>> Welcome back everybody. Jeff Frick here with theCUBE we're in San Francisco at the Minds and Machines conference.  Three thousand people the fifth year of the show. Really everything about GE all the players from GE are here but are really being driven by the digital and the digitization of what was a bunch of stuff and still a bunch of stuff. But now we're digitizing it all. Yeah I'm really excited to get this bill saw you what nine months ago six months ago Timeflies to the Chief Digital Officer of chief power. Welcome. Great to see you again. >> Thank you. Thanks for being here. >> Absolutely. So just first impressions of this event. Pretty amazing. >> Yes it's gotten really bad. Right and I I remember stories of people telling me that hey this is the fifth one we're doing the first one we almost had like pull people to come here. Now we are like figure out how do we get to a bigger location because this is getting mainstream. Everybody is looking at how does digital help their business. Because in the industrial sector productivity had slowed down right over the last four or five years. It had become only 25 percent of what it used to be. So the biggest lever for productivity efficiency and creating new value is through digital transformation. It's not just automation. It's about creating new value new revenue from digital assets and that's why you see the excitement across all of the industries here. What's interesting you came from the I.T. world. >> Yeah there's already kind of been the digital transformation in the I.T. world that a lot of the I.T. stuff has now been Olek been turned into electronic assets right. You have no paper but that that can't happen in the OT world right. We still got generator just for gadget engines. You still got physical things but it's still a digital transformation. So how are those things kind of meshing together. Yeah so you know having worked in software all my career in Silicon Valley you write like you think about Cambridge with a belief that every business every industry will be reimagined with software. We've seen it in retail and music and entertainment and travel but there the software our aid the world. Yes software is going to aid the world but here software is transforming the world too because the physical assets matter. But all of the machines that we make for example in power we make machines that power the world more than one third of the world's electricity comes from a machine. Right. So all of these machines generate electrons but they also generate a lot of data more than you know two terabytes of data a day from a power plant can be generated. That's more data and more consumers will generate across an entire year old social media. So this data matters we can learn a lot from this data and make these machines efficient more productive and kind of like a 360 sexiest word for some of the industrialist is no unplanned downtime right. Element breakdowns which turns into massive productivity and value for our customers. The thing I think that would surprise most people Jeff talked about it in his keynote yesterday is that there has not been the kind of the long traditional productivity gains in the industrial machines themselves and you think wow they've been around for a long time. I would think they would be pretty pretty efficient. But in fact there's still these huge inefficiency opportunities to take advantage of with software which is why there's this huge kind of value creation opportunity. Absolutely. So now also think where the cycle time of innovation. Right. All of these are mechanical machines right. We know with advances in materials science and engineering and you know brilliant manufacturing we can get more out of the physical asset but that requires a big upgrade cycle. What if we agreed to the machine with software and that's really what we did in our businesses across power right where we called them edge applications where it's about improving the flexibility of a machine or they 50 of them. All of these are modeled and algorithms and the way to think about it is all these machines in fact outside we have a giant machine that powers this entire event. And you can see the digital twin version of that machine right here on the screen. All that is is a virtual representation of that machine from the physical world where we have all the thermal models the Trancy models the heat models the performance models all connected. But now we can run the simulation in real time all of the operation data and apply algorithms to get more performance out. A great example as we just launched one of the world's most efficient most flexible gas turbine a giant turbine called H.A.. >> But with the additional software we were able to improve the efficiency it's now the Guinness World Record holder as the most efficient flexible power plant in the world. That was then a brand new unit that was developed with the benefit of software or was that really applying a Software to our approach that was a brand new unit. But overlaid with software was able to eke out more efficiency as well. But we're doing this an older power plants as well. In fact a great story is we had a customer and Italy called A2A their multi utility company in Italy. They have a power plant and Cuba also in northern Italy. They had shut it down because it was no longer competitive to operate that power plant in the modern world where there was so much renewables. Because you got to compete in a market called ancillary services meaning you need to be able to quickly ramp up power when the wind doesn't blow or the sun doesn't shine bright and shouted down right away. You can't do that with giant power plants. What we did was we completely model that's how plant and software and digital trend we show them that this actually can be competitive. So with the addition of software we were able to reopen a power plant that was mothballed and jobs were reinstated and the Paul plan is actually flexible in the open competitive ancillary services market. So all of this is possible because of software we're able to breathe new life into big giant heavy machines. So just a year in the power space I'm just tired. You know we've seen kind of in the US. No the nukes are being turned turned off. >> I grew up in Portland got trojan on the Columbia River we could take field trips with the smoke come out the cooling tower. We've got the rise of renewables are really really really going crazy. He's got this crazy dynamics and the price of oil. How's that played. How are you guys helping kind of deal with this multimodal. It's interesting here that oil and gas is still its own separate group. I'm like they got it like we want to be part of the renewables and didn't just become energy and not renewables oil and gas nuclear etc.. So you know that's a great question the industry is oil and gas has lots of other things and downstream stream and so on. And but at least across all of the electricity businesses we're coming together. And we call this the electricity Value Network. Think about where we used to think about a value chain where the Greens got generated and they traveled to the consumer. It was a linear model. And we know from Silicon Valley when digital anchors industries they all become network model. Right. Right. So we're calling this the electricity Value Network. And the interesting thing is our customers have different mix of fuel. And every part of the geography in the world in North America is still a good mix. Renewables is on the rise in California. We're going to have 50 percent power from renewables by 2030. But you still have to balance and optimize the mix of power from gas and nuclear and other sources of fuel and hydro and steam and so on. Right. And in Europe it's our abundance of renewables. >> They're struggling to integrate them into the great abundance of renewables or abundant capacity right. Renewables are growing and so they have to integrate them better in China and India for example still coal and steam is the big source of power because that's the fuel they have. They don't have as much gas. So the mix of fuel will change the world. The beauty of software as we can help optimize the mix. In the past we always talked about renewables as a silver bullet or gas silver bullet. Now we're saying software is a silver bullet regardless of what the mix of fuel we can optimize the generation of electrons and we're seeing this entire industry of electricity being transformer and digital and we call that the electricity Value Network. It's crazy interesting times so big show any big announcements happening here at the show yeah we know lots of big announcements one of the biggest ones is we're just dying day big enterprise wide digital transformation and relationship with Exelon Exelon is the largest utility in North America and they so are 10 million customers but they also generate a lot of power over 35000 megawatts of cross nuclear wind solar hydro gas and you know a year and a half ago we started a journey with them on understanding what the value of vigilance. There is such a believer and we learned a lot working with them as well and now they're deploying our Predix platform the industrial platform and APM which is our asset command and software and our food speed of operations optimization business optimization and cyber across the entire enterprise. >> So it's a big strategic agreement with them and where we're allowed to tell people is that you know a year and a half ago we were talking about what would happen if a wind farm went digital or a power plant. When you don't right now we're talking about what happens an entire utility goes digital or an entire industry of electricity goes digital and leaders like Exelon have the opportunity to create that tipping point in the industry. It does feel like this is the moment I think digital transformation of the electricity industry went real and this is it I presume not everything that they own is jii equipment no software is agnostic. Right. Right so this is really a software deal with their existing infrastructure that probably has a blend of G gear and who knows what other year that are generating. This is no different than how we in Silicon Valley would think about a enterprise software deal. It is the Enterprise subscription deal for them except it's to our cloud and our edge solutions and it's every machine right every single asset whether it's a giant gas turbine or a small little pump every machine has some sense or we will sense the rise or does the environment but all that data is being put into Predix. We will build digital twins of their entire power plants and give them more new insight and help them you know eliminate unplanned downtime and reduce operational costs citing times. We've got to get on buses to get those batteries done right till we get stored where we can we can connect them and optimize them as well. Right. Absolutely. >> I look forward to catching up six months from now and see where you guys are going out fast Bill and you and the team have grown you know from from a little bit of these kind of software skunkworks out there. Yeah I know many people are in San Ramon now. Now I think we're about a hundred people I think we're diversifying I think and it's a great challenge. So when we get the Adsit camping on the horizon. Oh and Sarah will be there. You can hit me up on Twitter again as well if you're interested in working in meaningful purposeful things like energy and the coolest things and software super. All right good. Thanks for stopping by. All right. Thank you. You have been asking us belum Jeffrey. You're watching the queue. We'll be back with our next segment after this short break.

(upbeat music) >> Welcome to this CUBE conversation. I'm Dave Nicholson and we are recapping the Citrix launchpad series. This series presents announcements on LinkedIn live on a variety of subjects, specifically cloud, security, and work. Three topics that I think all of us are keenly aware of going through the last 18 months of the pandemic. Citrix has taken time to sort of regroup and look at ways that security can be improved so that it isn't a hindrance for members of staff, but instead offers a unified integrated way of dealing with security across all of the variety of situations we find ourselves in today. Everything from a mobile device in a cafe through actually working back in the office when we get the opportunity to, to accessing information on a company issued laptop in a home office, secured networks, unsecured networks, secured browsers, unsecured browsers, the permutations are nearly endless. So Citrix has taken an interesting point of view, starting from the perspective of zero trust, meaning everything must be authenticated. They apply contextualism to their strategies. So the context and the posture of the access, the device, the location, all of those matter so that security protocols are tailored to help enhance productivity and security instead of, again, being a hindrance. So I highly recommend you go to the Citrix launchpad site dedicated to security. Two senior Citrix execs, Tim and Joe, will go through great detail on the announcements, but let's recap a little bit from an overview perspective. The first is this idea of secure private access. You combine that with secure internet access, and now you have a package that allows this contextual security posture that can change and adapt based upon varying conditions. Additionally, they have announced a partnership with Google where all of these capabilities are built into the Chrome OS. So now you have a device level native support for these protocols. They're also talking about bot management as something that is critical to security, moving forward. Bots out fishing, you want to kill them. You don't want them getting into your system, but there are some bots that are okay that have poking around in your environment. So again, go into the details with Tim and Joe. Having said that, I am delighted to have a very special guest here. Friend of theCUBE, veteran of theCUBE, author, advisor, author of the book, Cyber Minds and Tech Executive, Shira Rubinoff, is going to join us in just a moment. (upbeat music) Hello, and welcome to this special CUBE conversation. I'm Dave Nicholson, and we are recapping the Citrix launchpad series with a focus on the topic of security. Now, whenever we're going to talk security on theCUBE, we have a CUBE veteran and smartest person on cybersecurity that we know, Shira Rubinoff. She's a cybersecurity executive author and advisor, specifically author of the excellent book on the subject, Cyber Minds. Shira, welcome back to theCUBE. >> Thank you. Pleasure to be here. >> How are you today? >> Doing great, always great to be on theCUBE and talk to you folks and certainly be part of something from Citrix. >> Well, that might be the last pleasant thing that we say, because we are surrounded by security threats. So are you ready to get serious? >> Oh, always with a smile, serious with a smile. >> So, one kind of overriding question that a lot of people have now, if you're an IT executive, you've experienced a complete change in the world from so many different angles, but how has the pandemic changed the way you think of security? What are the dynamics at play, things that are different now that we couldn't have anticipated maybe two or three years ago? >> Interesting question. Certainly, if we look at the scope and the ecosystem of the way that organizations operated, it was pretty much in the high 90% of people being in the office with just the few percentage being working from home. And that had to shift literally overnight to literally the flip side of it, having the multitude of the organization work from home, work remotely, and maybe the few people that had to be in the office were there. So all of a sudden organizations were left with this, how do we secure down our organization? How do we keep our employees safe? How do we keep our organization safe? How do we connect to the outside world? What do we do to maintain the proper cyber? That's call it cyber hygiene within an organization. And that's a topic that I talk about quite frequently. When you look at cybersecurity as a whole, we look at the cyber posture of an organization. We also have to break it down and say, what does an organization need to do to be fully cyber secure? So of course, the ongoing training and that had to shift as well. We have now training for the organization and employees, but also think about the consumers and who else is interacting with organizations. We have to switch how that is done. And that has to be ongoing in the global awareness, the cybersecurity of course is at top of mind. And then that would lead to us to zero trust. Zero trust is a massive, massive piece of cybersecurity need for organizations. We think about it as who needs the data is king. Whoever has the data, they rule the world. They own the organization, they do what they need to do. Zero trust, limited access, knowledge of who gets in, why they get in, the need to get in, and the need for that within organization. So zero trust is a very key component and Citrix is very focused on as well. We talk about updated security and patching and all that has to happen, think about remotely. So not only are we thinking about all these topics, we have to think about them going at warp speed with people that might be working remote, who also have other things they have to take care of. Maybe they're taking care of elderly parents, maybe they're having to watch their kids on zoom, making sure they're staying on zoom, and all sorts of things with school, and other maybe roommates who are working for other organizations, not having important information in the backgrounds of their zoom while they're having these important conversations with organizations. But also think about the multiple devices people are using. They may have an area that's set up properly in order to do their work, but then again, they have to be in another room at the same time. Oh, let me just grab my device. So the whole area of the multiple devices, the warp speed of working and not, let's call this pause. And this is one of the key elements that I would tell all organizations to stop and pause, to think about what you're doing before you do it. Give the headaches, but that was not interplayed when the height of the pandemic. The height of the pandemic, we were worried about what's going on? Need knowledge of information, where we're getting this information, downloading it, clicking on links. Then we're working at the same time, taking care of people. So all these things are happening simultaneously, leaving these open vectors for the tax surface to be that much more heightened for the bad actors to get in. >> So, you advise some of the largest companies in the world on this subject, and obviously you're not going to reveal any names or specifics, but as a general overall view from your perspective, how are we doing right now? Is the average large organization now sort of back on cruise control, having figured everything out for this new reality? On a scale of 1 to 10, how well are we executing against all of these changes? >> That's a great question. Let me talk about the global whole. I think organizations are actually doing really well. I think there was a quick ramp up to figure out how to get it done, but because of also the shift of sharing of information that some of these largest companies across the world, they came together to share information with bad actors, to share information about the tax, to share information about what to do if something happens, who's out there and buying together almost like a whole. So it wasn't each finger on its own. It's a hand as a whole looking at it from a stronger perspective. So I think that shift coupled with the fact of the knowledge and understanding of what companies needed to do in terms of locking down the organization, but also allowing and helping their employees, empowering them to get their work done, but get it done in a secure safe fashion. And I believe now, obviously, we all know, they obviously, but the ransomware attacks are now prevalent and they're becoming even more intense with the rise of 5G, a way that attacks could happen, the warp speed. We're now having to understand that being reactive is not enough, being proactive is something that is wonderful to see organizations are doing as well. It used to be okay, let's be reactive. If something happens, what do we do? Let's have a plan in place. But that's not good enough and we've seen that happen because these attacks are coming a warp speed. So the proactivity of these organizations that they've taken is applaudable in general. I can't talk for all the companies, but the ones that I've been consulting to and have interactions with, I'm pleasantly surprised and not surprised as well, that the way that they've taken their cyber posture so seriously, and where they focus in, not only on the organization as a whole, but their employees as individuals, what their needs are and being able to give them what they need to do their jobs well. >> Yeah, that makes sense. You can almost think of it like cybersecurity is a team sport and to the extent that all of that proactive work that an organization can do can be absolutely undermined if we don't do our parts as endpoints, as endpoint people. And when someone reads Cyber Minds, I think there's an undercurrent that I definitely sensed. And then when I looked more closely into your background, I realized that, yes, in fact, you do have a background in psychology. I want to shift to a question along that line, if you don't mind. Thinking about the psychology of people who have lived through the pandemic, this concept of our personal hygiene and our personal security has been in the forefront of our mind. As you leave the house, and there's hand sanitizer and masks and maybe gloves, we're very, very aware of this. How has that affected us from a cybersecurity team sport perspective? Has that made us better players on the field? What are your thoughts in that regard? >> I actually love that question. As we saw the pandemic heightened, everyone became hyper aware of their own personal, what's called cleanliness. And in terms of where they are, what they're doing, if they're masking, if they're putting on gloves, the sanitizers are everywhere, six feet apart. Everybody's thinking about that. It's a forefront. It became a way of life. And if you then do you shift that and you're saying, okay, let's look at the technology or the cybersecurity part of it, your own personal safety, your own personal cybersecurity. I think we failed a lot in that area. I think because of the fact, if you think about the human psychology and the pieces that people needed to know information, everybody was hungry for the latest and greatest information. What's going on? What are the stats? How many people? Just terrible, terrible pandemic with so many people getting sick. So many people dying and wanting to know, what is going on? what are the latest rule sets? What can I do? What else can I do to protect myself? What is my business doing? So we also had bad actors sending out the phishing attacks, heightened tremendously. There is information being sent out, click here for the latest here. This is Dr. Fauchi, his latest report. Everything going out there was not necessarily to help us, but to hurt us. And because of people's human psychology of thinking, I need to protect myself, so I need the information. The stop and pause is, is this the right information? Is this a safe place to go? But then there's also the other flip side of, if I'm not interacting, I'm not there. Think about the different generational people we have going on. Gen Z, millennials, all sorts of it. Everybody's all over social media. And everybody needs to and wants to have a presence there, certainly in this world. So putting out lots of information and being present was very critical 'cause people weren't in-person anymore. So people were interacting online, whether it being on social, whether it being telling people where they're going, what they're doing, what they're eating, what their favorite animal is, all sorts of things that they were doing. But they were giving over personal information that made have be utilized as passwords or ways to get to know somebody, to either do a spear phishing attack or any types of attacks to gather information to hurt, not just a personal to steal money or to steal someone's identity or to come in and hurt the company, but information was everywhere. So we were taking care of our personal cleanliness, but our cyber hygiene with our psychologies aspect of cybersecurity itself, I think took a big dive. And I think that people started becoming aware as these attack surfaces grew. There were also different types of attacks that were happening where phone calls were coming in and saying, somebody is breaking into your bank account. Just verify yourself, give me the last four digits. I need to know who you are. So playing on the human psyche of fear, somebody is trying to get you nervous. So what are you going to do? You're going to act quickly without thinking. Or all sorts of, I think we were talking earlier about extended warranties for different things. That also grew extensively, but how did they do that? They were gathering information, personal information to give you something you want. So if you're playing again on the human psychology of people, when people get what they want, they're more likely to give over something they may not give to somebody else anyway. And one of my biggest example or a strong example is back in the day with Candy Crush. If you think about that game, before you sign up for that game, you literally have to give over your kidney. You're giving over access to your camera, to your contacts. If you look back at the permissions you are giving, it's really unbelievable that everybody was clicking yes, because they wanted to play a game. So take that example and transfer that into real life. We were doing the same thing. So the importance of brushing up on that personal cyber hygiene and really understanding what people needed to do to heighten their own security themselves, less sharing on social, not giving over information that they shouldn't, not allowing a trusted source who isn't really a trusted source into it. Having strong zero trust, not just organizations, but for yourself was very important. >> Yeah now, did we, Chuck. Chuck's my producer. Did we get Shira's social security number and her date of birth? Shira, can you give us that? >> Sure, it's 555-55-5555. >> Excellent Aha, phishing attack. >> There you go, go for it. (laughs) >> So you think there could be a little bit of security fatigue that might come into play when we're thinking of living up to our responsibilities as those end points? >> I think there was just fatigue in general and people were tired of being locked in the house. People were tired of having everybody under the same roof all the time, 24/7. Trying to get work done, trying to get school done, taking care of people, what they needed to do, having groceries delivered, going into groceries, all the thoughts that they had to do that was just a way of life before that we all took for granted during the pandemic. It was just a whole shift. People were just antsy, jumpy. We needed to connect and we need to connect in any way we could. So all these open vectors became a problem that ended up hurting us rather than helping us. So this has been something that was a big mind shift as a pandemic continued. People started realizing what was going on and organizations took a good stand on educating the population and telling them, look, these are the things that are happening. This is what we need to do. Certainly a lot of the companies I'm working with did such a great job with that. Giving their employees the wherewithal of wanting to connect, but doing in a secure manner. Giving them the tools of what they needed to do personal, only also in their personal lives, not just for their work lives. So that was helpful too. And as we're coming out of it, hopefully continue to come completely out of it, we'll see the shift back into, let's take that stop and pause. Let's think what we're doing. >> Yeah, well, we are all looking back to whatever resemblance of normal we can get to. Shira, I can spend hours picking your brain on a variety of subjects. Unfortunately, we are coming to the end of our time together. Do you promise to come back? >> Certainly, a big fan of theCUBE. >> Well, fantastic. Shira Rubinoff, thank you so much for your time. This is Dave Nicholson with a very special CUBE conversation, signing out. Thanks for watching. >> Shira: Thank you too. (gentle music)

>> Announcer: From around the globe, It's theCUBE. Presenting Enterprise Digital Resilience on Hybrid and Multicloud. Brought to you by Io-Tahoe. >> Okay, Let's now get into the next segment where we'll explore data automation but from the angle of digital resilience within and as a service consumption model. We're now joined by Yusef Khan, who heads data services for Io-Tahoe and Suresh Kanniappan who's the vice president and head of US sales at Happiest Minds. Gents, welcome to the program, great to have you in theCUBE. >> Thank you, David. >> Suresh, you guys talk about at Happiest Minds this notion of born digital, foreign agile, I like that but talk about your mission at the company. >> Sure, far in 2011, Happiest minds is a born digital, born agile company. The reason is that, we are focused on customers. Our customer centric approach and delivering digital and seamless solutions, have helped us be in the race along with the Tier 1 providers. Our mission, Happiest People, Happiest Customers is focused to enable customer happiness through people happiness. We have been ranked among the top 25 ID services company in the great places to work in service. Our Glassdoor ratings, of four dot one against the rating of five, is among the top in the Indian ID services company, that shows the mission and the culture what we have built on the values, right? Is sharing, mindful, integrity, learning and social responsibilities, are the core values of our company. And that's where the entire culture of the company has been built. >> That's great, sounds like a happy place to be. Now Yusef, you had updated services for Io-Tahoe, we've talked in the past year, of course you're at London. What's your day to day focus with customers and partners? What are you focused on? >> Well David, my team worked daily with customers and partners to help them better understand their data, improve their data quality, their data governance, and help them make that data more accessible in a self-service kind of way to the stakeholders within those businesses. And this is a key part of digital resilience that we allow. We'll come on to talk about a bit later. >> You're right, I mean that self-service theme is something that we're going to really accelerate this decade Yusef. And so, but I wonder before we get into that, maybe you could talk about the nature of the partnership with Happiest Minds, why do you guys choose to work closely together? >> Very good question. We see Io-Tahoe and Happiest Minds as a great mutual fit. As Suresh said, Happiest Minds are a very agile organization. I think that's one of the key things that attracts the customers. And Io-Tahoe is all about automation. We're using machine learning algorithms to make data discovery, data cataloging, understanding data redundancy much easier and we're enabling customers and partners to do it much more quickly. So when you combine our emphasis on automation, with the emphasis on agility that Happiest Minds have. That's a really nice combination, works very well together, very powerful. I think the other things that are key, both businesses as Suresh have said, are really innovative, digital native type companies. Very focused on newer technologies, the cloud, et cetera. And then finally I think they're both challenge brands and Happiest Minds have a really positive, fresh, ethical approach to people and customers that really resonates with us at Io-Tahoe too. >> That's great, thank you for that. Suresh, let's get into the whole notion of digital resilience. I want to sort of set it up with what I see and maybe you can comment. Being prior to the pandemic, a lot of customers that kind of equated disaster recovery with their business continuance or business resilience strategy and that's changed almost overnight. How have you seen your clients respond to that? What I sometimes call the forced match to become a digital business and maybe you could talk about some of the challenges that they've faced along the way. >> Absolutely, So especially during this pandemic times when you see Dave, customers have been having tough times managing their business. So Happiest Minds being a digital resilient company, we were able to react much faster in the industry apart from the other services company. So, one of the key things is, the organizations are trying to adapt onto the digital technologies, right? There has been lot of data which has to be managed by these customers, and there've been a lot of threats and risk which has to be managed by the CIOs. So Happiest Minds Digital Resilient Technology, right? We're bringing the data complaints as a service. We were able to manage the resilience much ahead of other competitors in the market. We were able to bring in our business continuity processes from day one, where we were able to deliver our services without any interruption to the services what we are delivering to our customers. So that is where the digital, the resilience with business continuity process enabled was very helpful for us to enable our customers continue their business without any interruptions during pandemics. >> So, I mean some of the challenges that customers tell me if I may obviously had to figure out how to get laptops to remote workers, that whole remote, work from home pivot, figure out how to secure the end points, and those were kind of looking back they're kind of table stakes. And it sounds like, you got, I mean digital business means, a data business, putting data at the core, I like to say it. But so, I wonder if you could talk a little bit more about, maybe the philosophy you have toward digital resilience and the specific approach you take with clients. >> Absolutely Dave, see in any organization, data becomes the key. And thus for the first step, is to identify the critical data, right? So, this is a six step process plot we follow in Happiest Minds. First of all, we take stock of the current state, though the customers think that they have a clear visibility of their data. However, we do more often assessment from an external point of view and see how critical their data is. Then we help the customers to strategize that, right? The most important thing is to identify the most important critical asset. Data being the most critical asset for any organization, identification of the data are key for the customers. Then we help in building a viable operating model to ensure these identified critical assets are secure and monitored duly so that they are consumed well as well as protected from external threats. Then as a fourth step, now we try to bring in awareness to the people. We train them, at all levels in the organization. That is a key for people to understand the importance of the digital lessons. And then, as a fifth step, we work as a backup plan. In terms of bringing in a very comprehensive and a wholistic distinct approach on people, process, as well as in technology, to see how the organization can withstand during a crisis time. And finally, we do a continuous governance of these data. Which is a key, right? It is not just a one-step process. We set up the environment, we do the initial analysis, and set up the strategy and continuously govern these data to ensure that they are not only not managed well, secure, as well as they also have to meet the compliance requirements of the organizations, right? That is where we help organizations to secure and meet the regulations of the organizations as per the privacy laws. So this is a constant process. It's not a one time effort, we do a constant process because every organization grows towards their digital journey, and they have to face all these as part of the evolving environment on digital journey. And that's where they should be kept ready in terms of recovering, rebounding and moving forward if things goes wrong. >> So, let's stick on that for a minute and then I want to bring Yusef into the conversation. So, you mentioned compliance and governance. When you're in digital business here as you say you're a data business, so that brings up issues, data sovereignty, there's governance, there's compliance, there's things like right to be forgotten, there's data privacy, so many things. These were often kind of afterthoughts for businesses that bolted on, if you will. I know a lot of executives are very much concerned that these are built in and it's not a one-shot deal. So, do you have solutions around compliance and governance? Can you deliver that as a service? Maybe you could talk about some of the specifics there. >> Sure, we offer multiple services to our customers on digital residents. And one of the key service is the data compliance as a service. Here, we help organizations to map the key data against the data compliance requirements. Some of the features includes in terms of the continuous discovery of data, right? Because organizations keep adding on data when they move more digital. And helping and understanding the actual data in terms of the resilience of data, it could be an heterogeneous data sources, It could be on data basis, or it could be even on the data lakes, or it could be even on on-prem or on the cloud environment. So, identifying the data across the various heterogeneous environment is a very key feature of our solution. Once we identify and classify these sensitive data, the data privacy regulations and the prevalent laws have to be mapped based on the business rules. So we define those rules and help map those data so that organizations know how critical their digital assets are. Then we work on a continuous monitoring of data for anomalies. Because that's one of the key features of the solution, which needs to be implemented on the day-to-day operational basis. So, we help in monitoring those anomalies of data, for data quality management on an ongoing basis. And finally, we also bring in the automated data governance where we can manage the sensitive data policies and their data relationships in terms of mapping and manage that business rules. And we drive limitations and also suggest appropriate actions to the customers to take on those specific data assets. >> Great, thank you. Yusef thanks for being patient. I want to bring in Io-Tahoe to the discussion and understand where your customers and Happiest Minds can leverage your data automation capability that you and I have talked about in the past. And I mean it'd be great if you had an example as well, but maybe you could pick it up from there. >> Sure, I mean at a high level as Suresh articulated really, Io-Tahoe delivers business agility. So that's by accelerating the times operationalized data, automating, putting in place controls, and also helping put in place digital resilience. I mean, if we stepped back a little bit in time, traditional resilience in relation to data, often meant manually making multiple copies of the same data. So you'd have a DBA, they would copy the data to various different places, and then business users would access it in those functional silos. And of course, what happened was you ended up with lots of different copies of the same data around the enterprise. Very inefficient, and of course ultimately increases your risk profile, your risk of a data breach, It's very hard to know where everything is. And I realized that expression you used David, the idea of the forced match to digital. So, with enterprises that are going on this forced match, what they're finding is, they don't have a single version of the truth. And almost nobody has an accurate view of where their critical data is. Then you have containers, and with containers that enables a big leap forward. So you can break applications down into microservices, updates are available via APIs, and so you don't have the same need to to build and to manage multiple copies of the data. So, you have an opportunity to just have a single version of a truth. Then your challenge is, how do you deal with these large legacy data states that Suresh has been referring to? Where you have to consolidate. And that's really where Io-Tahoe comes in. We massively accelerate that process of putting in a single version of truth into place. So by automatically discovering the data, discovering what's duplicate, what's redundant, that means you can consolidate it down to a single trusted version, much more quickly. We've seen many customers who've tried to do this manually and it's literally taken years using manual methods to cover even a small percentage of their IT estates. With Io-Tahoe you can do it really very quickly and you can have tangible results within weeks and months. And then you can apply controls to the data based on context. So, who's the user? What's the content? What's the use case? Things like data quality validations or access permissions, and then once you've done that, your applications and your enterprise are much more secure, much more resilient as a result. You've got to do these things whilst retaining agility though. So, coming full circle, this is where the partnership with Happiest Minds really comes in as well. You've got to be agile, you've got to have controls and you've got to drive towards the business outcomes. And it's doing those three things together, we really deliver for the customer. >> Thank you, Yusef. I mean you and I in previous episodes we've looked in detail at the business case you were just talking about the manual labor involved. We know that you can't scale, but also there's that compression of time to get to the next step in terms of ultimately getting to the outcome and we've to a number of customers in theCUBE and the conclusion is, it's really consistent that if you can accelerate the time to value, that's the key driver, reducing complexity, automating and getting to insights faster. That's where you see telephone numbers in terms of business impact. So my question is, where should customers start? I mean how can they take advantage of some of these opportunities that we've discussed today? >> Well, we've tried to make that easy for customers. So, with Io-Tahoe and Happiest Minds you can very quickly do what we call a data health check. And this is a two to three week process to really quickly start to understand and deliver value from your data. So, Io-Tahoe deploys into the customer environment, data doesn't go anywhere, we would look at a few data sources, and a sample of data and we can very rapidly demonstrate how data discovery, data cataloging and understanding duplicate data or redundant data can be done, using machine learning, and how those problems can be solved. And so what we tend to find is that we can very quickly as I said in a matter of a few weeks, show a customer how they can get to a more resilient outcome and then how they can scale that up, take it into production, and then really understand their data state better, and build resilience into the enterprise. >> Excellent, there you have it. We'll leave it right there guys. Great conversation. Thanks so much for coming into the program. Best of luck to you in the partnership, be well. >> Thank you David, Suresh. >> Thank you Yusef. >> And thank you for watching everybody. This is Dave Vellante for theCUBE and our ongoing series on Data Automation with Io-Tahoe. (soft upbeat music)

>>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

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hey welcome back all righty Jeff Rick here with the cube we are in our Palo Alto studios today and as we continue to go through week after week after week of the kovat crisis the kovat situation you know we've been focusing on leadership and we've been reaching out to the community to get their take on you know what's happening best practices things that they can share to help and to share knowledge with the rest of the community and we're really excited to have our next guest Rebecca Knight you know her as a guest host on the cube she's actually been a freelance journalist for decades and writes for all the top pubs it's how we met her in the first first place doing some working at mighty so Rebecca first off great to see you it's been too long we were supposed to be together this week but situation kind of changed the schedule a little bit indeed it's so it's so good to see your face Jeff and it's so fun to be working with the cube gang again even though we are we are many miles apart right now we should all be together but but I'm really happy to be you're happy to be talking to you great well I am too and let's let's jump into it because you know you've been writing about leadership but really why I wanted to reach out with you is instead of you kind of co-hosting our guests really get get your perspective on things because you've been writing about leadership for a very long time so now that we're I don't know six weeks into this thing what are you writing about what you know it has it has the the topics kind of shifted you know over the last several weeks what's kind of top of mind what do you publish in this week absolutely the topics have shifted in the sense that there is only one topic and that is hope at 19 and that is how our managers coping with this with this health crisis this pandemic that is all over the world of course and a huge part of our workplace right now managers are just dealing with this unprecedented event industry and trying to be a sense of strength for their colleagues and for their direct report at a time where they themselves don't really know what the future holds none of us know what the future holds and so this is a very our managers right now and so that's that's a lot of what I'm doing for her for Harvard Business trivia now there's so many pieces to that one you know we've been talking a lot about it as being kind of this light switch digital transformation moment because even if you had planned and people have been planning and things have been slowly moving whether it be working from home for jobs or remote education in higher education or a lot of these things they were kind of you know moving along and all sudden boom full stop ready set go everyone has to stay home so that there wasn't really a plan a rollout plan and it's quite a challenge and the other thing is not only for you the individual who's going through this but their significant other or spouses also home the kids are also home and again nobody really got an opportunity to plan and try to think some of these things through so it's it's it's not only just working from home but now it says pandemic that adds all these extra layers of complexity and to you to your point uncertainty which is always the hardest thing to deal with you know Jeff I've actually been working from home for over a decade now I work for the Financial Times for about four ten years and that and I even and then I was Boston corresponding for the FT working from home I was following a bunch of writers on trip Twitter people are writing and saying working from home is the worst and I'm constantly please like concentrate this I will never want to work from home and then all these writers were chiming if they hold up theirs working from home and then there's working from home during a global pandemic two totally different things um but you're absolutely right this is a time where our families are underfoot we're trying to homeschool our children we are quarantined with our spouse trying to make our marriages work and also trying to do the job that we're being paid to do if we're lucky enough they'll be employed or still have assignment I in the hoppers though you're right this is this is a very this is not necessarily the test of remote work and remote learning that I think we all deserve and we will some day have and we're showing this is obviously an experiment and in some ways that's showing that it can work in ways but there is also this is this isn't exact this is more oh hey you have eight days to get all your employees online right now or eight days to roll out your curriculum so this is not quite exactly what we'd all had in my remember talking about the future of online education or the digital organization but but it certainly interested the watch all happen so it's funny as part of this we had Martin make us on and he has been running distributed teams for decades and it was really funny his take on it which was that it's so much easier to fake it at the office right and and to many people we had Amy Hayworth on from Citrix and in a blog that she referenced you know eventually people will start judging people based on outcome versus behavior and activities and it just it strikes me that in 2020 you know is this what it's taken to get people to actually judge people by their output and I think you know Martin's other take was that when you work from home all you have is your output you know you don't have kind of looking busy or saying hi to the boss or the car looks really great today you know you only have your output in his take was it's actually a much easier way to decide who's doing the job and who's not doing the job yeah you know I'm of two minds with that because I think that there is so much to be said for the teamwork there so I mean you may not be the person who is definitely always pedal to the metal getting every single thing done checking all the boxes you you know I mean obviously you have to be sort of have a baseline of productivity and engagement but there's also just you're someone that other people like to work with you're someone who offers good ideas who can be a really good sounding board who just will have those moments of creativity that are really important for a theme to be to succeed and to get to get to the finish line and I can get again I'm not saying the people who are just have just been coasting oh yeah this is it for you but I'm just saying that there's a lot of different personalities and a lot of skills that then go into making a great high-functioning team it takes all type and so and so I think that we are missing that we are missing the camaraderie the collegiality of the watercooler chat and and that where teams do a lot of problem solving is is sort of that informal conversation that right now a lot of us are missing because we've all had way too much zoom and no one wants to just sort of shoot the breeze on zoom with anyone so what so what are you telling people so unfortunately you know this is not how we would have planned it and we would have probably transitioned it a little bit smoother matter but here we are and were actually now five six weeks into it and the I think the the Monday was I think March 16th was the big day here in the Bay Area when it all kind of got got official so what are some things that you're sharing with with leaders and managers you know some specific things they can do some specific tasks that they can do to help get through this better the first thing I would say and this is what I'm hearing from the experts that I'm talking to the people who really study crisis management is first of all it's deal yourself this is this is a challenge of a lifetime and you are leading through something that is hard and you need to understand that and and first of all don't be too hard on yourself because this is this is this is really difficult this is what they're going to be writing case studies about in business schools for decades for to come these are really big management challenges steal yourself be ready for the challenge make sure you are taking care of yourself getting enough sleep getting rest on the weekends time with your family and friends do exercise eat right don't just snack on Cheetos all day long make sure you are taking care of yourself in terms of interacting with your employees and your team obviously like I just said everyone everyone cannot everyone's zum fatigue is real um but at the same time you do need to make time to talk to your team and say hey how are you how are things make sure that people are you wait no baby we need to make sure that you have your your finger on the pulse of your team and make sure everyone's mental health it is they okay so yeah empathy humility it share with your team problems that your the your face singing yourself I mean obviously they should not be the repository for all of your fears and insecurities and worries about whoa I don't know if I got a turn am I gonna have a job next week but um but at the same time II talked about the challenges you're facing too your team needs to know that you aren't a superhuman you know you you're a human too you're going through this just like they are right that's what's such a weird thing about it - you know having been through a couple of events like the earthquake or Mount st. Helens blowing up you know the people that were into that area when something like that goes down have a common story right where were you in the earthquake where are you and mount st. Helens blew up but now this is a global thing where everyone will have a story where are you in March 20 20 so the fact that we're all going through it together and there's so many stories and impacts you know the more people you talk to you know the layers of The Onion's just keep on peeling - more and more and more impact but I'm curious to get your take on kind of how you see once we do get out of this because whether it's 12 months or 18 months or 24 months to get to a vaccine you know now it seems like forever and the grand scheme of things it's going to be a relatively short period of window but but over that time you know behaviors become habits and I'm just curious to get your take as to when it's okay to go back to work whenever that is I don't see it going back the way that it was because who's gonna want to sit on highway 101 for two hours every morning once you've figured out a pretty good routine and a pretty good workflow without doing that how do you see it kind of shaken out so I couldn't agree more and this is a night like I said I've worked from home for many many years and so I do think that people this is dispelling the myth that you need to work where you live you have a lot more agency and a lot more freedom to get your job done anywhere you want to live and if that's in a city because I mean God willing sports will come back and pewter will come back music and all the reasons we love living in cities but will one day be able to do that again but if you like living near the mountains or near the ocean you can do that and get your job done so I think we're I think you're absolutely right about that we're going to see many more people making a decision about you know this is the life I want to live and I can still might do my job and yet people still like being around other people I mean I think that's why we're all going a little stir-crazy right now is because we just we missed other people we miss interacting and so I think that we will have to think about some ways to create different kinds of offices and crap we work type things but I think they could just be different offices all over and they can be in the suburbs they can be in the mountains and it could just be a place where people come together and sometimes they're in the same industry field sometimes may be the same company but I think that they don't even necessarily need to be that way I think that some people will want to work from home and I think other people will want to go someplace even if it's not what we think of as the typical American office right but I even think in and I used to think this before right as you know I ride my bikes and do all my little eToys but you know even if people didn't commute one day a week or didn't commute one day every two weeks or two days a week you know the impact on the infrastructure to me some of these second-order effects is you know looking at empty freeways and empty streets demonstrate that we actually have a lot of infrastructure it just gets overwhelmed when everybody's on it at the same time so just the whole concept of going in the same time every day of course if you're in construction or you're in trades and you got a truck full of gear that you have to take that's one thing but for so many people now that our informational workers and they're just working on a laptop whether it be home that we work or we're at the office you know even shifting a couple of days a week I think has just a huge impact on infrastructure or quality of life you know the environment in terms of pollution gas consumption and on and on and on so yeah I don't think it will go a hundred percent one way or the other but I certainly don't think it'll go 100 percent back to you know going in the office every day from 8:00 to 5:00 I I couldn't agree more and just be the idea of the quality of life I mean you know I'm I have two children 9 and 12 and they are doing their school work from home and they're they're doing all right they're hanging in my older one in particular I say that she's sort of this mix between a graduate student and a young MBA because she's got her little devices already zooming with her science teacher than play rehearsal there but but um you know why I think that the slowing down has actually been kind of good for them too because they're busy kids and they have a lot going on and actually having family dinners having board games watching family movies going for family hikes in the weekends that has been really good but in her forever I mean obviously we're also indebted and grateful to the frontline workers and and we we also see there is a lot of loss around us people losing loved ones to this horrible disease and then losing livelihood but I think and then we are seeing a few silver linings than this too so I think sometimes our quality of life it has for some people this has been quarantines getting a little old but at the same time I think that there has been some bright for a lot of for a lot of people yeah I think I think you're right in again it's a horrible human toll people getting sick and dying and in the economic toll is gargantuan especially for people with no safety net and are in industries it's just don't exist in right now like travel and leisure and and and and things that are in the business of bringing people together when you can't bring people together but just final question before I let you go is is really on higher education so it's one thing with the kids and in k-12 and you know how sophisticated are an ability to learn online but I'm I'm really more interested to get your take on higher education because you know you've already got to kind of this scale back in terms of the number of physical classes that people attend when they're and when they're an undergrad and the actual amount of time that they spend you know in an lecture I mean this is this now knocking that right off of the table and I'm just really curious to get your take on higher education with distributed learning because it's it's something that's been talked about for a long time I think there's been a lot of resistance but again this light switch moment and if it goes on for into the next school year what's what what's that going to do to the kind in higher education and the stance of of how much infrastructure they actually need to support educating these kids well I am a Wesleyan grad and the president of Wesleyan was quoted in the New York Times this weekend talking about that this very topic thing that this has really shown us the value of a residential or not necessarily for year but residential education where people are together and they are able to Bure be creative have fierce debate in the classroom that is just frankly not possible with remote learning or at least not to the same degree since the same extent and the kind of accessibility you have with professors particularly at a small liberal arts school like the one that I went through I think that Jeff a lot of a lot of colleges are not going to be able to survive this because they're just they are so different tuition dependent and a lot of kids are going to defer if they if they say you know if I can't be at college in the fall I'm gonna take a year off and go to Community College or I'm going to you know do something else take a gap year and then reassess my options once this health crisis passes and I think that for a lot of colleges that's just that's just not tenable for them and for their for their operations so I'm afraid that a lot of businesses and a lot of colleges their point of closed yeah it's just it's just crazy the the impact and just showing you know as you said we are social beings we like to be together and when you when you stop people from being together it makes you really realize how often we are together whether it's you know weddings and funerals and and bar mitzvahs and and those kind of things in church and family stuff or whether it's business things conventions concerts sporting events means so many things street fairs you know are really about bringing people together and we do like to be together so this too will pass and and and hopefully you know the Warriors in this battle thankfully are super smart you know we're hopefully using a lot of modern compute that we didn't have in the past thankfully we have things like like the Internet and zoom that you and I can talk from 3,000 miles away so I'm glad you're hopeful I'm hopeful we'll get through it and and then we can get together on a set and do some interviews together I can't wait exactly all right Rebecca well thanks for checking in be safe look forward to seeing you in person and and until then have a great I guess May we're into May Mother's Day coming up so happy Mother's Day a few days early thank you very much Jeff it was a pleasure working with you again all right we'll take care she's Rebecca I'm Jeff you are watching the cube thanks for checking in wolf see you next time [Music]

>> From Times Square, in the heart of New York City, it's theCUBE. Covering IMAGINE 2018. Brought to you by Automation Anywhere. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in Manhattan, New York City, at Automation Anywhere's IMAGINE 2018. We've never been to this show. Pretty interesting, about 1,100 people talking about Bots, but it's really more than Bots. It's really how do we use digital employees, digital programs, to help people be more efficient, and take advantage of a lot of the opportunities as well as the challenges that we're facing as we keep innovating, I'm really excited to have our next guest. Jeffrey Immelt, the former chairman and CEO of GE, great to see you Jeff. >> Good to see you. >> Absolutely, last I saw you I think, was at Minds and Machines, and we're huge fans, >> A couple years ago, yep. >> Beth Comstock, I loved Bill Ruh, so you know, what a fantastic team. >> A great team. >> But here you are talking about Bots, and it's interesting because at GE you guys have been involved in big industrial equipment, as well as a huge software business, so you really figured out that you've gotta have software and people to really work with these machines. >> So you know Jeff, I really am a big believer that productivity is the key, and that we, we're seeing a bow wave of technology that's really gonna impact the workplace in a meaningful way. The reason why I like RPA, what we call Bots-- >> Right, RPA. >> Is because it can happen so quickly. It can happen across the organization. It has great productivity associated with it. So I kinda view RPA as being really one of the uh, let's say early wave technologies in terms of how to drive more automation and productivity in the workplace. >> That's funny, because people ask me they're like, what's the deal with some of these stock evaluations, is it real, and think back to the ERP days right, ERP unlocked this huge amount of inefficiency. That was a long, long time ago, and yet we still continue to find these huge buckets of inefficiency over and over. >> I think it's, I mean I think to your point, the early days of IT, really if you look at ERP manufacturing systems, even CRM. They were really more around governance. They were kind of connecting big enterprises. But they really weren't driving the kind of decision support, automation, AI, that companies really need to drive productivity. And I think the next wave of tools will operate inside that envelope. You know, ultimately these will all merge. But I think these are gonna get productivity much quicker than an ERP system or an MES system did. Which are really, at the end of the day, driven by CFOs to drive compliance more than operating people to drive productivity. >> Right, but what's driving this as we've seen over and over, that consumerization of IT, not only in terms of the expected behavior of applications, you know you want everything to act like Amazon, you want everything to act like Google. But also, in terms of expectations of feedback, expectations of performance. Now people can directly connect with the customer, with companies like they never could before, and the customers, and the companies can direct with their customer directly. Where before you had channels, you had a lot of distribution steps in between. Those things are kind of breaking down. >> I think that's for sure. I mean I think that's sure. I would say beyond that is the ability to empower employees more with some of these tools so you know, an employee used to have to go to the CIO with a work ticket, hey here's what I need. You know these Bots grow virally inside organizations. They're easy to implement. They're easy to see an impact very quickly. So I just think the tools are becoming more facile. It's no longer kind of a hierarchical IT-driven technology base. It's more of a grounds-up technology base, and I think it's gonna drive more speed and productivity inside companies. >> Right, so really it's kind of, there's always a discussion of are the machines gonna take our jobs, or are they? But really there's-- >> Jeff, I'm not that smart really I mean-- >> Well, but it's funny because they're not right? I mean, everyone's got requisitions out like crazy, we need the machines to help us do the jobs. >> Nobody has, nobody has easy jobs. The fact of the matter is, nobody has easy jobs. You know, a company like GE would have 300 ERP systems right? Because of acquisitions and things like that. And the METs not a complexity, manual journal entries, things like that. So to a certain extent these, this automation is really helping people do their jobs better. >> Better. >> More than thinking about you know, where does it all go some day. So I think, I think we're much better off as an economy getting these tools out there, getting people experience with them and, and uh, seeing what happens next. >> Right, it's funny they just showed the Bot store in the keynote before we sat down, and when you look closely, a lot of them look like relatively simple processes. But the problem is, they're relatively simple, but they take up a lot of time, and they're not that automated, most of them. >> One of my favorites Jeff, is doing a quote for a gas power plant would take eight weeks. Because now we have Bots, that can draw data from different data sources, you can do it in two and a half days right? So that's not what you naturally think of for an automation technology like this. But the ability to automate from the different data sources is what creates the cycle of time reduction. >> Right, and you're fortunate, you've sat in a position where you can really look down the road at some interesting things coming forward. And we always hear kind of these two views, there's kind of the dark view of where this is all going with the automation, and the robots. And then there's the more positive view that you just touched on you know, these are gonna enable us to do more with less and, and free people up to actually be productive, and not do the mundane. >> I think productivity, productivity enables growth. The world needs more productivity. These tools are gonna be used to drive more productivity. I think many more jobs will be technically enabled, than will be eliminated by technology. Clearly there's gonna be some that are, that are, that are impacted more dramatically than others. But I would actually say, for most people, the ability to have technology to help them do their day-to-day job is gonna have a much higher impact. >> Right. What do you think is the biggest misperception of this of this combining of people and machines to do better? Where do you think people kind of miss the boat? >> Oh look I mean, I think it's that people wanna gravitate towards a macro view. A theoretical view, versus actually watching how people work. If you actually spent time seeing how a Service Engineer works, how a Manufacturing person works, how an Administrative person works, then I think you would applaud the technology. Really, I think we tend to make these pronouncements that are philosophical or, coming from Silicon Valley about the rest of the world versus, if everybody just every day, would actually observe how tasks actually get done, you'd say bring on more technology. Because this is just shitty you know, these are just horrible, you know, these are tough, horrible jobs right? A Field Engineer fixing a turbine out in the, in the middle of Texas right, a wind turbine. If we can arm them with some virtual reality tools, and the ability to use analytics so that they can fix it right the first time, that's liberating for that person. They don't look at that and say, "Oh my God, if I use this they're gonna replace me." >> Right, right. >> They really need me to do all this stuff so, I think not enough people know how people actually work. That's the problem. >> It's a tool right? It's as if you took the guy's truck away, and made him ride out there on a horse I mean-- >> It's just a, it's just a, you know look-- >> It's just another tool. >> I remember sitting in a sales office in the early 80s, when the IT guy came out and installed Microsoft Outlook for the first time. And I remember sitting there saying, who would ever need this? You know, who needs spreadsheets? >> Right, right. >> I could do it all here. >> Yeah, little did you know. >> So I just think it's kind of one of those crazy things really. >> Yeah, little did you know those spreadsheets are still driving 80% of the world's computational demands. >> Exactly. >> Great, well alright I wanna give you a last word again. You're here, it's a very exciting spot. We call 'em Bots, or robotic process automation for those that aren't dialed in to RPA stands for. As you look forward, what are you really excited about? >> Oh look, I mean I always think back to the, to kind of the four A's really, which is uh you know, kind of artificial intelligence, automation, additive manufacturing and analytics. And I think if everybody could just hone in on those four things, it's gonna be immensely disruptive, as it pertains to just how people work, how things get built, how people do their work so, when you think about RPA, I put that in the automation. It's kind of a merger of automation and AI. It's just really exciting what's gonna be available. But this, this bow wave of technology, it's just a great time to be alive, really. >> Yeah, it is. People will forget. They focus on the negative, and don't really look at the track, but you can drop into any city, anywhere in the world, pull up your phone and find the directions to the local museum. Alright, well Jeff, thanks for uh taking a few minutes of your time. >> Great. >> Alright, he's Jeff Immelt and I'm Jeff Frick, you're watching theCUBE from Automation Anywhere IMAGINE 2018. Thanks for watching. (jazz music)

>> Narrator: Live from Las Vegas, it's theCUBE, covering AWS re:Invent 2017, presented by AWS, Intel, and our ecosystem of partners. >> Hello everyone, welcome to a special CUBE presentation here, live in Las Vegas for Amazon Web Service's AWS re:Invent 2017. This is theCUBE's fifth year here. We've been watching the progression. I'm John Furrier with Justin here as my co-host. Our two next guests are Bob Rogers, the chief data scientist at Intel, and Julie Cardoa, who's the CEO of Thorn. Great guests, showing some AI for good. Intel, obviously, good citizen and great technology partner. Welcome to theCUBE. >> Thank you, thanks for having us! >> So, I saw your talk you gave at the Public Sector Breakfast this morning here at re:Invent. Packed house, fire marshal was kicking people out. Really inspirational story. Intel, we've talked at South by Southwest. You guys are really doing a lot of AI for good. That's the theme here. You guys are doing incredible work. >> Julie: Thank you. >> Tell your story real quick. >> Yeah, so Thorn is a nonprofit, we started about five years ago, and we are just specifically dedicated to build new technologies to defend children form sexual abuse. We were seeing that, as, you know, new technologies emerge, there's new innovation out there, how child sexual abuse was presenting itself was changing dramatically. So, everything from child sex trafficking online, to the spread of child sexual abuse material, livestreaming abuse, and there wasn't a concentrated effort to put the best and brightest minds and technology together to be a part of the solution, and so that's what we do. We build products to stop child abuse. >> John: So you're a nonprofit? >> Julie: Yep! >> And you're in that public sector, but you guys have made a great progress. What's the story behind it? How did you get to do so effective work in such a short period of time as a nonprofit? >> Well, I think there's a couple things to that. One is, well, we learned a lot really quickly, so what we're doing today is not what we thought we would do five years ago. We thought we were gonna talk to big companies, and push them to do more, and then we realized that we actually needed to be a hub. We needed to build our own engineering teams, we needed to build product, and then bring in these companies to help us, and to add to that, but there had to be some there there, and so we actually have evolved. We're a nonprofit, but we are a product company. We have two products used in 23 countries around the world, stopping abuse every day. And I think the other thing we learned is that we really have to break down silos. So, we didn't, in a lot of our development, we didn't go the normal route of saying, okay, well this is a law enforcement job, so we're gonna go bid for a big government RFE. We just went and built a tool and gave it to a bunch of police officers and they said, "Wow, this works really well, "we're gonna keep using it." And it kinda spread like wildfire. >> And it's making a difference. It's really been a great inspirational story. Check out Thorn, amazing work, real use case, in my mind, a testimonial for how fast you can accelerate. Congratulations. Bob, I wanna get your take on this because it's a data problem that, actually, the technology's applying to a problem that people have been trying to crack the code on for a long time. >> Yeah, well, it's interesting, 'cause the context is that we're really in this era of AI explosion, and AI is really computer systems that can do things that only humans could do 10 years ago. That's kind of my basic way of thinking about it, so the problem of being able to recognize when you're looking at two images of the same child, which is the piece that we solved for Thorn, actually, you know, is a great example of using the current AI capabilities. You start with the problem of, if I show an algorithm two different images of the same child, can it recognize that they're the same? And you basically customize your training to create a very specific capability. Not a basic image recognition or facial recognition, but a very specific capability that's been trained with specific examples. I was gonna say something about what Julie was describing about their model. Their model to create that there there has been incredible because it allows them to really focus our energy into the right problems. We have lots of technology, we have lots of different ways of doing AI and machine learning, but when we get a focus on this is the data, this is the exact problem we need to solve, and this is the way it needs to work for law enforcement, for National Center for Missing and Exploited Children. It has really just turned the knob up to 11, so to speak. >> I mean, this is an example where, I mean, we always talk about how tech transformation can make things go faster. It's such an obvious problem. I mean, it's almost everyone kinda looks away because it's too hard. So, I wanna ask you, how do people make this happen for other areas for good? So, for instance, you know, what was the bottlenecks before? What solved the problem, because, I mean, you could really make a difference here. You guys are. >> Well, I think there's a couple things. I think you hit on one, which is this is a problem people turn away from. It's really hard to look at. And the other thing is is there's not a lot of money to be made in using advanced technology to find missing and exploited children, right? So, it did require the development of a nonprofit that said, "We're gonna do this, "and we're gonna fundraise to get it done." But it also required us to look at it from a technology angle, right? I think a lot of times people look at social issues from the impact angle, which we do, but we said, "What if we looked at it "from a different perspective? "How can technology disrupt in this area?" And then we made that the core of what we do, and we partnered with all the other amazing organizations that are doing the other work. And I think, then, what Bob said was that we created a hub where other experts could plug into, and I think, in any other issue area that you're working on, you can't just talk about it and convene people. You actually have to build, and when you build, you create a platform that others can add to, and I think that is one of the core reasons why we have seen so much progress, is we started out convening and really realized that wasn't gonna last very long, and then we built, and once we started building, we scaled. >> So, you got in the market quickly with something. >> Yeah. >> So, one of the issues with any sort of criminal enterprise is it tends to end up in a bit of an arms race, so you've built this great technology but then you've gotta keep one step ahead of the bad guys. So, how are you actually doing that? How are you continuing to invest in this and develop it to make sure that you're always one step ahead? >> So, I can address that on a couple of levels. One is, you know, working with Thorn, and I lead a program at Intel called the Safer Children Program, where we work with Thorn and also the National Center for Missing and Exploited Children. Those conversations bring in all of the tech giants, and there's a little bit of sibling rivalry. We're all trying to throw in our best tech. So, I think we all wanna do as well as we can for these partnerships. The other thing is, just in very tactical terms, working with Thorn, we've actually, Thorn and with Microsoft, we've created a capability to crowdsource more data to help improve the accuracy of these deep learning algorithms. So, by getting critical mass around this problem, we've actually now created enough visibility that we're getting more and more data. And as you said earlier, it's a data problem, so if you have enough data, you can actually create the models with the accuracy and the capability that you need. So, it starts to feed on itself. >> Julie talked about the business logic, how she attacked that. That's really, 'cause I think one thing notable, good use case, but from a tech perspective, how does the cloud fit in with Intel specifically? Because it really, the cloud is an enabler too. >> Bob: Yeah, absolutely. >> How's that all working with Intel? And you go on about whole new territory you guys are forging in here, it's awesome, but the cloud. >> Right, so, for us, the cloud is an incredible way for us to make our compute capability available to anyone who needs to do computing, especially in this data-driven algorithm era where more and more machine learning, more and more AI, more and more data-driven problems are coming to the fore, doing that work on the cloud and being able to scale your work according to how much data is coming in at any time, it makes the cloud a really natural place for us. And of course, Intel's hardware is a core component of pretty much all the cloud that you could connect to. >> And the compute that you guys provide, and Amazon adds to it, their cloud is impressive. Now, I'd like to know what you guys are gonna be talking about in your session. You have a session here at re:Invent. What's the title of the session, what's the agenda, is it the same stuff here, what's gonna be talked about? >> So, we're talking about life-changing AI applications, and in specific we're gonna talk about, at the end Julie will talk about what Thorn has done with the child-finder and the AI that we and Microsoft built for them. We'll also, I'll start out by talking about Intel's role broadly in the computing and AI space. Intel really looks to take all of its different hardware, and networking, and memory assets, and make it possible for anybody to do the kinds of artificial intelligence or machine learning they need to do. And then in the middle, there's a really cool deployment on AWS sandwich that (something) will talk about how they've taken the models and really dialed them up in terms of how fast you can go through this data, so that we can go through millions and millions of images in our searches, and come back with results really, really fast. So, it's a great sort of three piece story about the conception of AI, the deployment at scale and with high performance, and then how Thorn is really taking that and creating a human impact around it. >> So, Bob, I asked you the Intel question because no one calls up Intel and says, "Hey, give me some AI for good." I mean, I wish that would be the case. >> Well, they do now. >> If they do, well, share your strategy, because cloud makes sense. I could see how you could provision easily, get in there, really empowering people to do stuff that's passionable and relevant. But how do you guys play in all of this? 'Cause I know you supply stuff to the cloud guys. Is this a formal program you're doing at Intel? Is this a one-off? >> Yeah, so Safer Children is a formal program. It started with two other folks, Lisa Davis and Lisa Theinai, going to the VP of the entire data center group and saying, "There is an opportunity to make a big impact "with Intel technology, and we'd like to do this." And it started literally because Intel does actually want to do good work for humankind, and frankly, the fact that these people are using our technology and other technology to hurt children, it steams our dumplings, frankly. So, it started with that. >> You've been a team player with Amazon and everyone else. >> Exactly, so then, once we've been able to show that we can actually create technology and provide infrastructure to solve these problems, it starts to become a self-fulfilling prophecy where people are saying, "Hey, we've got this "interesting adjacent problem that "this kind of technology could solve. "Is there an opportunity to work together and solve that?" And that fits into our bigger, you know, people ask me all the time, "Why does Intel have a chief data scientist?" We're a hardware company, right? The answer is-- >> That processes a lot of data! >> Yes, that processes a lot of data. Literally, we need to help people know how to get value from their data. So, if people are successful with their analytics and their AI, guess what, they're gonna invest in their infrastructure, and it sort of lifts Intel's boat across the board. >> You guys have always been a great citizen, and great technology provider, and hats off to Intel. Julie, tell a story about an example people can get a feel for some of the impact, because I saw you on stage this morning with Theresa Carlson, and we've been tracking her efforts in the public sector have been amazing, and Intel's been part of that too, congratulations. But you were kind of emotional, and you got a lot of applause. What's some of the impact? Tell a story of how important this really is, and your work at Thorn. >> Yeah, well, I mean, one of the areas we work in is trying to identify children who are being sold online in the US. A lot of people, first of all, think that's happening somewhere else. No, that's here in this country. A lot of these kids are coming out of foster care, or are runaways, and they get convinced by a pimp or a trafficker to be sold into prostitution, basically. So, we have 150,000 escort ads posted every single day in this country, and somewhere in there are children, and it's really difficult to look through that with your eye, and determine what's a child. So, we built a tool called Spotlight that basically reads and analyzes every ad as it comes in, and we layer on smart algorithms to say to an officer, "Hey, this is an ad you need to pay attention to. "It looks like this could be a child." And we've had over 6,000 children identified over the last year. >> John: That's amazing. >> You know, it happens in a situation where, you know, you have online it says, you know, this girl's 18, and it's actually a 15-year-old girl who met a man who said he was 17, he was actually 30, had already been convicted of sex trafficking, and within 48 hours of meeting this girl, he had her up online for sale. So, that sounds like a unique incident. It is not unique, it happens every single day in almost every city and town across this country. And the work we're doing is to find those kids faster, and stop that trauma. >> Well, I just wanna say congratulations. That's great work. We had a CUBE alumni, founder of CloudAir, Jeff Hammerbacher, good friend of theCUBE. He had a famous quote that he said on theCUBE, then said on the Charlie Rose Show, "The best minds of our generations "are thinking about how to make people click ads. "That sucks." This is an example where you can really put the best minds on some of the real important things. >> Yeah, we love Jeff. I read that quote all the time. >> It's really a most important quote. Well, thanks so much. Congratulations, great inspiration, great story. Bob, thanks for coming on, appreciate it. CUBE live coverage here at AWS re:Invent 2017, kicking off day one of three days of wall-to-wall coverage here, live in Las Vegas. We'll be right back with more after this short break.

>> Hey welcome back here everybody. Jeff Frick here with theCUBE. We're at the Zuora Subscribe Conference 2017, downtown on San Francisco. 1,000, 2,000 people talking about the subscription economy. I think it's like the sixth year they've been doing the show. First time we've been here. We're excited to be here. But we're joined by a company that we spent a lot of time talking about IOT and the industrial internet, and that's GE, but a new guest, Gytis Barzdukas, he is the head of Predix Product Management for GE Digital. Welcome. >> Thanks Jeff, thanks for having me here. >> So you guys, I mean, we were there in 2013 when Beth and Bill launched the industrial internet initiative at the Jewish History Museum just across the street. So you guys have been in this space for a while, the GE Predix Cloud, industrial internet cloud, you guys have been doing a lot of stuff there. So give us a kind of update, where are you? Obviously picked to highlight one of the key stories here. People probably don't think of GE as necessarily a subscription economy type of play, but, >> So why are we here? >> Jeff: Yeah, so why are you here? >> Well we're here because we are a subscription economy. What we're really focusing on with Predix is building a platform that allows third-parties and first-party applications to be built around the industrial space, and so a lot of what we're hearing from our customers is that they want to subscribe to those services. They want to subscribe to either the production of the services, but more importantly maybe the different elements that bring together a solution. So think about the concept like a digital twin, a virtual representation of a physical asset. A lot of times what people want to do is they want to build twins specific to a specific asset. But they want to bring together the analytics, and the data associated with that, and maybe some environmental factors that they subscribe to from a third-party, bring those all together, do an analysis, And then basically give that stuff back. So they want to subscribe to things like analytics. They want to subscribe to data and the inputs, so that's why we're here. We've been using Zuora as part of our subscription service since we kicked off GE Predix last year. We went GA in February, and it's proven to be a very flexible solution for us. >> So the part that I don't think gets enough talk, and there really wasn't a lot of talk in the keynote, is how a subscription relationship changes the way that you engage with the customer. 'Cause if you just sell 'em something, here's the transaction, you know, go off, go run your jet engine, run your turbine, but if you have a subscription, and it's an ongoing value delivery to pay for that ongoing money that they're giving you, it's a much kind of deeper relationship than kind of a single transaction relationship. >> It can develop to be a much deeper relationship. I think the thing that it allows you to do is, it allows you to experiment a little bit, try a couple things, figure out what works best for you as a customer and then invest in those areas. You don't have to make a big purchase order. You don't have to go off and spend a lot of money on a bunch of software that may eventually go away. You can almost try, before you buy, or try as you buy is probably a better way of putting it. And so what we're seeing is we give people the ability to experiment. I think, we talk within GE about productivity and the impact we can make in our own productivity. To me Predix is as much about innovation. It's giving people the ability to try different things, to try and see what happens when you bring in environmental factors or usage data, or operational data, or we talk about jet engines a lot. Looking at the different pilots, how do they operate the engines? So there's all these scenarios you can sort of experiment with on a subscription model, find out what works and then go deep as necessary. >> And it's interesting, Tien in the keynote talked about how what's different now is that you can buy, you can upgrade, you can cancel, you can downgrade, so again this interaction as you just described, allows for a bunch of different types of engagement, not just the big bang. >> Yep, yeah. >> And the other thing that's consistent with who you're over and overwrite is the democratization. Democratization of the data, democratization of the tools so that if somebody does have a hypothesis, we've been looking at obviously a plane operating in the southwest United States is going to have different characteristics as one operating in Alaska. But as you just said maybe we should look at pilot characteristics. Maybe we should look at back ends, so when you open up that innovation platform, now you have so many more people coming up with hypothesis, testing hypothesis, and you open up the resources to your company to do so much better. >> Well, and you have little innovation, so we have a partner based in Israel, Plataine, who's doing some stuff in the manufacturing space with GE as we start thinking about additive manufacturing. You want to start thinking about the composites and the materials that actually go into the engine, and sort of how have those held up over time? So you can build a much more longitudinal view of that, and again, it can be a subscription service where you start experimenting, you start understanding, especially with additive being sort of a mechanism to decentralize a lot of the manufacturing. You don't need to make a huge investment to doing those analytics. You put some software alongside the additive systems, and you've got the ability to innovate and understand better what composites work better. You talk about the operation of the engine, but how about the manufacturing of the engine? Are there optimal environments where you want to build those engines? And I think we've done great work as an industrial company and understand how to optimize systems and probably even like what the environmental factors are to build an engine effectively, but when you start distributing that, you really want to gauge that real time to understand what the impact could be. >> All right, so we're on short time leash here, unfortunately, but I want to give you the last word, give a plug for the Predix Transform Show coming up as part of Minds and Machines. We went for the first year last year. It was 2,000 developers, pretty great turnout for really a development platform for an industrial internet cloud. >> Yeah, so what we've done this year is we're bringing together Transform, which is the event for our developer community with Minds and Machines which is more targeted towards the business leaders or some of the IT leaders in their organization and bringing them all under one roof. It'll be here in San Francisco mid-October. I don't have the exact dates. I probably should, but I think it's like-- >> I can look it up on the internet. That's why we have the internet. >> But we're bringing those together. So we can have a dialogue that spans the complete spectrum. It's the people that are building, and we'll have hackathons, we'll have places where people can actually work on that. We'll judge those different solutions that are being hacked together. And then we'll be presenting sort of the business value and the impact we're seeing with a lot of the industrial customers. Again, many of them are GE's existing customers. But we've got customers in the auto industry, elevator, escalator industry, fixtures, manufacturing, spaces that we haven't traditionally played, and so we'll be talking about all of the benefits. We're bringing in those customers plus some new product introductions which I can't talk about now. >> All right great event, IT meets OT. We went last year. Jeff was there, Beth was there, >> They will be there. >> Jeff: Bill was there, all the players. A great show. >> okay, Jeff. >> Jeff: Congratulations on your success with Zuora and we look forward to seeing you at Minds and Machines. >> Okay, thanks Jeff. >> All right, he's Gytis, I'm Jeff Frick. You're watching theCUBE from Zuora Subscribe 2017. We'll be right back after this short break. Thanks for watching.

>> Announcer: Big Data Silicon Valley 2017. >> Hey, welcome back, everybody, Jeff Frick here with The Cube. We are live at the historic Pagoda Lounge in San Jose for Big Data SV, which is associated with Strathead Dupe World, across the street, as well as Big Data week, so everything big data is happening in San Jose, we're happy to be here, love the new venue, if you're around, stop by, back of the Fairmount, Pagoda Lounge. We're excited to be joined in this next segment by, who's now become a regular, any time we're at a Big Data event, a Spark event, Holden always stops by. Holden Karau, she's the principal software engineer at IBM. Holden, great to see you. >> Thank you, it's wonderful to be back yet again. >> Absolutely, so the big data meme just keeps rolling, Google Cloud Next was last week, a lot of talk about AI and ML and of course you're very involved in Spark, so what are you excited about these days? What are you, I'm sure you've got a couple presentations going on across the street. >> Yeah, so my two presentations this week, oh wow, I should remember them. So the one that I'm doing today is with my co-worker Seth Hendrickson, also at IBM, and we're going to be focused on how to use structured streaming for machine learning. And sort of, I think that's really interesting, because streaming machine learning is something a lot of people seem to want to do but aren't yet doing in production, so it's always fun to talk to people before they've built their systems. And then tomorrow I'm going to be talking with Joey on how to debug Spark, which is something that I, you know, a lot of people ask questions about, but I tend to not talk about, because it tends to scare people away, and so I try to keep the happy going. >> Jeff: Bugs are never fun. >> No, no, never fun. >> Just picking up on that structured streaming and machine learning, so there's this issue of, as we move more and more towards the industrial internet of things, like having to process events as they come in, make a decision. How, there's a range of latency that's required. Where does structured streaming and ML fit today, and where might that go? >> So structured streaming for today, latency wise, is probably not something I would use for something like that right now. It's in the like sub second range. Which is nice, but it's not what you want for like live serving of decisions for your car, right? That's just not going to be feasible. But I think it certainly has the potential to get a lot faster. We've seen a lot of renewed interest in ML liblocal, which is really about making it so that we can take the models that we've trained in Spark and really push them out to the edge and sort of serve them in the edge, and apply our models on end devices. So I'm really excited about where that's going. To be fair, part of my excitement is someone else is doing that work, so I'm very excited that they're doing this work for me. >> Let me clarify on that, just to make sure I understand. So there's a lot of overhead in Spark, because it runs on a cluster, because you have an optimizer, because you have the high availability or the resilience, and so you're saying we can preserve the predict and maybe serve part and carve out all the other overhead for running in a very small environment. >> Right, yeah. So I think for a lot of these IOT devices and stuff like that it actually makes a lot more sense to do the predictions on the device itself, right. These models generally are megabytes in size, and we don't need a cluster to do predictions on these models, right. We really need the cluster to train them, but I think for a lot of cases, pushing the prediction out to the edge node is actually a pretty reasonable use case. And so I'm really excited that we've got some work going on there. >> Taking that one step further, we've talked to a bunch of people, both like at GE, and at their Minds and Machines show, and IBM's Genius of Things, where you want to be able to train the models up in the cloud where you're getting data from all the different devices and then push the retrained model out to the edge. Can that happen in Spark, or do we have to have something else orchestrating all that? >> So actually pushing the model out isn't something that I would do in Spark itself, I think that's better served by other tools. Spark is not really well suited to large amounts of internet traffic, right. But it's really well suited to the training, and I think with ML liblocal it'll essentially, we'll be able to provide both sides of it, and the copy part will be left up to whoever it is that's doing their work, right, because like if you're copying over a cell network you need to do something very different as if you're broadcasting over a terrestrial XM or something like that, you need to do something very different for satellite. >> If you're at the edge on a device, would you be actually running, like you were saying earlier, structured streaming, with the prediction? >> Right, I don't think you would use structured streaming per se on the edge device, but essentially there would be a lot of code share between structured streaming and the code that you'd be using on the edge device. And it's being vectored out now so that we can have this code sharing and Spark machine learning. And you would use structured streaming maybe on the training side, and then on the serving side you would use your custom local code. >> Okay, so tell us a little more about Spark ML today and how we can democratize machine learning, you know, for a bigger audience. >> Right, I think machine learning is great, but right now you really need a strong statistical background to really be able to apply it effectively. And we probably can't get rid of that for all problems, but I think for a lot of problems, doing things like hyperparameter tuning can actually give really powerful tools to just like regular engineering folks who, they're smart, but maybe they don't have a strong machine learning background. And Spark's ML pipelines make it really easy to sort of construct multiple stages, and then just be like, okay, I don't know what these parameters should be, I want you to do a search over what these different parameters could be for me, and it makes it really easy to do this as just a regular engineer with less of an ML background. >> Would that be like, just for those of us who are, who don't know what hyperparameter tuning is, that would be the knobs, the variables? >> Yeah, it's going to spin the knobs on like our regularization parameter on like our regression, and it can also spin some knobs on maybe the engram sizes that we're using on the inputs to something else, right. And it can compare how these knobs sort of interact with each other, because often you can tune one knob but you actually have six different knobs that you want to tune and you don't know, if you just explore each one individually, you're not going to find the best setting for them working together. >> So this would make it easier for, as you're saying, someone who's not a data scientist to set up a pipeline that lets you predict. >> I think so, very much. I think it does a lot of the, brings a lot of the benefits from sort of the SciPy world to the big data world. And SciPy is really wonderful about making machine learning really accessible, but it's just not ready for big data, and I think this does a good job of bringing these same concepts, if not the code, but the same concepts, to big data. >> The SciPy, if I understand, is it a notebook that would run essentially on one machine? >> SciPy can be put in a notebook environment, and generally it would run on, yeah, a single machine. >> And so to make that sit on Spark means that you could then run it on a cluster-- >> So this isn't actually taking SciPy and distributing it, this is just like stealing the good concepts from SciPy and making them available for big data people. Because SciPy's done a really good job of making a very intuitive machine learning interface. >> So just to put a fine sort of qualifier on one thing, if you're doing the internet of things and you have Spark at the edge and you're running the model there, it's the programming model, so structured streaming is one way of programming Spark, but if you don't have structured streaming at the edge, would you just be using the core batch Spark programming model? >> So at the edge you'd just be using, you wouldn't even be using batch, right, because you're trying to predict individual events, right, so you'd just be calling predict with every new event that you're getting in. And you might have a q mechanism of some type. But essentially if we had this batch, we would be adding additional latency, and I think at the edge we really, the reason we're moving the models to the edge is to avoid the latency. >> So just to be clear then, is the programming model, so it wouldn't be structured streaming, and we're taking out all the overhead that forced us to use batch with Spark. So the reason I'm trying to clarify is a lot of people had this question for a long time, which is are we going to have a different programming model at the edge from what we have at the center? >> Yeah, that's a great question. And I don't think the answer is finished yet, but I think the work is being done to try and make it look the same. Of course, you know, trying to make it look the same, this is Boosh, it's not like actually barking at us right now, even though she looks like a dog, she is, there will always be things which are a little bit different from the edge to your cluster, but I think Spark has done a really good job of making things look very similar on single node cases to multi node cases, and I think we can probably bring the same things to ML. >> Okay, so it's almost time, we're coming back, Spark took us from single machine to cluster, and now we have to essentially bring it back for an edge device that's really light weight. >> Yeah, I think at the end of the day, just from a latency point of view, that's what we have to do for serving. For some models, not for everyone. Like if you're building a website with a recommendation system, you don't need to serve that model like on the edge node, that's fine, but like if you've got a car device we can't depend on cell latency, right, you have to serve that in car. >> So what are some of the things, some of the other things that IBM is contributing to the ecosystem that you see having a big impact over the next couple years? >> So there's a lot of really exciting things coming out of IBM. And I'm obviously pretty biased. I spend a lot of time focused on Python support in Spark, and one of the most exciting things is coming from my co-worker Brian, I'm not going to say his last name in case I get it wrong, but Brian is amazing, and he's been working on integrating Arrow with Spark, and this can make it so that it's going to be a lot easier to sort of interoperate between JVM languages and Python and R, so I'm really optimistic about the sort of Python and R interfaces improving a lot in Spark and getting a lot faster as well. And we're also, in addition to the Arrow work, we've got some work around making it a lot easier for people in R and Python to get started. The R stuff is mostly actually the Microsoft people, thanks Felix, you're awesome. I don't actually know which camera I should have done that to but that's okay. >> I think you got it! >> But Felix is amazing, and the other people working on R are too. But I think we've both been pursuing sort of making it so that people who are in the R or Python spaces can just use like Pit Install, Conda Install, or whatever tool it is they're used to working with, to just bring Spark into their machine really easily, just like they would sort of any other software package that they're using. Because right now, for someone getting started in Spark, if you're in the Java space it's pretty easy, but if you're in R or Python you have to do sort of a lot of weird setup work, and it's worth it, but like if we can get rid of that friction, I think we can get a lot more people in these communities using Spark. >> Let me see, just as a scenario, the R server is getting fairly well integrated into Sequel server, so would it be, would you be able to use R as the language with a Spark execution engine to somehow integrate it into Sequel server as an execution engine for doing the machine learning and predicting? >> You definitely, well I shouldn't say definitely, you probably could do that. I don't necessarily know if that's a good idea, but that's the kind of stuff that this would enable, right, it'll make it so that people that are making tools in R or Python can just use Spark as another library, right, and it doesn't have to be this really special setup. It can just be this library and they point out the cluster and they can do whatever work it wants to do. That being said, the Sequel server R integration, if you find yourself using that to do like distributed computing, you should probably take a step back and like rethink what you're doing. >> George: Because it's not really scale out. >> It's not really set up for that. And you might be better off doing this with like, connecting your Spark cluster to your Sequel server instance using like JDBC or a special driver and doing it that way, but you definitely could do it in another inverted sort of way. >> So last question from me, if you look out a couple years, how will we make machine learning accessible to a bigger and bigger audience? And I know you touched on the tuning of the knobs, hyperparameter tuning, what will it look like ultimately? >> I think ML pipelines are probably what things are going to end up looking like. But I think the other part that we'll sort of see is we'll see a lot more examples of how to work with certain kinds of data, because right now, like, I know what I need to do when I'm ingesting some textural data, but I know that because I spent like a week trying to figure out what the hell I was doing once, right. And I didn't bother to write it down. And it looks like no one else bothered to write it down. So really I think we'll see a lot of tools that look very similar to the tools we have today, they'll have more options and they'll be a bit easier to use, but I think the main thing that we're really lacking right now is good documentation and sort of good books and just good resources for people to figure out how to use these tools. Now of course, I mean, I'm biased, because I work on these tools, so I'm like, yeah, they're pretty great. So there might be other people who are like, Holden, no, you're wrong, we need to rethink everything. But I think this is, we can go very far with the pipeline concept. >> And then that's good, right? The democratization of these things opens it up to more people, you get more creative people solving more different problems, that makes the whole thing go. >> You can like install Spark easily, you can, you know, set up an ML pipeline, you can train your model, you can start doing predictions, you can, people that haven't been able to do machine learning at scale can get started super easily, and build a recommendation system for their small little online shop and be like, hey, you bought this, you might also want to buy Boosh, he's really cute, but you can't have this one. No no no, not this one. >> Such a tease! >> Holden: I'm sorry, I'm sorry. >> Well Holden, that will, we'll say goodbye for now, I'm sure we will see you in June in San Francisco at the Spark Summit, and look forward to the update. >> Holden: I look forward to chatting with you then. >> Absolutely, and break a leg this afternoon at your presentation. >> Holden: Thank you. >> She's Holden Karau, I'm Jeff Frick, he's George Gilbert, you're watching The Cube, we're at Big Data SV, thanks for watching. (upbeat music)