>>Hi everyone. Welcome to this special event, pure Storage, the Path to Sustainable it. I'm your host, Lisa Martin. Very pleased to be joined by Nicole Johnson, the head of Social Impact and Sustainability at Pure Storage. Nicole, welcome to the >>Cube. Thanks for having me, Lisa. >>Sustainability is such an important topic to talk about, and I understand that Pure just announced a report today about sustainability. What can you tell me what nuggets are in this report? >>Well, actually quite a few really interesting nuggets, at least for us. And I, I think probably for you and your viewers as well. So we actually commissioned about a thousand sustainability leaders across the globe to understand, you know, what are their sustainability goals, what are they working on, and what are the impacts of buying decisions, particularly around infrastructure when it comes to sustainable goals. I think one of the things that was really interesting for us was the fact that around the world we did not see a significant variation in terms of sustainability being a top priority. You've, I'm sure you've heard about the energy crisis that's happening across Europe. And so, you know, there was some thought that perhaps that might play into AMEA being a larger, you know, having sustainability goals that were more significant. But we actually did not find that we found sustainability to be really important no matter where the respondents were located. >>So, very interesting at pure sustainability is really at the heart of what we do and has been since our founding. It's interesting because we set out to make storage really simple, but it turns out really simple, is also really sustainable and the products and services that we bring to our customers have really powerful outcomes when it comes to decreasing their, their own carbon footprints. And so, you know, we often hear from customers that we've actually really helped them to significantly improve their storage performance, but also allow them to save on space power and cooling costs and, and their footprint. So really significant findings. One example of that is a company called Cengage, which is a global education technology company. They recently shared with us that they have actually been able to reduce their overall storage footprint by 80% while doubling to tripling the performance of their storage systems. So it's really critical for, for companies who are thinking about their sustainability goals, to consider the dynamic between their sustainability program and their IT teams who are making these buying decisions. >>Right? Those two teams need to be really inextricably linked these days. You talked about the fact that there was really consistency across the regions in terms of sustainability being of high priority for organizations. You had a great customer story that you shared that showed significant impact can be made there by bringing the sustainability both together with it. But I'm wondering why are we seeing that so much of the vendor selection process still isn't revolving around sustainability or it's overlooked? What are some of the things that you see despite so many people saying sustainability huge priority? >>Well, in this survey, the most commonly cited challenge was really around the fact that there was a lack of management buy-in. 40% of respondents told us this was the top roadblock. So getting, I think getting that out of the way. And then we also just heard that sustainability teams were not brought into tech purchasing processes until after it's already rolling, right? So they're not even looped in. And that, that being said, you know, we know that it has been identified as one of the key departments to supporting a company's sustainability goals. So we, we really want to ensure that these two teams are talking more to each other. When we look even closer at the data from the respondents, we see some really positive correlations. We see that 65% of respondents reported that they're on track to meet their sustainability goals, and that it, of those 65%, it is significantly engaged with reporting data for those sustainability initiatives. We saw that, that for those who did report, the sustainability is a top priority for vendor selection. They were twice as likely to be on track with their goals and their sustainability directors said that they were getting involved at the beginning of the tech purchasing program. Our process, I'm sorry, rather than towards the end. And so, you know, we know that to curb the impact of climate crisis, we really need to embrace sustainability from a cross-functional viewpoint. >>Definitely has to be cross-functional. So, so strong correlations there in the report that organizations that had closer alignment between the sustainability folks and the IT folks were farther along in their sustainability program development, execution, et cetera, those CO was correlations, were they a surprise? >>Not entirely. You know, when we look at some of the statistics that come from the, you know, places like the World Economic Forum, they say that digitization generated 4% of greenhouse gas emissions in 2020. So, and that, you know, that's now almost three years ago, digital data only accelerates and by 2025, we expect that number could be almost double. And so we know that that communication and that correlation is gonna be really important because data centers are taking up such a huge footprint of when companies are looking at their emissions. And it's, I mean, quite frankly, a really interesting opportunity for it to be a trailblazer in the sustainability journey. And, you know, perhaps people that are in IT haven't thought about how they can make an impact in this area, but there really is some incredible ways to help us work on cutting carbon emissions, both from your company's perspective and from the world's perspective, right? >>Like we are, we're all doing this because it's something that we know we have to do to drive down climate change. So I think when you, when you think about how to be a trailblazer, how to do things differently, how to differentiate your own department, it's a really interesting connection that IT and sustainability work together. I would also say, you know, I'll just note that of the respondents to the survey we were discussing, we do over half of those respondents expect to see closer alignment between the organization's IT and sustainability teams as they move forward. >>And that's really a tip the, to those organizations embracing cultural change. That's always hard to do, but for those two, for sustainability and IT to come together as part of really the overall ethos of an organization, that's huge. And it's great to see the data demonstrating that, that those, that alignment, that close alignment is really on its way to helping organizations across industries make a big impact. And wanna dig in a little bit to peers, ESG goals. What can you share with us about >>That? Absolutely. So as I mentioned, peers kind of at the beginning of our formal ESG journey, but really has been working on the, on the sustainability front for a long time. I would, I, it's funny as we're, as we're doing a lot of this work and, and kind of building our own profile around this, we're coming back to some of the things that we have done in the past that consumers weren't necessarily interested in then, but are now because the world has changed, becoming more and more invested in. So that's exciting. So we did a baseline scope one, two, and three analysis and discovered, interestingly enough that 70% of our emissions comes from use of sold products. So our customers work running our products in their data centers. So we know that we, we've made some ambitious goals around our Scope one and two emissions, which is our own office, our utilities, you know, those, they only account for 6% of our emissions. So we know that to really address the issue of climate change, we need to work on the use of sold products. So we've also made a, a really ambitious commitment to decrease our carbon emissions by 66% per bed per petabyte by 2030 in our products. So decreasing our own carbon footprint, but also affecting our customers as well. And we've also committed to a science based target initiative and our road mapping how to achieve the ambitious goals set out in the Paris agreement. >>That's fantastic. It sounds like you really dialed in on where is the biggest opportunity for us as peer storage to make the biggest impact across our organization, across our customers' organizations. There lofty goals that pure set, but knowing what I know about Pure, you guys are probably well on track to, to accomplish those goals in record time. >>I hope So. >>Talk a little bit about advice that you would give to viewers who might be at the very beginning of their sustainability journey and really wondering what are the core elements besides it, sustainability, team alignment that I need to bring into this program to make it actually successful? >>Yeah, so I think, you know, understanding that you don't have to pick between really powerful technology and sustainable technology. There are opportunities to get both and not just in storage, right in, in your entire IT port portfolio. We know that, you know, we're in a place in the world where we have to look at things from the bigger picture. We have to solve new challenges and we have to approach business a little bit differently. So adopting solutions and services that are environmentally efficient can actually help to scale and deliver more effective and efficient IT solutions over time. So I think that that's something that we need to, to really remind ourselves, right? We have to go about business a little bit differently and that's okay. We also know that data centers utilize an incredible amount of, of energy and, and carbon. And so everything that we can do to drive that down is going to address the sustainability goals for us individually as well as, again, drive down that climate change. So we, we need to get out of the mindset that data centers are, are about reliability, your cost, et cetera. And really think about efficiency and carbon footprint when you're making those business decisions. I'll also say that, you know, the earlier that we can get sustainability teams into the conversation, the more impactful your business decisions are going to be and helping you to guide sustainable decision making. >>So shifting sustainability and it left almost together really shows that the correlation between those folks getting together in the beginning with intention, the report shows and the successes that peers had, demonstrate that that's very impactful for organizations to actually be able to implement even the cultural change that's needed for sustainability programs to be successful. My last question for you goes back to that report. You mentioned in there that the data show a lot of organizations are hampered by management buy-in, where sustainability is concerned. How can pure help its customers navigate around those barriers so that they get that management buy and they understand that the value in it for >>Them? Yeah, so I mean, I think that for me, my advice is always to speak to hearts and minds, right? And help the management to understand, first of all, the impact right on climate change. So I think that's the kind of hearts piece on the mind piece. I think it's addressing the sustainability goals that these companies have set for themselves and helping management understand how to, you know, how their IT buying decisions can actually really help them to reach these goals. We also, you know, we always run kind of TCOs for customers to understand what is the actual cost of, of the equipment. And so, you know, especially if you're in a, in a location in which energy costs are rising, I mean, I think we're seeing that around the world right now with inflation. Better understanding your energy costs can really help your management to understand the, again, the bigger picture and what that total cost is gonna be. Often we see, you know, that maybe the, the person who's buying the IT equipment isn't the same person who's purchasing, who's paying the, the electricity bills, right? And so sometimes even those two teams aren't talking. And there's a great opportunity there, I think, to just to just, you know, look at it from a more high level lens to better understand what total cost of ownership is. >>That's a great point. Great advice. Nicole. Thank you so much for joining me on the program today, talking about the new report that on sustainability that Pure put out some really compelling nuggets in there, but really also some great successes that you've already achieved internally on your own ESG goals and what you're helping customers to achieve in terms of driving down their carbon footprint and emissions. We so appreciate your insights and your thoughts. >>Thank you, Lisa. It's been great speaking with you. >>Pleasure speaking with you as well. We wanna thank you so much for watching. This is Pure Storage, the path to sustainable it. I'm Lisa Martin, we'll see you next time.

>>Hello, everyone. Welcome to the cubes presentation of the 80 startup showcase marketing technology, emerging cloud scale customer experiences. This is season two, episode three of the ongoing series covering the exciting startups from the, a AWS ecosystem to talk about all the top trends and also featuring the key customers. I'm your host, John ER, today we're joined by Louis Fernando, Denise vice president of peak pay social and John Kim, the CEO of Sandberg to learn about the future of what's going on in fostering deeper customer relationships. Gentlemen, thanks for joining us in the cube showcase, >>Excited to be here. >>So John talk about Sendbird real quick set the table for us. What you guys do, you got a customer here to highlight some of the key things you're doing with customers, the value proposition what's Sendbird and what's the showcase about, >>Yeah, I'm really excited to be here. Uh, I'm John founder, C of Sandberg. So Sandberg is the worst leading conversations platform for mobile applications. We can power user to user conversations in mobile applications, as well as the brand to user conversations such as marketing sales and support. So, uh, today we power over quarter billion users on a monthly basis. Uh, we have, you know, through over 300 employees across seven different countries around the world, we work with some of the world's leading, uh, uh, customers such as big pay that we are going to showcase today, along with other, uh, wonderful customers like DoorDash, Reddit, <inaudible> sports and so forth. We have collectively raised over 200 million in funding. Um, so that's kind of where we are today. >>Well, it's always great to have, uh, one great success. Uh, good funding, more important is the customers. And I love showcases where the customers do the talking, because that means you've got some success stories. Louise, talk about, um, are you happy customer? What's it like working with Sandberg? Give us the, give us the scoop. >>So sandbar is being a great partner with us. So pick pay is a Brazilian payment app. We're at a FinTech here with more than 30 million active users using everyday pick pay to pay everything. So the, the, the majority of the payments are between peers, between people. So sandbar is, is helping us to improve a lot this journey to make it more pleasant between every everyone who are using big, big. So we are here, let's talk and it's a >>Pleasure. Yeah, it's awesome. Well, I great to have you guys on great, great relationship. And one of the things we've been talking about on the cube, if the folks watching that know our audience, no we've been banging the, the drum hard on this new world and this new patterns of user expectations and building relationships in this new digital world is not about the old way, the old MarTech way. There are new new use cases, new expectations by the consumers, John, that are, that are bringing up new opportunities, but also expectations. It's not about, I mean, I mean, if someone's using discord, for example, cuz they're gamers, they're done discord. If they want to communicate with, with slack, they, I do slack, SMS, kind of old hat. You got WhatsApp, you've got all these now peer to peer organic connections, multiple channels. This is all the new world. What's your vision on this new relationship building digital communication world. >>Yeah. So I, I think you brought a really good point there. One of the most frequently used applications in the world today are messaging applications across any countries, any region, any culture, if you look at the most frequently used and most longest used applications are usually some form of a, a messaging application. Now the end users or the customers in the world are so used to using, uh, uh, such a, you know, frictionless ver very responsive, modern experience on those messaging applications. What we want to help with the business around the world, the 99.9% of the business around the world don't have those really te knowledge or user experience expertise in messaging. So we want to help our businesses, help our customers be able to harness the power of modern messaging capabilities and then be able to embed it in their own business so that they can retain their users on their platform, engage with them in the con context that their, uh, what their business is about so that they can not only, uh, control or provide a better user experience, but also be able to, uh, understand their users better, uh, understand what they're doing on their businesses, be able to own and, uh, control the data in a more secure and safe way. >>So really it's uh, we're like the Robin hood of the world trying to keep superpower yeah. Back to the businesses. >>Yeah. Deal from the rich idea, the messaging scale. Bring that to everybody else. I love that. Uh, and you got kind of this double int Robin hood kind of new for the new generation finance. This is about taking the advantage of scalable platforms, monopolies, right. And giving the entrepreneur an opportunity to have that same capability feature, rich Louise PPE. You guys used Sendbird together. You have to level up, you gotta compete with those big monopolies to pride, scalable conversations. Okay. How did you engage this? What was your success path look? What was it look like? >>Yeah. When we look to this majority, the bigger chat apps that we have nowadays in the market, we are looking to them and then Brazilians are using for their daily course, but Brazilians are paying every day millions and millions of payments. And these chat apps are not, uh, able to, to, to deal with these payments. So what we are doing here is that, uh, providing a solution where every conversation that are going to happen before, during, or after a payment between the, the people, they would, uh, uh, have a nice platform that could afford all, all of their emotions and discussions that they have to do before or after the payment. So we are putting together the chat platform and we with the payment platform. So that's, that's what we are doing now. >>Okay. So just so I get this right. You're using Sandberg essentially integrated your mobile payment experience. Okay. Which is your app you're Sandberg to bring that scalability into the, into the social app application into the app itself. Is that right? >>Yes. Perfect. Integrated with the payment journey. So everybody who is going to pay, they need to find the one, the, the one they want to pay and then they can chat and conclude the payment through the platform. Yeah. I >>Mean, why not have it right there at point of, uh, transaction. Right. Um, why did you, um, decide to, um, to use conversations in your mobile wallet? Just curious. >>So it's important to say that we were born social. We born in 2012. So when our main main product was peer to peer payments, so everybody were sending money to a friend requesting or charging their family. So a service provider. And once we, we started as a social platform in that period. In that moment, we are just focusing in likes comments and like public interactions and the word become more private. And as soon we under understood this situation, we decided to move from a public feed to a private, to a private interaction. So that's, uh, that then the, the conversational space was the solution for that moving from a public interaction to a private interaction. So between the peers, which are involved in the, the transaction. So that's why we are providing the chat solution integrated with payments. >>That's a great call. John, just give some context here, again, for the folks watching this is now expected, this integrated experience. What's your, how would you talk to folks out there? I mean, first of all, I, I, I see it clearly, you've got an app, you gotta have all this integration and you need it scaling to reach features. Talk about your view on that. Is that the, is that what's happening here? What's, what's the real dynamic here. What's the, the big trend. >>Yeah. One thing that's, uh, super interesting about, uh, uh, like messaging experience in general, if you think about any kind of conversations that's happening, uh, digitally between human beings, more and more conversations, just like what Louis mentioned earlier are happening between in a private setting, even on applications, whether it be slack or other forms of communication, uh, more hap uh, more conversations happen through either one-on-one conversations or in a private small group settings. And because people feel more secure, uh, safe to have, uh, more intimate conversations. So even when you're making transactions is more, you know, there's a higher trust and, uh, people tend to engage, uh, far better on platforms through these kind of private conversations. That's where we kind of come in, whether it be, you want to set a one-on-one conversations or with a group conversation. And then ultimately if you want to take it public in a large group setting, you can also support, you know, thousands, if not, you know, hundreds of thousands of people, uh, engaging a public forum as well. So all of those capabilities can be implemented using something Ember, but again, the world is, uh, right now the businesses and how the user are, are interacting with this with each other is all happening through digital conversations. And we're seeing more and more of that happening, uh, throughout the life cycle of our company. >>Yeah, just as a sidebar, I was just talking to a venture in San Francisco the other day, and we're talking about the future of security and SAS and cloud scale. And, you know, the conversation went to more of, is it SAS? Is it platform as a service Louis? I wanna get your thoughts because, you know, you're seeing more and more needs for customization, low code, no code. You're seeing these trends. You gotta built in security. So, you know, the different, the old SAS model was softwares a service, but now that's everything in the cloud is softwares a service. So, but you need to have that platform kind of vibe for scale customization, maybe some developer integration, cuz apps are becoming the, the touchpoint. So can you walk us through what your vision was when you decided to integrate, chat into your app and how did you see that chat, changing the customer experience for payments and across your user journey? Cause, I mean, it's obvious now looking at it, but it might not have been for some. What was your, what was your vision? And when you had to do that, >>When you looked to Brazilian reality, we can see those in, uh, payment apps. All of them are focused on the transactional moment. And as soon as we started to think, how could be, how could our journey be better, more pleased than the others and make people want to be here and to use and to open our app every day is just about making the interaction with the peers easier, even with a merchant or even with my friend. So the main point that our first step was just to connect all, all the users between themselves to payments. The second step we are providing now is using the chat platform, the send bird platform as a platform for peak pay. So we are going to provide more best information. We're going to provide a better customer experience through the support and everything. So, um, this, this, this interaction or this connection, this partnership with Sandberg are going to unlock a new level of service for our users. And at the same time, a much more pleasant or a more pleasant journey for them while they are using the, the app for a, a simple payment, or if they are going to look for a group objective or maybe a crowdfund in the future or a group to decide, or just to pay something. So we are then locking a new level of interaction between the peers between the people and the users that are, that are involved into this, this payment or this simple transaction, we are making it more conversational. >>Yeah. You're making the application more valuable. We're gonna get to that in the next segment about, you know, the future of apps one and done, you see a lot of sports apps, oh, this big tournament, you know, and then you use it and then you never use it again until next year. You know, you have very time specific apps, but now you guys are smart to kind of build this in, but I gotta ask you a question because a lot of developers and companies out there always have this buy versus build decision. Why did you decide to use Sendbird versus building it in house? It's always kind of like the big trade off. >>Yeah. First of all, it will take a long, long time for us to achieve a major platform as Sandberg. And we are not a chat platform. So we are going to use this social interaction to improve the payment platform that we have. So when we look to the market and we found Sandberg, then we thought, okay, this guys, they are a real platform. And through the conversations, we are seeing that they are roadmap working in synergy with our roadmap. And then we can, we could start to deliver value to our, to our users in a fastest way. Could you imagine it spending 2, 3, 4 years to develop something like sand? And even when we achieve this point, probably our solution will be, would be weaker than, than Sandberg. So it was like no brainer to do that. Yeah. Because we want to improve the payment journey, not to do a chat, only a chat platform. So that's why we are working together to prove it's >>Really, you start to see these plugins, these, you know, look at Stripe for payments, for instance, right. And here in the success they've had, you know, people want to plug in for services. So John, I gotta ask you about, um, about the, the complexity that goes into it. The trust required that they have for you, you have to do this heavy lifting, you gotta provide the confidence that your service is gonna have to scale the compliance. Talk about that. What do you guys do under the covers that make this easy again, great business model, heavy lifting done by you. Seamless integration provide that value. That's why business is good, but there's a lot going on share what's happening under the, under the covers. >>Yeah. Um, before going to like the technical, like intricacy of what we do just to provide a little bit of background context on why we even started this business is we, uh, this is my second startup. My first company was a gaming company. We had built like chat three, four times just for our own game. So we were basically, we felt like we were reinventing the wheel. And then we actually went on a buyer's journey when we were building a social application, uh, uh, for, for, uh, uh, building our own community. We tried to actually be a buyer to see if we can actually find a solution. We want to use turns out that there weren't a lot of like sophisticated, you know, top notch, modern, uh, uh, chat experience that we can build using some other third party solutions. So we had to build all of that ourselves, which became the foundation for se today. >>And what we realized is that for most companies like using a building, the most sophisticated chat is probably not going to be their highest priority in case a pick pay will be, you know, financial transactions and all the other business that can be built on and hosted by platform like pick pay. But, you know, building the most topnotch chat experience would be a priority for a company like let's say WhatsApp or, or telegram, but it will probably not be the priority for, you know, major gaming companies, food delivery companies, finance companies, chat is not the highest priority. That's kind of where we come in, cuz chat is the highest priority for us. And we also have a privilege of working with some of the other, uh, world industry, uh, industry leaders. So by, uh, having this collective experience, working with the industry leaders, we get, uh, uh, technological superiority, being able to, uh, scale to, you know, hundreds of millions of users on a monthly basis. Also the security and the compliances by working with some of the largest commercial banks on some of the largest FinTech applications across the globe. So we have, you know, security, compliances, all the industry, best practices that are built in and all the new topnotch user experience that we are, uh, building with other customers can be also be, uh, utilized by a customer like pick pay. So you get this collective almost like evolutionary benefit. Yeah. By, uh, working with a company like us, >>You get a lot of economies of scale. Could you mind just sharing the URL for the company? So folks watching can go get, do a deep dive. Cause I'm you guys got a lot of, lot of, um, certifications under the covers, a lot of things you guys do. So you mind just sharing URL real quick. >>Yeah. So our company, uh, you can find everything about our company on sandberg.com like carrot pigeon. So, uh, you're sending a bird to send a message. So, uh, yeah. send.com >>All so let's get it to the application, cuz this is really interesting cuz Chad is table stakes now, but things are evolving beyond Chad. You gotta integrate that user experience. It's data. Now you gotta have scale. I mean, you know, people who wanna roll their own chat will find out there's a lot of client side and backend scale issues. Right. You can have a tsunami river like on Twitch, you know, you chat. I mean that, could you got client side issues, data scale. <laugh> right. You got backend. Um, Louis, talk about that dynamic because you know, as you start to scale, you want to rely on that. Talk about this dynamic, how apps now are integrating all these new features. So is it, are apps gonna go like more multifunctional? Do you see apps one and done? What's the, how do you guys see this app world playing out and where does, does the Sendbird fit in? And >>Just, just let me know better John, about the performance or about the, just, just let me >>Oh, slow with performance. Uh, performance is huge, right? You gotta have no one wants to have lag on, on chat. >>Okay. So, um, big pay when we look to the payments have millions, thousands of, of, of payments happen happening every second. So what we are doing now is moving all the payments through a conversation. So it always happened inside the conversation. So since from the first moment, um, every second counts to convert this client. And since from the first moment we never saw in, on Sandberg, any issue about that. And even when we have a question or something that we need to improve the team we're working together. So that that's, those are the points that are making us to work together and to make things going pretty fast. When we look to the users who are going to use chat, they are, their intention is three times better than the users who are not using payments through the chat. They are average. Average spent is three times higher too. >>So they, they are making more connections. They are chatting with their friends. They are friends are here. So the network effect is stronger. So if they're going to pay and they need to wait one more second, two seconds to conclude the payment, probably they will not go into choose paying through the, again, they will use only the wallet, only the code, only the Alliance of the user. So that's is so important for us to perform really, really fast. And then this is what we are finding. And this is what is happening with the integration with Sandberg. >>And what's interesting is, is that the by build chat with conversation, we just had a minute ago kind of plays in here. You get the benefits of Sandberg, but now your transactional fidelity is in the chat <laugh> that you don't build that you rely on them on. So again, that's an interesting dynamic. This is the future of apps, John, this is where it matters. The engagement. This is what you talk about is the new, the new digital experience who would've thought that five, 10 years ago. I mean, chat was just like, Hey, what's going around direct message. Now it's integral part of the app. What's your reading. >>Yeah. I mean, we're seeing that across, uh, uh, to Lewis's point, not just transactions, but like marketing messages are now being sent through chat. So the marketing is no longer just about like giving discount calls, but you can actually reengage with the brand. Uh, also support is becoming more real time through chat. So you're actually building a relationship. The support agents have a better context about the previous conversations and the transactions, the sales conversations, even like building, uh, building alerts, notification, all those things are now, uh, happening through conversations. And that's a better way for customers to engage with the brand cuz you actually, you're actually building a better relationship and also, uh, being able to trust the brand more because there is a channel for you to communicate and, and, and be seen and be heard, uh, by the brand. So we do believe that that's the future of the business and how more and more, uh, brands will be building relationships with their customers. >>Yeah. I love, I love your business model. I think it's really critical. And I think that stickiness is a real, uh, call out point there and the brand, the co-branding and the branding capability, but also really quickly in the last minute we have John and Luis, if you don't mind talking about security, I mean, I can't go a day now without getting an SMS scam, uh, text, uh, you seeing it now on WhatsApp. I mean, I don't even use telegram anymore. I mean, come on. So like, like this is now a problem. The old way has been infiltrated with spam and security issues. Security has to be there. The trust and security real quick, John, we'll start with you and we all Louis go, go ahead. >>No, no. Just, just to, to say how important is that we are not only a chatting platform. We are a payment platform, so we have money now, the transaction. So here in Brazil, we have all this safe, the, the, the layers, the security layers that we have in, on our app. And then we have the security layers provided from Sandburg. So, and when we look to the features, Sandberg are providing to us a lot of features that help users to feel safer like per refined profiles, like announcements, where it's a profile from peak pay, where the users can recognize. So this is peak pay talking with me. It's not a user trying to pass, trying to use big Bay's name to talk with me. So these issues is something that we are really, really, we really care about here because we are not only a chat platform. As I said before, we are a payment platform. We are a FinTech, we're at a digital bank. So we need to take care a lot and we don't have any complaint about it because Sandberg understood it. And then they, they, they are providing since the first moment with the perfect solutions and the user interface to make it simpler for the users to recognize that we speak, pay who is chatting with them, not a user with, with bad, bad intentions. >>Great, great insight, Louis. Thanks for sharing that, John really appreciate you guys coming on. Great showcase. Real final word. John will give you the final word folks watching out there. How do they engage with Sendbird? I want to integrate, I want to use your chat service. What do I do? Do I have to connect in as it managed service is the line of code. What do I do to get Sendbird? >>Yeah. So if you're a developer building a mobile application, simply come visit our website, we have a open documentation and SDK you can download and simply plug into your application. You can have a chat experience up and running matter of minutes, if not ours using our UI kit. So we want to make it as easy as possible for all the builders in the world to be able to harness the superpower of digital conversations. >>All right, great. Congratulations, John, on your success and all the growth and Louis, thanks for coming in, sharing the customer perspective and great insight. Thanks for coming on the showcase. Really appreciate it. Thanks for your time. >>Yeah. Thank you for having me. >>Okay. The a of us startup showcase season two, episode three here I'm John for your host. Thanks for watching.

>> Live from Boston, Massachusetts, it's The Cube! Covering IBM Chief Data Officer Summit, brought to you by IBM. >> Welcome back to The Cube's live coverage of IBM's Chief Data Strategy Summit, I'm your host Rebecca Knight, along with my cohost Dave Vellante. We have a big panel today, these are our social influencers. Starting at the top, we have Christopher Penn, VP Marketing of Shift Communications, then Tripp Braden, Executive Coach and Growth Strategist at Strategic Performance Partners, Mike Tamir, Chief Data Science Officer at TACT, Bob Hayes, President of Business Over Broadway. Thanks so much for joining us. >> Thank you. >> So we're talking about data as a way to engage customers, a way to engage employees. What business functions would you say stand to benefit the most from using data? >> I'll take a whack at that. I don't know if it's the biggest function, but I think the customer experience and customer success. How do you use data to help predict what customers will do, and how do you then use that information to kind of personalize that experience for them and drive up recommendations, retention, upselling, things like that. >> So it's really the customer experience that you're focusing on? >> Yes, and I just released a study. I found that analytical-leading companies tend to use analytics to understand their customers more than say analytical laggards. So those kind of companies who can actually get value from data, they focus their efforts around improving customer loyalty by just gaining a deeper understanding about their customers. >> Chris, you want to jump in here with- >> I was just going to say, as many of us said, we have three things we really care about as business people, right? We want to save money, save time, or make money. So any function that meets those qualifications, is a functional benefit from data. >> I think there's also another interesting dimension to this, when you start to look at the leadership team in the company, now having the ability to anticipate the future. I mean now, we are no longer just looking at static data. We are now looking at anticipatory capability and seeing around corners, so that the person comes to the team, they're bringing something completely different than the team has had in the past. This whole competency of being able to anticipate the future and then take from that, where you take your organization in the future. >> So follow up on that, Tripp, does data now finally trump gut feel? Remember the HBR article of 10, 15 years ago, can't beat gut feel? Is that, we hit a new era now? >> Well, I think we're moving into an era where we have both. I think it's no longer an either or, we have intuition or we have data. Now we have both. The organizations who can leverage both at the same time and develop that capability and earn the trust of the other members by doing that. I see the Chief Data Officer really being a catalyst for organizational change. >> So Dr. Tamir I wonder if I could ask you a question? Maybe the whole panel, but so we've all followed the big data trend and the meme, AI, deep learning, machine learning, same wine, new bottle, or is there something substantive behind it? >> So certainly our capabilities are growing, our capabilities in machine learning, and I think that's part of why now there's this new branding of AI. AI is not what your mother might have thought AI is. It's not robots and cylons and that sort of thing that are going to be able to think intelligently. They just did intelligence tests on the different, like Siri and Alexa, quote AIs from different companies, and they scored horribly. They scored much worse than my, much worse than my very intelligent seven-year old. And that's not a comment on the deficiencies in Alexa or in Siri. It's a comment on these are not actually artificial intelligences. These are just tools that apply machine learning strategically. >> So you are all thinking about data and how it is going to change the future and one of the things you said, Tripp, is that we can now see the future. Talk to me about some of the most exciting things that you're seeing that companies do that are anticipating what customers want. >> Okay, so for example, in the customer success space, a lot of Sass businesses have a monthly subscription, so they're very worried about customer churn. So companies are now leveraging all the user behavior to understand which customers are likely to leave next month, and if they know that, they can reach out to them with maybe some retention campaigns, or even use that data to find out who's most likely to buy more from you in the next month, and then market to those in effective ways. So don't just do a blast for everybody, focus on particular customers, their needs, and try to service them or market to them in a way that resonates with them that increases retention, upselling, and recommendations. >> So they've already seen certain behaviors that show a customer is maybe not going to re-up? >> Exactly, so you just, you throw this data in a machine learning, right. You find the predictors of your outcome that interest you, and then using that information, you say oh, maybe predictors A, B, and C, are the ones that actually drive loyalty behaviors, then you can use that information to segment your customers and market to them appropriately. It's pretty cool stuff. >> February 18th, 2018. >> Okay. >> So we did a study recently just for fun of when people search for the term "Outlook, out of office." Yeah, and you really only search for that term for one reason, you're going on vacation, and you want to figure out how to turn the feature on. So we did a five-year data poll of people, of the search times for that and then inverted it, so when do people search least for that term. That's when they're in the office, and it's the week of February 18th, 2018, will be that time when people like, yep, I'm at the office, I got to work. And knowing that, prediction and data give us specificity, like yeah, we know the first quarter is busy, we know between memorial Day and Labor Day is not as busy in the B to B world. But as a marketer, we need to put specificity, data and predictive analytics gives us specificity. We know what week to send our email campaigns, what week to turn our ad budgets all the way to full, and so on and so forth. If someone's looking for The Cube, when will they be doing that, you know, going forward? That's the power of this stuff, is that specificity. >> They know what we're going to search for before we search for it. (laughter) >> I'd like to know where I'm going to be next week. Why that date? >> That's the date that people least search for the term, "Outlook, out of office." >> Okay. >> So, they're not looking for that feature, which logically means they're in the office. >> Or they're on vacation. (laughter) Right, I'm just saying. >> That brings up a good point on not just, what you're predicting for interactions right now, but also anticipating the trends. So Bob brought up a good point about figuring out when people are churning. There's a flip side to that, which is how do you get your customers to be more engaged? And now we have really an explosion in reinforcement learning in particular, which is a tool for figuring out, not just how to interact with you right now as a one off, statically. But how do I interact with you over time, this week, next week, the week after that? And using reinforcement learning, you can actually do that. This is the the sort-of technique that they used to beat Alpha-Go or to beat humans with Alpha-Go. Machine-learning algorithms, supervised learning, works well when you get that immediate feedback, but if you're playing a game, you don't get that feedback that you're going to win 300 turns from now, right now. You have to create more advanced value functions and ways of anticipating where things are going, this move, so that you see things are on track for winning in 20, 30, 40 moves, down the road. And it's the same thing when you're dealing with customer engagement. You want to, you can make a decision, I'm going to give this customer a coupon that's going to make them spend 50 cents more today, or you can make decisions algorithmically that are going to give them a 50 cent discount this week, next week, and the week after that, that are going to make them become a coffee drinker for life, or customer for life. >> It's about finding those customers for life. >> IBM uses the term cognitive business. We go to these conferences, everybody talks about digital transformation. At the end of the day it's all about how you use data. So my question is, if you think about the bell curve of organizations that you work with, how do they, what's the shape of that curve, part one. And then part two is, where do you see IBM on that curve? >> Well I think a lot of my clients make a living predicting the future, they're insurance companies and financial services. That's where the CDO currently resides and they get a lot of benefit. But one of things we're all talking about, but talking around, is that human element. So now, how do we take the human element and incorporate this into the structure of how we make our decisions? And how do we take this information, and how do we learn to trust that? The one thing I hear from most of the executives I talk to, when they talk about how data is being used in their organizations is the lack of trust. Now, when you have that, and you start to look at the trends that we're dealing with, and we call them data points verses calling them people, now you have a problem, because people become very, almost analytically challenged, right? So how do we get people to start saying, okay, let's look at this from the point of view of, it's not an either or solution in the world we live in today. Cognitive organizations are not going to happen tomorrow morning, even the most progressive organizations are probably five years away from really deploying them completely. But the organizations who take a little bit of an edge, so five, ten percent edge out of there, they now have a really, a different advantage in their markets. And that's what we're talking about, hyper-critical thinking skills. I mean, when you start to say, how do I think like Warren Buffet, how do I start to look and make these kinds of decisions analytically? How do I recreate an artificial intelligence when machine-learning practice, and program that's going to provide that solution for people. And that's where I think organizations that are forward-leaning now are looking and saying, how do I get my people to use these capabilities and ultimately trust the data that they're told. >> So I forget who said it, but it was early on in the big data movement, somebody said that we're further away from a single version of the truth than ever, and it's just going to get worse. So as a data scientist, what say you? >> I'm not familiar with the truth quote, but I think it's very relevant, well very relevant to where we are today. There's almost an arms race of, you hear all the time about automating, putting out fake news, putting out misinformation, and how that can be done using all the technology that we have at our disposal for disbursing that information. The only way that that's going to get solved is also with algorithmic solutions with creating algorithms that are going to be able to detect, is this news, is this something that is trying to attack my emotions and convince me just based on fear, or is this an article that's trying to present actual facts to me and you can do that with machine-learning algorithms. Now we have the technology to do that, algorithmically. >> Better algos than like and share. >> From a technological perspective, to your question about where IBM is, IBM has a ton of stuff that I call AI as a service, essentially where if you're a developer on Bluemix, for example, you can plug in to the different components of Watson at literally pennies per usage, to say I want to do sentiment analysis, I want to do tone analysis, I want personality insights, about this piece, who wrote this piece of content. And to Dr. Tamir's point, this is stuff that, we need these tools to do things like, fingerprint this piece of text. Did the supposed author actually write this? You can tell that, so of all the four magi, we call it, the Microsoft, Amazon, Google, IBM, getting on board, and adding that five or ten percent edge that Tripp was talking about, is easiest with IBM Bluemix. >> Great. >> Well, one of the other parts of this is you start to talk about what we're doing and you start to look at the players that are doing this. They are all organizations that I would not call classical technology organizations. They were 10 years ago, look at a Microsoft. But you look at the leadership of Microsoft today, and they're much more about figuring out what the formula is for success for business, and that's the other place I think we're seeing a transformation occurring, and the early adopters, is they have gone through the first generation, and the pain, you know, of having to have these kinds of things, and now they're moving to that second generation, where they're looking for the gain. And they're looking for people who can bring them capability and have the conversation, and discuss them in ways that they can see the landscape. I mean part of this is if you get caught in the bits and bites, you miss the landscape that you should be seeing in the market, and that's why I think there's a tremendous opportunity for us to really look at multiple markets of the same data. I mean, imagine looking and here's what I see, everyone in this group would have a different opinion in what they're seeing, but now we have the ability to see it five different ways and share that with our executive team and what we're seeing, so we can make better decisions. >> I wonder if we could have a frank conversation, an honest conversation about the data and the data ownership. You heard IBM this morning, saying hey we're going to protect your data, but I'd love you guys, as independents to weigh in. You got this data, you guys are involved with your clients, building models, the data trains the model. I got to believe that that model gets used at a lot of different places, within an industry, like insurance or across retail, whatever it is. So I'm afraid that my data is, my IP is going to seep across the industry. Should I not be worried about that? I wonder if you guys could weigh in. >> Well if you work with a particular vendor, sometimes vendors have a stipulation that we will not share your models with other clients, so you just got to stick to that. But in terms of science, I mean you build a model, right? You want to generalize that to other businesses. >> Right! >> (drowned out by others talking) So maybe if you could work somehow with your existing clients, say here, this is what we want to do, we just want to elevate the waters for everybody, right? So everybody wins when all boats rise, right? So if you can kind of convince your clients that we just want to help the world be better, and function better, make employees happier, customers happier, let's take that approach and just use models in a, that may be generalized to other situations and use them. If if you don't, then you just don't. >> Right, that's your choice. >> It's a choice, it's a choice you have to make. >> As long as you're transparent about it. >> I'm not super worried, I mean, you, Dave, Tripp, and I are all dressed similarly, right? We have the model of shirt and tie so, if I put on your clothes, we wouldn't, but if I were to put on your clothes, it would not be, even though it's the same model, it's just not going to be the same outcome. It's going to look really bad, right, so. Yes, companies can share the models and the general flows and stuff, but there's so much, if a company's doing machine learning well, there's so much feature engineering that's unique to that company that trying to apply that somewhere else, is just going to blow up. >> Yeah, but we could switch ties, like Tripp has got a really cool tie, I'd be using that tie on July 4th. >> This is turning into a different kind of panel (laughter) Chris, Tripp, Mike, and Bob, thanks so much for joining us. This has been a really fun and interesting panel. >> Thank you very much. Thank you. >> Thanks you guys. >> We will have more from the IBM Summit in Boston just after this. (techno music)

>> Narrator: Live from New York, it's the Cube, covering big data New York City 2016. Brought to you by headline sponsors, CISCO, IBM, NVIDIA, and our ecosystem sponsors, now here's your host, Dave Valante. >> Welcome back to New York City, everybody, this is the Cube, the worldwide leader in live tech coverage, and this is a cube first, we've got a nine person, actually eight person panel of experts, data scientists, all alike. I'm here with my co-host, James Cubelis, who has helped organize this panel of experts. James, welcome. >> Thank you very much, Dave, it's great to be here, and we have some really excellent brain power up there, so I'm going to let them talk. >> Okay, well thank you again-- >> And I'll interject my thoughts now and then, but I want to hear them. >> Okay, great, we know you well, Jim, we know you'll do that, so thank you for that, and appreciate you organizing this. Okay, so what I'm going to do to our panelists is ask you to introduce yourself. I'll introduce you, but tell us a little bit about yourself, and talk a little bit about what data science means to you. A number of you started in the field a long time ago, perhaps data warehouse experts before the term data science was coined. Some of you started probably after Hal Varian said it was the sexiest job in the world. (laughs) So think about how data science has changed and or what it means to you. We're going to start with Greg Piateski, who's from Boston. A Ph.D., KDnuggets, Greg, tell us about yourself and what data science means to you. >> Okay, well thank you Dave and thank you Jim for the invitation. Data science in a sense is the second oldest profession. I think people have this built-in need to find patterns and whatever we find we want to organize the data, but we do it well on a small scale, but we don't do it well on a large scale, so really, data science takes our need and helps us organize what we find, the patterns that we find that are really valid and useful and not just random, I think this is a big challenge of data science. I've actually started in this field before the term Data Science existed. I started as a researcher and organized the first few workshops on data mining and knowledge discovery, and the term data mining became less fashionable, became predictive analytics, now it's data science and it will be something else in a few years. >> Okay, thank you, Eves Mulkearns, Eves, I of course know you from Twitter. A lot of people know you as well. Tell us about your experiences and what data scientist means to you. >> Well, data science to me is if you take the two words, the data and the science, the science it holds a lot of expertise and skills there, it's statistics, it's mathematics, it's understanding the business and putting that together with the digitization of what we have. It's not only the structured data or the unstructured data what you store in the database try to get out and try to understand what is in there, but even video what is coming on and then trying to find, like George already said, the patterns in there and bringing value to the business but looking from a technical perspective, but still linking that to the business insights and you can do that on a technical level, but then you don't know yet what you need to find, or what you're looking for. >> Okay great, thank you. Craig Brown, Cube alum. How many people have been on the Cube actually before? >> I have. >> Okay, good. I always like to ask that question. So Craig, tell us a little bit about your background and, you know, data science, how has it changed, what's it all mean to you? >> Sure, so I'm Craig Brown, I've been in IT for almost 28 years, and that was obviously before the term data science, but I've evolved from, I started out as a developer. And evolved through the data ranks, as I called it, working with data structures, working with data systems, data technologies, and now we're working with data pure and simple. Data science to me is an individual or team of individuals that dissect the data, understand the data, help folks look at the data differently than just the information that, you know, we usually use in reports, and get more insights on, how to utilize it and better leverage it as an asset within an organization. >> Great, thank you Craig, okay, Jennifer Shin? Math is obviously part of being a data scientist. You're good at math I understand. Tell us about yourself. >> Yeah, so I'm a senior principle data scientist at the Nielsen Company. I'm also the founder of 8 Path Solutions, which is a data science, analytics, and technology company, and I'm also on the faculty in the Master of Information and Data Science program at UC Berkeley. So math is part of the IT statistics for data science actually this semester, and I think for me, I consider myself a scientist primarily, and data science is a nice day job to have, right? Something where there's industry need for people with my skill set in the sciences, and data gives us a great way of being able to communicate sort of what we know in science in a way that can be used out there in the real world. I think the best benefit for me is that now that I'm a data scientist, people know what my job is, whereas before, maybe five ten years ago, no one understood what I did. Now, people don't necessarily understand what I do now, but at least they understand kind of what I do, so it's still an improvement. >> Excellent. Thank you Jennifer. Joe Caserta, you're somebody who started in the data warehouse business, and saw that snake swallow a basketball and grow into what we now know as big data, so tell us about yourself. >> So I've been doing data for 30 years now, and I wrote the Data Warehouse ETL Toolkit with Ralph Timbal, which is the best selling book in the industry on preparing data for analytics, and with the big paradigm shift that's happened, you know for me the past seven years has been, instead of preparing data for people to analyze data to make decisions, now we're preparing data for machines to make the decisions, and I think that's the big shift from data analysis to data analytics and data science. >> Great, thank you. Miriam, Miriam Fridell, welcome. >> Thank you. I'm Miriam Fridell, I work for Elder Research, we are a data science consultancy, and I came to data science, sort of through a very circuitous route. I started off as a physicist, went to work as a consultant and software engineer, then became a research analyst, and finally came to data science. And I think one of the most interesting things to me about data science is that it's not simply about building an interesting model and doing some interesting mathematics, or maybe wrangling the data, all of which I love to do, but it's really the entire analytics lifecycle, and a value that you can actually extract from data at the end, and that's one of the things that I enjoy most is seeing a client's eyes light up or a wow, I didn't really know we could look at data that way, that's really interesting. I can actually do something with that, so I think that, to me, is one of the most interesting things about it. >> Great, thank you. Justin Sadeen, welcome. >> Absolutely, than you, thank you. So my name is Justin Sadeen, I work for Morph EDU, an artificial intelligence company in Atlanta, Georgia, and we develop learning platforms for non-profit and private educational institutions. So I'm a Marine Corp veteran turned data enthusiast, and so what I think about data science is the intersection of information, intelligence, and analysis, and I'm really excited about the transition from big data into smart data, and that's what I see data science as. >> Great, and last but not least, Dez Blanchfield, welcome mate. >> Good day. Yeah, I'm the one with the funny accent. So data science for me is probably the funniest job I've ever to describe to my mom. I've had quite a few different jobs, and she's never understood any of them, and this one she understands the least. I think a fun way to describe what we're trying to do in the world of data science and analytics now is it's the equivalent of high altitude mountain climbing. It's like the extreme sport version of the computer science world, because we have to be this magical unicorn of a human that can understand plain english problems from C-suite down and then translate it into code, either as soles or as teams of developers. And so there's this black art that we're expected to be able to transmogrify from something that we just in plain english say I would like to know X, and we have to go and figure it out, so there's this neat extreme sport view I have of rushing down the side of a mountain on a mountain bike and just dodging rocks and trees and things occasionally, because invariably, we do have things that go wrong, and they don't quite give us the answers we want. But I think we're at an interesting point in time now with the explosion in the types of technology that are at our fingertips, and the scale at which we can do things now, once upon a time we would sit at a terminal and write code and just look at data and watch it in columns, and then we ended up with spreadsheet technologies at our fingertips. Nowadays it's quite normal to instantiate a small high performance distributed cluster of computers, effectively a super computer in a public cloud, and throw some data at it and see what comes back. And we can do that on a credit card. So I think we're at a really interesting tipping point now where this coinage of data science needs to be slightly better defined, so that we can help organizations who have weird and strange questions that they want to ask, tell them solutions to those questions, and deliver on them in, I guess, a commodity deliverable. I want to know xyz and I want to know it in this time frame and I want to spend this much amount of money to do it, and I don't really care how you're going to do it. And there's so many tools we can choose from and there's so many platforms we can choose from, it's this little black art of computing, if you'd like, we're effectively making it up as we go in many ways, so I think it's one of the most exciting challenges that I've had, and I think I'm pretty sure I speak for most of us in that we're lucky that we get paid to do this amazing job. That we get make up on a daily basis in some cases. >> Excellent, well okay. So we'll just get right into it. I'm going to go off script-- >> Do they have unicorns down under? I think they have some strange species right? >> Well we put the pointy bit on the back. You guys have in on the front. >> So I was at an IBM event on Friday. It was a chief data officer summit, and I attended what was called the Data Divas' breakfast. It was a women in tech thing, and one of the CDOs, she said that 25% of chief data officers are women, which is much higher than you would normally see in the profile of IT. We happen to have 25% of our panelists are women. Is that common? Miriam and Jennifer, is that common for the data science field? Or is this a higher percentage than you would normally see-- >> James: Or a lower percentage? >> I think certainly for us, we have hired a number of additional women in the last year, and they are phenomenal data scientists. I don't know that I would say, I mean I think it's certainly typical that this is still a male-dominated field, but I think like many male-dominated fields, physics, mathematics, computer science, I think that that is slowly changing and evolving, and I think certainly, that's something that we've noticed in our firm over the years at our consultancy, as we're hiring new people. So I don't know if I would say 25% is the right number, but hopefully we can get it closer to 50. Jennifer, I don't know if you have... >> Yeah, so I know at Nielsen we have actually more than 25% of our team is women, at least the team I work with, so there seems to be a lot of women who are going into the field. Which isn't too surprising, because with a lot of the issues that come up in STEM, one of the reasons why a lot of women drop out is because they want real world jobs and they feel like they want to be in the workforce, and so I think this is a great opportunity with data science being so popular for these women to actually have a job where they can still maintain that engineering and science view background that they learned in school. >> Great, well Hillary Mason, I think, was the first data scientist that I ever interviewed, and I asked her what are the sort of skills required and the first question that we wanted to ask, I just threw other women in tech in there, 'cause we love women in tech, is about this notion of the unicorn data scientist, right? It's been put forth that there's the skill sets required to be a date scientist are so numerous that it's virtually impossible to have a data scientist with all those skills. >> And I love Dez's extreme sports analogy, because that plays into the whole notion of data science, we like to talk about the theme now of data science as a team sport. Must it be an extreme sport is what I'm wondering, you know. The unicorns of the world seem to be... Is that realistic now in this new era? >> I mean when automobiles first came out, they were concerned that there wouldn't be enough chauffeurs to drive all the people around. Is there an analogy with data, to be a data-driven company. Do I need a data scientist, and does that data scientist, you know, need to have these unbelievable mixture of skills? Or are we doomed to always have a skill shortage? Open it up. >> I'd like to have a crack at that, so it's interesting, when automobiles were a thing, when they first bought cars out, and before they, sort of, were modernized by the likes of Ford's Model T, when we got away from the horse and carriage, they actually had human beings walking down the street with a flag warning the public that the horseless carriage was coming, and I think data scientists are very much like that. That we're kind of expected to go ahead of the organization and try and take the challenges we're faced with today and see what's going to come around the corner. And so we're like the little flag-bearers, if you'd like, in many ways of this is where we're at today, tell me where I'm going to be tomorrow, and try and predict the day after as well. It is very much becoming a team sport though. But I think the concept of data science being a unicorn has come about because the coinage hasn't been very well defined, you know, if you were to ask 10 people what a data scientist were, you'd get 11 answers, and I think this is a really challenging issue for hiring managers and C-suites when the generants say I was data science, I want big data, I want an analyst. They don't actually really know what they're asking for. Generally, if you ask for a database administrator, it's a well-described job spec, and you can just advertise it and some 20 people will turn up and you interview to decide whether you like the look and feel and smell of 'em. When you ask for a data scientist, there's 20 different definitions of what that one data science role could be. So we don't initially know what the job is, we don't know what the deliverable is, and we're still trying to figure that out, so yeah. >> Craig what about you? >> So from my experience, when we talk about data science, we're really talking about a collection of experiences with multiple people I've yet to find, at least from my experience, a data science effort with a lone wolf. So you're talking about a combination of skills, and so you don't have, no one individual needs to have all that makes a data scientist a data scientist, but you definitely have to have the right combination of skills amongst a team in order to accomplish the goals of data science team. So from my experiences and from the clients that I've worked with, we refer to the data science effort as a data science team. And I believe that's very appropriate to the team sport analogy. >> For us, we look at a data scientist as a full stack web developer, a jack of all trades, I mean they need to have a multitude of background coming from a programmer from an analyst. You can't find one subject matter expert, it's very difficult. And if you're able to find a subject matter expert, you know, through the lifecycle of product development, you're going to require that individual to interact with a number of other members from your team who are analysts and then you just end up well training this person to be, again, a jack of all trades, so it comes full circle. >> I own a business that does nothing but data solutions, and we've been in business 15 years, and it's been, the transition over time has been going from being a conventional wisdom run company with a bunch of experts at the top to becoming more of a data-driven company using data warehousing and BI, but now the trend is absolutely analytics driven. So if you're not becoming an analytics-driven company, you are going to be behind the curve very very soon, and it's interesting that IBM is now coining the phrase of a cognitive business. I think that is absolutely the future. If you're not a cognitive business from a technology perspective, and an analytics-driven perspective, you're going to be left behind, that's for sure. So in order to stay competitive, you know, you need to really think about data science think about how you're using your data, and I also see that what's considered the data expert has evolved over time too where it used to be just someone really good at writing SQL, or someone really good at writing queries in any language, but now it's becoming more of a interdisciplinary action where you need soft skills and you also need the hard skills, and that's why I think there's more females in the industry now than ever. Because you really need to have a really broad width of experiences that really wasn't required in the past. >> Greg Piateski, you have a comment? >> So there are not too many unicorns in nature or as data scientists, so I think organizations that want to hire data scientists have to look for teams, and there are a few unicorns like Hillary Mason or maybe Osama Faiat, but they generally tend to start companies and very hard to retain them as data scientists. What I see is in other evolution, automation, and you know, steps like IBM, Watson, the first platform is eventually a great advance for data scientists in the short term, but probably what's likely to happen in the longer term kind of more and more of those skills becoming subsumed by machine unique layer within the software. How long will it take, I don't know, but I have a feeling that the paradise for data scientists may not be very long lived. >> Greg, I have a follow up question to what I just heard you say. When a data scientist, let's say a unicorn data scientist starts a company, as you've phrased it, and the company's product is built on data science, do they give up becoming a data scientist in the process? It would seem that they become a data scientist of a higher order if they've built a product based on that knowledge. What is your thoughts on that? >> Well, I know a few people like that, so I think maybe they remain data scientists at heart, but they don't really have the time to do the analysis and they really have to focus more on strategic things. For example, today actually is the birthday of Google, 18 years ago, so Larry Page and Sergey Brin wrote a very influential paper back in the '90s About page rank. Have they remained data scientist, perhaps a very very small part, but that's not really what they do, so I think those unicorn data scientists could quickly evolve to have to look for really teams to capture those skills. >> Clearly they come to a point in their career where they build a company based on teams of data scientists and data engineers and so forth, which relates to the topic of team data science. What is the right division of roles and responsibilities for team data science? >> Before we go, Jennifer, did you have a comment on that? >> Yeah, so I guess I would say for me, when data science came out and there was, you know, the Venn Diagram that came out about all the skills you were supposed to have? I took a very different approach than all of the people who I knew who were going into data science. Most people started interviewing immediately, they were like this is great, I'm going to get a job. I went and learned how to develop applications, and learned computer science, 'cause I had never taken a computer science course in college, and made sure I trued up that one part where I didn't know these things or had the skills from school, so I went headfirst and just learned it, and then now I have actually a lot of technology patents as a result of that. So to answer Jim's question, actually. I started my company about five years ago. And originally started out as a consulting firm slash data science company, then it evolved, and one of the reasons I went back in the industry and now I'm at Nielsen is because you really can't do the same sort of data science work when you're actually doing product development. It's a very very different sort of world. You know, when you're developing a product you're developing a core feature or functionality that you're going to offer clients and customers, so I think definitely you really don't get to have that wide range of sort of looking at 8 million models and testing things out. That flexibility really isn't there as your product starts getting developed. >> Before we go into the team sport, the hard skills that you have, are you all good at math? Are you all computer science types? How about math? Are you all math? >> What were your GPAs? (laughs) >> David: Anybody not math oriented? Anybody not love math? You don't love math? >> I love math, I think it's required. >> David: So math yes, check. >> You dream in equations, right? You dream. >> Computer science? Do I have to have computer science skills? At least the basic knowledge? >> I don't know that you need to have formal classes in any of these things, but I think certainly as Jennifer was saying, if you have no skills in programming whatsoever and you have no interest in learning how to write SQL queries or RR Python, you're probably going to struggle a little bit. >> James: It would be a challenge. >> So I think yes, I have a Ph.D. in physics, I did a lot of math, it's my love language, but I think you don't necessarily need to have formal training in all of these things, but I think you need to have a curiosity and a love of learning, and so if you don't have that, you still want to learn and however you gain that knowledge I think, but yeah, if you have no technical interests whatsoever, and don't want to write a line of code, maybe data science is not the field for you. Even if you don't do it everyday. >> And statistics as well? You would put that in that same general category? How about data hacking? You got to love data hacking, is that fair? Eaves, you have a comment? >> Yeah, I think so, while we've been discussing that for me, the most important part is that you have a logical mind and you have the capability to absorb new things and the curiosity you need to dive into that. While I don't have an education in IT or whatever, I have a background in chemistry and those things that I learned there, I apply to information technology as well, and from a part that you say, okay, I'm a tech-savvy guy, I'm interested in the tech part of it, you need to speak that business language and if you can do that crossover and understand what other skill sets or parts of the roles are telling you I think the communication in that aspect is very important. >> I'd like throw just something really quickly, and I think there's an interesting thing that happens in IT, particularly around technology. We tend to forget that we've actually solved a lot of these problems in the past. If we look in history, if we look around the second World War, and Bletchley Park in the UK, where you had a very similar experience as humans that we're having currently around the whole issue of data science, so there was an interesting challenge with the enigma in the shark code, right? And there was a bunch of men put in a room and told, you're mathematicians and you come from universities, and you can crack codes, but they couldn't. And so what they ended up doing was running these ads, and putting challenges, they actually put, I think it was crossword puzzles in the newspaper, and this deluge of women came out of all kinds of different roles without math degrees, without science degrees, but could solve problems, and they were thrown at the challenge of cracking codes, and invariably, they did the heavy lifting. On a daily basis for converting messages from one format to another, so that this very small team at the end could actually get in play with the sexy piece of it. And I think we're going through a similar shift now with what we're refer to as data science in the technology and business world. Where the people who are doing the heavy lifting aren't necessarily what we'd think of as the traditional data scientists, and so, there have been some unicorns and we've championed them, and they're great. But I think the shift's going to be to accountants, actuaries, and statisticians who understand the business, and come from an MBA star background that can learn the relevant pieces of math and models that we need to to apply to get the data science outcome. I think we've already been here, we've solved this problem, we've just got to learn not to try and reinvent the wheel, 'cause the media hypes this whole thing of data science is exciting and new, but we've been here a couple times before, and there's a lot to be learned from that, my view. >> I think we had Joe next. >> Yeah, so I was going to say that, data science is a funny thing. To use the word science is kind of a misnomer, because there is definitely a level of art to it, and I like to use the analogy, when Michelangelo would look at a block of marble, everyone else looked at the block of marble to see a block of marble. He looks at a block of marble and he sees a finished sculpture, and then he figures out what tools do I need to actually make my vision? And I think data science is a lot like that. We hear a problem, we see the solution, and then we just need the right tools to do it, and I think part of consulting and data science in particular. It's not so much what we know out of the gate, but it's how quickly we learn. And I think everyone here, what makes them brilliant, is how quickly they could learn any tool that they need to see their vision get accomplished. >> David: Justin? >> Yeah, I think you make a really great point, for me, I'm a Marine Corp veteran, and the reason I mentioned that is 'cause I work with two veterans who are problem solvers. And I think that's what data scientists really are, in the long run are problem solvers, and you mentioned a great point that, yeah, I think just problem solving is the key. You don't have to be a subject matter expert, just be able to take the tools and intelligently use them. >> Now when you look at the whole notion of team data science, what is the right mix of roles, like role definitions within a high-quality or a high-preforming data science teams now IBM, with, of course, our announcement of project, data works and so forth. We're splitting the role division, in terms of data scientist versus data engineers versus application developer versus business analyst, is that the right breakdown of roles? Or what would the panelists recommend in terms of understanding what kind of roles make sense within, like I said, a high performing team that's looking for trying to develop applications that depend on data, machine learning, and so forth? Anybody want to? >> I'll tackle that. So the teams that I have created over the years made up these data science teams that I brought into customer sites have a combination of developer capabilities and some of them are IT developers, but some of them were developers of things other than applications. They designed buildings, they did other things with their technical expertise besides building technology. The other piece besides the developer is the analytics, and analytics can be taught as long as they understand how algorithms work and the code behind the analytics, in other words, how are we analyzing things, and from a data science perspective, we are leveraging technology to do the analyzing through the tool sets, so ultimately as long as they understand how tool sets work, then we can train them on the tools. Having that analytic background is an important piece. >> Craig, is it easier to, I'll go to you in a moment Joe, is it easier to cross train a data scientist to be an app developer, than to cross train an app developer to be a data scientist or does it not matter? >> Yes. (laughs) And not the other way around. It depends on the-- >> It's easier to cross train a data scientist to be an app developer than-- >> Yes. >> The other way around. Why is that? >> Developing code can be as difficult as the tool set one uses to develop code. Today's tool sets are very user friendly. where developing code is very difficult to teach a person to think along the lines of developing code when they don't have any idea of the aspects of code, of building something. >> I think it was Joe, or you next, or Jennifer, who was it? >> I would say that one of the reasons for that is data scientists will probably know if the answer's right after you process data, whereas data engineer might be able to manipulate the data but may not know if the answer's correct. So I think that is one of the reasons why having a data scientist learn the application development skills might be a easier time than the other way around. >> I think Miriam, had a comment? Sorry. >> I think that what we're advising our clients to do is to not think, before data science and before analytics became so required by companies to stay competitive, it was more of a waterfall, you have a data engineer build a solution, you know, then you throw it over the fence and the business analyst would have at it, where now, it must be agile, and you must have a scrum team where you have the data scientist and the data engineer and the project manager and the product owner and someone from the chief data office all at the table at the same time and all accomplishing the same goal. Because all of these skills are required, collectively in order to solve this problem, and it can't be done daisy chained anymore it has to be a collaboration. And that's why I think spark is so awesome, because you know, spark is a single interface that a data engineer can use, a data analyst can use, and a data scientist can use. And now with what we've learned today, having a data catalog on top so that the chief data office can actually manage it, I think is really going to take spark to the next level. >> James: Miriam? >> I wanted to comment on your question to Craig about is it harder to teach a data scientist to build an application or vice versa, and one of the things that we have worked on a lot in our data science team is incorporating a lot of best practices from software development, agile, scrum, that sort of thing, and I think particularly with a focus on deploying models that we don't just want to build an interesting data science model, we want to deploy it, and get some value. You need to really incorporate these processes from someone who might know how to build applications and that, I think for some data scientists can be a challenge, because one of the fun things about data science is you get to get into the data, and you get your hands dirty, and you build a model, and you get to try all these cool things, but then when the time comes for you to actually deploy something, you need deployment-grade code in order to make sure it can go into production at your client side and be useful for instance, so I think that there's an interesting challenge on both ends, but one of the things I've definitely noticed with some of our data scientists is it's very hard to get them to think in that mindset, which is why you have a team of people, because everyone has different skills and you can mitigate that. >> Dev-ops for data science? >> Yeah, exactly. We call it insight ops, but yeah, I hear what you're saying. Data science is becoming increasingly an operational function as opposed to strictly exploratory or developmental. Did some one else have a, Dez? >> One of the things I was going to mention, one of the things I like to do when someone gives me a new problem is take all the laptops and phones away. And we just end up in a room with a whiteboard. And developers find that challenging sometimes, so I had this one line where I said to them don't write the first line of code until you actually understand the problem you're trying to solve right? And I think where the data science focus has changed the game for organizations who are trying to get some systematic repeatable process that they can throw data at and just keep getting answers and things, no matter what the industry might be is that developers will come with a particular mindset on how they're going to codify something without necessarily getting the full spectrum and understanding the problem first place. What I'm finding is the people that come at data science tend to have more of a hacker ethic. They want to hack the problem, they want to understand the challenge, and they want to be able to get it down to plain English simple phrases, and then apply some algorithms and then build models, and then codify it, and so most of the time we sit in a room with whiteboard markers just trying to build a model in a graphical sense and make sure it's going to work and that it's going to flow, and once we can do that, we can codify it. I think when you come at it from the other angle from the developer ethic, and you're like I'm just going to codify this from day one, I'm going to write code. I'm going to hack this thing out and it's just going to run and compile. Often, you don't truly understand what he's trying to get to at the end point, and you can just spend days writing code and I think someone made the comment that sometimes you don't actually know whether the output is actually accurate in the first place. So I think there's a lot of value being provided from the data science practice. Over understanding the problem in plain english at a team level, so what am I trying to do from the business consulting point of view? What are the requirements? How do I build this model? How do I test the model? How do I run a sample set through it? Train the thing and then make sure what I'm going to codify actually makes sense in the first place, because otherwise, what are you trying to solve in the first place? >> Wasn't that Einstein who said if I had an hour to solve a problem, I'd spend 55 minutes understanding the problem and five minutes on the solution, right? It's exactly what you're talking about. >> Well I think, I will say, getting back to the question, the thing with building these teams, I think a lot of times people don't talk about is that engineers are actually very very important for data science projects and data science problems. For instance, if you were just trying to prototype something or just come up with a model, then data science teams are great, however, if you need to actually put that into production, that code that the data scientist has written may not be optimal, so as we scale out, it may be actually very inefficient. At that point, you kind of want an engineer to step in and actually optimize that code, so I think it depends on what you're building and that kind of dictates what kind of division you want among your teammates, but I do think that a lot of times, the engineering component is really undervalued out there. >> Jennifer, it seems that the data engineering function, data discovery and preparation and so forth is becoming automated to a greater degree, but if I'm listening to you, I don't hear that data engineering as a discipline is becoming extinct in terms of a role that people can be hired into. You're saying that there's a strong ongoing need for data engineers to optimize the entire pipeline to deliver the fruits of data science in production applications, is that correct? So they play that very much operational role as the backbone for... >> So I think a lot of times businesses will go to data scientist to build a better model to build a predictive model, but that model may not be something that you really want to implement out there when there's like a million users coming to your website, 'cause it may not be efficient, it may take a very long time, so I think in that sense, it is important to have good engineers, and your whole product may fail, you may build the best model it may have the best output, but if you can't actually implement it, then really what good is it? >> What about calibrating these models? How do you go about doing that and sort of testing that in the real world? Has that changed overtime? Or is it... >> So one of the things that I think can happen, and we found with one of our clients is when you build a model, you do it with the data that you have, and you try to use a very robust cross-validation process to make sure that it's robust and it's sturdy, but one thing that can sometimes happen is after you put your model into production, there can be external factors that, societal or whatever, things that have nothing to do with the data that you have or the quality of the data or the quality of the model, which can actually erode the model's performance over time. So as an example, we think about cell phone contracts right? Those have changed a lot over the years, so maybe five years ago, the type of data plan you had might not be the same that it is today, because a totally different type of plan is offered, so if you're building a model on that to say predict who's going to leave and go to a different cell phone carrier, the validity of your model overtime is going to completely degrade based on nothing that you have, that you put into the model or the data that was available, so I think you need to have this sort of model management and monitoring process to take this factors into account and then know when it's time to do a refresh. >> Cross-validation, even at one point in time, for example, there was an article in the New York Times recently that they gave the same data set to five different data scientists, this is survey data for the presidential election that's upcoming, and five different data scientists came to five different predictions. They were all high quality data scientists, the cross-validation showed a wide variation about who was on top, whether it was Hillary or whether it was Trump so that shows you that even at any point in time, cross-validation is essential to understand how robust the predictions might be. Does somebody else have a comment? Joe? >> I just want to say that this even drives home the fact that having the scrum team for each project and having the engineer and the data scientist, data engineer and data scientist working side by side because it is important that whatever we're building we assume will eventually go into production, and we used to have in the data warehousing world, you'd get the data out of the systems, out of your applications, you do analysis on your data, and the nirvana was maybe that data would go back to the system, but typically it didn't. Nowadays, the applications are dependent on the insight coming from the data science team. With the behavior of the application and the personalization and individual experience for a customer is highly dependent, so it has to be, you said is data science part of the dev-ops team, absolutely now, it has to be. >> Whose job is it to figure out the way in which the data is presented to the business? Where's the sort of presentation, the visualization plan, is that the data scientist role? Does that depend on whether or not you have that gene? Do you need a UI person on your team? Where does that fit? >> Wow, good question. >> Well usually that's the output, I mean, once you get to the point where you're visualizing the data, you've created an algorithm or some sort of code that produces that to be visualized, so at the end of the day that the customers can see what all the fuss is about from a data science perspective. But it's usually post the data science component. >> So do you run into situations where you can see it and it's blatantly obvious, but it doesn't necessarily translate to the business? >> Well there's an interesting challenge with data, and we throw the word data around a lot, and I've got this fun line I like throwing out there. If you torture data long enough, it will talk. So the challenge then is to figure out when to stop torturing it, right? And it's the same with models, and so I think in many other parts of organizations, we'll take something, if someone's doing a financial report on performance of the organization and they're doing it in a spreadsheet, they'll get two or three peers to review it, and validate that they've come up with a working model and the answer actually makes sense. And I think we're rushing so quickly at doing analysis on data that comes to us in various formats and high velocity that I think it's very important for us to actually stop and do peer reviews, of the models and the data and the output as well, because otherwise we start making decisions very quickly about things that may or may not be true. It's very easy to get the data to paint any picture you want, and you gave the example of the five different attempts at that thing, and I had this shoot out thing as well where I'll take in a team, I'll get two different people to do exactly the same thing in completely different rooms, and come back and challenge each other, and it's quite amazing to see the looks on their faces when they're like, oh, I didn't see that, and then go back and do it again until, and then just keep iterating until we get to the point where they both get the same outcome, in fact there's a really interesting anecdote about when the UNIX operation system was being written, and a couple of the authors went away and wrote the same program without realizing that each other were doing it, and when they came back, they actually had line for line, the same piece of C code, 'cause they'd actually gotten to a truth. A perfect version of that program, and I think we need to often look at, when we're building models and playing with data, if we can't come at it from different angles, and get the same answer, then maybe the answer isn't quite true yet, so there's a lot of risk in that. And it's the same with presentation, you know, you can paint any picture you want with the dashboard, but who's actually validating when the dashboard's painting the correct picture? >> James: Go ahead, please. >> There is a science actually, behind data visualization, you know if you're doing trending, it's a line graph, if you're doing comparative analysis, it's bar graph, if you're doing percentages, it's a pie chart, like there is a certain science to it, it's not that much of a mystery as the novice thinks there is, but what makes it challenging is that you also, just like any presentation, you have to consider your audience. And your audience, whenever we're delivering a solution, either insight, or just data in a grid, we really have to consider who is the consumer of this data, and actually cater the visual to that person or to that particular audience. And that is part of the art, and that is what makes a great data scientist. >> The consumer may in fact be the source of the data itself, like in a mobile app, so you're tuning their visualization and then their behavior is changing as a result, and then the data on their changed behavior comes back, so it can be a circular process. >> So Jim, at a recent conference, you were tweeting about the citizen data scientist, and you got emasculated by-- >> I spoke there too. >> Okay. >> TWI on that same topic, I got-- >> Kirk Borne I hear came after you. >> Kirk meant-- >> Called foul, flag on the play. >> Kirk meant well. I love Claudia Emahoff too, but yeah, it's a controversial topic. >> So I wonder what our panel thinks of that notion, citizen data scientist. >> Can I respond about citizen data scientists? >> David: Yeah, please. >> I think this term was introduced by Gartner analyst in 2015, and I think it's a very dangerous and misleading term. I think definitely we want to democratize the data and have access to more people, not just data scientists, but managers, BI analysts, but when there is already a term for such people, we can call the business analysts, because it implies some training, some understanding of the data. If you use the term citizen data scientist, it implies that without any training you take some data and then you find something there, and they think as Dev's mentioned, we've seen many examples, very easy to find completely spurious random correlations in data. So we don't want citizen dentists to treat our teeth or citizen pilots to fly planes, and if data's important, having citizen data scientists is equally dangerous, so I'm hoping that, I think actually Gartner did not use the term citizen data scientist in their 2016 hype course, so hopefully they will put this term to rest. >> So Gregory, you apparently are defining citizen to mean incompetent as opposed to simply self-starting. >> Well self-starting is very different, but that's not what I think what was the intention. I think what we see in terms of data democratization, there is a big trend over automation. There are many tools, for example there are many companies like Data Robot, probably IBM, has interesting machine learning capability towards automation, so I think I recently started a page on KDnuggets for automated data science solutions, and there are already 20 different forums that provide different levels of automation. So one can deliver in full automation maybe some expertise, but it's very dangerous to have part of an automated tool and at some point then ask citizen data scientists to try to take the wheels. >> I want to chime in on that. >> David: Yeah, pile on. >> I totally agree with all of that. I think the comment I just want to quickly put out there is that the space we're in is a very young, and rapidly changing world, and so what we haven't had yet is this time to stop and take a deep breath and actually define ourselves, so if you look at computer science in general, a lot of the traditional roles have sort of had 10 or 20 years of history, and so thorough the hiring process, and the development of those spaces, we've actually had time to breath and define what those jobs are, so we know what a systems programmer is, and we know what a database administrator is, but we haven't yet had a chance as a community to stop and breath and say, well what do we think these roles are, and so to fill that void, the media creates coinages, and I think this is the risk we've got now that the concept of a data scientist was just a term that was coined to fill a void, because no one quite knew what to call somebody who didn't come from a data science background if they were tinkering around data science, and I think that's something that we need to sort of sit up and pay attention to, because if we don't own that and drive it ourselves, then somebody else is going to fill the void and they'll create these very frustrating concepts like data scientist, which drives us all crazy. >> James: Miriam's next. >> So I wanted to comment, I agree with both of the previous comments, but in terms of a citizen data scientist, and I think whether or not you're citizen data scientist or an actual data scientist whatever that means, I think one of the most important things you can have is a sense of skepticism, right? Because you can get spurious correlations and it's like wow, my predictive model is so excellent, you know? And being aware of things like leaks from the future, right? This actually isn't predictive at all, it's a result of the thing I'm trying to predict, and so I think one thing I know that we try and do is if something really looks too good, we need to go back in and make sure, did we not look at the data correctly? Is something missing? Did we have a problem with the ETL? And so I think that a healthy sense of skepticism is important to make sure that you're not taking a spurious correlation and trying to derive some significant meaning from it. >> I think there's a Dilbert cartoon that I saw that described that very well. Joe, did you have a comment? >> I think that in order for citizen data scientists to really exist, I think we do need to have more maturity in the tools that they would use. My vision is that the BI tools of today are all going to be replaced with natural language processing and searching, you know, just be able to open up a search bar and say give me sales by region, and to take that one step into the future even further, it should actually say what are my sales going to be next year? And it should trigger a simple linear regression or be able to say which features of the televisions are actually affecting sales and do a clustering algorithm, you know I think hopefully that will be the future, but I don't see anything of that today, and I think in order to have a true citizen data scientist, you would need to have that, and that is pretty sophisticated stuff. >> I think for me, the idea of citizen data scientist I can relate to that, for instance, when I was in graduate school, I started doing some research on FDA data. It was an open source data set about 4.2 million data points. Technically when I graduated, the paper was still not published, and so in some sense, you could think of me as a citizen data scientist, right? I wasn't getting funding, I wasn't doing it for school, but I was still continuing my research, so I'd like to hope that with all the new data sources out there that there might be scientists or people who are maybe kept out of a field people who wanted to be in STEM and for whatever life circumstance couldn't be in it. That they might be encouraged to actually go and look into the data and maybe build better models or validate information that's out there. >> So Justin, I'm sorry you had one comment? >> It seems data science was termed before academia adopted formalized training for data science. But yeah, you can make, like Dez said, you can make data work for whatever problem you're trying to solve, whatever answer you see, you want data to work around it, you can make it happen. And I kind of consider that like in project management, like data creep, so you're so hyper focused on a solution you're trying to find the answer that you create an answer that works for that solution, but it may not be the correct answer, and I think the crossover discussion works well for that case. >> So but the term comes up 'cause there's a frustration I guess, right? That data science skills are not plentiful, and it's potentially a bottleneck in an organization. Supposedly 80% of your time is spent on cleaning data, is that right? Is that fair? So there's a problem. How much of that can be automated and when? >> I'll have a shot at that. So I think there's a shift that's going to come about where we're going to move from centralized data sets to data at the edge of the network, and this is something that's happening very quickly now where we can't just hold everything back to a central spot. When the internet of things actually wakes up. Things like the Boeing Dreamliner 787, that things got 6,000 sensors in it, produces half a terabyte of data per flight. There are 87,400 flights per day in domestic airspace in the U.S. That's 43.5 petabytes of raw data, now that's about three years worth of disk manufacturing in total, right? We're never going to copy that across one place, we can't process, so I think the challenge we've got ahead of us is looking at how we're going to move the intelligence and the analytics to the edge of the network and pre-cook the data in different tiers, so have a look at the raw material we get, and boil it down to a slightly smaller data set, bring a meta data version of that back, and eventually get to the point where we've only got the very minimum data set and data points we need to make key decisions. Without that, we're already at the point where we have too much data, and we can't munch it fast enough, and we can't spin off enough tin even if we witch the cloud on, and that's just this never ending deluge of noise, right? And you've got that signal versus noise problem so then we're now seeing a shift where people looking at how do we move the intelligence back to the edge of network which we actually solved some time ago in the securities space. You know, spam filtering, if an emails hits Google on the west coast of the U.S. and they create a check some for that spam email, it immediately goes into a database, and nothing gets on the opposite side of the coast, because they already know it's spam. They recognize that email coming in, that's evil, stop it. So we've already fixed its insecurity with intrusion detection, we've fixed it in spam, so we now need to take that learning, and bring it into business analytics, if you like, and see where we're finding patterns and behavior, and brew that out to the edge of the network, so if I'm seeing a demand over here for tickets on a new sale of a show, I need to be able to see where else I'm going to see that demand and start responding to that before the demand comes about. I think that's a shift that we're going to see quickly, because we'll never keep up with the data munching challenge and the volume's just going to explode. >> David: We just have a couple minutes. >> That does sound like a great topic for a future Cube panel which is data science on the edge of the fog. >> I got a hundred questions around that. So we're wrapping up here. Just got a couple minutes. Final thoughts on this conversation or any other pieces that you want to punctuate. >> I think one thing that's been really interesting for me being on this panel is hearing all of my co-panelists talking about common themes and things that we are also experiencing which isn't a surprise, but it's interesting to hear about how ubiquitous some of the challenges are, and also at the announcement earlier today, some of the things that they're talking about and thinking about, we're also talking about and thinking about. So I think it's great to hear we're all in different countries and different places, but we're experiencing a lot of the same challenges, and I think that's been really interesting for me to hear about. >> David: Great, anybody else, final thoughts? >> To echo Dez's thoughts, it's about we're never going to catch up with the amount of data that's produced, so it's about transforming big data into smart data. >> I could just say that with the shift from normal data, small data, to big data, the answer is automate, automate, automate, and we've been talking about advanced algorithms and machine learning for the science for changing the business, but there also needs to be machine learning and advanced algorithms for the backroom where we're actually getting smarter about how we ingestate and how we fix data as it comes in. Because we can actually train the machines to understand data anomalies and what we want to do with them over time. And I think the further upstream we get of data correction, the less work there will be downstream. And I also think that the concept of being able to fix data at the source is gone, that's behind us. Right now the data that we're using to analyze to change the business, typically we have no control over. Like Dez said, they're coming from censors and machines and internet of things and if it's wrong, it's always going to be wrong, so we have to figure out how to do that in our laboratory. >> Eaves, final thoughts? >> I think it's a mind shift being a data scientist if you look back at the time why did you start developing or writing code? Because you like to code, whatever, just for the sake of building a nice algorithm or a piece of software, or whatever, and now I think with the spirit of a data scientist, you're looking at a problem and say this is where I want to go, so you have more the top down approach than the bottom up approach. And have the big picture and that is what you really need as a data scientist, just look across technologies, look across departments, look across everything, and then on top of that, try to apply as much skills as you have available, and that's kind of unicorn that they're trying to look for, because it's pretty hard to find people with that wide vision on everything that is happening within the company, so you need to be aware of technology, you need to be aware of how a business is run, and how it fits within a cultural environment, you have to work with people and all those things together to my belief to make it very difficult to find those good data scientists. >> Jim? Your final thought? >> My final thoughts is this is an awesome panel, and I'm so glad that you've come to New York, and I'm hoping that you all stay, of course, for the the IBM Data First launch event that will take place this evening about a block over at Hudson Mercantile, so that's pretty much it. Thank you, I really learned a lot. >> I want to second Jim's thanks, really, great panel. Awesome expertise, really appreciate you taking the time, and thanks to the folks at IBM for putting this together. >> And I'm big fans of most of you, all of you, on this session here, so it's great just to meet you in person, thank you. >> Okay, and I want to thank Jeff Frick for being a human curtain there with the sun setting here in New York City. Well thanks very much for watching, we are going to be across the street at the IBM announcement, we're going to be on the ground. We open up again tomorrow at 9:30 at Big Data NYC, Big Data Week, Strata plus the Hadoop World, thanks for watching everybody, that's a wrap from here. This is the Cube, we're out. (techno music)

this is the cute live from the Moscone Center in San Francisco this is silicon angles continuous coverage a vm world 2010 now inside the queue we're back I'm excited too I'm John furry with SiliconANGLE calm I'm really excited to have John Troyer here who is the senior community social media manager also executive producer who put the whole bloggers lounge social media lounge together John thanks so much for doing this and we'll get into a conversation but I just want to say it's been a pleasure working with you you know VMware at the show set new agenda and I think this blogger lounge we have some areas improve I think but from my standpoint really people were excited by it in setting the agenda so congratulations thanks it was really a pleasure working with you guys as well I think you've done a great job all this week this is really innovative what we've done here we always bring community to it we always bring multis multimedia and social media distribution to it but what an often conference the old school right the old style would be let's spend a couple million bucks let's bring in some TV crews and and do it that way we've done it kind of mean a much more agile for happen anywhere hackers you know it's awesome man it's great we were very pleased with the amount of content we produced the show coverage we're right here in the middle of the bloggers lounge so we got some interaction there we're going to have some bloggers on later and be experts on later today so I think it's really been a successful project today is dedicated to the to get the community out there but I mean I had an observation I didn't think it's gonna be this big that the broadcast sided with all the executives people were energized I had a personal element to it it wasn't the big heavy lights it wasn't the CNN trucks but we know we got the content out with good sound and HD you were going to fix the little audio thing but but you know from a Content standpoint it's going to look good and sound good and okay it's not the standard big equipment I don't I mean I don't care great to be here I don't thinks everybody's watching us now doesn't care only eight yeah only produce an H pump at HD onto the internet but the thing is BM world is really exciting I this is my fifth vmworld so I have not been to all of them this I think is the biggest what's certainly the biggest I think it's the most exciting the most there's a palpable buzz going on both in the industry and here on the show floor vendor after vendor attendee after attendee even the show staff has this kind of silly grin on their face because there's so much going on it all went so well everyone's so excited the connections that are being made and that's what I mean I'm a social media guy right and this is that this is an extension of that into the real world social media we're connecting online but that that activates when people actually get together in person and I've seen it happen over and over again what we've tried to do here on the cube is it is then convey a little bit of that excitement back out you know Alan to the internet for folks who couldn't come so I think anyway I've it's been great time here and I hope everybody else has enjoyed our coverage both Twitter or Facebook you know video etc it's been it's been great I mean it's just just we got sound bites we got content a mountain of content so that's phenomena it's all going out to the social grass and activity streams but you've been on the floor minutes exciting I what are your report I mean what's your report I mean you seeing a lot of tweets you seeing the connections are being made I'll see that the party was great great feedback from last night the vendors had their partners had their party what's to give us inside the ropes of the social media activity that went on because we didn't get much I see it much of it with the final year you know I work with the guys that work in the data center right and they're they're the hands-on practitioners that are out there and they need they have often been working with VMware for years and it's interesting you know some years you could come to VMware and I get the feedback well I knew everything so I talked to people but I didn't really go to the sessions and it was ok and it was good it was good to reconnect this year I'm hearing my god there's so much new going on I talked to some extra vendors that I'm interested in you know vCloud director is really interesting vshield is really interesting you know all that from the news from VMware was fascinating the labs were great and so what I'm hearing is a what I've seen from the guys I'm working with and talk to is a real sense of excitement I have to bring this back to my I have to bring this back to my company you have to bring this back to my data center and even though we are we I used to think of myself as a virtualization expert you know we've gotta broaden that conversation out to our whole the data center the operations of our IT department you know how our CIO runs things is going to also be brought along in this kind of technological shift social media has got a lot of social science to it it's all about you know connections and handshakes and all and going stuff online and is very social I mean do you think like the momentum i agree with device so i saw the same thing usually it's lobby con hey sessions i blew that off middle the party but i saw i saw the same thing you did but do you think that that's contagious i mean that momentum that excitement you think that was an element of it absolutely and I mean it was interesting I mean it in vm world's past I was the one doing a lot of the blogging I was open to it a lot of the tweeting you know you talk to people all day you you upload videos all night it's a very active it's a lot of work yeah you know if we have a blogger lounge here today in the lobby it was very visible it was very nice but I think if you really had to map out the bloggers social media activity lounge this year you'd have to draw a circle all around Moscone I mean everybody's belonging everybody's tweeting the tweet stream on the keynote was insane I had about 50 I had tables for about 50 bloggers and tweeters but the entire audience of 17,000 was tweeting blanket average we we trended in San Francisco number two on Twitter you know that day and with VMware and we were number five a little later on the day is insane it's exciting so everyone here is really engaged I seriously flow going on it really changes we were talking a couple of weeks ago it's like the old days CES or or or or not see yes Comdex you know you you get ready for the show you'd go to the show you come home for the show this year this build up the social media connectivity that happens before during and after it it's it turns the event into something completely different I think this is an event within the event i mean the social media flow was phenomenal I mean I still guys doing podcasts in the lobby video streaming the event as this phenomena it's exciting it's a whole new world out there we got a couple of podcasts coming up here next yeah we got some podcast any final comments about you know what you're what you take away is from a social media standpoint any goals for next year any things that you on your mind well you know I mean vmworld is a tribal event it's the gathering of a site of innovators who are really taking things to the next level so I think as we as we'll see what happens in them in the social media world in a year a lot can change all of our cameras will be smaller you know we'll probably do it more remotes it'll be it'll be interesting it bought by yahoo yeah I mean we have a lot of fourscore activities so I think you know we'll try to do more community stuff we might probably have multiple video streams I mean it our capabilities increase but it'll all be about people right at the end in the end social media is people right it's all about connecting people and will continue to do that I mean I'm excited about the fact that you know the industry is evolving and growing up together and this business that's being done I mean it's it's an uptick in the economy you know vmware is actually be a big part of that enablement and so you know when people are doing business it's just a different vibe in the social excitement that comes from meetings countless meetings where it's like yeah deals are going on like partnerships are being made I mean that's the 1 2 15 that Todd was talking about that yeah there's a there's definitely something in the air the industry the the technology and then something's going right at VMware too so I feel it internally congratulations John Shorey executive producer the blogger lounge social media the cube thanks for for supporting us and being a part of it and helping us a collaborate on the content thanks so much thanks for working with us had a great time

>>In the early part of this century, we're talking about the 2005 to 2007 timeframe. There was a lot of talk about so-called green it. And at that time there was some organizational friction. Like for example, the line was that the CIO never saw the power bill, so he or she didn't care, or that the facilities folks, they rarely talked to the IT department. So it was kind of that split brain. And, and then the oh 7 0 8 financial crisis really created an inflection point in a couple of ways. First, it caused organizations to kind of pump the brakes on it spending, and then they took their eye off the sustainability ball. And the second big trend, of course, was the cloud model, you know, kind of became a benchmark for it. Simplicity and automation and efficiency, the ability to dial down and dial up capacity as needed. >>And the third was by the end of the first decade of the, the two thousands, the technology of virtualization was really hitting its best stride. And then you had innovations like flash storage, which largely eliminated the need for these massive farms of spinning mechanical devices that sucked up a lot of power. And so really these technologies began their march to mainstream adoption. And as we progressed through the 2020s, the effect of climate change really come into focus as a critical component of esg. Environmental, social, and governance. Shareholders have come to demand metrics around sustainability. Employees are often choosing employers based on their ESG posture. And most importantly, companies are finding that savings on power cooling and footprint, it has a bottom line impact on the income statement. Now you add to that the energy challenges around the world, particularly facing Europe right now, the effects of global inflation and even more advanced technologies like machine intelligence. >>And you've got a perfect storm where technology can really provide some relief to organizations. Hello and welcome to the Path to Sustainable It Made Possible by Pure Storage and Collaboration with the Cube. My name is Dave Valante and I'm one of the host of the program, along with my colleague Lisa Martin. Now, today we're gonna hear from three leaders on the sustainability topic. First up, Lisa will talk to Nicole Johnson. She's the head of Social Impact and Sustainability at Pure Storage. Nicole will talk about the results from a study of around a thousand sustainability leaders worldwide, and she'll share some metrics from that study. And then next, Lisa will speak to AJ Singh. He's the Chief Product Officer at Pure Storage. We've had had him on the cube before, and not only will he share some useful stats in the market, I'll also talk about some of the technology innovations that customers can tap to address their energy consumption, not the least of which is ai, which is is entering every aspect of our lives, including how we deal with energy consumption. And then we'll bring it back to our Boston studio and go north of Italy with Mattia Ballero of Elec Informatica, a services provider with deep expertise on the topic of sustainability. We hope you enjoyed the program today. Thanks for watching. Let's get started >>At Pure Storage, the opportunity for change and our commitment to a sustainable future are a direct reflection of the way we've always operated and the values we live by every day. We are making significant and immediate impact worldwide through our environmental sustainability efforts. The milestones of change can be seen everywhere in everything we do. Pure's Evergreen Storage architecture delivers two key environmental benefits to customers, the reduction of wasted energy and the reduction of e-waste. Additionally, Pure's implemented a series of product packaging redesigns, promoting recycled and reuse in order to reduce waste that will not only benefit our customers, but also the environment. Pure is committed to doing what is right and leading the way with innovation. That has always been the pure difference, making a difference by enabling our customers to drive out energy usage and their data storage systems by up to 80%. Today, more than 97% of pure arrays purchased six years ago are still in service. And tomorrow our goal for the future is to reduce Scope three. Emissions Pure is committing to further reducing our sold products emissions by 66% per petabyte by 2030. All of this means what we said at the beginning, change that is simple and that is what it has always been about. Pure has a vision for the future today, tomorrow, forever. >>Hi everyone, welcome to this special event, pure Storage, the Path to Sustainable it. I'm your host, Lisa Martin. Very pleased to be joined by Nicole Johnson, the head of Social Impact and Sustainability at Pure Storage. Nicole, welcome to the Cube. Thanks >>For having me, Lisa. >>Sustainability is such an important topic to talk about and I understand that Pure just announced a report today about sustainability. What can you tell me what nuggets are in this report? >>Well, actually quite a few really interesting nuggets, at least for us. And I, I think probably for you and your viewers as well. So we actually commissioned about a thousand sustainability leaders across the globe to understand, you know, what are their sustainability goals, what are they working on, and what are the impacts of buying decisions, particularly around infrastructure when it comes to sustainable goals. I think one of the things that was really interesting for us was the fact that around the world we did not see a significant variation in terms of sustainability being a top priority. You've, I'm sure you've heard about the energy crisis that's happening across Europe. And so, you know, there was some thought that perhaps that might play into AMEA being a larger, you know, having sustainability goals that were more significant. But we actually did not find that we found sustainability to be really important no matter where the respondents were located. >>So very interesting at Pure sustainability is really at the heart of what we do and has been since our founding. It's interesting because we set out to make storage really simple, but it turns out really simple is also really sustainable. And the products and services that we bring to our customers have really powerful outcomes when it comes to decreasing their, their own carbon footprints. And so, you know, we often hear from customers that we've actually really helped them to significantly improve their storage performance, but also allow them to save on space power and cooling costs and, and their footprint. So really significant findings. One example of that is a company called Cengage, which is a global education technology company. They recently shared with us that they have actually been able to reduce their overall storage footprint by 80% while doubling to tripling the performance of their storage systems. So it's really critical for, for companies who are thinking about their sustainability goals, to consider the dynamic between their sustainability program and their IT teams who are making these buying decisions, >>Right? Those two teams need to be really inextricably linked these days. You talked about the fact that there was really consistency across the regions in terms of sustainability being of high priority for organizations. You had a great customer story that you shared that showed significant impact can be made there by bringing the sustainability both together with it. But I'm wondering why are we seeing that so much of the vendor selection process still isn't revolving around sustainability or it's overlooked? What are some of the things that you received despite so many people saying sustainability, huge priority? >>Well, in this survey, the most commonly cited challenge was really around the fact that there was a lack of management buy-in. 40% of respondents told us this was the top roadblock. So getting, I think getting that out of the way. And then we also just heard that sustainability teams were not brought into tech purchasing processes until after it's already rolling, right? So they're not even looped in. And that being said, you know, we know that it has been identified as one of the key departments to supporting a company sustainability goals. So we, we really want to ensure that these two teams are talking more to each other. When we look even closer at the data from the respondents, we see some really positive correlations. We see that 65% of respondents reported that they're on track to meet their sustainability goals. And the IT of those 65%, it is significantly engaged with reporting data for those sustainability initiatives. We saw that, that for those who did report, the sustainability is a top priority for vendor selection. They were twice as likely to be on track with their goals and their sustainability directors said that they were getting involved at the beginning of the tech purchasing program. Our process, I'm sorry, rather than towards the end. And so, you know, we know that to curb the impact of climate crisis, we really need to embrace sustainability from a cross-functional viewpoint. >>Definitely has to be cross-functional. So, so strong correlations there in the report that organizations that had closer alignment between the sustainability folks and the IT folks were farther along in their sustainability program development, execution, et cetera, those co was correlations, were they a surprise? >>Not entirely. You know, when we look at some of the statistics that come from the, you know, places like the World Economic Forum, they say that digitization generated 4% of greenhouse gas emissions in 2020. So, and that, you know, that's now almost three years ago, digital data only accelerates, and by 2025, we expect that number could be almost double. And so we know that that communication and that correlation is gonna be really important because data centers are taking up such a huge footprint of when companies are looking at their emissions. And it's, I mean, quite frankly, a really interesting opportunity for it to be a trailblazer in the sustainability journey. And, you know, perhaps people that are in IT haven't thought about how they can make an impact in this area, but there really is some incredible ways to help us work on cutting carbon emissions, both from your company's perspective and from the world's perspective, right? >>Like we are, we're all doing this because it's something that we know we have to do to drive down climate change. So I think when you, when you think about how to be a trailblazer, how to do things differently, how to differentiate your own department, it's a really interesting connection that IT and sustainability work together. I would also say, you know, I'll just note that of the respondents to the survey we were discussing, we do over half of those respondents expect to see closer alignment between the organization's IT and sustainability teams as they move forward. >>And that's really a, a tip a hat to those organizations embracing cultural change. That's always hard to do, but for those two, for sustainability in IT to come together as part of really the overall ethos of an organization, that's huge. And it's great to see the data demonstrating that, that those, that alignment, that close alignment is really on its way to helping organizations across industries make a big impact. I wanna dig in a little bit to here's ESG goals. What can you share with us about >>That? Absolutely. So as I mentioned peers kind of at the beginning of our formal ESG journey, but really has been working on the, on the sustainability front for a long time. I would, it's funny as we're, as we're doing a lot of this work and, and kind of building our own profile around this, we're coming back to some of the things that we have done in the past that consumers weren't necessarily interested in then but are now because the world has changed, becoming more and more invested in. So that's exciting. So we did a baseline scope one, two, and three analysis and discovered, interestingly enough that 70% of our emissions comes from use of sold products. So our customers work running our products in their data centers. So we know that we, we've made some ambitious goals around our Scope one and two emissions, which is our own office, our utilities, you know, those, they only account for 6% of our emissions. So we know that to really address the issue of climate change, we need to work on the use of sold products. So we've also made a, a really ambitious commitment to decrease our carbon emissions by 66% per bed per petabyte by 2030 in our product. So decreasing our own carbon footprint, but also affecting our customers as well. And we've also committed to a science-based target initiative and our road mapping how to achieve the ambitious goals set out in the Paris agreement. >>That's fantastic. It sounds like you really dialed in on where is the biggest opportunity for us as Pure Storage to make the biggest impact across our organization, across our customers organizations. There lofty goals that pure set, but knowing what I know about Pure, you guys are probably well on track to, to accomplish those goals in record time, >>I hope So. >>Talk a little bit about advice that you would give to viewers who might be at the very beginning of their sustainability journey and really wondering what are the core elements besides it, sustainability, team alignment that I need to bring into this program to make it actually successful? >>Yeah, so I think, you know, understanding that you don't have to pick between really powerful technology and sustainable technology. There are opportunities to get both and not just in storage right in, in your entire IT portfolio. We know that, you know, we're in a place in the world where we have to look at things from the bigger picture. We have to solve new challenges and we have to approach business a little bit differently. So adopting solutions and services that are environmentally efficient can actually help to scale and deliver more effective and efficient IT solutions over time. So I think that that's something that we need to, to really remind ourselves, right? We have to go about business a little bit differently and that's okay. We also know that data centers utilize an incredible amount of, of energy and, and carbon. And so everything that we can do to drive that down is going to address the sustainability goals for us individually as well as, again, drive down that climate change. So we, we need to get out of the mindset that data centers are, are about reliability or cost, et cetera, and really think about efficiency and carbon footprint when you're making those business decisions. I'll also say that, you know, the earlier that we can get sustainability teams into the conversation, the more impactful your business decisions are going to be and helping you to guide sustainable decision making. >>So shifting sustainability and IT left almost together really shows that the correlation between those folks getting together in the beginning with intention, the report shows and the successes that peers had demonstrate that that's very impactful for organizations to actually be able to implement even the cultural change that's needed for sustainability programs to be successful. My last question for you goes back to that report. You mentioned in there that the data show a lot of organizations are hampered by management buy-in, where sustainability is concerned. How can pure help its customers navigate around those barriers so that they get that management buy-in and they understand that the value in it for >>Them? Yeah, so I mean, I think that for me, my advice is always to speak to hearts and minds, right? And help the management to understand, first of all, the impact right on climate change. So I think that's the kind of hearts piece on the mind piece. I think it's addressing the sustainability goals that these companies have set for themselves and helping management understand how to, you know, how their IT buying decisions can actually really help them to reach these goals. We also, you know, we always run kind of TCOs for customers to understand what is the actual cost of, of the equipment. And so, you know, especially if you're in a, in a location in which energy costs are rising, I mean, I think we're seeing that around the world right now with inflation. Better understanding your energy costs can really help your management to understand the, again, the bigger picture and what that total cost is gonna be. Often we see, you know, that maybe the I the person who's buying the IT equipment isn't the same person who's purchasing, who's paying the, the electricity bills, right? And so sometimes even those two teams aren't talking. And there's a great opportunity there, I think, to just to just, you know, look at it from a more high level lens to better understand what total cost of ownership is. >>That's a great point. Great advice. Nicole, thank you so much for joining me on the program today, talking about the new report that on sustainability that Pure put out some really compelling nuggets in there, but really also some great successes that you've already achieved internally on your own ESG goals and what you're helping customers to achieve in terms of driving down their carbon footprint and emissions. We so appreciate your insights and your thoughts. >>Thank you, Lisa. It's been great speaking with you. >>AJ Singh joins me, the Chief Product Officer at Peer Storage. Aj, it's great to have you back on the program. >>Great to be back on, Lisa, good morning. >>Good morning. And sustainability is such an important topic to talk about. So we're gonna really unpack what PEER is doing, we're gonna get your viewpoints on what you're seeing and you're gonna leave the audience with some recommendations on how they can get started on their ESG journey. First question, we've been hearing a lot from pure AJ about the role that technology plays in organizations achieving sustainability goals. What's been the biggest environmental impact associated with, with customers achieving that given the massive volumes of data that keep being generated? >>Absolutely, Lisa, you can imagine that the data is only growing and exploding and, and, and, and there's a good reason for it. You know, data is the new currency. Some people call it the new oil. And the opportunity to go process this data gain insights is really helping customers drive an edge in the digital transformation. It's gonna make a difference between them being on the leaderboard a decade from now when the digital transformation kind of pans out versus, you know, being kind of somebody that, you know, quite missed the boat. So data is super critical and and obviously as part of that we see all these big benefits, but it has to be stored and, and, and that means it's gonna consume a lot of resources and, and the, and therefore data center usage has only accelerated, right? You can imagine the amount of data being generated, you know, recent study pointed to roughly by twenty twenty five, a hundred and seventy five zetabytes, which where each zettabyte is a billion terabytes. So just think of that size and scale of data. That's huge. And, and they also say that, you know, pretty soon, today, in fact in the developed world, every person is having an interaction with the data center literally every 18 seconds. So whether it's on Facebook or Twitter or you know, your email, people are constantly interacting with data. So you can imagine this data is only exploding. It has to be stored and it consumes a lot of energy. In fact, >>It, oh, go ahead. Sorry. >>No, I was saying in fact, you know, there's some studies have shown that data center usage literally consumes one to 2% of global energy consumption. So if there's one place we could really help climate change and, and all those aspects, if you can kind of really, you know, tamp down the data center, energy consumption, sorry, you were saying, >>I was just gonna say, it's, it's an incredibly important topic and the, the, the stats on data that you provided and also I, I like how you talked about, you know, every 18 seconds we're interacting with a data center, whether we know it or not, we think about the long term implications, the fact that data is growing massively. As you shared with the stats that you mentioned. If we think about though the responsibility that companies have, every company in today's world needs to be a data company, right? And we consumers expect it. We expect that you are gonna deliver these relevant, personalized experiences whether we're doing a transaction in our personal lives or in business. But what is the, what requirements do technology companies have to really start billing down their carbon footprints? >>No, absolutely. If you can think about it, just to kind of finish up the data story a little bit, the explosion is to the point where, in fact, if you just recently was in the news that Ireland went up and said, sorry, we can't have any more data centers here. We just don't have the power to supply them. That was big in the news and you know, all the hyperscale that was crashing the head. I know they've come around that and figured out a way around it, but it's getting there. Some, some organizations and and areas jurisdictions are saying pretty much no data center the law, you know, we're, we just can't do it. And so as you said, so companies like Pure, I mean, our view is that it has an opportunity here to really do our bit for climate change and be able to, you know, drive a sustainable environment. >>And, and at Pure we believe that, you know, today's data success really ultimately hinges on energy efficiency, you know, so to to really be energy efficient means you are gonna be successful long term with data. Because if you think of classic data infrastructures, the legacy infrastructures, you know, we've got disk infrastructures, hybrid infrastructures, flash infrastructures, low end systems, medium end systems, high end systems. So a lot of silos, you know, a lot of inefficiency across the silos. Cause the data doesn't get used across that. In fact, you know, today a lot of data centers are not really built with kind of the efficiency and environmental mindset. So there's a big opportunity there. >>So aj, talk to me about some of the steps that Pure is implementing as its chief product officer. Would love to get your your thoughts, what steps is it implementing to help Pures customers become more sustainable? >>No, absolutely. So essentially we are all inherently motivated, like pure and, and, and, and everybody else to solve problems for customers and really forward the status quo, right? You know, innovation, you know, that's what we are all about. And while we are doing that, the challenge is to how do you make technology and the data we feed into it faster, smarter, scalable obviously, but more importantly sustainable. And you can do all of that, but if you miss the sustainability bit, you're kind of missing the boat. And I also feel from an ethical perspective, that's really important for us. Not only you do all the other things, but also kind of make it sustainable. In fact, today 80% of the companies, the companies are realizing this, 80% today are in fact report out on sustainability, which is great. In fact, 80% of leadership at companies, you know, CEOs and senior executives say they've been impacted by some climate change event, you know, where it's a fire in the place they had to evacuate or floods or storms or hurricanes, you, you name it, right? >>So mitigating the carbon impact can in fact today be a competitive advantage for companies because that's where the puck is going and everybody's, you know, it's skating, wanting to skate towards the, and it's good, it's good business too to be sustainable and, and, and meet these, you know, customer requirements. In fact, the the recent survey that we released today is saying that more and more organizations are kickstarting, their sustainability initiatives and many take are aiming to make a significant progress against that over the next decade. So that's, that's really, you know, part of the big, the really, so our view is that that IT infrastructure, you know, can really make a big push towards greener it and not just kind of greenwash it, but actually, you know, you know, make things more greener and, and, and really take the, the lead in, in esg. And so it's important that organizations can reach alignment with their IT teams and challenge their IT teams to continue to lead, you know, for the organization, the sustainability aspects. >>I'm curious, aj, when you're in customer conversations, are you seeing that it's really the C-suite plus it coming together and, and how does peer help facilitate that? To your point, it needs to be able to deliver this, but it's, it's a board level objective these days. >>Absolutely. We're seeing increasingly, especially in Europe with the, you know, the war in Ukraine and the energy crisis that, you know, that's, that's, you know, unleashed. We definitely see it's becoming a bigger and bigger board level objective for, for a lot of companies. And we definitely see customers in starting to do that. So, so in particular, I do want to touch briefly on what steps we are taking as a company, you know, to to to make it sustainable. And obviously customers are doing all the things we talked about and, and we're also helping them become smarter with data. But the key difference is, you know, we have a big focus on efficiency, which is really optimizing performance per wat with unmatched storage density. So you can reduce the footprint and dramatically lower the power required. And and how efficient is that? You know, compared to other old flash systems, we tend to be one fifth, we tend to take one fifth the power compared to other flash systems and substantially lower compared to spinning this. >>So you can imagine, you know, cutting your, if data center consumption is a 2% of global consumption, roughly 40% of that tends to be storage cause of all the spinning disc. So you add about, you know, 0.8% to global consumption and if you can cut that by four fifths, you know, you can already start to make an impact. So, so we feel we can do that. And also we're quite a bit more denser, 10 times more denser. So imagine one fifth the power, one 10th the density, but then we take it a step further because okay, you've got the storage system in the data center, but what about the end of life aspect? What about the waste and reclamation? So we also have something called non-disruptive upgrades. We, using our AI technology in pure one, we can start to sense when a particular part is going to fail and just before it goes to failure, we actually replace it in a non-disruptive fashion. So customer's data is not impacted and then we recycle that so you get a full end to end life cycle, you know, from all the way from the time you deploy much lower power, much lower density, but then also at the back end, you know, reduction in e-waste and those kind of things. >>That's a great point you, that you bring up in terms of the reclamation process. It sounds like Pure does that on its own, the customer doesn't have to be involved in that. >>That's right. And we do that, it's a part of our evergreen, you know, service that we offer. A lot of customers sign up for that service and in fact they don't even, we tell them, Hey, you know, that part's about to go, we're gonna come in, we're gonna swap it out and, and then we actually recycle that part, >>The power of ai. Love that. What are some of the, the things that companies can do if they're, if they're early in this journey on sustainability, what are some of the specific steps companies can take to get started and maybe accelerate that journey as it's becoming climate change and things are becoming just more and more of a, of a daily topic on the news? >>No, absolutely. There's a lot of things companies can do. In fact, the four four item that we're gonna highlight, the first one is, you know, they can just start by doing a materiality assessment and a materiality assessment essentially engages all the stakeholders to find out which specific issues are important for the business, right? So you identify your key priorities that intersect with what the stakeholders want, you know, your different groups from sales, customers, partners, you know, different departments in the organization. And for example, for us, when we conducted our materiality assessment, for us, our product we felt was the biggest area of focus that could contribute a lot towards, you know, making an impact in, in, in from a sustainability standpoint. That's number one. I think number two companies can also think about taking an Azure service approach. The beauty of the Azure service approach is that you are buying a, your customer, they're buying outcomes with SLAs and, and when you are starting to buy outcomes with SLAs, you can start small and then grow as you consume more. >>So that way you don't have systems sitting idle waiting for you to consume more, right? And that's the beauty of the as service approach. And so for example, for us, you know, we have something called Evergreen one, which is our as service offer, where essentially customers are able to only use and have systems turned onto as much as they're consuming. So, so that reduces the waste associated with underutilized systems, right? That's number two. Number three is also you can optimize your supply chains end to end, right? Basically by making sure you're moving, recycling, packaging and eliminating waste in that thing so you can recycle it back to your suppliers. And you can also choose a sustainable supplier network that following sort of good practices, you know, you know, across the globe and such supply chains that are responsive and diverse can really help you. Also, the big business benefit benefited. >>You can also handle surges and demand, for example, for us during the pandemic with this global supply chain shortages, you know, whereas most of our competitors, you know, lead times went to 40, 50 weeks, our lead times went from three to six weeks cuz you know, we had this sustainable, you know, supply chain. And so all of these things, you know, the three things important, but the fourth thing I say more cultural and, and the cultural thing is how do you actually begin to have sustainability become a core part of your ethos at the company, you know, across all the departments, you know, and we've at Pure, definitely it's big for us, you know, you know, around sustainability starting with a product design, but all of the areas as well, if you follow those four items, they'll do the great place to start. >>That's great advice, great recommendations. You talk about the, the, the supply chain, sustainable supply chain optimization. We've been having a lot of conversations with businesses and vendors alike about that and how important it is. You bring up a great point too on supplier diversity, if we could have a whole conversation on that. Yes. But I'm also glad that you brought up culture that's huge to, for organizations to adopt an ESG strategy and really drive sustainability in their business. It has to become, to your point, part of their ethos. Yes. It's challenging. Cultural change management is challenging. Although I think with climate change and the things that are so public, it's, it's more on, on the top mindset folks. But it's a great point that the organization really as a whole needs to embrace the sustainability mindset so that it as a, as an organization lives and breathes that. Yes. And last question for you is advice. So you, you outlined the Four Steps organizations can take. I look how you made that quite simple. What advice would you give organizations who are on that journey to adopting those, those actions, as you said, as they look to really build and deploy and execute an ESG strategy? >>No, absolutely. And so obviously, you know, the advice is gonna come from, you know, a company like Pure, you know, our background kind of being a supplier of products. And so, you know, our advice is for companies that have products, usually they tend to be the biggest generator, the products that you sell to your, your customers, especially if they've got hardware components in it. But, you know, the biggest generator of e-waste and, and and, and, and, and kind of from a sustainability standpoint. So it's really important to have an intentional design approach towards your products with sustainability in mind. So it's not something that's, that you can handle at the very back end. You design it front in the product and so that sustainable design becomes very intentional. So for us, for example, doing these non-disruptive upgrades had to be designed up front so that, you know, a, you know, one of our repair person could go into a customer shop and be able to pull out a card and put in a new card without any change in the customer system. >>That non-receptive approach, it has to be designed into the hardware software systems to be able to pull that on. And that intentional design enables you to recover pieces just when they're about to fail and then putting them through a recovery, you know, waste recovery process. So that, that's kind of the one thing I would say that philosophy, again, it comes down to if that is, you know, seeping into the culture, into your core ethos, you will start to do, you know, you know, that type of work. So, so I mean it's important thing, you know, look, this year, you know, with the spike in energy prices, you know, you know, gas prices going up, it's super important that all of us, you know, do our bit in there and start to drive products that are fundamentally sustainable, not just at the initial, you know, install point, but from an end to end full life cycle standpoint. >>Absolutely. And I love that you brought up intention that is everything that peers doing is with, with such thought and intention and really for organizations and any industry to become more sustainable, to develop an ESG strategy. To your point, it all needs to start with intention. And of course that that cultural adoption, aj, it's been so great to have you on the program talking about what PEER is doing to help organizations really navigate that path to sustainable it. We appreciate your insights on your time. >>Thank you, Lisa. Pleasure being on board >>At Pure Storage. The opportunity for change and our commitment to a sustainable future are a direct reflection of the way we've always operated and the values we live by every day. We are making significant and immediate impact worldwide through our environmental sustainability efforts. The milestones of change can be seen everywhere in everything we do. Pures Evergreen storage architecture delivers two key environmental benefits to customers, the reduction of wasted energy and the reduction of e-waste. Additionally, pures implemented a series of product packaging redesigns, promoting recycle and reuse in order to reduce waste that will not only benefit our customers, but also the environment. Pure is committed to doing what is right and leading the way with innovation. That has always been the pure difference, making a difference by enabling our customers to drive out energy usage and their data storage systems by up to 80% today, more than 97% of Pure Array purchased six years ago are still in service. And tomorrow our goal for the future is to reduce Scope three emissions Pure is committing to further reducing our sold products emissions by 66% per petabyte by 2030. All of this means what we said at the beginning, change that is simple and that is what it has always been about. Pure has a vision for the future today, tomorrow, forever. >>We're back talking about the path to sustainable it and now we're gonna get the perspective from Mattia Valerio, who is with Elec Informatica and IT services firm and the beautiful Lombardi region of Italy north of Milano. Mattia, welcome to the Cube. Thanks so much for coming on. >>Thank you very much, Dave. Thank you. >>All right, before we jump in, tell us a little bit more about Elec Informatica. What's your focus, talk about your unique value add to customers. >>Yeah, so basically Alma Informatica is middle company from the north part of Italy and is managed service provider in the IT area. Okay. So the, the main focus area of Al Meca is reach digital transformation innovation to our clients with focus on infrastructure services, workplace services, and also cybersecurity services. Okay. And we try to follow the path of our clients to the digital transformation and the innovation through technology and sustainability. >>Yeah. Obviously very hot topics right now. Sustainability, environmental impact, they're growing areas of focus among leaders across all industries. A particularly acute right now in, in Europe with the, you know, the energy challenges you've talked about things like sustainable business. What does that mean? What does that term Yeah. You know, speak to and, and what can others learn from it? >>Yeah. At at, at our approach to sustainability is grounded in science and, and values and also in customer territory, but also employee centered. I mean, we conduct regular assessments to understand the most significant environment and social issues for our business with, with the goal of prioritizing what we do for a sustainability future. Our service delivery methodology, employee care relationship with the local supplier and local area and institution are a major factor for us to, to build a such a responsibility strategy. Specifically during the past year, we have been particularly focused on define sustainability governance in the company based on stakeholder engagement, defining material issues, establishing quantitative indicators to monitor and setting medium to long-term goals. >>Okay, so you have a lot of data. You can go into a customer, you can do an assessment, you can set a baseline, and then you have other data by which you can compare that and, and understand what's achievable. So what's your vision for sustainable business? You know, that strategy, you know, how has it affected your business in terms of the evolution? Cuz this wasn't, hasn't always been as hot a topic as it is today. And and is it a competitive advantage for you? >>Yeah, yeah. For, for, for all intense and proposed sustainability is a competitive advantage for elec. I mean, it's so, because at the time of profound transformation in the work, in the world of work, CSR issues make a company more attractive when searching for new talent to enter in the workforce of our company. In addition, efforts to ensure people's proper work life balance are a strong retention factor. And regarding our business proposition, ELEX attempts is to meet high standard of sustainability and reliability. Our green data center, you said is a prime example of this approach as at the same time, is there a conditioning activity that is done to give a second life to technology devices that come from back from rental? I mean, our customer inquiries with respect to sustainability are increasingly frequent and in depth and which is why we monitor our performance and invest in certification such as EcoVadis or ISO 14,001. Okay, >>Got it. So in a previous life I actually did some work with, with, with power companies and there were two big factors in it that affected the power consumption. Obviously virtualization was a big one, if you could consolidate servers, you know, that was huge. But the other was the advent of flash storage and that was, we used to actually go in with the, the engineers and the power company put in alligator clips to measure of, of, of an all flash array versus, you know, the spinning disc and it was a big impact. So you, I wanna talk about your, your experience with Pure Storage. You use Flash Array and the Evergreen architecture. Can you talk about what your experience there, why did you make that decision to select Pure Storage? How does that help you meet sustainability and operational requirements? Do those benefits scale as your customers grow? What's your experience been? >>Yeah, it was basically an easy and easy answer to our, to our business needs. Okay. Because you said before that in Elec we, we manage a lot of data, okay? And in the past we, we, we see it, we see that the constraints of managing so many, many data was very, very difficult to manage in terms of power consumption or simply for the, the space of storing the data. And when, when Pure came to us and share our products, their vision to the data management journey for Element Informatica, it was very easy to choose pure why with values and numbers. We, we create a business case and we said that we, we see that our power consumption usage was much less, more than 90% of previous technology that we used in the past. Okay. And so of course you have to manage a grade oil deploy of flash technology storage, but it was a good target. >>So we have tried to monitoring the adoption of flash technology and monitor monitoring also the power consumption and the efficiency that the pure technology bring to our, to our IT systems and of course the IT systems of our clients. And so this is one, the first part, the first good part of our trip with, with Pure. And after that we approach also the sustainability in long term of choosing pure technology storage. You mentioned the Evergreen models of Pure, and of course this was, again, challenge for us because it allows, it allow us to extend the life cycle management of our data centers, but also the, IT allows us to improve the facility of the facilities of using technology from our technical side. Okay. So we are much more efficient than in the past with the choose of Pure storage technologies. Okay. Of course, this easy users, easy usage mode, let me say it, allow us to bring this value to our, to all our clients that put their data in our data centers. >>So you talked about how you've seen a 90% improvement relative to previous technologies. I always, I haven't put you in the spot. Yeah, because I, I, I was on Pure's website and I saw in their ESG report some com, you know, it was a comparison with a generic competitor presuming that competitor was not, you know, a 2010 spinning disc system. But, but, so I'm curious as to the results that you're seeing with Pure in terms of footprint and power usage. You, you're referencing some of that. We heard some metrics from Nicole and AJ earlier in the program. Do you think, again, I'm gonna put you in the spot, do you think that Pure's architecture and the way they've applied, whether it's machine intelligence or the Evergreen model, et cetera, is more competitive than other platforms that you've seen? >>Yeah, of course. Is more competitor improve competitive because basically it allows to service provider to do much more efficient value proposition and offer services that are more, that brings more values to, to the customers. Okay. So the customer is always at the center of a proposition of a service provider and trying to adopt the methodology and also the, the value that pure as inside by design in the technology is, is for us very, very, very important and very, very strategic because, because with like a glass, we can, our self transfer try to transfer the values of pure, pure technologies to our service provider client. >>Okay. Matta, let's wrap and talk about sort of near term 2023 and then longer term it looks like sustainability is a topic that's here to stay. Unlike when we were putting alligator clips on storage arrays, trying to help customers get rebates that just didn't have legs. It was too complicated. Now it's a, a topic that everybody's measuring. What's next for elec in its sustainability journey? What advice would you might have? Sustainability leaders that wanna make a meaningful impact on the environment, but also on the bottom line. >>Okay, so sustainability is fortunately a widely spread concept. And our role in, in this great game is to define a strategy, align with the common and fundamentals goals for the future of planet and capable of expressing our inclination and the, and the particularities and accessibility goals in the near future. I, I say, I can say that are will be basically free one define sustainability plan. Okay? It's fundamentals to define a sustainability plan. Then it's very important to monitor the its emissions and we will calculate our carbon footprint. Okay? And least button list produces certifiable and comprehensive sustainability report with respect to the demands of customers, suppliers, and also partners. Okay. So I can say that this three target will be our direction in the, in the future. Okay. >>Yeah. So I mean, pretty straightforward. Make a plan. You gotta monitor and measure, you can't improve what you can't measure. So you gonna set a baseline, you're gonna report on that. Yep. You're gonna analyze the data and you're gonna make continuous improvement. >>Yep. >>Matea, thanks so much for joining us today in sharing your perspectives from the, the northern part of Italy. Really appreciate it. >>Yeah, thank you for having aboard. Thank you very >>Much. It was really our pleasure. Okay, in a moment, I'm gonna be back to wrap up the program and share some resources that could be valuable in your sustainability journey. Keep it right there. >>Sustainability is becoming increasingly important and is hitting more RFPs than ever before as a critical decision point for customers. Environmental benefits are not the only impetus. Rather bottom line cost savings are proving that sustainability actually means better business. You can make a strong business case around sustainability and you should, many more organizations are setting mid and long-term goals for sustainability and putting forth published metrics for shareholders and customers. Whereas early green IT initiatives at the beginning of this century, were met with skepticism and somewhat disappointing results. Today, vendor r and d is driving innovation in system design, semiconductor advancements, automation in machine intelligence that's really beginning to show tangible results. Thankfully. Now remember, all these videos are available on demand@thecube.net. So check them out at your convenience and don't forget to go to silicon angle.com for all the enterprise tech news of the day. You also want to check out pure storage.com. >>There are a ton of resources there. As an aside, pure is the only company I can recall to allow you to access resources like a Gartner Magic Quadrant without forcing you to fill out a lead gen form. So thank you for that. Pure storage, I love that. There's no squeeze page on that. No friction. It's kind of on brand there for pure well done. But to the topic today, sustainability, there's some really good information on the site around esg, Pure's Environmental, social and Governance mission. So there's more in there than just sustainability. You'll see some transparent statistics on things like gender and ethnic diversity, and of course you'll see that Pure has some work to do there. But kudos for publishing those stats transparently and setting goals so we can track your progress. And there's plenty on the sustainability topic as well, including some competitive benchmarks, which are interesting to look at and may give you some other things to think about. We hope you've enjoyed the path to Sustainable it made possible by Pure Storage produced with the Cube, your leader in enterprise and emerging tech, tech coverage.

>>Hello, and welcome to the cubes presentation of the AWS startup showcase. This is season two, episode four, the ongoing series covering exciting startups from the AWS ecosystem to talk about cyber security. I'm your host, John feer. And today we're excited to join by a Mediatel Walker, CEO of Quin security and sub IER, vice president of product management of sequence security gentlemen, thanks for joining us today on this showcase. >>Thank you, John PRAs. >>So the title of this session is continuous API protection life cycle to discover, detect, and defend security. APIs are part of it. They're hardened, everyone's using them, but they're they're target for malicious behavior. This is the focus of this segment. You guys are in the leading edge of this. What are the biggest challenges for organizations right now in assessing their security risks? Because you're seeing APIs all over the place in the news, just even this week, Twitter had a whistleblower come out from the security group, talking about their security plans, misleading the FTC on the bots and some of the malicious behavior inside the API interface of Twitter. This is really a mainstream Washington post is reporting on it. New York times, all the global outlets are talking about this story. This is the risk. I mean, yeah, this is what you guys do protect against this. >>Yeah, this is absolutely top of mind for a lot of security folks today. So obviously in the media and the type of attack that that is being discussed with this whistleblower coming out is called reputation bombing. This is not new. This has been going on since I would say at least eight to 10 years where the, the bad actors are using bots or automation and ultimately using APIs on these large social media platforms, whether it's Facebook, whether it's Twitter or some other social media platform and messing with the reputation system of those large platforms. And what I mean by that is they will do fake likes, fake commenting, fake retweeting in the case of Twitter. And what that means is that things that are, should not be very popular, all of a sudden become popular. That that way they're able to influence things like elections, shopping habits, personnel. >>We, we work with similar profile companies and we see this all the time. We, we mostly work on some of the secondary platforms like dating and other sort of social media platforms around music sharing and things like video sharing. And we see this all the time. These, these bots are bad. Actors are using bots, but ultimately it's an API problem. It's not just a bot problem. And that's what we've been trying to sort of preach to the world, which is your bot problem is subset of your API security challenges that you deal as an organization. >>You know, IMIA, we talked about this in the past on a previous conversation, but this really is front and center mainstream for the whole world to see around the challenges. All companies face, every CSO, every CIO, every board member organizations out there looking at this security posture that spans not just information technology, but physical and now social engineering. You have all kinds of new payloads of malicious behavior that are being compromised through, through things like APIs. This is not just about CSO, chief information security officer. This is chief security officer issues. What's your reaction >>Very much so I think the, this is a security problem, but it's also a reputation problem. In some cases, it's a data governance problem. We work with several companies which have very restrictive data governance and data regulations or data residency regulations there to conform to those regulations. And they have to look at that. It's not just a CSO problem anymore. In case of the, the news of the day to day, this is a platform problem. This goes all the way to the, that time CTO of Twitter. And now the CEO of Twitter, who was in charge of dealing with these problems. We see as just to give you an example, we, we work, we work with a similar sort of social media platform that allows Oop based login to their platform that is using tokens. You can sort of sign in with Facebook, sign in with Twitter, sign in with Google. These are API keys that are generated and trusted by these social media platforms. When we saw that Facebook leaked about 50 million of these login credentials or API keys, this was about three, four years ago. I wrote a blog about it. We saw a huge spike in those API keys being used to log to other social media platforms. So although one social platform might be taking care of its, you know, API or what problem, if something else gets reached somewhere else, it has a cascading impact on a variety of platforms. >>You know, that's a really interesting dynamic. And if you think about just the token piece that you mentioned, that's kind of under the coverage, that's a technology challenge, but also you get in the business logic. So let's go back and, and unpack that, okay, they discontinue the tokens. Now they're being reused here. In the case of Twitter, I was talking to an executive here in Silicon valley and they said, yeah, it's a cautionary tale, for sure. Although Twitter's a unique situation, but they abstract out the business value and say, Hey, they had an M and a deal on the table. And so if someone wants to unwind that deal, all I gotta say is, Hey, there's a bot problem. And now you have essentially new kinds of risk in the business have nothing to do with some sign the technology, okay. They got a security breach, but here with Twitter, you have an, an, an M and a deal, an acquisition that's being contested because of the, the APIs. So, so if you're in business, you gotta think to yourself, what am I risking with my API? So every organization should be assessing their security risks, tied to their APIs. This is a huge awakening for them. Where should they start? And that's the, that's the core question. Okay. You got my attention risks with the API. What do I do? >>So when I talked to you in my previous interview, the start is basically knowing what to, in most cases, you see these that are hitting the wire much. Every now there is a major in cases you'll find these APIs are targeted, that are not poorly protected. They're absolutely just not protected at all, which means the security team or any sort of team that is responsible for protecting these APIs are just completely unaware of these APIs being there in the first place. And this is where we talk about the shadow it or shadow API problem. Large enterprises have teams that are geo distributed, and this problem is escalated after the pandemic even more because now you have teams that are completely distributed. They do M and a. So they acquire new companies and have no visibility into their API or security practices. And so there are a lot of driving factors why these APIs are just not protected and, and just unknown even more to the security team. So the first step has to be discover your API attack surface, and then prioritize which APIs you wanna target in terms of runtime protection. >>Yeah. I wanna dig into that API kind of attack surface area management, runtime monitoring capability in a second, but so I wanna get you in here too, because we're talking about APIs, we're talking about attacks. What does an API attack look like? >>Yeah, that's a very good question, John, there are really two different forms of attacks of APIs, one type of attack, exploits, APIs that have known vulnerabilities or some form of vulnerabilities. For instance, APIs that may use a weak form of authentication or are really built with no authentication at all, or have some sort of vulnerability that makes them very good targets for an attacker to target. And the second form of attack is a more subtle one. It's called business logic abuse. It's, it's utilizing APIs in completely legitimate manner manners, but exploiting those APIs to exfiltrate information or key sensitive information that was probably not thought through by the developer or the designers or those APIs. And really when we do API protection, we really need to be able to handle both of those scenarios, protect against abuse of APIs, such as broken authentication, or broken object level authorization APIs with that problem, as well as protecting APIs from business logic abuse. And that's really how we, you know, differentiate against other vendors in this >>Market. So just what are the, those key differentiated ways to identify the, in the malicious intents with APIs? Can you, can you just summarize that real quick, the three ways? >>Sure. Yeah, absolutely. There are three key ways that we differentiate against our competition. One is in the, we have built out a, in the ability to actually detect such traffic. We have built out a very sophisticated threat intelligence network built over the entire lifetime of the company where we have very well curated information about malicious infrastructures, malicious operators around the world, including not just it address ranges, but also which infrastructures do they operate on and stuff like that, which actually helps a lot in, in many environments in especially B2C environments, that alone accounts for a lot of efficacy for us in detecting our weed out bad traffic. The second aspect is in analyzing the request that are coming in the API traffic that is coming in and from the request itself, being able to tell if there is credential abuse going on or credential stuffing going on or known patterns that the traffic is exhibiting, that looks like it is clearly trying to attack the attack, the APM. >>And the third one is, is really more sophisticated as they go farther and farther. It gets more sophisticated where sequence actually has a lot of machine learning models built in which actually profile the traffic that is coming in and separate. So the legitimate or learns the legitimate traffic from the anomalous or suspicious traffic. So as the traffic, as the API requests are coming in, it automatically can tell that this traffic does not look like legitimate traffic does not look like the traffic that this API typically gets and automatically uses that to figure out, okay, where is this traffic coming from? And automatically takes action to prevent that attack? >>You know, it's interesting APIs have been part of the goodness of cloud and cloud scale. And it reminds me of the old Andy Grove quote, founder of, in one of the founders of Intel, you know, let chaos, let, let the chaos happen, then reign it in it's APIs. You know, a lot of people have been creating them and you've got a lot of different stakeholders involved in creating them. And so now securing them and now manage them. So a lot of creation now you're starting to secure them and now you gotta manage 'em. This all is now big focus. As you pointed out, what are some of the dynamics that customers who have to deal with on the product side and, and organization, let, let chaos rain, and then rain in the chaos, as, as the saying goes, what, what do companies do? >>Yeah. Typically companies start off with like, like a mayor talked about earlier. Discovery is really the key thing to start with, like figuring out what your API attack surfaces and really getting your arms around that problem. And typically we are finding customers start that off from the security organization, the CSO organization to really go after that problem. And in some cases, in some customers, we even find like dedicated centers of excellence that are created for API security, which go after that problem to be able to get their arms around the whole API attack surface and the API protection problem statement. So that's where usually that problem starts to get addressed. >>I mean, organizations and your customers have to stop the attacks. A lot of different techniques, you know, run time. You mentioned that earlier, the surface area monitoring, what's the choice. What's the, where are, where are, where is everybody? Is everyone in the, in the boiling water, like the frog and boiling water or they do, they know it's happening? Like what did they do? What's their opportunity to get in >>Position? Yeah. So I, I think let's take a step back a little bit, right? What has happened is if you draw the cloud security market, if you will, right. Which is the journey to the cloud, the security of these applications or APIs at a container level, in terms of vulnerabilities and, and other things that market grew with the journey to the cloud, pretty much locked in lockstep. What has happened in the API side is the API space has kind of lacked behind the growth and explosion in the API space. So what that means is APIs are getting published way faster than the security teams are able to sort of control and secure them. APIs are getting published in environments that the security completely unaware of. We talked about in the past about the parameter, the parameter, as we know, it doesn't exist anymore. It used to be the case that you hit a CDN, you terminate your SSL, you stop your layer three and four DDoS. >>And then you go into the application and do the business logic. That parameter is just gone because it's now could be living in multi-cloud environment. It could be living in the on-prem environment, which is PubNet is friendly. And so security teams that are used to protecting apps, using a perimeter defense plus changes, it's gone. You need to figure out where your perimeter is. And therefore we sort of recommend an approach, which is have a uniform view across all your APIs, wherever they could be distributed and have a single point of control across those with a solution like sequence. And there are others also in this space, which is giving you that uniform view, which is first giving you that, you know, outside and looking view of what APIs to protect. And then let's, you sort of take the journey of securing the API life cycle. >>So I would say that every company now hear me out on this indulges me for a second. Every company in the world will be non perimeter based, except for maybe 5% because of maybe unique reason, proprietary lockdown, information, whatever. But for most, most companies, everyone will be in the cloud or some cloud native, non perimeter based security posture. So the question is, how does your platform fit into that trajectory? And specifically, why are you guys in the position in your mind to help customers solve this API problem? Because again, APIs have been the greatest thing about the cloud, right? Yeah. So the goodness is there because of APS. Now you gotta reign it in reign in the chaos. Yeah. What, what about your platform share? What is it, why is it win? Why should customers care about this? >>Absolutely. So if you think about it, you're right, the parameter doesn't exist. People have APIs deployed in multiple environments, multicloud hybrid, you name it sequence is uniquely positioned in a way that we can work with your environment. No matter what that environment is. We're the only player in this space that can protect your APIs purely as a SA solution or purely as an on-prem deployment. And that could be a SaaS platform. It doesn't need to be RackN, but we also support that and we could be a hybrid deployment. We have some deployments which are on your prem and the rest of this solution is in our SA. If you think about it, customers have secured their APIs with sequence with 15 minutes, you know, going live from zero to life and getting that protection instantaneously. We have customers that are processing a billion API calls per day, across variety of different cloud environments in sort of six different brands. And so that scale, that flexibility of where we can plug into your infrastructure or be completely off of your infrastructure is something unique to sequence that we offer that nobody else is offering >>Today. Okay. So I'll be, I'll be a naysayer. Yeah, look, it, we are perfectly coded APIs. We are the best in the business. We're locked down. Our APIs are as tight as a drum. Why do I need you? >>So that goes back to who's answer. Of course, >>Everyone's say that that's, that's great, but that's my argument. >>There are two types of API attacks. One is a tactic problem, which is exploiting a vulnerability in an API, right? So what you're saying is my APIs are secure. It does not have any vulnerability I've taken care of all vulnerabilities. The second type of attack that targets APIs is the business logic. Use this stuff in the news this week, which is the whistleblower problem, which is, if you think APIs that Twitter is publishing for users are perfectly secure. They are taking care of all the vulnerabilities and patching them when they find new ones. But it's the business logic of, you know, REWE liking or commenting that the bots are targeting, which they have no against. Right. And then none of the other social networks too. Yeah. So there are many examples. Uber wrote a program to impersonate users in different geo locations to find lifts, pricing, and driver information and passenger information, completely legitimate use of APIs for illegitimate, illegitimate purpose using bots. So you don't need bots by the way, don't, don't make this about bot versus not. Yeah. You can use APIs sort of for the, the purpose that they're not designed for sort of exploiting their business logic, either using a human interacting, a human farm, interacting with those APIs or a bot form targeting those APIs, I think. But that's the problem when you have, even when you've secured all your problem, all your APIs, you still have to worry about these of challenges. >>I think that's the big one. I think the business logic one, certainly the Twitter highlights that the Uber example is a good one. That is basically almost the, the backlash of having a simplistic API, which people design to. Right. Yeah. You know, as you point out, Twitter is very simple API, hardened, very strong security, but they're using it to maliciously manipulate what's inside. So in a way that perimeter's dead too. Right. So how do you stop that business logic? What's the, what's the solution what's the customer do about that? Because their goal is to create simple, scalable APIs. >>Yeah. I'll, I'll give you a little bit, and then I think Subaru should maybe go into a little bit of the depth of the problem, but what I think that the answer lies in what Subaru spoke earlier, which is our ML. AI is, is good at profiling plus split between the API users, are these legitimate users, humans versus bots. That's the first split we do. The split second split we do is even when these, these are classified users as bots, we will say there are some good bots that are necessary for the business and bad bots. So we are able to split this across three types of users, legitimate humans, good bots and bad bots. And just to give you an example of good bots is there are in the financial work, there are aggregators that are scraping your data and aggregating for end users to consume, right? Your, your, and other type of financial aggregators FinTech companies like MX. These are good bots and you wanna allow them to, you know, use your APIs, whereas you wanna stop the bad bots from using your APIs super, if you wanna add so, >>So good bots versus bad bots, that's the focus. Go ahead. Weigh in, weigh in on your thought on this >>Really breaks down into three key areas that we talk about here, sequence, right? One is you start by discovering all your APIs. How many APIs do I have in my environment that ly immediately highlight and say, Hey, you have, you know, 10,000 APIs. And that usually is an eye opener to many customers where they go, wow. I thought we had a 10th of that number. That usually is an eyeopener for them to, to at least know where they're at. The second thing is to tell them detection information. So discover, detect, and defend detect will tell them, Hey, your APIs are getting traffic from. So and so it addresses so and so infrastructure. So and so countries and so on that usually is another eye opener for them. They then get to see where their API traffic is coming from. Let's say, if you are a, if you're running a pizza delivery service out of California and your traffic is coming from Eastern Europe to go, wait a minute, nobody's trying, I'm not, I'm not, I don't deliver pizzas in Eastern Europe. Why am I getting traffic from that part of the world? So that sort of traffic immediately comes up and it will tell you that it is hitting your unauthenticated API. It is hitting your API. That has, that is vulnerable to a broken object level, that authorization, vulnerable be and so on. >>Yeah, I think, and >>Then comes the different aspect. Yeah. The different aspect is where you can take action and say, I wanna block certain types of traffic, or I wanna rate limit certain types of traffic. If, if you're seeing spikes there or you could maybe insert header so that it passes on to the end application and the application team can use that bit to essentially take a, a conscious response. And so, so the platform is very flexible in allowing them to take an action that suits their needs. >>Yeah. And I think this is the big trend. This is why I like what you guys are doing. One APIs we're built for the goodness of cloud. They're now the plumbing, you know, anytime you see plumbing involved, connection points, you know, that's pretty important. People are building it out and it has made the cloud what it is. Now, you got a security challenge. You gotta add more intelligence, more smarts to it. This is where I think platform versus tools matter. Can you guys just quickly share your thoughts on that? Cuz a lot of your customers and, and future customers have dealt with the sprawls of all these different tools. Right? I got a tool for this. I got a tool for that, but people are gravitating towards platforms, but how many platforms can a customer have? So again, this brings up the point point around how you guys are engaging with customers. Can you share your thoughts on tooling platforms? Your customers are constantly inundated with the same tsunami. Isn't new thing. Why, what, how should they look at this? >>Yeah, I mean, we don't wanna be, we don't wanna add to that alert fatigue problem that affects much of the cybersecurity industry by generating a whole bunch of alerts and so on. So what we do is we actually integrate very well with S IEM systems or so systems and allow customers to integrate the information that we are detecting or mitigating and feed them onto enterprise systems like a Splunk or a Datadog where they may have sophisticated processes built in to monitor, you know, spikes in anomalous traffic or actions that are taken by sequence. And that can be their dashboard where a whole bunch of alerting and reporting actually happens. So we play in the security ecosystem very well by integrating with other products and integrate very tightly with them, right outta the box. >>Okay. Mia, this is a wrap up now for the showcase. Really appreciate you guys sharing your awesome technology and very relevant product for your customers and where we are right now in this we call Supercloud or now multi-cloud or hybrid world of cloud. Share a, a little bit about the company, how people can get involved in your solution, how they can consume it and things they should know about, about sequence security. >>Yeah, we've been on this journey, an exciting journey it's been for, for about eight years. We have very large fortune 100 global 500 customers that use our platform on a daily basis. We have some amazing logos, both in Europe and, and, and in us customers are, this is basically not the shelf product customers not only use it, but depend on sequence. Several retailers. We are sitting in front of them handling, you know, black Friday, cyber, Monday, Christmas shopping, or any sort of holiday seasonality shopping. And we have handled that the journey starts by, by just simply looking at your API attack surface, just to a discover call with sequence, figure out where your APIs are posted work with you to prioritize how to protect them in a sort of a particular order and take the whole life cycle with sequence. This is, this is an exciting phase exciting sort of stage in the company's life. We just raised a very sort of large CDC round of funding in December from Menlo ventures. And we are excited to see, you know, what's next in, in, in the next, you know, 12 to 18 months. It certainly is the, you know, one of the top two or three items on the CSOs, you know, budget list for next year. So we are extremely busy, but we are looking for, for what the next 12 to 18 months are, are in store for us. >>Well, congratulations to all the success. So will you run the roadmap? You know, APIs are the plumbing. If you will, you know, they connection points, you know, you want to kind of keep 'em simple, as they say, keep the pipes dumb and make the intelligence around it. You seem to see more and more intelligence coming around, not just securing it, but does, where does this go in your mind? Where, where do we go beyond once we secure everything and manage it properly, APRs, aren't going away, they're only gonna get better and smarter. Where's the intelligence coming share a little bit. >>Absolutely. Yeah. I mean, there's not a dull moment in the space. As digital transformation happens to most enterprise systems, many applications are getting transformed. We are seeing an absolute explosion in the volume of APIs and the types of APIs as well. So the applications that were predominantly limited to data centers sort of deployments are now splintered across multiple different cloud environments are completely microservices based APIs, deep inside a Kubernetes cluster, for instance, and so on. So very exciting stuff in terms of proliferation of volume of APIs, as well as types of APIs, there's nature of APIs. And we are building very sophisticated machine learning models that can analyze traffic patterns of such APIs and automatically tell legitimate behavior from anomalous or suspicious behavior and so on. So very exciting sort of breadth of capabilities that we are looking at. >>Okay. I mean, yeah. I'll give you the final words since you're the CEO for the CSOs out there, the chief information security officers and the chief security officers, what do you want to tell them? If you could give them a quick shout out? What would you say to them? >>My shout out is just do an assessment with sequence. I think this is a repeating thing here, but really get to know your APIs first, before you decide what and where to protect them. That's the one simple thing I can mention for thes >>Am. Thank you so much for, for joining me today. Really appreciate it. >>Thank you. >>Thank you. Okay. That is the end of this segment of the eight of his startup showcase. Season two, episode four, I'm John for your host and we're here with sequin security. Thanks for watching.

(upbeat music) >> Hey everyone. Welcome to theCUBE's coverage of Women in Data Science, 2022. I'm Lisa Martin, excited to be coming to you live from Stanford University at the Ariaga alumni center. I'm pleased to welcome fresh keynote stage Alex Hanna the director of research at the dare Institute. Alex, it's great to have you on the program. >> Yeah, lovely to be here. >> Talk to me a little bit about yourself. I know your background is in sociology. We were talking before we went live about your hobbies and roller derby, which I love. >> Yes. >> But talk to me a little bit about your background and what the DAIR Institute this is, distributed AI research Institute, what it actually is doing. >> Sure, absolutely. So happy to be here talking to the women in data science community. So my background's in sociology, but also in computer science and machine learning. So my dissertation work was actually focusing on developing some machine learning and natural language processing tools for analyzing protest event data and generating that and applying it to pertinent questions within social movement scholarship. After that, I was a faculty at University of Toronto and then research scientist at Google on the ethical AI team where I met Dr. Timnit Gebru who is the founder of DAIR. And so, DAIR is a nonprofit research Institute oriented on around independent community based AI work, focused really on, the kind of, lots of discussions around AI are done by big companies or companies focus on solutions that are very much oriented around collecting as much data as they can. Not really knowing if it's going to be for community benefit. At DAIR, we want to flip that, we want to really want to prioritize what that would mean if communities had input into data driven technologies what it would mean for those communities and how we can help there. >> Double click and just some of your research, where do your passions lie? >> So I'm a sociologist and a lot of that being, I think one of the big insights of sociology is to really highlight at how society can be more just, how we can interrogate inequality and understanding how to make those distances between people who are underserved and over served who already have quite a lot, how we can reduce the disparities. So finding out where that lies, especially in technology that's really what I'm passionate about. So it's not just technology, which I think can be helpful but it's really understanding what it means to reduce those gaps and make the world more just. >> And that's so important. I mean, as more and more data is generated, exponentially growing, so are some of the biases and the challenges that that causes. You just gave your tech vision talk which I had a chance to see most of it. And you were talking about something that's very interesting. That is the biases in facial recognition software. Maybe on a little bit about what you talked about and why that is such a challenge. And also what are some of the steps being made in the right direction where that's concerned? >> Yeah. So there's the work I was talking about in the talk was highlighting, not work I've done, but the work by doctors (indistinct) and (indistinct) focusing on the distance that exists and the biases that exist in facial recognition as a technical system. The fact remains also that facial recognition is used and is disproportionately deployed on marginalized population. So in the U.S, that means black and brown communities. That's where facial recognition is used disproportionately. And we also see this in refugee context where refugees will be leaving the country. And those facial recognition software will be used in those contexts and surveilling them. So these are people already in a really precarious place. And so, some of the movements there have been to debias some of the facial recognition tools. I actually don't think that's far enough. I'm fundamentally against facial recognition. I think that it shouldn't be used as a technology because it is used so pervasively in surveillance and policing. And if we're going to approach that we really need to think, rethink our models of security models of immigration and whatnot. >> Right, it's such an important topic to discuss because I think it needs more awareness about some of the the biases, but also some to your point about some of those vulnerable communities that are really potentially being harmed by technologies like that. We have to be, there's a fine line. Or maybe it's not so fine. >> I don't think it's that fine. So like, I think it's used, in an incredibly harsh way. And for instance there's research that's being done in which, so I'm a transgender woman and there's a research being done by researchers who collected data sets that people had on YouTube documenting their transitions. And already there was a researcher collecting those data and saying, well, we could have terrorists or something take hormones and cross borders. And you talk to any trans person, you're like, well, that's not how it works, first off. Second off, it's already viewing trans people and a trans body as kind of a mode of deception. And so that's, whereas researchers in this space were collecting those data and saying that well, we should collect these data to help make these facial recognitions more fair. But that's not fair if it's going to be used on a population that's already intensely surveilled and held in suspicion. >> Right. That's, the question of fairness is huge, absolutely. Were you always interested in tech, you talked about your background in sociology. Was it something that you always, were you a stem kid from the time you were little? Talk to me about your background and how you got to where you are now? >> Yeah. I've been using computers since I was four. I've been using, I was taking a part, my parents' gateway computer. yeah, when I was 10. Going to computer shows, slapping hard drives into things, seeing how much we could upgrade computer on our own and ruining more than in one computer, to my parents chagrin but I've always been that. I went to undergrad in triple major to computer science, math and sociology, and originally just in computer science and then added the other two where I got interested in things and understanding that, was really interested in this section of tech and society. And I think the more and more I sat within the field and went and did my graduate work in sociology and other social sciences really found that there was a place to interrogate those, that intersection of the two. >> Exactly. What are some of the things that excite you now about where technology is going? What are some of the positives that you see? >> I talk so much about the negatives. It's really hard to, I mean, there's I think, some of the things that I think that are positive are really the community driven initiatives that are saying, well, what can we do to remake this in such a way that is going to more be more positive for our community? And so seeing projects like, that try to do community control over certain kinds of AI models or really try to tie together different kinds of fields. I mean, that's exciting. And I think right now we're seeing a lot of people that are super politically and justice literate and they how to work and they know what's behind all these data driven technologies and they can really try to flip the script and try to understand what would it mean to kind of turn this into something that empowers us instead of being something that is really becoming centralized in a few companies >> Right. We need to be empowered with that for sure. How did you get involved with WIS? >> So Margo, one of the co-directors, we sit on a board together, the human rights data analysis group and I've been a huge fan of HR dag for a really long time because HR dag is probably one of the first projects I've seen that's really focused on using data for accountability for justice. Their methodology has been, called on to hold perpetrators of genocide to accounts to hold state violence, perpetrators to account. And I always thought that was really admirable. And so being on their board is sort of, kind of a dream. Not that they're actually coming to me for advice. So I met Margo and she said, come on down and let's do a thing for WIS and I happily obliged >> Is this your first Wis? >> This is my very first Wis. >> Oh, excellent. >> Yeah. >> What's your interpretation so far? >> I'm having a great time. I'm learning a lot meeting a lot of great people and I think it's great to bring folks from all levels here. Not only, people who are a super senior which they're not going to get the most out of it it's going to be the high school students the undergrads, grad students, folks who, and you're never too old to be mentored, so, fighting your own mentors too. >> You know, it's so great to see the young faces here and the mature faces as well. But one of the things that I was, I caught in the panel this morning was the the talk about mentors versus sponsors. And that's actually, I didn't know the difference until a few years ago in another women in tech event. And I thought it was such great advice for those panelists to be talking to the audience, talking about the importance of mentors, but also the difference between a mentor and sponsor. Who are some of your mentors? >> Yeah, I mean, great question. It's going to sound cheesy, but my boss (indistinct) I mean, she's been a huge mentor for me and with her and another mentor (indistinct) Mitchell, I wouldn't have been a research scientist. I was the first social scientist on the research scientist ladder at Google before I left and if it wasn't for their, they did sponsor but then they all also mentored me greatly. My PhD advisor, (indistinct) huge mentor by, and I mean, lots of primarily and then peer mentors, people that are kind of at the same stage as me academically but also in professionally, but are mentors. So folks like Anna Lauren Hoffman, who's at the UDub, she's a great inspiration in collaborating, co-conspirator, so yeah. >> Co-conspirator, I like that. I'm sure you have quite a few mentees as well. Talk to me a little bit about that and what excites you about being a mentor. >> Yeah. I have a lot of mentees either informally or formally. And I sought that out purposefully. I think one of the speakers this morning on the panel was saying, if you can mentor do it. And that's what I did and sought out that, I mean, it excites me because folks, I don't have all the answers, no one person does. You only get to those places, if you have a large community. And I think being smart is often something that people think comes like, there's kind of like a smart gene or whatever but like there probably is, like I'm not a biologist or a cognitive, anything, but what really takes cultivation is being kind and really advocating for other people and building solidarity. And so that's what mentorship really means to me is building that solidarity and really trying to lift other people up. I mean, I'm only here and where I'm at in my career, because many people were mentors and sponsors to me and that's only right to pay that forward. >> I love that, paying that forward. That's so true. There's nothing like a good community, right? I mean, there's so much opportunity that that ground swell just generates, which is what I love. We are, tomorrow is international women's day. And if we look at the numbers, women are 50% of the workforce, but only less than a quarter in stem positions. What's your advice and recommendation for those young girls who might be intimidated or might be being told even to this day, no, you can't do physics. You can't do computer science. What can you tell them? >> Yeah, I mean, so individual solutions to that are putting a bandaid on a very big wound. And I mean I think, finding other people in a working to change it, I mean, I think building structures of solidarity and care are really the only way we'll get out of that. >> I agree. Well, Alex, it's been great to have you on the program. Thank you for coming and sharing what you're doing at DAIR. The intersection of sociology and technology was fascinating and your roller derby, we'll have to talk well about that. >> For sure. >> Excellent. >> Thanks for joining me. >> Yeah, thank you Lisa. >> For Alex Hanna, I'm Lisa Martin. You're watching theCUBE's coverage live, of women in data science worldwide conference, 2022. Stick around, my next guest is coming right up. (upbeat music)

okay welcome back to thecube's coverage at splunk.com 2021 virtual i'm john furrier with thecube we're here live in the studios of splunk's event here we're all together broadcasting out all over the world here with chris dieglemeyer chief social impact officer for splunk great to see you thanks for coming on great thanks for having me today i love the title chief social impact officer because we're bringing in data unlocks value well you know that and yes it's the theme of the show society has really been impacted by misinformation what context we've seen examples of how data has been good and been bad yes so there's a divide there so you're this is a big part of your talk yes it it's a big part of me and it's going to be even a bigger part of splunk going forward so as many people know they've heard of the digital divide right and that was about access to information communication technologies and it was coined 20 years ago 2001 and we've made progress on that digital divide but now we have all that infrastructure or a lot of it and so on top of that we have the data divide and that's the increasing and expanding use of data and the gap between using that to solve commercial and provide commercial value in contrast to solving our social and environmental challenges and so the the important thing about it is we're early enough that with urgent action we can try to close that gap um and really make a difference in the world so let's get started let's define the data divide and give some specific examples where you see it in action on the pro side and where there's some work needed yeah so all so the definition is again that that gap between using we we have all this data being used for commercial value and a relatively weak use of data being used to solve our social and environmental challenges and we've got four kind of key barriers that we've identified that need to be addressed which will get to you know the questions and how we solve it one is access so think about it think of the data that google has and where that is in access compared to probably the department of education in any country around the world so access is big second is capacity we need both financial resources investing in solving our social and environmental problems and we need data scientists data stewards great data people working to solve our social and environmental problems just as we are in the corporate sector and then the third one is investment choices and this one is a little bit of a be in my bonnet and this happens mostly in the private sector so we all know you know every year it's like what what hits the return on investment criteria and solving social and environmental challenges often does not uh doesn't have that quite time frame return on investment and think about if we'd identified this data divide 20 years ago for climate because companies are doing phenomenal work now about climate what if we had been doing that work 20 years ago around sustainability around efficiency and then the last piece is actionable solutions that we can replicate so those are kind of the four barriers um and again i think we've got a lot of potential and examples there isn't one issue i can think of where more data isn't going to help us you know this is so important i feel very strongly about this because i've seen examples where i've seen really strong people start ngos or non-profits or just building an app and they abandon it because they can't get there fast enough so the idea that cloud and data accessibility can be there you get to see some success and you can double down on that's the cloud way yes so i think this is something that people want to know the playbook so you know where where are people being successful what can people do yeah to take advantage of it yeah so i think that's a really good important point um is transitioning to the cloud so think of the nonprofit sector it's barely there yet so all of us who are investors philanthropists we need to be supporting the nonprofit sector be cloud enabled and cloud forward similarly with government i i you know there's example after example where you know whether it's health whether it's child and human services their data is in file cabinets think about that think of prime so we need to digitize those then we need to data enable that so that we can see those insights that are coming out around those solutions you know it's always the you know it's always a discussion in the industry inside the ropes and now on mainstream but getting data to the right place at the right time yeah is a really important thing it's a technical latency all these things but practically it has societal impact where would you rank the progress bar in terms of where we are on the digital divide because i can see healthcare for instance having access to the right information or it could be something on the government side where it could be related to climate change or hey get this involved where are we on this so i i would say on the digital divide which is the infrastructure piece um for most definitely high-income countries mid-income countries we've actually made progress and so they have that they're all you know network they're cloud but now they have all this data they don't know what to do with right and so what we need to kind of now build on that infrastructure to solve for that data and i'll just you know a splunk example one of our customers the netherlands um in their court system right with using splunk they were able to enable real-time data to inform court decisions so historically the judge would ask you know this happened in covid where are we on bankruptcy cases right and historically somebody would call somebody they'd call somebody they go dig the files and they get the information three months real time this is what's happening with bankruptcy in real time with covid is going to change those decisions that impact people's lives so you add that on top i mean we have environmental examples working with net zero schools we have it and we worked with the healthcare coalition with mitre to enable real-time data with a number of other companies so um where so i would say we're further along on the digital divide we're at step one on the data divide yeah doug merritt was talking earlier today about how you know this data plan that splunk has evolved into this catch basin for all the data and then it becomes useful and really taking us through the journal now security and it's this control plane that's enabling yeah i think to me that's a real key thing here so i have to ask do you see envision a future where we have a data commons where um citizens and could tap into the data and in the gov 2.0 is kind of on that vision yeah what do you where do you see this what do you say well i i think and i i know doug has talked about this before too from a values standpoint of especially with government moving to open data and then what we have to do is we have to protect privacy which actually splunk is really good at doing uh so you've got to take that individual data out of there but then once you get these big data pools into these big data lakes you'll be able to see insights that you couldn't see before you know it's interesting that i remember when the internet came around and how the u.s government's very active it seems now that that tech policy has always been kind of like oh yeah we're kind of involved in dc but now tech is so important and with all the backlash on the facebooks of the world of you know how democracy was broken there's an opportunity yeah and the lawmakers and the people who make the laws are kind of lawyers they're not really techies so so like policy's got to change how do we do that yeah oh gosh if i could solve that one on policy change but but i want to make a comment because i think it's really important because you reference and the situation facebook is in is common knowledge i give a lot of credit to splunk as you know a data platform company saying we see this data divide coming and we're going to step to the table now and do something about it because there's a lot of other companies that knew these challenges if they looked out three five years and they made personal or company choices not to do something about it so transparency is super important getting that out there and and being again in data and just saying it's not all roses right and and so take being a purpose-driven company is about making those decisions as a company to have an impact so then to answer your question on policy um i would say i think it's really complicated and tricky because data moves at the speed of sound and policy moves kind of like a turtle and so i think what we need to have happen is companies going to sometimes have to lead the way and hold themselves accountable and then work in partnership with policy to make you know policy changes that impact everybody so again we're strong advocates of open data you know we we can't make the government do it but we can be a voice for it in service of bridging the state this data divide is a great conversation i wish we had more time for the last minute just give a quick plug for what splunk's doing specifically and how people could get involved and participate yeah so i'll kind of i'd say three things one is at this early stage we're kind of raising the flag to governments out there to philanthropy to nonprofits like we all need to be paying attention to this we're going to be investing in more research on it because it is at such an early stage we've identified these barriers but we've got to go much deeper and build collaborations around the solution so we're going to be mobilizing our partners and our customers we have a 100 million dollar pledge where we donate our product nonprofits we and the equally important thing as i talked about it's our talent right it's getting the talent to help these organizations it's our strategic giving so we're mobilizing you know all of our assets around this pledge we have a 50 million dollar impact fund which is around four purpose data enabled companies so we're trying to do it across a multitude of platforms is that investment fund deploying now or has it been making investments in companies already yeah we've made um three investments refrain ai is one about using machine learning and ai around the jobs of the future and retraining so it's still or it was launched just a couple years ago so we're still early in the 50 million dollar fund so we'll be doing more of that sounds like a great opportunity for people out there watching enable enable the people to change the world yeah that's what splunk's all about right now exactly chris thanks for coming on appreciate great thank you okay the data divide we're bringing you all the data here from the cube live here in the splunk studios i'm john furrier with thecube thanks for watching thank you

>>From around the globe. It's the Cube with digital coverage of IBM think 2021 brought to you by >>IBM. Welcome back to IBM Think 2021. This is the cubes ongoing coverage where we go out to the events, we extract the signal from the noise of course, virtually in this case now we're going to talk about ecosystems, partnerships in the flywheel, they deliver in the technology business and with me or Jason kelly, general manager, global strategic partnerships, IBM global business services and Mani Das Gupta, who is the vice president of marketing for IBM Global Business services folks. It's great to see you again in which we're face to face. But this will have to do >>good to see you Dave and uh same, I wish we were face to face but uh we'll we'll go with this >>soon. We're being patient, Jason. Let's start with you. You have a partner strategy. I wonder if you could sort of summarize that and tell us more about it. >>So it's interesting that we start with the strategy because you said we have a partner strategy dave and I'd say that the market has dictated back to us a partner strategy something that we it's not new and we didn't start it yesterday. It's something that we continue to evolve and build even stronger. This thought of a partner strategy is it nothing is better than the thought of a partner ship. And people say oh well you know you got to work together as one team and as a partner And it sounds almost as a 1-1 type relationship. Our strategies is much different than that. David our execution is even better and that that execution is focused on now. The requirement that the market our clients are showing to us and our strategic partners that one player can't deliver all their needs, they can't Design solution and deliver that from one place. It does take an ecosystem to the word that you called out. This thought of an ecosystem and our strategy and execution is focused on that. And the reason why I say it evolves is because the market will continue to evolve and this thought of being able to look at a client's let's call it a a workflow, let's call it a value chain from one end to the other, wherever they start their process to wherever it ultimately hits that end user. It's going to take many players to cover that. And then we, as IBM want to make sure that we are the general contractor of that capability with the ability to convene the right strategic partners, bring out the best value for that outcome, not just technology for technology's sake, but the outcome that the incline is looking for so that we bring value to our strategic partners and that in client. >>I think about when you talk about the value chain, you know, I'm imagining, you know, the business books years ago you see the conceptual value chain, you can certainly understand that you can put processes together to connect them and now you've got technology, I think of a P. I. S. It's it's really supports that everything gets accelerated and and uh money. I wonder if you could address some of the the go to market how this notion of of ecosystem which is so important, is impacting the way in which you go to market. >>Absolutely. So modern business, you know, demands a new approach to working the ecosystem. Thought that Jason was just alluding to, it's a mutual benefit of all these companies working together in the market, it's a mutual halo of the brands, so as responsible for the championship of the IBM and the global business services brand. I am very, very interested in this mutual working together. It should be a win win win, as we say in the market, it should be a win for our clients, first and foremost, it should be a win for our partners and it should be a win for IBM and we are working together right now on an approach to bring this, go to market strategy to life. >>So I wonder if we could maybe talk about how this actually works and and pull in some examples, uh you must have some favorites that that we can touch on. Uh is that, is that fair? Can we, can we name some names, >>sure names, always working debut, right. And it's always in context of reality that we can talk about, as I said, this execution and not just a strategy. And I'll start with probably what's right in the front of many people's minds as we're doing this virtually because of what because of an unfortunate pandemic, um, this disastrous loss of life and things that have taken us down a path. We go well, how do we, how do we address that? Well, any time there's a tough task, IBM raises its hand first. You know, whether it was putting a person on the moon and bringing them home safely or standing up a system behind the current Social Security Administration, you know, during the Depression, you pick it well here we are now. And why not start with that as an example? Because I think it calls out just what we mentioned here first day, this thought of a, of an ecosystem because the first challenge, how do we create uh and address the biggest data puzzle of our lives, which is how do we get this vaccine created in record time, which it was the fastest before that was four years. This was a matter of months. Visor created the first one out and then had to get it out to distribution. Behind. That is a wonderful partner of R. S. A. P. Trying to work with that. So us working with S. A. P. Along with Pfizer in order to figure out how to get that value chain. And some would say supply chain, but I'll address that in a second. But there's many players there. And so we were in the middle of that with fires are committed to saying, how do we do that with S. A. P. So now you see players working together as one ecosystem. But then think about the ecosystem that that's happening where you have a federal government agency, a state, a local, you have healthcare, life science industry, you have consumer industry. Oh wait a second day. This is getting very complicated, Right? Well, this is the thought of convening an ecosystem and this is what I'm telling you is our execution and it has worked well. And so it's it's it's happening now. We still it's we see it's still developing and being, being, you know, very productive in real time. But then I said there was another example and that's with me, you mani whomever you pick the consumer. Ultimately we are that outcome of of the value chain. That's why I said, I don't want to just call it a supply chain because at the end is a someone consuming and in this case we need a shot. And so we partnered with Salesforce, IBM and Salesforce saying, wait a minute, that's not a small task. It's not just get the content there and put it in someone's arm instead they're scheduling that must be done. There's follow up an entire case management like system sells force is a master at this, so work dot com team with IBM, we sit now let's get that part done for the right type of UI UX capability that the user experience, user interaction interface and then also in bringing another player in the ecosystem, one of ours Watson health along with our block changing, we brought together something called a Digital Health pass. So I've just talked about two ecosystems work multiple ecosystems working together. So you think of an ecosystem of ecosystems. I called out Blockchain technology and obviously supply chain but there's also a I I O T. So you start to see where look this is truly an orchestration effort. It has to happen with very well designed capability and so of course we master and design and tying that that entire ecosystem together and convening it so that we get to the right outcome you me money all getting into shot being healthy. That's a real time example of us working with an ecosystem and teeming with key strategic partners, >>you know, money, I mean Jason you're right. I mean pandemics been horrible, I have to say. I'm really thankful it didn't happen 20 years ago because it would have been like okay here's some big pcs and a modem and go ahead and figure it out. So I mean the tech industry has saved business. I mean with not only we mentioned ai automation data, uh even things basic things like security at the end point. I mean so many things and you're right, I mean IBM in particular, other large companies you mentioned ASAP you have taken the lead and it's really I don't money, I don't think the tech industry gets enough credit, but I wonder if there's some of your favorite, you know, partnerships that you can talk about. >>Yeah, so I'm gonna I'm gonna build on what you just said. Dave IBM is in this unique position amongst this ecosystem. Not only the fact that we have the world leading most innovative technologies to bring to bear, but we also have the consulting capabilities that go with it now to make any of these technologies work towards the solution that Jason was referring to in this digital health pass, it could be any other solution you would need to connect these disparate systems, sometimes make them work towards a common outcome to provide value to the client. So I think our role as IBM within this ecosystem is pretty unique in that we are able to bring both of these capabilities to bear. In terms of you know, you asked about favorite there are this is really a coop petition market where everybody has products, everybody has service is the most important thing is how how are we bringing them all together to serve the need or the need of the hour in this case, I would say one important thing in this. As you observe how these stories are panning out in an ecosystem in in part in a partnership, it is about the value that we provide to our clients together. So it's almost like a cell with model from from a go to market perspective, there is also a question of our products and services being delivered through our partners. Right? So think about the span and scope of what we do here. And so that's the sell through. And then of course we have our products running within our partner companies and our partner products, for example. Salesforce running within IBM. So this is a very interesting and a new way of doing business. I would say it's almost like the modern way of doing business with modernity. >>Well. And you mentioned cooperation. I mean you're you're part of IBM that will work with anybody because your customer first, whether it's a W. S. Microsoft oracle is a is a is a really tough competitor. But your customers are using oracle and they're using IBM. So I mean as a those are some good examples. I think of your point about cooper Titian. >>Absolutely. If you pick on any other client, I'll mention in this case. Delta, Delta was working with us on moving, being more agile. Now this pandemic has impacted the airline sector particularly hard, right With travel stopping and anything. So they are trying to get to a model which will help them scale up, scale down, be more agile will be more secure, be closer to their customers, try and understand how they can provide value to their customers and customers better. So we are working with Delta on moving them to cloud on the journey to cloud. Now that public cloud could be anything. The beauty of this model and a hybrid cloud approach is that you are able to put them on red hat open shift, you're able to do and package the services into a microservices kind of a model. You want to make sure all the applications are running on a portable, almost platform. Agnostic kind of a model. This is the beauty of this ecosystem that we are discussing is the ability to do what's right for the end customer at the end of the day, >>how about some of the like sass players, like some of the more prominent ones and we watched the ascendancy of service now and and, and work day, you mentioned Salesforce. How do you work with those guys? Obviously there's an Ai opportunity, but maybe you could add some, you know, color there. >>So I like the fact that you call out the different hyper scholars for example, uh whether it's a W. S, whether it's Microsoft, knowing that they have their own cloud instances, for example. And when you, when you mentioned, he had this happened a long time ago, you know, you start talking about the heft of the technology, I started thinking of all the truckloads of servers or whatever they have to pull up. We don't need that now because it can happen in the cloud and you don't have to pick one cloud or the other. And so when people say hybrid cloud, that's what comes out, you start to think of what I I call, you know, a hybrid of hybrids because I told you before, you know, these roles are changing. People aren't just buyers or suppliers, they're both. And then you start to say what we're different people supplying well in that ecosystem, we know there's not gonna be one player, there's gonna be multiple. So we partner by doing just what monty called out is this thought of integrating in hybrid environments on hybrid platforms with hybrid clouds, Multi clouds, maybe I want something on my premises, something somewhere else. So in giving that capability that flexibility we empower and this is what's doing that cooperation, we empower our partners are strategic partners, we want them to be better with us. And this is this thought of being able to actually bring more together and move faster which is almost counterintuitive. You're like wait a minute you're adding more players but you're moving faster. Exactly because we have the capability to integrate those those technologies and get that outcome that monty mentioned, >>I would add to this one. Jason you mentioned something very very interesting. I think if you want to go just fast you go alone but if you want to go further, you go together. And that is the core of our point of view in this case is that we want to go further and we want to create value that is long lasting. >>What about like so I get the technology players and there may be things that you do that others don't or vice versa. So the gap fillers etcetera. But what about how to maybe customers that they get involved? Perhaps government agencies, may they be they be customer or an N. G. O. As another example, Are they part of this value chain? Part of this ecosystem? >>Absolutely. I'll give you I'll stick with the same example when I mentioned a digital health past that Digital Health Pass is something that we have as IBM and it's a credential Think of it as a health credential not a vaccine passport because it could be used for a test for a negative test on Covid, it could be used for antibiotics. So if you have this credential, it's something that we, as IBM created years back and we were using it for learning. When you think of getting people uh certifications versus a four year diploma, how do we get people into the workforce? That was what was original. That was a jenny Rometty thought, let's focus on new collar workers. So we had this asset that we'd already created and then it's wait, there's a place for it to work with, with health, with validation verification on someone's option, it's optional. They choose it. Hey, I want to do it this way. Well, the state of new york said that they wanted to do it that way and they said, listen, we are going to have a digital health pass for all of our, all of our new york citizens and we want to make sure that it's equitable, it could be printed or on a screen and we want it to be designed in this way and we wanted to work on this platform and we want to be able to, to work with the strategic Partners, a Salesforce and ASAP and work. I mean, I can just keep and we said okay let's do this. And this is the start of collaboration and doing it by design. So we haven't lost that day but this only brings it to the forefront just as you said, yes, that is what we want. We want to make sure that in this ecosystem we have a way to ensure that we are bringing together convening not just point products or different service providers but taking them together and getting the best outcome so that that end user can have it configured in the way that they want it >>guys, we got to leave it there but it's clear you're helping your customers and your partners on this this digital transformation journey that we already we all talk about. You get this massive portfolio of capabilities, deep, deep expertise, I love the hybrid cloud and AI Focus, Jason and money really appreciate you coming back in the cubes. Great to see you both. >>Thank you so much. Dave Fantastic. All >>Right. And thank you for watching everybody's day Vigilante for the Cuban. Our continuous coverage of IBM, think 2021, the virtual edition. Keep it right there. Yeah. Mhm. Mhm. >>Mhm.

>>Hello, and welcome to today's event, dealing government emergency responses beyond the pandemic. This is Bob Wooley, senior fellow for the center for digital government and formerly the chief tech clerk for the state of Utah. I'm excited to serve as moderator for today's event. And just want to say, thank you for joining us. I know we're in for an informative session over the next 60 minutes before we begin a couple of brief housekeeping notes or recording of this presentation will be emailed to all registrants within 48 hours. You can use the recording for your reference or feel free to pass it along to colleagues. This webcast is designed to be interactive and you can participate in Q and a with us by asking questions at any time during the presentation, you should see a Q and a box on the bottom left of the presentation panel. >>Please send in your questions as they come out throughout the presentation, our speakers will address as many of these questions as we can during the Q and a portion of the close of our webinar today, if you would like to download the PDF of the slides for this presentation, you can do so by clicking the webinar resources widget at the bottom of the console. Also during today's webinar, you'll be able to connect with your peers by LinkedIn, Twitter and Facebook. Please use the hashtag gov tech live to connect with your peers across the government technology platform, via Twitter. At the close of the webinar, we encourage you to complete a brief survey about the presentation. We would like to hear what you think if you're unable to see with us for the entire webinar, but we're just like to complete the survey. As much as you're able, please click the survey widget at the bottom of the screen to launch the survey. Otherwise it will pop up once the webinar concludes at this time, we recommend that you disable your pop-up blockers, and if you experiencing any media player issues or have any other problems, please visit our webcast help guide by clicking on the help button at the bottom of the console. >>Joining me today to discuss this very timely topic are Karen revolt and Tim Burch, Kim Berge currently serves as the administrator of human services for Clark County Nevada. He's invested over 20 years in improving health and human service systems of care or working in the private public and nonprofit sectors. 18 of those years have been in local government in Clark County, Las Vegas, where you served in a variety of capacities, including executive leadership roles as the director of department of social services, as well as the director for the department of family services. He has also served as CEO for provider of innovative hosted software solutions, as well as chief strategy officer for a boutique public sector consulting firm. Karen real-world is the social program management offering lead for government health and human services with IBM Watson health. Karen focuses delivering exciting new offerings by focusing on market opportunities, determining unmet needs and identifying innovative solutions. >>Much of her career has been in health and human services focused on snap, TANIF, Medicaid, affordable care act, and child welfare prior to joining IBM. Karen was the senior director of product management for a systems integrator. She naturally fell in love with being a project manager. She can take her user requirements and deliver offerings. Professionals would use to make their job easier and more productive. Karen has also found fulfillment in working in health and human services on challenges that could possibly impact the outcome of people's lives. Now, before we begin our discussion of the presentation, I want to one, we'd like to learn a little more about you as an audience. So I'm going to ask you a polling question. Please take a look at this. Give us an idea of what is your organization size. I won't bother to read all these to you, but there are other a range of sizes zero to 250 up to 50,000. Please select the one that is most appropriate and then submit. >>It looks like the vast majority are zero to two 50. Don't have too many over 250,000. So this is a very, very interesting piece of information. Now, just to set up our discussion today, what I want to do is just spend just a moment and talk about the issue that we're dealing with. So when you look the COVID-19 pandemic, it's put immense pressure on States. I've been a digital state judge and had been judging a lot of the responses from States around the country. It's been very interesting to me because they bifurcate really into two principle kinds of reactions to the stress providing services that COVID environment present. One is we're in a world of hurt. We don't have enough money. I think I'm going to go home and engage as little as I have to. Those are relatively uncommon. Thankfully, most of them have taken the COVID-19 pandemic has immense opportunity for them to really do a lot more with telework, to do more with getting people, employees, and citizens involved with government services. >>And I've done some really, really creative things along the way. I find that to be a really good thing, but in many States systems have been overloaded as individuals and families throughout the country submitted just an unprecedented number of benefit applications for social services. At the same time, government agencies have had to contend with social distance and the need for a wholly different approach to engage with citizens. Um, overall most public agencies, regardless of how well they've done with technology have certainly felt some strain. Now, today we have the opportunity to go into a discussion with our speakers, have some wonderful experience in these areas, and I'm going to be directing questions to them. And again, we encourage you as you hear what they have to say. Be sure and submit questions that we can pick up later at the time. So Tim, let's start with you. Given that Las Vegas is a hub for hospitality. An industry hit severely as a result of this pandemic. How's the County doing right now and how are you prioritizing the growing needs of the County? >>Thanks Bob. Thanks for having me. Let me start off by giving just a little, maybe context for Clark County too, to our audience today. So, uh, Clark County is, you know, 85% of the state of Nevada if we serve not just as a regional County by way of service provision, but also direct municipal services. Well, if, uh, the famous Las Vegas strip is actually in unincorporated Clark County, and if we were incorporated, we would be the largest city in the state. So I say all of that to kind of help folks understand that we provide a mix of services, not just regional services, like health and human services, the direct and, and missable, uh, services as well as we work with our other five jurisdiction partners, uh, throughout the area. Uh, we are very much, um, I think during the last recession we were called the Detroit of the West. >>And, uh, that was because we're very much seen as a one industry town. Uh, so most like when the car plants, the coal plants closed back East and in the communities fuel that very rapidly, the same thing happens to us when tourism, uh, it's cut. Uh, so of course, when we went into complete shutdown and March, uh, we felt it very rapidly, not just on, uh, uh, tax receipts and collectibles, but the way in which we could deliver services. So of course our first priority was to, uh, like I think you mentioned mobilized staff. We, we mobilized hundreds of staff overnight with laptops and phones and cars and the things they needed to do to get mobile and still provide the priority services that we're mandated to provide from a safety standpoint. Um, and then we got busy working for our clients and that's really where our partnership with IBM and Watson, uh, came in and began planning that in July. And we're able to open that portal up in October to, to really speed up the way in which we're giving assistance to, to our residents. Um, re focus has been on making sure that people stay housed. We have, uh, an estimated, uh, 2.5 million residents and over 150,000 of those households are anticipated to be facing eviction, uh, as of January one. So we, we've got a, a big task ahead of us. >>All of this sounds kind of expensive. Uh, one of the common threads as you know, runs throughout government is, ah, I don't really have the money for that. I think I'd be able to afford that a diaper too, as well. So what types of funding has been made available for counties, a result of a pandemic, >>Primarily our funding stream that we're utilizing to get these services out the door has been the federal cares act. Uh, now we had some jurisdictions regionally around us and even locally that prioritize those funds in a different way. Um, our board of County commissioners, uh, took, um, a sum total of about $85 million of our 240 million that said, this will go directly to residents in the form of rental assistance and basic needs support. No one should lose their home or go hungry during this pandemic. Uh, so we've really been again working through our community partners and through our IBM tools to make sure that happens. >>So how does, how does, how does the cares act funding then support Clark County? Cause it seems to me that the needs would be complex, diverse >>Pretty much so. So as you, as folks may know him a call there's several tronches of the cares act, the original cares act funding that has come down to us again, our board, uh, identified basic needs or rental assistance and, and gave that the department of social service to go to the tunicate, uh, through the community. We then have the cares act, uh, uh, coronavirus relief funds that have, uh, impacted our CDBG and our emergency solutions grants. We've taken those. And that's what we was going to keep a lot of the programs and services, uh, like our IBM Watson portal open past January one when the cares act dollars expire. Uh, our initial response was a very manual one, uh, because even though we have a great home grown homeless management information system, it does not do financials. Uh, so we had 14 local nonprofits adjudicating, uh, this rental assistance program. >>And so we could get our social service visitor portal up, uh, to allow us to take applications digitally and run that through our program. Uh, and, uh, so those partners were obviously very quickly overwhelmed and were able to stand up our portal, uh, which for the reason we were driving so hard, even from, uh, beginning of the conversations where after going into lockdown into contracting in July and getting the portal open in October, which was an amazing turnaround. Uh, so the kudos that IBM team, uh, for getting us up and out the door so quickly, uh, was a tie in, uh, to our, uh, Curam IBM, uh, case management system that we utilize to adjudicate benefits on daily basis in Clark County for all our local indigent population, uh, and high needs folks. Uh, and then that ties into our SAP IBM platform, which gets the checks out the door. >>So what, what we've been able to do with these dollars is created in Lucian, uh, that has allowed us in the last 60 days to get as much money out the door, as our nonprofits were able go out the door in the first six months pandemic. So it really has helped us. Uh, so I'm really grateful to our board of County commissioners for recognizing the investment in technology to, to not only get our teams mobile, but to create ease of access for our constituents and our local residents to give them the help they need quickly and the way that they need it. >>Just to follow up question to that, Tim, that I'm curious about having done a lot of work like this in government, sometimes getting procurement through in a timely way is a bit challenging. How were you able to work through those issues and getting this up and provision so quickly? >>Uh, yeah, so we, we put together a, what we call a pandemic playbook, which is kind of lessons learned. And what we've seen is the folks who were essential workers in the first 60 days of the, uh, pandemic. We were able to get a lot done quickly because we were taking full advantage of the emergency. Uh, it may sound a little crass to folks not inside the service world, but it was, uh, you know, don't want you to crisis. It was things we've been planning or trying to do for years. We need them yesterday. We should have had them yesterday, but let's get them tomorrow and get it moving very quickly. Uh, this IBM procurement was something we were able to step through very quickly because of our longstanding relationship. Our countywide, uh, system of record for our financials is SAP. Uh, we've worked with Curam, uh, solution, uh, for years. >>So we've got this long standing relationship and trust in the product and the teams, which helped us build the business case of why we did it, no need to go out for competitive procurement that we didn't have time. And we needed something that would integrate very quickly into our existing systems. Uh, so that part was there. Now when the folks who were non essential came back in June and the reopening, it was whiplash, uh, the speed at which we were moving, went back to the pace of normal business, uh, which feels like hitting a wall, doing a hundred miles an hour when you're used to having that, uh, mode of doing business. Uh, so that's certainly been a struggle, uh, for all of those involved, uh, in trying to continue to get things up. Um, but, uh, once again, the teams have been great because we've probably tripled our licensure on this portal since we opened it, uh, because of working with outside vendors, uh, to, uh, literally triple the size of our staff that are processing these applications by bringing on temporary staff, uh, and short-term professionals. Uh, and so we've been able to get those things through, uh, because we'd already built the purchasing vehicle during the early onset of the crisis. >>That's very helpful. Karen, IBM has played a really pivotal role in all of this. Uh, IBM Watson health works with a number of global government agencies, raging from counties like Clark County to federal governments. What are some of the major challenges you've seen with your clients as a result of the pandemic and how is technology supporting them in a time of need and give us some background Watson health too. So we kind of know a little more about it because this is really a fascinating area. >>Yeah. Thank you, Bob. And thanks Tim for the background on Clark County, because I think Clark County is definitely also an example of what federal governments and global governments are doing worldwide today. So, um, Watson health is our division within IBM where we really focus on health and human services. And our goal is to really focus in on, um, the outcomes that we're providing to individuals and families and looking at how we use data and insights to really make that impact and that change. And within that division, we have our government health and human services area, which is the focus of where we are with our clients around social program. But it also allows us to work with, um, different agencies and really look at how we can really move the ball in terms of, um, effecting change and outcomes for, um, really moving the needle of how we can, uh, make an impact on individuals and families. >>So as we look at the globe globally as well, you know, everything that Tim had mentioned about how the pandemic has really changed the way that government agencies operate and how they do services, I think it's amazing that you have that pandemic playbook because a lot of agencies in the same way also had these set of activities that they always wanted to go and take part on, but there was no impetus to really allow for that to happen. And with the pandemic, it allowed that to kind of open and say, okay, we can try this. And unfortunately I'm in a very partial house way to do that. And, um, what Tim has mentioned about the new program that they set up for the housing, some of those programs could take a number of years to really get a program online and get through and allowing, uh, the agencies to be able to do that in a matter of weeks is amazing. >>And I think that's really gonna set a precedent as we go forward and how you can bring on programs such as the housing and capability in Canada with the economic, uh, social, um, uh, development and, and Canada need that the same thing. They actually had a multi benefit delivery system that was designed to deliver benefits for three programs. And as part of the department of fisheries and oceans Canada, the, um, the state had an emergency and they really need to set up on how they could provide benefits to the fishermen who had been at that impacted, um, from that. And they also did set up a digital front-end using IBM citizen engagement to start to allow the applications that benefits, um, and they set it up in a matter of weeks. And as I mentioned, we, uh, Clark County had a backend legacy system where they could connect to and process those applications. And this case, this is a brand new program and the case management system that they brought up was on cloud. And they had to set up a new one, but allow them to set up a, what we used to call straight through processing, I think has been now turned, turned or coined contact less, uh, processing and allowing us to really start to move those benefits and get those capabilities out to the citizens in even a faster way than has been imagined. Uh, pre pandemic. >>Karen, I have one follow-up question. I want to ask you, having had a lot of experience with large projects in government. Sometimes there's a real gap between getting to identified real requirements and then actions. How do you, how do you work with clients to make sure that process time to benefit is shortened? >>So we really focus on the user themselves and we take a human centered design focus and really prioritizing what those needs are. Um, so working with the clients, uh, effectively, and then going through agile iterations of brain, that capability out as, um, in, in a phased approach to, so the idea of getting what we can bring out that provides quality and capability to the users, and then over time starting to really roll out additional functions and, um, other, uh, things that citizens or individuals and families would need >>Very helpful. Tim, this is an interesting partnership. It's always good to see partnerships between private sector and government. Tell us a little bit about how the partnership with IBM Watson health was established and what challenges or they were brought into assist, where they brought into assist with back to requirements. Again, within the requirements definitely shifted on us. You know, we had the con looking at, uh, Watson on our child welfare, uh, side of the house that I'm responsible for and how that we could, uh, increase access to everything from tele-health to, to, uh, foster parent benefit, uh, kinship, placement benefits, all those types of things that, that right now are very manual, uh, on the child welfare side. Uh, and then the pandemic kid. And we very quickly realized that we needed, uh, to stand up a, um, a new program because, uh, a little bit for context, uh, the park County, we don't administer TANIF or Medicaid at the County level. >>It is done at the state level. So we don't have, uh, unemployment systems or Medicaid, 10 of snap benefits systems to be able to augment and enroll out. We provide, uh, the indigent supports the, the homelessness prevention, referee housing continuum of care, long-term care, really deep emergency safety net services for our County, which is a little bit different and how those are done. So that was really our focus, which took a lot of in-person investigation. We're helping people qualify for disability benefits so they can get into permanent supportive housing, uh, things that are very intensive. And yet now we have a pandemic where we need things to happen quickly because the cares act money expires at the end of December. And people were facing eviction and eviction can help spread exposure to, to COVID. Uh, so, uh, be able to get in and very rapidly, think about what is the minimal pelvis to MVP. >>What's the minimum viable product that we can get out the door that will help people, uh, entrance to a system as contactless as possible, which again was a complete one 80 from how we had been doing business. Um, and, uh, so the idea that you could get on and you have this intelligent chat bot that can walk you through questions, help you figure out if you look like you might be eligible, roll you right into an application where you can upload the few documents that we're going to require to help verify your coat would impact and do that from a smartphone and under, you know, 20 minutes. Um, it, it, it is amazing. And the fact that we've stood that up and got it out the door in 90 days, it's just amazing to me, uh, when it shows the, uh, strength of partnership. Um, I think we can, we have some shared language because we had that ongoing partnership, but we were able to actually leverage some system architects that we had that were familiar with our community and our other products. So it really helped expedite, uh, getting this, uh, getting this out to the citizens. >>So, uh, I assume that there are some complexities in doing this. So overall, how has this deployment of citizen engagement with Watson gone and how do you measure success other than you got it out quick? How do you know if it's working? >>Yeah. Right. So it's the adage of, you know, quick, fast and good, right. Um, or fast, good and cheap. So, uh, we measure success in this way. Um, how are we getting access as our number one quality measurement here? So we were able to collect, uh, about 13,000 applications, uh, manual NRC, manually folks had to go onto our website, download a PDF, fill it out, email it, or physically drop it off along with their backup. One of their choice of 14 non-profits in town, whichever is closest to them. Um, and, uh, and then wait for that process. And they were able to get 13,000 of those, uh, process for the last six months. Uh, we have, I think we had about 8,000 applications the first month come into the portal and about an equal amount of folks who could not provide the same documentation that it was needed. >>And self-selected out. If we had not had the, the tool in place, we would have had 16,000 applications, half of which would have been non-eligible would have been jamming up the system, uh, when we don't have the bandwidth to deal to deal with that, we, we need to be able to focus in on, uh, Judy Kenny applications that we believe are like a 95% success rate from the moment our staff gets them, but because we have the complex and he was on already being dependent upon the landlord, having to verify the rent amount and be willing to work with us, um, which is a major hurdle. Um, but, uh, so w we knew we could not do is go, just reinvent the manual process digitally that that would have been an abject failure on our behalf. So, uh, the ideas that, uh, folks had can go on a very, had this very intuitive conversation to the chat bot, answer some questions and find out if they're eligible. >>And then self-select out was critical for us to not only make sure that the citizens got the help they needed, but not so burnt out and overload our workforce, which is already feeling the strain of the COVID pandemic on their own personal lives and in their homes and in the workplace. Um, so that was really critical for us. So it's not just about speed, ease of access was important. Uh, the ability to quickly automate things on the fly, uh, we have since changed, uh, the area median income, a qualifier for the rental assistance, because we were able to reallocate more money, uh, to the program. So we were able to open it up to more people. We were able to make that, uh, change to the system very quickly. Uh, the idea that we can go on the home page and put updates, uh, we recognized that, uh, some of our monolingual Hispanic residents were having difficulty even with some guidance getting through the system. >>So we're able to record a, a Spanish language walkthrough and get done on the home page the next day, right into the fordable, there'll be a fine, so they could literally run the YouTube video while they're walking through their application. Side-by-side so things like that, that those are how we are able to, for us measured success, not just in the raw dollars out the door, not just in the number of applications that have come in, but our ability to be responsive when we hear from our constituents and our elected officials that, Hey, I want, I appreciate the 15,000 applications as you all, a process and record time, I've got three, four, five, six, 10 constituents that having this type of problem and be able to go back and retool our systems to make them more intuitive, to do, be able to keep them responsive for us is definitely a measure of success and all of this, probably more qualitative than here we're looking >>For, but, uh, that's for us, that's important. Actually the qualitative side is what usually gets ignored. Uh, Karen, I've got a question that's a follow up for you on the same topic. How does IBM facilitate reporting within this kind of an environment given the different needs of stakeholders, online managers and citizens? What kinds of things do you, are you able to do >>So with, um, the influx of digitalization? I think it allows us to really take a more data-driven approach to start looking at that. So, as, as Tim was mentioning, you can see where potentially users are spending more time on certain questions, or if they're stuck on a question, you can see where the abandoned rate is. So using a more data-driven approach to go in to identify, you know, how do we actually go and, um, continue to drive that user experience that may not be something that we drive directly from the users. So I would say that analytics is really, uh, I think going to continue to be a driving force as government agencies go forward, because now they are capturing the data. But one thing that they have to be careful of is making sure that the data that they're getting is the right data to give them the information, to make the right next steps and decisions. >>And Tim, you know, use a really good example with, um, the chatbot in terms of, you know, with the influx of everything going on with COVID, the citizens are completely flooded with information and how do they get the right information to actually help them decide, can I apply for this chap program? Or should I, you know, not even try and what Tim mentioned just saved the citizens, you know, the people that may not be eligible a lot of time and going through and applying, and then getting denied by having that upfront, I have questions and I need answers. Um, so again, more data-driven of how do we provide that information? And, you know, we've seen traditionally citizens having to go on multiple website, web pages to get an answer to the question, because they're like, I think I have a question in this area, but I'm not exactly sure. And they, then they're starting to hunt and hunt and hunt and not even potentially get an answer. So the chocolate really like technology-wise helps to drive, you know, more data-driven answers to what, um, whether it's a citizen, whether it's, um, Tim who needs to understand how and where my citizens getting stuck, are they able to complete the application where they are? Can we really get the benefits to, um, this individual family for the housing needs >>Too many comments on the same thing. I know you have to communicate measures of success to County executives and others. How do you do that? I mean, are you, do you have enough information to do it? Yeah, we're able to, we actually have a standup meeting every morning where the first thing I learn is how many new applications came in overnight. How many of those were completed with full documentation? How many will be ported over into our system, assigned the staff to work, where they're waiting >>On landlord verification. So I can see the entire pipeline of applications, which helps us then determine, um, Oh, it's, it's not, you know, maybe urban legend is that folks are having difficulty accessing the system. When I see really the bottleneck there, it got gotten the system fine, the bottlenecks laying with our landlord. So let's do a landlord, a town hall and iterate and reeducate them about what their responsibilities are and how easy it is for them to respond with the form they need to attest to. And so it lets us see in real time where we're having difficulties, uh, because, uh, there's a constant pressure on this system. Not just that, uh, we don't want anyone to lose their home, uh, but these dollars also go away within a December. So we've got this dual pressure of get it right and get it right now. >>Uh, and so th the ability to see these data and these metrics on, on a daily basis is critical for us to, to continue to, uh, ModuLite our response. Um, and, and not just get comfortable are baked into well, that's why we developed the flowchart during requirements, and that's just the way things are gonna stay. Uh, that's not how you respond to a pandemic. Uh, and so having a tool and a partner that helps us, uh, stay flexible, state agile, I guess, to, to, to leverage some terminology, uh, is important. And, and it's, it's paid dividends for our citizens. Karen, again, is another up to the same thing. I'm kind of curious about one of the problems of government from time to time. And Tim, I think attest to this is how do you know when Dunn has been reached? How did you go about defining what done would look like for the initial rollout with this kind of a customer? >>So I think Doug, I guess in this case, um, is, is this, isn't able to get the benefits that they're looking for and how do we, uh, you know, starting from, I think what we were talking about earlier, like in terms of requirements and what is the minimum viable, um, part of that, and then you start to add on the bells and whistles that we're really looking to do. So, um, you know, our team worked with him to really define what are those requirements. I know it's a new program. So some of those policy decisions were still also being worked out as the requirements were being defined as well. So making sure that you are staying on top of, okay, what are the key things and what do we really need to do from a compliance standpoint, from a functionality, and obviously, um, the usability of how, uh, an assistant can come on and apply and, um, have those, uh, requirements, make sure that you can meet that, that version before you start adding on additional scope. >>Very helpful. Jim, what's your comment on this since I know done matters to you? Yeah. And look, I I've lived through a, again, multiple, uh, county-wide it implementations and some department wide initiatives as well. So I think we know that our staff always want more so nothing's ever done, uh, which is a challenge and that's on our side of the customer. Um, but, uh, for this, it really was our, our experience of recognizing the, the time was an essence. We didn't have a chance. We didn't have, uh, the space to get into these endless, uh, conversations, uh, the agile approach, rather than doing the traditional waterfall, where we would have been doing requirements tracking for months before we ever started coding, it was what do we need minimally to get a check in the hands of a landlord on behalf of a client, so they don't get evicted. >>And we kept just re honing on that. That's nice. Let's put that in the parking lot. We'll come back to it because again, we want to leverage this investment long term, uh, because we've got a we, and we've got the emergency solutions and CDBG, and then our, uh, mainstream, uh, services we brought on daily basis, but we will come back to those things speed and time are of the essence. So what do we need, uh, to, to get this? So a chance to really, um, educate our staff about the concepts of agile iteration, um, and say, look, this is not just on the it side. We're gonna roll a policy out today around how you're doing things. And we may figure out through data and metrics that it's not working next week, and we'll have to have that. You want it. And you're going to get the same way. >>You're getting updated guidance from the CDC on what to do and what not to do. Uh, health wise, you're getting the same from us, uh, and really to helping the staff understand that process from the beginning was key. And, uh, so, and, and that's, again, partnering with, with our development team in that way was helpful. Um, because once we gave them that kind of charter as I am project champion, this is what we're saying. They did an equally good job of staying on task and getting to the point of is this necessary or nice. And if it wasn't necessary, we put it in the nice category and we'll come back to it. So I think that's really helpful. My experience having done several hundred sheet applications also suggest the need for MBP matters, future stages really matter and not getting caught. My flying squirrels really matters. So you don't get distracted. So let's move on to, let's do a polling question before we go on to some of our other questions. So for our audience, do you have a digital front ends for your benefit delivery? Yes, no. Or we're planning to a lot of response here yet. There we go. Looks like about half, have one and half note. So that's an interesting question. What's going to one more polling question, learn a little more here. Has COVID-19 >>Accelerated or moved cloud. Yes, no. We already run a majority of applications on cloud. Take a moment and respond if you would, please. So this is interesting. No real acceleration was taken place and in terms of moving to cloud is not what I was expecting, but that's interesting. So let's go onto another question then. And Karen, let me direct this one to you, given that feedback, how do you envision technologies such as citizen engagement and watching the system will be used, respond to emergency situations like the pandemic moving forward? I mean, what should government agencies consider given the challenges? This kind of a pandemic is brought upon government and try to tie this in, if you would, what, what is the role of cloud in all of this for making this happen in a timely way? Karen, take it away. >>Okay. Thanks Bob. So as we started the discussion around the digital expansion, you know, we definitely see additional programs and additional capabilities coming online as we continue on. Um, I think, uh, agencies have really seen a way to connect with their citizens and families and landlords, um, in this case an additional way. And he prepared them like there were, uh, presuppose assumptions that the, um, the citizens or landlords really wanted to interact with agency face-to-face and have that high touch part. And I think, um, through this, the governments have really learned that there is a way to still have an impact on the citizen without having a slow, do a face to face. And so I think that's a big realization for them to now really explore other ways to digitally explain, expand their programs and capabilities. Another area that we touched on was around the AI and chat bot piece. >>So as we start to see capabilities like this, the reason why Clark County was able to bring it up quickly and everything was because it was housed on cloud, we are seeing the push of starting to move some of the workloads. I know from a polling question perspective that it's been, um, lighter in terms of getting, uh, moving to the cloud. But we have seen the surge of really chatbots. I think we've been talking about chatbots for a while now. And, um, agencies hadn't really had the ability to start to implement that and really put it into effect. But with the pandemic, they were able to bring things up and, you know, very short amount of time to solve, um, a big challenge of not having the call center be flooded and have a different way to direct that engagement between the citizen and the government. >>So really building a different type of channel for them to engage rather than having to call or to come into an office, which wasn't really allowed in terms of, um, the pandemic. Um, the other thing I'll touch on is, um, 10 mentioned, you know, the backlog of applications that are coming in and we're starting to see the, um, the increase in automation. How do we automate areas where it's administratively highly burdened, but it's really a way that we can start to automate those processes, to give our workers the ability to focus on more of those complex situations that really need attention. So we're starting to see where the trends of trying to push there of can we automate some of those processes, um, uh, uploading documents and verification documents is another way of like, trying to look at, is there a way that we can make that easier? >>Not only for the applicant that's applying, but also for the caseworker. So there's not having to go through that. Um, does the name match, um, the applicant, uh, information and what we're looking on here, and Bob, you mentioned cloud. So behind the scenes of, you know, why, uh, government agencies are really pushing the cloud is, um, you heard about, I mean, with the pandemic, you see a surge of applicants coming in for those benefits and how do we scale for that kind of demand and how do you do that in an inappropriate way, without the huge pressures that you put on to your data center or your staff who's already trying to help our citizens and applicants, applicants, and families get the benefits they need. And so the cloud, um, you know, proposition of trying, being able to be scalable and elastic is really a key driver that we've seen in terms of, uh, uh, government agencies going to cloud. >>We haven't really seen during a pandemic, the core competencies, some of them moving those to cloud, it's really been around that digital front end, the chat bot area of how do we start to really start with that from a cloud perspective and cloud journey, and then start to work in the other processes and other areas. Um, security is also huge, uh, focus right now with the pandemic and everything going online. And with cloud allows you to be able to make sure that you're secure and be able to apply the right security so that you're always covered in terms of the type of demand and, um, impact, uh, that is coming through >>Very helpful. Tim, I'm going to ask to follow up on this of a practical nature. So you brought this up very quickly. Uh, there's a certain amount of suspicion around state government County government about chatbots. How did you get a chat much and be functional so quickly? And were you able to leverage the cloud in this process? Yeah, so on the trust is important. Uh, and I'll go back to my previous statement about individuals being able to see upfront whether they believe they're eligible or not, because nothing will erode trust more than having someone in hours applying and weeks waiting to find out they were denied because they weren't eligible to begin with, uh, that erodes trust. So being able to let folks know right up front, here's what it looks like to be eligible, actually help us build some of that, uh, cause they don't feel like, uh, someone in the bureaucracy is just putting them through the ringer for no reason. >>Um, now in regard to how do we get the chat bot out? I will say, uh, we have a, uh, dynamic it and leadership, uh, team at the highest level of County government who we have been already having conversations over the last year about what it meant to be smart government, uh, the department of social service and family services that I'm responsible for. We're already, uh, hands up first in line, you know, Guinea pigs volunteering to be on the front end of, uh, certain projects. So w we have primed ourselves for, for some of this readiness in that aspect. Um, but for citizen trust, um, the timeliness of application right now is the biggest element of trust. Uh, so I've applied I've I feel like I put my housing future in your hands. Are you going to deliver and having the ability for us to rapidly scale up? >>Uh, we typically have 120 staff in the department of social service that, that are adjudicating benefits for programs on daily basis. We've doubled that with temporary staff, uh, through some partnerships, uh, we're, we're gonna, as of next week, probably have more temporary per professional staff helping an adjudicator applications. No, do full-time County staff, because again, this rush to get the dollars out, out the door. So having a system where I can easily, uh, ramp on new users and manage them without having to be solely dependent upon an already, uh, overworked it staff who were trying to support 37 other departments in the County, um, around infrastructure needs has been greatly helpful. Sounds to me like a strong outcome focus and one that seems to work. Let's move on now to our audience questions. We're getting close to the end of our time. So let's jump into some questions from the audience. A number of you have been asking about getting copies of today's presentation within the next 48 hours. Government technology will provide all attendees with the link to the recording for your reference, or to share with colleagues. Well, let's go to our first question. So this is an interesting one. And Karen, this is for you did IBM work with other counties and States to provide digital engagement portals. >>We did Bob, uh, we've worked, um, so globally we've provided guidance on this. We work closely with New York city. They've been the integral part of the development also with our citizen engagement offering. Um, we work closely with the States. So we worked with New York city. Um, North Carolina was also another state who, um, improved their, uh, citizen engagement piece, bring up their Medicaid and snap, um, applications along with Medicaid. COVID testing along that. And I mentioned, um, the economic and social development in Canada as well. And we also work with the ministry of social development in Singapore. So a number of our customers had put up, uh, a global, uh, or sorry, a citizen engagement frontend. And during this timeframe, >>Very helpful. I don't know how much did you hear your mom provide you, but how much did it cost for initial deployment and what are the ongoing costs in other words, is this thing going to be sustainable over time? >>Yeah, absolutely. So total, uh, to date, we've spent about a $1.8 million on development implementations and licensure. A big chunk of that again has been the rapid extended of licensure, uh, for this program. Um, I think over a third of that is probably licensing because again, we need to get the dollars out and we need staff to do that and making the short term several hundred thousand dollar investment in a professional support staff and having them be able to work this portal is much cheaper than the long-term investment of bringing on a staff, printing a job, uh, during a financial difficulty that we're facing, uh, the single largest fiscal cliff let's get into that us history. Um, so it's not smart to create jobs that have a 30 year, one way to retirement, uh, inside our in unionized government environment here. So having this, the staff that would come on and do this and get out the door on these federal dollars was critical for us. Um, and there is a $800,000 a year, I believe so ongoing costs associated with licensure and, and the programming support. Uh, but once again, we're going to be moving, um, our traditional services into this digital front end. We'll be continuing this because we're, we're, we're facing, it took us, I think, six and a half, seven years to come back from the previous recession. Undoubtedly, take a little longer to get back >>From this one. Here's another interesting question, I guess really primarily Tim Tim was the solution on primarily on premise or in the cloud. >>So we'll, we've done a mix. Uh, the, and I'm starting a lot of feedbacks. I don't know if you all can hear that or not, but the, uh, I think we went on prem for, uh, some people because of the, uh, bridge into our service case manager system, which is on prem. So we did some management there. I do believe the chat bot piece of it though is in the cloud. So we're bringing it down to, from one system to the other. Uh, and, and part of that was a student negotiations and costs and worrying about what long-term is that we have a very stated goal of moving, uh, our Curam platform, which is on-prem, this is the backend. So how are we? We, we set our IBM Watson, uh, portal up, uh, and moving all of that on cloud, uh, because I mean, we've got, uh, a workforce who, uh, has the ability to retire at a very high rate over the next five years. >>And, uh, having 24 seven support in the cloud is, is as a, someone who would be called to respond to emergency situations like the is, is a much better Cod deal for, for myself and the citizen. So migrating, uh, and, um, our typical on-prem stuff up into the cloud, uh, as we continue on this, uh, evolution of what IBM Watson, uh, and the plug into our Curam, uh, system looks like Karen related question for another user is the portal provided with Clara County and others linked to other third-party backend office apps, or can it be, >>Yeah, the answer is it can be it's interoperable. So through APIs, uh, rest, uh, however, um, assistance that they need to be integrated with can definitely be integrated with, uh, like, uh, Tim mentioned, we, we went to the case management solution, but it can be integrated with other applications as well. >>Tim, did you use some other backend third party apps with yours? Uh, we did not. Uh, again, just for speed of getting, uh, this MVP solution out the door. Uh, now what we do with that on the go forward, it is going to look different and probably will include some, another practical question. Given the cares funding should be expended by December. Can this application even be employed at this late date? And you want to take a cut at that? Yeah, for us, uh, once again, we brought up earlier, um, the emergency solutions grants and the community development block grants, which have a Corona virus, uh, CV traunch, each one of those, and those have two to three year expenditure timeframes on them. Uh, so we were going to leverage those to keep this system and some of these programs going once again, that the housing needs, uh, will outstrip our capacity for years to come. >>I guess probably I should have said upfront Las Vegas has one of the worst affordable housing inventories in the nation. Uh, so we know we're going to be facing a housing issue, um, because of this for, for a long time. So we'll be using those two traunches of dollars, ESE, ESPs, uh, CV CDBG, CB funds, uh, in addition to dollars earmarked through some, uh, recreational marijuana license fees that have been dedicated to our homelessness. And when you consider this housing, uh, stability program was part of that homelessness prevention. That's our funding mix locally. Very helpful. So questions maybe for bolts for you on this one, you can probably also teach respond is the system has been set up helping the small business community. Um, this user's been canvassing and the general feeling is that small businesses have been left behind and they've been unable to access funds. What's your response on that? Karen, do you want to take that first? >>Um, yes. So in terms of, uh, the security and sorry. Um, but, uh, can you repeat the last part of that? I just missed the last part when you >>Behind it, but unable to access funds. >>Uh, yeah, so I think from a funding perspective, there's different types of, I think what Tim mentioned in terms of the cares funding, there was different types of funding that came out from a government perspective. Uh, I think there were also other grants and things that are coming out one, uh, that we're still looking at. And I think as we go into the new year, it'll be interesting to see, you know, what additional funding, um, hopefully is, is provided. Uh, but in terms of creativity, we've seen other creative ways that organizations come together to kind of, uh, help with the different agencies, to provide some, some guidance to the community, um, and helping to, uh, provide efforts and, uh, maybe looking at different ways of, um, providing, uh, some of the capabilities that the, either at the County or at the state level that they're able to leverage. But Tim happy to maybe have you chime in here too. >>Yeah. So I'll first start with my wheelhouse and I'll expand out to, to some of my partners. Uh, so the primary, small business, we knew the idea was a daily basis inside this realm is going to be landlords. Uh, so actually this afternoon, we're doing a town hall with folks to be able to roll out, uh, which they will go to our portal to find a corporate landlord program. Uh, so that I seem a landlord for Camille the application pack and on behalf of a hundred residents, rather than us having to adjudicate a hundred individual applications and melon a hundred checks. Uh, so that is because we were listening to that particular segment of the, uh, the business community. Now I know early on, we were, we were really hoping that the, the paycheck protection program federally would have, uh, been dispersed in a way that helped our local small businesses. >>Uh, more we did a, our economic development team did a round of small business supports through our cares act. Uh, our quarterly unfortunate was not open yet. It was just about 15, 20 days shy. So we use, uh, another traditional grant mechanism that we have in place to dedicate that. Uh, but on a go forward board, willing to Congress passes something over the next 30 days, um, that if there's a round two of cares or some other programs, we absolutely now have a tool that we know we can create a digital opening for individuals to come figure out if they're eligible or not for whatever program it is, the it housing, the it, uh, small business operations supports, uh, and it would apply through that process and in a very lightweight, so we're looking forward to how we can expand our footprint to help all of the needs that are present in our community. This leads to another question which may be our last one, but this is an interesting question. How can agencies use COVID-19 as a proof point providing a low cost configurable solutions that can scale across government. Karen, do you want to respond to that? And then Tim also, >>Thanks, Bob. So I believe like, you know, some of the things that we've said in terms of examples of how we were able to bring up the solution quicker, I definitely see that scaling as you go forward and trying to really, um, focus in on the needs and getting that MVP out the door. Uh, and then Tim alluded to this as well. A lot of the change management processes that went into re-imagining what these processes look like. I definitely see a additional, you know, growth mindset of how do we get better processes in place, or really focusing on the core processes so that we can really move the ball forward and continuing to go that path of delivering on a quicker path, uh, leveraging cloud, as we mentioned of, um, some, some of the capabilities around the chat bot and other things to really start to push, um, uh, the capabilities out to those citizens quicker and really reduce that timeline that we have to take on the backend side, um, that that would be our hope and goal, um, given, you know, sort of what we've been able to accomplish and hoping using that as a proof point of how we can do this for other types of, uh, either programs or other processes. >>Yeah, I think, um, the, you know, the tool has given us capability now there, whether we use local leaders leverage that to the fullest really becomes a coming upon us. So do we take a beat, uh, when we can catch our breath and then, you know, work through our executive leadership to say, look, here's all the ways you can use this tool. You've made an enterprise investment in. Um, and I know for us, uh, at Clark County, we've stood up, uh, enterprise, uh, kind of governance team where we can come and talk through all of our enterprise solutions, uh, encourage our other department head peers, uh, to, to examine how you might be able to use this. Is there a way that, um, you know, parks and rec might use this to better access their scholarship programs to make sure that children get into youth sports leagues and don't get left out, uh, because we know youth suicide on the rise and they need something positive to do when this pandemic is clear, I'm there for them to get out and do those things. >>So the possibilities really are out there. It really becomes, um, how do we mind those internally? And I know that being a part of listservs and, uh, you know, gov tech and all the magazines and things are out there to help us think about how do we better use our solutions, um, as well as our IBM partners who are always eager to say, Hey, have you seen how they're using this? Um, it is important for us to continue to keep our imaginations open, um, so that we continue to iterate through this process. Um, cause I, I would hate to see the culture of, um, iteration go away with this pandemic. >>Okay. We have time for one final question. We've already addressed this in part two, and this one is probably for you and that you've used the cares act to eliminate some of the procurement red tape that's shown up. Well, how do you somehow that's been very positive. How do you see that impacting you going forward? What happens when the red tape all comes back? >>Yeah, so I think I mentioned a little bit, uh, about that when some of the folks who are deemed non essential came back during our reopening phases and they're operating at the speed of prior business and red tape where we had all been on this, these green tape, fast tracks, uh, it, it was a bit of a organizational whiplash. Uh, but it, for us, we've had the conversation with executive management of like, we cannot let this get in the way of what our citizens need. So like keep that pressure on our folks to think differently. Don't and, uh, we've gone so far as to, uh, even, uh, maybe take it a step further and investigate what had been done in, in, in Canada. Some other places around, um, like, like going right from in a 48 hour period, going from a procurement statement through a proof of concept and doing purchasing on the backside, like how can we even get this even more streamlined so that we can get the things we need quickly, uh, because the citizens don't understand, wait, we're doing our best, uh, your number 3000 and queue on the phone line that that's not what they need to hear or want to hear during times of crisis. >>Very helpful. Well, I want to be respectful of our one hour commitment, so we'll have to wrap it up here in closing. I want to thank everyone for joining us for today's event and especially a big, thank you goes to Karen and Tim. You've done a really great job of answering a lot of questions and laying this out for us and a special thanks to our partners at IBM for enabling us to bring this worthwhile discussion to our audience. Thanks once again, and we look forward to seeing you at another government technology event,

>>from around the globe. It's the Cube presenting adaptive data governance brought to you by Iota Ho. >>And we're back with the data automation. Siri's. In this episode, we're gonna learn more about what I owe Tahoe is doing in the field of adaptive data governance how it can help achieve business outcomes and mitigate data security risks. I'm Lisa Martin, and I'm joined by a J. Bihar on the CEO of Iot Tahoe and Lester Waters, the CEO of Bio Tahoe. Gentlemen, it's great to have you on the program. >>Thank you. Lisa is good to be back. >>Great. Staley's >>likewise very socially distant. Of course as we are. Listen, we're gonna start with you. What's going on? And I am Tahoe. What's name? Well, >>I've been with Iot Tahoe for a little over the year, and one thing I've learned is every customer needs air just a bit different. So we've been working on our next major release of the I O. Tahoe product. But to really try to address these customer concerns because, you know, we wanna we wanna be flexible enough in order to come in and not just profile the date and not just understand data quality and lineage, but also to address the unique needs of each and every customer that we have. And so that required a platform rewrite of our product so that we could, uh, extend the product without building a new version of the product. We wanted to be able to have plausible modules. We also focused a lot on performance. That's very important with the bulk of data that we deal with that we're able to pass through that data in a single pass and do the analytics that are needed, whether it's, uh, lineage, data quality or just identifying the underlying data. And we're incorporating all that we've learned. We're tuning up our machine learning we're analyzing on MAWR dimensions than we've ever done before. We're able to do data quality without doing a Nen initial rejects for, for example, just out of the box. So I think it's all of these things were coming together to form our next version of our product. We're really excited by it, >>So it's exciting a J from the CEO's level. What's going on? >>Wow, I think just building on that. But let's still just mentioned there. It's were growing pretty quickly with our partners. And today, here with Oracle are excited. Thio explain how that shaping up lots of collaboration already with Oracle in government, in insurance, on in banking and we're excited because we get to have an impact. It's real satisfying to see how we're able. Thio. Help businesses transform, Redefine what's possible with their data on bond. Having I recall there is a partner, uh, to lean in with is definitely helping. >>Excellent. We're gonna dig into that a little bit later. Let's let's go back over to you. Explain adaptive data governance. Help us understand that >>really adaptive data governance is about achieving business outcomes through automation. It's really also about establishing a data driven culture and pushing what's traditionally managed in I t out to the business. And to do that, you've got to you've got Thio. You've got to enable an environment where people can actually access and look at the information about the data, not necessarily access the underlying data because we've got privacy concerns itself. But they need to understand what kind of data they have, what shape it's in what's dependent on it upstream and downstream, and so that they could make their educated decisions on on what they need to do to achieve those business outcomes. >>Ah, >>lot of a lot of frameworks these days are hardwired, so you can set up a set of business rules, and that set of business rules works for a very specific database and a specific schema. But imagine a world where you could just >>say, you >>know, the start date of alone must always be before the end date of alone and having that generic rule, regardless of the underlying database and applying it even when a new database comes online and having those rules applied. That's what adaptive data governance about I like to think of. It is the intersection of three circles, Really. It's the technical metadata coming together with policies and rules and coming together with the business ontology ease that are that are unique to that particular business. And this all of this. Bringing this all together allows you to enable rapid change in your environment. So it's a mouthful, adaptive data governance. But that's what it kind of comes down to. >>So, Angie, help me understand this. Is this book enterprise companies are doing now? Are they not quite there yet. >>Well, you know, Lisa, I think every organization is is going at its pace. But, you know, markets are changing the economy and the speed at which, um, some of the changes in the economy happening is is compelling more businesses to look at being more digital in how they serve their own customers. Eh? So what we're seeing is a number of trends here from heads of data Chief Data Officers, CEO, stepping back from, ah, one size fits all approach because they've tried that before, and it it just hasn't worked. They've spent millions of dollars on I T programs China Dr Value from that data on Bennett. And they've ended up with large teams of manual processing around data to try and hardwire these policies to fit with the context and each line of business and on that hasn't worked. So the trends that we're seeing emerge really relate. Thio, How do I There's a chief data officer as a CEO. Inject more automation into a lot of these common tax. Andi, you know, we've been able toc that impact. I think the news here is you know, if you're trying to create a knowledge graph a data catalog or Ah, business glossary. And you're trying to do that manually will stop you. You don't have to do that manually anymore. I think best example I can give is Lester and I We we like Chinese food and Japanese food on. If you were sitting there with your chopsticks, you wouldn't eat the bowl of rice with the chopsticks, one grain at a time. What you'd want to do is to find a more productive way to to enjoy that meal before it gets cold. Andi, that's similar to how we're able to help the organizations to digest their data is to get through it faster, enjoy the benefits of putting that data to work. >>And if it was me eating that food with you guys, I would be not using chopsticks. I would be using a fork and probably a spoon. So eso Lester, how then does iota who go about doing this and enabling customers to achieve this? >>Let me, uh, let me show you a little story have here. So if you take a look at the challenges the most customers have, they're very similar, but every customers on a different data journey, so but it all starts with what data do I have? What questions or what shape is that data in? Uh, how is it structured? What's dependent on it? Upstream and downstream. Um, what insights can I derive from that data? And how can I answer all of those questions automatically? So if you look at the challenges for these data professionals, you know, they're either on a journey to the cloud. Maybe they're doing a migration oracle. Maybe they're doing some data governance changes on bits about enabling this. So if you look at these challenges and I'm gonna take you through a >>story here, E, >>I want to introduce Amanda. Man does not live like, uh, anyone in any large organization. She's looking around and she just sees stacks of data. I mean, different databases, the one she knows about, the one she doesn't know about what should know about various different kinds of databases. And a man is just tasking with understanding all of this so that they can embark on her data journey program. So So a man who goes through and she's great. I've got some handy tools. I can start looking at these databases and getting an idea of what we've got. Well, as she digs into the databases, she starts to see that not everything is as clear as she might have hoped it would be. You know, property names or column names, or have ambiguous names like Attribute one and attribute to or maybe date one and date to s Oh, man is starting to struggle, even though she's get tools to visualize. And look what look at these databases. She still No, she's got a long road ahead. And with 2000 databases in her large enterprise, yes, it's gonna be a long turkey but Amanda Smart. So she pulls out her trusty spreadsheet to track all of her findings on what she doesn't know about. She raises a ticket or maybe tries to track down the owner to find what the data means. And she's tracking all this information. Clearly, this doesn't scale that well for Amanda, you know? So maybe organization will get 10 Amanda's to sort of divide and conquer that work. But even that doesn't work that well because they're still ambiguities in the data with Iota ho. What we do is we actually profile the underlying data. By looking at the underlying data, we can quickly see that attribute. One looks very much like a U. S. Social Security number and attribute to looks like a I c D 10 medical code. And we do this by using anthologies and dictionaries and algorithms to help identify the underlying data and then tag it. Key Thio Doing, uh, this automation is really being able to normalize things across different databases, so that where there's differences in column names, I know that in fact, they contain contain the same data. And by going through this exercise with a Tahoe, not only can we identify the data, but we also could gain insights about the data. So, for example, we can see that 97% of that time that column named Attribute one that's got us Social Security numbers has something that looks like a Social Security number. But 3% of the time, it doesn't quite look right. Maybe there's a dash missing. Maybe there's a digit dropped. Or maybe there's even characters embedded in it. So there may be that may be indicative of a data quality issues, so we try to find those kind of things going a step further. We also try to identify data quality relationships. So, for example, we have two columns, one date, one date to through Ah, observation. We can see that date 1 99% of the time is less than date, too. 1% of the time. It's not probably indicative of a data quality issue, but going a step further, we can also build a business rule that says Day one is less than date to. And so then when it pops up again, we can quickly identify and re mediate that problem. So these are the kinds of things that we could do with with iota going even a step further. You could take your your favorite data science solution production ISAT and incorporated into our next version a zey what we call a worker process to do your own bespoke analytics. >>We spoke analytics. Excellent, Lester. Thank you. So a J talk us through some examples of where you're putting this to use. And also what is some of the feedback from >>some customers? But I think it helped do this Bring it to life a little bit. Lisa is just to talk through a case study way. Pull something together. I know it's available for download, but in ah, well known telecommunications media company, they had a lot of the issues that lasted. You spoke about lots of teams of Amanda's, um, super bright data practitioners, um, on baby looking to to get more productivity out of their day on, deliver a good result for their own customers for cell phone subscribers, Um, on broadband users. So you know that some of the examples that we can see here is how we went about auto generating a lot of that understanding off that data within hours. So Amanda had her data catalog populated automatically. A business class three built up on it. Really? Then start to see. Okay, where do I want Thio? Apply some policies to the data to to set in place some controls where they want to adapt, how different lines of business, maybe tax versus customer operations have different access or permissions to that data on What we've been able to do there is, is to build up that picture to see how does data move across the entire organization across the state. Andi on monitor that overtime for improvement, so have taken it from being a reactive. Let's do something Thio. Fix something. Thio, Now more proactive. We can see what's happening with our data. Who's using it? Who's accessing it, how it's being used, how it's being combined. Um, on from there. Taking a proactive approach is a real smart use of of the talents in in that telco organization Onda folks that worked there with data. >>Okay, Jason, dig into that a little bit deeper. And one of the things I was thinking when you were talking through some of those outcomes that you're helping customers achieve is our ally. How do customers measure are? Why? What are they seeing with iota host >>solution? Yeah, right now that the big ticket item is time to value on. And I think in data, a lot of the upfront investment cause quite expensive. They have been today with a lot of the larger vendors and technologies. So what a CEO and economic bio really needs to be certain of is how quickly can I get that are away. I think we've got something we can show. Just pull up a before and after, and it really comes down to hours, days and weeks. Um, where we've been able Thio have that impact on in this playbook that we pulled together before and after picture really shows. You know, those savings that committed a bit through providing data into some actionable form within hours and days to to drive agility, but at the same time being out and forced the controls to protect the use of that data who has access to it. So these are the number one thing I'd have to say. It's time on. We can see that on the the graphic that we've just pulled up here. >>We talk about achieving adaptive data governance. Lester, you guys talk about automation. You talk about machine learning. How are you seeing those technologies being a facilitator of organizations adopting adaptive data governance? Well, >>Azaz, we see Mitt Emmanuel day. The days of manual effort are so I think you know this >>is a >>multi step process. But the very first step is understanding what you have in normalizing that across your data estate. So you couple this with the ontology, that air unique to your business. There is no algorithms, and you basically go across and you identify and tag tag that data that allows for the next steps toe happen. So now I can write business rules not in terms of columns named columns, but I could write him in terms of the tags being able to automate. That is a huge time saver and the fact that we can suggest that as a rule, rather than waiting for a person to come along and say, Oh, wow. Okay, I need this rule. I need this will thes air steps that increased that are, I should say, decrease that time to value that A. J talked about and then, lastly, a couple of machine learning because even with even with great automation and being able to profile all of your data and getting a good understanding, that brings you to a certain point. But there's still ambiguities in the data. So, for example, I might have to columns date one and date to. I may have even observed the date. One should be less than day two, but I don't really know what date one and date to our other than a date. So this is where it comes in, and I might ask the user said, >>Can >>you help me identify what date? One and date You are in this in this table. Turns out they're a start date and an end date for alone That gets remembered, cycled into the machine learning. So if I start to see this pattern of date one day to elsewhere, I'm going to say, Is it start dating and date? And these Bringing all these things together with this all this automation is really what's key to enabling this This'll data governance. Yeah, >>great. Thanks. Lester and a j wanna wrap things up with something that you mentioned in the beginning about what you guys were doing with Oracle. Take us out by telling us what you're doing there. How are you guys working together? >>Yeah, I think those of us who worked in i t for many years we've We've learned Thio trust articles technology that they're shifting now to ah, hybrid on Prohm Cloud Generation to platform, which is exciting. Andi on their existing customers and new customers moving to article on a journey. So? So Oracle came to us and said, you know, we can see how quickly you're able to help us change mindsets Ondas mindsets are locked in a way of thinking around operating models of I t. That there may be no agile and what siloed on day wanting to break free of that and adopt a more agile A p I at driven approach. A lot of the work that we're doing with our recall no is around, uh, accelerating what customers conduce with understanding their data and to build digital APS by identifying the the underlying data that has value. Onda at the time were able to do that in in in hours, days and weeks. Rather many months. Is opening up the eyes to Chief Data Officers CEO to say, Well, maybe we can do this whole digital transformation this year. Maybe we can bring that forward and and transform who we are as a company on that's driving innovation, which we're excited about it. I know Oracle, a keen Thio to drive through and >>helping businesses transformed digitally is so incredibly important in this time as we look Thio things changing in 2021 a. J. Lester thank you so much for joining me on this segment explaining adaptive data governance, how organizations can use it benefit from it and achieve our Oi. Thanks so much, guys. >>Thank you. Thanks again, Lisa. >>In a moment, we'll look a adaptive data governance in banking. This is the Cube, your global leader in high tech coverage. >>Innovation, impact influence. Welcome to the Cube. Disruptors. Developers and practitioners learn from the voices of leaders who share their personal insights from the hottest digital events around the globe. Enjoy the best this community has to offer on the Cube, your global leader in high tech digital coverage. >>Our next segment here is an interesting panel you're gonna hear from three gentlemen about adaptive data. Governments want to talk a lot about that. Please welcome Yusuf Khan, the global director of data services for Iot Tahoe. We also have Santiago Castor, the chief data officer at the First Bank of Nigeria, and good John Vander Wal, Oracle's senior manager of digital transformation and industries. Gentlemen, it's great to have you joining us in this in this panel. Great >>to be >>tried for me. >>Alright, Santiago, we're going to start with you. Can you talk to the audience a little bit about the first Bank of Nigeria and its scale? This is beyond Nigeria. Talk to us about that. >>Yes, eso First Bank of Nigeria was created 125 years ago. One of the oldest ignored the old in Africa because of the history he grew everywhere in the region on beyond the region. I am calling based in London, where it's kind of the headquarters and it really promotes trade, finance, institutional banking, corporate banking, private banking around the world in particular, in relationship to Africa. We are also in Asia in in the Middle East. >>So, Sanjay, go talk to me about what adaptive data governance means to you. And how does it help the first Bank of Nigeria to be able to innovate faster with the data that you have? >>Yes, I like that concept off adaptive data governor, because it's kind of Ah, I would say an approach that can really happen today with the new technologies before it was much more difficult to implement. So just to give you a little bit of context, I I used to work in consulting for 16, 17 years before joining the president of Nigeria, and I saw many organizations trying to apply different type of approaches in the governance on by the beginning early days was really kind of a year. A Chicago A. A top down approach where data governance was seeing as implement a set of rules, policies and procedures. But really, from the top down on is important. It's important to have the battle off your sea level of your of your director. Whatever I saw, just the way it fails, you really need to have a complimentary approach. You can say bottom are actually as a CEO are really trying to decentralize the governor's. Really, Instead of imposing a framework that some people in the business don't understand or don't care about it, it really needs to come from them. So what I'm trying to say is that data basically support business objectives on what you need to do is every business area needs information on the detector decisions toe actually be able to be more efficient or create value etcetera. Now, depending on the business questions they have to solve, they will need certain data set. So they need actually to be ableto have data quality for their own. For us now, when they understand that they become the stores naturally on their own data sets. And that is where my bottom line is meeting my top down. You can guide them from the top, but they need themselves to be also empower and be actually, in a way flexible to adapt the different questions that they have in orderto be able to respond to the business needs. Now I cannot impose at the finish for everyone. I need them to adapt and to bring their answers toe their own business questions. That is adaptive data governor and all That is possible because we have. And I was saying at the very beginning just to finalize the point, we have new technologies that allow you to do this method data classifications, uh, in a very sophisticated way that you can actually create analitico of your metadata. You can understand your different data sources in order to be able to create those classifications like nationalities, a way of classifying your customers, your products, etcetera. >>So one of the things that you just said Santa kind of struck me to enable the users to be adaptive. They probably don't want to be logging in support ticket. So how do you support that sort of self service to meet the demand of the users so that they can be adaptive. >>More and more business users wants autonomy, and they want to basically be ableto grab the data and answer their own question. Now when you have, that is great, because then you have demand of businesses asking for data. They're asking for the insight. Eso How do you actually support that? I would say there is a changing culture that is happening more and more. I would say even the current pandemic has helped a lot into that because you have had, in a way, off course, technology is one of the biggest winners without technology. We couldn't have been working remotely without these technologies where people can actually looking from their homes and still have a market data marketplaces where they self serve their their information. But even beyond that data is a big winner. Data because the pandemic has shown us that crisis happened, that we cannot predict everything and that we are actually facing a new kind of situation out of our comfort zone, where we need to explore that we need to adapt and we need to be flexible. How do we do that with data. Every single company either saw the revenue going down or the revenue going very up For those companies that are very digital already. Now it changed the reality, so they needed to adapt. But for that they needed information. In order to think on innovate, try toe, create responses So that type of, uh, self service off data Haider for data in order to be able to understand what's happening when the prospect is changing is something that is becoming more, uh, the topic today because off the condemning because of the new abilities, the technologies that allow that and then you then are allowed to basically help your data. Citizens that call them in the organization people that no other business and can actually start playing and an answer their own questions. Eso so these technologies that gives more accessibility to the data that is some cataloging so they can understand where to go or what to find lineage and relationships. All this is is basically the new type of platforms and tools that allow you to create what are called a data marketplace. I think these new tools are really strong because they are now allowing for people that are not technology or I t people to be able to play with data because it comes in the digital world There. Used to a given example without your who You have a very interesting search functionality. Where if you want to find your data you want to sell, Sir, you go there in that search and you actually go on book for your data. Everybody knows how to search in Google, everybody's searching Internet. So this is part of the data culture, the digital culture. They know how to use those schools. Now, similarly, that data marketplace is, uh, in you can, for example, see which data sources they're mostly used >>and enabling that speed that we're all demanding today during these unprecedented times. Goodwin, I wanted to go to you as we talk about in the spirit of evolution, technology is changing. Talk to us a little bit about Oracle Digital. What are you guys doing there? >>Yeah, Thank you. Um, well, Oracle Digital is a business unit that Oracle EMEA on. We focus on emerging countries as well as low and enterprises in the mid market, in more developed countries and four years ago. This started with the idea to engage digital with our customers. Fear Central helps across EMEA. That means engaging with video, having conference calls, having a wall, a green wall where we stand in front and engage with our customers. No one at that time could have foreseen how this is the situation today, and this helps us to engage with our customers in the way we were already doing and then about my team. The focus of my team is to have early stage conversations with our with our customers on digital transformation and innovation. And we also have a team off industry experts who engaged with our customers and share expertise across EMEA, and we inspire our customers. The outcome of these conversations for Oracle is a deep understanding of our customer needs, which is very important so we can help the customer and for the customer means that we will help them with our technology and our resource is to achieve their goals. >>It's all about outcomes, right? Good Ron. So in terms of automation, what are some of the things Oracle's doing there to help your clients leverage automation to improve agility? So that they can innovate faster, which in these interesting times it's demanded. >>Yeah, thank you. Well, traditionally, Oracle is known for their databases, which have bean innovated year over year. So here's the first lunch on the latest innovation is the autonomous database and autonomous data warehouse. For our customers, this means a reduction in operational costs by 90% with a multi medal converts, database and machine learning based automation for full life cycle management. Our databases self driving. This means we automate database provisioning, tuning and scaling. The database is self securing. This means ultimate data protection and security, and it's self repairing the automates failure, detection fail over and repair. And then the question is for our customers, What does it mean? It means they can focus on their on their business instead off maintaining their infrastructure and their operations. >>That's absolutely critical use if I want to go over to you now. Some of the things that we've talked about, just the massive progression and technology, the evolution of that. But we know that whether we're talking about beta management or digital transformation, a one size fits all approach doesn't work to address the challenges that the business has, um that the i t folks have, as you're looking through the industry with what Santiago told us about first Bank of Nigeria. What are some of the changes that you're seeing that I owe Tahoe seeing throughout the industry? >>Uh, well, Lisa, I think the first way I'd characterize it is to say, the traditional kind of top down approach to data where you have almost a data Policeman who tells you what you can and can't do, just doesn't work anymore. It's too slow. It's too resource intensive. Uh, data management data, governments, digital transformation itself. It has to be collaborative on. There has to be in a personalization to data users. Um, in the environment we find ourselves in. Now, it has to be about enabling self service as well. Um, a one size fits all model when it comes to those things around. Data doesn't work. As Santiago was saying, it needs to be adapted toe how the data is used. Andi, who is using it on in order to do this cos enterprises organizations really need to know their data. They need to understand what data they hold, where it is on what the sensitivity of it is they can then any more agile way apply appropriate controls on access so that people themselves are and groups within businesses are our job and could innovate. Otherwise, everything grinds to a halt, and you risk falling behind your competitors. >>Yeah, that one size fits all term just doesn't apply when you're talking about adaptive and agility. So we heard from Santiago about some of the impact that they're making with First Bank of Nigeria. Used to talk to us about some of the business outcomes that you're seeing other customers make leveraging automation that they could not do >>before it's it's automatically being able to classify terabytes, terabytes of data or even petabytes of data across different sources to find duplicates, which you can then re mediate on. Deletes now, with the capabilities that iota offers on the Oracle offers, you can do things not just where the five times or 10 times improvement, but it actually enables you to do projects for Stop that otherwise would fail or you would just not be able to dio I mean, uh, classifying multi terrible and multi petabytes states across different sources, formats very large volumes of data in many scenarios. You just can't do that manually. I mean, we've worked with government departments on the issues there is expect are the result of fragmented data. There's a lot of different sources. There's lot of different formats and without these newer technologies to address it with automation on machine learning, the project isn't durable. But now it is on that that could lead to a revolution in some of these businesses organizations >>to enable that revolution that there's got to be the right cultural mindset. And one of the when Santiago was talking about folks really kind of adapted that. The thing I always call that getting comfortably uncomfortable. But that's hard for organizations to. The technology is here to enable that. But well, you're talking with customers use. How do you help them build the trust in the confidence that the new technologies and a new approaches can deliver what they need? How do you help drive the kind of a tech in the culture? >>It's really good question is because it can be quite scary. I think the first thing we'd start with is to say, Look, the technology is here with businesses like I Tahoe. Unlike Oracle, it's already arrived. What you need to be comfortable doing is experimenting being agile around it, Andi trying new ways of doing things. Uh, if you don't wanna get less behind that Santiago on the team that fbn are a great example off embracing it, testing it on a small scale on, then scaling up a Toyota, we offer what we call a data health check, which can actually be done very quickly in a matter of a few weeks. So we'll work with a customer. Picky use case, install the application, uh, analyzed data. Drive out Cem Cem quick winds. So we worked in the last few weeks of a large entity energy supplier, and in about 20 days, we were able to give them an accurate understanding of their critical data. Elements apply. Helping apply data protection policies. Minimize copies of the data on work out what data they needed to delete to reduce their infrastructure. Spend eso. It's about experimenting on that small scale, being agile on, then scaling up in a kind of very modern way. >>Great advice. Uh, Santiago, I'd like to go back to Is we kind of look at again that that topic of culture and the need to get that mindset there to facilitate these rapid changes, I want to understand kind of last question for you about how you're doing that from a digital transformation perspective. We know everything is accelerating in 2020. So how are you building resilience into your data architecture and also driving that cultural change that can help everyone in this shift to remote working and a lot of the the digital challenges and changes that we're all going through? >>The new technologies allowed us to discover the dating anyway. Toe flawed and see very quickly Information toe. Have new models off over in the data on giving autonomy to our different data units. Now, from that autonomy, they can then compose an innovator own ways. So for me now, we're talking about resilience because in a way, autonomy and flexibility in a organization in a data structure with platform gives you resilience. The organizations and the business units that I have experienced in the pandemic are working well. Are those that actually because they're not physically present during more in the office, you need to give them their autonomy and let them actually engaged on their own side that do their own job and trust them in a way on as you give them, that they start innovating and they start having a really interesting ideas. So autonomy and flexibility. I think this is a key component off the new infrastructure. But even the new reality that on then it show us that, yes, we used to be very kind off structure, policies, procedures as very important. But now we learn flexibility and adaptability of the same side. Now, when you have that a key, other components of resiliency speed, because people want, you know, to access the data and access it fast and on the site fast, especially changes are changing so quickly nowadays that you need to be ableto do you know, interact. Reiterate with your information to answer your questions. Pretty, um, so technology that allows you toe be flexible iterating on in a very fast job way continue will allow you toe actually be resilient in that way, because you are flexible, you adapt your job and you continue answering questions as they come without having everything, setting a structure that is too hard. We also are a partner off Oracle and Oracle. Embodies is great. They have embedded within the transactional system many algorithms that are allowing us to calculate as the transactions happened. What happened there is that when our customers engaged with algorithms and again without your powers, well, the machine learning that is there for for speeding the automation of how you find your data allows you to create a new alliance with the machine. The machine is their toe, actually, in a way to your best friend to actually have more volume of data calculated faster. In a way, it's cover more variety. I mean, we couldn't hope without being connected to this algorithm on >>that engagement is absolutely critical. Santiago. Thank you for sharing that. I do wanna rap really quickly. Good On one last question for you, Santiago talked about Oracle. You've talked about a little bit. As we look at digital resilience, talk to us a little bit in the last minute about the evolution of Oracle. What you guys were doing there to help your customers get the resilience that they have toe have to be not just survive but thrive. >>Yeah. Oracle has a cloud offering for infrastructure, database, platform service and a complete solutions offered a South on Daz. As Santiago also mentioned, We are using AI across our entire portfolio and by this will help our customers to focus on their business innovation and capitalize on data by enabling new business models. Um, and Oracle has a global conference with our cloud regions. It's massively investing and innovating and expanding their clouds. And by offering clouds as public cloud in our data centers and also as private cloud with clouded customer, we can meet every sovereignty and security requirements. And in this way we help people to see data in new ways. We discover insights and unlock endless possibilities. And and maybe 11 of my takeaways is if I If I speak with customers, I always tell them you better start collecting your data. Now we enable this partners like Iota help us as well. If you collect your data now, you are ready for tomorrow. You can never collect your data backwards, So that is my take away for today. >>You can't collect your data backwards. Excellently, John. Gentlemen, thank you for sharing all of your insights. Very informative conversation in a moment, we'll address the question. Do you know your data? >>Are you interested in test driving the iota Ho platform kick Start the benefits of data automation for your business through the Iota Ho Data Health check program. Ah, flexible, scalable sandbox environment on the cloud of your choice with set up service and support provided by Iota ho. Look time with a data engineer to learn more and see Io Tahoe in action from around the globe. It's the Cube presenting adaptive data governance brought to you by Iota Ho. >>In this next segment, we're gonna be talking to you about getting to know your data. And specifically you're gonna hear from two folks at Io Tahoe. We've got enterprise account execs to be to Davis here, as well as Enterprise Data engineer Patrick Simon. They're gonna be sharing insights and tips and tricks for how you could get to know your data and quickly on. We also want to encourage you to engage with the media and Patrick, use the chat feature to the right, send comments, questions or feedback so you can participate. All right, Patrick Savita, take it away. Alright. >>Thankfully saw great to be here as Lisa mentioned guys, I'm the enterprise account executive here in Ohio. Tahoe you Pat? >>Yeah. Hey, everyone so great to be here. I said my name is Patrick Samit. I'm the enterprise data engineer here in Ohio Tahoe. And we're so excited to be here and talk about this topic as one thing we're really trying to perpetuate is that data is everyone's business. >>So, guys, what patent I got? I've actually had multiple discussions with clients from different organizations with different roles. So we spoke with both your technical and your non technical audience. So while they were interested in different aspects of our platform, we found that what they had in common was they wanted to make data easy to understand and usable. So that comes back. The pats point off to being everybody's business because no matter your role, we're all dependent on data. So what Pan I wanted to do today was wanted to walk you guys through some of those client questions, slash pain points that we're hearing from different industries and different rules and demo how our platform here, like Tahoe, is used for automating Dozier related tasks. So with that said are you ready for the first one, Pat? >>Yeah, Let's do it. >>Great. So I'm gonna put my technical hat on for this one. So I'm a data practitioner. I just started my job. ABC Bank. I have, like, over 100 different data sources. So I have data kept in Data Lakes, legacy data, sources, even the cloud. So my issue is I don't know what those data sources hold. I don't know what data sensitive, and I don't even understand how that data is connected. So how can I saw who help? >>Yeah, I think that's a very common experience many are facing and definitely something I've encountered in my past. Typically, the first step is to catalog the data and then start mapping the relationships between your various data stores. Now, more often than not, this has tackled through numerous meetings and a combination of excel and something similar to video which are too great tools in their own part. But they're very difficult to maintain. Just due to the rate that we are creating data in the modern world. It starts to beg for an idea that can scale with your business needs. And this is where a platform like Io Tahoe becomes so appealing, you can see here visualization of the data relationships created by the I. O. Tahoe service. Now, what is fantastic about this is it's not only laid out in a very human and digestible format in the same action of creating this view, the data catalog was constructed. >>Um so is the data catalog automatically populated? Correct. Okay, so So what I'm using Iota hope at what I'm getting is this complete, unified automated platform without the added cost? Of course. >>Exactly. And that's at the heart of Iota Ho. A great feature with that data catalog is that Iota Ho will also profile your data as it creates the catalog, assigning some meaning to those pesky column underscore ones and custom variable underscore tents. They're always such a joy to deal with. Now, by leveraging this interface, we can start to answer the first part of your question and understand where the core relationships within our data exists. Uh, personally, I'm a big fan of this view, as it really just helps the i b naturally John to these focal points that coincide with these key columns following that train of thought, Let's examine the customer I D column that seems to be at the center of a lot of these relationships. We can see that it's a fairly important column as it's maintaining the relationship between at least three other tables. >>Now you >>notice all the connectors are in this blue color. This means that their system defined relationships. But I hope Tahoe goes that extra mile and actually creates thes orange colored connectors as well. These air ones that are machine learning algorithms have predicted to be relationships on. You can leverage to try and make new and powerful relationships within your data. >>Eso So this is really cool, and I can see how this could be leverage quickly now. What if I added new data sources or your multiple data sources and need toe identify what data sensitive can iota who detect that? >>Yeah, definitely. Within the hotel platform. There, already over 300 pre defined policies such as hip for C, C, P. A and the like one can choose which of these policies to run against their data along for flexibility and efficiency and running the policies that affect organization. >>Okay, so so 300 is an exceptional number. I'll give you that. But what about internal policies that apply to my organization? Is there any ability for me to write custom policies? >>Yeah, that's no issue. And it's something that clients leverage fairly often to utilize this function when simply has to write a rejects that our team has helped many deploy. After that, the custom policy is stored for future use to profile sensitive data. One then selects the data sources they're interested in and select the policies that meet your particular needs. The interface will automatically take your data according to the policies of detects, after which you can review the discoveries confirming or rejecting the tagging. All of these insights are easily exported through the interface. Someone can work these into the action items within your project management systems, and I think this lends to the collaboration as a team can work through the discovery simultaneously, and as each item is confirmed or rejected, they can see it ni instantaneously. All this translates to a confidence that with iota hope, you can be sure you're in compliance. >>So I'm glad you mentioned compliance because that's extremely important to my organization. So what you're saying when I use the eye a Tahoe automated platform, we'd be 90% more compliant that before were other than if you were going to be using a human. >>Yeah, definitely the collaboration and documentation that the Iot Tahoe interface lends itself to really help you build that confidence that your compliance is sound. >>So we're planning a migration. Andi, I have a set of reports I need to migrate. But what I need to know is, uh well, what what data sources? Those report those reports are dependent on. And what's feeding those tables? >>Yeah, it's a fantastic questions to be toe identifying critical data elements, and the interdependencies within the various databases could be a time consuming but vital process and the migration initiative. Luckily, Iota Ho does have an answer, and again, it's presented in a very visual format. >>Eso So what I'm looking at here is my entire day landscape. >>Yes, exactly. >>Let's say I add another data source. I can still see that unified 3 60 view. >>Yeah, One future that is particularly helpful is the ability to add data sources after the data lineage. Discovery has finished alone for the flexibility and scope necessary for any data migration project. If you only need need to select a few databases or your entirety, this service will provide the answers. You're looking for things. Visual representation of the connectivity makes the identification of critical data elements a simple matter. The connections air driven by both system defined flows as well as those predicted by our algorithms, the confidence of which, uh, can actually be customized to make sure that they're meeting the needs of the initiative that you have in place. This also provides tabular output in case you needed for your own internal documentation or for your action items, which we can see right here. Uh, in this interface, you can actually also confirm or deny the pair rejection the pair directions, allowing to make sure that the data is as accurate as possible. Does that help with your data lineage needs? >>Definitely. So So, Pat, My next big question here is So now I know a little bit about my data. How do I know I can trust >>it? So >>what I'm interested in knowing, really is is it in a fit state for me to use it? Is it accurate? Does it conform to the right format? >>Yeah, that's a great question. And I think that is a pain point felt across the board, be it by data practitioners or data consumers alike. Another service that I owe Tahoe provides is the ability to write custom data quality rules and understand how well the data pertains to these rules. This dashboard gives a unified view of the strength of these rules, and your dad is overall quality. >>Okay, so Pat s o on on the accuracy scores there. So if my marketing team needs to run, a campaign can read dependent those accuracy scores to know what what tables have quality data to use for our marketing campaign. >>Yeah, this view would allow you to understand your overall accuracy as well as dive into the minutia to see which data elements are of the highest quality. So for that marketing campaign, if you need everything in a strong form, you'll be able to see very quickly with these high level numbers. But if you're only dependent on a few columns to get that information out the door, you can find that within this view, eso >>you >>no longer have to rely on reports about reports, but instead just come to this one platform to help drive conversations between stakeholders and data practitioners. >>So I get now the value of IATA who brings by automatically capturing all those technical metadata from sources. But how do we match that with the business glossary? >>Yeah, within the same data quality service that we just reviewed, one can actually add business rules detailing the definitions and the business domains that these fall into. What's more is that the data quality rules were just looking at can then be tied into these definitions. Allowing insight into the strength of these business rules is this service that empowers stakeholders across the business to be involved with the data life cycle and take ownership over the rules that fall within their domain. >>Okay, >>so those custom rules can I apply that across data sources? >>Yeah, you could bring in as many data sources as you need, so long as you could tie them to that unified definition. >>Okay, great. Thanks so much bad. And we just want to quickly say to everyone working in data, we understand your pain, so please feel free to reach out to us. we are Website the chapel. Oh, Arlington. And let's get a conversation started on how iota Who can help you guys automate all those manual task to help save you time and money. Thank you. Thank >>you. Your Honor, >>if I could ask you one quick question, how do you advise customers? You just walk in this great example this banking example that you instantly to talk through. How do you advise customers get started? >>Yeah, I think the number one thing that customers could do to get started with our platform is to just run the tag discovery and build up that data catalog. It lends itself very quickly to the other needs you might have, such as thes quality rules. A swell is identifying those kind of tricky columns that might exist in your data. Those custom variable underscore tens I mentioned before >>last questions to be to anything to add to what Pat just described as a starting place. >>I'm no, I think actually passed something that pretty well, I mean, just just by automating all those manual task. I mean, it definitely can save your company a lot of time and money, so we we encourage you just reach out to us. Let's get that conversation >>started. Excellent. So, Pete and Pat, thank you so much. We hope you have learned a lot from these folks about how to get to know your data. Make sure that it's quality, something you can maximize the value of it. Thanks >>for watching. Thanks again, Lisa, for that very insightful and useful deep dive into the world of adaptive data governance with Iota Ho Oracle First Bank of Nigeria This is Dave a lot You won't wanna mess Iota, whose fifth episode in the data automation Siri's in that we'll talk to experts from Red Hat and Happiest Minds about their best practices for managing data across hybrid cloud Inter Cloud multi Cloud I T environment So market calendar for Wednesday, January 27th That's Episode five. You're watching the Cube Global Leader digital event technique

>> Narrator: From around the globe, it's theCUBE with digital coverage of AWS Public Sector Partner Awards brought to you by Amazon Web Services. Hello everyone, welcome back to theCUBE's Coverage of "AWS Public Sector Partner Awards Program". I'm John Furrier your host of theCUBE here in Palo Alto, California doing the remote interviews, during this pandemic we have our remote crews and getting all the stories and celebrating the award winners and here to feature the most Innovative Connect Deployment. We have Accenture of Canada and the Department of Employment and Social Development of Canada known as ESDC. Guys, congratulations Joel Marchildon, Accenture Canada, managing director and Benoit Long, ESDC of Canada chief transformation officer. Gentlemen, thanks for coming on, and congratulations on the award. >> Thank you. >> Thank you and nice to be here >> So obviously, during this pandemic, a lot of disruption and a lot of business still needs to go on including government services. But the citizens and people need to still do their thing you got a business got to run, and you got to get things going. But the disruptions caused a little bit of how the user experiences are. So this Connect has been interesting. Its been a featured part of what we've been hearing at the Public Sector Summit with Teresa Carlson. You guys, this is a key product. Tell us about the award. What is the solution that is starving and deserving of the award? >> Maybe I'll go first and then pass it over to Benoit. But I think the solution is Amazon Connect based Virtual Contact Center that was stood up fairly quickly, over the course of about four days and really in support of benefit that the Government of Canada was was releasing as part of its economic response to the pandemic. And in the end, its a fully functioning featured contact center solution. Includes an IVR. And, we stood it up for about 1500 to 2000 agents. So that's the the crux of the solution. And maybe Benoit can give a bit of insight as to how it came about so quickly. >> Yeah, we're happy to actually, we were obviously like every other government facing enormous pressures at that time to deliver benefits directly to people who were in true need. The jobs are being lost, our current systems were in trouble because of their age and their archaic nature. And so the challenge was quickly how do we actually support a lot of people really fast. And so it came through immediately that after our initial payments were made under what was called the Canada emergency response benefit that we had to support clients directly and so people turn to the transformation team of all teams. If you wish during a firestorm, to say, well, what could you do? And how could you help. And so we had an established relationship with a number of our system integrators, including Accenture. And we were able to run a competition very rapidly, and Accenture won. And then we deployed in, as Joel said, in a matter of four days, what for us was an exceptional and high quality solution to a significant client problem. And I say that because I think you can imagine how people feel in a pandemic of all things, but with the uncertainty that comes with loss of income, loss of jobs, the question of being able to deal with somebody a real; a human being, as well as to be able to efficiently answer a very simple but straightforward questions rapidly and with high quality, was pretty fundamental for us. So the the people in the groups that we're talking through here we're speaking to millions of people, who were literally being asked to accept the payment rapidly and to be able to connect with us quickly. And without this solution, which was exceptionally well done and of high quality personally as a technology solution, it would not have been possible to even answer any of these queries quickly. >> And well, that's a great point. One of the things that you see with the pandemic, its a disaster in the quote disaster kind of readiness thing. Unforeseen, right. So like other things, you can kind of plan for things, hypothetical, you got scenarios. But this is truly a case where every day counts, every minute counts, because humans are involved. There's no ROI calculation. Its not like, well, what's the payback of our system? The old kind of way to think. This is real results, fast. This is what cloud is all about. This is the promise of cloud, can I stand up something quick, and you did it with a partner, okay. This is like not, like normal. Its like, its like unheard of, right. Four days, with critical infrastructure, critical services that were unforeseen. Take us through what was going on in the war room. As you guys knew this was here. Take us through through what happened. >> So I think I can start. As you can imagine the set of executives that were overseeing the payment process was an exceptional, it was like a bunker, frankly, for about two weeks. We had to suspend the normal operations of the vast majority of our programming. We had to launch brand new payments and benefits systems and programs that nobody has seen before the level of simplicity was maximized in order to deliver the funds quickly. So you can imagine its a Warpath if you wish, because the campaign is really around timing. Timing is fundamental. People are literally losing their jobs, there is no support, there is no funding money for them to be able to buy groceries. So, and the trust that people have in the government is pretty much at risk right there. And there is straightforward but extraordinarily powerful magic moment, if you wish. If you can deliver a solution, then you make a difference for a long time. And so the speed is unheard of on all fronts. When it came to the call center capability and the ability for us to support in a service context, the clients that were desperate to reach us, and we're talking hundreds of thousands of calls a day. We're not talking a few thousand here, ultimately, at some point we were literally getting in overtaken by volumes, call centers, because we had our regular ones still operating. Over a million calls were coming in the day. With the capacity to answer 10s of thousands and so the reality is that the Call Centers that we put up here, very quickly became capable of answering more calls than our regular call centers. And that speaks to the the speed of delivery, the quality of the solution, of course, but the scalability of it. And I have to say maybe unheard of, it may be difficult to replicate the conditions to lead to this are rare. But I have to say that my bosses and most of the government is probably now wondering why we can't do this more often. Why can't we operate with that kind of speed and agility. So I think what you've got is a client in our case, under extreme circumstances, now realizing the new normal will never be the same. That these types of solutions and technology and their scalability, their agility, their speed of deployment, is frankly something we want we want all the time. Now we'd like to be able to do them during normal timeline conditions, but even those will be a fraction of what it used to take. It would have taken us a while I can actually tell you because I was the lead technologist to deploy at scale for the government, Canada, all the call center capabilities under a single software as a service platform. It took us two years to design it two years to procure it, and five years to install it. That's the last experience we have of call center, enterprise scale capabilities. And in this case we went from years, to literally days. >> Well, it takes a crisis sometimes to kind of wire up the simplicity solution that you say, why didn't we do this before? The waterfall meetings getting everyone arguing kind of gets in the way and the old software model, I want to come back to the transformation Benoit a minute, Because I think that's going to be a great success story and some learnings and I want to get your thoughts on that. But I want to go to Joel, because Joel, we've talked to many Accenture executives over the years and most recently, this past 24 months. And the message we've been hearing is, "We're going to be faster. We're not going to be seen as that, a consulting firm, taking our times trying to get a pound of flesh from the client." This is an example of my opinion of a partner working with a problem statement that kind of matches the cloud speed. So you guys have been doing this is not new to Accenture. So take us through how you guys reacted, because one, you got to sync up and get the cadence of what Benoit was trying to do sync up and execute take us through what happened on your side. >> Yeah, I mean, so its an unprecedented way of operating for us as well, frankly. And, we've had to look at, to get this specific solution out the door and respond to an RFP and the commercial requirements that go with that we had to get pretty agile ourselves internally on, how we go through approvals, etc, to make sure that we were there to support Benoit and his team and I think that we saw this as a broader opportunity to really respond to it. To help Canada in a time of need. So I think we had to streamline a lot of our internal processes and make quick decisions that normally even for our organization would have taken, could have taken weeks, right, and we were down to hours and a lot of instances. So it forces us to react and act differently as well. But I mean to Benoit's point I think this is really going to hopefully change the way... It illustrates the art of the possible and hopefully will change how quickly we can look at problems and we reduce deployment timeframes from years to months and months to weeks, etc. For solutions like this. And I think the AWS platform specifically in this case, Benoit touched on a lot of things beat the market scalability, but just as the benefit itself has to be simplified to do this quickly. I think one of the one of the benefits of the solution itself is, its simple to use technologically. I mean, we trained, as I said, I think 1600 agents on how to use the platform over the course of a weekend. And they're not normal agents. These were people who were furloughed from other jobs potentially within the government. So they're not necessarily contact center agents, by training, but they became contact center agents over the course of 48 hours. And I think, from that perspective, that was important as well to have something that people could use to answer those calls that we know that we knew were going to come. >> Benoit this is the transformation dream scenario in the sense of capabilities. I know its under circumstances of the pandemic and you guys did solve a big problem really fast and saved lives and then help people get on with their day. But transformation is about having people closest to the problem, execute. And also the people equation people process technology, as they say, is kind of playing out in real time. This is kind of the playbook. Amazon came in and said, "Hey, you want to stand something up?" You wired it together the solution quickly, you have close to it. Looking back now its almost like, hey, why aren't we doing this before, as you said, and then you had to bring people in, who weren't trained and stood them up and they were delivering the service. This is the playbook to share your thoughts on this because this is what you're you're thinking about all the time, and it actually is playing out in real time. >> Well, I would definitely endorse the idea that its a playbook. Its I would say its an ideal and dream playbook to bid like showing up on a basketball court with all the best players in the entire league playing together magically. It is exactly that. So a lot of things had to happen quickly but also correctly, because you can't pull all these things properly together without that. So I would say the partnership with the private sector here was fundamental. And I have to applaud the work that Accenture did particularly I think, as Canadians we were very proud of the fact that we needed to respond quickly. Everyone was in this our neighbors, we knew people who were without support and Accenture's team, I mean, all the way up and down across the organization was fundamental in and delivering this but also literally putting themselves into these roles and to make sure that we would be able to respond and quickly do so. I think the playbook around the readiness for change, I was shocked into existence. I mean, I won't talk about quantum physics, but clearly some higher level of energy was thrown in quickly, mobilize everybody all at once. Nobody was said he is sitting around saying, I wonder if we have changed management covered off, this was changed readiness at its best. And so I think for me from a learning perspective, apart from just the technology side, which is pretty fundamental, if you don't have ready enough technology to deploy quickly, then the best pay your plans in the world won't work. The reality is that to mobilize an organization going forward into that level of spontaneous driving change, exception, acceptance, and adoption, is really what I would aim for. And so our challenge now will be continuing that kind of progression going forward. And we now found the way and we certainly use the way to work with the private sector in an innovative capacity and innovative ways with brand new solutions that are truly agile and scalable, to be able to pull all of the organization all at once very rapidly and I have to admit that it is going to shift permanently our planning, we had 10 year plans for our big transformations, because some of our programs are the most important in the country in many ways. We support people about 8 million Canadians a month, depending on the benefits payments that we deliver. And they're the most marginal needing and requires our support from seniors, to the unemployed, to job seekers and whatnot. So if you think about that group itself, and to be able to support them clearly with the systems that we have its just unsustainable. But the new technologies are clearly going to show us a way that we had never forecast, and I have to say I had to throw up my 10 year plan. And now I'm working my way down from 10 to nine to eight year plans going forward. And so its exciting and nerve wracking sometimes, but then, obviously as a change leader, our goal is to get there as quickly as possible. So the benefits of all these solutions can make a difference in people's lives. >> What's interesting is that you can shorten that timetable, but also frees you up to be focused on what's contemporary and what's needed at the time to leverage the people and the resources you have. And take advantage of that versus having something that you're sitting on that's needs to be refreshed, you can always be on that bleeding edge. And this just brings up the DevOps kind of mindset, agility, the lean startup, the lean company, this is a team effort between Amazon Accenture and ESDC. Its, pass, shoot, score really fast. So this is the new reality. Any commentary from you guys on this, new pass, shoot, score combination because you got speed, you got agility, you're leaner, which makes you more flexible for being contemporary in solving problems? What's your thoughts? >> Yeah. So my perspective on that is most definitely right. I think what we were able to show in what's coming out of a lot of different responses to the pandemic by government is, perfection isn't the most important thing out of the gate, getting something out there that's going to reassure citizens, that's going to allow them to answer their questions or access benefits quickly, is what's becoming more important, obviously, security and privacy, those things are of the utmost importance as well. But its ability to get stuff out there, quickly, test it, change it, test it again, and just always be iterating on the solution. Like I can say what we put out on April 6, within four days, is the backbone of what's out there still today. But we've added an integrated workforce management solution from NICE, and we added some other ISVs to do outbound dialing from Acquia and things like that. So the solution has grown from that MVP. And I think that's one other thing that's going to be a big takeaway. If you're not going to do anything till you got the final end product out there, then its going to be late. So let's go quickly and let's adapt from there. >> Benoit, talk about that dynamic because that's about building blocks, on foundational things and then services. Its the cloud model. >> Yeah, I mean, before the pandemic, I had lunch with Mark Schwartz, which I believe you are quite familiar with. And, I spent an hour and a half with him. We were talking and he was so exciting and energized by what the technologies could do. And I was listening to him and I used to be the chief technology officer for the Government of Canada, right. And so I've seen a lot of stuff and I said, Well, that's really exciting. And I'm sure its possible in some other places, and maybe in some other countries where they didn't have infrastructure and legacy. I guess if I see him again soon. I'll have to apologize for not believing him enough. I think the building blocks of Agile the building blocks sprints and MVPs. I mean, they're enough fundamental to the way we're going to solve our biggest Harriers and scariest problems technologically. And then from a business perspective, service candidate itself has 18,000 employees involved in multiple channels, where the work has always been very lethargic, very difficult. Arduous you make change over years, not months, not days, for sure. And so I think that new method is not only a different way of working, its a completely revamped way of assembling solutions. And I think that the concept of engineering is probably going to be closer to what we're going to do. And I have to borrow the Lego metaphor, but the building blocks are going to be assembled. We know in working, I'm saying this in front of Joel, he doesn't know that yet. (all laughing) (indistinct) partners. We're going to be assembling MVP maps of an entire long program and its going to be iterative, it is going to be designed built, it will be agile as much as we can implement it. But more importantly, as much as we can govern it because the government is... We may have changed a lot, but the government is not necessarily caught on to most of these approaches. But the reality is that, that's where we're heading. And I will say, I'll close perhaps on this answer. The biggest reason for doing that apart from we've proved it is the fact that the appetite inside the organization for that level of mobilization, speed and solutioning, and being engaged rapidly, you just can't take that away from an organization once they've tasted that. If you let them down, well, they'll remember and frankly, they do remember now because they want more of this. And its going to be hard. But its a better hard, better challenge, than the one of having to do things over a decade, then to go fast and to kind of iterate quickly through the challenges and the issues and then move on very much to the next one as rapidly as possible. I think the the other comment I would add is most of this was driven by a client need. And that's not inconsequential because it mobilized everybody to a common focus. If it had been just about, well, we need to get people on side and solutions in place just to make our lives better as providers. Yeah, would it work perhaps, but it would have been different than the mobilization that comes when the client is put in the middle. The client is the focus, and then we drive everyone to that solution. >> Shared success and success is contagious. And when you ride the new wave, you're oh, we need a new board, right? So once you get it, it then spreads like wildfire. This is what we've been seeing. And it also translates down to the citizens because again, being contemporary, none of this just look could feel its success and performance. So as people in business start to adopt cloud. It becomes a nice synergy. This is a key! Joe, take us home here on the Accenture. The award winner, you guys did a great job. Final thoughts. >> Yeah, I mean, I think final thoughts would be happy to have had the opportunity to help. And it was a it was a complete team effort and continues to be. Its not a bunch of eccentric technologists in the background doing this. The commitment from everyone to get this in place and to continue to improve it from Benoit team and from other folks across the government has been paramount to the success. So its been a fantastic if world win like experience and look forward to continuing to build on it. And it has been well said, I think one thing that's done is its created demand for speed on some of these larger transformations. So I looking forward to continuing to innovate with with Benoit team. >> Well, congratulations for the most innovative Connect Deployment. And because you guys from Canada, I have to use the Hockey-Reference. You get multiple people working together in a cohesive manner. Its pass, shoot, score every time and its contagious. (Benoit laughs) Gentlemen, thank you very much for your time and congratulations for winning the election. Take care! >> Thanks. >> Take care. >> Okay, this is theCUBE's Coverage "AWS Public Sector Partner Awards" show. I'm John Furrier, host of theCUBE. Thanks for watching. (upbeat music)

>>from the Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. >>Welcome back to the Cube's coverage of AWS Public Sector Partner Awards program. I'm John Furrow, your host of the Cube here in Palo Alto, California In the remote interviews during this pandemic, we have our remote crews and getting all the stories and celebrating the award winners. And here to feature the most innovative connect deployment. We have a center of Canada and the Department of Employment and Social Development of Canada, known as E S D. C guys. Congratulations, Joel. More Children Censure Canada Managing director and Ben while long sdc of Canada Chief Transformation officer. Gentlemen, thanks for coming on. And congratulations on the award. >>Thank you. >>Thank you. >>So, Ashley, during this pandemic, a lot of disruption and a lot of business still needs to go on, including government services. But the citizens and people need to still do their thing. Business got to run, and you got to get things going. But the disruptions caused a little bit of how the user experiences are. So this connect has been interesting. It's been a featured part of where you've been hearing at the Public Sector summit with Theresa Carlson. You guys, this is a key product. Tell us about the award. What is the solution? That disturbing of deserving reward? >>Maybe I'll get I'll go first and then pass it over to Benoit. But I think the solution is Amazon Connect based Virtual Contact Center that we stood up fairly quickly over the course of about four days and really in support of of benefit that the government of Canada was was releasing as part of its economic response to the pandemic. And in the end that, you know, it's a fully functioning featured contact center solution includes an I V r. And, uh, you know, we stood it up for about 1500 to 2000 agents so that that's the crux of the solution. And maybe Benoit can give a bit of insight as to to how it came about so quickly. >>Yeah, happy to actually wear obviously, like every other government, facing enormous pressures at that time to deliver benefits directly to people who were in true need, the jobs are being lost. Our current systems were in trouble because of their age and barricade cake nature. And so the challenge is was quickly how to actually support a lot of people really fast. And so it came through immediately that after our initial payments were made under what was called Canada Emergency Response Benefit, then we have to support our clients directly. And so people turn to the transformation team of all teams. If you wish during a fire firestorm to say, Well, what could you do and how could you help? And so we had an established relationship with a number of other system integrators, including Accenture, and we were able to run a competition very rapidly. Accenture one. And then we deployed. And as you all said, in a matter of four days, what for us was a new, exceptional on high quality solution to a significant client problem. And I say that because I think you can imagine how people feel in the endemic of all of all things. But with the uncertainty that comes with the loss of income, loss of jobs, the question of being able to deal with somebody really a human being, as well as to be able to be efficiently answer a very simple but straightforward questions rapidly and with high quality, with pretty fundamental for us. So the people in the groups that were talking through here are talking, speaking to millions of people who were literally being asked to to accept the pavement rapidly and to be able to connect with us quickly. And without this solution, which was exceptionally well done and deployed and of high quality personally, just a technology, uh, solution. I would not have been possible to even answer any of these queries quickly. >>And while that's a great 0.1 of the things that you see with the pandemic it's a disaster in the quote disaster kind of readiness thing. Unforeseen, right? So, like other things, you can kind of plan for things that hypothetical. You've got scenarios, but this >>is >>truly a case where every day counts. Every minute counts because humans are involved is no our ROI calculation. It's not like it's not like, Well, what's the payback of our system? The old kind of way to think this is really results fast. This is what cloud is all about. This is the promise of cloud. Can I stand up something quick and you did it with a partner. Okay, this is, like, not, like, normal again. It's like it's, you know, it's like, unheard of, right? Four days with critical infrastructure, critical services that were unforeseen. Take us through what was going on in the war room, as you guys knew this was here. Take us through the through what happened. Yeah, >>So I think I can start a Z. You can imagine the set of executives that we're seeing a payment process. Uh, was an exceptional. It was like a bunker. Frankly, for about two weeks, we had to suspend the normal operations off the vast majority of our programming. We had to launch brand new payments and benefits systems and programs that nobody had seen before. The level of simplicity was maximized to delivered the funds quickly. So you could imagine it's a warpath if you wish, because the campaign is really around. A timing. Timing is fundamental. People are are literally losing their jobs. There is no support. There's no funding money for them to be able to buy groceries. So on the trust that people have in the government, Ai's pretty much at risk right there and then in a very straightforward but extraordinarily powerful magic moment. If you wish. If you can deliver a solution, then you make a difference for a long time. And so the speed unheard off on old friends when he came to the call center capability and the ability for us to support and service context the clients that were desperate to reach us on. We're talking hundreds of thousands of calls, right? We're not talking a few 1000 year. Ultimately, at some point we were literally getting in our over over, taken by volumes, call centers. But we had a regular one still operating over a 1,000,000 calls for coming in today with the capacity to answer, um, you know, tens of thousands. And so the reality is that the counselor that we put up here very quickly became capable of answering more calls than our regular costumes. And that speaks to the speed of delivery, the quality of the solution, of course, but the scalability of it and I have to say, maybe unheard of, it may be difficult to replicate. The conditions to lead to this are rare, but I have to say that my bosses and most of the government is probably now wondering why we can't do this more often, like we can't operate with that kind of speed and agility. So I think what you've got is a client in our case, under extreme circumstances. Now, realizing the new normal will never be the same, that these types of solutions and technology. And then there's scalability. There's agility there, the speed of deployment. It's frankly, something we want. We want all the time. Now we'd like to be able to do it under your whole timeline conditions. But even those will be a fraction of what it used to take. It would have taken us well, actually, I can actually tell you because I was the lead, Ah, technologists to deploy at scale for the government. Canada all the call center capabilities under a single software as a service platform. It took us two years to design it two years to procure it and five years to install it. That's the last experience. We have a call center enterprise scale capabilities, and in this case, we went from years to literally days. >>Well, you know, it takes a crisis sometimes to kind of wire up the simplicity solution that you say. Why didn't we do this before? You know, the waterfall meetings, Getting everyone arguing gets kind of gets in the way of the old the old software model. I want to come back to the transformation been wanna minute, cause I think that's gonna be a great success story and some learnings, and I want to get your thoughts on that. But I want to go to Joel because Joel, we've talked to many Accenture executives over the years and most recently this past 24 months. And the message we've been hearing is we're going to be faster. We're not going to be seen as that. You know, a consulting firm taking our times. Try and get a pound of flesh from the client. This is an example. In my opinion of a partner working with a problem statement that kind of matches the cloud speed. So you guys have been doing this. This is not new to a censure. So take us through how you guys reacted because one you got to sync up and get the cadence of what, Ben? What I was trying to do sync up and execute. Take us through what happened on your side. >>Yeah, I mean, so it's It's Ah, it's an unprecedented way of operating for us as well, frankly, and, um and, uh and, you know, we've had to look at to get this specific solution at the door and respond to an RFP and the commercial requirements that go with that way. Had Teoh get pretty agile ourselves internally on on how we go through approvals, etcetera, to make sure that that we were there to support Ben Wan is team. And I think you know that we saw this is a broader opportunity to really respond to it, to help Canada in a time of need. So So I think we, you know, we had to streamline a lot of our internal processes that make quick decisions that normally even for our organization, would have taken, um, could it could have taken weeks, right? And we were down to hours in a lot of instances. So it helps. It forces us to react and act differently as well. But I mean, to Benoit's point, I think this is really going to to hopefully change the way it illustrates the art of the possible and hopefully will change How, How quick We can look at problems and and we reduced deployment timeframes from from years to months and months to weeks, etcetera for solutions like this. Um, and I think that the AWS platform specifically in this case but what touched on a lot of things to beat the market scale ability But just as the benefit itself was, you know has to be simplified to do this quickly. I think one of the one of the benefits of the solution itself is it's simple to use technologically. I mean, we know least retrained. As I said, I think 1600 agents on how to use the platform over the course of a weekend on and and were able, and they're not normal agents. These were people who are firm from other jobs, potentially within the government. So they're not necessarily contact center agents by training. But they became contact center agents over the course of 48 hours, and I think from that perspective, you know, that was important as well have something that people could could use. The answer those calls that we know that when you were gonna come so >>Ben what this is. This is the transformation dream scenario in the sense of capabilities. I know it's under circumstances of the pandemic, and you guys didn't solve a big, big problem really fast and saved lives and help people get on with their day. But transformations about having people closest to the problem execute and the the also the people equation people process technology, as they say, is kind of playing out in real time. This >>is >>the this is kind of the playbook, you know? Amazon came in said, Hey, you want to stand something up? You wired it together. The solution quickly. You're close to it. Looking back now, it's almost like, Hey, why aren't we doing this before? As you said and then you had to bring people in who weren't trained and stood them up and they were delivering the service. This >>is >>the playbook to share your thoughts on this, because this is what you're you're thinking about all the time and it actually playing out in real time. >>Well, I would definitely endorsed the idea that it's a playbook. It's I would say it's an ideal and dream playbook timidly showing up on the basketball court with all the best players in the entire league playing together magically, it is exactly that. So a lot of things have to happen quickly, but also, um, correctly because you know, you can't pull these things properly together without that. So I would say the partnership with the private sector here was fundamental, and I have to applaud the work that Accenture did particularly, I think, as Canadians, we're very proud of the fact that we needed to respond quickly. Everyone was in this, our neighbors, we knew people who were without support and Accenture's team, I mean, all the way up and down across the organization was fundamental and delivering this, but also literally putting themselves into, uh, these roles and to make sure that we would be able to respond quickly to do so. I think the playbook around the readiness for change I was shocked into existence every night. I won't talk about quantum physics, but clearly some some high level of energy was thrown in very quickly, mobilized everybody all at once. Nobody was said. He's sitting around saying, I wonder if we have change management covered off, you know this was changed readiness at its best. And so I think for me from a learning perspective, apart from just the technology side, which is pretty fundamental if you don't have ready enough technology to deploy quickly than the best paid plans in the world won't work. The reality is that to mobilize an organization going for it into that level of of spontaneous driving, change, exception, acceptance and adoption is really what I would aim for. And so our challenge now we'll be continuing that kind of progression going forward, and we now found the way. We certainly use the way to work with private sector in an innovative capacity in the new, innovative ways with brand new solutions that are truly agile and and and scalable to be able to pull all of the organization. All that one's very rapidly, and I have to admit that it is going to shift permanently our planning. We had 10 year plans for our big transformation, so some of our programs are the most important in the country. In many ways. We support people about eight million Canadians a month and on the benefits payments that we deliver, and they're the most marginal needed meeting and and requires our support from senior study, unemployed jobseekers and whatnot. So if you think about that group itself and to be able to support them clearly with the systems that we have is just unsustainable. But the new technologies are clearly going to show us the way that we had never for forecast. And I have to say I had to throw up, like in your plan. And now I'm working my way down from 10 denying date your plants going forward. And so it's exciting and nerve wracking sometimes, but then obviously has a change leader. Our goal is to get there as quickly as possible, so the benefit of all of these solutions could make a difference in people's lives. >>What's interesting is that you can shorten that timetable but also frees you up to be focused on what's contemporary and what's needed at the time. So leverage the people on the resource is You have and take advantage of that versus having something that you're sitting on that need to be refreshed. You can always be on that bleeding edge, and this brings up the Dev ops kind of mindset agility. The lean startup glean company. You know this is a team effort between Amazon and center and SDC. It's pass, shoot, score really fast. So this isn't the new, the new reality. Any commentary from you guys on this, you know, new pass shoot score combination. Because you got speed, you got agility. You're leaner, which makes you more flexible for being contemporary and solving problems. What's your thoughts? >>So my perspective on that is most definitely right. I think what we what we were able to show and what's. You know, what's coming out of a lot of different responses to the pandemic by government is, um, you know, perfection isn't the most important thing out of the gate. Getting something out there that's going to reassure citizens that's gonna allow them to answer their questions or access benefits quickly is what's becoming more important. Obviously, security and privacy. Those things are of the utmost importance as well. But it's ability to get stuff out there, quickly, test it, change it, tested again and and just always be iterating on the solutions. Like I can say what we put out on April 6th within four days is the backbone of what's out there still today. But we've added, you know, we added an integrated workforce management solution from Nice, and we added some other eyes views to do outbound dialing from acquisition, things like that. So the solution has grown from that M v p. And I think that's one other thing that that's going to be a big takeaways if you're not gonna do anything. So you got the final and product out there, then it's going to be here, right? So let's go quickly and let's adapt from there. >>Then we'll talk about that dynamic cause that's about building blocks, fund foundational things and then services. It's the cloud model. >>Yeah, I mean, before the pandemic, I had lunch with Mark Schwarz, which I believe you're quite familiar with, and, you know, I spent an hour and 1/2 with it. We were talking, and he was so exciting and and energized by what the technologies could do. And I was listening to him, and I used to be the chief technology officer for the government can right? And so I've seen a lot of stuff and I said, Well, that's really exciting, and I'm sure it's possible in some other places. And maybe it's some other countries where you know they didn't have infrastructure and legacy. I guess if I see him again soon, I'll have to. I apologize for not believing him enough, I think the building blocks of edge of the building, blocks of sprints and MVP's I mean they're not fundamental to the way we're gonna. So our biggest, various and scariest problems, technologically and then from a business perspective, Service candidate itself has 18,000 employees involved in multiple channels where the work has always been very lethargic, very difficult, arduous. You make change over years, not months, not days for sure. And so I think that that new method is not only a different way of working, it's a completely re HVAC way of assembly solutions, and I think the concept of engineering is probably going to be closer to what we're going to do on. And I have to borrow the Lego metaphor, but the building blocks are gonna be assembled. We now and working. I'm saying this in front of goal. He doesn't know that you should practice partners. We're gonna be assembling MPP maps of an entire long program, and it's gonna be iterative. It is gonna be designed, built. It will be agile as much as we can implement it. But more importantly, and punches weaken govern. It is, you know, the government is we may have changed. A lot of the government is not necessarily can count on to Most of these things approaches, But the reality is that that's where we're heading. And I will say, Oh, close. Perhaps on this on this answer. The biggest reason for doing that apart from we've proved it is the fact that the appetite inside the organization for that level of globalization, speed solution ing and being engaged rapidly you just can't take that away from an organization. Must be a piece of that. Uh, if you let them down, well, they'll remember. And frankly, they do remember now, cause they want more and it's gonna be hard. But it's a better heart. Ah, a better challenge that the one of having to do things over a decade, then to go fast and to kind of iterating quickly through the challenges and the issues and then move on very much to the next one as rapidly as possible. I think the other company, I would add is most of this was driven by a client need, and that's not inconsequential because it mobilized everybody to comment focused. If you have been just about, well, you know, we need to get people on side and solutions in place just to make our lives better, it providers. Yeah, it would have worked, perhaps, but it would have been different than the mobilisation It comes when the client is put in the middle, the client is the focus, and then we drive. Everyone's with that solution, >>you know, shared success and success is contagious. And when you ride the new way to oh, we need a new board, right? So once you get it, it then spreads like wildfire. This is what we've been seeing. And it also translates down to the citizens because again, being contemporary, none of us just looked could feel it's success in performance. So, as you know, people in business start to adopt cloud. It becomes a nice, nice, nice synergy. This is key. I'll take a year on a center. Um, the award winner. You guys did a great job. Final thoughts. >>Yeah. I mean, I think final thoughts would be happy to have the opportunity that help. And it was a It was a complete team effort and continues to be, um, it's not. It's not a bunch of Accenture technologists in the background in this, you know the commitment from everyone to get this in place. And can you continue to improvement from Benoit's team and from other folks across the government has been, uh, has been paramount to the success. So, um um, it's been a fantastic if world win like experience and, uh, look forward to continuing to build on it. And it has been said, I think one thing this is done is it's created demand for speed on some of these larger transformations. So I'm looking forward to continuing to innovate with with Ben wanting. >>Well, congratulations. The most innovative connect deployment. And because you guys from Canada, I have to use the hockey reference. You get multiple people working together in a cohesive manner. It's pass, shoot, score every time. And you know it's contagious. Thank you very much for your time. And congratulations for winning the >>West. Thanks. Thank you. Okay, this is the >>Cube's coverage of AWS Public Sector Partner Award show. I'm John Furrier, host of the Cube. Thanks for watching. Yeah, Yeah, yeah, yeah.

>>from the Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. >>Welcome back to the Cube's coverage of AWS Public Sector Partner Awards program. I'm John Furrow, your host of the Cube here in Palo Alto, California In the remote interviews during this pandemic, we have our remote crews and getting all the stories and celebrating the award winners. And here to feature the most innovative connect deployment. We have a center of Canada and the Department of Employment and Social Development of Canada, known as E S D. C guys. Congratulations, Joel. More Children Censure Canada Managing director and Ben while long sdc of Canada Chief Transformation officer. Gentlemen, thanks for coming on. And congratulations on the award. >>Thank you. >>Thank you. >>So, Ashley, during this pandemic, a lot of disruption and a lot of business still needs to go on, including government services. But the citizens and people need to still do their thing. Business got to run, and you got to get things going. But the disruptions caused a little bit of how the user experiences are. So this connect has been interesting. It's been a featured part of what we've been hearing at the public sector summit with Theresa Carlson. You guys, this is a key product. Tell us about the award. What is the solution? That disturbing of deserving reward? >>Maybe I'll get I'll go first and then pass it over to Benoit. But I think the solution is Amazon. Connect a spiritual contact center that we stood up fairly quickly over the course of about four days and really in support of of benefit that the government of Canada was was releasing as part of its economic response to the pandemic. And in the end that, you know, it's a fully functioning featured contact center solution includes an ai VR and, uh, you know, we stood it up for 1500 to 2000 agents so that that's the crux of the solution. And maybe Benoit can give a bit of insight as to to how it came about so quickly. >>Yeah, I'd be happy to actually wear obviously, like every other government, facing enormous pressures at that time to deliver benefits directly to people who were in true need, the jobs are being lost. Our current systems were in trouble because of their age in the arcade cake Nature. And so the challenge is was quickly how to actually support a lot of people really fast. And so it came through immediately that after our initial payments were made under what was called Canada Emergency Response Benefit, then we have to support our clients directly. And so people turn to the transformation team of all teams. If you wish during a fire firestorm to say, Well, what could you do and how could you help? And so we had an established relationship with a number of other system integrators, including Accenture, and we were able to run a competition very rapidly. Accenture one. And then we deployed in, as you all said, in a matter of four days, what for us was a new, exceptional on high quality solution to a significant client problem. And I say that because I think you can imagine how people feel in that endemic of all of all things. But with the uncertainty that comes with the loss of income, loss of jobs, the question of being able to deal with somebody really a human being, as well as to be able to be efficiently answer a very simple but straightforward questions rapidly and with high quality, with pretty fundamental for us. So the people in the groups that were talking through here are talking, speaking to millions of people who were literally being asked to to accept the pavement rapidly and to be able to connect with us quickly. And without this solution, which was exceptionally well done and deployed and of high quality personally, just a technology, uh, solution. I would not have been possible to even answer any of these queries quickly. >>And while that's a great 0.1 of the things that you see with the pandemic it's a disaster in the quote disaster kind of readiness thing. Unforeseen, right? So, like other things, you can kind of plan for things that hypothetical. You've got scenarios, but this >>is >>truly a case where every day counts. Every minute counts because humans are involved is no our ROI calculation. It's not like it's not like, Well, what's the payback of our system? The old kind of way to think this is really results fast. This is what cloud is all about. This is the promise of cloud. Can I stand up something quick and you did it with a partner. Okay, this is, like, not, like, normal again. It's like it's, you know, it's like, unheard of right? Four days with critical infrastructure, critical services that were unforeseen. Take us through what was going on in the war room, as you guys knew this was here. Take us through the through what happened. Yeah, >>So I think I can start a Z. You can imagine the set of executives that we're seeing a payment process. Uh, was an exceptional. It was like a bunker. Frankly, for about two weeks, we had to suspend the normal operations off the vast majority of our programming. We had to launch brand new payments and benefits systems and programs that nobody had seen before. The level of simplicity was maximized to delivered the funds quickly. So you could imagine it's a warpath if you wish, because the campaign is really around. A timing. Timing is fundamental. People are are literally losing their jobs. There is no support. There's no funding money for them to be able to buy groceries. So on the trust that people have in the government, Ai's pretty much at risk right there and then, in a very straightforward but extraordinarily powerful magic moment. If you wish. If you can deliver a solution, then you make a difference for a long time. And so the speed unheard off on old friends when he came to the call center capability and the ability for us to support and service context the clients that were desperate to reach us on. We're talking hundreds of thousands of calls, right? We're not talking a few 1000 year. Ultimately, at some point we were literally getting in our over over, taken by volumes, call centers, but we had a regular one still operating over a 1,000,000 calls for coming in today. Uh, with the capacity to answer, um, you know, tens of thousands. And so the reality is that the counselor that we put up here very quickly became capable of answering more calls than our regular costumes. And that speaks to the speed of delivery, the quality of the solution, of course, but the scalability of it and I have to say, maybe unheard of, it may be difficult to replicate. The conditions to lead to this are rare, but I have to say that my bosses and most of the government is probably now wondering why we can't do this more often like we can't operate with that kind of speed and agility. So I think what you've got is a client in our case, under extreme circumstances. Now, realizing the new normal will never be the same, that these types of solutions and technology. And then there's scalability. There's agility there, the speed of deployment. It's frankly, something we want. We want all the time. Now we'd like to be able to do it under your whole timeline conditions. But even those will be a fraction of what it used to take. It would have taken us well, actually, I can actually tell you because I was the lead. Ah, technologist, to deploy at scale for the government, Canada, all the call center capabilities under a single software as a service platform. It took us two years to design it. Two years to procure it and five years to install it. That's the last experience. We have a call center enterprise scale capabilities, and in this case, we went from years to literally days. >>Well, you know, it takes a crisis sometimes to kind of wire up the simplicity solution that you say. Why didn't we do this before? You know the waterfall meetings, Getting everyone arguing gets kind of gets in the way of the old, the old software model. I want to come back to the transformation been wanna minute, cause I think that's going to be a great success story and some learnings, and I want to get your thoughts on that. But I want to go to Joel because Joel we've talked to many Accenture executives over the years and most recently this past 24 months, And the message we've been hearing is we're going to be faster. We're not going to be seen as that. You know, a consulting firm taking our times. Try and get a pound of flesh from the client. This is an example, in my opinion of a partner working with the problem statement that kind of matches the cloud speed. So you guys have been doing this. This is not new to a censure. So take us through how you guys reacted because one you got to sync up and get the cadence of what? Ben? What I was trying to do, sync up and execute. Take us through what happened on your side. >>Yeah. I mean, so it's It's Ah, It's an unprecedented way of operating for us as well, frankly, and, um and, uh and, you know, we've had to look at to get this specific solution at the door and respond to an RFP and the commercial requirements that go with that way. Had Teoh get pretty agile ourselves internally on on how we go through approvals, etcetera, to make sure that that we were there to support Ben Wan is team. And I think you know that we saw this is a broader opportunity to really respond to it, to help Canada in a time of need. So So I think we, you know, we had to streamline a lot of our internal processes and make quick decisions that normally, even for our organization, would have taken, um, could it could have taken weeks, right? And we were down to hours in a lot of instances. So it helps. It forces us to react and act differently as well. But I mean, to Benoit's point, I think this is really going to to hopefully change the way it illustrates the art of the possible and hopefully will change how, How quickly we can look at problems and and we reduce deployment timeframes from from years to months and months to weeks, etcetera for solutions like this. Um, and I think that the AWS platform specifically in this case but what touched on a lot of things to beat the market scale ability But just as the benefit itself was, you know has to be simplified to do this quickly. I think one of the one of the benefits of the solution itself is it's simple to use technologically. I mean, we know least retrained. As I said, I think 1600 agents on how to use the platform over the course of a weekend on and and were able, and they're not normal agents. These were people who are firm from other jobs, potentially within the government. So they're not necessarily contact center agents by training. But they became contact center agents over the course of 48 hours that I think from that perspective, you know, that was important as well have something that people could could use. The answer those calls that you know that when you're gonna come So, >>Ben, what this is This is the transformation dream scenario in the sense of capabilities. I know it's under circumstances of the pandemic, and you guys didn't solve a big, big problem really fast and saved lives and help people get on with their day. But transformations about having people closest to the problem execute and the the also the people equation. People process technology, as they say, is kind of playing out in real time. This >>is >>the this is kind of the playbook, you know, Amazon came in said, Hey, you want to stand something up? You wired it together. The solution quickly. You're close to it. Looking back now, it's almost like, Hey, why aren't we doing this before? As you said and then you had to bring people in who weren't trained and stood them up and they were delivering the service. This >>is >>the playbook to share your thoughts on this, because this is what you're you're thinking about all the time and it actually playing out in real time. >>Well, I would definitely endorsed the idea that it's a playbook. It's I would say it's an ideal and dream playbook to build like showing up on the basketball court with all the best players in the entire league playing together magically, it is exactly that. So a lot of things have to happen quickly, but also correctly because you know you can't pull these things properly together without that. So I would say the partnership with the private sector here was fundamental. And I have to applaud the work that Accenture did particularly, I think, as Canadians, we're very proud of the fact that we needed to respond quickly. Everyone was in this, our neighbors, we knew people who were without support and Accenture's team, I mean all the way up and down across the organization was fundamental in and delivering this, but also literally putting themselves into, uh, these roles and to make sure that we would be able to respond quickly, do so. I think the playbook around the readiness for change. I was shocked into existence every night. I won't talk about quantum physics, but clearly some some high level of energy was thrown in very quickly, mobilized everybody all at once. Nobody was said. He's sitting around saying, I wonder if we have change management covered off, you know this was changed readiness at its best. And so I think for me from a learning perspective, apart from just the technology side, which is pretty fundamental if you don't have ready enough technology to deploy quickly than the best plans in the world won't work. The reality is that to mobilize an organization going forward into that level of of spontaneous driving, change, exception, acceptance and adoption is really what I would ain't for. And so our challenge Now we'll be continuing that kind of progression going forward, and we now found a way. And we certainly use the way to work with private sector in an innovative capacity and in innovative ways with brand new solutions that are truly agile and and scalable to be able to pull all of the organization. All that one's very rapidly, and I have to admit that it is going to shift permanently our planning. We had 10 year plans for our big transformation, so some of our programs are the most important in the country. In many ways. We support people about eight million Canadians a month and on the benefits payments that we deliver, and they're the most marginal needed meeting and and requires our support from senior studio, unemployed jobseekers and whatnot. So if you think about that group itself and to be able to support them clearly with their systems that we have is just unsustainable. But the new technologies are clearly going to show us the way that we had never for forecast. And I have to say I had to throw up, like in your plan. And now I'm working my way down from 10 denying date your plants going forward. And so it's exciting and nerve wracking sometimes. But then, obviously, as a change leader, our goal is to get there as quickly as possible, so the benefit of all of these solutions could make a difference in people's lives. >>What's interesting is that you can shorten that timetable but also frees you up to be focused on what's contemporary and what's needed at the time. So leverage the people on the resource is You have and take advantage of that versus having something that you're sitting on that need to be refreshed. You can always be on that bleeding edge, and this brings up the Dev ops kind of mindset agility. The lean startup glean company. You know this is a team effort between Amazon and center and SDC. It's pass, shoot, score really fast. So this isn't the new, the new reality. Any commentary from you guys on this, you know, new pass shoot score combination. Because you got speed, you got agility. You're leaner, which makes you more flexible for being contemporary and solving problems. What's your thoughts? >>Yeah, So my perspective on that is most definitely right. I think what we what we were able to show and what's. You know, what's coming out of a lot of different responses to the pandemic by government is, um, you know, perfection isn't the most important thing out of the gate. Getting something out there that's going to reassure citizens that's going to allow them to answer their questions or access benefits quickly is what's becoming more important. Obviously, security and privacy. Those things are of the utmost importance as well. But it's ability to get stuff in there, quickly, test it, change it tested again and just always be iterating on the solutions. Like I can say what we put out on April 6th within four days is the backbone of what's out there still today. But we've added, you know, we added an integrated workforce management solution from Nice, and we added some other eyes views to do outbound dialing from acquisition, things like that. So the solution has grown from that M v p. And I think that's one other thing that that's going to be a big takeaways if you're not gonna do anything. So you got the final and product out there, then it's going to be here, right? So let's go quickly and let's adapt from there. >>Then we'll talk about that dynamic cause that's about building blocks, fund foundational things and then services. It's the cloud model. >>Yeah, I mean, before the pandemic, I had lunch with Mark Schwarz, which I believe you're quite familiar with, and, you know, I spent an hour and 1/2 with it. We were talking, and he was so exciting and and energized by what the technologies could do. And I was listening to him, and I used to be the chief technology officer for the government. Can't right. And so I've seen a lot of stuff and I said, Well, that's really exciting, and I'm sure it's possible in some other places. And maybe it's some other countries where you know they didn't have infrastructure and legacy. I guess if I see him again soon, I'll have to. I apologize for not believing him enough, I think the building blocks of agile, the building blocks of sprints and MVP's I mean, they're not fundamental to the way we're going to solve our biggest various and scariest problems technologically and then from a business perspective. Service candidate itself has 18,000 employees involved in multiple channels, where the work has always been very lethargic, very difficult, arduous. You make change over years, not months, not days for sure. And so I think that that new method is not only a different way of working, it's a completely revamped way of assembly solutions, and I think the concept of engineering is probably going to be closer to what we're going to do. Um, and I have to borrow the Lego metaphor, but the building blocks are gonna be assembled. We now and working. I'm saying this in front of goal. He doesn't know that you should practice partners. We're gonna be assembling MPP maps of an entire long program, and it's gonna be iterative. It is gonna be designed, built. It will be agile as much as we can implement it. But more importantly, and punches weaken govern. It is, you know, the government is we may have changed. A lot of the government is not necessarily can count on to Most of these things approaches. But the reality is that that's where we're headed. And I will say, Oh, close. Perhaps on this on this answer. The biggest reason for doing that apart from we've proved it is the fact that the appetite inside the organization for that level of globalization, speed solution ing and being engaged rapidly you just can't take that away from an organization. Must be a piece of that. Uh, if you let them down, well, they don't remember. And frankly, they do remember now, cause they want more and it's gonna be hard. But it's a better heart. Ah, a better challenge that the one of having to do things over a decade, then to go fast and to kind of iterating quickly through the challenges and the issues and then move on very much to the next one as rapidly as possible. I think The other company, I would add, is most of this was driven by a client need, and that's not inconsequential because it mobilized everybody to comment focused. It could have been just about well, you know, we need to get people on side and solutions in place just to make our lives better. It is his providers. Yeah, it would have worked, perhaps, but it would have been different than the mobilisation It comes when the client is put in the middle. The client is the focus. And then we drive. Everyone's with that, >>you know, shared success and and successes contagious. And when you ride the new way to oh, we need a new board, right? So once you get it, it then spreads like wildfire. This is what we've been seeing. And it also translates down to the citizens because again, being contemporary numbers just look and feel. It's success in performance. So, as you know, people in business start to adopt cloud. It becomes a nice, nice, nice synergy. This is key. I'll take a year on a center. Um, the award winner. You guys did a great job. Final thoughts. >>Yeah. I mean, I think final thoughts would be happy to have the opportunity that help. And it was a It was a complete team effort and continues to be, um, it's not. It's not a bunch of Accenture technologists in the background in this, you know the commitment from everyone to get this in place. And can you continue to improvement from Benoit's team and from other folks across the government has been has been paramount to the success. So, um um, it's been a fantastic world win like experience and, uh, look forward to continuing to build on it. And it has been said, I think one thing this is done is it's created demand for speed on some of these larger transformations. So I'm looking forward to continuing to innovate with with Ben wanting. >>Well, congratulations. The most innovative connect deployment. And because you guys from Canada, I have to use the hockey reference. You get multiple people working together in a cohesive manner. It's pass, shoot, score every time. And you know it's contagious. Thank you very much for your time. And congratulations for winning the West. Thanks. Okay, this is the Cube's coverage of AWS Public Sector Partner Award show. I'm John Furrier, host of the Cube. Thanks for watching. Yeah, Yeah, >>yeah, yeah, yeah