their customers it was their biggest SMB customer of last year. The Oracle cloud is very, very important, but it's not as tool. It doesn't have the same level of tooling as a lot of the other ones. And to, to close that deal, their regulatory customer wanted to use Datadog. So they have hundreds and hundreds of metrics. And what TriggerMesh did was ingest the hundreds and hundreds of metrics and filter them and connect them to Datadog so that, they could, use Datadog to measure, to monitor workloads on Oracle cloud. So that, would be an example of the data flow on the workflow. PNC bank is, is probably our best example and PNC bank. They want to do. I talked about infrastructure code integration is code. They want to do policy as code. So they're very highly regulatory regulated. And what they used to do is they had policies that they applied against all their systems once a month, to determine how much they were in compliance. Well, theoretically if you do that once a month, it could be 30 days before you knew where you were out of compliance. What we did was, we provided them a way to take all of the changes within their systems and for them to a server less cluster. And they codified all of these policies into server less functions and TriggerMesh is triggering their policies as code. So upon change, they're getting almost real-time updates on whether or not they're in compliance or not. And that's a huge thing. And they're going to, they have, within their first division, we worked with, you know, tens of policies throughout PNC. They have thousands of policies. And so that's really going to revolutionize what they're able to do as far as compliance. And that's a huge use case across the whole banking system. >> That's also a huge business outcome. >> Yes. >> So Mark, where can folks go to learn more about TriggerMesh, maybe even read about more specifically about the announcement that you made this week. >> TriggerMesh.com is the best way to get an overview. The open source project is get hub.com/triggermesh/trigger mesh. >> Awesome Mark, thank you for joining Dave and me talking to us about TriggerMesh, what you guys are doing. The use cases that you're enabling customers. We appreciate your time and we wish you best of luck as you continue to forge into financial services and other industries. >> Thanks, it was great to be here. >> All right. For Dave Nicholson, I'm Lisa Martin coming to you live from Los Angeles at KubeCon and CloudNativeCon North America 21, stick around Dave and I, will be right back with our next guest.

(upbeat music) >> Announcer: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hey, welcome back everybody. Jeff Frick here with the CUBE, coming to you from our Palo Alto studios today for a CUBE conversation. we've got a couple of a CUBE alumni veterans who've been on a lot of times. They've got some exciting announcements to tell us today, so we're excited to jump into it, So let's go. First we're joined by Eric Herzog. He's the CMO and VP worldwide storage channels for IBM Storage, made his time on theCUBE Eric, great to see you. >> Great, thanks very much for having us today. >> Jeff: Absolutely. And joining him, I think all the way from North Carolina, Sam Werner, the VP of, and offering manager business line executive storage for IBM. Sam, great to see you as well. >> Great to be here, thank you. >> Absolutely. So let's jump into it. So Sam you're in North Carolina, I think that's where the Red Hat people are. You guys have Red Hat, a lot of conversations about containers, containers are going nuts. We know containers are going nuts and it was Docker and then Kubernetes. And really a lot of traction. Wonder if you can reflect on, on what you see from your point of view and how that impacts what you guys are working on. >> Yeah, you know, it's interesting. We talk, everybody hears about containers constantly. Obviously it's a hot part of digital transformation. What's interesting about it though is most of those initiatives are being driven out of business lines. I spend a lot of time with the people who do infrastructure management, particularly the storage teams, the teams that have to support all of that data in the data center. And they're struggling to be honest with you. These initiatives are coming at them, from application developers and they're being asked to figure out how to deliver the same level of SLAs the same level of performance, governance, security recovery times, availability. And it's a scramble for them to be quite honest they're trying to figure out how to automate their storage. They're trying to figure out how to leverage the investments they've made as they go through a digital transformation and keep in mind, a lot of these initiatives are accelerating right now because of this global pandemic we're living through. I don't know that the strategy's necessarily changed, but there's been an acceleration. So all of a sudden these storage people kind of trying to get up to speed or being thrown right into the mix. So we're working directly with them. You'll see, in some of our announcements, we're helping them, you know, get on that journey and provide the infrastructure their teams need. >> And a lot of this is driven by multicloud and hybrid cloud, which we're seeing, you know, a really aggressive move to before it was kind of this rush to public cloud. And that everybody figured out, "Well maybe public cloud isn't necessarily right for everything." And it's kind of this horses for courses, if you will, with multicloud and hybrid cloud, another kind of complexity thrown into the storage mix that you guys have to deal with. >> Yeah, and that's another big challenge. Now in the early days of cloud, people were lifting and shifting applications trying to get lower capex. And they were also starting to deploy DevOps, in the public cloud in order to improve agility. And what they found is there were a lot of challenges with that, where they thought lifting and shifting an application will lower their capital costs the TCO actually went up significantly. Where they started building new applications in the cloud. They found they were becoming trapped there and they couldn't get the connectivity they needed back into their core applications. So now we're at this point where they're trying to really, transform the rest of it and they're using containers, to modernize the rest of the infrastructure and complete the digital transformation. They want to get into a hybrid cloud environment. What we found is, enterprises get two and a half X more value out of the IT when they use a hybrid multicloud infrastructure model versus an all public cloud model. So what they're trying to figure out is how to piece those different components together. So you need a software-driven storage infrastructure that gives you the flexibility, to deploy in a common way and automate in a common way, both in a public cloud but on premises and give you that flexibility. And that's what we're working on at IBM and with our colleagues at Red Hat. >> So Eric, you've been in the business a long time and you know, it's amazing as it just continues to evolve, continues to evolve this kind of unsexy thing under the covers called storage, which is so foundational. And now as data has become, you know, maybe a liability 'cause I have to buy a bunch of storage. Now it is the core asset of the company. And in fact a lot of valuations on a lot of companies is based on its value, that's data and what they can do. So clearly you've got a couple of aces in the hole you always do. So tell us what you guys are up to at IBM to take advantage of the opportunity. >> Well, what we're doing is we are launching, a number of solutions for various workloads and applications built with a strong container element. For example, a number of solutions about modern data protection cyber resiliency. In fact, we announced last year almost a year ago actually it's only a year ago last week, Sam and I were on stage, and one of our developers did a demo of us protecting data in a container environment. So now we're extending that beyond what we showed a year ago. We have other solutions that involve what we do with AI big data and analytic applications, that are in a container environment. What if I told you, instead of having to replicate and duplicate and have another set of storage right with the OpenShift Container configuration, that you could connect to an existing external exabyte class data lake. So that not only could your container apps get to it, but the existing apps, whether they'll be bare-metal or virtualized, all of them could get to the same data lake. Wow, that's a concept saving time, saving money. One pool of storage that'll work for all those environments. And now that containers are being deployed in production, that's something we're announcing as well. So we've got a lot of announcements today across the board. Most of which are container and some of which are not, for example, LTO-9, the latest high performance and high capacity tape. We're announcing some solutions around there. But the bulk of what we're announcing today, is really on what IBM is doing to continue to be the leader in container storage support. >> And it's great, 'cause you talked about a couple of very specific applications that we hear about all the time. One obviously on the big data and analytics side, you know, as that continues to do, to kind of chase history of honor of ultimately getting the right information to the right people at the right time so they can make the right decision. And the other piece you talked about was business continuity and data replication, and to bring people back. And one of the hot topics we've talked to a lot of people about now is kind of this shift in a security threat around ransomware. And the fact that these guys are a little bit more sophisticated and will actually go after your backup before they let you know that they're into your primary storage. So these are two, really important market areas that we could see continue activity, as all the people that we talk to every day. You must be seeing the same thing. >> Absolutely we are indeed. You know, containers are the wave. I'm a native California and I'm coming to you from Silicon Valley and you don't fight the wave, you ride it. So at IBM we're doing that. We've been the leader in container storage. We, as you know, way back when we invented the hard drive, which is the foundation of almost this entire storage industry and we were responsible for that. So we're making sure that as container is the coming wave that we are riding that in and doing the right things for our customers, for our channel partners that support those customers, whether they be existing customers, and obviously, with this move to containers, is going to be some people searching for probably a new vendor. And that's something that's going to go right into our wheelhouse because of the things we're doing. And some of our capabilities, for example, with our FlashSystems, with our Spectrum Virtualize, we're actually going to be able to support CSI snapshots not only for IBM Storage, but our Spectrum Virtualize products supports over 500 different arrays, most of which aren't ours. So if you got that old EMC VNX2 or that HPE, 3PAR or aNimble or all kinds of other storage, if you need CSI snapshot support, you can get it from IBM, with our Spectrum Virtualize software that runs on our FlashSystems, which of course cuts capex and opex, in a heterogeneous environment, but gives them that advanced container support that they don't get, because they're on older product from, you know, another vendor. We're making sure that we can pull our storage and even our competitor storage into the world of containers and do it in the right way for the end user. >> That's great. Sam, I want to go back to you and talk about the relationship with the Red Hat. I think it was about a year ago, I don't have my notes in front of me, when IBM purchased Red Hat. Clearly you guys have been working very closely together. What does that mean for you? You've been in the business for a long time. You've been at IBM for a long time, to have a partner you know, kind of embed with you, with Red Hat and bringing some of their capabilities into your portfolio. >> It's been an incredible experience, and I always say my friends at Red Hat because we spend so much time together. We're looking at now, leveraging a community that's really on the front edge of this movement to containers. They bring that, along with their experience around storage and containers, along with the years and years of enterprise class storage delivery that we have in the IBM Storage portfolio. And we're bringing those pieces together. And this is a case of truly one plus one equals three. And you know, an example you'll see in this announcement is the integration of our data protection portfolio with their container native storage. We allow you to in any environment, take a snapshot of that data. You know, this move towards modern data protection is all about a movement to doing data protection in a different way which is about leveraging snapshots, taking instant copies of data that are application aware, allowing you to reuse and mount that data for different purposes, be able to protect yourself from ransomware. Our data protection portfolio has industry leading ransomware protection and detection in it. So we'll actually detect it before it becomes a problem. We're taking that, industry leading data protection software and we are integrating it into Red Hat, Container Native Storage, giving you the ability to solve one of the biggest challenges in this digital transformation which is backing up your data. Now that you're moving towards, stateful containers and persistent storage. So that's one area we're collaborating. We're working on ensuring that our storage arrays, that Eric was talking about, that they integrate tightly with OpenShift and that they also work again with, OpenShift Container Storage, the Cloud Native Storage portfolio from, Red Hat. So we're bringing these pieces together. And on top of that, we're doing some really, interesting things with licensing. We allow you to consume the Red Hat Storage portfolio along with the IBM software-defined Storage portfolio under a single license. And you can deploy the different pieces you need, under one single license. So you get this ultimate investment protection and ability to deploy anywhere. So we're, I think we're adding a lot of value for our customers and helping them on this journey. >> Yeah Eric, I wonder if you could share your perspective on multicloud management. I know that's a big piece of what you guys are behind and it's a big piece of kind of the real world as we've kind of gotten through the hype and now we're into production, and it is a multicloud world and it is, you got to manage this stuff it's all over the place. I wonder if you could speak to kind of how that challenge you know, factors into your design decisions and how you guys are about, you know, kind of the future. >> Well we've done this in a couple of ways in things that are coming out in this launch. First of all, IBM has produced with a container-centric model, what they call the Multicloud Manager. It's the IBM Cloud Pak for multicloud management. That product is designed to manage multiple clouds not just the IBM Cloud, but Amazon, Azure, et cetera. What we've done is taken our Spectrum Protect Plus and we've integrated it into the multicloud manager. So what that means, to save time, to save money and make it easier to use, when the customer is in the multicloud manager, they can actually select Spectrum Protect Plus, launch it and then start to protect data. So that's one thing we've done in this launch. The other thing we've done is integrate the capability of IBM Spectrum Virtualize, running in a FlashSystem to also take the capability of supporting OCP, the OpenShift Container Platform in a Clustered environment. So what we can do there, is on-premise, if there really was an earthquake in Silicon Valley right now, that OpenShift is sitting on a server. The servers just got crushed by the roof when it caved in. So you want to make sure you've got disaster recovery. So what we can do is take that OpenShift Container Platform Cluster, we can support it with our Spectrum Virtualize software running on our FlashSystem, just like we can do heterogeneous storage that's not ours, in this case, we're doing it with Red Hat. And then what we can do is to provide disaster recovery and business continuity to different cloud vendors not just to IBM Cloud, but to several cloud vendors. We can give them the capability of replicating and protecting that Cluster to a cloud configuration. So if there really was an earthquake, they could then go to the cloud, they could recover that Red Hat Cluster, to a different data center and run it on-prem. So we're not only doing the integration with a multicloud manager, which is multicloud-centric allowing ease of use with our Spectrum Protect Plus, but incase of a really tough situation of fire in a data center, earthquake, hurricane, whatever, the Red Hat OpenShift Cluster can be replicated out to a cloud, with our Spectrum Virtualize Software. So in most, in both cases, multicloud examples because in the first one of course the multicloud manager is designed and does support multiple clouds. In the second example, we support multiple clouds where our Spectrum Virtualize for public clouds software so you can take that OpenShift Cluster replicate it and not just deal with one cloud vendor but with several. So showing that multicloud management is important and then leverage that in this launch with a very strong element of container centricity. >> Right >> Yeah, I just want to add, you know, and I'm glad you brought that up Eric, this whole multicloud capability with, the Spectrum Virtualize. And I could see the same for our Spectrum Scale Family, which is our storage infrastructure for AI and big data. We actually, in this announcement have containerized the client making it very simple to deploy in Kubernetes Cluster. But one of the really special things about Spectrum Scale is it's active file management. This allows you to build out a file system not only on-premises for your, Kubernetes Cluster but you can actually extend that to a public cloud and it automatically will extend the file system. If you were to go into a public cloud marketplace which it's available in more than one, you can go in there click deploy, for example, in AWS Marketplace, click deploy it will deploy your Spectrum Scale Cluster. You've now extended your file system from on-prem into the cloud. If you need to access any of that data, you can access it and it will automatically cash you on locally and we'll manage all the file access for you. >> Yeah, it's an interesting kind of paradox between, you know, kind of the complexity of what's going on in the back end, but really trying to deliver simplicity on the front end. Again, this ultimate goal of getting the right data to the right person at the right time. You just had a blog post Eric recently, that you talked about every piece of data isn't equal. And I think it's really highlighted in this conversation we just had about recovery and how you prioritize and how you, you know, think about, your data because you know, the relative value of any particular piece might be highly variable, which should drive the way that you treated in your system. So I wonder if you can speak a little bit, you know, to helping people think about data in the right way. As you know, they both have all their operational data which they've always had, but now they've got all this unstructured data that's coming in like crazy and all data isn't created equal, as you said. And if there is an earthquake or there is a ransomware attack, you need to be smart about what you have available to bring back quickly. And maybe what's not quite so important. >> Well, I think the key thing, let me go to, you know a modern data protection term. These are two very technical terms was, one is the recovery time. How long does it take you to get that data back? And the second one is the recovery point, at what point in time, are you recovering the data from? And the reason those are critical, is when you look at your datasets, whether you replicate, you snap, you do a backup. The key thing you've got to figure out is what is my recovery time? How long is it going to take me? What's my recovery point. Obviously in certain industries you want to recover as rapidly as possible. And you also want to have the absolute most recent data. So then once you know what it takes you to do that, okay from an RPO and an RTO perspective, recovery point objective, recovery time objective. Once you know that, then you need to look at your datasets and look at what does it take to run the company if there really was a fire and your data center was destroyed. So you take a look at those datasets, you see what are the ones that I need to recover first, to keep the company up and rolling. So let's take an example, the sales database or the support database. I would say those are pretty critical to almost any company, whether you'd be a high-tech company, whether you'd be a furniture company, whether you'd be a delivery company. However, there also is probably a database of assets. For example, IBM is a big company. We have buildings all over, well, guess what? We don't lease a chair or a table or a whiteboard. We buy them. Those are physical assets that the company has to pay, you know, do write downs on and all this other stuff, they need to track it. If we close a building, we need to move the desk to another building. Like even if we leasing a building now, the furniture is ours, right? So does an asset database need to be recovered instantaneously? Probably not. So we should focus on another thing. So let's say on a bank. Banks are both online and brick and mortar. I happened to be a Wells Fargo person. So guess what? There's Wells Fargo banks, two of them in the city I'm in, okay? So, the assets of the money, in this case now, I don't think the brick and mortar of the building of Wells Fargo or their desks in there but now you're talking financial assets or their high velocity trading apps. Those things need to be recovered almost instantaneously. And that's what you need to do when you're looking at datasets, is figure out what's critical to the business to keep it up and rolling, what's the next most critical. And you do it in basically the way you would tear anything. What's the most important thing, what's the next most important thing. It doesn't matter how you approach your job, how you used to approach school, what are the classes I have to get an A and what classes can I not get an A and depending on what your major was, all that sort of stuff, you're setting priorities, right? And the dataset, since data is the most critical asset of any company, whether it's a Global Fortune 500 or whether it's Herzog Cigar Store, all of those assets, that data is the most valuable. So you've got to make sure, recover what you need as rapidly as you need it. But you can't recover all of it. You just, there's just no way to do that. So that's why you really ranked the importance of the data to use sameware, with malware and ransomware. If you have a malware or ransomware attack, certain data you need to recover as soon as you can. So if there, for example, as a, in fact there was one Jeff, here in Silicon Valley as well. You've probably read about the University of California San Francisco, ended up having to pay over a million dollars of ransom because some of the data related to COVID research University of California, San Francisco, it was the health care center for the University of California in Northern California. They are working on COVID and guess what? The stuff was held for ransom. They had no choice, but to pay them. And they really did pay, this is around end of June, of this year. So, okay, you don't really want to do that. >> Jeff: Right >> So you need to look at everything from malware and ransomware, the importance of the data. And that's how you figure this stuff out, whether be in a container environment, a traditional environment or virtualized environment. And that's why data protection is so important. And with this launch, not only are we doing the data protection we've been doing for years, but now taking it to the heart of the new wave, which is the wave of containers. >> Yeah, let me add just quickly on that Eric. So think about those different cases you talked about. You're probably going to want for your mission critically. You're going to want snapshots of that data that can be recovered near instantaneously. And then, for some of your data, you might decide you want to store it out in cloud. And with Spectrum Protect, we just announced our ability to now store data out in Google cloud. In addition to, we already supported AWS Azure IBM Cloud, in various on-prem object stores. So we already provided that capability. And then we're in this announcement talking about LTL-9. And you got to also be smart about which data do you need to keep, according to regulation for long periods of time, or is it just important to archive? You're not going to beat the economics nor the safety of storing data out on tape. But like Eric said, if all of your data is out on tape and you have an event, you're not going to be able to restore it quickly enough at least the mission critical things. And so those are the things that need to be in snapshot. And that's one of the main things we're announcing here for Kubernetes environments is the ability to quickly snapshot application aware backups, of your mission critical data in your Kubernetes environments. It can very quickly to be recovered. >> That's good. So I'll give you the last word then we're going to sign off, we are out of time, but I do want to get this in it's 2020, if I didn't ask the COVID question, I would be in big trouble. So, you know, you've all seen the memes and the jokes about really COVID being an accelerant to digital transformation, not necessarily change, but certainly a huge accelerant. I mean, you guys have a, I'm sure a product roadmap that's baked pretty far and advanced, but I wonder if you can speak to, you know, from your perspective, as COVID has accelerated digital transformation you guys are so foundational to executing that, you know, kind of what is it done in terms of what you're seeing with your customers, you know, kind of the demand and how you're seeing this kind of validation as to an accelerant to move to these better types of architectures? Let's start with you Sam. >> Yeah, you know I, and I think i said this, but I mean the strategy really hasn't changed for the enterprises, but of course it is accelerating it. And I see storage teams more quickly getting into trouble, trying to solve some of these challenges. So we're working closely with them. They're looking for more automation. They have less people in the data center on-premises. They're looking to do more automation simplify the management of the environment. We're doing a lot around Ansible to help them with that. We're accelerating our roadmaps around that sort of integration and automation. They're looking for better visibility into their environments. So we've made a lot of investments around our storage insights SaaS platform, that allows them to get complete visibility into their data center and not just in their data center. We also give them visibility to the stores they're deploying in the cloud. So we're making it easier for them to monitor and manage and automate their storage infrastructure. And then of course, if you look at everything we're doing in this announcement, it's about enabling our software and our storage infrastructure to integrate directly into these new Kubernetes, initiatives. That way as this digital transformation accelerates and application developers are demanding more and more Kubernetes capabilities. They're able to deliver the same SLAs and the same level of security and the same level of governance, that their customers expect from them, but in this new world. So that's what we're doing. If you look at our announcement, you'll see that across, across the sets of capabilities that we're delivering here. >> Eric, we'll give you the last word, and then we're going to go to Eric Cigar Shop, as soon as this is over. (laughs) >> So it's clearly all about storage made simple, in a Kubernetes environment, in a container environment, whether it's block storage, file storage, whether it be object storage and IBM's goal is to offer ever increasing sophisticated services for the enterprise at the same time, make it easier and easier to use and to consume. If you go back to the old days, the storage admins manage X amount of gigabytes, maybe terabytes. Now the same admin is managing 10 petabytes of data. So the data explosion is real across all environments, container environments, even old bare-metal. And of course the not quite so new anymore virtualized environments. The admins need to manage that more and more easily and automated point and click. Use AI based automated tiering. For example, we have with our Easy Tier technology, that automatically moves data when it's hot to the fastest tier. And when it's not as hot, it's cool, it pushes down to a slower tier, but it's all automated. You point and you click. Let's take our migration capabilities. We built it into our software. I buy a new array, I need to migrate the data. You point, you click, and we automatic transparent migration in the background on the fly without taking the servers or the storage down. And we always favor the application workload. So if the application workload is heavy at certain times a day, we slow the migration. At night for sake of argument, If it's a company that is not truly 24 by seven, you know, heavily 24 by seven, and at night, it slows down, we accelerate the migration. All about automation. We've done it with Ansible, here in this launch, we've done it with additional integration with other platforms. So our Spectrum Scale for example, can use the OpenShift management framework to configure and to grow our Spectrum Scale or elastic storage system clusters. We've done it, in this case with our Spectrum Protect Plus, as you saw integration into the multicloud manager. So for us, it's storage made simple, incredibly new features all the time, but at the same time we do that, make sure that it's easier and easier to use. And in some cases like with Ansible, not even the real storage people, but God forbid, that DevOps guy messes with a storage and loses that data, wow. So by, if you're using something like Ansible and that Ansible framework, we make sure that essentially the DevOps guy, the test guy, the analytics guy, basically doesn't lose the data and screw up the storage. And that's a big, big issue. So all about storage made simple, in the right way with incredible enterprise features that essentially we make easy and easy to use. We're trying to make everything essentially like your iPhone, that easy to use. That's the goal. And with a lot less storage admins in the world then there has been an incredible storage growth every single year. You'd better make it easy for the same person to manage all that storage. 'Cause it's not shrinking. It is, someone who's sitting at 50 petabytes today, is 150 petabytes the next year and five years from now, they'll be sitting on an exabyte of production data, and they're not going to hire tons of admins. It's going to be the same two or four people that were doing the work. Now they got to manage an exabyte, which is why this storage made simplest is such a strong effort for us with integration, with the Open, with the Kubernetes frameworks or done with OpenShift, heck, even what we used to do in the old days with vCenter Ops from VMware, VASA, VAAI, all those old VMware tools, we made sure tight integration, easy to use, easy to manage, but sophisticated features to go with that. Simplicity is really about how you manage storage. It's not about making your storage dumb. People want smarter and smarter storage. Do you make it smarter, but you make it just easy to use at the same time. >> Right. >> Well, great summary. And I don't think I could do a better job. So I think we'll just leave it right there. So congratulations to both of you and the teams for these announcement after a whole lot of hard work and sweat went in, over the last little while and continued success. And thanks for the, check in, always great to see you. >> Thank you. We love being on theCUBE as always. >> All right, thanks again. All right, he's Eric, he was Sam, I'm I'm Jeff, you're watching theCUBE. We'll see you next time, thanks for watching. (upbeat music)

>>Data is at the heart of transformation and the change. Every company needs to succeed, but it takes more than new technology. It's about teams, talent and cultural change. Empowering everyone on the front lines to make decisions all at the speed of digital. The transformation starts with you. It's time to lead the way it's time for thought leaders. >>Welcome to thought leaders, a digital event brought to you by ThoughtSpot. My name is Dave Volante. The purpose of this day is to bring industry leaders and experts together to really try and understand the important issues around digital transformation. We have an amazing lineup of speakers and our goal is to provide you with some best practices that you can bring back and apply to your organization. Look, data is plentiful, but insights are not. ThoughtSpot is disrupting analytics by using search and machine intelligence to simplify data analysis and really empower anyone with fast access to relevant data. But in the last 150 days, we've had more questions than answers. Creating an organization that puts data and insights at their core requires not only modern technology, but leadership, a mindset and a culture that people often refer to as data-driven. What does that mean? How can we equip our teams with data and fast access to quality information that can turn insights into action. >>And today we're going to hear from experienced leaders who are transforming their organizations with data insights and creating digital first cultures. But before we introduce our speakers, I'm joined today by two of my cohosts from ThoughtSpot first chief data strategy officer, the ThoughtSpot is Cindy Hausen. Cindy is an analytics and BI expert with 20 plus years experience and the author of successful business intelligence unlock the value of BI and big data. Cindy was previously the lead analyst at Gartner for the data and analytics magic quadrant. And early last year, she joined ThoughtSpot to help CDOs and their teams understand how best to leverage analytics and AI for digital transformation. Cindy. Great to see you welcome to the show. Thank you, Dave. Nice to join you virtually. Now our second cohost and friend of the cube is ThoughtSpot CEO, sedition air. Hello. Sudheesh how are you doing today? I am validating. It's good to talk to you again. That's great to see you. Thanks so much for being here now Sateesh please share with us why this discussion is so important to your customers and of course, to our audience and what they're going to learn today. >>Thanks, Dave. >>I wish you were there to introduce me into every room that I walk into because you have such an amazing way of doing it. It makes me feel also good. Um, look, since we have all been, you know, cooped up in our homes, I know that the vendors like us, we have amped up know sort of effort to reach out to you with invites for events like this. So we are getting very more invites for events like this than ever before. So when we started planning for this, we had three clear goals that we wanted to accomplish. And our first one that when you finish this and walk away, we want to make sure that you don't feel like it was a waste of time. We want to make sure that we value your time. Then this is going to be used. Number two, we want to put you in touch with industry leaders and thought leaders, generally good people that you want to hang around with long after this event is over. >>And number three, has we planned through this? You know, we are living through these difficult times. You want an event to be this event, to be more of an uplifting and inspiring event. Now, the challenge is how do you do that with the team being change agents? Because teens can, as much as we romanticize it, it is not one of those uplifting things that everyone wants to do, or like through the VA. I think of it changes sort of like if you've ever done bungee jumping and it's like standing on the edges waiting to make that one more step, uh, you know, all you have to do is take that one step and gravity will do the rest, but that is the hardest step to take change requires a lot of courage. And when we are talking about data and analytics, which is already like such a hard topic, not necessarily an uplifting and positive conversation, most businesses, it is somewhat scary. >>Change becomes all the more difficult, ultimately change requires courage, courage. To first of all, challenge the status quo. People sometimes are afraid to challenge the status quo because they are thinking that, you know, maybe I don't have the power to make the change that the company needs. Sometimes they feel like I don't have the skills. Sometimes they've may feel that I'm, I'm probably not the right person to do it. Or sometimes the lack of courage manifest itself as the inability to sort of break the silos that are formed within the organizations, when it comes to data and insights that you talked about, you know, that are people in the company who are going to have the data because they know how to manage the data, how to inquire and extract. They know how to speak data. They have the skills to do that, but they are not the group of people who have sort of the knowledge, the experience of the business to ask the right questions off the data. >>So there is the silo of people with the answers, and there is a silo of people with the questions. And there is gap. This sort of silos are standing in the way of making that necessary change that we all know the business needs. And the last change to sort of bring an external force. Sometimes it could be a tool. It could be a platform, it could be a person, it could be a process, but sometimes no matter how big the company is or how small the company is, you may need to bring some external stimuli to start the domino of the positive changes that are necessarily the group of people that we are brought in. The four people, including Cindy, that you will hear from today are really good at practically telling you how to make that step, how to step off that edge, how to trust the rope, that you will be safe. And you're going to have fun. You will have that exhilarating feeling of jumping for a bungee jump. >>So we're going to take a hard pivot now and go from football to Ternopil Chernobyl. What went wrong? 1986, as the reactors were melting down, they had the data to say, this is going to be catastrophic. And yet the culture said, no, we're perfect. Hide it. Don't dare tell anyone which meant they went ahead and had celebrations in Kiev. Even though that increased the exposure, the additional thousands, getting cancer and 20,000 years before the ground around there and even be inhabited again, this is how powerful and detrimental a negative culture, a culture that is unable to confront the brutal facts that hides data. This is what we have to contend with. And this is why I want you to focus on having fostering a data driven culture. I don't want you to be a laggard. I want you to be a leader in using data to drive your digital transformation. >>So I'll talk about culture and technology. Isn't really two sides of the same coin, real world impacts. And then some best practices you can use to disrupt and innovate your culture. Now, oftentimes I would talk about culture and I talk about technology. And recently a CDO said to me, you know, Cindy, I actually think this is two sides of the same coin. One reflects the other. What do you think? Let me walk you through this. So let's take a laggard. What does the technology look like? Is it based on 1990s BI and reporting largely parameterized reports on premises, data, warehouses, or not even that operational reports at best one enterprise, nice data warehouse, very slow moving and collaboration is only email. What does that culture tell you? Maybe there's a lack of leadership to change, to do the hard work that Sudheesh referred to, or is there also a culture of fear, afraid of failure, resistance to change complacency. >>And sometimes that complacency it's not because people are lazy. It's because they've been so beaten down every time a new idea is presented. It's like, no we're measured on least cost to serve. So politics and distrust, whether it's between business and it or individual stakeholders is the norm. So data is hoarded. Let's contrast that with a leader, a data and analytics leader, what is their technology look like? Augmented analytics search and AI driven insights, not on premises, but in the cloud and maybe multiple clouds. And the data is not in one place, but it's in a data Lake and in a data warehouse, a logical data warehouse, the collaboration is being a newer methods, whether it's Slack or teams allowing for that real time decisioning or investigating a particular data point. So what is the culture in the leaders? It's transparent and trust. There is a trust that data will not be used to punish that there is an ability to confront the bad news. >>It's innovation, valuing innovation in pursuit of the company goals, whether it's the best fan experience and player safety in the NFL or best serving your customers. It's innovative and collaborative. None of this. Oh, well, I didn't invent that. I'm not going to look at that. There's still proud of that ownership, but it's collaborating to get to a better place faster. And people feel empowered to present new ideas, fail fast, and they're energized knowing that they're using the best technology and innovating at the pace that business requires. So data is democratized and double monetized, not just for people, how are users or analysts, but really at the of impact what we like to call the new decision makers or really the front line workers. So Harvard business review partnered with us to develop this study to say, just how important is this? We've been working at BI and analytics as an industry for more than 20 years. >>Why is it not at the front lines? Whether it's a doctor, a nurse, a coach, a supply chain manager, a warehouse manager, a financial services advisor, 87% said they would be more successful if frontline workers were empowered with data driven insights, but they recognize they need new technology to be able to do that. It's not about learning hard tools. The sad reality only 20% of organizations are actually doing this. These are the data driven leaders. So this is the culture and technology. How did we get here? It's because state of the art keeps changing. So the first generation BI and analytics platforms were deployed on premises on small datasets, really just taking data out of ERP systems that were also on premises. And state-of-the-art was maybe getting a management report, an operational report over time, visual based data discovery vendors disrupted these traditional BI vendors, empowering now analysts to create visualizations with the flexibility on a desktop, sometimes larger data sometimes coming from a data warehouse, the current state of the art though, Gartner calls it augmented analytics at ThoughtSpot, we call it search and AI driven analytics. >>And this was pioneered for large scale data sets, whether it's on premises or leveraging the cloud data warehouses. And I think this is an important point. Oftentimes you, the data and analytics leaders will look at these two components separately, but you have to look at the BI and analytics tier in lockstep with your data architectures to really get to the granular insights and to leverage the capabilities of AI. Now, if you've never seen ThoughtSpot, I'll just show you what this looks like. Instead of somebody's hard coding of report, it's typing in search keywords and very robust keywords contains rank top bottom, getting to a visual visualization that then can be pinned to an existing Pinboard that might also contain insights generated by an AI engine. So it's easy enough for that new decision maker, the business user, the non analyst to create themselves modernizing the data and analytics portfolio is hard because the pace of change has accelerated. >>You use to be able to create an investment place. A bet for maybe 10 years, a few years ago, that time horizon was five years now, it's maybe three years and the time to maturity has also accelerated. So you have these different components, the search and AI tier the data science, tier data preparation and virtualization. But I would also say equally important is the cloud data warehouse and pay attention to how well these analytics tools can unlock the value in these cloud data warehouses. So thoughts about was the first to market with search and AI driven insights, competitors have followed suit, but be careful if you look at products like power BI or SAP analytics cloud, they might demo well, but do they let you get to all the data without moving it in products like snowflake, Amazon Redshift, or, or Azure synapse or Google big query, they do not. >>They re require you to move it into a smaller in memory engine. So it's important how well these new products inter operate the pace of change. It's acceleration Gartner recently predicted that by 2022, 65% of analytical queries will be generated using search or NLP or even AI. And that is roughly three times the prediction they had just a couple years ago. So let's talk about the real world impact of culture. And if you read any of my books or used any of the maturity models out there, whether the Gardner it score that I worked on, or the data warehousing Institute also has the maturity model. We talk about these five pillars to really become data driven. As Michelle spoke about it's focusing on the business outcomes, leveraging all the data, including new data sources, it's the talent, the people, the technology, and also the processes. >>And often when I would talk about the people in the talent, I would lump the culture as part of that. But in the last year, as I've traveled the world and done these digital events for thought leaders, you have told me now culture is absolutely so important. And so we've pulled it out as a separate pillar. And in fact, in polls that we've done in these events, look at how much more important culture is as a barrier to becoming data driven. It's three times as important as any of these other pillars. That's how critical it is. And let's take an example of where you can have great data, but if you don't have the right culture, there's devastating impacts. And I will say, I have been a loyal customer of Wells Fargo for more than 20 years. But look at what happened in the face of negative news with data, it said, Hey, we're not doing good cross selling customers do not have both a checking account and a credit card and a savings account and a mortgage. >>They opened fake accounts, basing billions in fines, change in leadership that even the CEO attributed to a toxic sales culture, and they're trying to fix this. But even recently there's been additional employee backlash saying the culture has not changed. Let's contrast that with some positive examples, Medtronic, a worldwide company in 150 countries around the world. They may not be a household name to you, but if you have a loved one or yourself, you have a pacemaker spinal implant diabetes, you know, this brand and at the start of COVID when they knew their business would be slowing down, because hospitals would only be able to take care of COVID patients. They took the bold move of making their IP for ventilators publicly available. That is the power of a positive culture or Verizon, a major telecom organization looking at late payments of their customers. And even though the us federal government said, well, you can't turn them off. >>He said, we'll extend that even beyond the mandated guidelines and facing a slow down in the business because of the tough economy, he said, you know what? We will spend the time upskilling our people, giving them the time to learn more about the future of work, the skills and data and analytics for 20,000 of their employees, rather than furloughing them. That is the power of a positive culture. So how can you transform your culture to the best in class? I'll give you three suggestions, bring in a change agent, identify the relevance, or I like to call it with them and organize for collaboration. So the CDO, whatever your title is, chief analytics, officer chief, digital officer, you are the most important change agent. And this is where you will hear that. Oftentimes a change agent has to come from outside the organization. So this is where, for example, in Europe, you have the CDO of just eat a takeout food delivery organization coming from the airline industry or in Australia, national Australian bank, taking a CDO within the same sector from TD bank going to NAB. >>So these change agents come in disrupt. It's a hard job. As one of you said to me, it often feels like Sisyphus. I make one step forward and I get knocked down again. I get pushed back. It is not for the faint of heart, but it's the most important part of your job. The other thing I'll talk about is with them, what is in it for me? And this is really about understanding the motivation, the relevance that data has for everyone on the frontline, as well as those analysts, as well as the executives. So if we're talking about players in the NFL, they want to perform better and they want to stay safe. That is why data matters to them. If we're talking about financial services, this may be a wealth management advisor, okay. We could say commissions, but it's really helping people have their dreams come true, whether it's putting their children through college or being able to retire without having to work multiple jobs still into your seventies or eighties for the teachers, teachers, you ask them about data. They'll say we don't, we don't need that. I care about the student. So if you can use data to help a student perform better, that is with them. And sometimes we spend so much time talking the technology, we forget, what is the value we're trying to deliver with this? And we forget the impact on the people that it does require change. In fact, the Harvard business review study found that 44% said lack of change. Management is the biggest barrier to leveraging both new technology, but also being empowered to act on those data driven insights. >>The third point organize for collaboration. This does require diversity of thought, but also bringing the technology, the data and the business people together. Now there's not a single one size fits all model for data and analytics. At one point in time, even having a BICC a BI competency center was considered state of the art. Now for the biggest impact, what I recommend is that you have a federated model centralized for economies of scale. That could be the common data, but then in bed, these evangelists, these analysts of the future within every business unit, every functional domain. And as you see this top bar, all models are possible, but the hybrid model has the most impact the most leaders. So as we look ahead to the months ahead to the year ahead and exciting time, because data is helping organizations better navigate a tough economy, lock in the customer loyalty. And I look forward to seeing how you foster that culture. That's collaborative with empathy and bring the best of technology, leveraging the cloud, all your data. So thank you for joining us at thought leaders. And next I'm pleased to introduce our first change agent, Tom Masa, Pharaoh, chief data officer of Western union. And before joining Western union, Tom made his Mark at HSBC and JP Morgan chase spearheading digital innovation in technology, operations, risk compliance, and retail banking. Tom, thank you so much for joining us today. >>Very happy to be here and, uh, looking forward to, uh, to talking to all of you today. So as we look to move organizations to a data-driven, uh, capability into the future, there is a lot that needs to be done on the data side, but also how did it connect and enable different business teams and technology teams into the future. As we look across, uh, our data ecosystems and our platforms and how we modernize that to the cloud in the future, it all needs to basically work together, right? To really be able to drive an organization from a data standpoint into the future. That includes being able to have the right information with the right quality of data at the right time to drive informed business decisions, to drive the business forward. As part of that, we actually have partnered with ThoughtSpot to actually bring in the technology to help us drive that as part of that partnership. >>And it's how we've looked to integrate it into our overall business as a whole we've looked at how do we make sure that our, that our business and our professional lives right, are enabled in the same ways as our personal lives. So for example, in your personal lives, when you want to go and find something out, what do you do? You go on to google.com or you go on to being, you gone to Yahoo and you search for what you want search to find an answer ThoughtSpot for us, it's the same thing, but in the business world. So using ThoughtSpot and other AI capability is it's allowed us to actually enable our overall business teams in our company to actually have our information at our fingertips. So rather than having to go and talk to someone or an engineer to go pull information or pull data, we actually can have the end users or the business executives, right. >>Search for what they need, what they want at the exact time that action needed to go and drive the business forward. This is truly one of those transformational things that we've put in place on top of that, we are on the journey to modernize our larger ecosystem as a whole. That includes modernizing our underlying data warehouses, our technology or our Elequil environments. And as we move that we've actually picked to our cloud providers going to AWS and GCP. We've also adopted snowflake to really drive into organize our information and our data then drive these new solutions and capabilities forward. So the portion of us though, is culture. So how do we engage with the business teams and bring the, the, the it teams together to really hit the drive, these holistic end to end solution, the capabilities to really support the actual business into the future. >>That's one of the keys here, as we look to modernize and to really enhance our organizations to become data driven. This is the key. If you can really start to provide answers to business questions before they're even being asked and to predict based upon different economic trends or different trends in your business, what does this is maybe be made and actually provide those answers to the business teams before they're even asking for it, that is really becoming a data driven organization. And as part of that, it's really then enables the business to act quickly and take advantage of opportunities as they come in based upon industries, based upon markets, as upon products, solutions or partnerships into the future. These are really some of the keys that, uh, that become crucial as you move forward, right, uh, into this, uh, into this new age, especially with COVID with COVID now taking place across the world, right? >>Many of these markets, many of these digital transformations are celebrating and are changing rapidly to accommodate and to support customers. And these, these very difficult times as part of that, you need to make sure you have the right underlying foundation ecosystems and solutions to really drive those, those capabilities. And those solutions forward as we go through this journey, uh, boasted both of my career, but also each of your careers into the future, right? It also needs to evolve, right? Technology has changed so drastically in the last 10 years, and that change has only a celebrating. So as part of that, you have to make sure that you stay up to speed up to date with new technology changes both on the platform standpoint tools, but also what our customers want, what our customers need and how do we then surface them with our information, with our data, with our platform, with our products and our services to meet those needs and to really support and service those customers into the future. >>This is all around becoming a more data driven organization, such as how do you use your data to support the current business lines, but how do you actually use your information, your data, to actually better support your customers and to support your business there's important, your employees, your operations teams, and so forth, and really creating that full integration in that ecosystem is really when he talked to get large dividends from his investments into the future. But that being said, uh, I hope you enjoyed the segment on how to become and how to drive a data driven organization. And I'm looking forward to talking to you again soon. Thank you, >>Tom. That was great. Thanks so much. Now I'm going to have to brag on you for a second as a change agent. You've come in this rusted. And how long have you been at Western union? >>Uh, well in nine months. So just, uh, just started this year, but, uh, there'd be some great opportunities and great changes and we were a lot more to go, but we're really driving things forward in partnership with our business teams and our colleagues to support those customers going forward. >>Tom, thank you so much. That was wonderful. And now I'm excited to introduce you to Gustavo Canton, a change agent that I've had the pleasure of working with meeting in Europe, and he is a serial change agent most recently, Schneider electric, but even going back to Sam's clubs. Gustavo. Welcome. >>So hi everyone. My name is Gustavo Canton and thank you so much, Cindy, for the intro, as you mentioned, doing transformations is a high effort, high reward situation. I have empowerment transformations and I have less many transformations. And what I can tell you is that it's really hard to predict the future, but if you have a North star and you know where you're going, the one thing that I want you to take away from this discussion today is that you need to be bold to evolve. And so in today I'm going to be talking about culture and data, and I'm going to break this down in four areas. How do we get started barriers or opportunities as I see it, the value of AI, and also, how do you communicate, especially now in the workforce of today with so many different generations, you need to make sure that you are communicating in ways that are nontraditional sometimes. >>And so how do we get started? So I think the answer to that is you have to start for you yourself as a leader and stay tuned. And by that, I mean, you need to understand not only what is happening in your function or your field, but you have to be very into what is happening, society, socioeconomically speaking, wellbeing. You know, the common example is a great example. And for me personally, it's an opportunity because the number one core value that I have is wellbeing. I believe that for human potential, for customers and communities to grow wellbeing should be at the center of every decision. And as somebody mentioned is great to be, you know, stay in tune and have the skillset and the Koresh. But for me personally, to be honest, to have this courage is not about Nadina afraid. You're always afraid when you're making big changes in your swimming upstream. >>But what gives me the courage is the empathy part. Like I think empathy is a huge component because every time I go into an organization or a function, I try to listen very attentively to the needs of the business and what the leaders are trying to do. What I do it thinking about the mission of how do I make change for the bigger, eh, you know, workforce? So the bigger, good, despite the fact that this might have a perhaps implication. So my own self interest in my career, right? Because you have to have that courage sometimes to make choices that are not well seeing politically speaking, what are the right thing to do and you have to push through it. So the bottom line for me is that I don't think they're transforming fast enough. And the reality is I speak with a lot of leaders and we have seen stories in the past. >>And what they show is that if you look at the four main barriers that are basically keeping us behind budget, inability to add cultural issues, politics, and lack of alignment, those are the top four. But the interesting thing is that as Cindy has mentioned, these topic about culture is sexually gaining, gaining more and more traction. And in 2018, there was a story from HBR and he wants about 45%. I believe today it's about 55%, 60% of respondents say that this is the main area that we need to focus on. So again, for all those leaders and all the executives who understand and are aware that we need to transform, commit to the transformation in set us state, eh, deadline to say, Hey, in two years, we're going to make this happen. Why do we need to do, to empower and enable this change engines to make it happen? >>You need to make the tough choices. And so to me, when I speak about being bold is about making the right choices now. So I'll give you examples of some of the roadblocks that I went through. As I think the transformations most recently, as Cindy mentioned in Schneider, there are three main areas, legacy mindset. And what that means is that we've been doing this in a specific way for a long time. And here is how having successful while working the past is not going to work. Now, the opportunity there is that there is a lot of leaders who have a digital mindset and their up and coming leaders that are perhaps not yet fully developed. We need to mentor those leaders and take bets on some of these talents, including young talent. We cannot be thinking in the past and just wait for people, you know, three to five years for them to develop because the world is going to in a, in a way that is super fast, the second area, and this is specifically to implementation of AI is very interesting to me because just the example that I have with ThoughtSpot, right? >>We went on implementation and a lot of the way the it team function. So the leaders look at technology, they look at it from the prison of the prior auth success criteria for the traditional BIS. And that's not going to work again, your opportunity here is that you need to really find what success look like. In my case, I want the user experience of our workforce to be the same as this experience you have at home is a very simple concept. And so we need to think about how do we gain that user experience with this augmented analytics tools and then work backwards to have the right talent processes and technology to enable that. And finally, and obviously with, with COVID a lot of pressuring organizations and companies to do more with less. And the solution that most leaders I see are taking is to just minimize costs sometimes and cut budget. >>We have to do the opposite. We have to actually invest some growth areas, but do it by business question. Don't do it by function. If you actually invest. And these kind of solutions, if you actually invest on developing your talent, your leadership to see more digitally, if you actually invest on fixing your data platform, it's not just an incremental cost. It's actually this investment is going to offset all those hidden costs and inefficiencies that you have on your system, because people are doing a lot of work in working very hard, but it's not efficiency, and it's not working in the way that you might want to work. So there is a lot of opportunity there. And you just to put into some perspective, there have been some studies in the past about, you know, how do we kind of measure the impact of data? And obviously this is going to vary by your organization. >>Maturity is going to be a lot of factors. I've been in companies who have very clean, good data to work with. And I've been with companies that we have to start basically from scratch. So it all depends on your maturity level, but in this study, what I think is interesting is they try to put a tagline or attack price to what is the cost of incomplete data. So in this case, it's about 10 times as much to complete a unit of work. When you have data that is flawed as opposed to have imperfect data. So let me put that just in perspective, just as an example, right? Imagine you are trying to do something and you have to do a hundred things in a project, and each time you do something, it's going to cost you a dollar. So if you have perfect data, the total cost of that project might be a hundred dollars. >>But now let's say you have 80% perfect data and 20% flow data by using this assumption that Florida is 10 times as costly as perfect data. Your total costs now becomes $280 as opposed to a hundred dollars. This just for you to really think about as a CIO CTO, CSRO CEO, are we really paying attention and really close in the gaps that we have on our data infrastructure. If we don't do that, it's hard sometimes to see this snowball effect or to measure the overall impact. But as you can tell, the price tag goes up very, very quickly. So now, if I were to say, how do I communicate this? Or how do I break through some of these challenges or some of these various, right. I think the key is I am in analytics. I know statistics obviously, and, and, and love modeling and, you know, data and optimization theory and all that stuff. >>That's what I came to analytics. But now as a leader and as a change agent, I need to speak about value. And in this case, for example, for Schneider, there was this tagline coffee of your energy. So the number one thing that they were asking from the analytics team was actually efficiency, which to me was very interesting. But once I understood that I understood what kind of language to use, how to connect it to the overall strategy and basically how to bring in the right leaders, because you need to focus on the leaders that you're going to make the most progress. You know, again, low effort, high value. You need to make sure you centralize all the data as you can. You need to bring in some kind of augmented analytics solution. And finally you need to make it super simple for the, you know, in this case, I was working with the HR teams and other areas, so they can have access to one portal. >>They don't have to be confused and looking for 10 different places to find information. I think if you can actually have those four foundational pillars, obviously under the guise of having a data driven culture, that's where you can actually make the impact. So in our case, it was about three years total transformation, but it was two years for this component of augmented analytics. It took about two years to talk to, you know, it, get leadership support, find the budgeting, you know, get everybody on board, make sure the success criteria was correct. And we call this initiative, the people analytics, I pulled up, it was actually launched in July of this year. And we were very excited and the audience was very excited to do this. In this case, we did our pilot in North America for many, many manufacturers. But one thing that is really important is as you bring along your audience on this, you know, you're going from Excel, you know, in some cases or Tablo to other tools like, you know, you need to really explain them. >>What is the difference in how these two can truly replace some of the spreadsheets or some of the views that you might have on these other kinds of tools? Again, Tableau, I think it's a really good tool. There are other many tools that you might have in your toolkit. But in my case, personally, I feel that you need to have one portal going back to Cindy's point. I really truly enable the end user. And I feel that this is the right solution for us, right? And I will show you some of the findings that we had in the pilot in the last two months. So this was a huge victory, and I will tell you why, because it took a lot of effort for us to get to the station. Like I said, it's been years for us to kind of lay the foundation, get the leadership in shape the culture so people can understand why you truly need to invest, but I meant analytics. >>And so what I'm showing here is an example of how do we use basically to capture in video the qualitative findings that we had, plus the quantitative insights that we have. So in this case, our preliminary results based on our ambition for three main metrics, our safe user experience and adoption. So for our safe or a mission was to have 10 hours per week per employee save on average user experience or ambition was 4.5 and adoption, 80% in just two months, two months and a half of the pilot, we were able to achieve five hours per week per employee savings. I used to experience for 4.3 out of five and adoption of 60%, really, really amazing work. But again, it takes a lot of collaboration for us to get to the stage from it, legal communications, obviously the operations teams and the users in HR safety and other areas that might be, eh, basically stakeholders in this whole process. >>So just to summarize this kind of effort takes a lot of energy. You hire a change agent, you need to have the courage to make this decision and understand that. I feel that in this day and age, with all this disruption happening, we don't have a choice. We have to take the risk, right? And in this case, I feel a lot of satisfaction in how we were able to gain all these very souls for this organization. And that gave me the confidence to know that the work has been done and we are now in a different stage for the organization. And so for me, it says to say, thank you for everybody who has believed, obviously in our vision, everybody wants to believe in, you know, the word that we were trying to do and to make the life for, you know, workforce or customers that in community better, as you can tell, there is a lot of effort. >>There is a lot of collaboration that is needed to do something like this. In the end, I feel very satisfied. We, the accomplishments of this transformation, and I just, I just want to tell for you, if you are going right now in a moment that you feel that you have to swim upstream, you know, what would mentors, where we, people in this industry that can help you out and guide you on this kind of a transformation is not easy to do is high effort bodies, well worth it. And with that said, I hope you are well. And it's been a pleasure talking to you. Take care. Thank you, Gustavo. That was amazing. All right, let's go to the panel. >>I think we can all agree how valuable it is to hear from practitioners. And I want to thank the panel for sharing their knowledge with the community. And one common challenge that I heard you all talk about was bringing your leadership and your teams along on the journey with you. We talk about this all the time, and it is critical to have support from the top. Why? Because it directs the middle and then it enables bottoms up innovation effects from the cultural transformation that you guys all talked about. It seems like another common theme we heard is that you all prioritize database decision making in your organizations and you combine two of your most valuable assets to do that and create leverage employees on the front lines. And of course the data, as you rightly pointed out, Tom, the pandemic has accelerated the need for really leaning into this. You know, the old saying, if it ain't broke, don't fix it. We'll COVID is broken everything. And it's great to hear from our experts, you know, how to move forward. So let's get right into, so Gustavo, let's start with you. If, if I'm an aspiring change agent and let's say I'm a, I'm a budding data leader. What do I need to start doing? What habits do I need to create for long lasting success? >>I think curiosity is very important. You need to be, like I say, in tune to what is happening, not only in your specific field, like I have a passion for analytics, I can do this for 50 years plus, but I think you need to understand wellbeing other areas across not only a specific business, as you know, I come from, you know, Sam's club, Walmart, retail, I mean energy management technology. So you have to try to push yourself and basically go out of your comfort zone. I mean, if you are staying in your comfort zone and you want to use lean continuous improvement, that's just going to take you so far. What you have to do is, and that's what I try to do is I try to go into areas, different certain transformations that make me, you know, stretch and develop as a leader. That's what I'm looking to do. So I can help to inform the functions organizations and do the change management decision of mindset as required for these kinds of efforts. A thank you for that, that is inspiring. And, and Sydney, you love data. And the data's pretty clear that diversity is a good business, but I wonder if you can add your perspective to this conversation. >>Yeah. So Michelle has a new fan here because she has found her voice. I'm still working on finding mine. And it's interesting because I was raised by my dad, a single dad. So he did teach me how to work in a predominantly male environment, but why I think diversity matters more now than ever before. And this is by gender, by race, by age, by just different ways of working in thinking is because as we automate things with AI, if we do not have diverse teams looking at the data and the models and how they're applied, we risk having bias at scale. So this is why I think I don't care what type of minority you are finding your voice, having a seat at the table and just believing in the impact of your work has never been more important. And as Michelle said more possible, >>Great perspectives. Thank you, Tom. I want to go to you. I mean, I feel like everybody in our businesses in some way, shape or form become a COVID expert, but what's been the impact of the pandemic on your organization's digital transformation plans. We've seen a massive growth actually in a digital business over the last 12 months, really, uh, even in celebration, right? Once, once COBIT hit, uh, we really saw that, uh, that, uh, in the 200 countries and territories that we operate in today and service our customers. And today that, uh, been a huge need, right? To send money, to support family, to support, uh, friends and loved ones across the world. And as part of that, uh, we, you know, we we're, we are, uh, very, uh, honored to get to support those customers that we across all the centers today. But as part of that acceleration, we need to make sure that we had the right architecture and the right platforms to basically scale, right, to basically support and provide the right kind of security for our customers going forward. >>So as part of that, uh, we, we did do some, uh, some the pivots and we did, uh, a solo rate, some of our plans on digital to help support that overall growth coming in there to support our customers going forward, because there were these times during this pandemic, right? This is the most important time. And we need to support those, those that we love and those that we care about and doing that it's one of those ways is actually by sending money to them, support them financially. And that's where, uh, really our part that our services come into play that, you know, we really support those families. So it was really a, a, a, a, a great opportunity for us to really support and really bring some of our products to the next level and supporting our business going forward. Awesome. Thank you. Now, I want to come back to Gustavo, Tom. I'd love for you to chime in too. Did you guys ever think like you were, you were pushing the envelope too much in, in doing things with, with data or the technology that was just maybe too bold, maybe you felt like at some point it was, it was, it was failing or you're pushing your people too hard. Can you share that experience and how you got through it? >>Yeah, the way I look at it is, you know, again, whenever I go to an organization, I ask the question, Hey, how fast you would like to conform. And, you know, based on the agreements on the leadership and the vision that we want to take place, I take decisions. And I collaborate in a specific way now, in the case of COVID, for example, right? It forces us to remove silos and collaborate in a faster way. So to me, it was an opportunity to actually integrate with other areas and drive decisions faster, but make no mistake about it. When you are doing a transformation, you are obviously trying to do things faster than sometimes people are comfortable doing, and you need to be okay with that. Sometimes you need to be okay with tension, or you need to be okay, you know, the varying points or making repetitive business cases onto people, connect with the decision because you understand, and you are seeing that, Hey, the CEO is making a one two year, you know, efficiency goal. >>The only way for us to really do more with less is for us to continue this path. We cannot just stay with the status quo. We need to find a way to accelerate it's information. That's the way, how, how about Utah? We were talking earlier was sedation Cindy, about that bungee jumping moment. What can you share? Yeah. You know, I think you hit upon, uh, right now, the pace of change will be the slowest pace that you see for the rest of your career. So as part of that, right, that's what I tell my team. This is that you need to be, need to feel comfortable being uncomfortable. I mean, that we have to be able to basically, uh, scale, right, expand and support that the ever changing needs in the marketplace and industry and our customers today, and that pace of change that's happening. >>Right. And what customers are asking for and the competition in the marketplace, it's only going to accelerate. So as part of that, you know, as you look at what, uh, how you're operating today and your current business model, right. Things are only going to get faster. So you have to plan into align and to drive the actual transformation so that you can scale even faster in the future. So as part of that is what we're putting in place here, right. Is how do we create that underlying framework and foundation that allows the organization to basically continue to scale and evolve into the future? Yeah, we're definitely out of our comfort zones, but we're getting comfortable with it. So, Cindy, last question, you've worked with hundreds of organizations, and I got to believe that, you know, some of the advice you gave when you were at Gartner, which is pre COVID, maybe sometimes clients didn't always act on it. You know, they're not on my watch for whatever variety of reasons, but it's being forced on them now. But knowing what you know now that you know, we're all in this isolation economy, how would you say that advice has changed? Has it changed? What's your number one action and recommendation today? >>Yeah. Well, first off, Tom just freaked me out. What do you mean? This is the slowest ever even six months ago. I was saying the pace of change in data and analytics is frenetic. So, but I think you're right, Tom, the business and the technology together is forcing this change. Now, um, Dave, to answer your question, I would say the one bit of advice, maybe I was a little more, um, very aware of the power and politics and how to bring people along in a way that they are comfortable. And now I think it's, you know, what? You can't get comfortable. In fact, we know that the organizations that were already in the cloud have been able to respond and pivot faster. So if you really want to survive as, as Tom and Gustavo said, get used to being uncomfortable, the power and politics are gonna happen. Break the rules, get used to that and be bold. Do not, do not be afraid to tell somebody they're wrong and they're not moving fast enough. I do think you have to do that with empathy, as Michelle said, and Gustavo, I think that's one of the key words today besides the bungee jumping. So I want to know where's the dish gonna go on to junk >>Guys. Fantastic discussion, really, thanks again, to all the panelists and the guests. It was really a pleasure speaking with you today. Really virtually all of the leaders that I've spoken to in the cube program. Recently, they tell me that the pandemic is accelerating so many things, whether it's new ways to work, we heard about new security models and obviously the need for cloud. I mean, all of these things are driving true enterprise wide digital transformation, not just as I said before, lip service is sometimes we minimize the importance and the challenge of building culture and in making this transformation possible. But when it's done, right, the right culture is going to deliver tournament, tremendous results. Know what does that mean? Getting it right? Everybody's trying to get it right. My biggest takeaway today is it means making data part of the DNA of your organization. >>And that means making it accessible to the people in your organization that are empowered to make decisions, decisions that can drive you revenue, cut costs, speed, access to critical care, whatever the mission is of your organization. Data can create insights and informed decisions that drive value. Okay. Let's bring back Sudheesh and wrap things up. So these please bring us home. Thank you. Thank you, Dave. Thank you. The cube team, and thanks. Thanks goes to all of our customers and partners who joined us and thanks to all of you for spending the time with us. I want to do three quick things and then close it off. The first thing is I want to summarize the key takeaways that I had from all four of our distinguished speakers. First, Michelle, I was simply put it. She said it really well. That is be brave and drive. >>Don't go for a drive along. That is such an important point. Often times, you know that I think that you have to make the positive change that you want to see happen when you wait for someone else to do it, not just, why not you? Why don't you be the one making that change happen? That's the thing that I picked up from Michelle's talk, Cindy talked about finding the importance of finding your voice, taking that chair, whether it's available or not, and making sure that your ideas, your voices are heard, and if it requires some force and apply that force, make sure your ideas are we start with talking about the importance of building consensus, not going at things all alone, sometimes building the importance of building the Koran. And that is critical because if you want the changes to last, you want to make sure that the organization is fully behind it, Tom, instead of a single take away. >>What I was inspired by is the fact that a company that is 170 years old, 170 years sold 200 companies, 200 countries they're operating in and they were able to make the change that is necessary through this difficult time. So in a matter of months, if they could do it, anyone could. The second thing I want to do is to leave you with a takeaway that is I would like you to go to topspot.com/nfl because our team has made an app for NFL on snowflake. I think you will find this interesting now that you are inspired and excited because of Michelle stock. And the last thing is these go to topspot.com/beyond our global user conferences happening in this December, we would love to have you join us. It's again, virtual, you can join from anywhere. We are expecting anywhere from five to 10,000 people, and we would love to have you join and see what we've been up to since last year, we, we have a lot of amazing things in store for you, our customers, our partners, our collaborators, they will be coming and sharing. You'll be sharing things that you have been working to release something that will come out next year. And also some of the crazy ideas or engineers. All of those things will be available for you at hotspot beyond. Thank you. Thank you so much.

>>So we're going to take a hard pivot now and go from football to Ternopil Chernobyl. What went wrong? 1986, as the reactors were melting down, they had the data to say, this is going to be catastrophic. And yet the culture said, no, we're perfect. Hide it. Don't dare tell anyone which meant they went ahead and had celebrations in Kiev. Even though that increased the exposure, the additional thousands, getting cancer and 20,000 years before the ground around there and even be inhabited again, this is how powerful and detrimental a negative culture, a culture that is unable to confront the brutal facts that hides data. This is what we have to contend with, and this is why I want you to focus on having fostering a data driven culture. I don't want you to be a laggard. I want you to be a leader in using data to drive your digital transformation. >>So I'll talk about culture and technology. Isn't really two sides of the same coin, real world impacts, and then some best practices you can use to disrupt and innovate your culture. Now, oftentimes I would talk about culture and I talk about technology. And recently a CDO said to me, you know, Cindy, I actually think this is two sides of the same coin. One reflects the other. What do you think? Let me walk you through this. So let's take a laggard. What is the technology look like? Is it based on 1990s BI and reporting largely parameterized reports on premises, data, warehouses, or not even that operational reports at best one enterprise data warehouse, very slow moving and collaboration is only email. What does that culture tell you? Maybe there's a lack of leadership to change, to do the hard work that Sudheesh referred to, or is there also a culture of fear, afraid of failure, resistance to change complacency. >>And sometimes that complacency it's not because people are lazy. It's because they've been so beaten down every time a new idea is presented. It's like, no we're measured least cost to serve. So ticks and distrust there it's between business and it or individual stakeholders is the norm. So data is hoarded. Let's contrast that with a leader, a data and analytics leader, what is their technology look like? Augmented analytics search and AI driven insights, not on premises, but in the cloud and maybe multiple clouds. And the data is not in one place, but it's in a data Lake and in a data warehouse, a logical data warehouse, the collaboration is via newer methods, whether it's Slack or teams allowing for that real time decisioning or investigating a particular data point. So what is the culture in the leaders? It's transparent and trust. There is a trust that data will not be used to punish. >>There is an ability to confront the bad news. It's innovation, valuing innovation in pursuit of the company goals, whether it's the best fan experience and player safety in the NFL or best serving your customers. It's innovative and collaborative. There's none of this. Oh, well, I didn't invent that. I'm not going to look at that. There's still pride of ownership, but it's collaborating to get to a better place faster. And people feel empowered to present new ideas, to fail fast. And they're energized knowing that they're using the best technology and innovating at the pace that business requires. So data is democratized and democratized, not just for power users or analysts, but really at the point of impact what we like to call the new decision makers or really the frontline workers. So Harvard business review partnered with us to develop this study to say, just how important is this? >>They've been working at BI and analytics as an industry for more than 20 years. Why is it not at the front lines? Whether it's a doctor, a nurse, a coach, a supply chain manager, a warehouse manager, a financial services advisor, 87% said they would be more successful if frontline workers were empowered with data driven insights, but they recognize they need new technology to be able to do that. It's not about learning hard tools. The sad reality only 20% of organizations are actually doing this. These are the data-driven leaders. So this is the culture and technology. How did we get here? It's because state of the art keeps changing. So the first generation BI and analytics platforms were deployed on premises on small datasets, really just taking data out of ERP systems that were also on premises and state of the art was maybe getting a management report, an operational report over time, visual based data discovery vendors disrupted these traditional BI vendors, empowering now analysts to create visualizations with the flexibility on a desktop, sometimes larger data sometimes coming from a data warehouse, the current state of the art though, Gartner calls it augmented analytics at ThoughtSpot, we call it search and AI driven analytics. >>And this was pioneered for large scale data sets, whether it's on premises or leveraging the cloud data warehouses. And I think this is an important point. Oftentimes you, the data and analytics leaders will look at these two components separately, but you have to look at the BI and analytics tier in lockstep with your data architectures to really get to the granular insights and to leverage the capabilities of AI. Now, if you've never seen ThoughtSpot, I'll just show you what this looks like. Instead of somebody's hard coding of report, it's typing in search keywords and very robust keywords contains rank top bottom, getting to a visual visualization that then can be pinned to an existing Pinboard that might also contain insights generated by an AI engine. So it's easy enough for that new decision maker, the business user, the non analyst to create themselves modernizing the data and analytics portfolio is hard because the pace of change has accelerated. >>You used to be able to create an investment place. A bet for maybe 10 years, a few years ago, that time horizon was five years now, it's maybe three years and the time to maturity has also accelerated. So you have these different, the search and AI tier the data science, tier data preparation and virtualization. But I would also say equally important is the cloud data warehouse and pay attention to how well these analytics tools can unlock the value in these cloud data warehouses. So thoughts about was the first to market with search and AI driven insights, competitors have followed suit, but be careful if you look at products like power BI or SAP analytics cloud, they might demo well, but do they let you get to all the data without moving it in products like snowflake, Amazon Redshift, or, or Azure synapse or Google big query, they do not. >>They re require you to move it into a smaller in memory engine. So it's important how well these new products inter operate the pace of change. It's acceleration Gartner recently predicted that by 2020 to 65% of analytical queries will be generated using search or NLP or even AI. And that is roughly three times the prediction they had just a couple years ago. So let's talk about the real world impact of culture. And if you read any of my books or used any of the maturity models out there, whether the Gardner it score that I worked on, or the data warehousing Institute also has the maturity model. We talk about these five pillars to really become data driven. As Michelle spoke about it's focusing on the business outcomes, leveraging all the data, including new data sources, it's the talent, the people, the technology, and also the processes. >>And often when I would talk about the people in the talent, I would lump the culture as part of that. But in the last year, as I've traveled the world and done these digital events for thought leaders, you have told me now culture is absolutely so important. And so we've pulled it out as a separate pillar. And in fact, in polls that we've done in these events, look at how much more important culture is as a barrier to becoming data driven. It's three times as important as any of these other pillars. That's how critical it is. And let's take an example of where you can have great, but if you don't have the right culture, there's devastating impacts. And I will say I have been a loyal customer of Wells Fargo for more than 20 years, but look at what happened in the face of negative news with data, it said, Hey, we're not doing good cross selling customers do not have both a checking account and a credit card and a savings account and a mortgage. >>The opened fake accounts, basing billions in fines, change in leadership that even the CEO attributed to a toxic sales culture, and they're trying to fix this. But even recently there's been additional employee backlash saying the culture has not changed. Let's contrast that with some positive effects, samples, Medtronic, a worldwide company in 150 countries around the world. They may not be a household name to you, but if you have a loved one or yourself, you have a pacemaker spinal implant diabetes, you know, this brand and at the start of COVID when they knew their business would be slowing down, because hospitals would only be able to take care of COVID patients. They took the bold move of making their IP for ventilators publicly available. That is the power of a positive culture or Verizon, a major telecom organization looking at late payments of their customers. And even though the us federal government said, well, you can't turn them off. >>He said, we'll extend that even beyond the mandated guidelines and facing a slow down in the business because of the tough economy, he said, you know what? We will spend the time upskilling our people, giving them the time to learn more about the future of work, the skills and data and analytics for 20,000 of their employees, rather than furloughing them. That is the power of a positive culture. So how can you transform your culture to the best in class? I'll give you three suggestions, bring in a change agent, identify the relevance, or I like to call it with them and organize for collaboration. So the CDO, whatever your title is, chief analytics, officer chief, digital officer, you are the most important change agent. And this is where you will hear that. Oftentimes a change agent has to come from outside organization. So this is where, for example, in Europe, you have the CDO of just eat a takeout food delivery organization coming from the airline industry or in Australia, national Australian bank, taking a CDO within the same sector from TD bank going to NAB. >>So these change agents come in disrupt. It's a hard job. As one of you said to me, it often feels like Sisyphus. I make one step forward and I get knocked down again. I get pushed back. It is not for the faint of heart, but it's the most important part of your job. The other thing I'll talk about is with them, what is in it for me? And this is really about understanding the motivation, the relevance that data has for everyone on the frontline, as well as those analysts, as well as the executives. So if we're talking about players in the NFL, they want to perform better and they want to stay safe. That is why data matters to them. If we're talking about financial services, this may be a wealth management advisor, okay. We could say commissions, but it's really helping people have their dreams come true, whether it's putting their children through college or being able to retire without having to work multiple jobs still into your seventies or eighties for the teachers, teachers, you ask them about data. They'll say we don't, we don't need that. I care about the student. So if you can use data to help a student perform better, that is with them. And sometimes we spend so much time talking the technology, we forget what is the value we're trying to deliver with it? And we forget the impact on the people that it does require change. In fact, the Harvard business review study found that 44% said lack of change. Management is the biggest barrier to leveraging both new technology, but also being empowered to act on those data driven insights. >>The third point organize for collaboration. This does require diversity of thought, but also bringing the technology, the data and the business people together. Now there's not a single one size fits all model for data and analytics. At one point in time, even having a BICC a BI competency center was considered state of the art. Now for the biggest impact, what I recommend is that you have a federated model centralized for economies of scale. That could be the common data, but then in bed, these evangelists, these analysts of the future within every business unit, every functional domain. And as you see this top bar, all models are possible, but the hybrid model has the most impact the most leaders. So as we look ahead said to the months ahead to the year ahead and exciting time, because data is helping organizations better navigate a tough economy, lock in the customer loyalty. And I look forward to seeing how you foster that culture. That's collaborative with empathy and bring the best of technology, leveraging the cloud, all your data. So thank you for joining us at thoughtless.

>> Voice Over: Data is at the heart of transformation, and the change every company needs to succeed. But it takes more than new technology. It's about teams, talent and cultural change. Empowering everyone on the front lines to make decisions, all at the speed of digital. The transformation starts with you, it's time to lead the way, it's time for thought leaders. (soft upbeat music) >> Welcome to Thought.Leaders a digital event brought to you by ThoughtSpot, my name is Dave Vellante. The purpose of this day is to bring industry leaders and experts together to really try and understand the important issues around digital transformation. We have an amazing lineup of speakers, and our goal is to provide you with some best practices that you can bring back and apply to your organization. Look, data is plentiful, but insights are not, ThoughtSpot is disrupting analytics, by using search and machine intelligence to simplify data analysis and really empower anyone with fast access to relevant data. But in the last 150 days, we've had more questions than answers. Creating an organization that puts data and insights at their core, requires not only modern technology but leadership, a mindset and a culture, that people often refer to as data-driven. What does that mean? How can we equip our teams with data and fast access to quality information that can turn insights into action? And today we're going to hear from experienced leaders who are transforming their organizations with data, insights, and creating digital first cultures. But before we introduce our speakers, I'm joined today by two of my co-hosts from ThoughtSpot. First, chief data strategy officer of the ThoughtSpot is Cindi Howson, Cindi is an analytics and BI expert with 20 plus years experience, and the author of Successful Business Intelligence: Unlock the Value of BI & Big Data. Cindi was previously the lead analyst at Gartner for the data and analytics Magic Quadrant. In early last year, she joined ThoughtSpot to help CEOs and their teams understand how best to leverage analytics and AI for digital transformation. Cindi great to see you, welcome to the show. >> Thank you Dave, nice to join you virtually. >> Now our second cohost and friend of theCUBE is ThoughtSpot CEO Sudheesh Nair Hello Sudheesh, how are you doing today? >> I'm well, good to talk to you again. >> That's great to see you, thanks so much for being here. Now Sudheesh, please share with us why this discussion is so important to your customers and of course to our audience, and what they're going to learn today. (upbeat music) >> Thanks Dave, I wish you were there to introduce me into every room that I walk into because you have such an amazing way of doing it. It makes me feel also good. Look, since we have all been you know, cooped up in our homes, I know that the vendors like us, we have amped up our sort of effort to reach out to you with, invites for events like this. So we are getting very more invites for events like this than ever before. So when we started planning for this, we had three clear goals that we wanted to accomplish. And our first one, that when you finish this and walk away, we want to make sure that you don't feel like it was a waste of time, we want to make sure that we value your time, then this is going to be used. Number two, we want to put you in touch with industry leaders and thought leaders, generally good people, that you want to hang around with long after this event is over. And number three, as we plan through this, you know we are living through these difficult times we want this event to be more of an uplifting and inspiring event too. Now, the challenge is how do you do that with the team being change agents, because teens and as much as we romanticize it, it is not one of those uplifting things that everyone wants to do or likes to do. The way I think of it, changes sort of like, if you've ever done bungee jumping, and it's like standing on the edges, waiting to make that one more step you know, all you have to do is take that one step and gravity will do the rest, but that is the hardest step today. Change requires a lot of courage, and when we are talking about data and analytics, which is already like such a hard topic not necessarily an uplifting and positive conversation most businesses, it is somewhat scary, change becomes all the more difficult. Ultimately change requires courage, courage to first of all, challenge the status quo. People sometimes are afraid to challenge the status quo because they are thinking that you know, maybe I don't have the power to make the change that the company needs, sometimes they feel like I don't have the skills, sometimes they may feel that I'm probably not the right person to do it. Or sometimes the lack of courage manifest itself as the inability to sort of break the silos that are formed within the organizations when it comes to data and insights that you talked about. You know, that are people in the company who are going to have the data because they know how to manage the data, how to inquire and extract, they know how to speak data, they have the skills to do that. But they are not the group of people who have sort of the knowledge, the experience of the business to ask the right questions off the data. So there is the silo of people with the answers, and there is a silo of people with the questions, and there is gap, this sort of silos are standing in the way of making that necessary change that we all know the business needs. And the last change to sort of bring an external force sometimes. It could be a tool, it could be a platform, it could be a person, it could be a process but sometimes no matter how big the company is or how small the company is you may need to bring some external stimuli to start the domino of the positive changes that are necessary. The group of people that we are brought in, the four people, including Cindi that you will hear from today are really good at practically telling you how to make that step, how to step off that edge, how to dress the rope, that you will be safe and you're going to have fun, you will have that exhilarating feeling of jumping for a bungee jump, all four of them are exceptional, but my owner is to introduce Michelle. And she's our first speaker, Michelle I am very happy after watching our presentation and reading your bio that there are no country vital worldwide competition for cool parents, because she will beat all of us. Because when her children were small, they were probably into Harry Potter and Disney and she was managing a business and leading change there. And then as her kids grew up and got to that age where they like football and NFL, guess what? She's the CIO of NFL, what a cool mom. I am extremely excited to see what she's going to talk about. I've seen this slides, a bunch of amazing pictures, I'm looking to see the context behind it, I'm very thrilled to make that client so far, Michelle, I'm looking forward to her talk next. Welcome Michelle, it's over to you. (soft upbeat music) >> I'm delighted to be with you all today to talk about thought leadership. And I'm so excited that you asked me to join you because today I get to be a quarterback. I always wanted to be one, and I thought this is about as close as I'm ever going to get. So I want to talk to you about quarterbacking our digital revolution using insights data, and of course as you said, leadership. First a little bit about myself, a little background as I said, I always wanted to play football, and this is something that I wanted to do since I was a child, but when I grew up, girls didn't get to play football. I'm so happy that that's changing and girls are now doing all kinds of things that they didn't get to do before. Just this past weekend on an NFL field, we had a female coach on two sidelines, and a female official on the field. I'm a lifelong fan and student of the game of football, I grew up in the South, you can tell from the accent and in the South is like a religion and you pick sides. I chose Auburn University working in the Athletic Department, so I'm testament to you can start the journey can be long it took me many, many years to make it into professional sports. I graduated in 1987 and my little brother, well, not actually not so little, he played offensive line for the Alabama Crimson Tide. And for those of you who know SEC football you know, this is a really big rivalry. And when you choose sides, your family is divided, so it's kind of fun for me to always tell the story that my dad knew his kid would make it to the NFL he just bet on the wrong one. My career has been about bringing people together for memorable moments at some of America's most iconic brands. Delivering memories and amazing experiences that delight from Universal Studios, Disney to my current position as CIO of the NFL. In this job I'm very privileged to have the opportunity to work with the team, that gets to bring America's game to millions of people around the world. Often I'm asked to talk about how to create amazing experiences for fans, guests, or customers. But today I really wanted to focus on something different and talk to you about being behind the scenes and backstage. Because behind every event every game, every awesome moment is execution, precise repeatable execution. And most of my career has been behind the scenes, doing just that, assembling teams to execute these plans, and the key way that companies operate at these exceptional levels, is making good decisions, the right decisions at the right time and based upon data, so that you can translate the data into intelligence and be a data-driven culture. Using data and intelligence is an important way that world-class companies do differentiate themselves. And it's the lifeblood of collaboration and innovation. Teams that are working on delivering these kinds of world-class experiences are often seeking out and leveraging next generation technologies and finding new ways to work. I've been fortunate to work across three decades of emerging experiences, which each required emerging technologies to execute. A little bit first about Disney, in the 90s I was at Disney, leading a project called destination Disney, which it's a data project, it was a data project, but it was CRM before CRM was even cool. And then certainly before anything like a data-driven culture was ever brought up. But way back then we were creating a digital backbone that enabled many technologies for the things that you see today, like the magic band, just these magical express. My career at Disney began in finance, but Disney was very good about rotating you around, and it was during one of these rotations that I became very passionate about data. I kind of became a pain in the butt to the IT team, asking for data more and more data. And I learned that all of that valuable data was locked up in our systems, all of our point of sales systems, our reservation systems, our operation systems, and so I became a shadow IT person in marketing, ultimately leading to moving into IT, and I haven't looked back since. In the early 2000s I was at Universal Studios Theme Park as their CIO, preparing for and launching the wizarding world of Harry Potter. Bringing one of history's most memorable characters to life required many new technologies and a lot of data. Our data and technologies were embedded into the rides and attractions. I mean, how do you really think a wand selects you at a wine shop. As today at the NFL, I am constantly challenged to do leading edge technologies using things like sensors, AI, machine learning, and all new communication strategies, and using data to drive everything from player performance, contracts to where we build new stadiums and hold events. With this year being the most challenging, yet rewarding year in my career at the NFL. In the middle of a global pandemic, the way we are executing on our season is leveraging data from contract tracing devices joined with testing data. Talk about data, actually enabling your business without it we wouldn't be having a season right now. I'm also on the board of directors of two public companies, where data and collaboration are paramount. First RingCentral, it's a cloud based unified communications platform, and collaboration with video message and phone, all in one solution in the cloud. And Quotient Technologies, whose product is actually data. The tagline at quotient is the result in knowing. I think that's really important, because not all of us are data companies, where your product is actually data. But we should operate more like your product is data. I'd also like to talk to you about four areas of things to think about, as thought leaders in your companies. First just hit on it is change, how to be a champion and a driver of change. Second, how to use data to drive performance for your company, and measure performance of your company. Third, how companies now require intense collaboration to operate, and finally, how much of this is accomplished through solid data-driven decisions. First let's hit on change. I mean, it's evident today more than ever, that we are in an environment of extreme change. I mean, we've all been at this for years and as technologists we've known it, believed it, lived it, and thankfully for the most part knock on wood we were prepared for it. But this year everyone's cheese was moved, all the people in the back rooms, IT, data architects and others, were suddenly called to the forefront. Because a global pandemic has turned out to be the thing that is driving intense change in how people work and analyze their business. On March 13th, we closed our office at the NFL in the middle of preparing for one of our biggest events, our kickoff event, the 2020 Draft. We went from planning, a large event in Las Vegas under the bright lights red carpet stage to smaller events in club facilities. And then ultimately to one where everyone coaches, GMs, prospects and even our commissioner were at home in their basements. And we only had a few weeks to figure it out. I found myself for the first time being in the live broadcast event space, talking about bungee dress jumping, this is really what it felt like. It was one in which no one felt comfortable, because it had not been done before. But leading through this, I stepped up, but it was very scary, it was certainly very risky but it ended up being Oh, so rewarding when we did it. And as a result of this, some things will change forever. Second, managing performance. I mean, data should inform how you're doing and how to get your company to perform at this level, highest level. As an example, the NFL has always measured performance obviously, and it is one of the purest examples of how performance directly impacts outcome. I mean, you can see performance on the field, you can see points being scored and stats, and you immediately know that impact, those with the best stats, usually win the games. The NFL has always recorded stats, since the beginning of time, here at the NFL a little this year as our 100 and first year and athletes ultimate success as a player has also always been greatly impacted by his stats. But what has changed for us, is both how much more we can measure, and the immediacy with which it can be measured. And I'm sure in your business, it's the same, the amount of data you must have has got to have quadrupled recently and how fast you need it and how quickly you need to analyze it, is so important. And it's very important to break the silos between the keys to the data and the use of the data. Our next generation stats platform is taking data to a next level, it's powered by Amazon Web Services, and we gathered this data real time from sensors that are on players' bodies. We gather it in real time, analyze it, display it online and on broadcast, and of course it's used to prepare week to week in addition to what is a normal coaching plan would be. We can now analyze, visualize, route patterns speed, matchups, et cetera, so much faster than ever before. We're continuing to roll out sensors too, that we'll gather more and more information about player's performance as it relates to their health and safety. The third trend is really I think it's a big part of what we're feeling today and that is intense collaboration. And just for sort of historical purposes it's important to think about for those of you that are IT professionals and developers, you know more than 10 years ago, agile practices began sweeping companies or small teams would work together rapidly in a very flexible, adaptive and innovative way, and it proved to be transformational. However today, of course, that is no longer just small teams the next big wave of change, and we've seen it through this pandemic is that it's the whole enterprise that must collaborate and be agile. If I look back on my career when I was at Disney, we owned everything 100%, we made a decision, we implemented it, we were a collaborative culture but it was much easier to push change because you own the whole decision. If there was buy in from the top down, you got the people from the bottom up to do it, and you executed. At Universal, we were a joint venture, our attractions and entertainment was licensed, our hotels were owned and managed by other third parties. So influence and collaboration and how to share across companies became very important. And now here I am at the NFL and even the bigger ecosystem. We have 32 clubs that are all separate businesses 31 different stadiums that are owned by a variety of people. We have licensees, we have sponsors, we have broadcast partners. So it seems that as my career has evolved centralized control has gotten less and less and has been replaced by intense collaboration not only within your own company, but across companies. The ability to work in a collaborative way across businesses and even other companies that has been a big key to my success in my career. I believe this whole vertical integration and big top down decision making is going by the wayside in favor of ecosystems that require cooperation, yet competition to coexist. I mean the NFL is a great example of what we call coopertition, which is cooperation and competition. When in competition with each other, but we cooperate to make the company the best it can be. And at the heart of these items really are data-driven decisions and culture. Data on its own isn't good enough, you must be able to turn it to insights, partnerships between technology teams who usually hold the keys to the raw data, and business units who have the knowledge to build the right decision models is key. If you're not already involved in this linkage, you should be, data mining isn't new for sure. The availability of data is quadrupling and it's everywhere. How do you know what to even look at? How do you know where to begin? How do you know what questions to ask? It's by using the tools that are available for visualization and analytics and knitting together strategies of the company. So it begins with first of all making sure you do understand the strategy of the company. So in closing, just to wrap up a bit, many of you joined today looking for thought leadership on how to be a change agent, a change champion, and how to lead through transformation. Some final thoughts are be brave, and drive, don't do the ride along program, it's very important to drive, driving can be high risk but it's also high reward. Embracing the uncertainty of what will happen, is how you become brave, get more and more comfortable with uncertainty be calm and let data be your map on your journey, thanks. >> Michelle, thank you so much. So you and I share a love of data, and a love of football. You said you want to be the quarterback, I'm more an old wine person. (Michelle laughing) >> Well, then I can do my job without you. >> Great, and I'm getting the feeling now you know, Sudheesh is talking about bungee jumping. My boat is when we're past this pandemic, we both take them to the Delaware Water Gap and we do the cliff jumping. >> That sounds good, I'll watch. >> You'll watch, okay, so Michelle, you have so many stakeholders when you're trying to prioritize the different voices, you have the players, you have the owners you have the league, as you mentioned to the broadcasters your, your partners here and football mamas like myself. How do you prioritize when there's so many different stakeholders that you need to satisfy? I think balancing across stakeholders starts with aligning on a mission. And if you spend a lot of time understanding where everyone's coming from, and you can find the common thread ties them all together you sort of do get them to naturally prioritize their work, and I think that's very important. So for us at the NFL, and even at Disney, it was our core values and our core purpose is so well known, and when anything challenges that we're able to sort of lay that out. But as a change agent, you have to be very empathetic, and I would say empathy is probably your strongest skill if you're a change agent. And that means listening to every single stakeholder even when they're yelling at you, even when they're telling you your technology doesn't work and you know that it's user error, or even when someone is just emotional about what's happening to them and that they're not comfortable with it. So I think being empathetic and having a mission and understanding it, is sort of how I prioritize and balance. >> Yeah, empathy, a very popular word this year. I can imagine those coaches and owners yelling. So I thank you for your metership here. So Michelle, I look forward to discussing this more with our other customers and disruptors joining us in a little bit. (soft upbeat music) >> So we're going to take a hard pivot now and go from football to Chernobyl, Chernobyl, what went wrong? 1986, as the reactors were melting down they had the data to say, this is going to be catastrophic and yet the culture said, "No, we're perfect, hide it. Don't dare tell anyone," which meant they went ahead and had celebrations in Kiev. Even though that increased the exposure the additional thousands getting cancer, and 20,000 years before the ground around there and even be inhabited again, This is how powerful and detrimental a negative culture, a culture that is unable to confront the brutal facts that hides data. This is what we have to contend with, and this is why I want you to focus on having fostering a data-driven culture. I don't want you to be a laggard, I want you to be a leader in using data to drive your digital transformation. So I'll talk about culture and technology, isn't really two sides of the same coin, real-world impacts and then some best practices you can use to disrupt and innovate your culture. Now, oftentimes I would talk about culture and I talk about technology, and recently a CDO said to me, "You know Cindi, I actually think this is two sides of the same coin. One reflects the other, what do you think?" Let me walk you through this, so let's take a laggard. What is the technology look like? Is it based on 1990s BI and reporting largely parameterized reports on-premises data warehouses, or not even that operational reports, at best one enterprise data warehouse very slow moving and collaboration is only email. What does that culture tell you? Maybe there's a lack of leadership to change, to do the hard work that Sudheesh referred to. Or is there also a culture of fear, afraid of failure, resistance to change complacency and sometimes that complacency it's not because people are lazy, it's because they've been so beaten down every time a new idea is presented. It's like, no we're measured on least cost to serve. So politics and distrust, whether it's between business and IT or individual stakeholders is the norm. So data is hoarded, let's contrast that with a leader, a data and analytics leader, what is their technology look like? Augmented analytics, search and AI-driven insights not on-premises, but in the cloud and maybe multiple clouds. And the data is not in one place, but it's in a data lake, and in a data warehouse, a logical data warehouse. The collaboration is being a newer methods whether it's Slack or teams allowing for that real time decisioning or investigating a particular data point. So what is the culture in the leaders? It's transparent and trust, there is a trust that data will not be used to punish, that there is an ability to confront the bad news. It's innovation, valuing innovation in pursuit of the company goals, whether it's the best fan experience and player safety in the NFL or best serving your customers. It's innovative and collaborative. There's none of this, oh, well, I didn't invent that, I'm not going to look at that. There's still pride of ownership, but it's collaborating to get to a better place faster. And people feel empowered to present new ideas to fail fast, and they're energized, knowing that they're using the best technology and innovating at the pace that business requires. So data is democratized and democratized, not just for power users or analysts, but really at the point of impact what we like to call the new decision makers. Or really the frontline workers. So Harvard business review partnered with us to develop this study to say, just how important is this? They've been working at BI and analytics as an industry for more than 20 years. Why is it not at the front lines? Whether it's a doctor, a nurse, a coach, a supply chain manager a warehouse manager, a financial services advisor. 87% said they would be more successful if frontline workers were empowered with data-driven insights, but they recognize they need new technology to be able to do that. It's not about learning hard tools, the sad reality only 20% of organizations are actually doing this, these are the data-driven leaders. So this is the culture and technology, how did we get here? It's because state of the art keeps changing. So the first generation BI and analytics platforms were deployed on-premises, on small datasets really just taking data out of ERP systems that were also on-premises, and state of the art was maybe getting a management report, an operational report. Over time visual based data discovery vendors, disrupted these traditional BI vendors, empowering now analysts to create visualizations with the flexibility on a desktop, sometimes larger data sometimes coming from a data warehouse, the current state of the art though, Gartner calls it augmented analytics, at ThoughtSpot, we call it search and AI-driven analytics. And this was pioneered for large scale data sets, whether it's on-premises or leveraging the cloud data warehouses, and I think this is an important point. Oftentimes you, the data and analytics leaders, will look at these two components separately, but you have to look at the BI and analytics tier in lockstep with your data architectures to really get to the granular insights, and to leverage the capabilities of AI. Now, if you've never seen ThoughtSpot I'll just show you what this looks like, instead of somebody's hard coding a report, it's typing in search keywords and very robust keywords contains rank, top, bottom getting to a visualization that then can be pinned to an existing Pinboard that might also contain insights generated by an AI engine. So it's easy enough for that new decision maker, the business user, the non analyst to create themselves. Modernizing the data and analytics portfolio is hard, because the pace of change has accelerated. You used to be able to create an investment, place a bet for maybe 10 years. A few years ago, that time horizon was five years, now it's maybe three years, and the time to maturity has also accelerated. So you have these different components the search and AI tier, the data science tier, data preparation and virtualization. But I would also say equally important is the cloud data warehouse. And pay attention to how well these analytics tools can unlock the value in these cloud data warehouses. So ThoughtSpot was the first to market with search and AI-driven insights. Competitors have followed suit, but be careful if you look at products like Power BI or SAP Analytics Cloud, they might demo well, but do they let you get to all the data without moving it in products like Snowflake, Amazon Redshift or Azure Synapse or Google BigQuery, they do not. They require you to move it into a smaller in memory engine. So it's important how well these new products inter operate. The pace of change, it's acceleration, Gartner recently predicted that by 2022, 65% of analytical queries will be generated using search or NLP or even AI, and that is roughly three times the prediction they had just a couple years ago. So let's talk about the real world impact of culture. And if you've read any of my books or used any of the maturity models out there whether the Gartner IT score that I worked on, or the data warehousing institute also has a maturity model. We talk about these five pillars to really become data-driven, as Michelle spoke about, it's focusing on the business outcomes, leveraging all the data, including new data sources. It's the talent, the people, the technology, and also the processes, and often when I would talk about the people in the talent, I would lump the culture as part of that. But in the last year, as I've traveled the world and done these digital events for thought leaders you have told me now culture is absolutely so important. And so we've pulled it out as a separate pillar, and in fact, in polls that we've done in these events, look at how much more important culture is, as a barrier to becoming data-driven. It's three times as important as any of these other pillars. That's how critical it is, and let's take an example of where you can have great data but if you don't have the right culture there's devastating impacts. And I will say, I have been a loyal customer of Wells Fargo for more than 20 years, but look at what happened in the face of negative news with data, that said, "Hey, we're not doing good cross selling, customers do not have both a checking account and a credit card and a savings account and a mortgage." They opened fake accounts, facing billions in fines, change in leadership, that even the CEO attributed to a toxic sales culture, and they're trying to fix this. But even recently there's been additional employee backlash saying that culture has not changed. Let's contrast that with some positive examples, Medtronic a worldwide company in 150 countries around the world, they may not be a household name to you, but if you have a loved one or yourself, you have a pacemaker, spinal implant, diabetes you know, this brand. And at the start of COVID when they knew their business would be slowing down, because hospitals would only be able to take care of COVID patients, they took the bold move of making their IP for ventilators publicly available, that is the power of a positive culture. Or Verizon, a major telecom organization, looking at late payments of their customers, and even though the US federal government said "Well, you can't turn them off." They said, "We'll extend that even beyond the mandated guidelines," and facing a slow down in the business because of the tough economy, he said, "You know what? We will spend the time upskilling our people giving them the time to learn more about the future of work, the skills and data and analytics," for 20,000 of their employees, rather than furloughing them. That is the power of a positive culture. So how can you transform your culture to the best in class? I'll give you three suggestions, bring in a change agent identify the relevance, or I like to call it WIIFM, and organize for collaboration. So the CDO whatever your title is, chief analytics officer chief digital officer, you are the most important change agent. And this is where you will hear, that oftentimes a change agent has to come from outside the organization. So this is where, for example in Europe, you have the CDO of Just Eat takeout food delivery organization, coming from the airline industry or in Australia, National Australian Bank, taking a CDO within the same sector from TD Bank going to NAB. So these change agents come in disrupt, it's a hard job. As one of you said to me, it often feels like Sisyphus, I make one step forward and I get knocked down again, I get pushed back. It is not for the faint of heart, but it's the most important part of your job. The other thing I'll talk about is WIIFM, what is in it for me? And this is really about understanding the motivation, the relevance that data has for everyone on the frontline as well as those analysts, as well as the executives. So if we're talking about players in the NFL they want to perform better, and they want to stay safe. That is why data matters to them. If we're talking about financial services this may be a wealth management advisor, okay, we could say commissions, but it's really helping people have their dreams come true whether it's putting their children through college, or being able to retire without having to work multiple jobs still into your 70s or 80s. For the teachers, teachers, you asked them about data, they'll say, "We don't need that, I care about the student." So if you can use data to help a student perform better that is WIIFM. And sometimes we spend so much time talking the technology, we forget what is the value we're trying to deliver with it. And we forget the impact on the people that it does require change. In fact, the Harvard Business Review Study, found that 44% said lack of change management is the biggest barrier to leveraging both new technology but also being empowered to act on those data-driven insights. The third point, organize for collaboration. This does require diversity of thought, but also bringing the technology, the data and the business people together. Now there's not a single one size fits all model for data and analytics. At one point in time, even having a BICC, a BI Competency Center was considered state of the art. Now for the biggest impact, what I recommend is that you have a federated model, centralized for economies of scale, that could be the common data, but then in bed, these evangelists, these analysts of the future, within every business unit, every functional domain, and as you see this top bar, all models are possible but the hybrid model has the most impact, the most leaders. So as we look ahead to the months ahead, to the year ahead, an exciting time, because data is helping organizations better navigate a tough economy lock in the customer loyalty, and I look forward to seeing how you foster that culture that's collaborative with empathy and bring the best of technology, leveraging the cloud, all your data. So thank you for joining us at thought leaders, and next I'm pleased to introduce our first change agent Thomas Mazzaferro, chief data officer of Western Union, and before joining Western Union, Tom made his mark at HSBC and JP Morgan Chase spearheading digital innovation in technology operations, risk compliance, and retail banking. Tom, thank you so much for joining us today. (soft upbeat music) >> Very happy to be here and looking forward to talking to all of you today. So as we look to move organizations to a data-driven capability into the future, there is a lot that needs to be done on the data side, but also how does data connect and enable, different business teams and technology teams into the future. As we look across our data ecosystems and our platforms and how we modernize that to the cloud in the future, it all needs to basically work together, right? To really be able to drive over the shift from a data standpoint, into the future. That includes being able to have the right information with the right quality of data at the right time to drive informed business decisions, to drive the business forward. As part of that, we actually have partnered with ThoughtSpot to actually bring in the technology to help us drive that, as part of that partnership, and it's how we've looked to integrated into our overall business as a whole. We've looked at how do we make sure that our business and our professional lives, right? Are enabled in the same ways as our personal lives. So for example, in your personal lives, when you want to go and find something out, what do you do? You go on to google.com or you go on to Bing, or go to Yahoo and you search for what you want, search to find an answer. ThoughtSpot for us as the same thing, but in the business world. So using ThoughtSpot and other AI capability is allowed us to actually enable our overall business teams in our company, to actually have our information at our fingertips. So rather than having to go and talk to someone or an engineer to go pull information or pull data, we actually can have the end users or the business executives, right? Search for what they need, what they want, at the exact time that action needed, to go and drive the business forward. This is truly one of those transformational things that we've put in place. On top of that, we are on the journey to modernize our larger ecosystem as a whole. That includes modernizing our underlying data warehouses, our technology or our (indistinct) environments, and as we move that we've actually picked to our cloud providers going to AWS and GCP. We've also adopted Snowflake to really drive into organize our information and our data, then drive these new solutions and capabilities forward. So big portion of us though is culture, so how do we engage with the business teams and bring the IT teams together to really drive these holistic end to end solutions and capabilities, to really support the actual business into the future. That's one of the keys here, as we look to modernize and to really enhance our organizations to become data-driven, this is the key. If you can really start to provide answers to business questions before they're even being asked, and to predict based upon different economic trends or different trends in your business, what does is be made and actually provide those answers to the business teams before they're even asking for it. That is really becoming a data-driven organization. And as part of that, it's really then enables the business to act quickly and take advantage of opportunities as they come in based upon industries, based upon markets, based upon products, solutions, or partnerships into the future. These are really some of the keys that become crucial as you move forward right into this new age, especially with COVID, with COVID now taking place across the world, right? Many of these markets, many of these digital transformations are celebrating, and are changing rapidly to accommodate and to support customers in these very difficult times. As part of that, you need to make sure you have the right underlying foundation, ecosystems and solutions to really drive those capabilities, and those solutions forward. As we go through this journey, both of my career but also each of your careers into the future, right? It also needs to evolve, right? Technology has changed so drastically in the last 10 years, and that change is only a celebrating. So as part of that, you have to make sure that you stay up to speed, up to date with new technology changes both on the platform standpoint, tools, but also what our customers want, what do our customers need, and how do we then surface them with our information, with our data, with our platform, with our products and our services, to meet those needs and to really support and service those customers into the future. This is all around becoming a more data-driven organization such as how do you use your data to support the current business lines. But how do you actually use your information your data, to actually better support your customers better support your business, better support your employees, your operations teams and so forth, and really creating that full integration in that ecosystem is really when you start to get large dividends from these investments into the future. With that being said I hope you enjoyed the segment on how to become and how to drive a data-driven organization, and looking forward to talking to you again soon, thank you. >> Tom, that was great, thanks so much. Now I'm going to have to brag on you for a second, as a change agent you've come in disrupted, and how long have you been at Western Union? >> Only nine months, I just started this year, but there'd be some great opportunities and big changes, and we have a lot more to go, but we're really driving things forward in partnership with our business teams, and our colleagues to support those customers forward. >> Tom, thank you so much that was wonderful. And now I'm excited to introduce you to Gustavo Canton, a change agent that I've had the pleasure of working with meeting in Europe, and he is a serial change agent. Most recently with Schneider Electric, but even going back to Sam's Club, Gustavo welcome. (soft upbeat music) >> So hi everyone my name is Gustavo Canton and thank you so much Cindi for the intro. As you mentioned, doing transformations is a you know, high effort, high reward situation. I have empowerment in transformation and I have led many transformations. And what I can tell you is that it's really hard to predict the future, but if you have a North Star and you know where you're going, the one thing that I want you to take away from this discussion today, is that you need to be bold to evolve. And so in today, I'm going to be talking about culture and data, and I'm going to break this down in four areas. How do we get started barriers or opportunities as I see it, the value of AI, and also how do you communicate, especially now in the workforce of today with so many different generations, you need to make sure that you are communicating in ways that are nontraditional sometimes. And so how do we get started? So I think the answer to that is, you have to start for you, yourself as a leader and stay tuned. And by that, I mean you need to understand not only what is happening in your function or your field, but you have to be very into what is happening in society, socioeconomically speaking, wellbeing, you know, the common example is a great example. And for me personally, it's an opportunity because the number one core value that I have is wellbeing. I believe that for human potential, for customers and communities to grow, wellbeing should be at the center of every decision. And as somebody mentioned, it's great to be you know, stay in tune and have the skillset and the courage. But for me personally, to be honest to have this courage is not about not being afraid. You're always afraid when you're making big changes and your swimming upstream. But what gives me the courage is the empathy part, like I think empathy is a huge component because every time I go into an organization or a function, I try to listen very attentively to the needs of the business, and what the leaders are trying to do, what I do it thinking about the mission of how do I make change for the bigger, you know workforce so the bigger good, despite the fact that this might have a perhaps implication, so my own self interest in my career, right? Because you have to have that courage sometimes to make choices, that are not well seeing politically speaking what are the right thing to do, and you have to push through it. So the bottom line for me is that, I don't think they're transforming fast enough. And the reality is I speak with a lot of leaders and we have seen stories in the past, and what they show is that if you look at the four main barriers, that are basically keeping us behind budget, inability to add, cultural issues, politics, and lack of alignment, those are the top four. But the interesting thing is that as Cindi has mentioned, this topic about culture is actually gaining more and more traction, and in 2018, there was a story from HBR and it was for about 45%. I believe today, it's about 55%, 60% of respondents say that this is the main area that we need to focus on. So again, for all those leaders and all the executives who understand, and are aware that we need to transform, commit to the transformation and set us deadline to say, "Hey, in two years, we're going to make this happen, what do we need to do to empower and enable these search engines to make it happen?" You need to make the tough choices. And so to me, when I speak about being bold is about making the right choices now. So I'll give you samples of some of the roadblocks that I went through, as I think the intro information most recently as Cindi mentioned in Schneider. There are three main areas, legacy mindset, and what that means is that we've been doing this in a specific way for a long time, and here is how we have been successful. We're working the past is not going to work now, the opportunity there is that there is a lot of leaders who have a digital mindset, and their up and coming leaders that are perhaps not yet fully developed. We need to mentor those leaders and take bets on some of these talents, including young talent. We cannot be thinking in the past and just wait for people you know, three to five years for them to develop, because the world is going to in a way that is super fast. The second area and this is specifically to implementation of AI is very interesting to me, because just example that I have with ThoughtSpot, right? We went to an implementation and a lot of the way the IT team functions, so the leaders look at technology, they look at it from the prism of the prior or success criteria for the traditional BIs, and that's not going to work. Again, your opportunity here is that you need to really find what success look like, in my case, I want the user experience of our workforce to be the same as your experience you have at home. It's a very simple concept, and so we need to think about how do we gain that user experience with this augmented analytics tools, and then work backwards to have the right talent, processes and technology to enable that. And finally, and obviously with COVID a lot of pressure in organizations and companies to do more with less, and the solution that most leaders I see are taking is to just minimize cost sometimes and cut budget. We have to do the opposite, we have to actually invest some growth areas, but do it by business question. Don't do it by function, if you actually invest in these kind of solutions, if you actually invest on developing your talent, your leadership, to see more digitally, if you actually invest on fixing your data platform is not just an incremental cost, it's actually this investment is going to offset all those hidden costs and inefficiencies that you have on your system, because people are doing a lot of work in working very hard but it's not efficiency, and it's not working in the way that you might want to work. So there is a lot of opportunity there, and you just to put it into some perspective, there have been some studies in the past about you know, how do we kind of measure the impact of data? And obviously this is going to vary by organization, maturity there's going to be a lot of factors. I've been in companies who have very clean, good data to work with, and I think with companies that we have to start basically from scratch. So it all depends on your maturity level, but in this study what I think is interesting is, they try to put a tagline or attack price to what is a cost of incomplete data. So in this case, it's about 10 times as much to complete a unit of work, when you have data that is flawed as opposed to have imperfect data. So let me put that just in perspective, just as an example, right? Imagine you are trying to do something and you have to do 100 things in a project, and each time you do something it's going to cost you a dollar. So if you have perfect data, the total cost of that project might be a $100. But now let's say you have any percent perfect data and 20% flow data, by using this assumption that flow data is 10 times as costly as perfect data, your total costs now becomes $280 as opposed to $100, this just for you to really think about as a CIO, CTO, you know CSRO, CEO, are we really paying attention and really closing the gaps that we have on our infrastructure? If we don't do that, it's hard sometimes to see the snowball effect or to measure the overall impact, but as you can tell, the price tag goes up very, very quickly. So now, if I were to say, how do I communicate this? Or how do I break through some of these challenges or some of these barriers, right? I think the key is I am in analytics, I know statistics obviously, and love modeling and you know, data and optimization theory and all that stuff, that's what I can do analytics, but now as a leader and as a change agent, I need to speak about value, and in this case, for example for Schneider, there was this tagline coffee of your energy. So the number one thing that they were asking from the analytics team was actually efficiency, which to me was very interesting. But once I understood that I understood what kind of language to use, how to connect it to the overall strategy and basically how to bring in the right leaders, because you need to, you know, focus on the leaders that you're going to make the most progress. You know, again, low effort, high value, you need to make sure you centralize all the data as you can, you need to bring in some kind of augmented analytics, you know, solution, and finally you need to make it super simple for the you know, in this case, I was working with the HR teams and other areas, so they can have access to one portal. They don't have to be confused and looking for 10 different places to find information. I think if you can actually have those four foundational pillars, obviously under the guise of having a data-driven culture, that's when you can actually make the impact. So in our case, it was about three years total transformation but it was two years for this component of augmented analytics. It took about two years to talk to, you know, IT, get leadership support, find the budgeting, you know, get everybody on board, make sure the success criteria was correct. And we call this initiative, the people analytics, I pulled up, it was actually launched in July of this year. And we were very excited and the audience was very excited to do this. In this case, we did our pilot in North America for many, many manufacturers, but one thing that is really important is as you bring along your audience on this, you know, you're going from Excel, you know in some cases or Tableau to other tools like you know, ThoughtSpot, you need to really explain them, what is the difference, and how these two can truly replace some of the spreadsheets or some of the views that you might have on these other kind of tools. Again, Tableau, I think it's a really good tool, there are other many tools that you might have in your toolkit. But in my case, personally I feel that you need to have one portal going back to seeing these points that really truly enable the end user. And I feel that this is the right solution for us, right? And I will show you some of the findings that we had in the pilot in the last two months. So this was a huge victory, and I will tell you why, because it took a lot of effort for us to get to these stations. Like I said it's been years for us to kind of lay the foundation, get the leadership and chasing culture, so people can understand why you truly need to invest what I meant analytics. And so what I'm showing here is an example of how do we use basically, you know a tool to capturing video, the qualitative findings that we had, plus the quantitative insights that we have. So in this case, our preliminary results based on our ambition for three main metrics, hours saved, user experience and adoption. So for hours saved, our ambition was to have 10 hours per week per employee save on average, user experience or ambition was 4.5 and adoption 80%. In just two months, two months and a half of the pilot we were able to achieve five hours, per week per employee savings. I used to experience for 4.3 out of five, and adoption of 60%. Really, really amazing work. But again, it takes a lot of collaboration for us to get to the stage from IT, legal, communications obviously the operations things and the users, in HR safety and other areas that might be basically stakeholders in this whole process. So just to summarize this kind of effort takes a lot of energy, you are a change agent, you need to have a courage to make these decision and understand that, I feel that in this day and age with all this disruption happening, we don't have a choice. We have to take the risk, right? And in this case, I feel a lot of satisfaction in how we were able to gain all these very souls for this organization, and that gave me the confidence to know that the work has been done, and we are now in a different stage for the organization. And so for me it safe to say, thank you for everybody who has believed obviously in our vision, everybody who has believed in, you know, the word that we were trying to do and to make the life for, you know workforce or customers that are in community better. As you can tell, there is a lot of effort, there is a lot of collaboration that is needed to do something like this. In the end, I feel very satisfied with the accomplishments of this transformation, and I just want to tell for you, if you are going right now in a moment that you feel that you have to swim upstream you know, what would mentors what people in this industry that can help you out and guide you on this kind of a transformation is not easy to do is high effort but is well worth it. And with that said, I hope you are well and it's been a pleasure talking to you, talk to you soon, take care. >> Thank you Gustavo, that was amazing. All right, let's go to the panel. (soft upbeat music) >> I think we can all agree how valuable it is to hear from practitioners, and I want to thank the panel for sharing their knowledge with the community, and one common challenge that I heard you all talk about was bringing your leadership and your teams along on the journey with you. We talk about this all the time, and it is critical to have support from the top, why? Because it directs the middle, and then it enables bottoms up innovation effects from the cultural transformation that you guys all talked about. It seems like another common theme we heard, is that you all prioritize database decision making in your organizations, and you combine two of your most valuable assets to do that, and create leverage, employees on the front lines, and of course the data. That was rightly pointed out, Tom, the pandemic has accelerated the need for really leaning into this. You know, the old saying, if it ain't broke, don't fix it, well COVID's broken everything. And it's great to hear from our experts, you know, how to move forward, so let's get right into it. So Gustavo let's start with you if I'm an aspiring change agent, and let's say I'm a budding data leader. What do I need to start doing? What habits do I need to create for long lasting success? >> I think curiosity is very important. You need to be, like I say, in tune to what is happening not only in your specific field, like I have a passion for analytics, I can do this for 50 years plus, but I think you need to understand wellbeing other areas across not only a specific business as you know, I come from, you know, Sam's Club Walmart retail, I mean energy management technology. So you have to try to push yourself and basically go out of your comfort zone. I mean, if you are staying in your comfort zone and you want to use lean continuous improvement that's just going to take you so far. What you have to do is and that's what I tried to do is I try to go into areas, businesses and transformations that make me, you know stretch and develop as a leader. That's what I'm looking to do, so I can help transform the functions organizations, and do these change management and decisions mindset as required for these kinds of efforts. >> Thank you for that is inspiring and Cindi, you love data, and the data is pretty clear that diversity is a good business, but I wonder if you can add your perspectives to this conversation. >> Yeah, so Michelle has a new fan here because she has found her voice, I'm still working on finding mine. And it's interesting because I was raised by my dad, a single dad, so he did teach me how to work in a predominantly male environment. But why I think diversity matters more now than ever before, and this is by gender, by race, by age, by just different ways of working and thinking is because as we automate things with AI, if we do not have diverse teams looking at the data and the models, and how they're applied, we risk having bias at scale. So this is why I think I don't care what type of minority, you are finding your voice, having a seat at the table and just believing in the impact of your work has never been more important. And as Michelle said more possible >> Great perspectives thank you, Tom, I want to go to you. I mean, I feel like everybody in our businesses in some way, shape or form become a COVID expert but what's been the impact of the pandemic on your organization's digital transformation plans? >> We've seen a massive growth actually you know, in a digital business over the last 12 months really, even in celebration, right? Once COVID hit, we really saw that in the 200 countries and territories that we operate in today and service our customers and today, that there's been a huge need, right? To send money, to support family, to support friends and loved ones across the world. And as part of that, you know, we are very honored to support those customers that we across all the centers today. But as part of that celebration, we need to make sure that we had the right architecture and the right platforms to basically scale, right? To basically support and provide the right kind of security for our customers going forward. So as part of that, we did do some pivots and we did celebrate some of our plans on digital to help support that overall growth coming in, and to support our customers going forward. Because there were these times during this pandemic, right? This is the most important time, and we need to support those that we love and those that we care about. And in doing that, it's one of those ways is actually by sending money to them, support them financially. And that's where really are part of that our services come into play that, you know, I really support those families. So it was really a great opportunity for us to really support and really bring some of our products to this level, and supporting our business going forward. >> Awesome, thank you. Now I want to come back to Gustavo, Tom, I'd love for you to chime in too. Did you guys ever think like you were pushing the envelope too much and doing things with data or the technology that was just maybe too bold, maybe you felt like at some point it was failing, or you pushing your people too hard, can you share that experience and how you got through it? >> Yeah, the way I look at it is, you know, again, whenever I go to an organization I ask the question, Hey, how fast you would like to conform?" And, you know, based on the agreements on the leadership and the vision that we want to take place, I take decisions and I collaborate in a specific way. Now, in the case of COVID, for example, right? It forces us to remove silos and collaborate in a faster way, so to me it was an opportunity to actually integrate with other areas and drive decisions faster. But make no mistake about it, when you are doing a transformation, you are obviously trying to do things faster than sometimes people are comfortable doing and you need to be okay with that. Sometimes you need to be okay with tension, or you need to be okay, you know debating points or making repetitive business cases onto people connect with the decision because you understand, and you are seeing that, hey, the CEO is making a one, two year, you know, efficiency goal, the only way for us to really do more with less is for us to continue this path. We cannot just stay with the status quo, we need to find a way to accelerate transformation... >> How about you Tom, we were talking earlier was Sudheesh had said about that bungee jumping moment, what can you share? >> Yeah you know, I think you hit upon it. Right now, the pace of change will be the slowest pace that you see for the rest of your career. So as part of that, right? That's what I tell my team is that you need to feel comfortable being uncomfortable. I mean, that we have to be able to basically scale, right? Expand and support that the ever changing needs the marketplace and industry and our customers today and that pace of change that's happening, right? And what customers are asking for, and the competition the marketplace, it's only going to accelerate. So as part of that, you know, as we look at what how you're operating today in your current business model, right? Things are only going to get faster. So you have to plan into align, to drive the actual transformation, so that you can scale even faster into the future. So as part of that, so we're putting in place here, right? Is how do we create that underlying framework and foundation that allows the organization to basically continue to scale and evolve into the future? >> We're definitely out of our comfort zones, but we're getting comfortable with it. So, Cindi, last question, you've worked with hundreds of organizations, and I got to believe that you know, some of the advice you gave when you were at Gartner, which is pre COVID, maybe sometimes clients didn't always act on it. You know, they're not on my watch for whatever variety of reasons, but it's being forced on them now, but knowing what you know now that you know, we're all in this isolation economy how would you say that advice has changed, has it changed? What's your number one action and recommendation today? >> Yeah well, first off, Tom just freaked me out. What do you mean this is the slowest ever? Even six months ago, I was saying the pace of change in data and analytics is frenetic. So, but I think you're right, Tom, the business and the technology together is forcing this change. Now, Dave, to answer your question, I would say the one bit of advice, maybe I was a little more, very aware of the power in politics and how to bring people along in a way that they are comfortable, and now I think it's, you know what? You can't get comfortable. In fact, we know that the organizations that were already in the cloud, have been able to respond and pivot faster. So if you really want to survive as Tom and Gustavo said, get used to being uncomfortable, the power and politics are going to happen. Break the rules, get used to that and be bold. Do not be afraid to tell somebody they're wrong and they're not moving fast enough. I do think you have to do that with empathy as Michelle said, and Gustavo, I think that's one of the key words today besides the bungee jumping. So I want to know where's Sudheesh going to go on bungee jumping? (all chuckling) >> That's fantastic discussion really. Thanks again to all the panelists and the guests, it was really a pleasure speaking with you today. Really virtually all of the leaders that I've spoken to in theCUBE program recently, they tell me that the pandemic is accelerating so many things, whether it's new ways to work, we heard about new security models and obviously the need for cloud. I mean, all of these things are driving true enterprise wide digital transformation, not just as I said before lip service. And sometimes we minimize the importance and the challenge of building culture and in making this transformation possible. But when it's done right, the right culture is going to deliver tremendous results. Yeah, what does that mean getting it right? Everybody's trying to get it right. My biggest takeaway today, is it means making data part of the DNA of your organization. And that means making it accessible to the people in your organization that are empowered to make decisions that can drive you revenue, cut costs, speed, access to critical care, whatever the mission is of your organization. Data can create insights and informed decisions that drive value. Okay, let's bring back Sudheesh and wrap things up. Sudheesh please bring us home. >> Thank you, thank you Dave, thank you theCUBE team, and thanks goes to all of our customers and partners who joined us, and thanks to all of you for spending the time with us. I want to do three quick things and then close it off. The first thing is I want to summarize the key takeaways that I had from all four of our distinguished speakers. First, Michelle, I was simply put it, she said it really well, that is be brave and drive. Don't go for a drive along, that is such an important point. Often times, you know that I think that you have to do to make the positive change that you want to see happen. But you wait for someone else to do it, why not you? Why don't you be the one making that change happen? That's the thing that I picked up from Michelle's talk. Cindi talked about finding the importance of finding your voice, taking that chair, whether it's available or not and making sure that your ideas, your voices are heard and if it requires some force then apply that force, make sure your ideas are good. Gustavo talked about the importance of building consensus, not going at things all alone sometimes building the importance of building the courtroom. And that is critical because if you want the changes to last, you want to make sure that the organization is fully behind it. Tom instead of a single take away, what I was inspired by is the fact that a company that is 170 years old, 170 years old, 200 companies and 200 countries they're operating in, and they were able to make the change that is necessary through this difficult time. So in a matter of months, if they could do it, anyone could. The second thing I want to do is to leave you with a takeaway that is I would like you to go to thoughtspot.com/nfl because our team has made an app for NFL on Snowflake. I think you will find this interesting now that you are inspired and excited because of Michelle's talk. And the last thing is, please go to thoughtspot.com/beyond, our global user conferences happening in this December, we would love to have you join us. It's again, virtual, you can join from anywhere, we are expecting anywhere from five to 10,000 people, and we would love to have you join and see what we would have been up to since the last year. We have a lot of amazing things in store for you, our customers, our partners, our collaborators, they will be coming and sharing, you'll be sharing things that you have been working to release something that will come out next year. And also some of the crazy ideas for engineers I've been cooking up. All of those things will be available for you at ThoughtSpot Beyond, thank you, thank you so much.

(speaks in foreign language) >> Narrator: Data is at the heart of transformation and the change every company needs to succeed, but it takes more than new technology. It's about teams, talent, and cultural change. Empowering everyone on the front lines to make decisions, all at the speed of digital. The transformation starts with you. It's time to lead the way, it's time for thought leaders. >> Welcome to Thought Leaders, a digital event brought to you by ThoughtSpot. My name is Dave Vellante. The purpose of this day is to bring industry leaders and experts together to really try and understand the important issues around digital transformation. We have an amazing lineup of speakers and our goal is to provide you with some best practices that you can bring back and apply to your organization. Look, data is plentiful, but insights are not. ThoughtSpot is disrupting analytics by using search and machine intelligence to simplify data analysis, and really empower anyone with fast access to relevant data. But in the last 150 days, we've had more questions than answers. Creating an organization that puts data and insights at their core, requires not only modern technology, but leadership, a mindset and a culture that people often refer to as data-driven. What does that mean? How can we equip our teams with data and fast access to quality information that can turn insights into action. And today, we're going to hear from experienced leaders, who are transforming their organizations with data, insights and creating digital-first cultures. But before we introduce our speakers, I'm joined today by two of my co-hosts from ThoughtSpot. First, Chief Data Strategy Officer for ThoughtSpot is Cindi Hausen. Cindi is an analytics and BI expert with 20 plus years experience and the author of Successful Business Intelligence Unlock The Value of BI and Big Data. Cindi was previously the lead analyst at Gartner for the data and analytics magic quadrant. And early last year, she joined ThoughtSpot to help CDOs and their teams understand how best to leverage analytics and AI for digital transformation. Cindi, great to see you, welcome to the show. >> Thank you, Dave. Nice to join you virtually. >> Now our second cohost and friend of theCUBE is ThoughtSpot CEO Sudheesh Nair. Hello Sudheesh, how are you doing today? >> I am well Dave, it's good to talk to you again. >> It's great to see you. Thanks so much for being here. Now Sudheesh, please share with us why this discussion is so important to your customers and of course, to our audience and what they're going to learn today? (gentle music) >> Thanks, Dave, I wish you were there to introduce me into every room that I walk into because you have such an amazing way of doing it. It makes me feel also good. Look, since we have all been cooped up in our homes, I know that the vendors like us, we have amped up our, you know, sort of effort to reach out to you with invites for events like this. So we are getting way more invites for events like this than ever before. So when we started planning for this, we had three clear goals that we wanted to accomplish. And our first one that when you finish this and walk away, we want to make sure that you don't feel like it was a waste of time. We want to make sure that we value your time, and this is going to be useful. Number two, we want to put you in touch with industry leaders and thought leaders, and generally good people that you want to hang around with long after this event is over. And number three, as we plan through this, you know, we are living through these difficult times, we want an event to be, this event to be more of an uplifting and inspiring event too. Now, the challenge is, how do you do that with the team being change agents? Because change and as much as we romanticize it, it is not one of those uplifting things that everyone wants to do or likes to do. The way I think of it, change is sort of like, if you've ever done bungee jumping. You know, it's like standing on the edges, waiting to make that one more step. You know, all you have to do is take that one step and gravity will do the rest, but that is the hardest step to take. Change requires a lot of courage and when we are talking about data and analytics, which is already like such a hard topic, not necessarily an uplifting and positive conversation, in most businesses it is somewhat scary. Change becomes all the more difficult. Ultimately change requires courage. Courage to to, first of all, challenge the status quo. People sometimes are afraid to challenge the status quo because they are thinking that, "You know, maybe I don't have the power to make the change that the company needs. Sometimes I feel like I don't have the skills." Sometimes they may feel that, I'm probably not the right person to do it. Or sometimes the lack of courage manifest itself as the inability to sort of break the silos that are formed within the organizations, when it comes to data and insights that you talked about. You know, there are people in the company, who are going to hog the data because they know how to manage the data, how to inquire and extract. They know how to speak data, they have the skills to do that, but they are not the group of people who have sort of the knowledge, the experience of the business to ask the right questions off the data. So there is this silo of people with the answers and there is a silo of people with the questions, and there is gap. These sort of silos are standing in the way of making that necessary change that we all I know the business needs, and the last change to sort of bring an external force sometimes. It could be a tool, it could be a platform, it could be a person, it could be a process, but sometimes no matter how big the company is or how small the company is. You may need to bring some external stimuli to start that domino of the positive changes that are necessary. The group of people that we have brought in, the four people, including Cindi, that you will hear from today are really good at practically telling you how to make that step, how to step off that edge, how to trust the rope that you will be safe and you're going to have fun. You will have that exhilarating feeling of jumping for a bungee jump. All four of them are exceptional, but my honor is to introduce Michelle and she's our first speaker. Michelle, I am very happy after watching her presentation and reading her bio, that there are no country vital worldwide competition for cool patents, because she will beat all of us because when her children were small, you know, they were probably into Harry Potter and Disney and she was managing a business and leading change there. And then as her kids grew up and got to that age, where they like football and NFL, guess what? She's the CIO of NFL. What a cool mom. I am extremely excited to see what she's going to talk about. I've seen the slides with a bunch of amazing pictures, I'm looking to see the context behind it. I'm very thrilled to make the acquaintance of Michelle. I'm looking forward to her talk next. Welcome Michelle. It's over to you. (gentle music) >> I'm delighted to be with you all today to talk about thought leadership. And I'm so excited that you asked me to join you because today I get to be a quarterback. I always wanted to be one. This is about as close as I'm ever going to get. So, I want to talk to you about quarterbacking our digital revolution using insights, data and of course, as you said, leadership. First, a little bit about myself, a little background. As I said, I always wanted to play football and this is something that I wanted to do since I was a child but when I grew up, girls didn't get to play football. I'm so happy that that's changing and girls are now doing all kinds of things that they didn't get to do before. Just this past weekend on an NFL field, we had a female coach on two sidelines and a female official on the field. I'm a lifelong fan and student of the game of football. I grew up in the South. You can tell from the accent and in the South football is like a religion and you pick sides. I chose Auburn University working in the athletic department, so I'm testament. Till you can start, a journey can be long. It took me many, many years to make it into professional sports. I graduated in 1987 and my little brother, well not actually not so little, he played offensive line for the Alabama Crimson Tide. And for those of you who know SEC football, you know this is a really big rivalry, and when you choose sides your family is divided. So it's kind of fun for me to always tell the story that my dad knew his kid would make it to the NFL, he just bet on the wrong one. My career has been about bringing people together for memorable moments at some of America's most iconic brands, delivering memories and amazing experiences that delight. From Universal Studios, Disney, to my current position as CIO of the NFL. In this job, I'm very privileged to have the opportunity to work with a team that gets to bring America's game to millions of people around the world. Often, I'm asked to talk about how to create amazing experiences for fans, guests or customers. But today, I really wanted to focus on something different and talk to you about being behind the scenes and backstage. Because behind every event, every game, every awesome moment, is execution. Precise, repeatable execution and most of my career has been behind the scenes doing just that. Assembling teams to execute these plans and the key way that companies operate at these exceptional levels is making good decisions, the right decisions, at the right time and based upon data. So that you can translate the data into intelligence and be a data-driven culture. Using data and intelligence is an important way that world-class companies do differentiate themselves, and it's the lifeblood of collaboration and innovation. Teams that are working on delivering these kind of world class experiences are often seeking out and leveraging next generation technologies and finding new ways to work. I've been fortunate to work across three decades of emerging experiences, which each required emerging technologies to execute. A little bit first about Disney. In '90s I was at Disney leading a project called Destination Disney, which it's a data project. It was a data project, but it was CRM before CRM was even cool and then certainly before anything like a data-driven culture was ever brought up. But way back then we were creating a digital backbone that enabled many technologies for the things that you see today. Like the MagicBand, Disney's Magical Express. My career at Disney began in finance, but Disney was very good about rotating you around. And it was during one of these rotations that I became very passionate about data. I kind of became a pain in the butt to the IT team asking for data, more and more data. And I learned that all of that valuable data was locked up in our systems. All of our point of sales systems, our reservation systems, our operation systems. And so I became a shadow IT person in marketing, ultimately, leading to moving into IT and I haven't looked back since. In the early 2000s, I was at Universal Studio's theme park as their CIO preparing for and launching the Wizarding World of Harry Potter. Bringing one of history's most memorable characters to life required many new technologies and a lot of data. Our data and technologies were embedded into the rides and attractions. I mean, how do you really think a wand selects you at a wand shop. As today at the NFL, I am constantly challenged to do leading edge technologies, using things like sensors, AI, machine learning and all new communication strategies, and using data to drive everything, from player performance, contracts, to where we build new stadiums and hold events. With this year being the most challenging, yet rewarding year in my career at the NFL. In the middle of a global pandemic, the way we are executing on our season is leveraging data from contact tracing devices joined with testing data. Talk about data actually enabling your business. Without it we wouldn't be having a season right now. I'm also on the board of directors of two public companies, where data and collaboration are paramount. First, RingCentral, it's a cloud based unified communications platform and collaboration with video message and phone, all-in-one solution in the cloud and Quotient Technologies, whose product is actually data. The tagline at Quotient is The Result in Knowing. I think that's really important because not all of us are data companies, where your product is actually data, but we should operate more like your product is data. I'd also like to talk to you about four areas of things to think about as thought leaders in your companies. First, just hit on it, is change. how to be a champion and a driver of change. Second, how to use data to drive performance for your company and measure performance of your company. Third, how companies now require intense collaboration to operate and finally, how much of this is accomplished through solid data-driven decisions. First, let's hit on change. I mean, it's evident today more than ever, that we are in an environment of extreme change. I mean, we've all been at this for years and as technologists we've known it, believed it, lived it. And thankfully, for the most part, knock on wood, we were prepared for it. But this year everyone's cheese was moved. All the people in the back rooms, IT, data architects and others were suddenly called to the forefront because a global pandemic has turned out to be the thing that is driving intense change in how people work and analyze their business. On March 13th, we closed our office at the NFL in the middle of preparing for one of our biggest events, our kickoff event, The 2020 Draft. We went from planning a large event in Las Vegas under the bright lights, red carpet stage, to smaller events in club facilities. And then ultimately, to one where everyone coaches, GMs, prospects and even our commissioner were at home in their basements and we only had a few weeks to figure it out. I found myself for the first time, being in the live broadcast event space. Talking about bungee jumping, this is really what it felt like. It was one in which no one felt comfortable because it had not been done before. But leading through this, I stepped up, but it was very scary, it was certainly very risky, but it ended up being also rewarding when we did it. And as a result of this, some things will change forever. Second, managing performance. I mean, data should inform how you're doing and how to get your company to perform at its level, highest level. As an example, the NFL has always measured performance, obviously, and it is one of the purest examples of how performance directly impacts outcome. I mean, you can see performance on the field, you can see points being scored and stats, and you immediately know that impact. Those with the best stats usually win the games. The NFL has always recorded stats. Since the beginning of time here at the NFL a little... This year is our 101st year and athlete's ultimate success as a player has also always been greatly impacted by his stats. But what has changed for us is both how much more we can measure and the immediacy with which it can be measured and I'm sure in your business it's the same. The amount of data you must have has got to have quadrupled recently. And how fast do you need it and how quickly you need to analyze it is so important. And it's very important to break the silos between the keys to the data and the use of the data. Our next generation stats platform is taking data to the next level. It's powered by Amazon Web Services and we gather this data, real-time from sensors that are on players' bodies. We gather it in real time, analyze it, display it online and on broadcast. And of course, it's used to prepare week to week in addition to what is a normal coaching plan would be. We can now analyze, visualize, route patterns, speed, match-ups, et cetera, so much faster than ever before. We're continuing to roll out sensors too, that will gather more and more information about a player's performance as it relates to their health and safety. The third trend is really, I think it's a big part of what we're feeling today and that is intense collaboration. And just for sort of historical purposes, it's important to think about, for those of you that are IT professionals and developers, you know, more than 10 years ago agile practices began sweeping companies. Where small teams would work together rapidly in a very flexible, adaptive and innovative way and it proved to be transformational. However today, of course that is no longer just small teams, the next big wave of change and we've seen it through this pandemic, is that it's the whole enterprise that must collaborate and be agile. If I look back on my career, when I was at Disney, we owned everything 100%. We made a decision, we implemented it. We were a collaborative culture but it was much easier to push change because you own the whole decision. If there was buy-in from the top down, you got the people from the bottom up to do it and you executed. At Universal, we were a joint venture. Our attractions and entertainment was licensed. Our hotels were owned and managed by other third parties, so influence and collaboration, and how to share across companies became very important. And now here I am at the NFL an even the bigger ecosystem. We have 32 clubs that are all separate businesses, 31 different stadiums that are owned by a variety of people. We have licensees, we have sponsors, we have broadcast partners. So it seems that as my career has evolved, centralized control has gotten less and less and has been replaced by intense collaboration, not only within your own company but across companies. The ability to work in a collaborative way across businesses and even other companies, that has been a big key to my success in my career. I believe this whole vertical integration and big top-down decision-making is going by the wayside in favor of ecosystems that require cooperation, yet competition to co-exist. I mean, the NFL is a great example of what we call co-oppetition, which is cooperation and competition. We're in competition with each other, but we cooperate to make the company the best it can be. And at the heart of these items really are data-driven decisions and culture. Data on its own isn't good enough. You must be able to turn it to insights. Partnerships between technology teams who usually hold the keys to the raw data and business units, who have the knowledge to build the right decision models is key. If you're not already involved in this linkage, you should be, data mining isn't new for sure. The availability of data is quadrupling and it's everywhere. How do you know what to even look at? How do you know where to begin? How do you know what questions to ask? It's by using the tools that are available for visualization and analytics and knitting together strategies of the company. So it begins with, first of all, making sure you do understand the strategy of the company. So in closing, just to wrap up a bit, many of you joined today, looking for thought leadership on how to be a change agent, a change champion, and how to lead through transformation. Some final thoughts are be brave and drive. Don't do the ride along program, it's very important to drive. Driving can be high risk, but it's also high reward. Embracing the uncertainty of what will happen is how you become brave. Get more and more comfortable with uncertainty, be calm and let data be your map on your journey. Thanks. >> Michelle, thank you so much. So you and I share a love of data and a love of football. You said you want to be the quarterback. I'm more an a line person. >> Well, then I can't do my job without you. >> Great and I'm getting the feeling now, you know, Sudheesh is talking about bungee jumping. My vote is when we're past this pandemic, we both take him to the Delaware Water Gap and we do the cliff jumping. >> Oh that sounds good, I'll watch your watch. >> Yeah, you'll watch, okay. So Michelle, you have so many stakeholders, when you're trying to prioritize the different voices you have the players, you have the owners, you have the league, as you mentioned, the broadcasters, your partners here and football mamas like myself. How do you prioritize when there are so many different stakeholders that you need to satisfy? >> I think balancing across stakeholders starts with aligning on a mission and if you spend a lot of time understanding where everyone's coming from, and you can find the common thread that ties them all together. You sort of do get them to naturally prioritize their work and I think that's very important. So for us at the NFL and even at Disney, it was our core values and our core purpose is so well known and when anything challenges that, we're able to sort of lay that out. But as a change agent, you have to be very empathetic, and I would say empathy is probably your strongest skill if you're a change agent and that means listening to every single stakeholder. Even when they're yelling at you, even when they're telling you your technology doesn't work and you know that it's user error, or even when someone is just emotional about what's happening to them and that they're not comfortable with it. So I think being empathetic, and having a mission, and understanding it is sort of how I prioritize and balance. >> Yeah, empathy, a very popular word this year. I can imagine those coaches and owners yelling, so thank you for your leadership here. So Michelle, I look forward to discussing this more with our other customers and disruptors joining us in a little bit. >> (gentle music) So we're going to take a hard pivot now and go from football to Chernobyl. Chernobyl, what went wrong? 1986, as the reactors were melting down, they had the data to say, "This is going to be catastrophic," and yet the culture said, "No, we're perfect, hide it. Don't dare tell anyone." Which meant they went ahead and had celebrations in Kiev. Even though that increased the exposure, additional thousands getting cancer and 20,000 years before the ground around there can even be inhabited again. This is how powerful and detrimental a negative culture, a culture that is unable to confront the brutal facts that hides data. This is what we have to contend with and this is why I want you to focus on having, fostering a data-driven culture. I don't want you to be a laggard. I want you to be a leader in using data to drive your digital transformation. So I'll talk about culture and technology, is it really two sides of the same coin? Real-world impacts and then some best practices you can use to disrupt and innovate your culture. Now, oftentimes I would talk about culture and I talk about technology. And recently a CDO said to me, "You know, Cindi, I actually think this is two sides of the same coin, one reflects the other." What do you think? Let me walk you through this. So let's take a laggard. What does the technology look like? Is it based on 1990s BI and reporting, largely parametrized reports, on-premises data warehouses, or not even that operational reports. At best one enterprise data warehouse, very slow moving and collaboration is only email. What does that culture tell you? Maybe there's a lack of leadership to change, to do the hard work that Sudheesh referred to, or is there also a culture of fear, afraid of failure, resistance to change, complacency. And sometimes that complacency, it's not because people are lazy. It's because they've been so beaten down every time a new idea is presented. It's like, "No, we're measured on least to serve." So politics and distrust, whether it's between business and IT or individual stakeholders is the norm, so data is hoarded. Let's contrast that with the leader, a data and analytics leader, what does their technology look like? Augmented analytics, search and AI driven insights, not on-premises but in the cloud and maybe multiple clouds. And the data is not in one place but it's in a data lake and in a data warehouse, a logical data warehouse. The collaboration is via newer methods, whether it's Slack or Teams, allowing for that real-time decisioning or investigating a particular data point. So what is the culture in the leaders? It's transparent and trust. There is a trust that data will not be used to punish, that there is an ability to confront the bad news. It's innovation, valuing innovation in pursuit of the company goals. Whether it's the best fan experience and player safety in the NFL or best serving your customers, it's innovative and collaborative. There's none of this, "Oh, well, I didn't invent that. I'm not going to look at that." There's still pride of ownership, but it's collaborating to get to a better place faster. And people feel empowered to present new ideas, to fail fast and they're energized knowing that they're using the best technology and innovating at the pace that business requires. So data is democratized and democratized, not just for power users or analysts, but really at the point of impact, what we like to call the new decision-makers or really the frontline workers. So Harvard Business Review partnered with us to develop this study to say, "Just how important is this? We've been working at BI and analytics as an industry for more than 20 years, why is it not at the front lines? Whether it's a doctor, a nurse, a coach, a supply chain manager, a warehouse manager, a financial services advisor." 87% said they would be more successful if frontline workers were empowered with data-driven insights, but they recognize they need new technology to be able to do that. It's not about learning hard tools. The sad reality only 20% of organizations are actually doing this. These are the data-driven leaders. So this is the culture and technology, how did we get here? It's because state-of-the-art keeps changing. So the first generation BI and analytics platforms were deployed on-premises, on small datasets, really just taking data out of ERP systems that were also on-premises and state-of-the-art was maybe getting a management report, an operational report. Over time, visual based data discovery vendors disrupted these traditional BI vendors, empowering now analysts to create visualizations with the flexibility on a desktop, sometimes larger data, sometimes coming from a data warehouse. The current state-of-the-art though, Gartner calls it augmented analytics. At ThoughtSpot, we call it search and AI driven analytics, and this was pioneered for large scale data sets, whether it's on-premises or leveraging the cloud data warehouses. And I think this is an important point, oftentimes you, the data and analytics leaders, will look at these two components separately. But you have to look at the BI and analytics tier in lock-step with your data architectures to really get to the granular insights and to leverage the capabilities of AI. Now, if you've never seen ThoughtSpot, I'll just show you what this looks like. Instead of somebody hard coding a report, it's typing in search keywords and very robust keywords contains rank, top, bottom, getting to a visual visualization that then can be pinned to an existing pin board that might also contain insights generated by an AI engine. So it's easy enough for that new decision maker, the business user, the non-analyst to create themselves. Modernizing the data and analytics portfolio is hard because the pace of change has accelerated. You used to be able to create an investment, place a bet for maybe 10 years. A few years ago, that time horizon was five years. Now, it's maybe three years and the time to maturity has also accelerated. So you have these different components, the search and AI tier, the data science tier, data preparation and virtualization but I would also say, equally important is the cloud data warehouse. And pay attention to how well these analytics tools can unlock the value in these cloud data warehouses. So ThoughtSpot was the first to market with search and AI driven insights. Competitors have followed suit, but be careful, if you look at products like Power BI or SAP analytics cloud, they might demo well, but do they let you get to all the data without moving it in products like Snowflake, Amazon Redshift, or Azure Synapse, or Google BigQuery, they do not. They require you to move it into a smaller in-memory engine. So it's important how well these new products inter-operate. The pace of change, its acceleration, Gartner recently predicted that by 2022, 65% of analytical queries will be generated using search or NLP or even AI and that is roughly three times the prediction they had just a couple of years ago. So let's talk about the real world impact of culture and if you've read any of my books or used any of the maturity models out there, whether the Gartner IT Score that I worked on or the Data Warehousing Institute also has a maturity model. We talk about these five pillars to really become data-driven. As Michelle spoke about, it's focusing on the business outcomes, leveraging all the data, including new data sources, it's the talent, the people, the technology and also the processes. And often when I would talk about the people in the talent, I would lump the culture as part of that. But in the last year, as I've traveled the world and done these digital events for thought leaders. You have told me now culture is absolutely so important, and so we've pulled it out as a separate pillar. And in fact, in polls that we've done in these events, look at how much more important culture is as a barrier to becoming data-driven. It's three times as important as any of these other pillars. That's how critical it is. And let's take an example of where you can have great data, but if you don't have the right culture, there's devastating impacts. And I will say I have been a loyal customer of Wells Fargo for more than 20 years, but look at what happened in the face of negative news with data. It said, "Hey, we're not doing good cross-selling, customers do not have both a checking account and a credit card and a savings account and a mortgage." They opened fake accounts facing billions in fines, change in leadership that even the CEO attributed to a toxic sales culture and they're trying to fix this, but even recently there's been additional employee backlash saying the culture has not changed. Let's contrast that with some positive examples. Medtronic, a worldwide company in 150 countries around the world. They may not be a household name to you, but if you have a loved one or yourself, you have a pacemaker, spinal implant, diabetes, you know this brand. And at the start of COVID when they knew their business would be slowing down, because hospitals would only be able to take care of COVID patients. They took the bold move of making their IP for ventilators publicly available. That is the power of a positive culture. Or Verizon, a major telecom organization looking at late payments of their customers and even though the U.S. Federal Government said, "Well, you can't turn them off." They said, "We'll extend that even beyond the mandated guidelines," and facing a slow down in the business because of the tough economy, They said, "You know what? We will spend the time upskilling our people, giving them the time to learn more about the future of work, the skills and data and analytics for 20,000 of their employees rather than furloughing them. That is the power of a positive culture. So how can you transform your culture to the best in class? I'll give you three suggestions. Bring in a change agent, identify the relevance or I like to call it WIIFM and organize for collaboration. So the CDO, whatever your title is, Chief Analytics Officer, Chief Digital Officer, you are the most important change agent. And this is where you will hear that oftentimes a change agent has to come from outside the organization. So this is where, for example, in Europe you have the CDO of Just Eat, a takeout food delivery organization coming from the airline industry or in Australia, National Australian Bank taking a CDO within the same sector from TD Bank going to NAB. So these change agents come in, disrupt. It's a hard job. As one of you said to me, it often feels like. I make one step forward and I get knocked down again, I get pushed back. It is not for the faint of heart, but it's the most important part of your job. The other thing I'll talk about is WIIFM What's In It For Me? And this is really about understanding the motivation, the relevance that data has for everyone on the frontline, as well as those analysts, as well as the executives. So, if we're talking about players in the NFL, they want to perform better and they want to stay safe. That is why data matters to them. If we're talking about financial services, this may be a wealth management advisor. Okay, we could say commissions, but it's really helping people have their dreams come true, whether it's putting their children through college or being able to retire without having to work multiple jobs still into your 70s or 80s. For the teachers, teachers you ask them about data. They'll say, "We don't need that, I care about the student." So if you can use data to help a student perform better, that is WIIFM and sometimes we spend so much time talking the technology, we forget, what is the value we're trying to deliver with this? And we forget the impact on the people that it does require change. In fact, the Harvard Business Review study found that 44% said lack of change management is the biggest barrier to leveraging both new technology, but also being empowered to act on those data-driven insights. The third point, organize for collaboration. This does require diversity of thought, but also bringing the technology, the data and the business people together. Now there's not a single one size fits all model for data and analytics. At one point in time, even having a BICC, a BI competency center was considered state of the art. Now for the biggest impact, what I recommend is that you have a federated model centralized for economies of scale. That could be the common data, but then embed these evangelists, these analysts of the future within every business unit, every functional domain. And as you see this top bar, all models are possible, but the hybrid model has the most impact, the most leaders. So as we look ahead to the months ahead, to the year ahead, an exciting time because data is helping organizations better navigate a tough economy, lock in the customer loyalty and I look forward to seeing how you foster that culture that's collaborative with empathy and bring the best of technology, leveraging the cloud, all your data. So thank you for joining us at Thought Leaders. And next, I'm pleased to introduce our first change agent, Tom Mazzaferro Chief Data Officer of Western Union and before joining Western Union, Tom made his Mark at HSBC and JP Morgan Chase spearheading digital innovation in technology, operations, risk compliance and retail banking. Tom, thank you so much for joining us today. (gentle music) >> Very happy to be here and looking forward to talking to all of you today. So as we look to move organizations to a data-driven capability into the future, there is a lot that needs to be done on the data side, but also how does data connect and enable different business teams and the technology teams into the future? As we look across our data ecosystems and our platforms, and how we modernize that to the cloud in the future, it all needs to basically work together, right? To really be able to drive an organization from a data standpoint, into the future. That includes being able to have the right information with the right quality of data, at the right time to drive informed business decisions, to drive the business forward. As part of that, we actually have partnered with ThoughtSpot to actually bring in the technology to help us drive that. As part of that partnership and it's how we've looked to integrate it into our overall business as a whole. We've looked at, how do we make sure that our business and our professional lives, right? Are enabled in the same ways as our personal lives. So for example, in your personal lives, when you want to go and find something out, what do you do? You go onto google.com or you go onto Bing or you go onto Yahoo and you search for what you want, search to find an answer. ThoughtSpot for us is the same thing, but in the business world. So using ThoughtSpot and other AI capability is it's allowed us to actually enable our overall business teams in our company to actually have our information at our fingertips. So rather than having to go and talk to someone, or an engineer to go pull information or pull data. We actually can have the end users or the business executives, right. Search for what they need, what they want, at the exact time that they actually need it, to go and drive the business forward. This is truly one of those transformational things that we've put in place. On top of that, we are on a journey to modernize our larger ecosystem as a whole. That includes modernizing our underlying data warehouses, our technology, our... The local environments and as we move that, we've actually picked two of our cloud providers going to AWS and to GCP. We've also adopted Snowflake to really drive and to organize our information and our data, then drive these new solutions and capabilities forward. So a big portion of it though is culture. So how do we engage with the business teams and bring the IT teams together, to really help to drive these holistic end-to-end solutions and capabilities, to really support the actual business into the future. That's one of the keys here, as we look to modernize and to really enhance our organizations to become data-driven. This is the key. If you can really start to provide answers to business questions before they're even being asked and to predict based upon different economic trends or different trends in your business, what decisions need to be made and actually provide those answers to the business teams before they're even asking for it. That is really becoming a data-driven organization and as part of that, it really then enables the business to act quickly and take advantage of opportunities as they come in based upon industries, based upon markets, based upon products, solutions or partnerships into the future. These are really some of the keys that become crucial as you move forward, right, into this new age, Especially with COVID. With COVID now taking place across the world, right? Many of these markets, many of these digital transformations are celebrating and are changing rapidly to accommodate and to support customers in these very difficult times. As part of that, you need to make sure you have the right underlying foundation, ecosystems and solutions to really drive those capabilities and those solutions forward. As we go through this journey, both in my career but also each of your careers into the future, right? It also needs to evolve, right? Technology has changed so drastically in the last 10 years, and that change is only accelerating. So as part of that, you have to make sure that you stay up to speed, up to date with new technology changes, both on the platform standpoint, tools, but also what do our customers want, what do our customers need and how do we then service them with our information, with our data, with our platform, and with our products and our services to meet those needs and to really support and service those customers into the future. This is all around becoming a more data-driven organization, such as how do you use your data to support your current business lines, but how do you actually use your information and your data to actually better support your customers, better support your business, better support your employees, your operations teams and so forth. And really creating that full integration in that ecosystem is really when you start to get large dividends from these investments into the future. With that being said, I hope you enjoyed the segment on how to become and how to drive a data-driven organization, and looking forward to talking to you again soon. Thank you. >> Tom, that was great. Thanks so much and now going to have to drag on you for a second. As a change agent you've come in, disrupted and how long have you been at Western Union? >> Only nine months, so just started this year, but there have been some great opportunities to integrate changes and we have a lot more to go, but we're really driving things forward in partnership with our business teams and our colleagues to support those customers going forward. >> Tom, thank you so much. That was wonderful. And now, I'm excited to introduce you to Gustavo Canton, a change agent that I've had the pleasure of working with meeting in Europe and he is a serial change agent. Most recently with Schneider Electric but even going back to Sam's Clubs. Gustavo, welcome. (gentle music) >> So, hey everyone, my name is Gustavo Canton and thank you so much, Cindi, for the intro. As you mentioned, doing transformations is, you know, a high reward situation. I have been part of many transformations and I have led many transformations. And, what I can tell you is that it's really hard to predict the future, but if you have a North Star and you know where you're going, the one thing that I want you to take away from this discussion today is that you need to be bold to evolve. And so, in today, I'm going to be talking about culture and data, and I'm going to break this down in four areas. How do we get started, barriers or opportunities as I see it, the value of AI and also, how you communicate. Especially now in the workforce of today with so many different generations, you need to make sure that you are communicating in ways that are non-traditional sometimes. And so, how do we get started? So, I think the answer to that is you have to start for you yourself as a leader and stay tuned. And by that, I mean, you need to understand, not only what is happening in your function or your field, but you have to be very in tune what is happening in society socioeconomically speaking, wellbeing. You know, the common example is a great example and for me personally, it's an opportunity because the number one core value that I have is wellbeing. I believe that for human potential for customers and communities to grow, wellbeing should be at the center of every decision. And as somebody mentioned, it's great to be, you know, stay in tune and have the skillset and the courage. But for me personally, to be honest, to have this courage is not about not being afraid. You're always afraid when you're making big changes and you're swimming upstream, but what gives me the courage is the empathy part. Like I think empathy is a huge component because every time I go into an organization or a function, I try to listen very attentively to the needs of the business and what the leaders are trying to do. But I do it thinking about the mission of, how do I make change for the bigger workforce or the bigger good despite the fact that this might have perhaps implication for my own self interest in my career. Right? Because you have to have that courage sometimes to make choices that are not well seen, politically speaking, but are the right thing to do and you have to push through it. So the bottom line for me is that, I don't think we're they're transforming fast enough. And the reality is, I speak with a lot of leaders and we have seen stories in the past and what they show is that, if you look at the four main barriers that are basically keeping us behind budget, inability to act, cultural issues, politics and lack of alignment, those are the top four. But the interesting thing is that as Cindi has mentioned, these topic about culture is actually gaining more and more traction. And in 2018, there was a story from HBR and it was about 45%. I believe today, it's about 55%, 60% of respondents say that this is the main area that we need to focus on. So again, for all those leaders and all the executives who understand and are aware that we need to transform, commit to the transformation and set a deadline to say, "Hey, in two years we're going to make this happen. What do we need to do, to empower and enable these change agents to make it happen? You need to make the tough choices. And so to me, when I speak about being bold is about making the right choices now. So, I'll give you examples of some of the roadblocks that I went through as I've been doing transformations, most recently, as Cindi mentioned in Schneider. There are three main areas, legacy mindset and what that means is that, we've been doing this in a specific way for a long time and here is how we have been successful. What worked in the past is not going to work now. The opportunity there is that there is a lot of leaders, who have a digital mindset and they're up and coming leaders that are perhaps not yet fully developed. We need to mentor those leaders and take bets on some of these talents, including young talent. We cannot be thinking in the past and just wait for people, you know, three to five years for them to develop because the world is going in a way that is super-fast. The second area and this is specifically to implementation of AI. It's very interesting to me because just the example that I have with ThoughtSpot, right? We went on implementation and a lot of the way the IT team functions or the leaders look at technology, they look at it from the prism of the prior or success criteria for the traditional BIs, and that's not going to work. Again, the opportunity here is that you need to redefine what success look like. In my case, I want the user experience of our workforce to be the same user experience you have at home. It's a very simple concept and so we need to think about, how do we gain that user experience with these augmented analytics tools and then work backwards to have the right talent, processes, and technology to enable that. And finally and obviously with COVID, a lot of pressure in organizations and companies to do more with less. And the solution that most leaders I see are taking is to just minimize costs sometimes and cut budget. We have to do the opposite. We have to actually invest on growth areas, but do it by business question. Don't do it by function. If you actually invest in these kind of solutions, if you actually invest on developing your talent and your leadership to see more digitally, if you actually invest on fixing your data platform, it's not just an incremental cost. It's actually this investment is going to offset all those hidden costs and inefficiencies that you have on your system, because people are doing a lot of work and working very hard but it's not efficient and it's not working in the way that you might want to work. So there is a lot of opportunity there and just to put in terms of perspective, there have been some studies in the past about, you know, how do we kind of measure the impact of data? And obviously, this is going to vary by organization maturity, there's going to be a lot of factors. I've been in companies who have very clean, good data to work with and I've been with companies that we have to start basically from scratch. So it all depends on your maturity level. But in this study, what I think is interesting is they try to put a tagline or a tag price to what is the cost of incomplete data. So in this case, it's about 10 times as much to complete a unit of work when you have data that is flawed as opposed to having perfect data. So let me put that just in perspective, just as an example, right? Imagine you are trying to do something and you have to do 100 things in a project, and each time you do something, it's going to cost you a dollar. So if you have perfect data, the total cost of that project might be $100. But now let's say you have 80% perfect data and 20% flawed data. By using this assumption that flawed data is 10 times as costly as perfect data, your total costs now becomes $280 as opposed to $100. This just for you to really think about as a CIO, CTO, you know CHRO, CEO, "Are we really paying attention and really closing the gaps that we have on our data infrastructure?" If we don't do that, it's hard sometimes to see the snowball effect or to measure the overall impact, but as you can tell, the price tag goes up very, very quickly. So now, if I were to say, how do I communicate this or how do I break through some of these challenges or some of these barriers, right? I think the key is, I am in analytics, I know statistics obviously and love modeling, and, you know, data and optimization theory, and all that stuff. That's what I came to analytics, but now as a leader and as a change agent, I need to speak about value and in this case, for example, for Schneider. There was this tagline, make the most of your energy. So the number one thing that they were asking from the analytics team was actually efficiency, which to me was very interesting. But once I understood that, I understood what kind of language to use, how to connect it to the overall strategy and basically, how to bring in the right leaders because you need to, you know, focus on the leaders that you're going to make the most progress, you know. Again, low effort, high value. You need to make sure you centralize all the data as you can, you need to bring in some kind of augmented analytics, you know, solution. And finally, you need to make it super-simple for the, you know, in this case, I was working with the HR teams and other areas, so they can have access to one portal. They don't have to be confused and looking for 10 different places to find information. I think if you can actually have those four foundational pillars, obviously under the guise of having a data-driven culture, that's when you can actually make the impact. So in our case, it was about three years total transformation, but it was two years for this component of augmented analytics. It took about two years to talk to, you know, IT, get leadership support, find the budgeting, you know, get everybody on board, make sure the success criteria was correct. And we call this initiative, the people analytics portal. It was actually launched in July of this year and we were very excited and the audience was very excited to do this. In this case, we did our pilot in North America for many, many, many factors but one thing that is really important is as you bring along your audience on this, you know. You're going from Excel, you know, in some cases or Tableu to other tools like, you know, ThoughtSpot. You need to really explain them what is the difference and how this tool can truly replace some of the spreadsheets or some of the views that you might have on these other kinds of tools. Again, Tableau, I think it's a really good tool. There are other many tools that you might have in your toolkit but in my case, personally, I feel that you need to have one portal. Going back to Cindi's points, that really truly enable the end user. And I feel that this is the right solution for us, right? And I will show you some of the findings that we had in the pilot in the last two months. So this was a huge victory and I will tell you why, because it took a lot of effort for us to get to this stage and like I said, it's been years for us to kind of lay the foundation, get the leadership, initiating culture so people can understand, why you truly need to invest on augmented analytics. And so, what I'm showing here is an example of how do we use basically, you know, a tool to capturing video, the qualitative findings that we had, plus the quantitative insights that we have. So in this case, our preliminary results based on our ambition for three main metrics. Hours saved, user experience and adoption. So for hours saved, our ambition was to have 10 hours per week for employee to save on average. User experience, our ambition was 4.5 and adoption 80%. In just two months, two months and a half of the pilot, we were able to achieve five hours per week per employee savings, a user experience for 4.3 out of five and adoption of 60%. Really, really amazing work. But again, it takes a lot of collaboration for us to get to the stage from IT, legal, communications, obviously the operations things and the users. In HR safety and other areas that might be basically stakeholders in this whole process. So just to summarize, this kind of effort takes a lot of energy. You are a change agent, you need to have courage to make this decision and understand that, I feel that in this day and age with all this disruption happening, we don't have a choice. We have to take the risk, right? And in this case, I feel a lot of satisfaction in how we were able to gain all these great resource for this organization and that give me the confident to know that the work has been done and we are now in a different stage for the organization. And so for me, it's just to say, thank you for everybody who has belief, obviously in our vision, everybody who has belief in, you know, the work that we were trying to do and to make the life of our, you know, workforce or customers and community better. As you can tell, there is a lot of effort, there is a lot of collaboration that is needed to do something like this. In the end, I feel very satisfied with the accomplishments of this transformation and I just want to tell for you, if you are going right now in a moment that you feel that you have to swim upstream, you know, work with mentors, work with people in the industry that can help you out and guide you on this kind of transformation. It's not easy to do, it's high effort, but it's well worth it. And with that said, I hope you are well and it's been a pleasure talking to you. Talk to you soon. Take care. >> Thank you, Gustavo. That was amazing. All right, let's go to the panel. (light music) Now I think we can all agree how valuable it is to hear from practitioners and I want to thank the panel for sharing their knowledge with the community. Now one common challenge that I heard you all talk about was bringing your leadership and your teams along on the journey with you. We talk about this all the time and it is critical to have support from the top. Why? Because it directs the middle and then it enables bottoms up innovation effects from the cultural transformation that you guys all talked about. It seems like another common theme we heard is that you all prioritize database decision making in your organizations. And you combine two of your most valuable assets to do that and create leverage, employees on the front lines, and of course the data. Now as as you rightly pointed out, Tom, the pandemic has accelerated the need for really leaning into this. You know, the old saying, if it ain't broke, don't fix it, well COVID has broken everything and it's great to hear from our experts, you know, how to move forward, so let's get right into it. So Gustavo, let's start with you. If I'm an aspiring change agent and let's say I'm a budding data leader, what do I need to start doing? What habits do I need to create for long-lasting success? >> I think curiosity is very important. You need to be, like I said, in tune to what is happening, not only in your specific field, like I have a passion for analytics, I've been doing it for 50 years plus, but I think you need to understand wellbeing of the areas across not only a specific business. As you know, I come from, you know, Sam's Club, Walmart retail. I've been in energy management, technology. So you have to try to push yourself and basically go out of your comfort zone. I mean, if you are staying in your comfort zone and you want to just continuous improvement, that's just going to take you so far. What you have to do is, and that's what I try to do, is I try to go into areas, businesses and transformations, that make me, you know, stretch and develop as a leader. That's what I'm looking to do, so I can help transform the functions, organizations, and do the change management, the essential mindset that's required for this kind of effort. >> Well, thank you for that. That is inspiring and Cindi you love data and the data is pretty clear that diversity is a good business, but I wonder if you can, you know, add your perspectives to this conversation? >> Yeah, so Michelle has a new fan here because she has found her voice. I'm still working on finding mine and it's interesting because I was raised by my dad, a single dad, so he did teach me how to work in a predominantly male environment, but why I think diversity matters more now than ever before and this is by gender, by race, by age, by just different ways of working and thinking, is because as we automate things with AI, if we do not have diverse teams looking at the data, and the models, and how they're applied, we risk having bias at scale. So this is why I think I don't care what type of minority you are, finding your voice, having a seat at the table and just believing in the impact of your work has never been more important and as Michelle said, more possible. >> Great perspectives, thank you. Tom, I want to go to you. So, I mean, I feel like everybody in our businesses is in some way, shape, or form become a COVID expert, but what's been the impact of the pandemic on your organization's digital transformation plans? >> We've seen a massive growth, actually, in our digital business over the last 12 months really, even acceleration, right, once COVID hit. We really saw that in the 200 countries and territories that we operate in today and service our customers in today, that there's been a huge need, right, to send money to support family, to support friends, and to support loved ones across the world. And as part of that we are very honored to be able to support those customers that, across all the centers today, but as part of the acceleration, we need to make sure that we have the right architecture and the right platforms to basically scale, right? To basically support and provide the right kind of security for our customers going forward. So as part of that, we did do some pivots and we did accelerate some of our plans on digital to help support that overall growth coming in and to support our customers going forward, because during these times, during this pandemic, right, this is the most important time and we need to support those that we love and those that we care about. And doing that some of those ways is actually by sending money to them, support them financially. And that's where really our products and our services come into play that, you know, and really support those families. So, it was really a great opportunity for us to really support and really bring some of our products to the next level and supporting our business going forward. >> Awesome, thank you. Now, I want to come back to Gustavo. Tom, I'd love for you to chime in too. Did you guys ever think like you were pushing the envelope too much in doing things with data or the technology that it was just maybe too bold, maybe you felt like at some point it was failing, or you're pushing your people too hard? Can you share that experience and how you got through it? >> Yeah, the way I look at it is, you know, again, whenever I go to an organization, I ask the question, "Hey, how fast you would like to conform?" And, you know, based on the agreements on the leadership and the vision that we want to take place, I take decisions and I collaborate in a specific way. Now, in the case of COVID, for example, right, it forces us to remove silos and collaborate in a faster way. So to me, it was an opportunity to actually integrate with other areas and drive decisions faster, but make no mistake about it, when you are doing a transformation, you are obviously trying to do things faster than sometimes people are comfortable doing, and you need to be okay with that. Sometimes you need to be okay with tension or you need to be okay, you know, debating points or making repetitive business cases until people connect with the decision because you understand and you are seeing that, "Hey, the CEO is making a one, two year, you know, efficiency goal. The only way for us to really do more with less is for us to continue this path. We can not just stay with the status quo, we need to find a way to accelerate the transformation." That's the way I see it. >> How about Utah, we were talking earlier with Sudheesh and Cindi about that bungee jumping moment. What can you share? >> Yeah, you know, I think you hit upon it. Right now, the pace of change will be the slowest pace that you see for the rest of your career. So as part of that, right, this is what I tell my team, is that you need to be, you need to feel comfortable being uncomfortable. Meaning that we have to be able to basically scale, right? Expand and support the ever changing needs in the marketplace and industry and our customers today, and that pace of change that's happening, right? And what customers are asking for and the competition in the marketplace, it's only going to accelerate. So as part of that, you know, as you look at how you're operating today in your current business model, right? Things are only going to get faster. So you have to plan and to align and to drive the actual transformation, so that you can scale even faster into the future. So it's part of that, that's what we're putting in place here, right? It's how do we create that underlying framework and foundation that allows the organization to basically continue to scale and evolve into the future? >> Yeah, we're definitely out of our comfort zones, but we're getting comfortable with it. So Cindi, last question, you've worked with hundreds of organizations and I got to believe that, you know, some of the advice you gave when you were at Gartner, which was pre-COVID, maybe sometimes clients didn't always act on it. You know, not my watch or for whatever, variety of reasons, but it's being forced on them now. But knowing what you know now that, you know, we're all in this isolation economy, how would you say that advice has changed? Has it changed? What's your number one action and recommendation today? >> Yeah, well first off, Tom, just freaked me out. What do you mean, this is the slowest ever? Even six months ago I was saying the pace of change in data and analytics is frenetic. So, but I think you're right, Tom, the business and the technology together is forcing this change. Now, Dave, to answer your question, I would say the one bit of advice, maybe I was a little more very aware of the power in politics and how to bring people along in a way that they are comfortable and now I think it's, you know what, you can't get comfortable. In fact, we know that the organizations that were already in the cloud have been able to respond and pivot faster. So, if you really want to survive, as Tom and Gustavo said, get used to being uncomfortable. The power and politics are going to happen, break the rules, get used to that and be bold. Do not be afraid to tell somebody they're wrong and they're not moving fast enough. I do think you have to do that with empathy, as Michelle said and Gustavo, I think that's one of the key words today besides the bungee jumping. So I want to know where Sudheesh is going to go bungee jumping. (all chuckling) >> Guys, fantastic discussion, really. Thanks again to all the panelists and the guests, it was really a pleasure speaking with you today. Really, virtually all of the leaders that I've spoken to in theCUBE program recently, they tell me that the pandemic is accelerating so many things. Whether it's new ways to work, we heard about new security models and obviously the need for cloud. I mean, all of these things are driving true enterprise-wide digital transformation, not just as I said before, lip service. You know, sometimes we minimize the importance and the challenge of building culture and in making this transformation possible. But when it's done right, the right culture is going to deliver tournament results. You know, what does that mean? Getting it right. Everybody's trying to get it right. My biggest takeaway today is it means making data part of the DNA of your organization. And that means making it accessible to the people in your organization that are empowered to make decisions, decisions that can drive new revenue, cut costs, speed access to critical care, whatever the mission is of your organization, data can create insights and informed decisions that drive value. Okay, let's bring back Sudheesh and wrap things up. Sudheesh, please bring us home. >> Thank you, thank you, Dave. Thank you, theCUBE team, and thanks goes to all of our customers and partners who joined us, and thanks to all of you for spending the time with us. I want to do three quick things and then close it off. The first thing is I want to summarize the key takeaways that I heard from all four of our distinguished speakers. First, Michelle, I will simply put it, she said it really well. That is be brave and drive, don't go for a drive alone. That is such an important point. Often times, you know the right thing that you have to do to make the positive change that you want to see happen, but you wait for someone else to do it, not just, why not you? Why don't you be the one making that change happen? That's the thing that I picked up from Michelle's talk. Cindi talked about finding, the importance of finding your voice. Taking that chair, whether it's available or not, and making sure that your ideas, your voice is heard and if it requires some force, then apply that force. Make sure your ideas are heard. Gustavo talked about the importance of building consensus, not going at things all alone sometimes. The importance of building the quorum, and that is critical because if you want the changes to last, you want to make sure that the organization is fully behind it. Tom, instead of a single takeaway, what I was inspired by is the fact that a company that is 170 years old, 170 years old, 200 companies and 200 countries they're operating in and they were able to make the change that is necessary through this difficult time in a matter of months. If they could do it, anyone could. The second thing I want to do is to leave you with a takeaway, that is I would like you to go to ThoughtSpot.com/nfl because our team has made an app for NFL on Snowflake. I think you will find this interesting now that you are inspired and excited because of Michelle's talk. And the last thing is, please go to ThoughtSpot.com/beyond. Our global user conference is happening in this December. We would love to have you join us, it's, again, virtual, you can join from anywhere. We are expecting anywhere from five to 10,000 people and we would love to have you join and see what we've been up to since last year. We have a lot of amazing things in store for you, our customers, our partners, our collaborators, they will be coming and sharing. We'll be sharing things that we have been working to release, something that will come out next year. And also some of the crazy ideas our engineers have been cooking up. All of those things will be available for you at ThoughtSpot Beyond. Thank you, thank you so much.

>> Data is at the heart of transformation and the change every company needs to succeed, but it takes more than new technology. It's about teams, talent and cultural change. Empowering everyone on the front lines to make decisions all at the speed of digital. The transformation starts with you. It's time to lead the way it's time for Thought leaders. >> Welcome to "Thought Leaders" a digital event brought to you by ThoughtSpot. My name is Dave Vellante. The purpose of this day is to bring industry leaders and experts together to really try and understand the important issues around digital transformation. We have an amazing lineup of speakers and our goal is to provide you with some best practices that you can bring back and apply to your organization. Look, data is plentiful, but insights are not. ThoughtSpot is disrupting analytics by using search and machine intelligence to simplify data analysis and really empower anyone with fast access to relevant data. But in the last 150 days, we've had more questions than answers. Creating an organization that puts data and insights at their core requires not only modern technology, but leadership, a mindset and a culture that people often refer to as data-driven. What does that mean? How can we equip our teams with data and fast access to quality information that can turn insights into action. And today we're going to hear from experienced leaders who are transforming their organizations with data, insights and creating digital first cultures. But before we introduce our speakers, I'm joined today by two of my co-hosts from ThoughtSpot first chief data strategy officer at the ThoughtSpot is Cindi Howson. Cindi is an analytics and BI expert with 20 plus years experience and the author of "Successful Business Intelligence "Unlock the Value of BI & Big Data." Cindi was previously the lead analyst at Gartner for the data and analytics magic quadrant. And early last year, she joined ThoughtSpot to help CDOs and their teams understand how best to leverage analytics and AI for digital transformation. Cindi, great to see you welcome to the show. >> Thank you, Dave. Nice to join you virtually. >> Now our second cohost and friend of the cube is ThoughtSpot CEO Sudheesh Nair Hello, Sudheesh how are you doing today? >> I'm well Dave, it's good to talk to you again. >> It's great to see you thanks so much for being here. Now Sudheesh please share with us why this discussion is so important to your customers and of course, to our audience and what they're going to learn today. (upbeat music) >> Thanks, Dave. I wish you were there to introduce me into every room and that I walk into because you have such an amazing way of doing it. Makes me feel all so good. Look, since we have all been cooped up in our homes, I know that the vendors like us, we have amped up our sort of effort to reach out to you with invites for events like this. So we are getting very more invites for events like this than ever before. So when we started planning for this, we had three clear goals that we wanted to accomplish. And our first one that when you finish this and walk away, we want to make sure that you don't feel like it was a waste of time. We want to make sure that we value your time and this is going to be useful. Number two, we want to put you in touch with industry leaders and thought leaders, generally good people that you want to hang around with long after this event is over. And number three, as we plan through this, we are living through these difficult times. We want an event to be this event, to be more of an uplifting and inspiring event too. Now, the challenge is how do you do that with the team being change agents because change and as much as we romanticize it, it is not one of those uplifting things that everyone wants to do, or like to do. The way I think of it sort of like a, if you've ever done bungee jumping and it's like standing on the edges waiting to make that one more step, all you have to do is take that one step and gravity will do the rest, but that is the hardest step to take. Change requires a lot of courage. And when we are talking about data and analytics, which is already like such a hard topic, not necessarily an uplifting and positive conversation in most businesses, it is somewhat scary. Change becomes all the more difficult. Ultimately change requires courage. Courage to first of all challenge the status quo. People sometimes are afraid to challenge the status quo because they are thinking that maybe I don't have the power to make the change that the company needs. Sometimes they feel like I don't have the skills. Sometimes they may feel that I'm probably not the right person do it. Or sometimes the lack of courage manifest itself as the inability to sort of break the silos that are formed within the organizations, when it comes to data and insights that you talked about. There are people in the company who are going to hog the data because they know how to manage the data, how to inquire and extract. They know how to speak data. They have the skills to do that. But they are not the group of people who have sort of the knowledge, the experience of the business to ask the right questions off the data. So there is the silo of people with the answers, and there is a silo of people with the questions. And there is gap. This sort of silos are standing in the way of making that necessary change that we all know the business needs. And the last change to sort of bring an external force sometimes. It could be a tool. It could be a platform, it could be a person, it could be a process, but sometimes no matter how big the company is or how small the company is, you may need to bring some external stimuli to start the domino of the positive changes that are necessary. The group of people that we are brought in, the four people, including Cindi, that you will hear from today are really good at practically telling you how to make that step, how to step off that edge, how to dress the rope, that you will be safe and you're going to have fun. You will have that exhilarating feeling of jumping, for a bungee jump. All four of them are exceptional, but my honor is to introduce Michelle and she's our first speaker. Michelle, I am very happy after watching her presentation and reading our bio, that there are no country vital worldwide competition for cool patterns, because she will beat all of us because when her children were small, they were probably into Harry Potter and Disney. She was managing a business and leading change there. And then as her kids grew up and got to that age where they like football and NFL, guess what? She's the CIO of NFL. What a cool mom? I am extremely excited to see what she's going to talk about. I've seen the slides, tons of amazing pictures. I'm looking to see the context behind it. I'm very thrilled to make the acquaintance of Michelle and looking forward to her talk next. Welcome Michelle, it's over to you. (upbeat music) >> I'm delighted to be with you all today to talk about thought leadership. And I'm so excited that you asked me to join you because today I get to be a quarterback. I always wanted to be one. And I thought this is about as close as I'm ever going to get. So I want to talk to you about quarterbacking, our digital revolution using insights data. And of course, as you said, leadership, first a little bit about myself, a little background, as I said, I always wanted to play football. And this is something that I wanted to do since I was a child. But when I grew up, girls didn't get to play football. I'm so happy that that's changing and girls are now doing all kinds of things that they didn't get to do before. Just this past weekend on an NFL field, we had a female coach on two sidelines and a female official on the field. I'm a lifelong fan and student of the game of football. I grew up in the South. You can tell from the accent. And in the South football is like a religion and you pick sides. I chose Auburn university working in the athletic department. So I'm Testament to you can start the journey can be long. It took me many, many years to make it into professional sports. I graduated in 1987 and my little brother, well, not actually not so little. He played offensive line for the Alabama Crimson Tide. And for those of you who know SCC football, you know this is a really big rivalry. And when you choose sides, your family is divided. So it's kind of fun for me to always tell the story that my dad knew his kid would make it to the NFL. He just bet on the wrong one. My career has been about bringing people together for memorable moments at some of America's most iconic brands, delivering memories and amazing experiences that delight from Universal Studios, Disney to my current position as CIO of the NFL. In this job I'm very privileged to have the opportunity to work with the team that gets to bring America's game to millions of people around the world. Often I'm asked to talk about how to create amazing experiences for fans, guests, or customers. But today I really wanted to focus on something different and talk to you about being behind the scenes and backstage because behind every event, every game, every awesome moment is execution, precise, repeatable execution. And most of my career has been behind the scenes doing just that assembling teams to execute these plans. And the key way that companies operate at these exceptional levels is making good decisions, the right decisions at the right time and based upon data so that you can translate the data into intelligence and be a data-driven culture. Using data and intelligence is an important way that world-class companies do differentiate themselves. And it's the lifeblood of collaboration and innovation. Teams that are working on delivering these kinds of world casts experiences are often seeking out and leveraging next-generation technologies and finding new ways to work. I've been fortunate to work across three decades of emerging experiences, which each required emerging technologies to execute a little bit first about Disney in the 90s, I was at Disney leading a project called destination Disney, which it's a data project. It was a data project, but it was CRM before CRM was even cool. And then certainly before anything like a data-driven culture was ever brought up, but way back then we were creating a digital backbone that enabled many technologies for the things that you see today, like the magic band, Disney's magical express. My career at Disney began in finance, but Disney was very good about rotating you around. And it was during one of these rotations that I became very passionate about data. I kind of became a pain in the butt to the IT team asking for data more and more data. And I learned that all of that valuable data was locked up in our systems. All of our point of sales systems, our reservation systems, our operation systems. And so I became a shadow IT person in marketing, ultimately leading to moving into IT. And I haven't looked back since. In the early two thousands, I was at universal studios theme park as their CIO preparing for and launching "The Wizarding World of Harry Potter" bringing one of history's most memorable characters to life required many new technologies and a lot of data. Our data and technologies were embedded into the rides and attractions. I mean, how do you really think a wan selects you at a wan shop. As today at the NFL? I am constantly challenged to do leading edge technologies, using things like sensors, AI, machine learning, and all new communication strategies and using data to drive everything from player performance, contracts, to where we build new stadiums and hold events with this year being the most challenging yet rewarding year in my career at the NFL. In the middle of a global pandemic, the way we are executing on our season is leveraging data from contract tracing devices joined with testing data, talk about data, actually enabling your business without it w wouldn't be having a season right now. I'm also on the board of directors of two public companies where data and collaboration are paramount. First RingCentral, it's a cloud based unified communications platform and collaboration with video message and phone all in one solution in the cloud and Quotient technologies whose product is actually data. The tagline at Quotient is the result in knowing I think that's really important because not all of us are data companies where your product is actually data, but we should operate more like your product is data. I'd also like to talk to you about four areas of things to think about as thought leaders in your companies. First just hit on it is change how to be a champion and a driver of change. Second, how do you use data to drive performance for your company and measure performance of your company? Third, how companies now require intense collaboration to operate. And finally, how much of this is accomplished through solid data driven decisions. First let's hit on change. I mean, it's evident today more than ever, that we are in an environment of extreme change. I mean, we've all been at this for years and as technologists we've known it, believed it, lived it and thankfully for the most part, knock on what we were prepared for it. But this year everyone's cheese was moved. All the people in the back rooms, IT, data architects and others were suddenly called to the forefront because a global pandemic has turned out to be the thing that is driving intense change in how people work and analyze their business. On March 13th, we closed our office at the NFL in the middle of preparing for one of our biggest events, our kickoff event, the 2020 draft. We went from planning a large event in Las Vegas under the bright lights, red carpet stage to smaller events in club facilities. And then ultimately to one where everyone coaches GM's prospects and even our commissioner were at home in their basements. And we only had a few weeks to figure it out. I found myself for the first time being in the live broadcast event space, talking about bungee jumping. This is really what it felt like. It was one in which no one felt comfortable because it had not been done before. But leading through this, I stepped up, but it was very scary. It was certainly very risky, but it ended up being all so rewarding when we did it. And as a result of this, some things will change forever. Second, managing performance. I mean, data should inform how you're doing and how to get your company to perform at it's level. Highest level. As an example, the NFL has always measured performance, obviously, and it is one of the purest examples of how performance directly impacts outcome. I mean, you can see performance on the field. You can see points being scored in stats, and you immediately know that impact those with the best stats usually when the games. The NFL has always recorded stats since the beginning of time here at the NFL a little this year is our 101 year and athletes ultimate success as a player has also always been greatly impacted by his stats. But what has changed for us is both how much more we can measure and the immediacy with which it can be measured. And I'm sure in your business it's the same. The amount of data you must have has got to have quadrupled and how fast you need it and how quickly you need to analyze it is so important. And it's very important to break the silos between the keys, to the data and the use of the data. Our next generation stats platform is taking data to a next level. It's powered by Amazon web services. And we gathered this data real-time from sensors that are on players' bodies. We gather it in real time, analyze it, display it online and on broadcast. And of course it's used to prepare week to week in addition to what is a normal coaching plan would be. We can now analyze, visualize route patterns, speed match-ups, et cetera. So much faster than ever before. We're continuing to roll out sensors too that will gather more and more information about a player's performance as it relates to their health and safety. The third trend is really, I think it's a big part of what we're feeling today and that is intense collaboration. And just for sort of historical purposes, it's important to think about for those of you that are IT professionals and developers, more than 10 years ago, agile practices began sweeping companies where small teams would work together rapidly in a very flexible, adaptive, and innovative way. And it proved to be transformational. However, today, of course, that is no longer just small teams, the next big wave of change. And we've seen it through this pandemic is that it's the whole enterprise that must collaborate and be agile. If I look back on my career, when I was at Disney, we owned everything 100%. We made a decision, we implemented it. We were a collaborative culture, but it was much easier to push change because you own the whole decision. If there was buy-in from the top down, you've got the people from the bottom up to do it and you executed. At Universal we were a joint venture. Our attractions and entertainment was licensed. Our hotels were owned and managed by other third parties. So influence and collaboration and how to share across companies became very important. And now here I am at the NFL and even the bigger ecosystem, we have 32 clubs that are all separate businesses. 31 different stadiums that are owned by a variety of people. We have licensees, we have sponsors, we have broadcast partners. So it seems that as my career has evolved, centralized control has gotten less and less and has been replaced by intense collaboration, not only within your own company, but across companies. The ability to work in a collaborative way across businesses and even other companies that has been a big key to my success in my career. I believe this whole vertical integration and big top-down decision-making is going by the wayside in favor of ecosystems that require cooperation yet competition to co-exist. I mean, the NFL is a great example of what we call co-op petition, which is cooperation and competition. We're in competition with each other, but we cooperate to make the company the best it can be. And at the heart of these items really are data driven decisions and culture. Data on its own isn't good enough. You must be able to turn it to insights. Partnerships between technology teams who usually hold the keys to the raw data and business units who have the knowledge to build the right decision models is key. If you're not already involved in this linkage, you should be. Data mining isn't new for sure. The availability of data is quadrupling and it's everywhere. How do you know what to even look at? How do you know where to begin? How do you know what questions to ask it's by using the tools that are available for visualization and analytics and knitting together strategies of the company. So it begins with first of all, making sure you do understand the strategy of the company. So in closing, just to wrap up a bit, many of you joined today, looking for thought leadership on how to be a change agent, a change champion, and how to lead through transformation. Some final thoughts are be brave and drive. Don't do the ride along program. It's very important to drive. Driving can be high risk, but it's also high reward. Embracing the uncertainty of what will happen is how you become brave. Get more and more comfortable with uncertainty, be calm and let data be your map on your journey. Thanks. >> Michelle, tank you so much. So you and I share a love of data and a love of football. You said you want to be the quarterback. I'm more an old line person. (Michelle and Cindi laughing) >> Well, then I can do my job without you. >> Great. And I'm getting the feeling now, Sudheesh is talking about bungee jumping. My vote is when we're past this pandemic, we both take them to the Delaware water gap and we do the cliff jumping. >> That sounds good, I'll watch. >> Yeah, you'll watch, okay. So Michelle, you have so many stakeholders when you're trying to prioritize the different voices. You have the players, you have the owners, you have the league, as you mentioned, the broadcasters, your partners here and football mamas like myself. How do you prioritize when there's so many different stakeholders that you need to satisfy? >> I think balancing across stakeholders starts with, aligning on a mission. And if you spend a lot of time understanding where everyone's coming from, and you can find the common thread that ties them all together, you sort of do get them to naturally prioritize their work. And I think that's very important. So for us, at the NFL and even at Disney, it was our core values and our core purpose, is so well known and when anything challenges that we're able to sort of lay that out. But as a change agent, you have to be very empathetic. And I would say empathy is probably your strongest skill if you're a change agent. And that means listening to every single stakeholder, even when they're yelling at you, even when they're telling you your technology doesn't work and you know that it's user error, or even when someone is just emotional about what's happening to them and that they're not comfortable with it. So I think being empathetic and having a mission and understanding it is sort of how I prioritize and balance. >> Yeah, empathy, a very popular word this year. I can imagine those coaches and owners yelling. So, thank you for your leadership here. So Michelle, I look forward to discussing this more with our other customers and disruptors joining us in a little bit. (upbeat music) So we're going to take a hard pivot now and go from football to Chernobyl. Chernobyl what went wrong? 1986, as the reactors were melting down, they had the data to say, this is going to be catastrophic. And yet the culture said, "no, we're perfect, hide it. "Don't dare tell anyone." Which meant they went ahead and had celebrations in Kiev. Even though that increased the exposure, the additional thousands getting cancer and 20,000 years before the ground around there can even be inhabited again, this is how powerful and detrimental a negative culture, a culture that is unable to confront the brutal facts that hides data. This is what we have to contend with. And this is why I want you to focus on having, fostering a data-driven culture. I don't want you to be a laggard. I want you to be a leader in using data to drive your digital transformation. So I'll talk about culture and technology. Is it really two sides of the same coin, real-world impacts and then some best practices you can use to and innovate your culture. Now, oftentimes I would talk about culture and I talk about technology. And recently a CDO said to me, "Cindi, I actually think this is two sides "of the same coin. "One reflects the other." What do you think? Let me walk you through this. So let's take a laggard. What does the technology look like? Is it based on 1990s BI and reporting largely parametrized reports, on premises data, warehouses, or not even that operational reports at best one enterprise data warehouse, very slow moving and collaboration is only email. What does that culture tell you? Maybe there's a lack of leadership to change, to do the hard work that Sudheesh referred to, or is there also a culture of fear, afraid of failure, resistance to change complacency. And sometimes that complacency it's not because people are lazy. It's because they've been so beaten down every time a new idea is presented. It's like, no we're measured on least cost to serve. So politics and distrust, whether it's between business and IT or individual stakeholders is the norm. So data is hoarded. Let's contrast that with a leader, a data and analytics leader, what is their technology look like? Augmented analytics search and AI driven insights, not on premises, but in the cloud and maybe multiple clouds. And the data is not in one place, but it's in a data Lake and in a data warehouse, a logical data warehouse. The collaboration is being a newer methods, whether it's Slack or teams allowing for that real time decisioning or investigating a particular data point. So what is the culture in the leaders? It's transparent and trust. There is a trust that data will not be used to punish that there is an ability to confront the bad news. It's innovation, valuing innovation in pursuit of the company goals, whether it's the best fan experience and player safety in the NFL or best serving your customers. It's innovative and collaborative. There's none of this. Oh, well, I didn't invent that. I'm not going to look at that. There's still pride of ownership, but it's collaborating to get to a better place faster. And people feel empowered to present new ideas to fail fast, and they're energized knowing that they're using the best technology and innovating at the pace that business requires. So data is democratized. And democratized, not just for power users or analysts, but really at the point of impact what we like to call the new decision-makers or really the frontline workers. So Harvard business review partnered with us to develop this study to say, just how important is this? We've been working at BI and analytics as an industry for more than 20 years. Why is it not at the front lines? Whether it's a doctor, a nurse, a coach, a supply chain manager, a warehouse manager, a financial services advisor. Everyone said that if our 87% said, they would be more successful if frontline workers were empowered with data driven insights, but they recognize they need new technology to be able to do that. It's not about learning hard tools. The sad reality, only 20% of organizations are actually doing this. These are the data-driven leaders. So this is the culture in technology. How did we get here? It's because state-of-the-art keeps changing. So the first-generation BI and analytics platforms were deployed on premises on small datasets, really just taking data out of ERP systems that were also on premises. And state-of-the-art was maybe getting a management report, an operational report. Over time visual-based data discovery vendors disrupted these traditional BI vendors, empowering now analysts to create visualizations with the flexibility on a desktop, sometimes larger data, sometimes coming from a data warehouse. The current state of the art though, Gartner calls it augmented analytics at ThoughtSpot, we call it search and AI driven analytics. And this was pioneered for large scale datasets, whether it's on premises or leveraging the cloud data warehouses. And I think this is an important point. Oftentimes you, the data and analytics leaders will look at these two components separately, but you have to look at the BI and analytics tier in lockstep with your data architectures to really get to the granular insights and to leverage the capabilities of AI. Now, if you've never seen ThoughtSpot, I'll just show you what this looks like. Instead of somebody hard coding, a report it's typing in search keywords and very robust keywords contains rank top bottom, getting to a visual visualization that then can be pinned to an existing Pin board that might also contain insights generated by an AI engine. So it's easy enough for that new decision maker, the business user, the non analyst to create themselves. Modernizing the data and analytics portfolio is hard because the pace of change has accelerated. You use to be able to create an investment place a bet for maybe 10 years, a few years ago, that time horizon was five years, now it's maybe three years and the time to maturity has also accelerated. So you have these different components, the search and AI tier, the data science tier, data preparation and virtualization. But I would also say equally important is the cloud data warehouse and pay attention to how well these analytics tools can unlock the value in these cloud data warehouses. So ThoughtSpot was the first to market with search and AI driven insights. Competitors have followed suit, but be careful if you look at products like power BI or SAP analytics cloud, they might demo well, but do they let you get to all the data without moving it in products like Snowflake, Amazon Redshift, or Azure synapse or Google big query, they do not. They require you to move it into a smaller in memory engine. So it's important how well these new products inter operate. the pace of change, its acceleration Gartner recently predicted that by 2022, 65% of analytical queries will be generated using search or NLP or even AI. And that is roughly three times the prediction they had just a couple years ago. So let's talk about the real world impact of culture. And if you read any of my books or used any of the maturity models out there, whether the Gartner IT score that I worked on, or the data warehousing Institute also has the money surety model. We talk about these five pillars to really become data-driven. As Michelle, I spoke about it's focusing on the business outcomes, leveraging all the data, including new data sources, it's the talent, the people, the technology, and also the processes. And often when I would talk about the people and the talent, I would lump the culture as part of that. But in the last year, as I've traveled the world and done these digital events for Thought leaders, you have told me now culture is absolutely so important. And so we've pulled it out as a separate pillar. And in fact, in polls that we've done in these events, look at how much more important culture is as a barrier to becoming data-driven it's three times as important as any of these other pillars. That's how critical it is. And let's take an example of where you can have great data, but if you don't have the right culture, there's devastating impacts. And I will say, I have been a loyal customer of Wells Fargo for more than 20 years. But look at what happened in the face of negative news with data, it said, "hey, we're not doing good cross selling, "customers do not have both a checking account "and a credit card and a savings account and a mortgage." They opened fake accounts facing billions in fines, change in leadership that even the CEO attributed to a toxic sales culture, and they're trying to fix this. But even recently there's been additional employee backlash saying the culture has not changed. Let's contrast that with some positive examples, Medtronic, a worldwide company in 150 countries around the world. They may not be a household name to you, but if you have a loved one or yourself, you have a pacemaker, spinal implant diabetes, you know this brand. And at the start of COVID when they knew their business would be slowing down, because hospitals would only be able to take care of COVID patients. They took the bold move of making their IP for ventilators publicly available. That is the power of a positive culture. Or Verizon, a major telecom organization looking at late payments of their customers. And even though the U.S federal government said, "well, you can't turn them off. They said, "we'll extend that even beyond "the mandated guidelines." And facing a slow down in the business because of the tough economy, they said, you know what? "We will spend the time up skilling our people, "giving them the time to learn more "about the future of work, the skills and data "and analytics," for 20,000 of their employees, rather than furloughing them. That is the power of a positive culture. So how can you transform your culture to the best in class? I'll give you three suggestions, bring in a change agent, identify the relevance, or I like to call it WIFM and organize for collaboration. So the CDO, whatever your title is, chief analytics officer, chief digital officer, you are the most important change agent. And this is where you will hear that oftentimes a change agent has to come from outside the organization. So this is where, for example, in Europe, you have the CDO of Just Eat a takeout food delivery organization coming from the airline industry or in Australia, National Australian bank, taking a CDO within the same sector from TD bank going to NAB. So these change agents come in disrupt. It's a hard job. As one of you said to me, it often feels like Sisyphus. I make one step forward and I get knocked down again. I get pushed back. It is not for the faint of heart, but it's the most important part of your job. The other thing I'll talk about is WIFM. What is in it for me? And this is really about understanding the motivation, the relevance that data has for everyone on the frontline, as well as those analysts, as well as the executives. So if we're talking about players in the NFL, they want to perform better and they want to stay safe. That is why data matters to them. If we're talking about financial services, this may be a wealth management advisor. Okay we could say commissions, but it's really helping people have their dreams come true, whether it's putting their children through college or being able to retire without having to work multiple jobs still into your 70s or 80s for the teachers, teachers, you ask them about data. They'll say we don't, we don't need that. I care about the student. So if you can use data to help a student perform better, that is WIFM. And sometimes we spend so much time talking the technology, we forget what is the value we're trying to deliver with it. And we forget the impact on the people that it does require change. In fact, the Harvard business review study found that 44% said lack of change management is the biggest barrier to leveraging both new technology, but also being empowered to act on those data-driven insights. The third point organize for collaboration. This does require diversity of thought, but also bringing the technology, the data and the business people together. Now there's not a single one size fits all model for data and analytics. At one point in time, even having a BICC, a BI competency center was considered state-of-the-art. Now for the biggest impact what I recommend is that you have a federated model centralized for economies of scale. That could be the common data, but then in bed, these evangelists, these analysts of the future within every business unit, every functional domain. And as you see this top bar, all models are possible, but the hybrid model has the most impact, the most leaders. So as we look ahead to the months ahead, to the year ahead an exciting time, because data is helping organizations better navigate a tough economy, lock in the customer loyalty. And I look forward to seeing how you foster that culture that's collaborative with empathy and bring the best of technology, leveraging the cloud, all your data. So thank you for joining us at Thought Leaders. And next I'm pleased to introduce our first change agent, Tom Mazzaferro chief data officer of Western union. And before joining Western union, Tom made his Mark at HSBC and JPMorgan Chase spearheading digital innovation in technology, operations, risk compliance, and retail banking. Tom, thank you so much for joining us today. (upbeat music) >> Very happy to be here and looking forward to talking to all of you today. So as we look to move organizations to a data-driven, capability into the future, there is a lot that needs to be done on the data side, but also how does data connect and enable different business teams and technology teams into the future. As you look across, our data ecosystems and our platforms and how we modernize that to the cloud in the future, it all needs to basically work together, right? To really be able to drive and over the shift from a data standpoint, into the future, that includes being able to have the right information with the right quality of data, at the right time to drive informed business decisions, to drive the business forward. As part of that, we actually have partnered with ThoughtSpot, to actually bring in the technology to help us drive that as part of that partnership. And it's how we've looked to integrate it into our overall business as a whole we've looked at how do we make sure that our business and our professional lives right, are enabled in the same ways as our personal lives. So for example, in your personal lives, when you want to go and find something out, what do you do? You go onto google.com or you go on to Bing we go onto Yahoo and you search for what you want search to find and answer. ThoughtSpot for us as the same thing, but in the business world. So using ThoughtSpot and other AI capability it's allowed us to actually, enable our overall business teams in our company to actually have our information at our fingertips. So rather than having to go and talk to someone or an engineer to go pull information or pull data, we actually can have the end-users or the business executives, right. Search for what they need, what they want at the exact time that action need it to go and drive the business forward. This is truly one of those transformational things that we've put in place. On top of that, we are on the journey to modernize our larger ecosystem as a whole. That includes modernizing our underlying data warehouses, our technology, or our Eloqua environments. And as we move that, we've actually picked two of our cloud providers going to AWS and GCP. We've also adopted Snowflake to really drive and to organize our information and our data then drive these new solutions and capabilities forward. So they portion of us though is culture. So how do we engage with the business teams and bring the IT teams together to really drive these holistic end to end solutions and capabilities to really support the actual business into the future? That's one of the keys here, as we look to modernize and to really enhance our organizations to become data-driven, this is the key. If you can really start to provide answers to business questions before they're even being asked and to predict based upon different economic trends or different trends in your business, what does this is maybe be made and actually provide those answers to the business teams before they're even asking for it, that is really becoming a data-driven organization. And as part of that, it's really then enables the business to act quickly and take advantage of opportunities as they come in based upon, industries based upon markets, based upon products, solutions, or partnerships into the future. These are really some of the keys that become crucial as you move forward, right, into this new age, especially with COVID. With COVID now taking place across the world, right? Many of these markets, many of these digital transformations are accelerating and are changing rapidly to accommodate and to support customers in these very difficult times, as part of that, you need to make sure you have the right underlying foundation ecosystems and solutions to really drive those capabilities and those solutions forward. As we go through this journey, both of my career, but also each of your careers into the future, right? It also needs to evolve, right? Technology has changed so drastically in the last 10 years, and that change is only accelerating. So as part of that, you have to make sure that you stay up to speed, up to date with new technology changes both on the platform standpoint tools, but also what do our customers want? What do our customers need and how do we then service them with our information, with our data, with our platform and with our products and our services to meet those needs and to really support and service those customers into the future. This is all around becoming a more data organization such as how do you use your data to support the current business lines, but how do you actually use your information, your data to actually put a better support your customers, better support your business, better support your employees, your operations teams, and so forth, and really creating that full integration in that ecosystem is really when you start to get large dividends from this investments into the future. But that being said, hope you enjoy the segment on how to become and how to drive it data driven organization. And, looking forward to talking to you again soon. Thank you. >> Tom that was great thanks so much. Now I'm going to have to brag on you for a second as a change agent you've come in disrupted and how long have you been at Western union? >> Only nine months, so just started this year, but, doing some great opportunities and great changes. And we have a lot more to go, but, we're really driving things forward in partnership with our business teams and our colleagues to support those customers going forward. >> Tom, thank you so much. That was wonderful. And now I'm excited to introduce you to Gustavo Canton, a change agent that I've had the pleasure of working with meeting in Europe, and he is a serial change agent, most recently with Schneider electric, but even going back to Sam's clubs, Gustavo welcome. (upbeat music) >> So, hey everyone, my name is Gustavo Canton and thank you so much, Cindi, for the intro, as you mentioned, doing transformations is high effort, high reward situation. I have empowered many transformations and I have led many transformations. And what I can tell you is that it's really hard to predict the future, but if you have a North star and where you're going, the one thing that I want you to take away from this discussion today is that you need to be bold to evolve. And so in today, I'm going to be talking about culture and data, and I'm going to break this down in four areas. How do we get started barriers or opportunities as I see it, the value of AI, and also, how do you communicate, especially now in the workforce of today with so many different generations, you need to make sure that you are communicating in ways that are non-traditional sometimes. And so how do we get started? So I think the answer to that is you have to start for you yourself as a leader and stay tuned. And by that, I mean, you need to understand not only what is happening in your function or your field, but you have to be varying into what is happening in society, socioeconomically speaking wellbeing. The common example is a great example. And for me personally, it's an opportunity because the one core value that I have is well-being, I believe that for human potential, for customers and communities to grow wellbeing should be at the center of every decision. And as somebody mentioned is great to be, stay in tune and have the skillset and the courage. But for me personally, to be honest, to have this courage is not about not being afraid. You're always afraid when you're making big changes when you're swimming upstream, but what gives me the courage is the empathy part. Like I think empathy is a huge component because every time I go into an organization or a function, I try to listen very attentively to the needs of the business and what the leaders are trying to do. What I do it thinking about the mission of how do I make change for the bigger, workforce? for the bigger good. Despite this fact that this might have a perhaps implication on my own self-interest in my career, right? Because you have to have that courage sometimes to make choices that I know we'll see in politically speaking, what are the right thing to do? And you have to push through it. And you have to push through it. So the bottom line for me is that I don't think they're transforming fast enough. And the reality is I speak with a lot of leaders and we have seen stories in the past. And what they show is that if you look at the four main barriers that are basically keeping us behind budget, inability to act cultural issues, politics, and lack of alignment, those are the top four. But the interesting thing is that as Cindi has mentioned, these topics culture is actually gaining, gaining more and more traction. And in 2018, there was a story from HBR and it was about 45%. I believe today it's about 55%, 60% of respondents say that this is the main area that we need to focus on. So again, for all those leaders and all the executives who understand and are aware that we need to transform, commit to the transformation and set a state, deadline to say, "hey, in two years, we're going to make this happen. "What do we need to do to empower and enable "this change engines to make it happen?" You need to make the tough choices. And so to me, when I speak about being bold is about making the right choices now. So I'll give you samples of some of the roadblocks that I went through as I think transformation most recently, as Cindi mentioned in Schneider. There are three main areas, legacy mindset. And what that means is that we've been doing this in a specific way for a long time and here is how we have been successful what was working the past is not going to work now. The opportunity there is that there is a lot of leaders who have a digital mindset and there're up and coming leaders that are not yet fully developed. We need to mentor those leaders and take bets on some of these talent, including young talent. We cannot be thinking in the past and just wait for people, three to five years for them to develop because the world is going to in a way that is super fast. The second area, and this is specifically to implementation of AI is very interesting to me because just example that I have with ThoughtSpot, right, we went to implementation and a lot of the way is the IT team function of the leaders look at technology, they look at it from the prism of the prior all success criteria for the traditional Bi's. And that's not going to work. Again the opportunity here is that you need to really find what successful look like. In my case, I want the user experience of our workforce to be the same as user experience you have at home is a very simple concept. And so we need to think about how do we gain the user experience with this augmented analytics tools and then work backwards to have the right talent processes and technology to enable that. And finally, with COVID a lot of pressuring organizations, and companies to do more with less. And the solution that most leaders I see are taking is to just minimize costs, sometimes in cut budget, we have to do the opposite. We have to actually invest some growth areas, but do it by business question. Don't do it by function. If you actually invest in these kind of solutions, if you actually invest on developing your talent, your leadership to see more digitally, if you actually invest on fixing your data platform, it's not just an incremental cost. It's actually this investment is going to offset all those hidden costs and inefficiencies that you have on your system, because people are doing a lot of work and working very hard, but it's not efficiency, and it's not working in the way that you might want to work. So there is a lot of opportunity there. And you just to put into some perspective, there have studies in the past about, how do we kind of measure the impact of data. And obviously this is going to vary by your organization maturity, is going to, there's going to be a lot of factors. I've been in companies who have very clean, good data to work with. And I think with companies that we have to start basically from scratch. So it all depends on your maturity level, but in this study, what I think is interesting is they try to put attack line or attack price to what is the cost of incomplete data. So in this case, it's about 10 times as much to complete a unit of work when you have data that is flawed as opposed to have perfect data. So let me put that just in perspective, just as an example, right? Imagine you are trying to do something and you have to do 100 things in a project, and each time you do something, it's going to cost you a dollar. So if you have perfect data, the total cost of that project might be $100. But now let's say you have any percent perfect data and 20% flawed data by using this assumption that flawed data is 10 times as costly as perfect data. Your total costs now becomes $280 as opposed to $100. This is just for you to really think about as a CIO CTO, CHRO CEO, are we really paying attention and really closing the gaps that we have on our data infrastructure. If we don't do that, it's hard sometimes to see the snowball effect or to measure the overall impact. But as you can tell the price that goes up very, very quickly. So now, if I were to say, how do I communicate this? Or how do I break through some of these challenges or some of these various, right. I think the key is I am in analytics. I know statistics obviously, and love modeling and data and optimization theory and all that stuff. That's what I came to analytics. But now as a leader and as a change agent, I need to speak about value. And in this case, for example, for Schneider, there was this tagline called free up your energy. So the number one thing that they were asking from the analytics team was actually efficiency, which to me was very interesting. But once I understood that I understood what kind of language to use, how to connect it to the overall strategy and basically how to bring in the, the right leaders, because you need to focus on the leaders that you're going to make the most progress. Again, low effort, high value. You need to make sure you centralize all the data as you can. You need to bring in some kind of augmented analytics solution. And finally you need to make it super simple for the, in this case, I was working with the HR teams in other areas, so they can have access to one portal. They don't have to be confused in looking for 10 different places to find information. I think if you can actually have those four foundational pillars, obviously under the guise of having a data-driven culture, that's when you can actually make the impact. So in our case, it was about three years total transformation, but it was two years for this component of augmented analytics. It took about two years to talk to IT get leadership support, find the budgeting, get everybody on board, make sure the safe criteria was correct. And we call this initiative, the people analytics portal, it was actually launched in July of this year. And we were very excited and the audience was very excited to do this. In this case, we did our pilot in North America for many, many manufacturers. But one thing that is really important is as you bring along your audience on this, you're going from Excel, in some cases or Tableau to other tools like, ThoughtSpot, you need to really explain them what is the difference and how these tools can truly replace, some of the spreadsheets or some of the views that you might have on these other kind of tools. Again, Tableau, I think it's a really good tool. There are other many tools that you might have in your toolkit. But in my case, personally, I feel that you need to have one portal going back to Cindi's point. I really truly enable the end user. And I feel that this is the right solution for us, right? And I will show you some of the findings that we had in the pilot in the last two months. So this was a huge victory, and I will tell you why, because it took a lot of effort for us to get to the station. Like I said, it's been years for us to kind of lay the foundation, get the leadership, and shaping culture so people can understand why you truly need to invest on (indistinct) analytics. And so what I'm showing here is an example of how do we use basically, a tool to capture in video the qualitative findings that we had, plus the quantitative insights that we have. So in this case, our preliminary results based on our ambition for three main metrics, hours saved user experience and adoption. So for hours saved or a mission was to have 10 hours per week per employee save on average user experience, or ambition was 4.5. And adoption, 80%. In just two months, two months and a half of the pilot, we were able to achieve five hours per week per employee savings. Our user experience for 4.3 out of five and adoption of 60%. Really, really amazing work. But again, it takes a lot of collaboration for us to get to the stage from IT, legal, communications, obviously the operations teams and the users in HR safety and other areas that might be, basically stakeholders in this whole process. So just to summarize this kind of effort takes a lot of energy. You are a change agent. You need to have a courage to make the decision and understand that I feel that in this day and age, with all this disruption happening, we don't have a choice. We have to take the risk, right? And in this case, I feel a lot of satisfaction in how we were able to gain all these very source for this organization. And that gave me the confidence to know that the work has been done and we are now in a different stage for the organization. And so for me, it to say, thank you for everybody who has believed, obviously in our vision, everybody who has believe in the word that we were trying to do and to make the life of four workforce or customers or in community better. As you can tell, there is a lot of effort. There is a lot of collaboration that is needed to do something like this. In the end, I feel very satisfied. With the accomplishments of this transformation, and I just want to tell for you, if you are going right now in a moment that you feel that you have to swim upstream what would mentors, what would people in this industry that can help you out and guide you on this kind of a transformation is not easy to do is high effort, but is well worth it. And with that said, I hope you are well, and it's been a pleasure talking to you. Talk to you soon, take care. >> Thank you, Gustavo, that was amazing. All right, let's go to the panel. (air whooshing) >> Okay, now we're going to go into the panel and bring Cindi, Michelle, Tom, and Gustavo back and have an open discussion. And I think we can all agree how valuable it is to hear from practitioners. And I want to thank the panel for sharing their knowledge with the community. And one common challenge that I heard you all talk about was bringing your leadership and your teams along on the journey with you. We talk about this all the time, and it is critical to have support from the top. Why? Because it directs the middle and then it enables bottoms up innovation effects from the cultural transformation that you guys all talked about. It seems like another common theme we heard is that you all prioritize database decision-making in your organizations and you combine two of your most valuable assets to do that and create leverage, employees on the front lines. And of course the data. And as you rightly pointed out, Tom, the pandemic has accelerated the need for really leaning into this. The old saying, if it ain't broke don't fix it. Well COVID is broken everything. And it's great to hear from our experts, how to move forward. So let's get right into it. So Gustavo, let's start with you if I'm an aspiring change agent and let's say I'm a budding data leader. What do I need to start doing? What habits do I need to create for long lasting success? >> I think curiosity is very important. You need to be, like I say, in tune to what is happening, not only in your specific field, like I have a passion for analytics, I can do this for 50 years plus, but I think you need to understand wellbeing other areas across not only a specific business, as you know I come from, Sam's club Walmart, retail, I mean energy management technology. So you have to try to push yourself and basically go out of your comfort zone. I mean, if you are staying in your comfort zone and you want to use lean continuous improvement, that's just going to take you so far. What you have to do is, and that's what I try to do is I try to go into areas, businesses, and transformation that make me stretch and develop as a leader. That's what I'm looking to do so I can help transform the functions organizations and do the change management, change of mindset required for these kinds of efforts. >> Michelle, you're at the intersection of tech and sports and what a great combination, but they're both typically male oriented fields. I mean, we've talked a little bit about how that's changing, but two questions. Tell us how you found your voice and talk about why diversity matters so much more than ever now. >> No, I found my voice really as a young girl, and I think I had such amazing support from men in my life. And I think the support and sponsorship as well as sort of mentorship along the way, I've had amazing male mentors who have helped me understand that my voice is just as important as anyone else's. I mean, I have often heard, and I think it's been written about that a woman has to believe they'll 100% master topic before they'll talk about it where a man can feel much less mastery and go on and on. So I was that way as well. And I learned just by watching and being open, to have my voice. And honestly at times demand a seat at the table, which can be very uncomfortable. And you really do need those types of, support networks within an organization. And diversity of course is important and it has always been. But I think if anything, we're seeing in this country right now is that diversity among all types of categories is front and center. And we're realizing that we don't all think alike. We've always known this, but we're now talking about things that we never really talked about before. And we can't let this moment go unchecked and on, and not change how we operate. So having diverse voices within your company and in the field of tech and sports, I am often the first and only I'm was the first, CIO at the NFL, the first female senior executive. It was fun to be the first, but it's also, very challenging. And my responsibility is to just make sure that, I don't leave anyone behind and make sure that I leave it good for the next generation. >> Well, thank you for that. That is inspiring. And Cindi, you love data and the data's pretty clear that diversity is a good business, but I wonder if you can add your perspectives to this conversation? >> Yeah, so Michelle has a new fan here because she has found her voice. I'm still working on finding mine. And it's interesting because I was raised by my dad, a single dad. So he did teach me how to work in a predominantly male environment, but why I think diversity matters more now than ever before. And this is by gender, by race, by age, by just different ways of working in thinking is because as we automate things with AI, if we do not have diverse teams looking at the data and the models and how they're applied, we risk having bias at scale. So this is why I think I don't care what type of minority you are finding your voice, having a seat at the table and just believing in the impact of your work has never been more important. And as Michelle said more possible. >> Great perspectives, thank you. Tom I want to go to you. I mean, I feel like everybody in our businesses in some way, shape or form become a COVID expert, but what's been the impact of the pandemic on your organization's digital transformation plans? >> We've seen a massive growth actually in a digital business over the last, 12 months, really, even in celebration, right? Once COVID hit, we really saw that in the 200 countries and territories that we operate in today and service our customers, today, that there's been a huge need, right? To send money, to support family, to support, friends and support loved ones across the world. And as part of that we are very, honored to get to support those customers that we, across all the centers today. But as part of that acceleration we need to make sure that we had the right architecture and the right platforms to basically scale, right, to basically support and provide the right kind of security for our customers going forward. So as part of that, we did do some pivots and we did accelerate some of our plans on digital to help support that overall growth coming in and to support our customers going forward, because there were these times during this pandemic, right? This is the most important time. And we need to support those that we love and those that we care about and doing that it's one of those ways is actually by sending money to them, support them financially. And that's where, really our part of that our services come into play that we really support those families. So it was really a great opportunity for us to really support and really bring some of our products to this level and supporting our business going forward. >> Awesome, thank you. Now I want to come back to Gustavo, Tom I'd love for you to chime in too. Did you guys ever think like you were, you were pushing the envelope too much in doing things with data or the technology that was just maybe too bold, maybe you felt like at some point it was failing or you're pushing your people too hard. Can you share that experience and how you got through it? >> Yeah, the way I look at it is, again, whenever I go to an organization, I ask the question, hey, how fast you would like transform. And, based on the agreements from the leadership and the vision that we want to take place, I take decisions. And I collaborate in a specific way now, in the case of COVID, for example, right. It forces us to remove silos and collaborate in a faster way. So to me, it was an opportunity to actually integrate with other areas and drive decisions faster, but make no mistake about it. When you are doing a transformation, you are obviously trying to do things faster than sometimes people are comfortable doing, and you need to be okay with that. Sometimes you need to be okay with tension, or you need to be okay debating points or making repetitive business cases until people connect with the decision because you understand, and you are seeing that, "hey, the CEO is making a one two year, efficiency goal. "The only way for us to really do more with less "is for us to continue this path. "We cannot just stay with the status quo. "We need to find a way to accelerate the transformation." That's the way I see it. >> How about you Tom, we were talking earlier with Sudheesh and Cindi, about that bungee jumping moment. What could you share? >> Yeah, I think you hit upon it, right now, the pace of change with the slowest pace that you see for the rest of your career. So as part of that, right, that's what I tell my team is that you need to be, you need to feel comfortable being uncomfortable. I mean, that we have to be able to basically scale, right, expand and support that the ever-changing needs in the marketplace and industry our customers today, and that pace of change that's happening, right. And what customers are asking for and the competition in the marketplace, it's only going to accelerate. So as part of that, as you look at what, how you're operating today in your current business model, right. Things are only going to get faster. So you have to plan into a line into drive the agile transformation so that you can scale even faster in the future. So as part of that, that's what we're putting in place here, right, is how do we create that underlying framework and foundation that allows the organization to basically continue to scale and evolve into the future? >> Yeah, we're definitely out of our comfort zones, but we're getting comfortable with it. So, Cindi, last question, you've worked with hundreds of organizations, and I got to believe that, some of the advice you gave when you were at Gartner, which is pre COVID, maybe sometimes clients didn't always act on it. They're not on my watch for whatever variety of reasons, but it's being forced on them now. But knowing what you know now that we're all in this isolation economy, how would you say that advice has changed? Has it changed? What's your number one action and recommendation today? >> Yeah, well, first off, Tom just freaked me out. What do you mean? This is the slowest ever even six months ago I was saying the pace of change in data and analytics is frenetic. So, but I think you're right, Tom, the business and the technology together is forcing this change. Now, Dave, to answer your question, I would say the one bit of advice, maybe I was a little more, very aware of the power and politics and how to bring people along in a way that they are comfortable. And now I think it's, you know what you can't get comfortable. In fact, we know that the organizations that were already in the cloud have been able to respond and pivot faster. So if you really want to survive as Tom and Gustavo said, get used to being uncomfortable, the power and politics are going to happen. Break the rules, get used to that and be bold. Do not be afraid to tell somebody they're wrong and they're not moving fast enough. I do think you have to do that with empathy, as Michelle said, and Gustavo, I think that's one of the key words today besides the bungee jumping. So I want to know where's the dish going to go bungee jumping. >> Guys fantastic discussion, really. Thanks again to all the panelists and the guests. It was really a pleasure speaking with you today. Really virtually all of the leaders that I've spoken to in the Cube program. Recently, they tell me that the pandemic is accelerating so many things, whether it's new ways to work, we heard about new security models and obviously the need for cloud. I mean, all of these things are driving true enterprise wide digital transformation, not just, as I said before, lip service. Sometimes we minimize the importance and the challenge of building culture and in making this transformation possible. But when it's done, right, the right culture is going to deliver tremendous results. Yeah, what does that mean getting it right? Everybody's trying to get it right. My biggest takeaway today is it means making data part of the DNA of your organization. And that means making it accessible to the people in your organization that are empowered to make decisions, decisions that can drive new revenue, cut costs, speed access to critical care, whatever the mission is of your organization. Data can create insights and informed decisions that drive value. Okay. Let's bring back Sudheesh and wrap things up. Sudheesh, please bring us home. >> Thank you. Thank you, Dave. Thank you, the Cube team, and thank goes to all of our customers and partners who joined us and thanks to all of you for spending the time with us. I want to do three quick things and then close it off. The first thing is I want to summarize the key takeaways that I had from all four of our distinguished speakers. First, Michelle, I will simply put it. She said it really well. That is be brave and drive. Don't go for a drive along. That is such an important point. Oftentimes, you know that I think that you have to do to make the positive change that you want to see happen but you wait for someone else to do it, not just, why not you? Why don't you be the one making that change happen? That's the thing that I've picked up from Michelle's talk. Cindi talked about finding the importance of finding your voice. Taking that chair, whether it's available or not, and making sure that your ideas, your voices are heard, and if it requires some force, then apply that force. Make sure your ideas are heard. Gustavo talked about the importance of building consensus, not going at things all alone sometimes building the importance of building the quorum. And that is critical because if you want the changes to last, you want to make sure that the organization is fully behind it. Tom, instead of a single takeaway, what I was inspired by is the fact that a company that is 170 years old, 170 years old, 200 companies and 200 countries they're operating in. And they were able to make the change that is necessary through this difficult time. So in a matter of months, if they could do it, anyone could. The second thing I want to do is to leave you with a takeaway that is I would like you to go to topspot.com/nfl because our team has made an app for NFL on Snowflake. I think you will find this interesting now that you are inspired and excited because of Michelle's talk. And the last thing is please go to thoughtspot.com/beyond our global user conference is happening in this December. We would love to have you join us. It's again, virtual, you can join from anywhere. We are expecting anywhere from five to 10,000 people, and we would love to have you join and see what we've been up to since last year. We have a lot of amazing things in store for you, our customers, our partners, our collaborators, they will be coming and sharing. We'll be sharing things that we've have been working to release something that will come out next year. And also some of the crazy ideas our engineers have been cooking up. All of those things will be available for you at the Thought Spot Beyond. Thank you. Thank you so much.

>> connecting with thought leaders all around the world, this is a CUBE Conversation. Hi, everybody this is Dave Vellante of theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SEER model, the most popular SEER model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O our open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these great Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

>> Starting the record, Dave in five, four, three. Hi, everybody this is Dave Vellante, theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SaaS model, the most popular SaaS model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O or open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these Greek Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

(upbeat music) >> Announcer: From our studios, in the heart of Silicon Valley, Palo Alto, California, this is a Cube conversation. >> Everyone, welcome to this special Cube conversation here in Palo Alto Cube studios. I'm John Furrier, host of the Cube. We have special breaking news here, with Sri Ambati who is the founder and CEO of H2O.ai with big funding news. Great to see you Cube alumni, hot startup, you got some hot funding news, share with us. >> We are very excited to announce our Series D. Goldman Sachs, one of our leading customers and Ping An from China are leading our round. It's a round of $72 million, and bringing our total fundraise to 147. This is an endorsement of their support of our mission to democratize AI and an endorsement of the amazing teamwork behind the company and its customer centricity. Customers have now come to lead two of our rounds. Last round was Series C led by Wells Fargo and NVIDIA and I think it just goes to say how critical a thing we are for their success in AI. >> Well congratulations, I've been watching you guys build this company from scratch, we've had many conversations going back to 2013, '14 on The Cube. You call it-- >> You covered us long before. >> You guys were always on the wave, and you really created a category, this is a new category that Cloud 2.0 is creating which is a DevOps mindset, entrepreneurial mindset, creating a category to enable people to have the kind of infrastructure and tooling and software to enable them to do all the heavy lifting of AI without doing the heavy lifting. As the quote for cloud is, that Amazon always quotes is you do all of the undifferentiated heavy lifting that's required to stand up stuff and then provide tooling for the heavy differentiated lifting to make it easy to use. This has been a key thing. Has that been the-- >> Customers have be core to our, company building. H2O is here to build an amazing piece of innovation and technology and innovation is not new for Silicon Valley, as you know. But I think innovation, with a purpose and with a focus of customer success is something we represent and that's been kind of the key north finder for us. In terms of making things simpler, when we started, it was a grassroots movement in open source and we wanted the mind share of millions of users worldwide and that mind share got us a lot of feedback. And that feedback is how we then built the second generation of the product lines, which is driverless AI. We are also announcing our mission to make every company an AI company, this funding will power that transformation of several businesses that can then go on to build the AI superpower. >> And certainly, cloud computing, more compute more elastic resources is always a great tailwind. What are you guys going to do with the funding in terms of focus? >> You mentioned cloud which is a great story. We're obviously going to make things easier for folks who are doing the cloud, but they are the largest players, as well, Google, Microsoft, Amazon. They're right there, trying to innovate. AI is at the center of every software moment because AI eating software, software is eating the world. And so, all the software players are right there, trying to build a large AI opportunity for the world and we think in ecosystems, not just empires. So our mission is to uplift the entire AI to the place where businesses can use it, verticalize it, build new products, globalize. We are building our sales and marketing efforts now with a much bigger, faster systems-- >> So a lot of, go to market expansion, more customer focus. More field sales and support kind of thing. >> Build our center for AI research in Prague, within the CND, now we are building it in Chennai and Ottawa, and so globalizing the operation, going to China, going to build focus in Asia as well. >> So nice step up on funding at 72 million, you said? >> 72.5 million. >> 72.5 million, that's almost double what you've raised to date, nice kickup. So global expansion, nice philosophy. That's important to you guys, isn't it? >> The world has become a small village. There's no changing that, and data is global. Things are a wide global trend, it's amazing to see that AI is not just transforming the US, it's also transforming China, it's also transforming India. It's transforming Africa. Pay through mobile is a very common theme worldwide and I think data is being collected globally. I think there is no way to unbox it and box it back to a small place, so our vision is very borderless and global and we want the AI companies of the valley to also compete in a global arena and I think that's kind of why we think it's important to be-- >> Love competition, that's certainly going to force everyone to be more open. I got to ask you about the role of the developer. I love the democratization, putting AI in the hands of everybody, it's a great mission. You guys do a lot of AI for Good efforts. So congratulations on that, but how does this change the nature of the developer, because you're seeing with cloud and DevOps, developers are becoming closer to the front lines, they're becoming kingmakers. They're becoming really, really important. So the role of the developer is important. How do you change that role, if any. How do you expand it, what happens? >> There are two important transformations happening right now in the tech world. One is the role of data scientists and the role of the software engineer. Right, so they're coming closer in many ways, in actually in some of the newer places, software engineers are deploying data science models, data scientists are deploying software engineering. So Python has been a good new language, the new languages that are coming up that help that happen more closely. Software engineering as we know it, which was looking at data creating the rules and the logic that runs a program is now being automated to a degree where that logic is being generated from data using data science. So that's where the brains behind how programs run how computers build is now being, is AI inside. And so that's where the world is transforming, software engineers now get to do a lot more with a lot less of tinkering on a daily basis for little modules. They can probably build a whole slew of an application what would take 18 months to build is now compressing into 18 weeks or 18 days. >> Sri, I love how you talk about software engineering and data scientists, very specific. I was having a debate with my young son around what is computer science was the question. Well, computer science is the study of computers the science of computers. It used to be if you were a CS or a comp sci major which is not cool to say anymore but, when you were a computer science major, you were really a software engineer, that was the discipline. Now, computer science as a field has spread so far and so broad, you've got software engineering you've got data science, you have newer roles are emerging. But that brings up the question I want to put to you which is, the whole idea of, I'm a full stack developer. Well, if what you're saying you're doing is true, you're essentially cutting the stack in half. So it's a half stack developer on one end and a data scientist that's got the other half. So the notion of the full stack developer kind of goes away with the idea of horizontally scalable infrastructure and vertically specialized data and AI. Your thoughts, what's your reaction to that? >> I think the most... I would say the most scarce resource in the world is empathy, right? When developers have empathy for their users, they now start building design that cares for the users. So the design becomes still the limiting factor where you can't really automate a lot of that design. So the full stack engineer is now going closer to the front and understanding their users and making applications that are perceptive of how the users are using them and building that empathy into the product. A lot of the full stack, we used to learn how to build up a kernel, deploy it on cloud, scale it on your own servers. All of that is coming together in reasonably easier ways. With cloud is helping there, AI is helping there, data is helping there, and lessons from the data. But I think what has not gone away is imagination, creativity, and how to power that creativity with AI and get it in the hands of someone quickly. Marketing has become easier in the new world. So it's not just enough to make products, you have to make markets for your products and then deliver and get that success for customers-- >> So what you're saying-- >> The developers become-- >> The consistency of the lower end of the stack of wiring together the plumbing and the kernel and everything else is done for you. So you can move up. >> Up the stack. >> So the stack's growing, so it's still kind of full. No one calls themselves a half stack developer. I haven't met anyone say "Yeah I'm a half stack developer." They're full stack developers, but the roles are changing. >> I think what-- >> There's more to do on the front end of creativity so the stack's extending. >> Creativity is changing, I think the one thing we have learned. We've gone past Moore's Law in the valley and people are innovating architectures to run AI faster. So AI is beginning to eat hardware. So you've seen the transformation in microprocessors as well I think once AI starts being part of the overall conversation, you'll see a much more richer coexistence with being how a human programmer and a computer programmer is going to be working closely. But I think this is just the beginning of a real richness when you talk about rich interactive applications, you're going to talk about rich interactive appliances, where you start seeing intelligence really spread around the form. >> Sri, if we really want to have some fun we can just talk about what a 10x engineer is. No I'm only kidding, we're not going to go there. It's always a good debate on Twitter what a 10x engineer is. Sri, congratulations on the funding. $72.5 million in finance for global expansion on the team side as well as in geographies, congratulations. >> Thank you. >> H2O.ai >> The full stack engineer of the future is, finishing up your full stack engineer conversation is going to get that courage and become a leader. Going from managers to leaders, developers to founders. I think it's become easier to democratize entrepreneurship now than ever before and part of our mission as a company is to democratize things, democratize AI, democratize H2O like in the AI for Good, democratize water. But also democratize the art of making more entrepreneurs and remove the common ways to fail and that's also a way to create more opportunity more ownership in the world and so-- >> And I think society will benefit from this globally because in the data is truth, in the data is the notion of being transparent, if it's all there and we're going to get to the data faster and that's where AI helps us. >> That's what it is. >> Sri, congratulations, $72 million of funding for H2O. We're here with the founder and CEO Sri Ambati. Great success story here in Silicon Valley and around the world. I'm John Furrier with the Cube, thanks for watching. >> Sri: Thank you. (upbeat music)

>> Live from San Francisco, California, it's theCUBE covering the IBM Chief Data Officers Summit, brought to you by IBM. >> Welcome back to San Francisco everybody. I'm Dave Vellante and you're watching theCUBE, the leader in live tech coverage. This is the end of the day panel at the IBM Chief Data Officer Summit. This is the 10th CDO event that IBM has held and we love to to gather these panels. This is a data all-star panel and I've recruited Seth Dobrin who is the CDO of the analytics group at IBM. Seth, thank you for agreeing to chip in and be my co-host in this segment. >> Yeah, thanks Dave. Like I said before we started, I don't know if this is a promotion or a demotion. (Dave laughing) >> We'll let you know after the segment. So, the data all-star panel and the data all-star awards that you guys are giving out a little later in the event here, what's that all about? >> Yeah so this is our 10th CDU Summit. So two a year, so we've been doing this for 5 years. The data all-stars are those people that have been to four at least of the ten. And so these are five of the 16 people that got the award. And so thank you all for participating and I attended these like I said earlier, before I joined IBM they were immensely valuable to me and I was glad to see 16 other people that think it's valuable too. >> That is awesome. Thank you guys for coming on. So, here's the format. I'm going to introduce each of you individually and then ask you to talk about your role in your organization. What role you play, how you're using data, however you want to frame that. And the first question I want to ask is, what's a good day in the life of a data person? Or if you want to answer what's a bad day, that's fine too, you choose. So let's start with Lucia Mendoza-Ronquillo. Welcome, she's the Senior Vice President and the Head of BI and Data Governance at Wells Fargo. You told us that you work within the line of business group, right? So introduce your role and what's a good day for a data person? >> Okay, so my role basically is again business intelligence so I support what's called cards and retail services within Wells Fargo. And I also am responsible for data governance within the business. We roll up into what's called a data governance enterprise. So we comply with all the enterprise policies and my role is to make sure our line of business complies with data governance policies for enterprise. >> Okay, good day? What's a good day for you? >> A good day for me is really when I don't get a call that the regulators are knocking on our doors. (group laughs) Asking for additional reports or have questions on the data and so that would be a good day. >> Yeah, especially in your business. Okay, great. Parag Shrivastava is the Director of Data Architecture at McKesson, welcome. Thanks so much for coming on. So we got a healthcare, couple of healthcare examples here. But, Parag, introduce yourself, your role, and then what's a good day or if you want to choose a bad day, be fun the mix that up. >> Yeah, sounds good. Yeah, so mainly I'm responsible for the leader strategy and architecture at McKesson. What that means is McKesson has a lot of data around the pharmaceutical supply chain, around one-third of the world's pharmaceutical supply chain, clinical data, also around pharmacy automation data, and we want to leverage it for the better engagement of the patients and better engagement of our customers. And my team, which includes the data product owners, and data architects, we are all responsible for looking at the data holistically and creating the data foundation layer. So I lead the team across North America. So that's my current role. And going back to the question around what's a good day, I think I would say the good day, I'll start at the good day. Is really looking at when the data improves the business. And the first thing that comes to my mind is sort of like an example, of McKesson did an acquisition of an eight billion dollar pharmaceutical company in Europe and we were creating the synergy solution which was based around the analytics and data. And actually IBM was one of the partners in implementing that solution. When the solution got really implemented, I mean that was a big deal for me to see that all the effort that we did in plumbing the data, making sure doing some analytics, is really helping improve the business. I think that is really a good day I would say. I mean I wouldn't say a bad day is such, there are challenges, constant challenges, but I think one of the top priorities that we are having right now is to deal with the demand. As we look at the demand around the data, the role of data has got multiple facets to it now. For example, some of the very foundational, evidentiary, and compliance type of needs as you just talked about and then also profitability and the cost avoidance and those kind of aspects. So how to balance between that demand is the other aspect. >> All right good. And we'll get into a lot of that. So Carl Gold is the Chief Data Scientist at Zuora. Carl, tell us a little bit about Zuora. People might not be as familiar with how you guys do software for billing et cetera. Tell us about your role and what's a good day for a data scientist? >> Okay, sure, I'll start by a little bit about Zuora. Zuora is a subscription management platform. So any company who wants to offer a product or service as subscription and you don't want to build your billing and subscription management, revenue recognition, from scratch, you can use a product like ours. I say it lets anyone build a telco with a complicated plan, with tiers and stuff like that. I don't know if that's a good thing or not. You guys'll have to make up your own mind. My role is an interesting one. It's split, so I said I'm a chief data scientist and we work about 50% on product features based on data science. Things like churn prediction, or predictive payment retries are product areas where we offer AI-based solutions. And then but because Zuora is a subscription platform, we have an amazing set of data on the actual performance of companies using our product. So a really interesting part of my role has been leading what we call the subscription economy index and subscription economy benchmarks which are reports around best practices for subscription companies. And it's all based off this amazing dataset created from an anonymized data of our customers. So that's a really exciting part of my role. And for me, maybe this speaks to our level of data governance, I might be able to get some tips from some of my co-panelists, but for me a good day is when all the data for me and everyone on my team is where we left it the night before. And no schema changes, no data, you know records that you were depending on finding removed >> Pipeline failures. >> Yeah pipeline failures. And on a bad day is a schema change, some crucial data just went missing and someone on my team is like, "The code's broken." >> And everybody's stressed >> Yeah, so those are bad days. But, data governance issues maybe. >> Great, okay thank you. Jung Park is the COO of Latitude Food Allergy Care. Jung welcome. >> Yeah hi, thanks for having me and the rest of us here. So, I guess my role I like to put it as I'm really the support team. I'm part of the support team really for the medical practice so, Latitude Food Allergy Care is a specialty practice that treats patients with food allergies. So, I don't know if any of you guys have food allergies or maybe have friends, kids, who have food allergies, but, food allergies unfortunately have become a lot more prevalent. And what we've been able to do is take research and data really from clinical trials and other research institutions and really use that from the clinical trial setting, back to the clinical care model so that we can now treat patients who have food allergies by using a process called oral immunotherapy. It's fascinating and this is really personal to me because my son as food allergies and he's been to the ER four times. >> Wow. >> And one of the scariest events was when he went to an ER out of the country and as a parent, you know you prepare your child right? With the food, he takes the food. He was 13 years old and you had the chaperones, everyone all set up, but you get this call because accidentally he ate some peanut, right. And so I saw this unfold and it scared me so much that this is something I believe we just have to get people treated. So this process allows people to really eat a little bit of the food at a time and then you eat the food at the clinic and then you go home and eat it. Then you come back two weeks later and then you eat a little bit more until your body desensitizes. >> So you build up that immunity >> Exactly. >> and then you watch the data obviously. >> Yeah. So what's a good day for me? When our patients are done for the day and they have a smile on their face because they were able to progress to that next level. >> Now do you have a chief data officer or are you the de facto CFO? >> I'm the de facto. So, my career has been pretty varied. So I've been essentially chief data officer, CIO, at companies small and big. And what's unique about I guess in this role is that I'm able to really think about the data holistically through every component of the practice. So I like to think of it as a patient journey and I'm sure you guys all think of it similarly when you talk about your customers, but from a patient's perspective, before they even come in, you have to make sure the data behind the science of whatever you're treating is proper, right? Once that's there, then you have to have the acquisition part. How do you actually work with the community to make sure people are aware of really the services that you're providing? And when they're with you, how do you engage them? How do you make sure that they are compliant with the process? So in healthcare especially, oftentimes patients don't actually succeed all the way through because they don't continue all the way through. So it's that compliance. And then finally, it's really long-term care. And when you get the long-term care, you know that the patient that you've treated is able to really continue on six months, a year from now, and be able to eat the food. >> Great, thank you for that description. Awesome mission. Rolland Ho is the Vice President of Data and Analytics at Clover Health. Tell us a little bit about Clover Health and then your role. >> Yeah, sure. So Clover is a startup Medicare Advantage plan. So we provide Medicare, private Medicare to seniors. And what we do is we're because of the way we run our health plan, we're able to really lower a lot of the copay costs and protect seniors against out of pocket. If you're on regular Medicare, you get cancer, you have some horrible accident, your out of pocket is infinite potentially. Whereas with Medicare Advantage Plan it's limited to like five, $6,000 and you're always protected. One of the things I'm excited about being at Clover is our ability to really look at how can we bring the value of data analytics to healthcare? Something I've been in this industry for close to 20 years at this point and there's a lot of waste in healthcare. And there's also a lot of very poor application of preventive measures to the right populations. So one of the things that I'm excited about is that with today's models, if you're able to better identify with precision, the right patients to intervene with, then you fundamentally transform the economics of what can be done. Like if you had to pa $1,000 to intervene, but you were only 20% of the chance right, that's very expensive for each success. But, now if your model is 60, 70% right, then now it opens up a whole new world of what you can do. And that's what excites me. In terms of my best day? I'll give you two different angles. One as an MBA, one of my best days was, client calls me up, says, "Hey Rolland, you know, "your analytics brought us over $100 million "in new revenue last year." and I was like, cha-ching! Excellent! >> Which is my half? >> Yeah right. And then on the data geek side the best day was really, run a model, you train a model, you get ridiculous AUC score, so area under the curve, and then you expect that to just disintegrate as you go into validation testing and actual live production. But the 98 AUC score held up through production. And it's like holy cow, the model actually works! And literally we could cut out half of the workload because of how good that model was. >> Great, excellent, thank you. Seth, anything you'd add to the good day, bad day, as a CDO? >> So for me, well as a CDO or as CDO at IBM? 'Cause at IBM I spend most of my time traveling. So a good day is a day I'm home. >> Yeah, when you're not in an (group laughing) aluminum tube. >> Yeah. Hurdling through space (laughs). No, but a good day is when a GDPR compliance just happened, a good day for me was May 20th of last year when IBM was done and we were, or as done as we needed to be for GDPR so that was a good day for me last year. This year is really a good day is when we start implementing some new models to help IBM become a more effective company and increase our bottom line or increase our margins. >> Great, all right so I got a lot of questions as you know and so I want to give you a chance to jump in. >> All right. >> But, I can get it started or have you got something? >> I'll go ahead and get started. So this is a the 10th CDO Summit. So five years. I know personally I've had three jobs at two different companies. So over the course of the last five years, how many jobs, how many companies? Lucia? >> One job with one company. >> Oh my gosh you're boring. (group laughing) >> No, but actually, because I support basically the head of the business, we go into various areas. So, we're not just from an analytics perspective and business intelligence perspective and of course data governance, right? It's been a real journey. I mean there's a lot of work to be done. A lot of work has been accomplished and constantly improving the business, which is the first goal, right? Increasing market share through insights and business intelligence, tracking product performance to really helping us respond to regulators (laughs). So it's a variety of areas I've had to be involved in. >> So one company, 50 jobs. >> Exactly. So right now I wear different hats depending on the day. So that's really what's happening. >> So it's a good question, have you guys been jumping around? Sure, I mean I think of same company, one company, but two jobs. And I think those two jobs have two different layers. When I started at McKesson I was a solution leader or solution director for business intelligence and I think that's how I started. And over the five years I've seen the complete shift towards machine learning and my new role is actually focused around machine learning and AI. That's why we created this layer, so our own data product owners who understand the data science side of things and the ongoing and business architecture. So, same company but has seen a very different shift of data over the last five years. >> Anybody else? >> Sure, I'll say two companies. I'm going on four years at Zuora. I was at a different company for a year before that, although it was kind of the same job, first at the first company, and then at Zuora I was really focused on subscriber analytics and churn for my first couple a years. And then actually I kind of got a new job at Zuora by becoming the subscription economy expert. I become like an economist, even though I don't honestly have a background. My PhD's in biology, but now I'm a subscription economy guru. And a book author, I'm writing a book about my experiences in the area. >> Awesome. That's great. >> All right, I'll give a bit of a riddle. Four, how do you have four jobs, five companies? >> In five years. >> In five years. (group laughing) >> Through a series of acquisition, acquisition, acquisition, acquisition. Exactly, so yeah, I have to really, really count on that one (laughs). >> I've been with three companies over the past five years and I would say I've had seven jobs. But what's interesting is I think it kind of mirrors and kind of mimics what's been going on in the data world. So I started my career in data analytics and business intelligence. But then along with that I had the fortune to work with the IT team. So the IT came under me. And then after that, the opportunity came about in which I was presented to work with compliance. So I became a compliance officer. So in healthcare, it's very interesting because these things are tied together. When you look about the data, and then the IT, and then the regulations as it relates to healthcare, you have to have the proper compliance, both internal compliance, as well as external regulatory compliance. And then from there I became CIO and then ultimately the chief operating officer. But what's interesting is as I go through this it's all still the same common themes. It's how do you use the data? And if anything it just gets to a level in which you become closer with the business and that is the most important part. If you stand alone as a data scientist, or a data analyst, or the data officer, and you don't incorporate the business, you alienate the folks. There's a math I like to do. It's different from your basic math, right? I believe one plus one is equal to three because when you get the data and the business together, you create that synergy and then that's where the value is created. >> Yeah, I mean if you think about it, data's the only commodity that increases value when you use it correctly. >> Yeah. >> Yeah so then that kind of leads to a question that I had. There's this mantra, the more data the better. Or is it more of an Einstein derivative? Collect as much data as possible but not too much. What are your thoughts? Is more data better? >> I'll take it. So, I would say the curve has shifted over the years. Before it used to be data was the bottleneck. But now especially over the last five to 10 years, I feel like data is no longer oftentimes the bottleneck as much as the use case. The definition of what exactly we're going to apply to, how we're going to apply it to. Oftentimes once you have that clear, you can go get the data. And then in the case where there is not data, like in Mechanical Turk, you can all set up experiments, gather data, the cost of that is now so cheap to experiment that I think the bottleneck's really around the business understanding the use case. >> Mm-hmm. >> Mm-hmm. >> And I think the wave that we are seeing, I'm seeing this as there are, in some cases, more data is good, in some cases more data is not good. And I think I'll start it where it is not good. I think where quality is more required is the area where more data is not good. For example like regulation and compliance. So for example in McKesson's case, we have to report on opioid compliance for different states. How much opioid drugs we are giving to states and making sure we have very, very tight reporting and compliance regulations. There, highest quality of data is important. In our data organization, we have very, very dedicated focus around maintaining that quality. So, quality is most important, quantity is not if you will, in that case. Having the right data. Now on the other side of things, where we are doing some kind of exploratory analysis. Like what could be a right category management for our stores? Or where the product pricing could be the right ones. Product has around 140 attributes. We would like to look at all of them and see what patterns are we finding in our models. So there you could say more data is good. >> Well you could definitely see a lot of cases. But certainly in financial services and a lot of healthcare, particularly in pharmaceutical where you don't want work in process hanging around. >> Yeah. >> Some lawyer could find a smoking gun and say, "Ooh see." And then if that data doesn't get deleted. So, let's see, I would imagine it's a challenge in your business, I've heard people say, "Oh keep all the, now we can keep all the data, "it's so inexpensive to store." But that's not necessarily such a good thing is it? >> Well, we're required to store data. >> For N number of years, right? >> Yeah, N number of years. But, sometimes they go beyond those number of years when there's a legal requirements to comply or to answer questions. So we do keep more than, >> Like a legal hold for example. >> Yeah. So we keep more than seven years for example and seven years is the regulatory requirement. But in the case of more data, I'm a data junkie, so I like more data (laughs). Whenever I'm asked, "Is the data available?" I always say, "Give me time I'll find it for you." so that's really how we operate because again, we're the go-to team, we need to be able to respond to regulators to the business and make sure we understand the data. So that's the other key. I mean more data, but make sure you understand what that means. >> But has that perspective changed? Maybe go back 10 years, maybe 15 years ago, when you didn't have the tooling to be able to say, "Give me more data." "I'll get you the answer." Maybe, "Give me more data." "I'll get you the answer in three years." Whereas today, you're able to, >> I'm going to go get it off the backup tapes (laughs). >> (laughs) Yeah, right, exactly. (group laughing) >> That's fortunately for us, Wells Fargo has implemented data warehouse for so many number of years, I think more than 10 years. So we do have that capability. There's certainly a lot of platforms you have to navigate through, but if you are able to navigate, you can get to the data >> Yeah. >> within the required timeline. So I have, astonished you have the technology, team behind you. Jung, you want to add something? >> Yeah, so that's an interesting question. So, clearly in healthcare, there is a lot of data and as I've kind of come closer to the business, I also realize that there's a fine line between collecting the data and actually asking our folks, our clinicians, to generate the data. Because if you are focused only on generating data, the electronic medical records systems for example. There's burnout, you don't want the clinicians to be working to make sure you capture every element because if you do so, yes on the back end you have all kinds of great data, but on the other side, on the business side, it may not be necessarily a productive thing. And so we have to make a fine line judgment as to the data that's generated and who's generating that data and then ultimately how you end up using it. >> And I think there's a bit of a paradox here too, right? The geneticist in me says, "Don't ever throw anything away." >> Right. >> Right? I want to keep everything. But, the most interesting insights often come from small data which are a subset of that larger, keep everything inclination that we as data geeks have. I think also, as we're moving in to kind of the next phase of AI when you can start doing really, really doing things like transfer learning. That small data becomes even more valuable because you can take a model trained on one thing or a different domain and move it over to yours to have a starting point where you don't need as much data to get the insight. So, I think in my perspective, the answer is yes. >> Yeah (laughs). >> Okay, go. >> I'll go with that just to run with that question. I think it's a little bit of both 'cause people touched on different definitions of more data. In general, more observations can never hurt you. But, more features, or more types of things associated with those observations actually can if you bring in irrelevant stuff. So going back to Rolland's answer, the first thing that's good is like a good mental model. My PhD is actually in physical science, so I think about physical science, where you actually have a theory of how the thing works and you collect data around that theory. I think the approach of just, oh let's put in 2,000 features and see what sticks, you know you're leaving yourself open to all kinds of problems. >> That's why data science is not democratized, >> Yeah (laughing). >> because (laughing). >> Right, but first Carl, in your world, you don't have to guess anymore right, 'cause you have real data. >> Well yeah, of course, we have real data, but the collection, I mean for example, I've worked on a lot of customer churn problems. It's very easy to predict customer churn if you capture data that pertains to the value customers are receiving. If you don't capture that data, then you'll never predict churn by counting how many times they login or more crude measures of engagement. >> Right. >> All right guys, we got to go. The keynotes are spilling out. Seth thank you so much. >> That's it? >> Folks, thank you. I know, I'd love to carry on, right? >> Yeah. >> It goes fast. >> Great. >> Yeah. >> Guys, great, great content. >> Yeah, thanks. And congratulations on participating and being data all-stars. >> We'd love to do this again sometime. All right and thank you for watching everybody, it's a wrap from IBM CDOs, Dave Vellante from theCUBE. We'll see you next time. (light music)

(upbeat music) [Announcer] From our studios in the heart of Silicon Valley Palo Alto, California. This is a CUBE conversation. >> Hi, I'm Peter Burris, and welcome to another CUBE conversation from our studios in beautiful Palo Alto, California. One of the biggest challenges that every user faces is how are they going to arrange their resources that are responsible for storing, managing, delivering, and protecting data. And that's a significant challenge, but it gets even worse when we start talking about multi-cloud. So, today we've got Eric Herzog who's the CMO and VP of Worldwide Storage Channels at IBM Storage to talk a bit about the evolving relationship of what constitutes a modern, comprehensive storage portfolio and multi-cloud. Eric, welcome to theCUBE. >> Peter, Thank you, thank you. >> So, start off, what's happening with IBM Storage these days, and let's get into this kind of how multi-cloud is affecting some of your decisions, and some of your customer's decisions. >> So, what we've done, is we've started talking about multi-cloud over two years ago. When Ed Walsh joined the company as a general manager, we went on an analyst roadshow, in fact, we came here to theCUBE and shot a video, and we talked about how the IBM Storage Division is all about multi-cloud. And we look about that in three ways. First of all, if you are creating a private cloud, we work with you. From a container, whether you're Vmware based, whether you are doing a more traditional cloud- private cloud. Now the modern private cloud, all container based. Second is Hybrid Cloud, data on parem, out to a public cloud provider. And the third aspect, and in fact, you guys have written about it in one of your studies is that no one is going to use one public cloud provider, they're going to use multiple cloud providers. So whether that be IBM Cloud, which of course we love because we're IBM shareholders, but we work with Amazon, we work with Google, and in fact we work with any cloud provider. Our Spectrum Protect backup product, which is one of the most awarded enterprise backup packages can backup to any cloud. In fact, over 350 small to medium cloud providers, the engine for their backup as a service, is Spectrum Protect. Again, completely heterogeneous, we don't care what cloud you use, we support everyone. And we started that mantra two and a half years ago, when Ed first joined the company. >> Now, I remember when you came on, we talked a lot about this notion of data first and the idea that data driven was what we talked about >> Right, data driven. >> And increasingly, we talked about, or we made the observation that enterprises were going to take a look at the natural arrangement of their data, and that was going to influence a lot of their cloud, a lot of their architecture, and certainly a lot of their storage divisions or decisions. How is that playing out? Is that still obtaining? Are you still seeing more enterprises taking this kind of data driven approach to thinking about their overall cloud architectures? >> Well the world is absolutely data-centric. Where does the data go? What are security issues with that data? How is it close to the compute when I need it? How do I archive I, how do I back it up? How do I protect it? We're here in Silicon Valley. I'm a native Palo Alton, by the way, and we really do have earthquakes here, and they really do have earthquakes in Japan and China and there is all kinds of natural disasters. And of course as you guys have pointed out, as have almost all of the analysts, the number one cause of data loss besides humans is actually still fire. Even with fire suppressant data centers. >> And we have fires out here in Northern California too. >> That's true. So, you've got to make sure that you're backing up that data, you're archiving the data. Cloud could be part of that strategy. When does it need to be on parem, when does it need to be off parem? So, it's all about being a data-driven, and companies look at the data, profile the date and time, What sort of storage do I need? Can I go high end, mid-range and entry, profile that data, figure that out, what they need to do. And then do the same thing now with on parem and off parem. For certain data sets, for security reasons, legal reasons you probably are not going to put it out into a public cloud provider. But other data sets are ideal for that and so all of those decisions that are being made by: What's the security of the data? What's the legality of that data? What's the performance I need of that data? And, how often do I need the data? If you're going to constantly go back and forth, pull data back in, going to a public cloud provider, which charge both for in and out of the data, that actually may cost more than buying an Array on parem. And so, everyone's using that data-centricity to figure out how do they spend their money, and how do they optimize the data to use it in their applications, workloads and use cases. >> So, if you think about it, the reality is by application, workload, location, regulatory issues, we're seeing enterprises start to recognize and increase specialization of their data assets. And that's going to lead to a degree of specializations in the classes of data management and storage technologies that they utilize. Now, what is the challenge of choosing a specific solution versus looking at more of a portfolio of solutions, that perhaps provide a little bit more commonality? How are customers, how are the IMB customer base dealing with that question. >> Well, for us the good thing was to have a broad portfolio. When you look at the base storage Arrays we have file, block and object, they're all award winning. We can go big, we can go medium, and we can go small. And because of what we do with our Array family we have products that tend to be expensive because of what they do, products that mid-price and products that are perfect for Herzog's Bar and Grill. Or maybe for 5,000 different bank branches, 'cause that bank is not going to buy expensive storage for every branch. They have a small Array there in case core goes down, of course. When you or I go in to get a check or transact, if the core data center is down, that Wells Fargo, BofA, Bank of Tokyo. >> Still has to do business. >> They are all transacting. There's a small Array there. Well you don't want to spend a lot of money for that, you need a good, reliable all flash Array with the right RAS capability, right? The availability, capability, that's what you need, And we can do that. The other thing we do is, we have very much, cloud-ified everything we do. We can tier to the cloud, we can backup to the cloud. With object storage we can place it in the cloud. So we've made the cloud, if you will, a seamless tier to the storage infrastructure for our customers. Whether that be backup data, archive data, primary data, and made it so it's very easy to do. Remember, with that downturn in '08 and '09 a lot of storage people left their job. And while IT headcount is back up to where it used to be, in fact it's actually exceeded, if there was 50 storage guys at Company X, and they had to let go 25 of them, they didn't hire 25 storage guys now, but they got 10 times the data. So they probably have 2 more storage guys, they're from 25 to 27, except they're managing 10 times the data, so automation, seamless integration with clouds, and being multi-cloud, supporting hybrid clouds is a critical thing in today's storage world. >> So you've talked a little bit about format, data format issues still impact storage decisions. You've talked about how disasters or availability still impact storage decisions, certainly cost does. But you've also talked about some of the innovative things that are happening, security, encryption, evolved backup and and restore capabilities, AI and how that's going to play, what are some of the key thing that your customer base is asking for that's really driving some of your portfolio decisions? >> Sure, well when we look beyond making sure we integrate with every cloud and make it seamless, the other aspect is AI. AI has taken off, machine learning, big data, all those. And there it's all about having the right platform from an Array perspective, but then marrying it with the right software. So for example, our scale-out file system, Spectrum Scale can go to Exabyte Class, in fact the two fastest super computers on this planet have almost half an exabyte of IBM Spectrum Scale for big data, analytics, and machine learning workloads. At the same time you need to have Object Store. If you're generating that huge amount of data set in AI world, you want to be able to put it out. We also now have Spectrum discover, which allows you to use Metadata, which is the data about the data, and allow and AI app, a machine learning app, or an analytics app to actually access the metadata through an API. So that's one area, so cloud, then AI, is a very important aspect. And of course, cyber resiliency, and cyber security is critical. Everyone thinks, I got to call a security company, so the IBM Security Division, RSA, Check Point, Symantec, McAfee, all of these things. But the reality is, as you guys have noted, 98% of all enterprises are going to get broken into. So while they're in your house, they can steal you blind. Before the cops show up, like the old movie, what are they doing? They're loading up the truck before the cops show up. Well guess what, what if that happened, cops didn't show up for 20 minutes, but they couldn't steal anything, or the TV was tied to your fingerprint? So guess what, they couldn't use the TV, so they couldn't steal it, that's what we've done. So, whether it be encryption everywhere, we can encrypt backup sets, we can encrypt data at rest, we can even encrypt Arrays that aren't ours with our Spectrum Virtualize family. Air gapping, so that if you have ransomware or malware you can air-gap to tape. We've actually created air gapping out with a cloud snapshot. We have a product called Safeguard Copy which creates what I'll call a faux air gap in the mainframe space, but allows that protection so it's almost as if it was air gapped even though it's on an Array. So that's a ransomware and malware, being able to detect that, our backup products when they see an unusual activity will flag the backup restore jam and say there is unusual activity. Why, because ransomware and malware generate unusual activity on back up data sets in particular, so it's flaky. Now we don't go out and say, "By the way, that's Herzog ransomware, or "Peter Burris ransomware." But we do say "something is wrong, you need to take a look." So, integrating that sort of cyber resiliency and cyber security into the entire storage portfolio doesn't mean we solve everything. Which is why when you get an overall security strategy, you've got that Great Wall of China to keep the enemy out, you've got the what I call, chase software to get the bad guy once he's in the house, the cops that are coming to get the bad guy. But you've got to be able to lock everything down, you'll do it. So a comprehensive security strategy, and resiliency strategy involves not only your security vendor, but actually your storage vendor. And IBM's got the right cyber resiliency and security technology on the storage side to marry up, regardless of which security vendor they choose. >> Now you mention a number of things that are associated with how an enterprise is going to generate greater leverage, greater value out of data that you already know. So, you mentioned, you know, encryption end to end, you mention being able to look at metadata for AI applications. As we move to a software driven world of storage where physical volumes can still be made more virtual so you can move them around to different workloads. >> Right. >> And associate the data more easily, tell us a little bit about how data movement becomes an issue in the storage world, because the storage has already been associated with it's here. But increasingly, because of automation, because of AI, because of what businesses are trying to do, it's becoming more associated with intelligent, smart, secure, optimized movement of data. How is that starting to impact the portfolio? >> So we look at that really as data mobility. And data mobility can be another number of different things, for example, we already mentioned, we treat clouds as transparent tiers. We can backup to cloud, that's data mobility. We also tier data, we can tier data within an Array, or the Spectrum Virtualize product. We can tier data, block data cross 450 Arrays, most of which aren't IBM logo'd. We can tier from IBM to EMC, EMC can then tier to HDS, HDS can tier to Hitachi, and we do that on Arrays that aren't ours. So in that case what you're doing is looking for the optimal price point, whether it be- >> And feature set. >> And feature sets, and you move things, data around all transparently, so it's all got to be automated, that's another thing, in the old days we thought we had Nirvana when the tiering was automatically moved the data when it's 30 days old. What if we automatically move data with our Easy Tier technology through AI, when the data is hot moves it to the hottest tier, when the data is cold it puts it out to the lowest cost tier. That's real automation leveraging AI technology. Same thing, something simple, migration. How much money have all the storage companies made on migration services? What if you could do transparent block migration in the background on the fly, without ever taking your servers down, we can do that. And what we do is, it's so intelligent we always favor the data set, so when the data is being worked on, migration slows down. When the data set slows down, guess what? Migration picks up. But the point is, data mobility, in this case from an old Array to an new Array. So whether it be migrating data, whether it be tiering data, whether you're moving data out to the cloud, whether it be primary data or backup data, or object data for archive, the bottom line is we've infused not only the cloudification of our storage portfolio, but the mobility aspects of the portfolio. Which does of course include cloud. But all tiering more likely is on premise. You could tier to the cloud, but all flash Array to a cheap 7200 RPM Array, you save a lot of money and we can do that using AI technology with Easy Tier. All examples of moving data around transparently, quickly, efficiently, to save cost both in CapEx, using 7200 RPM Arrays of course to cut costs, but actually OpEx the storage admin, there aren't a hundred storage admins at Burris Incorporated. You had to let them go, you've hired 100 of the people back, but you hired them all for DevOps so you have 50 guys in storage >> Actually there are, but I'm a lousy businessman so I'm not going to be in business long. (laughing) One more question, Eric. I mean look you're an old style road warrior, you're out with customers a lot. Increasingly, and I know this because we've talked about it, you're finding yourself trying to explain to business people, not just IT people how digital business, data and storage come together. When you're having these conversations with executives on the business side, how does this notion of data services get discussed? What are some of the conversations like? >> Well I think the key thing you got to point out is storage guys love to talk speeds and feeds. I'm so old I can still talk TPI and BPI on hard drives and no one does that anymore, right? But, when you're talking to the CEO or the CFO or the business owner, it's all about delivering data at the right performance level you need for your applications, workloads and use cases, your right resiliency for applications, workloads and use cases, your right availability, so it's all about application, workloads, and use cases. So you don't talk about storage speeds and feeds that you would with Storage Admin, or maybe in the VP of infrastructure in the Fortune 500, you'd talk about it's all about the data, keeping the data secure, keeping the data reliable, keeping it at right performance. So if it's on the type of workload that needs performance, for example, let's take the easy one, Flash. Why do I need Flash? Well, Mr. CEO, do you use logistics? Of course we do! Who do you use, SAP. Oh, how long does that logistics workload take? Oh, it takes like 24 hours to run. What if I told you you could run that every night, in an hour? That's the power of Flash. So you translate what you and I are used to, storage nerdiness, we translate it into businessfied, in this case, running that SAP workload in an hour vs. 24 has a real business impact. And that's the way you got to talk about storage these days. When you're out talking to a storage admin, with the admin, yes, you want to talk latency and IOPS and bandwidth. But the CEO is just going to turn his nose up. But when you say I can run the MongoDB workload, or I can do this or do that, and I can do it. What was 24 hours in an hour, or half an hour. That translates to real data, and real value out of that data. And that's what they're looking for, is how to extract value from the data. If the data isn't performant, you get less value. If the data isn't there, you clearly have no value. And if the data isn't available enough so that it's down part time, if you are doing truly digital business. So, if Herzog's Bar and Grill, actually everything is done digitally, so before you get that pizza, or before you get that cigar, you have to order it online. If my website, which has a database underneath, of course, so I can handle the transactions right, I got to take the credit card, I got to get the orders right. If that is down half the time, my business is down, and that's an example of taking IT and translating it to something as simple as a Bar and Grill. And everyone is doing it these days. So when you talk about, do you want that website up all the time? Do you need your order entry system up all the time? Do you need your this or that? Then they actually get it, and then obviously, making sure that the applications run quickly, swiftly, and smoothly. And storage is, if you will, that critical foundation underneath everything. It's not the fancy windows, it's not the fancy paint. But if that foundation isn't right, what happens? The whole building falls down. And that's exactly what storage delivers regardless of the application workload. That right critical foundation of performance, availability, reliability. That's what they need, when you have that all applications run better, and your business runs better. >> Yeah, and the one thing I'd add to that, Eric, is increasingly the conversations that we're having is options. And one of the advantages of a large portfolio or a platform approach is that the things you're doing today, you'll discover new things that you didn't anticipate, and you want the option to be able to do them quickly. >> Absolutely. >> Very, very important thing. So, applications, workload, use cases, multi-cloud storage portfolio. Eric, thanks again for coming on theCUBE, always love having you. >> Great, thank you. >> And once again, I'm Peter Burris, talking with Eric Herzog, CMO, VP of Worldwide Storage Channels at IBM Storage. Thanks again for watching this CUBE conversation, until next time. (upbeat music)