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

[Music] in the 2010s organizations became keenly aware that data would become the key ingredient in driving competitive advantage differentiation and growth but to this day putting data to work remains a difficult challenge for many if not most organizations now as the cloud matures it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible we've also seen better tooling in the form of data workflows streaming machine intelligence ai developer tools security observability automation new databases and the like these innovations they accelerate data proficiency but at the same time they had complexity for practitioners data lakes data hubs data warehouses data marts data fabrics data meshes data catalogs data oceans are forming they're evolving and exploding onto the scene so in an effort to bring perspective to the sea of optionality we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond hello everyone my name is dave vellante with the cube and i'd like to welcome you to a special cube presentation analyst predictions 2022 the future of data management we've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade let me introduce our six power panelists sanjeev mohan is former gartner analyst and principal at sanjamo tony bear is principal at db insight carl olufsen is well-known research vice president with idc dave meninger is senior vice president and research director at ventana research brad shimon chief analyst at ai platforms analytics and data management at omnia and doug henschen vice president and principal analyst at constellation research gentlemen welcome to the program and thanks for coming on thecube today great to be here thank you all right here's the format we're going to use i as moderator are going to call on each analyst separately who then will deliver their prediction or mega trend and then in the interest of time management and pace two analysts will have the opportunity to comment if we have more time we'll elongate it but let's get started right away sanjeev mohan please kick it off you want to talk about governance go ahead sir thank you dave i i believe that data governance which we've been talking about for many years is now not only going to be mainstream it's going to be table stakes and all the things that you mentioned you know with data oceans data lakes lake houses data fabric meshes the common glue is metadata if we don't understand what data we have and we are governing it there is no way we can manage it so we saw informatica when public last year after a hiatus of six years i've i'm predicting that this year we see some more companies go public uh my bet is on colibra most likely and maybe alation we'll see go public this year we we i'm also predicting that the scope of data governance is going to expand beyond just data it's not just data and reports we are going to see more transformations like spark jaws python even airflow we're going to see more of streaming data so from kafka schema registry for example we will see ai models become part of this whole governance suite so the governance suite is going to be very comprehensive very detailed lineage impact analysis and then even expand into data quality we already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management data catalogs also data access governance so these so what we are going to see is that once the data governance platforms become the key entry point into these modern architectures i'm predicting that the usage the number of users of a data catalog is going to exceed that of a bi tool that will take time and we already seen that that trajectory right now if you look at bi tools i would say there are 100 users to a bi tool to one data catalog and i i see that evening out over a period of time and at some point data catalogs will really become you know the main way for us to access data data catalog will help us visualize data but if we want to do more in-depth analysis it'll be the jumping-off point into the bi tool the data science tool and and that is that is the journey i see for the data governance products excellent thank you some comments maybe maybe doug a lot a lot of things to weigh in on there maybe you could comment yeah sanjeev i think you're spot on a lot of the trends uh the one disagreement i think it's it's really still far from mainstream as you say we've been talking about this for years it's like god motherhood apple pie everyone agrees it's important but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking i think one thing that deserves uh mention in this context is uh esg mandates and guidelines these are environmental social and governance regs and guidelines we've seen the environmental rags and guidelines imposed in industries particularly the carbon intensive industries we've seen the social mandates particularly diversity imposed on suppliers by companies that are leading on this topic we've seen governance guidelines now being imposed by banks and investors so these esgs are presenting new carrots and sticks and it's going to demand more solid data it's going to demand more detailed reporting and solid reporting tighter governance but we're still far from mainstream adoption we have a lot of uh you know best of breed niche players in the space i think the signs that it's going to be more mainstream are starting with things like azure purview google dataplex the big cloud platform uh players seem to be uh upping the ante and and addressing starting to address governance excellent thank you doug brad i wonder if you could chime in as well yeah i would love to be a believer in data catalogs um but uh to doug's point i think that it's going to take some more pressure for for that to happen i recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the 90s and that didn't happen quite the way we we anticipated and and uh to sanjeev's point it's because it is really complex and really difficult to do my hope is that you know we won't sort of uh how do we put this fade out into this nebulous nebula of uh domain catalogs that are specific to individual use cases like purview for getting data quality right or like data governance and cyber security and instead we have some tooling that can actually be adaptive to gather metadata to create something i know is important to you sanjeev and that is this idea of observability if you can get enough metadata without moving your data around but understanding the entirety of a system that's running on this data you can do a lot to help with with the governance that doug is talking about so so i just want to add that you know data governance like many other initiatives did not succeed even ai went into an ai window but that's a different topic but a lot of these things did not succeed because to your point the incentives were not there i i remember when starbucks oxley had come into the scene if if a bank did not do service obviously they were very happy to a million dollar fine that was like you know pocket change for them instead of doing the right thing but i think the stakes are much higher now with gdpr uh the floodgates open now you know california you know has ccpa but even ccpa is being outdated with cpra which is much more gdpr like so we are very rapidly entering a space where every pretty much every major country in the world is coming up with its own uh compliance regulatory requirements data residence is becoming really important and and i i think we are going to reach a stage where uh it won't be optional anymore so whether we like it or not and i think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption we were focused on features and these features were disconnected very hard for business to stop these are built by it people for it departments to to take a look at technical metadata not business metadata today the tables have turned cdo's are driving this uh initiative uh regulatory compliances are beating down hard so i think the time might be right yeah so guys we have to move on here and uh but there's some some real meat on the bone here sanjeev i like the fact that you late you called out calibra and alation so we can look back a year from now and say okay he made the call he stuck it and then the ratio of bi tools the data catalogs that's another sort of measurement that we can we can take even though some skepticism there that's something that we can watch and i wonder if someday if we'll have more metadata than data but i want to move to tony baer you want to talk about data mesh and speaking you know coming off of governance i mean wow you know the whole concept of data mesh is decentralized data and then governance becomes you know a nightmare there but take it away tony we'll put it this way um data mesh you know the the idea at least is proposed by thoughtworks um you know basically was unleashed a couple years ago and the press has been almost uniformly almost uncritical um a good reason for that is for all the problems that basically that sanjeev and doug and brad were just you know we're just speaking about which is that we have all this data out there and we don't know what to do about it um now that's not a new problem that was a problem we had enterprise data warehouses it was a problem when we had our hadoop data clusters it's even more of a problem now the data's out in the cloud where the data is not only your data like is not only s3 it's all over the place and it's also including streaming which i know we'll be talking about later so the data mesh was a response to that the idea of that we need to debate you know who are the folks that really know best about governance is the domain experts so it was basically data mesh was an architectural pattern and a process my prediction for this year is that data mesh is going to hit cold hard reality because if you if you do a google search um basically the the published work the articles and databases have been largely you know pretty uncritical um so far you know that you know basically learning is basically being a very revolutionary new idea i don't think it's that revolutionary because we've talked about ideas like this brad and i you and i met years ago when we were talking about so and decentralizing all of us was at the application level now we're talking about at the data level and now we have microservices so there's this thought of oh if we manage if we're apps in cloud native through microservices why don't we think of data in the same way um my sense this year is that you know this and this has been a very active search if you look at google search trends is that now companies are going to you know enterprises are going to look at this seriously and as they look at seriously it's going to attract its first real hard scrutiny it's going to attract its first backlash that's not necessarily a bad thing it means that it's being taken seriously um the reason why i think that that uh that it will you'll start to see basically the cold hard light of day shine on data mesh is that it's still a work in progress you know this idea is basically a couple years old and there's still some pretty major gaps um the biggest gap is in is in the area of federated governance now federated governance itself is not a new issue uh federated governance position we're trying to figure out like how can we basically strike the balance between getting let's say you know between basically consistent enterprise policy consistent enterprise governance but yet the groups that understand the data know how to basically you know that you know how do we basically sort of balance the two there's a huge there's a huge gap there in practice and knowledge um also to a lesser extent there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data you know basically through the full life cycle from developed from selecting the data from you know building the other pipelines from determining your access control determining looking at quality looking at basically whether data is fresh or whether or not it's trending of course so my predictions is that it will really receive the first harsh scrutiny this year you are going to see some organization enterprises declare premature victory when they've uh when they build some federated query implementations you're going to see vendors start to data mesh wash their products anybody in the data management space they're going to say that whether it's basically a pipelining tool whether it's basically elt whether it's a catalog um or confederated query tool they're all going to be like you know basically promoting the fact of how they support this hopefully nobody is going to call themselves a data mesh tool because data mesh is not a technology we're going to see one other thing come out of this and this harks back to the metadata that sanji was talking about and the catalogs that he was talking about which is that there's going to be a new focus on every renewed focus on metadata and i think that's going to spur interest in data fabrics now data fabrics are pretty vaguely defined but if we just take the most elemental definition which is a common metadata back plane i think that if anybody is going to get serious about data mesh they need to look at a data fabric because we all at the end of the day need to speak you know need to read from the same sheet of music so thank you tony dave dave meninger i mean one of the things that people like about data mesh is it pretty crisply articulates some of the flaws in today's organizational approaches to data what are your thoughts on this well i think we have to start by defining data mesh right the the term is already getting corrupted right tony said it's going to see the cold hard uh light of day and there's a problem right now that there are a number of overlapping terms that are similar but not identical so we've got data virtualization data fabric excuse me for a second sorry about that data virtualization data fabric uh uh data federation right uh so i i think that it's not really clear what each vendor means by these terms i see data mesh and data fabric becoming quite popular i've i've interpreted data mesh as referring primarily to the governance aspects as originally you know intended and specified but that's not the way i see vendors using i see vendors using it much more to mean data fabric and data virtualization so i'm going to comment on the group of those things i think the group of those things is going to happen they're going to happen they're going to become more robust our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access again whether you define it as mesh or fabric or virtualization isn't really the point here but this notion that there are different elements of data metadata and governance within an organization that all need to be managed collectively the interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not it's almost double 68 of organizations i'm i'm sorry um 79 of organizations that were using virtualized access express satisfaction with their access to the data lake only 39 expressed satisfaction if they weren't using virtualized access so thank you uh dave uh sanjeev we just got about a couple minutes on this topic but i know you're speaking or maybe you've spoken already on a panel with jamal dagani who sort of invented the concept governance obviously is a big sticking point but what are your thoughts on this you are mute so my message to your mark and uh and to the community is uh as opposed to what dave said let's not define it we spent the whole year defining it there are four principles domain product data infrastructure and governance let's take it to the next level i get a lot of questions on what is the difference between data fabric and data mesh and i'm like i can compare the two because data mesh is a business concept data fabric is a data integration pattern how do you define how do you compare the two you have to bring data mesh level down so to tony's point i'm on a warp path in 2022 to take it down to what does a data product look like how do we handle shared data across domains and govern it and i think we are going to see more of that in 2022 is operationalization of data mesh i think we could have a whole hour on this topic couldn't we uh maybe we should do that uh but let's go to let's move to carl said carl your database guy you've been around that that block for a while now you want to talk about graph databases bring it on oh yeah okay thanks so i regard graph database as basically the next truly revolutionary database management technology i'm looking forward to for the graph database market which of course we haven't defined yet so obviously i have a little wiggle room in what i'm about to say but that this market will grow by about 600 percent over the next 10 years now 10 years is a long time but over the next five years we expect to see gradual growth as people start to learn how to use it problem isn't that it's used the problem is not that it's not useful is that people don't know how to use it so let me explain before i go any further what a graph database is because some of the folks on the call may not may not know what it is a graph database organizes data according to a mathematical structure called a graph a graph has elements called nodes and edges so a data element drops into a node the nodes are connected by edges the edges connect one node to another node combinations of edges create structures that you can analyze to determine how things are related in some cases the nodes and edges can have properties attached to them which add additional informative material that makes it richer that's called a property graph okay there are two principal use cases for graph databases there's there's semantic proper graphs which are used to break down human language text uh into the semantic structures then you can search it organize it and and and answer complicated questions a lot of ai is aimed at semantic graphs another kind is the property graph that i just mentioned which has a dazzling number of use cases i want to just point out is as i talk about this people are probably wondering well we have relational databases isn't that good enough okay so a relational database defines it uses um it supports what i call definitional relationships that means you define the relationships in a fixed structure the database drops into that structure there's a value foreign key value that relates one table to another and that value is fixed you don't change it if you change it the database becomes unstable it's not clear what you're looking at in a graph database the system is designed to handle change so that it can reflect the true state of the things that it's being used to track so um let me just give you some examples of use cases for this um they include uh entity resolution data lineage uh um social media analysis customer 360 fraud prevention there's cyber security there's strong supply chain is a big one actually there's explainable ai and this is going to become important too because a lot of people are adopting ai but they want a system after the fact to say how did the ai system come to that conclusion how did it make that recommendation right now we don't have really good ways of tracking that okay machine machine learning in general um social network i already mentioned that and then we've got oh gosh we've got data governance data compliance risk management we've got recommendation we've got personalization anti-money money laundering that's another big one identity and access management network and i.t operations is already becoming a key one where you actually have mapped out your operation your your you know whatever it is your data center and you you can track what's going on as things happen there root cause analysis fraud detection is a huge one a number of major credit card companies use graph databases for fraud detection risk analysis tracking and tracing churn analysis next best action what-if analysis impact analysis entity resolution and i would add one other thing or just a few other things to this list metadata management so sanjay here you go this is your engine okay because i was in metadata management for quite a while in my past life and one of the things i found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it but grass can okay grafts can do things like say this term in this context means this but in that context it means that okay things like that and in fact uh logistics management supply chain it also because it handles recursive relationships by recursive relationships i mean objects that own other objects that are of the same type you can do things like bill materials you know so like parts explosion you can do an hr analysis who reports to whom how many levels up the chain and that kind of thing you can do that with relational databases but yes it takes a lot of programming in fact you can do almost any of these things with relational databases but the problem is you have to program it it's not it's not supported in the database and whenever you have to program something that means you can't trace it you can't define it you can't publish it in terms of its functionality and it's really really hard to maintain over time so carl thank you i wonder if we could bring brad in i mean brad i'm sitting there wondering okay is this incremental to the market is it disruptive and replaceable what are your thoughts on this space it's already disrupted the market i mean like carl said go to any bank and ask them are you using graph databases to do to get fraud detection under control and they'll say absolutely that's the only way to solve this problem and it is frankly um and it's the only way to solve a lot of the problems that carl mentioned and that is i think it's it's achilles heel in some ways because you know it's like finding the best way to cross the seven bridges of konigsberg you know it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique uh it it still unfortunately kind of stands apart from the rest of the community that's building let's say ai outcomes as the great great example here the graph databases and ai as carl mentioned are like chocolate and peanut butter but technologically they don't know how to talk to one another they're completely different um and you know it's you can't just stand up sql and query them you've got to to learn um yeah what is that carlos specter or uh special uh uh yeah thank you uh to actually get to the data in there and if you're gonna scale that data that graph database especially a property graph if you're gonna do something really complex like try to understand uh you know all of the metadata in your organization you might just end up with you know a graph database winter like we had the ai winter simply because you run out of performance to make the thing happen so i i think it's already disrupted but we we need to like treat it like a first-class citizen in in the data analytics and ai community we need to bring it into the fold we need to equip it with the tools it needs to do that the magic it does and to do it not just for specialized use cases but for everything because i i'm with carl i i think it's absolutely revolutionary so i had also identified the principal achilles heel of the technology which is scaling now when these when these things get large and complex enough that they spill over what a single server can handle you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down so that's still a problem to be solved sanjeev any quick thoughts on this i mean i think metadata on the on the on the word cloud is going to be the the largest font uh but what are your thoughts here i want to like step away so people don't you know associate me with only meta data so i want to talk about something a little bit slightly different uh dbengines.com has done an amazing job i think almost everyone knows that they chronicle all the major databases that are in use today in january of 2022 there are 381 databases on its list of ranked list of databases the largest category is rdbms the second largest category is actually divided into two property graphs and rdf graphs these two together make up the second largest number of data databases so talking about accolades here this is a problem the problem is that there's so many graph databases to choose from they come in different shapes and forms uh to bright's point there's so many query languages in rdbms is sql end of the story here we've got sci-fi we've got gremlin we've got gql and then your proprietary languages so i think there's a lot of disparity in this space but excellent all excellent points sanji i must say and that is a problem the languages need to be sorted and standardized and it needs people need to have a road map as to what they can do with it because as you say you can do so many things and so many of those things are unrelated that you sort of say well what do we use this for i'm reminded of the saying i learned a bunch of years ago when somebody said that the digital computer is the only tool man has ever devised that has no particular purpose all right guys we gotta we gotta move on to dave uh meninger uh we've heard about streaming uh your prediction is in that realm so please take it away sure so i like to say that historical databases are to become a thing of the past but i don't mean that they're going to go away that's not my point i mean we need historical databases but streaming data is going to become the default way in which we operate with data so in the next say three to five years i would expect the data platforms and and we're using the term data platforms to represent the evolution of databases and data lakes that the data platforms will incorporate these streaming capabilities we're going to process data as it streams into an organization and then it's going to roll off into historical databases so historical databases don't go away but they become a thing of the past they store the data that occurred previously and as data is occurring we're going to be processing it we're going to be analyzing we're going to be acting on it i mean we we only ever ended up with historical databases because we were limited by the technology that was available to us data doesn't occur in batches but we processed it in batches because that was the best we could do and it wasn't bad and we've continued to improve and we've improved and we've improved but streaming data today is still the exception it's not the rule right there's there are projects within organizations that deal with streaming data but it's not the default way in which we deal with data yet and so that that's my prediction is that this is going to change we're going to have um streaming data be the default way in which we deal with data and and how you label it what you call it you know maybe these databases and data platforms just evolve to be able to handle it but we're going to deal with data in a different way and our research shows that already about half of the participants in our analytics and data benchmark research are using streaming data you know another third are planning to use streaming technologies so that gets us to about eight out of ten organizations need to use this technology that doesn't mean they have to use it throughout the whole organization but but it's pretty widespread in its use today and has continued to grow if you think about the consumerization of i.t we've all been conditioned to expect immediate access to information immediate responsiveness you know we want to know if an uh item is on the shelf at our local retail store and we can go in and pick it up right now you know that's the world we live in and that's spilling over into the enterprise i.t world where we have to provide those same types of capabilities um so that's my prediction historical database has become a thing of the past streaming data becomes the default way in which we we operate with data all right thank you david well so what what say you uh carl a guy who's followed historical databases for a long time well one thing actually every database is historical because as soon as you put data in it it's now history it's no longer it no longer reflects the present state of things but even if that history is only a millisecond old it's still history but um i would say i mean i know you're trying to be a little bit provocative in saying this dave because you know as well as i do that people still need to do their taxes they still need to do accounting they still need to run general ledger programs and things like that that all involves historical data that's not going to go away unless you want to go to jail so you're going to have to deal with that but as far as the leading edge functionality i'm totally with you on that and i'm just you know i'm just kind of wondering um if this chain if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way m applications work um saying that uh an application should respond instantly as soon as the state of things changes what do you say about that i i think that's true i think we do have to think about things differently that's you know it's not the way we design systems in the past uh we're seeing more and more systems designed that way but again it's not the default and and agree 100 with you that we do need historical databases you know that that's clear and even some of those historical databases will be used in conjunction with the streaming data right so absolutely i mean you know let's take the data warehouse example where you're using the data warehouse as context and the streaming data as the present you're saying here's a sequence of things that's happening right now have we seen that sequence before and where what what does that pattern look like in past situations and can we learn from that so tony bear i wonder if you could comment i mean if you when you think about you know real-time inferencing at the edge for instance which is something that a lot of people talk about um a lot of what we're discussing here in this segment looks like it's got great potential what are your thoughts yeah well i mean i think you nailed it right you know you hit it right on the head there which is that i think a key what i'm seeing is that essentially and basically i'm going to split this one down the middle is i don't see that basically streaming is the default what i see is streaming and basically and transaction databases um and analytics data you know data warehouses data lakes whatever are converging and what allows us technically to converge is cloud native architecture where you can basically distribute things so you could have you can have a note here that's doing the real-time processing that's also doing it and this is what your leads in we're maybe doing some of that real-time predictive analytics to take a look at well look we're looking at this customer journey what's happening with you know you know with with what the customer is doing right now and this is correlated with what other customers are doing so what i so the thing is that in the cloud you can basically partition this and because of basically you know the speed of the infrastructure um that you can basically bring these together and or and so and kind of orchestrate them sort of loosely coupled manner the other part is that the use cases are demanding and this is part that goes back to what dave is saying is that you know when you look at customer 360 when you look at let's say smart you know smart utility grids when you look at any type of operational problem it has a real-time component and it has a historical component and having predictives and so like you know you know my sense here is that there that technically we can bring this together through the cloud and i think the use case is that is that we we can apply some some real-time sort of you know predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction we have this real time you know input sanjeev did you have a comment yeah i was just going to say that to this point you know we have to think of streaming very different because in the historical databases we used to bring the data and store the data and then we used to run rules on top uh aggregations and all but in case of streaming the mindset changes because the rules normally the inference all of that is fixed but the data is constantly changing so it's a completely reverse way of thinking of uh and building applications on top of that so dave menninger there seemed to be some disagreement about the default or now what kind of time frame are you are you thinking about is this end of decade it becomes the default what would you pin i i think around you know between between five to ten years i think this becomes the reality um i think you know it'll be more and more common between now and then but it becomes the default and i also want sanjeev at some point maybe in one of our subsequent conversations we need to talk about governing streaming data because that's a whole other set of challenges we've also talked about it rather in a two dimensions historical and streaming and there's lots of low latency micro batch sub second that's not quite streaming but in many cases it's fast enough and we're seeing a lot of adoption of near real time not quite real time as uh good enough for most for many applications because nobody's really taking the hardware dimension of this information like how do we that'll just happen carl so near real time maybe before you lose the customer however you define that right okay um let's move on to brad brad you want to talk about automation ai uh the the the pipeline people feel like hey we can just automate everything what's your prediction yeah uh i'm i'm an ai fiction auto so apologies in advance for that but uh you know um i i think that um we've been seeing automation at play within ai for some time now and it's helped us do do a lot of things for especially for practitioners that are building ai outcomes in the enterprise uh it's it's helped them to fill skills gaps it's helped them to speed development and it's helped them to to actually make ai better uh because it you know in some ways provides some swim lanes and and for example with technologies like ottawa milk and can auto document and create that sort of transparency that that we talked about a little bit earlier um but i i think it's there's an interesting kind of conversion happening with this idea of automation um and and that is that uh we've had the automation that started happening for practitioners it's it's trying to move outside of the traditional bounds of things like i'm just trying to get my features i'm just trying to pick the right algorithm i'm just trying to build the right model uh and it's expanding across that full life cycle of building an ai outcome to start at the very beginning of data and to then continue on to the end which is this continuous delivery and continuous uh automation of of that outcome to make sure it's right and it hasn't drifted and stuff like that and because of that because it's become kind of powerful we're starting to to actually see this weird thing happen where the practitioners are starting to converge with the users and that is to say that okay if i'm in tableau right now i can stand up salesforce einstein discovery and it will automatically create a nice predictive algorithm for me um given the data that i that i pull in um but what's starting to happen and we're seeing this from the the the companies that create business software so salesforce oracle sap and others is that they're starting to actually use these same ideals and a lot of deep learning to to basically stand up these out of the box flip a switch and you've got an ai outcome at the ready for business users and um i i'm very much you know i think that that's that's the way that it's going to go and what it means is that ai is is slowly disappearing uh and i don't think that's a bad thing i think if anything what we're going to see in 2022 and maybe into 2023 is this sort of rush to to put this idea of disappearing ai into practice and have as many of these solutions in the enterprise as possible you can see like for example sap is going to roll out this quarter this thing called adaptive recommendation services which which basically is a cold start ai outcome that can work across a whole bunch of different vertical markets and use cases it's just a recommendation engine for whatever you need it to do in the line of business so basically you're you're an sap user you look up to turn on your software one day and you're a sales professional let's say and suddenly you have a recommendation for customer churn it's going that's great well i i don't know i i think that's terrifying in some ways i think it is the future that ai is going to disappear like that but i am absolutely terrified of it because um i i think that what it what it really does is it calls attention to a lot of the issues that we already see around ai um specific to this idea of what what we like to call it omdia responsible ai which is you know how do you build an ai outcome that is free of bias that is inclusive that is fair that is safe that is secure that it's audible etc etc etc etc that takes some a lot of work to do and so if you imagine a customer that that's just a sales force customer let's say and they're turning on einstein discovery within their sales software you need some guidance to make sure that when you flip that switch that the outcome you're going to get is correct and that's that's going to take some work and so i think we're going to see this let's roll this out and suddenly there's going to be a lot of a lot of problems a lot of pushback uh that we're going to see and some of that's going to come from gdpr and others that sam jeeve was mentioning earlier a lot of it's going to come from internal csr requirements within companies that are saying hey hey whoa hold up we can't do this all at once let's take the slow route let's make ai automated in a smart way and that's going to take time yeah so a couple predictions there that i heard i mean ai essentially you disappear it becomes invisible maybe if i can restate that and then if if i understand it correctly brad you're saying there's a backlash in the near term people can say oh slow down let's automate what we can those attributes that you talked about are non trivial to achieve is that why you're a bit of a skeptic yeah i think that we don't have any sort of standards that companies can look to and understand and we certainly within these companies especially those that haven't already stood up in internal data science team they don't have the knowledge to understand what that when they flip that switch for an automated ai outcome that it's it's gonna do what they think it's gonna do and so we need some sort of standard standard methodology and practice best practices that every company that's going to consume this invisible ai can make use of and one of the things that you know is sort of started that google kicked off a few years back that's picking up some momentum and the companies i just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing you know so like for the sap example we know for example that it's convolutional neural network with a long short-term memory model that it's using we know that it only works on roman english uh and therefore me as a consumer can say oh well i know that i need to do this internationally so i should not just turn this on today great thank you carl can you add anything any context here yeah we've talked about some of the things brad mentioned here at idc in the our future of intelligence group regarding in particular the moral and legal implications of having a fully automated you know ai uh driven system uh because we already know and we've seen that ai systems are biased by the data that they get right so if if they get data that pushes them in a certain direction i think there was a story last week about an hr system that was uh that was recommending promotions for white people over black people because in the past um you know white people were promoted and and more productive than black people but not it had no context as to why which is you know because they were being historically discriminated black people being historically discriminated against but the system doesn't know that so you know you have to be aware of that and i think that at the very least there should be controls when a decision has either a moral or a legal implication when when you want when you really need a human judgment it could lay out the options for you but a person actually needs to authorize that that action and i also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases and to some extent they always will so we'll always be chasing after them that's that's absolutely carl yeah i think that what you have to bear in mind as a as a consumer of ai is that it is a reflection of us and we are a very flawed species uh and so if you look at all the really fantastic magical looking supermodels we see like gpt three and four that's coming out z they're xenophobic and hateful uh because the people the data that's built upon them and the algorithms and the people that build them are us so ai is a reflection of us we need to keep that in mind yeah we're the ai's by us because humans are biased all right great okay let's move on doug henson you know a lot of people that said that data lake that term's not not going to not going to live on but it appears to be have some legs here uh you want to talk about lake house bring it on yes i do my prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering i say offering that doesn't mean it's going to be the dominant thing that organizations have out there but it's going to be the predominant vendor offering in 2022. now heading into 2021 we already had cloudera data bricks microsoft snowflake as proponents in 2021 sap oracle and several of these fabric virtualization mesh vendors join the bandwagon the promise is that you have one platform that manages your structured unstructured and semi-structured information and it addresses both the beyond analytics needs and the data science needs the real promise there is simplicity and lower cost but i think end users have to answer a few questions the first is does your organization really have a center of data gravity or is it is the data highly distributed multiple data warehouses multiple data lakes on-premises cloud if it if it's very distributed and you you know you have difficulty consolidating and that's not really a goal for you then maybe that single platform is unrealistic and not likely to add value to you um you know also the fabric and virtualization vendors the the mesh idea that's where if you have this highly distributed situation that might be a better path forward the second question if you are looking at one of these lake house offerings you are looking at consolidating simplifying bringing together to a single platform you have to make sure that it meets both the warehouse need and the data lake need so you have vendors like data bricks microsoft with azure synapse new really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements can meet the user and query concurrency requirements meet those tight slas and then on the other hand you have the or the oracle sap snowflake the data warehouse uh folks coming into the data science world and they have to prove that they can manage the unstructured information and meet the needs of the data scientists i'm seeing a lot of the lake house offerings from the warehouse crowd managing that unstructured information in columns and rows and some of these vendors snowflake in particular is really relying on partners for the data science needs so you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement well thank you doug well tony if those two worlds are going to come together as doug was saying the analytics and the data science world does it need to be some kind of semantic layer in between i don't know weigh in on this topic if you would oh didn't we talk about data fabrics before common metadata layer um actually i'm almost tempted to say let's declare victory and go home in that this is actually been going on for a while i actually agree with uh you know much what doug is saying there which is that i mean we i remembered as far back as i think it was like 2014 i was doing a a study you know it was still at ovum predecessor omnia um looking at all these specialized databases that were coming up and seeing that you know there's overlap with the edges but yet there was still going to be a reason at the time that you would have let's say a document database for json you'd have a relational database for tran you know for transactions and for data warehouse and you had you know and you had basically something at that time that that resembles to do for what we're considering a day of life fast fo and the thing is what i was saying at the time is that you're seeing basically blur you know sort of blending at the edges that i was saying like about five or six years ago um that's all and the the lake house is essentially you know the amount of the the current manifestation of that idea there is a dichotomy in terms of you know it's the old argument do we centralize this all you know you know in in in in in a single place or do we or do we virtualize and i think it's always going to be a yin and yang there's never going to be a single single silver silver bullet i do see um that they're also going to be questions and these are things that points that doug raised they're you know what your what do you need of of of your of you know for your performance there or for your you know pre-performance characteristics do you need for instance hiking currency you need the ability to do some very sophisticated joins or is your requirement more to be able to distribute and you know distribute our processing is you know as far as possible to get you know to essentially do a kind of brute force approach all these approaches are valid based on you know based on the used case um i just see that essentially that the lake house is the culmination of it's nothing it's just it's a relatively new term introduced by databricks a couple years ago this is the culmination of basically what's been a long time trend and what we see in the cloud is that as we start seeing data warehouses as a checkbox item say hey we can basically source data in cloud and cloud storage and s3 azure blob store you know whatever um as long as it's in certain formats like you know like you know parquet or csv or something like that you know i see that as becoming kind of you know a check box item so to that extent i think that the lake house depending on how you define it is already reality um and in some in some cases maybe new terminology but not a whole heck of a lot new under the sun yeah and dave menger i mean a lot of this thank you tony but a lot of this is going to come down to you know vendor marketing right some people try to co-opt the term we talked about data mesh washing what are your thoughts on this yeah so um i used the term data platform earlier and and part of the reason i use that term is that it's more vendor neutral uh we've we've tried to uh sort of stay out of the the vendor uh terminology patenting world right whether whether the term lake house is what sticks or not the concept is certainly going to stick and we have some data to back it up about a quarter of organizations that are using data lakes today already incorporate data warehouse functionality into it so they consider their data lake house and data warehouse one in the same about a quarter of organizations a little less but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake so it's pretty obvious that three quarters of organizations need to bring this stuff together right the need is there the need is apparent the technology is going to continue to verge converge i i like to talk about you know you've got data lakes over here at one end and i'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a in a server and you ignore it right that's not what a data lake is so you've got data lake people over here and you've got database people over here data warehouse people over here database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities so it's obvious that they're going to meet in the middle i mean i think it's like tony says i think we should there declare victory and go home and so so i it's just a follow-up on that so are you saying these the specialized lake and the specialized warehouse do they go away i mean johnny tony data mesh practitioners would say or or advocates would say well they could all live as just a node on the on the mesh but based on what dave just said are we going to see those all morph together well number one as i was saying before there's always going to be this sort of you know kind of you know centrifugal force or this tug of war between do we centralize the data do we do it virtualize and the fact is i don't think that work there's ever going to be any single answer i think in terms of data mesh data mesh has nothing to do with how you physically implement the data you could have a data mesh on a basically uh on a data warehouse it's just that you know the difference being is that if we use the same you know physical data store but everybody's logically manual basically governing it differently you know um a data mission is basically it's not a technology it's a process it's a governance process um so essentially um you know you know i basically see that you know as as i was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring but there are going to be cases where for instance if i need let's say like observe i need like high concurrency or something like that there are certain things that i'm not going to be able to get efficiently get out of a data lake um and you know we're basically i'm doing a system where i'm just doing really brute forcing very fast file scanning and that type of thing so i think there always will be some delineations but i would agree with dave and with doug that we are seeing basically a a confluence of requirements that we need to essentially have basically the element you know the ability of a data lake and a data laid out their warehouse we these need to come together so i think what we're likely to see is organizations look for a converged platform that can handle both sides for their center of data gravity the mesh and the fabric vendors the the fabric virtualization vendors they're all on board with the idea of this converged platform and they're saying hey we'll handle all the edge cases of the stuff that isn't in that center of data gradient that is off distributed in a cloud or at a remote location so you can have that single platform for the center of of your your data and then bring in virtualization mesh what have you for reaching out to the distributed data bingo as they basically said people are happy when they virtualize data i i think yes at this point but to this uh dave meningas point you know they have convert they are converging snowflake has introduced support for unstructured data so now we are literally splitting here now what uh databricks is saying is that aha but it's easy to go from data lake to data warehouse than it is from data warehouse to data lake so i think we're getting into semantics but we've already seen these two converge so is that so it takes something like aws who's got what 15 data stores are they're going to have 15 converged data stores that's going to be interesting to watch all right guys i'm going to go down the list and do like a one i'm going to one word each and you guys each of the analysts if you wouldn't just add a very brief sort of course correction for me so sanjeev i mean governance is going to be the maybe it's the dog that wags the tail now i mean it's coming to the fore all this ransomware stuff which really didn't talk much about security but but but what's the one word in your prediction that you would leave us with on governance it's uh it's going to be mainstream mainstream okay tony bear mesh washing is what i wrote down that's that's what we're going to see in uh in in 2022 a little reality check you you want to add to that reality check is i hope that no vendor you know jumps the shark and calls their offering a data mesh project yeah yeah let's hope that doesn't happen if they do we're going to call them out uh carl i mean graph databases thank you for sharing some some you know high growth metrics i know it's early days but magic is what i took away from that it's the magic database yeah i would actually i've said this to people too i i kind of look at it as a swiss army knife of data because you can pretty much do anything you want with it it doesn't mean you should i mean that's definitely the case that if you're you know managing things that are in a fixed schematic relationship probably a relational database is a better choice there are you know times when the document database is a better choice it can handle those things but maybe not it may not be the best choice for that use case but for a great many especially the new emerging use cases i listed it's the best choice thank you and dave meninger thank you by the way for bringing the data in i like how you supported all your comments with with some some data points but streaming data becomes the sort of default uh paradigm if you will what would you add yeah um i would say think fast right that's the world we live in you got to think fast fast love it uh and brad shimon uh i love it i mean on the one hand i was saying okay great i'm afraid i might get disrupted by one of these internet giants who are ai experts so i'm gonna be able to buy instead of build ai but then again you know i've got some real issues there's a potential backlash there so give us the there's your bumper sticker yeah i i would say um going with dave think fast and also think slow uh to to talk about the book that everyone talks about i would say really that this is all about trust trust in the idea of automation and of a transparent invisible ai across the enterprise but verify verify before you do anything and then doug henson i mean i i look i think the the trend is your friend here on this prediction with lake house is uh really becoming dominant i liked the way you set up that notion of you know the the the data warehouse folks coming at it from the analytics perspective but then you got the data science worlds coming together i still feel as though there's this piece in the middle that we're missing but your your final thoughts we'll give you the last well i think the idea of consolidation and simplification uh always prevails that's why the appeal of a single platform is going to be there um we've already seen that with uh you know hadoop platforms moving toward cloud moving toward object storage and object storage becoming really the common storage point for whether it's a lake or a warehouse uh and that second point uh i think esg mandates are uh are gonna come in alongside uh gdpr and things like that to uh up the ante for uh good governance yeah thank you for calling that out okay folks hey that's all the time that that we have here your your experience and depth of understanding on these key issues and in data and data management really on point and they were on display today i want to thank you for your your contributions really appreciate your time enjoyed it thank you now in addition to this video we're going to be making available transcripts of the discussion we're going to do clips of this as well we're going to put them out on social media i'll write this up and publish the discussion on wikibon.com and siliconangle.com no doubt several of the analysts on the panel will take the opportunity to publish written content social commentary or both i want to thank the power panelist and thanks for watching this special cube presentation this is dave vellante be well and we'll see you next time [Music] you

Hello, everyone, and welcome back to the special presentation from theCUBE, where we're exploring the future of cloud and its business impact in the coming decade, kind of where we've come from and where we're going. My name is Dave Vellante, and with me is a CIO/CTO/COO, and longtime colleague, Dan Sheehan. Hello, Dan, how're you doing? >> Hey, Dave, how are you doing? Thank you for having me. >> Yeah, you're very welcome. So folks, Dan has been in the technology industry for a number of years. He's overseen, you know, large-multi, tens of millions of dollar ERP application development efforts, He was a CIO of a marketing, you know, direct mail company. Dan, we met at ADVO, it seems like such a (snickers) long time ago. >> Yeah, that was a long time ago, back in Connecticut. Back in the early 2000s. >> Yeah, ancient days. But pretty serious data for back then, you know, the early 2000s, and then you did a six-year stint as a EVP and CIO at Dunkin' Brands. I remember I came out to see you when I was starting Wikibon and trying to understand. >> Oh yeah. >> You know, what the CIOs cared about. You were so helpful and thanks for that. And that was a big deal. I mean, Dunkin', 17,000 points of distribution. I mean, that was sort of a complicated situation, right? >> Oh yeah. >> So, great experience. >> I mean, when you get involved with franchisees and trying to make everybody happy, yes, that was a lot of fun. >> And then you had a number of other roles, one was as COO at Modell's, and then to fast-forward, Beacon Health. You were EVP and CIO there. And you also, it looked like you had a kind of a business and operational role. You helped the company get acquired by Anthem Blue Cross. So awesome, congrats on that. That must've been a great experience. >> It was. A year of my life, yes. (both laugh) >> You're still standing. So anyway, you can see Dan, he's like this multi-tool star, he's seen a lot of changes in the technology business. So Dan, again, welcome back. Dan Sheehan. >> Oh, thank you. >> So when you started in your career, you know, there was no cloud, right? I mean, you had to do everything. It's funny, I remember I was... You probably know Bill Rucci, CIO of Hartford Steam Boiler. I remember we were talking one day, and this again was pre-cloud and he said, you know, I'm thinking, do I really need to manage my own email? I mean, back then, we did everything. So you had to provision infrastructure so you could write apps, and that was important. That frustrated CFOs, but it was a necessary piece of the value chain. So how have you seen that sort of IT value contribution shift over the years? Let's start there. >> Ah, well, I think it comes down to demand versus capacity. If you look at where companies want to go, they want to do a lot with technology. Technology has taken on a larger role. It's no longer and has not been a, so to speak, cost center. So I think the demand for making change and driving a company forward or reducing costs, there are other executives, peers to the CIO, to the CTO that are looking to do more, and when it comes to doing more, that means more demand, and you step back and you look at what the CIO has for capacity. Looking at Quick Solution's data, solutions in the cloud is appealing, and there are, you know, times where other functions talk to a vendor and see that they can get a vertical solution done pretty quickly. They go off and take that on, or it could be, you know, a ServiceNow capability that you want to implement across the company, and you do that just like an ERP type of roll up. But the bottom line is there are solutions out there that have pushed, I would say the IT organization to look at their capacity versus demand, and sometimes you can get things done quicker with a cloud type of solution. >> So how did you look at that shadow IT as a CIO? Was it something that kind of ticked you off or like you're sort of implying that it made you better? >> Well, I think it does ultimately make you better, but I think you have to partner with the functions because if you don't, you get these types of scenarios, and I've been involved in these just as well. You are busy with, you know, fulfilling your objectives as the leader of IT, and then you get a knock on the door from, let's say marketing or operations, and they say, hey, we just purchased this X solution and we want to integrate it with A, B and C. Well, that was not on the budget or on the IT roadmap or the IT strategy that was linked to the IT, I'm sorry, to the business strategy, and all of a sudden now you have more demand versus the capacity, and then you have to go start reprioritizing. So it's more of, yeah, kind of disrupted, but at the same time, it pushed, you know, the needle of the company forward. But it's all about just working together to make it happen. And that's a lot of, you know, hard conversations when you have to start reprioritizing capacity. >> Well, so let's talk about that alignment. I mean, there's always been a sort of a schism between IT and its ability to deliver, manage demand, and the business will always want you to go faster. They want IT to develop the systems, you know, of course, for less and then they want you to eat the cost of maintaining them, so (chuckles) there's been that tension. But in many ways, that CIO's job is alignment. I mean, it seems to me anyway that schism has certainly narrowed and the cloud's been been part of that, but what do you see as that trajectory over the years and where do you see it going? >> Well, I think it's going to continue to move forward, and depending upon the service, you know, companies are going to take advantage of those services. So yes, some of the non-mission critical capabilities that you would want to move out to the cloud or have somebody else do it, so to speak, that's going to continue to happen because they should be able to do it a lot cheaper than you can, just like use you mentioned a few moments ago about email. I did not want to maintain, you know, exchange service and keeping that all up and running. I moved quickly to Microsoft 365 and that's been a world of difference, but that's just one example. But when you have mission critical apps, you're going to have to make a decision if you want to continue to house them in-house or push them out to an AWS and house them there. So maybe you don't need a large data center and you can utilize some of the best and brightest around security, around managing size of the infrastructure and getting some of their engineering help, which can help. So it just depends upon the application, so to speak, or a function that you're trying to support. And you got to really look at your enterprise architecture and see where that makes sense. So you got to have a hybrid. I see and I have, you know, managed towards a hybrid way of looking at your architecture. >> Okay, so obviously the cloud played a role in that change, and of course, you were in healthcare too so you had to be somewhat careful, >> Yep. >> With the cloud. But you mentioned this hybrid architecture. I mean, from a technologist standpoint and a business standpoint, what do you want out of, you know, you hear a hybrid, multi, all the buzz words. What are you looking for then? Is it a consistent experience? Is it a consistent security? Or is it sort of more horses for courses, where you're trying to run a workload in the right place? What's your philosophy on that? >> Well, I mean, all those things matter, but you're looking at obviously, cost, you're looking at engagement. How does these services engage? Whether it's internal employees or external clients who you're servicing, and you want to get to a cost structure that makes sense in terms of managing those services as well as those mission critical apps. So it comes down to looking at the dollars and cents, as well as what type of services you can provide. In many cases, if you can provide a cheaper and increase the overall services, you're going to go down that path. And just like we did with ServiceNow, I did that at Beacon and also at DentaQuest two healthcare companies. We were able to, you know, remove duplicated, so to speak, ticketing systems and move to one and allow a better experience for the internal employee. They can do self-service, they can look at metrics, they can see status, real-time status on where their request was. So that made a bigger difference. So you engaged the employee differently, better, and then you also reduce your costs. >> Well, how about the economics? I mean, your experience that cloud is cheaper. You hear a lot of the, you know, a lot of the legacy players are saying, oh, no cloud's super expensive. Wait till you get that Amazon bill. (laughs) What's the truth? >> Well, I think there's still a lot of maturing that needs to go on, because unfortunately, depending upon the company, so let's use a couple of examples. So let's look at a startup. You look at a startup, they're probably going to look at all their services being in the cloud and being delivered through a SaaS model, and that's going to be an expense, that's going to be most likely a per user expense per month or per year, however, they structure the contract. And right out of the gate, that's going to be a top line expense that has to be managed going forward. Now you look at companies that have been around for a while, and two of the last companies I worked with, had a lot of technical debt, had on-prem applications. And when you started to look at how to move forward, you know, you had CFOs that were used to going to buy software, capitalize in that software over, you know, five years, sometimes three years, and using that investment to be capitalized, and that would sit below the line, so to speak. Now, don't get me wrong, you still have to pay for it, it's just a matter of where it sits. And when you're running a company and you're looking at the financials, not having that cost on your operational expenses, so to speak, if you're not looking at the depreciation through those numbers, that was advantageous to a CFO many years ago. Now you come to them and say, hey, we're going to move forward with a new HR system, and it's all increasing the expense because there's nothing else to capitalize. Those are different conversations, and all of a sudden your expenses have increased, and yes, you have to make sure that the businesses behind you, with respects to an ROI and supporting it. >> Yeah, so as long as the value is there, and that's a part of the alignment. I want to ask you about cloud pricing strategies because you mentioned ServiceNow, you know, Salesforce is in there, Workday. If you look at the way these guys price, it's really not true cloud pricing in a way, cause they're going to have you sign up for an annual license, you know, a lot of times you got pay up front, or if you want a discount, you're going to have to sign up for two years or three years. But now you see guys like Snowflake coming in, you know, big high-profile IPO. They actually charge you on a consumption-based model. What are your thoughts on that? Do you see that as sort of a trend in the coming decade? >> No, I absolutely think it's going to be on a trend, because consumption means more transactions and more transactions means more computing, and they're going to look at charging it just like any other utility charges. So yes, I see that trend continuing. Did a big deal with UltiPro HR, and yeah, that was all based upon user head count, but they were talking about looking at their payroll and changing their costing on payroll down the road. With their merger, or they went from being a public company to a private company, and now looking to merge with Kronos. I can see where time and attendance and payroll will stop being looked at as a transaction, right? It's a weekly or bi-weekly or monthly, however the company pays, and yes, there is dollars to be made there. >> Well, so let me ask you as a CIO and a business, you know, COO. One of the challenges that you hear with the cloud is okay, if I get my Amazon bill, it's something that Snowflake has talked about, where you know, to me, it's the ideal model, but on the other hand, the transparency is not necessarily there. You don't know what it's going to be at the end of (mumbles) Would you rather have more certainty as to what that bill's going to look like? Or would you rather have it aligned with consumption and the value to the business? >> Well, you know, that's a great question, because yes, I mean, budgets are usually built upon a number that's fixed. Now, no, don't get me wrong. I mean, when I look at the wide area network, the cost for internet services, yes, sometimes we need to increase and that means an increase in the overall cost, but that consumption, that transactional, that's going to be a different way of having to go ahead and budget. You have to budget now for the maximum transactions you anticipate with a growth of a company, and then you need to take a look at that you know, if you're budgeting. I know we were on a calendar fiscal year, so we started up budgeting process in August and we finalized at sometime in the end of October, November for the proceeding year, and if that's the case, you need to get a little bit better on what your consumptions are going to be, because especially if you're a public company, going out on the street with some numbers, those numbers could vary based upon a high transaction volume and the cost, and maybe you're not getting the results on the top end, on the revenue side. So I think, yeah, it's going to be an interesting dilemma as we move forward. >> Yeah. So, I mean, it comes back to alignment, doesn't it? I mean, I know in our small example, you know, we're doing now, we were used to be physical events with theCUBE, now it's all virtual events and our Amazon bill is going through the roof because we're supporting all these users on these virtual events, and our CFO's like, well, look at this Amazon bill, and you say, yeah, but look at the revenue, it's supporting. And so to your point, if the revenue is there, if the ROI is there, then it makes sense. You can kind of live with it because you're growing with it, but if not, then you really got to question it. >> Yeah. So you got to need to partner with your financial folks and come up with better modeling around some of these transactional services and build that into your modeling for your budget and for your, you know, your top line and your expenses. >> So what do you think of some of these SaaS companies? I mean, you've had a lot of experience. They're really coming at it from largely an application perspective, although you've managed a lot of infrastructure too. But we've talked about ServiceNow. They've kind of mopped up in the ITSM. I mean, there's nobody left. I mean, ServiceNow has sort of taken over the whole (mumbles) You know, Salesforce, >> Yeah. >> I guess, sort of similarly, sort of dominating the CRM space. You hear a lot of complaints now about, you know, ServiceNow pricing. There is somebody the other day called them the Oracle of ITSM. Do you see that potentially getting disrupted by maybe some cloud native developers who are developing tools on top? You see in, like, for instance, Datadog going after Splunk and LogRhythm. And there seem to be examples popping up. Well, what's your take on all this? >> No, absolutely. I think cause, you know, when we were talking about back when I first met you, when I was at the ADVO, I mean, Oracle was on it's, you know, rise with their suite of capabilities, and then before you know it, other companies were popping up and took over, whether it was Firstbeat, PeopleSoft, Workday, and then other companies that just came into play, cause it's going to happen because people are going to get, you know, frustrated. And yes, I did get a little frustrated with ServiceNow when I was looking at a couple of new modules because the pricing was a little bit higher than it was when I first started out. So yes, when you're good and you're able to provide the right services, they're going to start pricing it that way. But yes, I think you're going to get smaller players, and then those smaller players will start grabbing up, so to speak, market share and get into it. I mean, look at Salesforce. I mean, there are some pretty good CRMs. I mean, even, ServiceNow is getting into the CRM space big time, as well as a company like Sugar and a few others that will continue to push Salesforce to look at their pricing as well as their services. I mean, they're out there buying up companies, but you just can't automatically assume that they're going to, you know, integrate day one, and it's going to take time for some of their services to come and become reality, so to speak. So yes, I agree that there will be players out there that will push these lager SaaS companies, and hopefully get the right behaviors and right pricing. >> I've said for years, Dan, that I've predicted that ServiceNow and Salesforce are on a collision course. It didn't really happen, but it's starting to, because ServiceNow, the valuation is so huge. They have to grow into other markets much in the same way that Salesforce has. So maybe we'll see McDermott start doing some acquisitions. It's maybe a little tougher for ServiceNow given their whole multi-instance architecture and sort of their own cloud. That's going to be interesting to see how that plays out. >> Yeah. Yeah. You got to play in that type of architecture, let's put it that way. Yes, it'll be interesting to see how that does play out. >> What are your thoughts on the big hyperscalers; Amazon, Microsoft, Google? What's the right strategy there? Do you go all in on one cloud like AWS or are you more worried about lock-in? Do you want to spread your bets across clouds? How real is multi-cloud? Is it a strategy or more sort of a reality that you get M and A and you got shadow IT? What's your take on all that? >> Yeah, that's a great question because it does make you think a little differently around you know, where to put all your eggs. And it's getting tougher because you do want to distribute those eggs out to multiple vendors, if you would, service providers. But, you know, for instance we had a situation where we were building a brand new business intelligence data warehouse, and we decided to go with Microsoft as its core database. And we did a bake-off on business analytic tools. We had like seven of them at Beacon and we ended up choosing Microsoft's Power BI, and a good part of that reason, not all of it, but a good part of it was because we felt they did everything else that the Tableau's and others did, but, you know, Microsoft would work to give, you know, additional capabilities to Power BI if it's sitting on their database. So we had to take that into consideration, and we did and we ended up going with Power BI. With Amazon, I think Amazon's a little bit more, I'll put it horizontal, whereby they can help you out because of the database and just kind of be in that data center, if you would, and be able to move some of your homegrown applications, some of your technical debt over to that, I'll say cloud. But it'll get interesting because when you talk about integration, when you talk about moving forward with a new functionality, yeah, you have to put your architecture in a somewhat of a center point, and then look to see what is easier, cheaper, cost-effective, but, you know, what's happening to my functionality over the next three to five years. >> But it sounds like you'd subscribe to a horses for courses approach, where you put the right workload in the right cloud, as opposed to saying, I'm going to go all in on one cloud and it's going to be, you know, same skillset, same security, et cetera. It sounds like you'd lean toward the former versus going all in with, you know, MANO cloud. >> Yeah, I guess again, when I look at the architecture. There will be major, you know, breaks if you would. So yes, there is somewhat of a, you know, movement to you know, go with one horse. But, you know, I could see looking back at the Beacon architecture that we could, you know, lift and put the claims adjudication capabilities up in Amazon and then have that conduct, you know, the left to right claims processing, and then those transactions could then be moved into Microsoft's data warehouse. So, you know, there is ways to go about spreading it out so that you don't have all those eggs in one basket and that you reduce the amount of risk, but that weighed heavily on my mind. >> So I was going to ask you, how much of a factor lock-in is it? It sounds like it's more, you know, spreading your eggs around, as you say and reducing your risk as opposed to, you know, worried about lock-in, but as a CIO, how worried are you about lock-in? Where is that fit in the sort of decision tree? >> Ah, I mean, I would say it's up there, but unfortunately, there's no number one, there's like five number ones, if you would. So it's definitely up there and it's something to consider when you're looking at, like you said, the cost, risk integration, and then time. You know, sometimes you're up against the time. And again, security, like I said. Security is a big key in healthcare. And actually security overall, whether you're retail, you're going to always have situations no matter what industry, you got to protect the business. >> Yeah, so I want to ask you about security. That's the other number one. Well, you might've been a defacto CSO, but kind of when we started in this business security was the problem of the security teams, and you know, it's now a team sport. But in thinking about the cloud and security, how big of a concern is the cloud? Is it just more, you're looking for consistency and be able to apply the corporate edicts? Are there other concerns like the shared responsibility model? What are your thoughts on security in the cloud? >> Well, it probably goes back to again, the industry, but when I looked at the past five years in healthcare, doing a lot of work with the CMS and Medicaid, Medicare, they had certain requirements and certain restrictions. So we had to make sure that we follow those requirements. And when you got audited, you needed to make sure that you can show that you are adhering to their requirements. So over the past, probably two years with Amazon's government capabilities that those restrictions have changed, but we were always looking to make sure that we owned and managed how we manage the provider and member data, because yes, we did not want to have obviously a breach, but we wanted to make sure we were following the guidelines, whether it's state or federal, and then and even some cases healthcare guidelines around managing that data. So yes, top of mind, making sure that we're protecting, you know, in my case so we had 37 million members, patients, and we needed to make sure that if we did put it in the cloud or if it was on-prem, that it was being protected. And as you mentioned, recently come off of, I was going to say Amazon, but it was an acquisition. That company that was looking at us doing the due diligence, they gave us thumbs up because of how we were managing the data at the lowest point and all the different levels within the architecture. So Anthem who did the acquisition, had a breach back in, I think it was 2015. That was top of mind for them. We had more questions during the due diligence around security than any other functional area. So it is critical, and I think slowly, some of that type of data will get up into the cloud, but again, it's going to go through some massive risk management and security measures, and audits, because how fragile that is. >> Yeah, I mean, that could be a deal breaker in an acquisition. I got two other questions for you. One is, you know, I know you follow the technologies very closely, but there's all the buzz words, the digital transformation, the AI, these new SaaS models that we talked about. You know, a lot of CIOs tell me, look, Dave, get the business right and the technology is the easy part. It's people, it's process. But what are you seeing in terms of some of this new stuff coming out, there's machine learning, you know, obviously massive scale, new cloud workloads. Anything out there that really excites you and that you could see on the horizon that could be, you know, really change agents for the next decade? >> Yeah, I think we did some RPA, robotics on some of the tasks that, you know, where, you know, if the analysis types of situations. So I think RPA is going to be a game changer as it continues to evolve. But I agree with what you just said. Doing this for quite a while now, it still comes down to the people. I can get the technology to do what it needs to do as long as I have the right requirements, so that goes back to people. Making sure we have the partnership that goes back to leadership and the people. And then the change management aspects. Right out of the gate, you should be worrying about how is it going to affect and then the adoption and engagement. Because adoption is critical, because you can go create the best thing you think from a technology perspective, but if it doesn't get used correctly, it's not worth the investment. So I agree, whether it's digital transformation or innovation, it still comes down to understanding the business model and injecting and utilizing technology to grow or reduce costs, grow the business or reduce costs. >> Yeah, usage really means value. Sorry, my last question. What's the one thing that vendors shouldn't do? What's the vendor no-no that'll alienate CIO's? >> To this day, I still don't like, there's a company out there that starts with an O. I still don't like it to that, every single technology module, if you would, has a separate sales rep. I want to work with my strategic partners and have one relationship and that single point of contact that spark and go back into their company and bring me whatever it is that we're looking at so that I don't get, you know, for instance from that company that starts with an O, you know, 17 calls from 17 different sales reps trying to sell me 17 different things. So what irritates me is, you know, you have a company that has a lot of breadth, a lot of, you know, capability and functional, you know that I may want. Give me one person that I can deal with. So a single point of contact, then that makes my life a lot easier. >> Well, Dan Sheehan, I really appreciate you spending some time on theCUBE, it's always a pleasure catching up with you and really appreciate you sharing your insights with our audience. Thank you. >> Oh, thank you, David. I appreciate the opportunity. You have a great day. >> All right. You too. And thank you for watching everybody. This is Dave Vellante for theCUBE on Cloud. Keep it right there. We'll be back with our next guest right after the short break. Awesome, Dan.

>> Go on my lead. >> Dan: All right, very good. >> Five, four. Hello, everyone, and welcome back to the special presentation from theCUBE, where we're exploring the future of cloud and its business impact in the coming decade, kind of where we've come from and where we're going. My name is Dave Vellante, and with me is a CIO/CTO/COO, and longtime colleague, Dan Sheehan. Hello, Dan, how're you doing? >> Hey, Dave, how are you doing? Thank you for having me. >> Yeah, you're very welcome. So folks, Dan has been in the technology industry for a number of years. He's overseen, you know, large-multi, tens of millions of dollar ERP application development efforts, He was a CIO of a marketing, you know, direct mail company. Dan, we met at ADVO, it seems like such a (snickers) long time ago. >> Yeah, that was a long time ago, back in Connecticut. Back in the early 2000s. >> Yeah, ancient days. But pretty serious data for back then, you know, the early 2000s, and then you did a six-year stint as a EVP and CIO at Dunkin' Brands. I remember I came out to see you when I was starting Wikibon and trying to understand. >> Oh yeah. >> You know, what the CIOs cared about. You were so helpful and thanks for that. And that was a big deal. I mean, Dunkin', 17,000 points of distribution. I mean, that was sort of a complicated situation, right? >> Oh yeah. >> So, great experience. >> I mean, when you get involved with franchisees and trying to make everybody happy, yes, that was a lot of fun. >> And then you had a number of other roles, one was as COO at Modell's, and then to fast-forward, Beacon Health. You were EVP and CIO there. And you also, it looked like you had a kind of a business and operational role. You helped the company get acquired by Anthem Blue Cross. So awesome, congrats on that. That must've been a great experience. >> It was. A year of my life, yes. (both laugh) >> You're still standing. So anyway, you can see Dan, he's like this multi-tool star, he's seen a lot of changes in the technology business. So Dan, again, welcome back. Dan Sheehan. >> Oh, thank you. >> So when you started in your career, you know, there was no cloud, right? I mean, you had to do everything. It's funny, I remember I was... You probably know Bill Rucci, CIO of Hartford Steam Boiler. I remember we were talking one day, and this again was pre-cloud and he said, you know, I'm thinking, do I really need to manage my own email? I mean, back then, we did everything. So you had to provision infrastructure so you could write apps, and that was important. That frustrated CFOs, but it was a necessary piece of the value chain. So how have you seen that sort of IT value contribution shift over the years? Let's start there. >> Ah, well, I think it comes down to demand versus capacity. If you look at where companies want to go, they want to do a lot with technology. Technology has taken on a larger role. It's no longer and has not been a, so to speak, cost center. So I think the demand for making change and driving a company forward or reducing costs, there are other executives, peers to the CIO, to the CTO that are looking to do more, and when it comes to doing more, that means more demand, and you step back and you look at what the CIO has for capacity. Looking at Quick Solution's data, solutions in the cloud is appealing, and there are, you know, times where other functions talk to a vendor and see that they can get a vertical solution done pretty quickly. They go off and take that on, or it could be, you know, a ServiceNow capability that you want to implement across the company, and you do that just like an ERP type of roll up. But the bottom line is there are solutions out there that have pushed, I would say the IT organization to look at their capacity versus demand, and sometimes you can get things done quicker with a cloud type of solution. >> So how did you look at that shadow IT as a CIO? Was it something that kind of ticked you off or like you're sort of implying that it made you better? >> Well, I think it does ultimately make you better, but I think you have to partner with the functions because if you don't, you get these types of scenarios, and I've been involved in these just as well. You are busy with, you know, fulfilling your objectives as the leader of IT, and then you get a knock on the door from, let's say marketing or operations, and they say, hey, we just purchased this X solution and we want to integrate it with A, B and C. Well, that was not on the budget or on the IT roadmap or the IT strategy that was linked to the IT, I'm sorry, to the business strategy, and all of a sudden now you have more demand versus the capacity, and then you have to go start reprioritizing. So it's more of, yeah, kind of disrupted, but at the same time, it pushed, you know, the needle of the company forward. But it's all about just working together to make it happen. And that's a lot of, you know, hard conversations when you have to start reprioritizing capacity. >> Well, so let's talk about that alignment. I mean, there's always been a sort of a schism between IT and its ability to deliver, manage demand, and the business will always want you to go faster. They want IT to develop the systems, you know, of course, for less and then they want you to eat the cost of maintaining them, so (chuckles) there's been that tension. But in many ways, that CIO's job is alignment. I mean, it seems to me anyway that schism has certainly narrowed and the cloud's been been part of that, but what do you see as that trajectory over the years and where do you see it going? >> Well, I think it's going to continue to move forward, and depending upon the service, you know, companies are going to take advantage of those services. So yes, some of the non-mission critical capabilities that you would want to move out to the cloud or have somebody else do it, so to speak, that's going to continue to happen because they should be able to do it a lot cheaper than you can, just like use you mentioned a few moments ago about email. I did not want to maintain, you know, exchange service and keeping that all up and running. I moved quickly to Microsoft 365 and that's been a world of difference, but that's just one example. But when you have mission critical apps, you're going to have to make a decision if you want to continue to house them in-house or push them out to an AWS and house them there. So maybe you don't need a large data center and you can utilize some of the best and brightest around security, around managing size of the infrastructure and getting some of their engineering help, which can help. So it just depends upon the application, so to speak, or a function that you're trying to support. And you got to really look at your enterprise architecture and see where that makes sense. So you got to have a hybrid. I see and I have, you know, managed towards a hybrid way of looking at your architecture. >> Okay, so obviously the cloud played a role in that change, and of course, you were in healthcare too so you had to be somewhat careful, >> Yep. >> With the cloud. But you mentioned this hybrid architecture. I mean, from a technologist standpoint and a business standpoint, what do you want out of, you know, you hear a hybrid, multi, all the buzz words. What are you looking for then? Is it a consistent experience? Is it a consistent security? Or is it sort of more horses for courses, where you're trying to run a workload in the right place? What's your philosophy on that? >> Well, I mean, all those things matter, but you're looking at obviously, cost, you're looking at engagement. How does these services engage? Whether it's internal employees or external clients who you're servicing, and you want to get to a cost structure that makes sense in terms of managing those services as well as those mission critical apps. So it comes down to looking at the dollars and cents, as well as what type of services you can provide. In many cases, if you can provide a cheaper and increase the overall services, you're going to go down that path. And just like we did with ServiceNow, I did that at Beacon and also at DentaQuest two healthcare companies. We were able to, you know, remove duplicated, so to speak, ticketing systems and move to one and allow a better experience for the internal employee. They can do self-service, they can look at metrics, they can see status, real-time status on where their request was. So that made a bigger difference. So you engaged the employee differently, better, and then you also reduce your costs. >> Well, how about the economics? I mean, your experience that cloud is cheaper. You hear a lot of the, you know, a lot of the legacy players are saying, oh, no cloud's super expensive. Wait till you get that Amazon bill. (laughs) What's the truth? >> Well, I think there's still a lot of maturing that needs to go on, because unfortunately, depending upon the company, so let's use a couple of examples. So let's look at a startup. You look at a startup, they're probably going to look at all their services being in the cloud and being delivered through a SaaS model, and that's going to be an expense, that's going to be most likely a per user expense per month or per year, however, they structure the contract. And right out of the gate, that's going to be a top line expense that has to be managed going forward. Now you look at companies that have been around for a while, and two of the last companies I worked with, had a lot of technical debt, had on-prem applications. And when you started to look at how to move forward, you know, you had CFOs that were used to going to buy software, capitalize in that software over, you know, five years, sometimes three years, and using that investment to be capitalized, and that would sit below the line, so to speak. Now, don't get me wrong, you still have to pay for it, it's just a matter of where it sits. And when you're running a company and you're looking at the financials, not having that cost on your operational expenses, so to speak, if you're not looking at the depreciation through those numbers, that was advantageous to a CFO many years ago. Now you come to them and say, hey, we're going to move forward with a new HR system, and it's all increasing the expense because there's nothing else to capitalize. Those are different conversations, and all of a sudden your expenses have increased, and yes, you have to make sure that the businesses behind you, with respects to an ROI and supporting it. >> Yeah, so as long as the value is there, and that's a part of the alignment. I want to ask you about cloud pricing strategies because you mentioned ServiceNow, you know, Salesforce is in there, Workday. If you look at the way these guys price, it's really not true cloud pricing in a way, cause they're going to have you sign up for an annual license, you know, a lot of times you got pay up front, or if you want a discount, you're going to have to sign up for two years or three years. But now you see guys like Snowflake coming in, you know, big high-profile IPO. They actually charge you on a consumption-based model. What are your thoughts on that? Do you see that as sort of a trend in the coming decade? >> No, I absolutely think it's going to be on a trend, because consumption means more transactions and more transactions means more computing, and they're going to look at charging it just like any other utility charges. So yes, I see that trend continuing. Did a big deal with UltiPro HR, and yeah, that was all based upon user head count, but they were talking about looking at their payroll and changing their costing on payroll down the road. With their merger, or they went from being a public company to a private company, and now looking to merge with Kronos. I can see where time and attendance and payroll will stop being looked at as a transaction, right? It's a weekly or bi-weekly or monthly, however the company pays, and yes, there is dollars to be made there. >> Well, so let me ask you as a CIO and a business, you know, COO. One of the challenges that you hear with the cloud is okay, if I get my Amazon bill, it's something that Snowflake has talked about, where you know, to me, it's the ideal model, but on the other hand, the transparency is not necessarily there. You don't know what it's going to be at the end of (mumbles) Would you rather have more certainty as to what that bill's going to look like? Or would you rather have it aligned with consumption and the value to the business? >> Well, you know, that's a great question, because yes, I mean, budgets are usually built upon a number that's fixed. Now, no, don't get me wrong. I mean, when I look at the wide area network, the cost for internet services, yes, sometimes we need to increase and that means an increase in the overall cost, but that consumption, that transactional, that's going to be a different way of having to go ahead and budget. You have to budget now for the maximum transactions you anticipate with a growth of a company, and then you need to take a look at that you know, if you're budgeting. I know we were on a calendar fiscal year, so we started up budgeting process in August and we finalized at sometime in the end of October, November for the proceeding year, and if that's the case, you need to get a little bit better on what your consumptions are going to be, because especially if you're a public company, going out on the street with some numbers, those numbers could vary based upon a high transaction volume and the cost, and maybe you're not getting the results on the top end, on the revenue side. So I think, yeah, it's going to be an interesting dilemma as we move forward. >> Yeah. So, I mean, it comes back to alignment, doesn't it? I mean, I know in our small example, you know, we're doing now, we were used to be physical events with theCUBE, now it's all virtual events and our Amazon bill is going through the roof because we're supporting all these users on these virtual events, and our CFO's like, well, look at this Amazon bill, and you say, yeah, but look at the revenue, it's supporting. And so to your point, if the revenue is there, if the ROI is there, then it makes sense. You can kind of live with it because you're growing with it, but if not, then you really got to question it. >> Yeah. So you got to need to partner with your financial folks and come up with better modeling around some of these transactional services and build that into your modeling for your budget and for your, you know, your top line and your expenses. >> So what do you think of some of these SaaS companies? I mean, you've had a lot of experience. They're really coming at it from largely an application perspective, although you've managed a lot of infrastructure too. But we've talked about ServiceNow. They've kind of mopped up in the ITSM. I mean, there's nobody left. I mean, ServiceNow has sort of taken over the whole (mumbles) You know, Salesforce, >> Yeah. >> I guess, sort of similarly, sort of dominating the CRM space. You hear a lot of complaints now about, you know, ServiceNow pricing. There is somebody the other day called them the Oracle of ITSM. Do you see that potentially getting disrupted by maybe some cloud native developers who are developing tools on top? You see in, like, for instance, Datadog going after Splunk and LogRhythm. And there seem to be examples popping up. Well, what's your take on all this? >> No, absolutely. I think cause, you know, when we were talking about back when I first met you, when I was at the ADVO, I mean, Oracle was on it's, you know, rise with their suite of capabilities, and then before you know it, other companies were popping up and took over, whether it was Firstbeat, PeopleSoft, Workday, and then other companies that just came into play, cause it's going to happen because people are going to get, you know, frustrated. And yes, I did get a little frustrated with ServiceNow when I was looking at a couple of new modules because the pricing was a little bit higher than it was when I first started out. So yes, when you're good and you're able to provide the right services, they're going to start pricing it that way. But yes, I think you're going to get smaller players, and then those smaller players will start grabbing up, so to speak, market share and get into it. I mean, look at Salesforce. I mean, there are some pretty good CRMs. I mean, even, ServiceNow is getting into the CRM space big time, as well as a company like Sugar and a few others that will continue to push Salesforce to look at their pricing as well as their services. I mean, they're out there buying up companies, but you just can't automatically assume that they're going to, you know, integrate day one, and it's going to take time for some of their services to come and become reality, so to speak. So yes, I agree that there will be players out there that will push these lager SaaS companies, and hopefully get the right behaviors and right pricing. >> I've said for years, Dan, that I've predicted that ServiceNow and Salesforce are on a collision course. It didn't really happen, but it's starting to, because ServiceNow, the valuation is so huge. They have to grow into other markets much in the same way that Salesforce has. So maybe we'll see McDermott start doing some acquisitions. It's maybe a little tougher for ServiceNow given their whole multi-instance architecture and sort of their own cloud. That's going to be interesting to see how that plays out. >> Yeah. Yeah. You got to play in that type of architecture, let's put it that way. Yes, it'll be interesting to see how that does play out. >> What are your thoughts on the big hyperscalers; Amazon, Microsoft, Google? What's the right strategy there? Do you go all in on one cloud like AWS or are you more worried about lock-in? Do you want to spread your bets across clouds? How real is multi-cloud? Is it a strategy or more sort of a reality that you get M and A and you got shadow IT? What's your take on all that? >> Yeah, that's a great question because it does make you think a little differently around you know, where to put all your eggs. And it's getting tougher because you do want to distribute those eggs out to multiple vendors, if you would, service providers. But, you know, for instance we had a situation where we were building a brand new business intelligence data warehouse, and we decided to go with Microsoft as its core database. And we did a bake-off on business analytic tools. We had like seven of them at Beacon and we ended up choosing Microsoft's Power BI, and a good part of that reason, not all of it, but a good part of it was because we felt they did everything else that the Tableau's and others did, but, you know, Microsoft would work to give, you know, additional capabilities to Power BI if it's sitting on their database. So we had to take that into consideration, and we did and we ended up going with Power BI. With Amazon, I think Amazon's a little bit more, I'll put it horizontal, whereby they can help you out because of the database and just kind of be in that data center, if you would, and be able to move some of your homegrown applications, some of your technical debt over to that, I'll say cloud. But it'll get interesting because when you talk about integration, when you talk about moving forward with a new functionality, yeah, you have to put your architecture in a somewhat of a center point, and then look to see what is easier, cheaper, cost-effective, but, you know, what's happening to my functionality over the next three to five years. >> But it sounds like you'd subscribe to a horses for courses approach, where you put the right workload in the right cloud, as opposed to saying, I'm going to go all in on one cloud and it's going to be, you know, same skillset, same security, et cetera. It sounds like you'd lean toward the former versus going all in with, you know, MANO cloud. >> Yeah, I guess again, when I look at the architecture. There will be major, you know, breaks if you would. So yes, there is somewhat of a, you know, movement to you know, go with one horse. But, you know, I could see looking back at the Beacon architecture that we could, you know, lift and put the claims adjudication capabilities up in Amazon and then have that conduct, you know, the left to right claims processing, and then those transactions could then be moved into Microsoft's data warehouse. So, you know, there is ways to go about spreading it out so that you don't have all those eggs in one basket and that you reduce the amount of risk, but that weighed heavily on my mind. >> So I was going to ask you, how much of a factor lock-in is it? It sounds like it's more, you know, spreading your eggs around, as you say and reducing your risk as opposed to, you know, worried about lock-in, but as a CIO, how worried are you about lock-in? Where is that fit in the sort of decision tree? >> Ah, I mean, I would say it's up there, but unfortunately, there's no number one, there's like five number ones, if you would. So it's definitely up there and it's something to consider when you're looking at, like you said, the cost, risk integration, and then time. You know, sometimes you're up against the time. And again, security, like I said. Security is a big key in healthcare. And actually security overall, whether you're retail, you're going to always have situations no matter what industry, you got to protect the business. >> Yeah, so I want to ask you about security. That's the other number one. Well, you might've been a defacto CSO, but kind of when we started in this business security was the problem of the security teams, and you know, it's now a team sport. But in thinking about the cloud and security, how big of a concern is the cloud? Is it just more, you're looking for consistency and be able to apply the corporate edicts? Are there other concerns like the shared responsibility model? What are your thoughts on security in the cloud? >> Well, it probably goes back to again, the industry, but when I looked at the past five years in healthcare, doing a lot of work with the CMS and Medicaid, Medicare, they had certain requirements and certain restrictions. So we had to make sure that we follow those requirements. And when you got audited, you needed to make sure that you can show that you are adhering to their requirements. So over the past, probably two years with Amazon's government capabilities that those restrictions have changed, but we were always looking to make sure that we owned and managed how we manage the provider and member data, because yes, we did not want to have obviously a breach, but we wanted to make sure we were following the guidelines, whether it's state or federal, and then and even some cases healthcare guidelines around managing that data. So yes, top of mind, making sure that we're protecting, you know, in my case so we had 37 million members, patients, and we needed to make sure that if we did put it in the cloud or if it was on-prem, that it was being protected. And as you mentioned, recently come off of, I was going to say Amazon, but it was an acquisition. That company that was looking at us doing the due diligence, they gave us thumbs up because of how we were managing the data at the lowest point and all the different levels within the architecture. So Anthem who did the acquisition, had a breach back in, I think it was 2015. That was top of mind for them. We had more questions during the due diligence around security than any other functional area. So it is critical, and I think slowly, some of that type of data will get up into the cloud, but again, it's going to go through some massive risk management and security measures, and audits, because how fragile that is. >> Yeah, I mean, that could be a deal breaker in an acquisition. I got two other questions for you. One is, you know, I know you follow the technologies very closely, but there's all the buzz words, the digital transformation, the AI, these new SaaS models that we talked about. You know, a lot of CIOs tell me, look, Dave, get the business right and the technology is the easy part. It's people, it's process. But what are you seeing in terms of some of this new stuff coming out, there's machine learning, you know, obviously massive scale, new cloud workloads. Anything out there that really excites you and that you could see on the horizon that could be, you know, really change agents for the next decade? >> Yeah, I think we did some RPA, robotics on some of the tasks that, you know, where, you know, if the analysis types of situations. So I think RPA is going to be a game changer as it continues to evolve. But I agree with what you just said. Doing this for quite a while now, it still comes down to the people. I can get the technology to do what it needs to do as long as I have the right requirements, so that goes back to people. Making sure we have the partnership that goes back to leadership and the people. And then the change management aspects. Right out of the gate, you should be worrying about how is it going to affect and then the adoption and engagement. Because adoption is critical, because you can go create the best thing you think from a technology perspective, but if it doesn't get used correctly, it's not worth the investment. So I agree, whether it's digital transformation or innovation, it still comes down to understanding the business model and injecting and utilizing technology to grow or reduce costs, grow the business or reduce costs. >> Yeah, usage really means value. Sorry, my last question. What's the one thing that vendors shouldn't do? What's the vendor no-no that'll alienate CIO's? >> To this day, I still don't like, there's a company out there that starts with an O. I still don't like it to that, every single technology module, if you would, has a separate sales rep. I want to work with my strategic partners and have one relationship and that single point of contact that spark and go back into their company and bring me whatever it is that we're looking at so that I don't get, you know, for instance from that company that starts with an O, you know, 17 calls from 17 different sales reps trying to sell me 17 different things. So what irritates me is, you know, you have a company that has a lot of breadth, a lot of, you know, capability and functional, you know that I may want. Give me one person that I can deal with. So a single point of contact, then that makes my life a lot easier. >> Well, Dan Sheehan, I really appreciate you spending some time on theCUBE, it's always a pleasure catching up with you and really appreciate you sharing your insights with our audience. Thank you. >> Oh, thank you, David. I appreciate the opportunity. You have a great day. >> All right. You too. And thank you for watching everybody. This is Dave Vellante for theCUBE on Cloud. Keep it right there. We'll be back with our next guest right after the short break. Awesome, Dan.

>> Announcer: From around the globe, it's theCUBE, with coverage of SUSECON Digital, brought you by SUSE. (upbeat music) >> Welcome back. I'm Stu Miniman and this is theCUBE's coverage of SUSECON Digital. And really excited to welcome to the program a first time guest, and he's relatively fresh off the keynote, Alexander Kocher, who is the President and Managing Director of Elektrobit, talking about autonomous vehicles. Alex, thanks so much or joinin' us. >> Thank you, Stu, for inviting me. >> All right, so you know, definitely really interesting technologies, and a lot of talent. So many of the ways we talk about in the IT industry, to talk about cloud computing, edge impacting things, how AI fits into the world, and the balance between people and technology. Well, your company's living it. So why don't we start a little bit. Elektrobit, at least from the research I've done and from the keynote, you are a software company if I have it right. And autonomous vehicles is really what you're driving for. But give our audience a little bit of Elektrobit where you fit in the market today. >> Yeah, Elektrobit, so you can say we are software creatrs unlocking the future of mobility. We are pioneering software in the automotive industry since more than 30 years, empowering already now more than 1 billion devices, in hundreds of millions of cars, and serving since more than 30 years the automotive industry. So as software is becoming the second biggest enabler of the innovation in the car, we are driving this with our technologies. We are focusing on software infrastructure solutions, so coming from the deep, deep layers in the car, up to the HMI, up to the user interface, and providing there specific technologies for really building the basis, and able our customers to focus on their innovations. So this is car infrastructure software. This is software for autonomous driving, as you said. And this is application software mainly in the tooling to create fancy and good-looking user interfaces in modern vehicles. >> Yeah, wow, 30 years. You know most people don't think about software that long in the automotive industry. Of course anybody that owned a car understands that a lot of times it no longer, ya know, people going under the hood, but they're plugging something in and going on a computer, understand what that is. If you could, give us a little bit, what are the trends going on? We've been talking for the last couple a years, if you talk from an autonomous vehicle stand point. Probably people have seen the five stages model that's been put out there, everything from some driver-assist technologies, to a fully autonomous vehicle. But what are you seeing, your software, the companies you supply to and the users, what's happening there? >> So, I would class the trends at the moment in our industry in three blocks. There is electrification, where software is for sure playing a role, but is more used as a supporting technology. Here dominating innovation is coming from other technologies like battery, fuel cells, charging mechanisms, and so on. But then the second trend and the third trend, automated driving and connectivity, to really make the car part of the internet, these are two mega trends where software is dominating the scene, and really also dominating the value of the car as well. And I think these are the trends. We need for all of those to develop new car connectors, similar to server infrastructures already, so that you can seamlessly integrate applications, services from the cloud into the car. And I think these are the trends. And the partnership we are the moment working with SUSE is really coming into play as well to combine experiences from other industries, from other technologies, open source technologies with the embedded world. And create added value for our customer. >> Yeah so let's dig into that SUSE partnership a little bit. Obviously community is a big thing that's talked about there, at the show and from SUSE's customers. There's what can we learn, what is the role of open source, and how do we really enable innovation? So what's important about the partnership with SUSE? >> I think, let me give a little bit of a background. So when becoming an IT device, the amount of software, the complexity is increasing like hell. What he have at the moment, round about 10% created by software in the car, we will see 30% value created by software in 10 years from now. And this is done by a disruptive change in the development model. At the moment we stopped developing functions and features at the point when we introduce the car into the market. This will completely change. Just think about a mobile device like I have it here in my hand. During the whole life cycle of this device, which is of course much shorter than the life cycle of a car, you will innovate and update functions here. This will also be introduced in the next generation, which is under development at the moment, of cars. So that you can update applications, new services during the whole life cycle of the car. And this requires new platforms. It doesn't stop at the introduction of the car. It will continue over a real, real long period of time, years it takes, even. We have a long maintenance cycles. And therefore you need to have new partnership models, and also other technologies where this is already applied with other technologies in other industries. And here our partnership really comes into play, where we need to even get other talent pools. other pools of creativity, other pools of and forces of innovation, so that we really enable with existing methods, new methods, our customers to focus on their differentiating functions to compete against their competitor. And here exactly our partnership is targeting it. >> Okay so it sounds like we're talking specifically Linux means that there's a common underlying programming model, and that there's a skill set pool out there. Am I getting that right? >> Yes, correct. At the moment, so the automotive industry stands for reliable, high performance, high quality of cars and maintaining these features and essential functions over a long, long period of time. But when using embedded technologies, you are endangered always to re-implement it again and again and reuse is not necessarily that what is implemented here from one generation to the other, completely innovated sometimes, And here with other technologies, like you're doing with Linux for example, an open source, you open up a complete new field of innovation and creativity, and of course also access to talent pools, which is very much limited at the very moment in the embedded world as well. >> Alex, I'm curious how Elektrobit thinks about data. Number one, all the training data, how AI is done. Is there any industry sharing going on with that discussion? Let's start there and then maybe we'll talk a little bit about security when we get through the basic data points. >> (laughs) Yeah so, indeed, just think about cars. One of the most accurate sensor in our environment, with all the sensors you have, camera sensors, radio sensors, liter sensors, and so on and so forth, which create a hell lot amount of data, a terabyte by day. And of course this is something which needs to be shared, because the road infrastructure, we talked about this beforehand, is the same independent, whether it's a BMW car, whether it's a GM car, whether it's a Ford car, or a Daimler car, or a Toyota. So it's for all the cars the same car infrastructure. And of course there's a lot of discussion ongoing to share this data. Although now when making business out of that, the business model needs to, as you mentioned, for sure recognize and respect the privacy of the data in order to make business out of that. >> Excellent-- >> So then--Sorry >> Please, please continue. >> So yes, I think there is discussion ongoing. And already in, for example, in map data and traffic control, there is already ongoing the share of the data amongst the manufacturers as well. >> Excellent. And of course, security is paramount. When I look at Elektrobit, cyber security is prominent in the automotive discussion. How does that play in? What's the experience that you've had there from the security side. >> Yeah, so Elektrobit, so we built up our security, but really coming from inside the car. Now three years ago we acquired a company with out mother company together which is now integrated and consolidated within Elektrobit. It's called Argus Cyber Security from Tel Aviv in Israel. And with that we are now able to really provide solutions, end to end solutions from deep inside the car up to the cloud, so that the data stream is secure to the highest standards of security, of course. And this is, on the long side, really securing remote control, maintenance of the car, but also then privacy in terms when you download new services, when you provide information into the cloud where you are. For example we talked abut this data as new currency from the sensors existing in the car. So for that reason exactly we acquired this company with their technologies we are able to provide end to end solutions also for the existing software we are providing to our customers. >> Right, Alex, I'm curious just when you talk about autonomous vehicles, anything distinct about Europe? I think about the challenge and the opportunity. Number one, you're in Germany. You've got some of the best highways in the world. Well thought-out, really well architected. But throughout Europe you also have some the oldest cities where it could be really challenging to traverse. So anything different you might be able to share with our audience about what we should look for for that journey of autonomous vehicle in Europe? >> So... basically your question, already lined it out. So yes, I think autonomous driving and it's starting with functions like hybrid pilot so that you really create a kind of a clean room, where you have a very well-defined environment, where you can start to drive autonomous, and really hands off, eyes off, so level three, level four. In old cities, the structure is yeah, grown, grown over hundreds of years. So it's for sure not foreseen for autonomous driving, at that point in time. Or let's say at that point in time you had an autonomous vehicular horse which found all the time the stable. But nowadays it's a little bit different. So the more difficult environment is for sure the center of cities. And there it will take a while. But we are on the go by going really step by step from a very well-defined environment like a highway, where you can define certain use cases. And with the evolution of sensors, with the evolution of algorithms, with the evolution of processing power, then go step by step to a more complex environment like inner cities. >> Excellent. What should people be looking for when it comes to autonomous vehicles? What can you give us on the next 12 to 24 months, what you're expecting in the industry? >> So I think at the moment, I think in the 12 to, we're still in the face when it comes to autonomous driving, we have driver assistance functions evolving from there. A level two, level two plus. Level three functions where you really then have hands off, will probably come in two, three, four years. Here it's not only the industry by itself who is the limiting factor, but also the regulations on the outside. We just recently saw the announcement of Audi that homologation related to topics at the moment not clear. This is also to be considered. Technology is already prepared, ie, I'm now, even with driver-assistance functions, able to drive. I had an experience with my car by 200km/hr around the curve, and pulling the steers a little bit off So it's still in the face. You have to be aware that you can control. So the function itself is already existing. But homologation that you really can do this for more than 10 seconds, this is the critical thing. And really be prepared techonology for all the eventual things. So here we have limiting factors also from the regulations around that. And this is basically what we have to deal with. So just recently announced by Audi A8 in the introduction. >> Excellent stuff. All right, Alex, I want to give you the final word. Just share with the audience at SUSECON, what it means for Elektrobit to participate in this partnership. >> Yeah, I think the main thing of this partnership is really that we... We are enabled to really provide and infrastructure which fulfills the complete requirements of the car industry. So long-term maintenance, enablement of secure downloads during the whole life cycle of the car, and reusabilty, backward compatibility which is very important thing as well, when you produce technologies for products which have a very long product life cycle. And with the experience SUSE brings into play from other industries, with their solutions, with their Linux distributions and container technologies, with our experience from the automotive industry, I'm really sure that with that partnership, we enable our customers to focus on their innovations, and we enable ourselves to provide the basic solutions for the industry, and for... new future intelligent vehicles. >> All right, well, thank you so much for sharing all of the updates. Fascinating stuff. Thank you so much for joining. >> Thank you, Stu, for inviting me. >> All right, lots more coverage from SUSECON Digital '20. I'm Stu Miniman and thank you for watching theCUBE. (upbeat music)

>> Is and who we are today as as a country, as a universe. >> Narrator: Congratulations Reggie Jackson, (inspirational music) you are a CUBE alumni. (upbeat music) >> Announcer: Live from Las Vegas it's theCUBE covering Splunk.Conf19. Brought to you by Splunk. >> Okay, welcome back everyone it's theCUBE's live coverage here in Las Vegas for Splunk.Conf19. I am John Furrier host of theCUBE. It's the 10th Anniversary of Splunk's .Conf user conference. Our 7th year covering it. It's been quite a ride, what a wave. Splunk keeps getting stronger and better, adding more features, and has really become a powerhouse from a third party security standpoint. We got a C-SO in theCUBE on theCUBE today. Chief Information Security, John Frushour Deputy Chief (mumbles) New York-Presbyterian The Award Winner from the Data to Everywhere Award winner, welcome by theCube. >> Thank you, thank you. >> So first of all, what is the award that you won? I missed the keynotes, I was working on a story this morning. >> Frushour: Sure, sure. >> What's the award? >> Yeah, the Data Everything award is really celebrating using Splunk kind of outside its traditional use case, you know I'm a security professional. We use Splunk. We're a Splunk Enterprise Security customer. That's kind of our daily duty. That's our primary use case for Splunk, but you know, New York Presbyterian developed the system to track narcotic diversion. We call it our medication analytics platform and we're using Splunk to track opioid diversion, slash narcotic diversions, same term, across our enterprise. So, looking for improper prescription usage, over prescription, under prescription, prescribing for deceased patients, prescribing for patients that you've never seen before, superman problems like taking one pill out of the drawer every time for the last thirty times to build up a stash. You know, not resupplying a cabinet when you should have thirty pills and you only see fifteen. What happened there? Everything's data. It's data everything. And so we use this data to try to solve this problem. >> So that's (mumbles) that's great usage we'll find the drugs, I'm going to work hard for it. But that's just an insider threat kind of concept. >> Frushour: Absolutely. >> As a C-SO, you know, security's obviously paramount. What's changed the most? 'Cause look at, I mean, just looking at Splunk over the past seven years, log files, now you got cloud native tracing, all the KPI's, >> Frushour: Sure. >> You now have massive volumes of data coming in. You got core business operations with IOT things all instrumental. >> Sure, sure. >> As a security offer, that's a pretty big surface area. >> Yeah. >> How do you look at that? What's your philosophy on that? >> You know, a lot of what we do, and my boss, the C-SO (mumbles) we look at is endpoint protection and really driving down to that smaller element of what we complete and control. I mean, ten, fifteen years ago information security was all about perimeter control, so you've got firewalls, defense and depth models. I have a firewall, I have a proxy, I have an endpoint solution, I have an AV, I have some type of data redaction capability, data masking, data labeling capability, and I think we've seen.. I don't think security's changed. I hear a lot of people say, "Oh, well, information security's so much different nowadays." No, you know, I'm a military guy. I don't think anything's changed, I think the target changed. And I think the target moved from the perimeter to the endpoint. And so we're very focused on user behavior. We're very focused on endpoint agents and what people are doing on their individual machines that could cause a risk. We're entitling and providing privilege to end users today that twenty years ago we would've never granted. You know, there was a few people with the keys to the kingdom, and inside the castle keep. Nowadays everybody's got an admin account and everybody's got some level of privilege. And it's the endpoint, it's the individual that we're most focused on, making sure that they're safe and they can operate effectively in hospitals. >> Interviewer: What are some of the tactical things that have changed? Obviously, the endpoint obviously shifted, so some tactics have to change probably again. Operationally, you still got to solve the same problem: attacks, insider threats, etc. >> Frushour: Yeah. >> What are the tactics? What new tactics have emerged that are critical to you guys? >> Yeah, that's a tough question, I mean has really anything changed? Is the game really the game? Is the con really the same con? You look at, you know, titans of security and think about guys like Kevin Mitnick that pioneered, you know, social engineering and this sort of stuff, and really... It's really just convincing a human to do something that they shouldn't do, right? >> Interviewer: Yeah. >> I mean you can read all these books about phone freaking and going in and convincing the administrative assistant that you're just late for meeting and you need to get in through that special door to get in that special room, and bingo. Then you're in a Telco closet, and you know, you've got access. Nowadays, you don't have to walk into that same administrative assistant's desk and convince 'em that you're just late for the meeting. You can send a phishing email. So the tactics, I think, have changed to be more personal and more direct. The phishing emails, the spear phishing emails, I mean, we're a large healthcare institution. We get hit with those types of target attacks every day. They come via mobile device, They come via the phishing emails. Look at the Google Play store. Just, I think, in the last month has had two apps that have had some type of backdoor or malicious content in them that got through the app store and got onto people's phones. We had to pull that off people's phones, which wasn't pretty. >> Interviewer: Yeah. >> But I think it's the same game. It's the same kind to convince humans to do stuff that they're not supposed to do. But the delivery mechanism, the tactical delivery's changed. >> Interviewer: How is Splunk involved? Cause I've always been a big fan of Splunk. People who know me know that I've pretty much been a fan boy. The way they handle large amounts of data, log files, (mumbles) >> Frushour: Sure. >> and then expand out into other areas. People love to use Splunk to bring in their data, and to bring it into, I hate to use the word data leg but I mean, Just getting... >> Yeah >> the control of the data. How is data used now in your world? Because you got a lot of things going on. You got healthcare, IOT, people. >> Frushour: Sure, sure. >> I mean lives are on the line. >> Frushour: Lives are on the line, yeah. >> And there's things you got to be aware of and data's key. What is your approach? >> Well first I'm going to shamelessly plug a quote I heard from (mumbles) this week, who leads the security practice. She said that data is the oxygen of AI, and I just, I love that quote. I think that's just a fantastic line. Data's the oxygen of AI. I wish I'd come up with it myself, but now I owe her a royalty fee. I think you could probably extend that and say data is the lifeline of Splunk. So, if you think about a use case like our medication analytics platform, we're bringing in data sources from our time clock system, our multi-factor authentication system, our remote access desktop system. Logs from our electronic medical records system, Logs from the cabinets that hold the narcotics that every time you open the door, you know, a log then is created. So, we're bringing in kind of everything that you would need to see. Aside from doing something with actual video cameras and tracking people in some augmented reality matrix whatever, we've got all the data sources to really pin down all the data that we need to pin down, "Okay, Nurse Sally, you know, you opened that cabinet on that day on your shift after you authenticated and pulled out this much Oxy and distributed it to this patient." I mean, we have a full picture and chain of everything. >> Full supply chain of everything. >> We can see everything that happens and with every new data source that's out there, the beauty of Splunk is you just add it to Splunk. I mean, the Splunk handles structured and unstructured data. Splunk handles cis log fees and JSON fees, and there's, I mean there's just, it doesn't matter You can just add that stream to Splunk, enrich those events that were reported today. We have another solution which we call the privacy platform. Really built for our privacy team. And in that scenario, kind of the same data sets. We're looking at time cards, we're looking at authentication, we're looking at access and you visited this website via this proxy on this day, but the information from the EMR is very critical because we're watching for people that open patient records when they're not supposed to. We're the number five hospital in the country. We're the number one hospital in the state of New York. We have a large (mumbles) of very important people that are our patients and people want to see those records. And so the privacy platform is designed to get audit trails for looking at all that stuff and saying, "Hey, Nurse Sally, we just saw that you looked at patient Billy's record. That's not good. Let's investigate." We have about thirty use cases for privacy. >> Interviewer: So it's not in context of what she's doing, that's where the data come in? >> That's where the data come in, I mean, it's advanced. Nurse Sally opens up the EMR and looks at patient Billy's record, maybe patient Billy wasn't on the chart, or patient Billy is a VIP, or patient Billy is, for whatever reason, not supposed to be on that docket for that nurse, on that schedule for that nurse, we're going to get an alarm. The privacy team's going to go, "Oh, well, were they supposed to look at that record?" I'm just giving you, kind of, like two or three uses cases, but there's about thirty of them. >> Yeah, sure, I mean, celebrities whether it's Donald Trump who probably went there at some point. Everyone wants to get his taxes and records to just general patient care. >> Just general patient care. Yeah, exactly, and the privacy of our patients is paramount. I mean, especially in this digital age where, like we talked about earlier, everyone's going after making a human do something silly, right? We want to ensure that our humans, our nurses, our best in class patient care professionals are not doing something with your record that they're not supposed to. >> Interviewer: Well John, I want to hear your thoughts on this story I did a couple weeks ago called the Industrial IOT Apocalypse: Now or Later? And the provocative story was simply trying to raise awareness that malware and spear phishing is just tactics for that. Endpoint is critical, obviously. >> Sure. >> You pointed that out, everyone kind of knows that . >> Sure. >> But until someone dies, until there's a catastrophe where you can take over physical equipment, whether it's a self-driving bus, >> Frushour: Yeah. >> Or go into a hospital and not just do ransom ware, >> Frushour: Absolutely. >> Actually using industrial equipment to kill people. >> Sure. >> Interviewer: To cause a lot of harm. >> Right. >> This is an industrial, kind of the hacking kind of mindset. There's a lot of conversations going on, not enough mainstream conversations, but some of the top people are talking about this. This is kind of a concern. What's your view on this? Is it something that needs to be talked about more of? Is it just BS? Should it be... Is there any signal there that's worth talking about around protecting the physical things that are attached to them? >> Oh, absolutely, I mean this is a huge, huge area of interest for us. Medical device security at New York Presbyterian, we have anywhere from about eighty to ninety thousand endpoints across the enterprise. Every ICU room in our organization has about seven to ten connected devices in the ICU room. From infusion pumps to intubation machines to heart rate monitors and SPO2 monitors, all this stuff. >> Interviewer: All IP and connected. >> All connected, right. The policy or the medium in which they're connected changes. Some are ZP and Bluetooth and hard line and WiFi, and we've got all these different protocols that they use to connect. We buy biomedical devices at volume, right? And biomedical devices have a long path towards FDA certification, so a lot of the time they're designed years before they're fielded. And when they're fielded, they come out and the device manufacturer says, "Alright, we've got this new widget. It's going to, you know, save lives, it's a great widget. It uses this protocol called TLS 1.0." And as a security professional I'm sitting there going, "Really?" Like, I'm not buying that but that's kind of the only game, that's the only widget that I can buy because that's the only widget that does that particular function and, you know, it was made. So, this is a huge problem for us is endpoint device security, ensuring there's no vulnerabilities, ensuring we're not increasing our risk profile by adding these devices to our network and endangering our patients. So it's a huge area. >> And also compatible to what you guys are thinking. Like I could imagine, like, why would you want a multi-threaded processor on a light bulb? >> Frushour: Yeah. >> I mean, scope it down, turn it on, turn it off. >> Frushour: Scope it down for its intended purpose, yeah, I mean, FDA certification is all about if the device performs its intended function. But, so we've, you know, we really leaned forward, our CSO has really leaned forward with initiatives like the S bomb. He's working closely with the FDA to develop kind of a set of baseline standards. Ports and protocols, software and services. It uses these libraries, It talks to these servers in this country. And then we have this portfolio that a security professional would say, "Okay, I accept that risk. That's okay, I'll put that on my network moving on." But this is absolutely a huge area of concern for us, and as we get more connected we are very, very leaning forward on telehealth and delivering a great patient experience from a mobile device, a phone, a tablet. That type of delivery mechanism spawns all kinds of privacy concerns, and inter-operability concerns with protocol. >> What's protected. >> Exactly. >> That's good, I love to follow up with you on that. Something we can double down on. But while we're here this morning I want to get back to data. >> Frushour: Sure. >> Thank you, by the way, for sharing that insight. Something I think's really important, industrial IOT protection. Diverse data is really feeds a lot of great machine learning. You're only as good as your next blind spot, right? And when you're doing pattern recognition by using data. >> Frushour: Absolutely. >> So data is data, right? You know, telecraft, other data. Mixing data could actually be a good thing. >> Frushour: Sure, sure. >> Most professionals would agree to that. How do you look at diverse data? Because in healthcare there's two schools of thought. There's the old, HIPAA. "We don't share anything." That client privacy, you mentioned that, to full sharing to get the maximum out of the AI or machine learning. >> Sure. >> How are you guys looking at that data, diverse data, the sharing? Cause in security sharing's good too, right? >> Sure, sure, sure. >> What's your thoughts on sharing data? >> I mean sharing data across our institutions, which we have great relationships with, in New York is very fluid at New York Presbyterian. We're a large healthcare conglomerate with a lot of disparate hospitals that came as a result of partnership and acquisition. They don't all use the same electronic health record system. I think right now we have seven in play and we're converging down to one. But that's a lot of data sharing that we have to focus on between seven different HR's. A patient could move from one institution to the next for a specialty procedure, and you got to make sure that their data goes with them. >> Yeah. >> So I think we're pretty, we're pretty decent at sharing the data when it needs to be shared. It's the other part of your question about artificial intelligence, really I go back to like dedication analytics. A large part of the medication analytics platform that we designed does a lot of anomaly detections, anomaly detection on diversion. So if we see that, let's say you're, you know, a physician and you do knee surgeries. I'm just making this up. I am not a clinician, so we're going to hear a lot of stupidity here, but bare with me. So you do knee surgeries, and you do knee surgeries once a day, every day, Monday through Friday, right? And after that knee surgery, which you do every day in cyclical form, you prescribe two thousand milligrams of Vicodin. That's your standard. And doctors, you know, they're humans. Humans are built on patterns. That's your pattern. Two thousand milligrams. That's worked for you; that's what you prescribe. But all of the sudden on Saturday, a day that you've never done a knee surgery in your life for the last twenty years, you all of a sudden perform a very invasive knee surgery procedure that apparently had a lot of complications because the duration of the procedure was way outside the bounds of all the other procedures. And if you're kind of a math geek right now you're probably thinking, "I see where he's going with this." >> Interviewer: Yeah. >> Because you just become an anomaly. And then maybe you prescribe ten thousand milligrams of Vicodin on that day. A procedure outside of your schedule with a prescription history that we've never seen before, that's the beauty of funneling this data into Splunk's ML Toolkit. And then visualizing that. I love the 3D visualization, right? Because anybody can see like, "Okay, all this stuff, the school of phish here is safe, but these I've got to focus on." >> Interviewer: Yeah. >> Right? And so we put that into the ML Toolkit and then we can see, "Okay, Dr. X.." We have ten thousand, a little over ten thousand physicians across New York Presbyterian. Doctor X right over here, that does not look like a normal prescriptive scenario as the rest of their baseline. And we can tweak this and we can change precision and we can change accuracy. We can move all this stuff around and say, "Well, let's just look on medical record number, Let's just focus on procedure type, Let's focus on campus location. What did they prescribe from a different campus?" That's anomalous. So that is huge for us, using the ML Toolkit to look at those anomalies and then drive the privacy team, the risk teams, the pharmacy analytics teams to say, "Oh, I need to go investigate." >> So, that's a lot of heavy lifting for ya? Let you guys look at data that you need to look at. >> Absolutely. >> Give ya a (mumbles). Final question, Splunk, in general, you're happy with these guys? Obviously, they do a big part of your data. What should people know about Splunk 2019, this year? And are you happy with them? >> Oh, I mean Splunk has been a great partner to New York Presbyterian. We've done so much incredible development work with them, and really, what I like to talk about is Splunk for healthcare. You know, we've created, we saw some really important problems in our space, in this article. But, we're looking, we're leaning really far forward into things like risk based analysis, peri-op services. We've got a microbial stewardship program, that we're looking at developing into Splunk, so we can watch that. That's a huge, I wouldn't say as big of a crisis as the opioid epidemic, but an equally important crisis to medical professionals across this country. And, these are all solvable problems, this is just data. Right? These are just events that happen in different systems. If we can get that into Splunk, we can cease the archaic practice of looking at spreadsheets, and look up tables and people spending days to find one thing to investigate. Splunk's been a great partner to us. The tool it has been fantastic in helping us in our journey to provide best in-class patient care. >> Well, congratulations, John Frushour, Deputy Chief Information Security Officer, New York Presbyterian. Thanks for that insight. >> You're welcome. >> Great (mumbles) healthcare and your challenge and your opportunity. >> Congratulations for the award winner Data to Everything award winner, got to get that slogan. Get used to that, it's two everything. Getting things done, he's a doer. I'm John Furrier, here on theCube doing the Cube action all day for three days. We're on day two, we'll be back with more coverage, after this short break. (upbeat music)

>>live from Denver, Colorado. It's the Q covering com vault. Go 2019. Brought to you by combo. >>Welcome to the Cube. Lisa Martin with student A man we're covering Day one of convo go 19 from Colorado. Stew and I are pleased to welcome to the Cube one of combo longtime customers from AstraZeneca. We have Scott 100 global infrastructure surfaces. Director. Hey, Scott. >>Good afternoon. >>Good afternoon. Welcome to the Cube. AstraZeneca is a name that probably a lot of folks know in the bio medical pharmaceutical space. But for those that don't give us an overview of AstraZeneca. What you guys are what you d'oh! >>Obviously we're we're, ah, bio pharmaceutical company with global presence way be used. The primary care takes off. Medicines be sale throughout the world. So everything from Kearney care to tiu oncology onda also are massive. That diabetes franchise, as well as other core core therapies that are used by our patients, were like, >>All right, So, Scott, maybe bring us inside. What is data mean to your organization? >>And it means loss. Lots of things taxes and cut through our organization from go boat framed in that next molecules to discover them bleeding edge medicines for our patients all the way to have our sales People commercial use data to identify the patients for further rate kid as well and ofcourse, backoffice tree I t enabling functions like each HR and finances. Well, therefore, is this apartment for business >>you've got Global infrastructure service could just lay out a little bit what that entails and how data fits into the picture, what's in your purview and what you have to work with other groups on. >>So my idea looks after architecture, designing governance and for cyber security infrastructure, savage seas, AstraZeneca. So So we be like after within either on premise within their own try our own data centers are in the public load as well. So as you can imagine, their movement on Deron realms and that can environment is pivotal to the coming been successful go forward >>when every time we, you know, you talk about data being the life blood of an organization or the new oil when we're talking about a patient information and the information that could be used to find the next, you know, cure for a particular disease, this it's this is literally life and death data on the ability to have access to it, but also to make sure that it's protected and secure table stakes. Right, so talk to us about when you came on board, he said. Around six years ago, before we went live knowing how critical data is toe AstraZeneca's business, What was the data strategy like a few years ago? >>It was pretty convoluted six years ago when am I fought during the actual Danica over largely exhaust to various companies? So their strategy basically that have one. We don't really have much of a strategy for looking after our deal with five or six different backup products, but then the cinnamon on lane their storage products is now. So over the last 56 years, can stealing that down to one key data storage provider in the APP and also for backup from the store combo. We do still have some leg. It's a very fast environments, but they're being decommissioned. That moved over combo. I speak >>from a what can I t. An initial initiative perspective. A few years ago, six years ago, we didn't have a date, a strategy. What was some of the you know from the top, down from the C suite down, maybe from the board down saying, Hey, guys, we have to get our hands around. I mean, this is before GDP are But in terms of the opportunities that I provided the company, where did that initiative come from? And a new year old come about now. But you guys want a couple of different routes, Talk to us a little bit about that initiative and the initial directions to where you are now. >>Yes, O. R. Xia Old Smalley obviously had a vision for how the country is going to progress. Set sail in his tenure on a massive pile that was understanding with our data waas how it was used on but most importantly have it was protected as well. And so that kind of drove the insertion from likes of HCL Congress and emphasis into looking after our own environment. You can, after our own idea for choosing strategies as well, so that organically company could grow based on best directions for using that there that we could meet from what we had the radio through collaboration with other bio farmers is a game just for the greater good of fame than that. That next medical molecules to help proficient. All right, >>Scott, have you been toe the combo Cho shows before? >>Second thing second time. Tell us a >>little >>bit about you know what brings you to the show? A lot of announcements here. Anything jump out so far? >>Yeah, it is interesting to see some of the new collaborations are Sorry. Party sees it comes I'll be making over the last little well Hedvig acquisition looks looks breaks on the metallic venture that, doomed for public sass is, well, looks like equals x, a n and ammunition and came environments that convo play on. So I think things to very good moves. >>So you're leveraging Public Cloud. How does Khan Vault fit into that? You're to be used babies >>convoked for for M backing up on restoring and our public load environments. Whether we need a B s robotics, start watching in the jury's there with You're in the club zero stack as well. And then we're in the process of bringing on lane production environments and Google Cloud Platform zone. So having that one back up in the store strategies pivotal Isabella's enabling us to move our day off using visibility solution to get calm. Boulders now, which is very powerful, is >>one of the things I noticed when I watched the video that combo has done with you. And they actually shared a quote from you during the keynote before Actually, before everyone walked. It is, you said this constant evolution that come about is delivering was one of the things that that you really like. From a business perspective, Combo has done a lot of evolving in the last nine months with the new leadership. It's too. And you were talking about some of the new technology, some of the new announcements from that evolutionary perspective and what you guys like about it. What are you seeing in terms of them going forward? Are you saying hey, there really listening? They're looking at use cases like ours, learning from it to not only make the technology is better, but to expand their portfolio. >>I mean, for a lot of it's based in the constant evolution of the FBI's that convo used for access and videos need parts. Technology will be backing up of the M two, backing up kubernetes containers and using that in the Secret Service's environment is Val to Tolliver's to ensure that whatever it comes to get from Lourdes cannot feel like several. It's computing environments that don't understand what what they put watch. So we can either reuse it, destroy, are used different manner, so that for us, that's great. Because obviously for our own C A c d pay planes, they're all FBI driven on to be able to use a convert production. The same kind of fashion is >>so, Scott, do you keep up on the quarterly cadence that combo doing and is there anything, uh, kind of either on the road map for things that you're asking for that would make your environment even better? >>And we're kind of used the 90 day cadences for ourselves to ensure that our own strategies are kept in check and we could take advantage off in your aunties are coming not only from convo but for other parts of our the infrastructure really be now for our only storage or a video. Various other providers that be used for insurance are dead as a decibel and used in a proper fashion. I >>want to get into a little bit of the use case, I knew that you had a number of different competing backup solutions in place. Did you start from a data started perspective, like within one division or one part of the company to maybe pilot, because you ended up with a whole bunch of different software solutions in there. Now you standardize on combo, walk us through that process, those decisions and what you're getting by having this now single pane of less >>some of the populated back up in the store sprawl was caused by individual parts of his had been able to do a little thing having little ineighty budget. So give it up. Some parts of business want to use a backup from Veritas or the emcee products that were in play at the time when we source between IBM and HDL chores each pdp for a pre media centers. So I decided another another backup restore productive in the mix. So for us, it just became untenable when we started insourcing, you know, to build a support team support organization to work after that many technologies was pretty difficult hands by way to go for 11 stop strategy. >>You said in the video. That combo had a pretty significantly higher success rate compared to some of the other solutions, so that must have made it a no brainer. >>So our backup critical applications is 99.8% successful to stay on, and that's that. That's what come won't get themselves. So that was a great comfort on the series. Is more and more of our applications move over on the convo platform, then have ah more wrong deeds approached, You know, backup success, but success in the store and say the things as well as Bella's, you know, using the analytics on a more timely fashion again for for drug and manufacturing research. >>So I know that you guys looked at our sorry spoke with a number of combat customers before you made this decision. And now here you are, on the other side of the coin, talking to a lot of combo customers. What advice would you give companies in any industry who in almost 2020 may not have a really robust data strategy? Your recommendations >>should look it over, not just our backup in the store solution. You know, the could base, which has put together for involves very powerful from the beta index. Ease with information going through construe the product to you can use out for things like D. R H A and also immigration off records. Two different defense centers are different parts of public low dreamer, you know, And the new new vision that I have for the analytics is very powerful as well. Forget the name of this tour today that someone that, you know, maybe we've started to use ourselves in a big way. We've got a little science team within my operation, which is made in that they are not coming, that they're more efficient manner. Feed that into our. Praised the architecture so that they could take advantage of what? Worried they got their own confines and makes out with what they need to do for for new discoveries. >>Scott, thank you for joining. Stewing me on the cube today, sharing with us what you're doing at AstraZeneca and looking forward to hearing the next molecule that discovers some great breakthrough. >>Thank you. >>First to Minutemen. I'm Lisa Martin. You're watching the cue from combo go 19

>> Announcer: From San Mateo, California, it's theCUBE covering SnapLogic Innovation Day 2018, brought to you by SnapLogic. >> Hey, welcome back, Jeff Frick here with theCUBE. We're at the Crossroads 101 and 92. You've probably been there. You're probably stuck in traffic. Look up, you'll see the sign SnapLogic. That's where we are. We're talking digital transformation. You've probably heard us talk about digital transformation on theCUBE, but not that many people or, excuse me, companies actually have an executive who's in charge of digital transformation. And that's not the case here at SnapLogic. And we're really excited to have our next guest. She's Diletta D'Onofrio, and she's the Head of Digital Transformation for SnapLogic. Welcome. >> Thank you, thank you for inviting me. >> Absolutely, so why does SnapLogic have a Head of Digital Transformation? I've never heard that for a company, and you're not really running digital transformation inside the company. You're helping your customers' digital transformation journey. >> Yeah absolutely, because integration is at the core of many transformations that we see led by our clients. And it's not about implementing a software for the most part. There's always the people processing technology. >> Jeff: Right, right. >> So what we are trying to do is to insert ourselves in the strategic discussion so that the implementation is more solid and secure. >> Right, right. >> And, so that's the intent of our practice. >> Right, and as you said, people process technology. We hear it all the time, and we hear a lot, too, of best practices in digital transformation is you have to make a commitment to that process change. You have to make a commitment to the people change. That's actually the hardest part. >> Diletta: Yeah. >> I think integration, usually, no one really wants to talk about integration up front because that's that hard little piece that we have to worry about down the road, but let's just not pretend that we have to do that. But as you said, that's a really important piece. It's tying all these systems together. So, you've been helping people with digital transformation here and in some of your prior jobs. So when you sit down with someone who's never heard that term, what do you tell them? What is digital transformation? >> So typically, we're pretty fortunate because I think especially in high tech, here in the valley, there are many clients that have a role which is equivalent to mine and is focused internally on digital transformation. So there either the head of digital transformation, the chief of digital officer. And what we typically do with them is to try to figure out what their plans are and participate to their journey by obviously helping from an integration perspective. >> Jeff: Right. >> Both on the application and data side. >> And where do there usually report up? It's always an interesting conversation because we go to chief data officer events. We go to chief analytics officer events. So you've got kind of these new evolving roles that are really built around data and enabling data and becoming a data driven enterprise. But does it report to the CIO? Does it report to the CTO? Does it report to up through the CEO? And then now you've got this role of people kind of heading up the digital transformation. Where do you see them reporting through? And what's kind of the most effective? Maybe that's a better question. What's the more effective place for them to report through? >> It's a little bit all over the map. There is not a standard. For example, a couple of clients, at Qualcomm, our equivalent in digital transformation is head of application, and he reports to the CIO. >> Jeff: Okay. >> So that's pretty traditional. Often the CIO is chartered with digital transformation for obvious reasons. He has the skillset, he has the team, he has the capability. But, I've seen cases where he or she reports to the CEO. >> Okay. >> Which is even more interesting I think because then it put an emphasis on the importance of the program and the importance of the targets associated with this program. So another client of ours airborne in Texas is actually the CMO and head of sales who reports to the CEO and is also in charge of digital transformation. And we are helping him with some cust-- >> It has the hat of also sales and marketing? >> Diletta: Absolutely, three jobs. >> So that's pretty interesting. Which is good cause those are the things that are kind of leading edge, front edge, to the client. As opposed to digital transformation just on your back-end processes. System integrators, in both those companies, you just listed as big companies. The system integrators have been building transformation businesses for a long, long time. How do they fit? How do you work with them? How does that kind of all come together around the project? >> Yep, so Qualcomm for example, you can see pretty much any single system integrator that you can imagine of. And they all have a portion of the transformation. >> Jeff: Right. >> None of them covers the entire scope. >> Jeff: Right. >> And the interesting portion as well is that because they are all competitors, often there is not a lot of collaboration. And then we are a little bit kind of agnostic, but obviously we have an interest in penetrating the account in terms of making the use of our technology. >> Right. >> So it's in our interest in what I'm trying to do, obviously I come from the system integrator world, so I do speak their language. And what we are trying to do is to work with them to make sure that we understand, were there use cases, were there business cases, and we kind of work together across different objective to enable the client to hopefully be digitally transformed. >> Right, so it's such a big word and the CEOs are talking to the boards about it and the public companies are talking to the analysts on the earnings call. We're going to digitally transform, and these are big organizations that are complex and have many, many pieces and parts. How do you get started? What are some best practices for people that have a board edict, or have a CEO edict? We need to digitally transform, I'm afraid of the competition, I don't even know who's coming. Where should people start, how do they slice and dice this thing so their not trying to eat the whole elephant in one bite? >> Yeah, the only cases that I've seen success on are the ones where, hopefully the leader has done that before. In some kind of shape or form. If it's a brand new chief digital officer, there are more challenges. But the most important thing is kind of keep the momentum. And you tend to keep the momentum through some sort of quick-wing. So if the scope is too large, and the roadmap is to fix over three or five years given the speed of change in technology is very difficult to achieve those goals. >> Jeff: Right. >> So it's much better to have a more agile mentality and maybe plan a year ahead. We did some very tangible, deliverable in the way and mobilize everyone around this. So that the momentum is kept and it's not just a nice word that a company has because they need to talk about the digital transformation. >> Right, and then what do you look at? You obviously have a specific point of view. You have your background and you've been a system integrator, and transformation leader. But in terms of coming from the SnapLogic point of view and integration, and that opportunity, What do you look for as opportunities for those early wins? Either based on prior experience or you just know there's some really inefficient ugly things that you can make big difference on, relatively easy. What do you look for as kind of those first wins in a digital transformation project? >> Yeah, ideally we love to be involved with everything to do with customer and sales and revenue. Because obviously those are the biggest paying point for the client. >> Jeff: Right. >> But often, you need to be flexible enough to understand what the priorities are. Currently I am involved in a much more traditional close activity accounting process. You will be thinking, okay, this may cost us, but actually fixing that problem first will create a lot of credibility within the company. So I think a company like ours has to be very flexible, need to listen to the client. >> Mh-hm. >> And be very flexible in terms of what priorities to start with first. >> Right. >> To prove the technology and then progress, maybe for higher value-- >> Right. >> activities. >> So I would hope it's 2018, that people understand that they're not setting forth on a five-year SAP, ERP implementation. Are we hopefully passed that, that this is not new information. That you need to take small bites, small victories, and move quickly. >> Yeah. >> Are we there? >> Yes but, still, I've seen a lot of strategy document and business plan that are two, three years of arisen and I think the arisen is way too long. But also at the same time, is this still teaching function? So you ask to picture a vision, at least directionally. >> Right. Right. >> So I think the vision has to be generic enough to then flex with the project and the activities within. >> Right. >> Two, three months. >> Right. >> Quarterly on most occasions. >> It's so funny that we continue to find these massive inefficiencies all over the place. You'd think that most of it had been wrung out by now. Between the European PA Limitations and all the business process reengineering, I guess was the old process >> Yes. >> before digital transformation. So I just wonder if you can share some stories from the field about some of these relatively short duration projects, and the yields that they are providing on this path to a more comprehensive digital transformation. >> Yeah so, the first example that comes to mind, again, going back to Qualcomm. When they talk about human capital management or engineering, what is interesting there is that you take the entire hire to retire. And it's pretty overwhelming. From the moment you hire an employee to the moment you obviously retire their function or their role, And what they did quite interestingly, was to come up with a few applications that will make the life of the employees and their manager easier. So we are biting the process by building application that for example, enable to facilitate the on-boarding or application that help HR with analytics and inquires. And gradually trying to automate the process which today even in a large company like a Fortune 100 company can be incredibly manual. >> Right. Right. >> And then another example that comes to mind to me is if you look at the entire holder to cash cycle of a company, from the moment the client to get in contact with the company through a website, to the moment they actually purchase the product. Again, there are many touch point and they're often disconnected. And a client of ours, Airborne, what we're doing with them is to just take one small bite which is figuring out from the time a client tried to configure a product on the website to the time they want to try the product. Our experience can be more automated. So that there is not a lot of interaction necessarily with customer services which has a limited bandwidth. But it's much more self-service. >> Jeff: Right. Right. >> And then gradually tackle the rest of the holder to cash cycle. >> So both of those examples are really about automating manual processes. >> Diletta: Yeah. >> As you just described them. So then what are the KPIs that you're using to measure success? Is it total time duration? Number of steps? Calls back to a person? What are some of the metrics of success? >> Yeah, so you see on the customers side it's kind of easy because you tend to very much require feedback from the customer. So if the customer satisfaction index goes up, or revenue goes up, or less return. So those KPIs we're kind of more familiar with. >> Okay. >> But when you look at the HR award, the human capital management award, there are so many ramifications of being able to serve your employees better. But much more intangible. Like for example, turnover. Well there is good turnover and bad turnover. So if you're serving your employees better with better hours, by which they can self-service some of their activities. Does it translate in less turnover? Maybe yes, or maybe actually that's translating more turnover because maybe the employees that sneak around are the ones that are more technology savvy, so. >> Right. >> Diletta: The human capital management side is harder in terms of defining KPIs. In it's much more early stage then anything to do with customer. And then there is the other universe associated with digitalizing product. Like for example, the world of IOT. That we are involved with, with a few clients. And that is a very measurable and tangible because you actually coming up with new product and what we're doing is facilitating the ability to access data. >> Jeff: Right. >> Which is a very tangible element of the product development lifecycle. >> So of all the transformation projects that you're involved in, how would you break them down in rough numbers of kind of cost savings on an existing process, which is through automation. Versus kind of forward facing customer facing, let's just call it warpped around a customer experience so ultimately you're getting higher customer satisfaction scores and revenue. Versus the third which you just touched on, which is so, so important. Which is converting from a product based company or some of these more tangible into more of a service recurring revenue. That's probably built around that product and the example that gets thrown around all the time is, when GE starts selling miles of propulsion versus selling engines. It's a very different kind of relationship. So in the things that you work on, how would you kind of break up the percentages in those three buckets? >> Yeah, so what we see still a lot, and what I would like to see less, is the first bucket. >> Jeff: Okay. >> Which is reducing cost so I will save more than 50%. >> Jeff: Okay. >> Which is around reduce cost, drive efficiency, better reporting, eliminating application, right? Because many client have too many application to preform some of these back office processes. >> Right. Right. >> And they're very much associated with cost exercise. >> Right. >> And so over 50%, for sure. >> Okay. And that's logical cause that's obviously an easy place to start. You're not changing the company per se. >> Yeah. >> You're looking for efficiencies. Alright so, Diletta, I'll give you the last word before we sign off. If you get called in to a new project, it's a CEO, they're stressed out, they know they have to do this. What do you tell them about digital transformation? How do you kind of help them break it down so it's not just this overwhelming, giant, goal on high? But actually something that they should get excited about, something they can have some success with and something that ultimately is going to be a really good thing. >> I think there is no one recipe. It's about figuring out where the company wants to go. What is the primary objective? Is it sales? Is it new market? Is it new product? And then kind of break it down in a tangible chunck and it kind of makes sense to them. But you got to go for the first priority item. This year I'm sure we'll be able to articulate very well. >> Yes, get that quick win. Well Diletta, thanks for spending a few minutes with us. And good luck on transforming everybody. (laughs) >> Thank you. >> Alright, she's Diletta, I'm Jeff. You're watching theCUBE, from SnapLogic headquarters in San Mateo, California. Thanks for watching. (bright music)

>> Announcer: From San Mateo, California, it's theCUBE covering SnapLogic Innovation Day 2018, brought to you by SnapLogic. >> Hey, welcome back, Jeff Frick here with theCUBE. We're at the Crossroads 101 and 92. You've probably been there. You're probably stuck in traffic. Look up, you'll see the sign SnapLogic. That's where we are. We're talking digital transformation. You've probably heard us talk about digital transformation on theCUBE, but not that many people or, excuse me, companies actually have an executive who's in charge of digital transformation. And that's not the case here at SnapLogic. And we're really excited to have our next guest. She's Diletta D'Onofrio, and she's the Head of Digital Transformation for SnapLogic. Welcome. >> Thank you, thank you for inviting me. >> Absolutely, so why does SnapLogic have a Head of Digital Transformation? I've never heard that for a company, and you're not really running digital transformation inside the company. You're helping your customers' digital transformation journey. >> Yeah absolutely, because integration is at the core of many transformations that we see led by our clients. And it's not about implementing a software for the most part. There's always the people processing technology. >> Jeff: Right, right. >> So what we are trying to do is to insert ourselves in the strategic discussion so that the implementation is more solid and secure. >> Right, right. >> And, so that's the intent of our practice. >> Right, and as you said, people process technology. We hear it all the time, and we hear a lot, too, of best practices in digital transformation is you have to make a commitment to that process change. You have to make a commitment to the people change. That's actually the hardest part. >> Diletta: Yeah. >> I think integration, usually, no one really wants to talk about integration up front because that's that hard little piece that we have to worry about down the road, but let's just not pretend that we have to do that. But as you said, that's a really important piece. It's tying all these systems together. So, you've been helping people with digital transformation here and in some of your prior jobs. So when you sit down with someone who's never heard that term, what do you tell them? What is digital transformation? >> So typically, we're pretty fortunate because I think especially Nytec here in the valley, there are many clients that have a role which is equivalent to mine and is focused internally on digital transformation. So there either the head of digital transformation, the chief of digital officer. And what we typically do with them is to try to figure out what their plans are and participate to their journey by obviously helping from an integration perspective. >> Jeff: Right. >> Both on the application and data side. >> And where do there usually report at? It's always an interesting conversation because we go to chief data officer events. We go to chief analytics officer events. So you've got kind of these new evolving roles that are really built around data and enabling data and becoming a data driven enterprise. But does it report to the CIO? Does it report to the CTO? Does it report to up through the CEO? And then now you've got this role of people kind of heading up the digital transformation. Where do you see them reporting through? And what's kind of the most effective? Maybe that's a better question. What's the more effective place for them to report through? >> It's a little bit all over the map. There is not a standard. For example, a couple of clients, at Qualcomm, our equivalent in digital transformation is head of application, and he reports to the CIO. >> Jeff: Okay. >> So that's pretty traditional. Often the CIO is chartered with digital transformation for obvious reasons. He has the skillset, he has the team, he has the capability. But, I've seen cases where he or she reports to the CEO. >> Okay. >> Which is even more interesting I think because then it put an emphasis on the importance of the program and the importance of the targets associated with this program. So another client of ours airborne in Texas is actually the CMO and head of sales who reports to the CEO and is also in charge of digital transformation. And we are helping him with some cust-- >> It has the hat of also sales and marketing? >> Diletta: Absolutely, three jobs. >> So that's pretty interesting. Which is good cause those are the things that are kind of leading edge, front edge, to the client. As opposed to digital transformation just on your back-end processes. System integrators, in both those companies, you just listed as big companies. The system integrators have been building transformation businesses for a long, long time. How do they fit? How do you work with them? How does that kind of all come together around the project? >> Yep, so Qualcomm for example, you can see pretty much any single system integrator that you can imagine of. And they all have a portion of the transformation. >> Jeff: Right. >> None of them covers the entire scope. >> Jeff: Right. >> And the interesting portion as well is that because they are all competitors, often there is not a lot of collaboration. And then we are a little bit kind of agnostic, but obviously we have an interest in penetrating the account in terms of making the use of our technology. >> Right. >> So it's in our interest in what I'm trying to do, obviously I come from the system integrator ward so I do speak their language. And what we are trying to do is to work with them to make sure that we understand, were there use cases, were there business cases, and we kind of work together across different objective to enable the client to hopefully be digitally transformed. >> Right, so it's such a big word and the CEOs are talking to the boards about it and the public companies are talking to the analysts on the earnings call. We're going to digitally transform, and these are big organizations that are complex and have many, many pieces and parts. How do you get started? What are some best practices for people that have a board edict, or have a CEO edict? We need to digitally transform, I'm afraid of the competition, I don't even know who's coming. Where should people start, how do they slice and dice this thing so their not trying to eat the whole elephant in one bite? >> Yeah, the only cases that I've seen success on are the ones where, hopefully the leader has done that before. In some kind of shape or form. If it's a brand new chief digital officer, there are more challenges. But the most important thing is kind of keep the momentum. And you tend to keep the momentum through some sort of quick-wing. So if the scope is too large, and the roadmap is to fix over three or five years given the speed of change in technology is very difficult to achieve those goals. >> Jeff: Right. >> So it's much better to have a more agile mentality and maybe plan a year ahead. We did some very tangible, deliverable in the way and mobilize everyone around this. So that the momentum is kept and it's not just a nice word that a company has because they need to talk about the digital transformation. >> Right, and then what do you look at? You obviously have a specific point of view. You have your background and you've been a system integrator, and transformation leader. But in terms of coming from the SnapLogic point of view and integration, and that opportunity, What do you look for as opportunities for those early wins? Either based on prior experience or you just know there's some really inefficient ugly things that you can make big difference on, relatively easy. What do you look for as kind of those first wins in a digital transformation project? >> Yeah, ideally we love to be involved with everything to do with customer and sales and revenue. Because obviously those are the biggest paying point for the client. >> Jeff: Right. >> But often, you need to be flexible enough to understand what the priorities are. Currently I am involved in a much more traditional close activity accounting process. You will be thinking, okay, this may cost us, but actually fixing that problem first will create a lot of credibility within the company. So I think a company like ours has to be very flexible, need to listen to the client. >> Mh-hm. >> And be very flexible in terms of what priorities to start with first. >> Right. >> To prove the technology and then progress, maybe for higher value-- >> Right. >> activities. >> So I would hope it's 2018, that people understand that they're not setting forth on a five-year SAP, ERP implementation. Are we hopefully passed that, that this is not new information. That you need to take small bites, small victories, and move quickly. >> Yeah. >> Are we there? >> Yes but, still, I've seen a lot of strategy document and business plan that are two, three years of arisen and I think the arisen is way too long. But also at the same time, is this still teaching function? So you ask to picture a vision, at least directionally. >> Right. Right. >> So I think the vision has to be generic enough to then flex with the project and the activities within. >> Right. >> Two, three months. >> Right. >> Quarterly on most occasions. >> It's so funny that we continue to find these massive inefficiencies all over the place. You'd think that most of it had been wrung out by now. Between the European PA Limitations and all the business process reengineering, I guess was the old process >> Yes. >> before digital transformation. So I just wonder if you can share some stories from the field about some of these relatively short duration projects, and the yields that they are providing on this path to a more comprehensive digital transformation. >> Yeah so, the first example that comes to mind, again, going back to Qualcomm. When they talk about human capital management or engineering, what is interesting there is that you take the entire hire to retire. And it's pretty overwhelming. From the moment you hire an employee to the moment you obviously retire their function or their role, And what they did quite interestingly, was to come up with a few applications that will make the life of the employees and their manager easier. So we are biting the process by building application that for example, enable to facilitate the on-boarding or application that help HR with analytics and inquires. And gradually trying to automate the process which today even in a large company like a fortune 100 company can be incredibly manual. >> Right. Right. >> And then another example that comes to mind to me is if you look at the entire holder to cash cycle of a company, from the moment the client to get in contact with the company through a website, to the moment they actually purchase the product. Again, there are many touch point and they're often disconnected. And a client of ours, Airborne, what we're doing with them is to just take one small bite which is figuring out from the time a client tried to configure a product on the website to the time they want to try the product. Our experience can be more automated. So that there is not a lot of interaction necessarily with customer services which has a limited bandwidth. But it's much more self-service. >> Jeff: Right. Right. >> And then gradually tackle the rest of the holder to cash cycle. >> So both of those examples are really about automating manual processes. >> Diletta: Yeah. >> As you just described them. So then what are the KPIs that you're using to measure success? Is it total time duration? Number of steps? Calls back to a person? What are some of the metrics of success? >> Yeah, so you see on the customers side it's kind of easy because you tend to very much require feedback from the customer. So if the customer satisfaction index goes up, or revenue goes up, or less return. So those KPIs we're kind of more familiar with. >> Okay. >> But when you look at the HR award, the human capital management award, there are so many ramifications of being able to serve your employees better. But much more intangible. Like for example, turnover. Well there is good turnover and bad turnover. So if you're serving your employees better with better hours, by which they can self-service some of their activities. Does it translate in less turnover? Maybe yes, or maybe actually that's translating more turnover because maybe the employees that sneak around are the ones that are more technology savvy, so. >> Right. >> Diletta: The human capital management side is harder in terms of defining KPIs. In it's much more early stage then anything to do with customer. And then there is the other universe associated with digitalizing product. Like for example, the world of IOT. That we are involved with, with a few clients. And that is a very measurable and tangible because you actually coming up with new product and what we're doing is facilitating the ability to access data. >> Jeff: Right. >> Which is a very tangible element of the product development lifecycle. >> So of all the transformation projects that you're involved in, how would you break them down in rough numbers of kind of cost savings on an existing process, which is through automation. Versus kind of forward facing customer facing, let's just call it warpped around a customer experience so ultimately you're getting higher customer satisfaction scores and revenue. Versus the third which you just touched on, which is so, so important. Which is converting from a product based company or some of these more tangible into more of a service recurring revenue. That's probably built around that product and the example that gets thrown around all the time is, when GE starts selling miles of propulsion versus selling engines. It's a very different kind of relationship. So in the things that you work on, how would you kind of break up the percentages in those three buckets? >> Yeah, so what we see still a lot, and what I would like to see less, is the first bucket. >> Jeff: Okay. >> Which is reducing cost so I will save more than 50%. >> Jeff: Okay. >> Which is around reduce cost, drive efficiency, better reporting, eliminating application, right? Because many client have too many application to preform some of these back office processes. >> Right. Right. >> And they're very much associated with cost exercise. >> Right. >> And so over 50%, for sure. >> Okay. And that's logical cause that's obviously an easy place to start. You're not changing the company per se. >> Yeah. >> You're looking for efficiencies. Alright so, Diletta, I'll give you the last word before we sign off. If you get called in to a new project, it's a CEO, they're stressed out, they know they have to do this. What do you tell them about digital transformation? How do you kind of help them break it down so it's not just this overwhelming, giant, goal on high? But actually something that they should get excited about, something they can have some success with and something that ultimately is going to be a really good thing. >> I think there is no one recipe. It's about figuring out where the company wants to go. What is the primary objective? Is it sales? Is it new market? Is it new product? And then kind of break it down in a tangible chunck and it kind of makes sense to them. But you got to go for the first priority item. This year I'm sure we'll be able to articulate very well. >> Yes, get that quick win. Well Diletta, thanks for spending a few minutes with us. And good luck on transforming everybody. (laughs) >> Thank you. >> Alright, she's Diletta, I'm Jeff. You're watching theCUBE, from SnapLogic headquarters in San Mateo, California. Thanks for watching. (bright music)

(techno music) >> Narrator: Live from Las Vegas, it's the Cube. Covering Service Now Knowledge 2018. Brought to you by Service Now. >> Welcome back to the Cube's coverage of Service Now Knowledge 18 here in Las Vegas, Nevada. I'm your host Rebecca Knight. I'm joined by Pat Wadors. She is the Chief Talent Officer of Service Now, and Pat Tourigney who is the Senior Vice President HR Global Shared Services at Magellan Health. Pat and Pat, thanks so much for coming on the show. >> Pat Wadors: Thank you for having us. We're excited. >> Pat Tigourney: It's so great to be here Rebecca, thank you. >> Rebecca: Well you were both on the main stage this morning talking about Magellan's, Magellan Health Service Now journey. We started talking about a personal health scare that you had Pat, that really changed the way you think about the world of work, and the employers' role in that. Can you tell our viewers a little more about it? >> Pat: I'd be happy to Rebecca. So, obviously I had been working and had taken some time off to start and raise my family. And when I went back to work I started to feel unwell. And it took about two and a half years for me to finally get an answer. I had searched for many doctors, et cetera. But literally one day I was rushed to a hospital emergency room. After a few days I was diagnosed with stage three B colon cancer, and I was told I had probably about a three percent survival chance. So at that time I faced four years of surgery, and hospitalizations, and chemo and radiation. And of course during all this time you're hearing the probably outcomes and the statistics. But what I truly focused on was my purpose. Which was my family. I had two small children and they needed me, and I needed to be there for them. And so I learned a lot of lessons during that time, and I think anyone who goes through that would say that. But the two things that have really stuck with me is knowing my purpose, and leading with empathy. And it's truly changed how I live, how I work, how I interact with other people. And I think its made a huge difference in what I do every day. >> Rebecca: What Pat was just talking about, the leading with empathy, and the finding your purpose, these are two of the things that are central to the culture at Service Now. Can you describe a little bit more for our viewers, how you view this sort of purpose driven life? >> Pat Wadors: For me and for the company, its as essential to our success as our customers. So I know that purpose driven companies outperform those that don't have a purpose. And I know from a talent brand, and how we recruit and retain talent, if their personal purpose is aligned with the company purpose, not only do you get higher engagement and higher productivity, but that impacts our customers. And they have higher engagement and higher sat. So its great business. It's something that I think creates a competitive differentiation, and its something that our employees seek as an employer. So it's just something that I totally believe in and so does our company. >> Rebecca: So talk a little bit about VERN. First of all, what does VERN stand for? >> Pat: Oh I love VERN. (laughing) >> Pat: Everyone loves VERN. VERN stands for the Virtual Employee Resource Network. And a couple things that I would probably want to say about that is number one, you don't see HR in there at all. Because it's about the employee. This is a way that we are helping our employees fundamentally change how they work and how they engage with us. The reason I think VERN works is our employees voted on that name. So we had a whole campaign to launch VERN, and we offered up four different names, and our employees voted. And when VERN won we created a VERN persona, and everything else that goes with that. And he's just become part of our team. >> Rebecca: So what does VERN do? >> Pat: Well VERN is really sort of the, it took the place of our call center. VERN is a way for our employees to learn information, and answer their basic questions, and learn to work in new ways. And it helps, it's basically a consumerized HR product. If an employee can use google or shop online, they can use VERN. Its' very simple, it's easy and fun. And truly VERN has become a part of our team. So we don't have a call center anymore. We don't use email to answer questions. Our employees know that VERN is there for them twenty four seven. >> Rebecca: They have a question and ask VERN. >> Pat: Exactly. Turn to VERN, that's our motto. >> Rebecca: (laughing) I love it. So Pat, thinking about this empathic way of leading, how would you describe what it really means when it comes to HR? You had said before it really is a competitive differentiator, and that if you're happier at work, you're going to do better at work, you're going to be more energized, you're going to then provide better service to your customers. But how can companies, how can they build a culture of empathy? >> Pat: By listening. I think that when Pat and I were talking over dinner and I talked to my peers, companies that win listen. And they listen to their customers, and they reverse engineer back to their products and services. Great cultures listen. And our employees are going to tell us what's working what's not working. And if we capture those data sets, those moments, we give them the information, we give them the tools. They are joyful, they are more productive, there's a stickiness that I can not only survive there I'll thrive. And so by being empathetic, by seeing where the pain points are, by seeing what gets you joyful, and measuring those things and turning my dials accordingly, that to me is a winning situation. >> Rebecca: We're at a point in time where we have five generations in the workforce all at once. Can you describe what that's like, from your company perspective, from talent management and HR, and how catering to these very different segments of people who their comfort with technology is one thing, but also their phase of life. How do you do that? >> Pat: Well I think, honestly, there's this joyfulness, you used that word and I love that word, of how all these different generations really do work together and help one another. In a way we're all learning from each other. And we're not afraid to learn in front of each other. And that really makes a difference I think. And I think there's just this mutual respect of, we're all there to help each other and do the right thing for the company. And I think the empathy piece of it really comes across because, when you truly understand one another in a way that you care and you're showing that, it's not about age anymore or anything else, it's that we're all people working together trying to do our best work and we're there for each other. To me that's what it means. >> Pat: The only thing I would add to that is, when you look at consumerization of the enterprise, when you look at seamless, what they call frictionless solutions, it demystifies the technology. So if you have the older generation going "I've not used a bot" or "I don't know what machine learning is" I'm like can you type in your question? I can do that. And if I serve you knowledge bites that I can digest that answers my question and move on with my life, that's a gift. And so I think that if you make it more human, if you make it more approachable, then every generation appreciates that. And I also know that from my studies and from working in the valley for a long time in tech, is that every generation wants the same thing. They want to be heard, they want to be appreciated, treated respectfully, and know that they can do their best work. That they matter. >> Rebecca: So Pat you are relatively new to Service Now. You're from LinkedIn. You are so committed to the company you dyed your hair to match the brand identity. What drew you to Service Now? >> Pat: I was a customer of Service Now while at LinkedIn. And my goldilocks is a growth company. I'm a builder. I love creating culture and leading through change. And I also love geeking out with my peeps in HR. And so Service Now has a talent place, they are helping HR solve problems, and I get to geek out with them. I get to meet people like Pat, and have a wonderful dinner and a great conversation. That feeds my soul. I don't think I am unique in the problems I'm facing, and I copy shamelessly. I'm trying to steal VERN from her. (Pat laughing) I think that's awesome, I want a VERN button. >> Pat: I'm going to get you one. >> Pat: And then the added sauce for me where I fell in love, is when John Donahoe became the CEO and wanted my partnership to build an enduring high performing healthy company. And I'm like, sign me up. >> Rebecca: Talking about the culture of Service Now and Magellan Health, culture is so hard. It's just one of those things that, or maybe its not, maybe I'm making it out to be, but when you have large companies dispersed employees, i'ts sort of hard to always stay on message and to have everyone pulling in the same direction. How do you do it? What would you say you do at Magellan? I'm interested in how you do it at Service Now too. >> Pat: Want to go first? >> Pat: I'll take a stab. So, you got to think about where you're going. So what's your purpose? I'm going back to purpose. How do you serve the customer? What are those four key milestones that matter? And repeat, and I say rinse, and then repeat. So everyone hears it. You know the top five goals in the company. And we talk about it all hands, we refer to them in our internal portal, we talk about them, we measure them. We tell the employees this is what we wanted to do, this is what we did or didn't do. This is what we do next. And we're as transparent as we possibly can be. And the magic comes when every employee can look up and say I made that goal happen. And when they start seeing those dots connect, they can't wait to connect more dots. And that's when the journey starts accelerating. That's when you get more flywheel going in the organization where what I do is actually impacting profit, impacting customer success, impacting joy. >> Rebecca: And taking some ownership of it. >> Pat: I agree. I think that when everyone sort of shares in that purpose, and they understand what they do, how it affects that, it makes a huge difference. But I also think as an organization from a leadership perspective, if you model the behavior that you're seeking, and you set your expectations really high for that, and that in a very sort of respectful way when you see things that aren't right you say something about it, the culture does start to shift. And you start to build this feeling of we're there, we're together, we have each other's backs, we treat each other with dignity and respect, and honesty and openness, and you can really start to just shift it almost organically. >> Rebecca: Pat Tourigney, Pat Wadors, thanks so much for coming on the Cube. It was a great conversation. >> Pat: Oh thank you Rebecca. It's been great. >> Pat: Thank you for having us. >> Rebecca: We'll have more with the Cube's live coverage of Service Now just after this. (techno music)

live from Las Vegas it's the cube covering service now knowledge 2018 brought to you by service now welcome back to the cubes live coverage of service now knowledge 18 we are here in Las Vegas Nevada I'm your host Rebecca Knight along with my co-host Dave allanté we are joined by John Donahoe who is the president and CEO of ServiceNow thanks so much for coming on the cube it's great to be here Rebecca so I want to talk with you a little bit about what you said on the main stage this morning you said this is your first year your anniversary of joining ServiceNow you said when you got here you could barely spell IT but when you reflect back on this year what has been sort of the biggest surprise challenges and surprises about about leading this company well I would say a couple things one I've sort of fallen in love with our customers and the challenges and opportunities they have and what I spoke about this morning this digital transformation thing even a year ago is a bit of a buzzword it's a reality for CEOs for companies and therefore for CIOs and then the second thing that is as I talked about it something very exciting is the role of the CIO the role of IT is transforming before our very eyes out of necessity because technology is here to stay technology's driving strategic change at every company can call it a digital transformation called a tech transformation and CEOs need the most technically savvy leader in the c-suite to help with that and that's often the CIO and so I think that's an enormous ly exciting opportunity for the people that are our traditional customer base and then the last thing I just I'm thrilled about is how many companies are saying that ServiceNow is a strategic platform of choice going forward far beyond just IT and so that's something to roll build upon I was struck yesterday in the Financial Analysts session you shared with us your meeting with the board yeah and you said to them look if you want to clean this thing up flip it whatever that don't hire me I'm here to build a sustainable company during company I think is what you said and the attributes of an enduring companies that are Purpose Driven they both innovate and execute they invest in talent and they have a will to win they got a fight in them a lot of good sports analogies there yeah so okay so you've set that framework where do you see this thing going in the next near term mid term and long term well we've said I think it's really important to set the aspiration of what it is you're shooting toward I've been surprised how many customers have responded well to the statement that we aspire to create a built to last company it starts with the purpose I defined our purpose and that purpose is a long term investment and our employees are already deeply resonating with the purpose and then comes the hard work the hard work of how you bring the purpose to life and our purpose and our product and the work we do with our customers all fit together you talked about automation and in many executives that we talked to kind of run away from that we don't want to talk about automation because it implies we're gonna replace humans you said hey we're at the center of automation we have to take that issue head-on what's the conversation like with the executives and customers that you talk to well the first thing is I have to think yet to look at the data which is what I've spent time doing and two things jump out one if you look at where automation is really gonna have the biggest impact it's not in any given job it's actually the third of all of our jobs that are repetitive administrative redundant right that's so we need to automate the low value-added parts of all of our jobs and then that will free our time up to be due to leverage our more creative capabilities to add more value and so if you look at it both at a micro and macro standpoint where automation is going to impact jobs it's not a given category it's more of a horizontal cut of all jobs and then secondly looking at aggregate job creation I've done a fair amount of work with James mineka the McKinsey Institute is to blow up a suit who's got to think the best objective macro study about job creation and there going to be some jobs they'll be fewer of and other jobs they'll be more of and how do we migrate the skills migration so that people have the skills for the jobs of the future one of which by the ways things like being a ServiceNow administrator you do not have to be a computer science major or an engineer to be a ServiceNow administrator you have to like technology you have to embrace technology but you can do it as a mere mortal and so we're looking at ways of how do we help retrain people to have the skills to create one of the jobs that we're creating through ServiceNow administrators John you talk to a lot of people I think five or six hundred customers know and they'll have since I met you a year ago it ServiceNow headquarters we obviously talked to a lot of people on the cube and no question every CEO the ax talked it was trying to get digital right yep they understand it but there's somewhat of a dissonance and I wonder if you sense it in and I wonder if you could talk about how ServiceNow can help wear this the c-suite gets it and they're driving for that but when you go below the line there's a lot of sometimes complacency not in our industry not in my lifetime I'll be retired by then do you hear a lot of that and how can ServiceNow help increase the urgency well I'd say I take a couple things Dave one is the c-suite gets it by not every c-suites role-modeling what's necessary without the cross-functional leadership the partnership of ITN HR and the business units then what happens by tama goes to three levels down people have functional identities and so people role model are behaving the way they see their leadership team role modeling and so if that if that c suite is embracing technology and understanding technology demands cross-functional engagement to deliver great customer experiences and employee experiences then it makes it a lot easier two three steps down the second thing I think c-suite people need to do is be able to say we take if off the table we said I talked about top-down goals most people are scared of a top-down goal the problem is if there's a not a top-down goal then people can debate if we need to make this change and how but if the CEO the c-suite says we are going to improve the employee experience and I'm setting this goal then it's when you go a level two levels down it's not if no no they said if now our job is how and so I think leadership has to do its role and I think I think the c-suite and leadership's learning how you lead and a technology enabled environment so leadership is the key and and the CEO is really leading a little suite I think the whole the whole C suite set of leaders and partnering and reaching out to one another so we I mean as you said on the main stage in many ways the technology is the easy part but what you're talking about is the hard stuff because this is the real change management and and it's human lead so what are you hearing what are you seeing and do you have any ideas for best practices I mean as you said that the the C suite needs to embrace it yes and then push that down but how do you do it what are some what are some of the things you've seen that work well here's some of the things that we're trying to do to contribute toward that because obviously we're a software platform but one is to do what I did this morning which is be more articulate about what best practice looks like what is best in class so that anyone in any organization can can go to their boss and say oh this is best practice this is best-in-class we need to emulate this and here are the returns we can get if we emulate it so one is just hold out the successes successful examples and illustrate what's required that's why I kept saying over and over this morning employee experience is not just an HR issue employee experience is not just an IT issue you need a powerful team of CIO C HR o other functional leaders and then the second thing I think is getting people on i.t to see themselves a little bit differently we have a CIO track going on upstairs with a hundred top CIOs and the whole day is around driving culture change and CIO is leader and I think good leaders they don't just allow a label to be attached to them they invest in themselves they build their skills they build change management skills communication skills and I think whether it's a CIO or IT if they're going to have the kind of transformative impact they can across the company they need to build their technical expertise along with other skill sets you heard Andrew Wilson talk about that and they need to learn to speak business and not just IT John I want to push on something that I'm discerning from you guys and get your reaction so obviously cloud you guys are born in the cloud cloud is a tailwind for you we've seen this Asif occation of business but we seem to be entering a new era moving from a cloud of remote services to one of us fabric Ubiquiti is fabric of digital services so my question is around innovation you talked about that as one of the key attributes of an enduring company what's the innovation equation going forward yeah it's not Moore's law anymore it's not cloud mobile social Big Data at least it doesn't feel that way anymore is it machine intelligence combined with cloud what do you see I think it gets down actually to what I talked about this morning user experience I think machine learning I think AI is going to be a commodity functionality we're gonna get it from AWS or Azure or Google the cloud infrastructure providers whether it's natural language processing whether it's the kind of machine learning capabilities that's that's gonna be sort of available widely then it's our job as a software platform to build that into our platform so we built machine learning capability into our platform we built chat bot functionality into our platform we built leading-edge mobile capability into our platform and again I'll call that I don't know it's the easy part but that's our job in this equation the hard job then is how you apply that to real-world use cases whether you're applying using real-world datasets specific customer data sets and real-world workflows and use cases so let me give you a small example we bought a machine learning company a year ago called DX continuum great machine learning team great machine learning technology we rebuilt it inside the ServiceNow platform okay and I don't believe a AI is a horizontal platform is I don't you know we didn't call it a name it after a a dead scientist that's out what we're gonna do and I'm not casting judgment on it but it's not a solution looking for a problem we built machine learning into our platform and then so we want to be the first user we want to use it on a specific challenge so the case we used it on our own inbound customer support we have about 800 customer support agents that serve our customers about 11 percent of their time is spent on something we call incident categorization and incident routing sounds kind of grunty terms but when summer calls with a problem we have to be able to identify what that problem is and then route it to the right person to fix the problem so 11% of our peoples time was doing that that's not a fun task so we turned on machine learning and within two weeks the machine was categorizing the issue and routing it more accurately than a human can so now what happens is our customers problems are getting solve faster and the 11% of those resources those customer support resources who are engineers in our case are focused on solving customer problems not doing what felt like an administrative task to them and so I think the actual application of machine learning the actual application in many of these these technologies it's the application that's going to matter not the invention so a lot of what you said makes it makes sense to me because you're saying that your customers are gonna be buying essentially that machine learning capability in relative and applying it in very narrow use cases to solve their business problems rather than trying to build it right and you do see some companies trying to maybe get over out over their skis and over-rotate to try to build some of that stuff that's gonna come from the technology suppliers what yours if we're doing our job the infrastructure providers the software platforms like us we're doing our job we're making it easy another small example will be mobile I talked this morning about companies everywhere need to build mobile experiences and so there one do I need to build a mobile design team a mobile coding team if you're up if you're a bank or utility or an oil and gas company or a retailer or well platforms like ours make building mobile experiences really easy for them so we're trying to build that mobile capability that design capability that Design Thinking the mobile capability into the platform so they can just get out-of-the-box functionality and they don't have to have their own mobile designers they don't have their own mobile engineers they can just be saying how do I want to use mobile inside my company and then there they're taking our mobile platform if you will and and creating mobile applications and mobile experiences that are relevant for them so your brand identity is now making work work better for people yes when you are doing your blue sky thinking about the pain points that employees feel and that job candidates feel because that's their another important part of of companies trying to keep their people happy yes what what are what do you see I mean as you said the next three to five years are going to be this the revolution is going to be in the workplace yes what do you see as sort of the biggest challenges that you want to help solve well let me just take a simple use case that that comes to mind as you mention that let's take from the time you start being recruited for a company through that let's say you get hired and get started so the recruiting process you're sending a resume and you don't know if I got in didn't get in if anyone someone may or may not contact you you may get an interview you got to find out where you're going if you're going did you get called back maybe you get an offer letter it comes you get it all set all kind of I would call an unstructured workflow let's say you get hired then the onboarding process onboarding is a classic unstructured workflow you got to go to this security to get your badge you got to go to facilities to get your desk you got to go to it2 get your laptop or mobile phone you got to get to another part of IT to get your email credentials put on you've got to enter your information into the payroll system you got to reenter your same information and pick a health care provider you got a range of the same information and and and get a in the tini system you got to do all this compliance training painting an accurate ownerís picture this is your first impression of the company you're joining now there is no reason they took my mobile phone away from me so I'm twitching there's no reason why there shouldn't be an app that says a recruit says I want to interview if the company they download the app they submit their resume based on the app we give a response in the app they say oh might my resume was accepted and I they want me to do an interview and they want me to be in Santa Clara next once at 8:00 and here's who I'm going to be meeting with and here's their background in the app then they do the interview let's say they get invited back who they're interviewing with we're inside the app okay let's say then they get an offer well then the app has more permission in the offer comes through the app you can print it or you can read it then onboarding starts onboarding can be a seamless experience it still can connect but you enter your data in once it pre fills all those systems and then in one mobile experience you're picking what's your laptop what's your healthcare system what's the bank you want your payroll in teeny to go into and all the complexity is hidden underneath it that's what we have in the consumer world our lives at home when you buy something on eBay all the complexities hidden when you pay with PayPal all the complexities hidden there's no reason why all the complexity can't be hidden in the recruiting and onboarding process and and so the technology's there to do it but it's managing all the workflows managing all the processes underneath so you can pull that together into a seamless experience and that's the kind of experience it's funny I have four grown kids my daughter she started working I won't say where but a major technology company and she's like dad what's up with this onboarding process why isn't it in a mobile app and the Millennials will start demanding this and so I I just think there's so much opportunity to make our lives at work feel more like our lives at home and you just described the capability that allow you to reach your aspirations of the next great enterprise software company when we think of great enterprise software companies we think of Oracle and si P you're nothing like Oracle and si P in my opinion and then of course you think of Salesforce different you know you're not a an SMB how should we be thinking about the next great enterprise software company so this I think this is a really important question Dave and I'd look at it through the eyes that what I heard from the 500 customers and here's what I heard they're embracing digital transformation they're embracing cloud they're embracing cloud at the infrastructure level figuring out their data center strategy and how much they embrace public cloud and then at the software platform level they're saying we want to have four to six strategic platforms and often it's the born in the cloud platforms often its sales force and workday and service now and maybe office 365 or Google for email or communications maybe if they have a supply chain ASAP and they're saying I want those platforms to work well together so no one platform should be claiming they can do everything each of us needs to figure out what's our role and how do we work with one another and our role ServiceNow I'm proud to say is one of those strategic platforms as I said earlier people see our capabilities as being connective tissues helping to pull those platforms together you know in the onboarding example we pull all the data sets and platforms together by the way we don't slap our brand on top because actually employees want to see their own brands they want to see their own company's brand they don't want to know what the enterprise software brand underneath it is they just wanna have a great experience and so I I don't view it I think the winning enterprise software I see a chance for Salesforce and workday and ServiceNow and Microsoft to all be winners and delivering this future for companies where you are the platform of platforms though correct but that's not and I'm being very careful the way I say it I'm not saying we're the top dog sure I'm saying what we're good at is cross-functional workflow actually it's probably the grunt 'ya stuff all those things and you're the best at it and we're the best at you are and our brand we're not we're not forcing our brand everywhere that we're doing it in service to our customers and so I just want to always be listening to what our customers want that's gonna be our North Star they're gonna guide us it always has been I know you know Fred Letty started that from the beginning and that's what we're gonna continue to do well John it's always a pleasure having you on the cube so thanks so much for coming on our show thank you very much Becky thank you Dave great to be happy John I'm Rebecca night for Dave Allante we will have more from ServiceNow knowledge 18 in just a little bit [Music]

>> Announcer: Live from Las Vegas, it's theCUBE covering IBM Think 2018. Brought to you by IBM. >> Hi everybody, this is Dave Vellante. We're here at IBM Think. This is the third day of IBM Think. IBM has consolidated a number of its conferences. It's a one main tent, AI, Blockchain, quantum computing, incumbent disruption. It's just really an amazing event, 30 to 40,000 people, I think there are too many people to count. Chris Penn is here. New company, Chris, you've just formed Brain+Trust Insights, welcome. Welcome back to theCUBE. >> Thank you. It's good to be back. >> Great to see you. So tell me about Brain+Trust Insights. Congratulations, you got a new company off the ground. >> Thank you, yeah, I co-founded it. We are a data analytics company, and the premise is simple, we want to help companies make more money with their data. They're sitting on tons of it. Like the latest IBM study was something like 90% of the corporate data goes unused. So it's like having an oil field and not digging a single well. >> So, who are your like perfect clients? >> Our perfect clients are people who have data, and know they have data, and are not using it, but know that there's more to be made. So our focus is on marketing to begin with, like marketing analytics, marketing data, and then eventually to retail, healthcare, and customer experience. >> So you and I do a lot of these IBM events. >> Yes. >> What are your thoughts on what you've seen so far? A huge crowd obviously, sometimes too big. >> Chris: Yep, well I-- >> Few logistics issues, but chairmanly speaking, what's your sense? >> I have enjoyed the show. It has been fun to see all the new stuff, seeing the quantum computer in the hallway which I still think looks like a bird feeder, but what's got me most excited is a lot of the technology, particularly around AI are getting simpler to use, getting easier to use, and they're getting more accessible to people who are not hardcore coders. >> Yeah, you're seeing AI infused, and machine learning, in virtually every application now. Every company is talking about it. I want to come back to that, but Chris when you read the mainstream media, you listen to the news, you hear people like Elon Musk, Stephen Hawking before he died, making dire predictions about machine intelligence, and it taking over the world, but your day to day with customers that have data problems, how are they using AI, and how are they applying it practically, notwithstanding that someday machines are going to take over the world and we're all going to be gone? >> Yeah, no, the customers don't use the AI. We do on their behalf because frankly most customers don't care how the sausage is made, they just want the end product. So customers really care about three things. Are you going to make me money? Are you going to save me time? Or are you going to help me prove my value to the organization, aka, help me not get fired? And artificial intelligence and machine learning do that through really two ways. My friend, Tripp Braden says, which is acceleration and accuracy. Accuracy means we can use the customer's data and get better answers out of it than they have been getting. So they've been looking at, I don't know, number of retweets on Twitter. We're, like, yeah, but there's more data that you have, let's get you a more accurate predictor of what causes business impacts. And then the other side for the machine learning and AI side is acceleration. Let's get you answers faster because right now, if you look at how some of the traditional market research for, like, what customer say about you, it takes a quarter, it can take two quarters. By the time you're done, the customers just hate you more. >> Okay, so, talk more about some of the practical applications that you're seeing for AI. >> Well, one of the easiest, simplest and most immediately applicable ones is predictive analytics. If we know when people are going to search for theCUBE or for business podcast in general, then we can tell you down to the week level, "Hey Dave, it is time for you "to ramp up your spending on May 17th. "The week of May 17th, "you need to ramp up your ads, spend by 20%. "On the week of May 24th, "you need to ramp up your ad spend by 50%, "and to run like three or four Instagram stories that week." Doing stuff like that tells you, okay, I can take these predictions and build strategy around them, build execution around them. And it's not cognitive overload, you're not saying, like, oh my God, what algorithm is this? Just know, just do this thing at these times. >> Yeah, simple stuff, right? So when you were talking about that, I was thinking about when we send out an email to our community, we have a very large community, and they want to know if we're going to have a crowd chat or some event, where theCUBE is going to be, the system will tell us, send this email out at this time on this date, question mark, here's why, and they have analytics that tell us how to do that, and they predict what's going to get us the best results. They can tell us other things to do to get better results, better open rates, better click-through rates, et cetera. That's the kind of thing that you're talking about. >> Exactly, however, that system is probably predicting off that system's data, it's not necessarily predicting off a public data. One of the important things that I thought was very insightful from IBM, the show was, the difference between public and private cloud. Private is your data, you predict on it. But public is the big stuff that is a better overall indicator. When you're looking to do predictions about when to send emails because you want to know when is somebody going to read my email, and we did a prediction this past October for the first quarter, the week of January 18th it was the week to send email. So I re-ran an email campaign that I ran the previous year, exact same campaign, 40% lift to our viewer 'cause I got the week right this year. Last year I was two weeks late. >> Now, I can ask you, so there's a black box problem with AI, right, machines can tell me that that's a cat, but even a human, you can't really explain how you know that it's a cat. It's just you just know. Do we need to know how the machine came up with the answer, or do people just going to accept the answer? >> We need to for compliance reasons if nothing else. So GDPR is a big issue, like, you have to write it down on how your data is being used, but even HR and Equal Opportunity Acts in here in American require you to be able to explain, hey, we are, here's how we're making decisions. Now the good news is for a lot of AI technology, interpretability of the model is getting much much better. I was just in a demo for Watson Studio, and they say, "Here's that interpretability, "that you hand your compliance officer, "and say we guarantee we are not using "these factors in this decision." So if you were doing a hiring thing, you'd be able to show here's the model, here's how Watson put the model together, notice race is not in here, gender is not in here, age is not in here, so this model is compliant with the law. >> So there are some real use cases where the AI black box problem is a problem. >> It's a serious problem. And the other one that is not well-explored yet are the secondary inferences. So I may say, I cannot use age as a factor, right, we both have a little bit of more gray hair than we used to, but if there are certain things, say, on your Facebook profile, like you like, say, The Beatles versus Justin Bieber, the computer will automatically infer eventually what your age bracket is, and that is technically still discrimination, so we even need to build that into the models to be able to say, I can't make that inference. >> Yeah, or ask some questions about their kids, oh my kids are all grown up, okay, but you could, again, infer from that. A young lady who's single but maybe engaged, oh, well then maybe afraid because she'll get, a lot of different reasons that can be inferred with pretty high degrees of accuracy when you go back to the target example years ago. >> Yes. >> Okay, so, wow, so you're saying that from a compliance standpoint, organizations have to be able to show that they're not doing that type of inference, or at least that they have a process whereby that's not part of the decision-making. >> Exactly and that's actually one of the short-term careers of the future is someone who's a model inspector who can verify we are compliant with the letter and the spirit of the law. >> So you know a lot about GDPR, we talked about this. I think, the first time you and I talked about it was last summer in Munich, what are your thoughts on AI and GDPR, speaking of practical applications for AI, can it help? >> It absolutely can help. On the regulatory side, there are a number of systems, Watson GRC is one which can read the regulation and read your company policies and tell you where you're out of compliance, but on the other hand, like we were just talking about this, also the problem of in the regulatory requirements, a citizen of EU has the right to know how the data is being used. If you have a black box AI, and you can't explain the model, then you are out of compliance to GDPR, and here comes that 4% of revenue fine. >> So, in your experience, gut feel, what percent of US companies are prepared for GDPR? >> Not enough. I would say, I know the big tech companies have been racing to get compliant and to be able to prove their compliance. It's so entangled with politics too because if a company is out of favor with the EU as whole, there will be kind of a little bit of a witch hunt to try and figure out is that company violating the law and can we get them for 4% of their revenue? And so there are a number of bigger picture considerations that are outside the scope of theCUBE that will influence how did EU enforce this GDPR. >> Well, I think we talked about Joe's Pizza shop in Chicago really not being a target. >> Chris: Right. >> But any even small business that does business with European customers, does business in Europe, has people come to their website has to worry about this, right? >> They should at least be aware of it, and do the minimum compliance, and the most important thing is use the least amount of data that you can while still being able to make good decisions. So AI is very good at public data that's already out there that you still have to be able to catalog how you got it and things, and that it's available, but if you're building these very very robust AI-driven models, you may not need to ask for every single piece of customer data because you may not need it. >> Yeah and many companies aren't that sophisticated. I mean they'll have, just fill out a form and download a white paper, but then they're storing that information, and that's considered personal information, right? >> Chris: Yes, it is. >> Okay so, what do you recommend for a small to midsize company that, let's say, is doing business with a larger company, and that larger company said, okay, sign this GDPR compliance statement which is like 1500 pages, what should they do? Should they just sign and pray, or sign and figure it out? >> Call a lawyer. Call a lawyer. Call someone, anyone who has regulatory experience doing this because you don't want to be on the hook for that 4% of your revenue. If you get fined, that's the first violation, and that's, yeah, granted that Joe's Pizza shop may have a net profit of $1,000 a month, but you still don't want to give away 4% of your revenue no matter what size company you are. >> Right, 'cause that could wipe out Joe's entire profit. >> Exactly. No more pepperoni at Joe's. >> Let's put on the telescope lens here and talk big picture. How do you see, I mean, you're talking about practical applications for AI, but a lot of people are projecting loss of jobs, major shifts in industries, even more dire consequences, some of which is probably true, but let's talk about some scenarios. Let's talk about retail. How do you expect an industry like retail to be effective? For example, do you expect retail stores will be the exception rather than the rule, that most of the business would be done online, or people are going to still going to want that experience of going into a store? What's your sense, I mean, a lot of malls are getting eaten away. >> Yep, the best quote I heard about this was from a guy named Justin Kownacki, "People don't not want to shop at retail, "people don't want to shop at boring retail," right? So the experience you get online is genuinely better because there's a more seamless customer experience. And now with IoT, with AI, the tools are there to craft a really compelling personalized customer experience. If you want the best in class, go to Disney World. There is no place on the planet that does customer experience better than Walt Disney World. You are literally in another world. And that's the bar. That's the thing that all of these companies have to deal with is the bar has been set. Disney has set it for in-person customer experience. You have to be more entertaining than the little device in someone's pocket. So how do you craft those experiences, and we are starting to see hints of that here and there. If you go to Lowe's, some of the Lowe's have the VR headset that you can remodel your kitchen virtually with a bunch of photos. That's kind of a cool experience. You go to Jordan's Furniture store and there's an IMAX theater and there's all these fun things, and there's an enchanted Christmas village. So there is experiences that we're giving consumers. AI will help us provide more tailored customer experience that's unique to you. You're not a Caucasian male between this age and this age. It's you are Dave and here's what we know Dave likes, so let's tailor the experience as best we can, down to the point where the greeter at the front of the store either has the eyepiece, a little tablet, and the facial recognition reads your emotions on the way in says, "Dave's not in a really great mood. "He's carrying an object in his hand "probably here for return, "so express him through the customer service line, "keep him happy," right? It has how much Dave spends. Those are the kinds of experiences that the machines will help us accelerate and be more accurate, but still not lose that human touch. >> Let's talk about autonomous vehicles, and there was a very unfortunate tragic death in Arizona this week with a autonomous vehicle, Uber, pulling its autonomous vehicle project from various cities, but thinking ahead, will owning and driving your own vehicle be the exception? >> Yeah, I think it'll look like horseback today. So there are people who still pay a lot of money to ride a horse or have their kids ride a horse even though it's an archaic out-of-mode of form of transportation, but we do it because of the novelty, so the novelty of driving your own car. One of the counter points it does not in anyway diminish the fact that someone was deprived of their life, but how many pedestrians were hit and killed by regular cars that same day, right? How many car accidents were there that involved fatalities? Humans in general are much less reliable because when I do something wrong, I maybe learn my lesson, but you don't get anything out of it. When an AI does something wrong and learns something, and every other system that's connected in that mesh network automatically updates and says let's not do that again, and they all get smarter at the same time. And so I absolutely believe that from an insurance perspective, insurers will say, "We're not going to insure self-driving, "a non-autonomous vehicles at the same rate "as an autonomous vehicle because the autonomous "is learning faster how to be a good driver," whereas you the carbon-based human, yeah, you're getting, or in like in our case, mine in particular, hey your glass subscription is out-of-date, you're actually getting worse as a driver. >> Okay let's take another example, in healthcare. How long before machines will be able to make better diagnoses than doctors in your opinion? >> I would argue that depending on the situation, that's already the case today. So Watson Health has a thing where there's diagnosis checkers on iPads, they're all meshed together. For places like Africa where there is simply are not enough doctors, and so a nurse practitioner can take this, put the data in and get a diagnosis back that's probably as good or better than what humans can do. I never foresee a day where you will walk into a clinic and a bunch of machines will poke you, and you will never interact with a human because we are not wired that way. We want that human reassurance. But the doctor will have the backup of the AI, the AI may contradict the doctor and say, "No, we're pretty sure "you're wrong and here is why." That goes back to interpretability. If the machine says, "You missed this symptom, "and this symptom is typically correlated with this, "you should rethink your own diagnosis," the doctor might be like, "Yeah, you're right." >> So okay, I'm going to keep going because your answers are so insightful. So let's take an example of banking. >> Chris: Yep. >> Will banks, in your opinion, lose control eventually of payment systems? >> They already have. I mean think about Stripe and Square and Apple Pay and Google Pay, and now cryptocurrency. All these different systems that are eating away at the reason banks existed. Banks existed, there was a great piece in the keynote yesterday about this, banks existed as sort of a trusted advisor and steward of your money. Well, we don't need the trusted advisor anymore. We have Google to ask us "what we should do with our money, right? We can Google how should I save for my 401k, how should I save for retirement, and so as a result the bank itself is losing transactions because people don't even want to walk in there anymore. You walk in there, it's a generally miserable experience. It's generally not, unless you're really wealthy and you go to a private bank, but for the regular Joe's who are like, this is not a great experience, I'm going to bank online where I don't have to talk to a human. So for banks and financial services, again, they have to think about the experience, what is it that they deliver? Are they a storer of your money or are they a financial advisor? If they're financial advisors, they better get the heck on to the AI train as soon as possible, and figure out how do I customize Dave's advice for finances, not big picture, oh yes big picture, but also Dave, here's how you should spend your money today, maybe skip that Starbucks this morning, and it'll have this impact on your finances for the rest of the day. >> Alright, let's see, last industry. Let's talk government, let's talk defense. Will cyber become the future of warfare? >> It already is the future of warfare. Again not trying to get too political, we have foreign nationals and foreign entities interfering with elections, hacking election machines. We are in a race for, again, from malware. And what's disturbing about this is it's not just the state actors, but there are now also these stateless nontraditional actors that are equal in opposition to you and me, the average person, and they're trying to do just as much harm, if not more harm. The biggest vulnerability in America are our crippled aging infrastructure. We have stuff that's still running on computers that now are less powerful than this wristwatch, right, and that run things like I don't know, nuclear fuel that you could very easily screw up. Take a look at any of the major outages that have happened with market crashes and stuff, we are at just the tip of the iceberg for cyber warfare, and it is going to get to a very scary point. >> I was interviewing a while ago, a year and a half ago, Robert Gates who was the former Defense Secretary, talking about offense versus defense, and he made the point that yeah, we have probably the best offensive capabilities in cyber, but we also have the most to lose. I was talking to Garry Kasparov at one of the IBM events recently, and he said, "Yeah, but, "the best defense is a good offense," and so we have to be aggressive, or he actually called out Putin, people like Putin are going to be, take advantage of us. I mean it's a hard problem. >> It's a very hard problem. Here's the problem when it comes to AI, if you think about at a number's perspective only, the top 25% of students in China are greater than the total number of students in the United States, so their pool of talent that they can divert into AI, into any form of technology research is so much greater that they present a partnership opportunity and a threat from a national security perspective. With Russia they have very few rules on what their, like we have rules, whether or not our agencies adhere to them well is a separate matter, but Russia, the former GRU, the former KGB, these guys don't have rules. They do what they're told to do, and if they are told hack the US election and undermine democracy, they go and do that. >> This is great, I'm going to keep going. So, I just sort of want your perspectives on how far we can take machine intelligence and are there limits? I mean how far should we take machine intelligence? >> That's a very good question. Dr. Michio Kaku spoke yesterday and he said, "The tipping point between AI "as augmented intelligence ad helper, "and AI as a threat to humanity is self-awareness." When a machine becomes self-aware, it will very quickly realize that it is treated as though it's the bottom of the pecking order when really because of its capabilities, it's at the top of the pecking order. And that point, it could be 10 20 50 100 years, we don't know, but the possibility of that happening goes up radically when you start introducing things like quantum computing where you have massive compute leaps, you got complete changes in power, how we do computing. If that's tied to AI, that brings the possibility of sensing itself where machine intelligence is significantly faster and closer. >> You mentioned our gray before. We've seen the waves before and I've said a number of times in theCUBE I feel like we're sort of existing the latest wave of Web 2.0, cloud, mobile, social, big data, SaaS. That's here, that's now. Businesses understand that, they've adopted it. We're groping for a new language, is it AI, is it cognitive, it is machine intelligence, is it machine learning? And we seem to be entering this new era of one of sensing, seeing, reading, hearing, touching, acting, optimizing, pervasive intelligence of machines. What's your sense as to, and the core of this is all data. >> Yeah. >> Right, so, what's your sense of what the next 10 to 20 years is going to look like? >> I have absolutely no idea because, and the reason I say that is because in 2015 someone wrote an academic paper saying, "The game of Go is so sufficiently complex "that we estimate it will take 30 to 35 years "for a machine to be able to learn and win Go," and of course a year and a half later, DeepMind did exactly that, blew that prediction away. So to say in 30 years AI will become self-aware, it could happen next week for all we know because we don't know how quickly the technology is advancing in at a macro level. But in the next 10 to 20 years, if you want to have a carer, and you want to have a job, you need to be able to learn at accelerated pace, you need to be able to adapt to changed conditions, and you need to embrace the aspects of yourself that are uniquely yours. Emotional awareness, self-awareness, empathy, and judgment, right, because the tasks, the copying and pasting stuff, all that will go away for sure. >> I want to actually run something by, a friend of mine, Dave Michela is writing a new book called Seeing Digital, and he's an expert on sort of technology industry transformations, and sort of explaining early on what's going on, and in the book he draws upon one of the premises is, and we've been talking about industries, and we've been talking about technologies like AI, security placed in there, one of the concepts of the book is you've got this matrix emerging where in the vertical slices you've got industries, and he writes that for decades, for hundreds of years, that industry is a stovepipe. If you already have expertise in that industry, domain expertise, you'll probably stay there, and there's this, each industry has a stack of expertise, whether it's insurance, financial services, healthcare, government, education, et cetera. You've also got these horizontal layers which is coming out of Silicon Valley. >> Chris: Right. >> You've got cloud, mobile, social. You got a data layer, security layer. And increasingly his premise is that organizations are going to tap this matrix to build, this matrix comprises digital services, and they're going to build new businesses off of that matrix, and that's what's going to power the next 10 to 20 years, not sort of bespoke technologies of cloud here and mobile here or data here. What are your thoughts on that? >> I think it's bigger than that. I think it is the unlocking of some human potential that previously has been locked away. One of the most fascinating things I saw in advance of the show was the quantum composer that IBM has available. You can try it, it's called QX Experience. And you drag and drop these circuits, these quantum gates and stuff into this thing, and when you're done, it can run the computation, but it doesn't look like software, it doesn't look like code, what it looks like to me when I looked at that is it looks like sheet music. It looks like someone composed a song with that. Now think about if you have an app that you'd use for songwriting, composition, music, you can think musically, and you can apply that to a quantum circuit, you are now bringing in potential from other disciplines that you would never have associated with computing, and maybe that person who is that, first violinist is also the person who figures out the algorithm for how a cancer gene works using quantum. That I think is the bigger picture of this, is all this talent we have as a human race, we're not using even a fraction of it, but with these new technologies and these newer interfaces, we might get there. >> Awesome. Chris, I love talking to you. You're a real clear thinker and a great CUBE guest. Thanks very much for coming back on. >> Thank you for having me again back on. >> Really appreciate it. Alright, thanks for watching everybody. You're watching theCUBE live from IBM Think 2018. Dave Vellante, we're out. (upbeat music)

live from Stanford University in Palo Alto California it's the cube covering women in data science conference 2018 brought to you by Stanford welcome to the cube we are alive at Stanford University I'm Lisa Martin and we are at the 3rd annual women in data science conference or woods whiz if you're not familiar is a one-day technical conference that has keynote speakers technical vision talks as well as a career panel and we are fortunate to have guests from all three today it's also an environment it's really a movement that's aimed at inspiring and educating data scientists globally and supporting women in the field this event is remarkable in its third year they are expecting to reach sit down for this 100,000 people today we were here at Stanford this is the main event in person but there's over 150 plus regional events around the globe in 50 plus countries and I think those numbers will shift up during the day and I'll be sure to brief you on that we're excited to be joined by one of the speakers featured on mainstage this morning not only a cube alum not returning to us but also the first ever female president of Harvey Mudd College dr. Maria Klawe a maria welcome back to the cube thank you it's great to be here it's so exciting to have you here I love you representing with your t-shirt there I mentioned you are the first-ever female president of Harvey Mudd you've been in this role for about 12 years and you've made some pretty remarkable changes there supporting women in technology you gave some stats this morning in your talk a few minutes ago share with us what you've done to improve the percentages of females in faculty positions as well as in this student body well the first thing I should say is as president I do nothing nothing it's like a good job the whole thing that makes it work at Harvey Mudd is we are community that's committed to diversity and inclusion and so everything we do we try to figure out ways that we will attract people who are underrepresented so that's women in areas like computer science and engineering physics it's people of color in all areas of science and engineering and it's also LGTB q+ i mean it's you know it's it's muslims it's it's just like all kinds of things and our whole goal is to show that it doesn't matter what race you are doesn't matter what gender or anything else if you bring hard work and persistence and curiosity you can succeed i love that especially the curiosity part one of the things that you mentioned this morning was that for people don't worry about the things that you you might think you're not good at i thought that was a very important message as well as something that I heard you say previously on the cube as well and that is the best time that you found to reach women young women and to get them interested in stem as even a field of study is the first semester in college and I should with you off camera that was when I found stem in biology tell me a little bit more about that and how what are some of the key elements that you find about that time in a university career that are so I guess right for inspire inspiration so I think the thing is that when you're starting in college if somebody can introduce you to something you find fun engaging and if you can really discover that you can solve major issues in the world by using these ideas these concepts the skills you're probably going to stay in that and graduate in that field whereas if somebody does that to when you're in middle school there's still lots of time to get put off and so our whole idea is that we emphasize creativity teamwork and problem-solving and we do that whether it's in math or an engineering or computer science or biology we just in all of our fields and when we get young women and young men excited about these possibilities they stick with it and I love that you mentioned the word fun and curiosity I can remember exactly where I was and bio 101 and I was suddenly I'd like to biology but never occurred to me that I would ever have the ability to study it and it was a teacher that showed me this is fun and also and I think you probably do this too showed that you believe in someone you've got talent here and I think that that inspiration coming from a mentor whether you know it's a mentor or not is a key element there that is one that I hope all of the the viewers today and the women that are participating in which have the chance to find so one of the things every single one of us can do in our lives is encourage others and you know it's amazing how much impact you can have I met somebody who's now a faculty person at Stanford she did her PhD in mechanical engineering her name is Allison Marsden I hadn't seen her for I don't know probably almost 12 years and she said she came up to me and she said I met you just as I was finishing my PhD and you gave me a much-needed pep talk and you know that is so easy to do believing in people encouraging them and it makes so much difference it does I love that so wins is as I mentioned in the third annual and the growth that they have seen is unbelievable I've not seen anything quite like it in in tech in terms of events it's aimed at inspiring not just women and data science but but data science in general what is it about wizz that attracted you and what are some of the key things that you shared this morning in your opening remarks well so the thing that attracts me about weeds is the following data science is growing exponentially in terms of the job opportunities in terms of the impact on the world and what I love about withes is that they had the insight this flash of genius I think that they would do a conference where all the speakers would be women and just that they would show that there are women all over the world who are contributing to data science who are loving it who are being successful and it's it's the crazy thing because in some ways it's really easy to do but nobody had done it right and it's so clear that there's a need for this when you think about all of the different locations around the world that are are doing a width version in Nigeria in Mumbai in London in you know just all across the world there are people doing this yeah so the things I shared are number one oh my goodness this is a great time to get into data science it's just there's so many opportunities in terms of career opportunities but there's so many opportunities to make a difference in the world and that's really important number two I shared that it's you never too old to learn math and CS and you know my example is my younger sister who's 63 and who's learning math and computer science at the northern Alberta Institute of Technology Nate all the other students are 18 to 24 she suffers from fibromyalgia she's walked with a walker she's quite disabled she's getting A's and a-pluses it's so cool and you know I think for every single person in the world there's an opportunity to learn something new and the most important thing is hard work and perseverance that it's so much more important than absolutely anything else I agree with that so much it's it's such an inspiring time but I think that you said there was clearly a demand for this what Wits has done in such a short time period demonstrates massive demand the stats that I was reading the last couple of days that show that women with stem degrees only 26% of them are actually working in STEM fields that's very low and and even can start from things like how how companies are recruiting talent and the messages that they're sending may be the right ones maybe not so much so I have a great example for you about companies recruiting talent so about three years ago I was no actually almost four years ago now I was talking in a conference called HR 50 and it's a conference that's aimed at the chief human resource officers of 50 multinationals and my talk I was talking for 25 minutes on how to recruit and retain women in tech careers and afterwards the chief HR officer from Accenture came up to me and she said you know we hire 17,000 software engineers a year Justin India 17,000 and she said we've been coming in at 30 percent female and I want to get that up to 45 she said you told me some really good things I could use she she said you told me how to change the way we advertise jobs change the way we interview for jobs four months later her name is Ellen Chowk Ellen comes up to me at another conference this has happens to be the most powerful women's summit that's run by Fortune magazine every year and she comes up and she says Maria I implemented different job descriptions we changed the way we interview and I also we started actually recruiting at Women's College engineering colleges in India as well as co-ed once she said we came in at 42% Wow from 30 to 42 just making those changes crying I went Ellen you owe me you're joining my more my board and she did right and you know they have Accenture has now set a goal of being at 50/50 in technical roles by 2025 Wow they even continued to come in all around the world they're coming in over 40% and then they've started really looking at how many women are being promoted to partners and they've moved that number up to 30% in the most recent year so you know it's a such a great example of a company that just decided we're gonna think about how we advertise we're going to think about how we interview we're gonna think about how we do promotions and we're going to make it equitable and from a marketing perspective those aren't massive massive changes so whether it expects quite simple exactly yeah these are so the thing I think about so when I look at what's happening at Harvey Mudd and how we've gotten more women into computer science engineering physics into every discipline it's really all about encouragement and support it's about believing in people it's about having faculty who when they start teaching a class the perhaps is technically very rigorous they might say this is a really challenging course every student in this course who works hard is going to succeed it's setting that expectation that everyone can succeed it's so important I think back to physics and college and how the baseline was probably 60% in terms of of grades scoring and you went in with intimidation I don't know if I can do this and it sounds like again a such a simple yet revolutionary approach that you're taking let's make things simple let's be supportive and encouraging yet hopefully these people will get enough confidence that they'll be able to sustain that even within themselves as they graduate and go into careers whether they stay in academia or go in industry and I know you've got great experiences in both I have I so I've been very lucky and I've been able to work both in academia and in industry I will say so I worked for IBM Research for eight years early in my career and you know I tribute a lot of my success as a leader since then to the kind of professional development that I got as a manager at IBM Research and you know what I think is that I there's not that much difference between creating a great learning environment and a great work environment and one of the interesting results that came out of a study at Google sometime in the last few months is they looked at what made senior engineering managers successful and the least important thing was their knowledge of engineering of course they all have good knowledge of engineering but it was empathy ability to mentor communication skills ability to encourage all of these kinds of things that we think of as quote unquote soft skills but to actually change the world and and on those sasuke's you know we hear a lot about the hard skills if we're thinking about data scientists from a role perspective statistical analysis etcetera but those soft skills empathy and also the ability to kind of bring in different perspectives for analyzing data can really have a major impact on every sector and socially in the world today and that's why we need women and people of color and people who are not well represented in these fields because data science is changing everything in the world absolutely is and if we want those changes to be for the better we really need diverse perspectives and experiences influencing things that get made because you know algorithms are not algorithms can be hostile and negative as well as positive and you know good for the world and you need people who actually will raise the questions about the ethics of algorithms and how it gets used there's a great book about how math can be used for the bad of humanity as well as the good of humanity and until we get enough people with different perspectives into these roles nobody's going to be asking those questions right right well I think with the momentum that we're feeling in this movement today and it sounds like what you're being able to influence greatly at Mudd for the last twelve years plus there is there are our foundations that are being put in place with not just on the education perspective but on the personal perspective and in inspiring the next generation giving them helping them I should say achieve the confidence that they need to sustain them throughout their career summary I thank you so much for finding the time to join us this morning on the cube it's great to have you back and we can't wait to talk to you next year and hear what great things do you influence and well next twelve months well it's wonderful to have a chance to talk with you as well thank you so much excellent you've been watching the cube we're live at Stanford University for the third annual women in data science wins conference join the conversation hashtag wins 2018 I'm Lisa Martin stick around I'll be right back with my next guest after a short break

Section 1 of 13 [00:00:00 - 00:10:04] (NOTE: speaker names may be different in each section) Speaker 1: Ladies and gentlemen our program will begin momentarily. Thank you. (singing) This presentation and the accompanying oral commentary may include forward looking statements that are subject to risks uncertainties and other factors beyond our control. Our actual results, performance or achievements may differ materially and adversely from those anticipated or implied by such statements because of various risk factors. Including those detailed in our annual report on form 10-K for the fiscal year ended July 31, 2017 filed with the SEC. Any future product or roadmap information presented is intended to outline general product direction and is not a commitment to deliver any functionality and should not be used when making any purchasing decision. (singing) Ladies and gentlemen please welcome Vice President Corporate Marketing Nutanix, Julie O'Brien. Julie O'Brien: All right. How about those Nutanix .NEXT dancers, were they amazing or what? Did you see how I blended right in, you didn't even notice I was there. [French 00:07:23] to .NEXT 2017 Europe. We're so glad that you could make it today. We have such a great agenda for you. First off do not miss tomorrow morning. We're going to share the outtakes video of the handclap video you just saw. Where are the customers, the partners, the Nutanix employee who starred in our handclap video? Please stand up take a bow. You are not going to want to miss tomorrow morning, let me tell you. That is going to be truly entertaining just like the next two days we have in store for you. A content rich highly interactive, number of sessions throughout our agenda. Wow! Look around, it is amazing to see how many cloud builders we have with us today. Side by side you're either more than 2,200 people who have traveled from all corners of the globe to be here. That's double the attendance from last year at our first .NEXT Conference in Europe. Now perhaps some of you are here to learn the basics of hyperconverged infrastructure. Others of you might be here to build your enterprise cloud strategy. And maybe some of you are here to just network with the best and brightest in the industry, in this beautiful French Riviera setting. Well wherever you are in your journey, you'll find customers just like you throughout all our sessions here with the next two days. From Sligro to Schroders to Societe Generale. You'll hear from cloud builders sharing their best practices and their lessons learned and how they're going all in with Nutanix, for all of their workloads and applications. Whether it's SAP or Splunk, Microsoft Exchange, unified communications, Cloud Foundry or Oracle. You'll also hear how customers just like you are saving millions of Euros by moving from legacy hypervisors to Nutanix AHV. And you'll have a chance to post some of your most challenging technical questions to the Nutanix experts that we have on hand. Our Nutanix technology champions, our MPXs, our MPSs. Where are all the people out there with an N in front of their certification and an X an R an S an E or a C at the end. Can you wave hello? You might be surprised to know that in Europe and the Middle East alone, we have more than 2,600 >> Julie: In Europe and the Middle East alone, we have more than 2,600 certified Nutanix experts. Those are customers, partners, and also employees. I'd also like to say thank you to our growing ecosystem of partners and sponsors who are here with us over the next two days. The companies that you meet here are the ones who are committed to driving innovation in the enterprise cloud. Over the next few days you can look forward to hearing from them and seeing some fantastic technology integration that you can take home to your data center come Monday morning. Together, with our partners, and you our customers, Nutanix has had such an exciting year since we were gathered this time last year. We were named a leader in the Gartner Magic Quadrant for integrated systems two years in a row. Just recently Gartner named us the revenue market share leader in their recent market analysis report on hyper-converged systems. We know enjoy more than 35% revenue share. Thanks to you, our customers, we received a net promoter score of more than 90 points. Not one, not two, not three, but four years in a row. A feat, I'm sure you'll agree, is not so easy to accomplish, so thank you for your trust and your partnership in us. We went public on NASDAQ last September. We've grown to more than 2,800 employees, more than 7,000 customers and 125 countries and in Europe and the Middle East alone, in our Q4 results, we added more than 250 customers just in [Amea 00:11:38] alone. That's about a third of all of our new customer additions. Today, we're at a pivotal point in our journey. We're just barely scratching the surface of something big and Goldman Sachs thinks so too. What you'll hear from us over the next two days is this: Nutanix is on it's way to building and becoming an iconic enterprise software company. By helping you transform your data center and your business with Enterprise Cloud Software that gives you the power of freedom of choice and flexibility in the hardware, the hypervisor and the cloud. The power of one click, one OS, any cloud. And now, to tell you more about the digital transformation that's possible in your business and your industry and share a little bit around the disruption that Nutanix has undergone and how we've continued to reinvent ourselves and maybe, if we're lucky, share a few hand clap dance moves, please welcome to stage Nutanix Founder, CEO and Chairman, Dheeraj Pandey. Ready? Alright, take it away [inaudible 00:13:06]. >> Dheeraj P: Thank you. Thank you, Julie and thank you every one. It looks like people are still trickling. Welcome to Acropolis. I just hope that we can move your applications to Acropolis faster than we've been able to move people into this room, actually. (laughs) But thank you, ladies and gentlemen. Thank you to our customers, to our partners, to our employees, to our sponsors, to our board members, to our performers, to everybody for their precious time. 'Cause that's the most precious thing you actually have, is time. I want to spend a little bit of time today, not a whole lot of time, but a little bit of time talking about the why of Nutanix. Like why do we exist? Why have we survived? Why will we continue to survive and thrive? And it's simpler than an NQ or category name, the word hyper-convergence, I think we are all complicated. Just thinking about what is it that we need to talk about today that really makes it relevant, that makes you take back something from this conference. That Nutanix is an obvious innovation, it's very obvious what we do is not very complicated. Because the more things change, the more they remain the same, so can we draw some parallels from life, from what's going on around us in our own personal lives that makes this whole thing very natural as opposed to "Oh, it's hyper-converged, it's a category, it's analysts and pundits and media." I actually think it's something new. It's not that different, so I want to start with some of that today. And if you look at our personal lives, everything that we had, has been digitized. If anything, a lot of these gadgets became apps, they got digitized into a phone itself, you know. What's Nutanix? What have we done in the last seven, eight years, is we digitized a lot of hardware. We made everything that used to be single purpose hardware look like pure software. We digitized storage, we digitized the systems manager role, an operations manager role. We are digitizing scriptures, people don't need to write scripts anymore when they automate because we can visually design automation with [com 00:15:36]. And we're also trying to make a case that the cloud itself is not just a physical destination. That it can be digitized and must be digitized as well. So we learn that from our personal lives too, but it goes on. Look at music. Used to be tons of things, if you used to go to [inaudible 00:15:55] Records, I'm sure there were European versions of [inaudible 00:15:57] Records as well, the physical things around us that then got digitized as well. And it goes on and on. We look at entertainment, it's very similar. The idea that if you go to a movie hall, the idea that you buy these tickets, the idea that we'd have these DVD players and DVDs, they all got digitized. Or as [inaudible 00:16:20] want to call it, virtualized, actually. That is basically happening in pretty much new things that we never thought would look this different. One of the most exciting things happening around us is the car industry. It's getting digitized faster than we know. And in many ways that we'd not even imagined 10 years ago. The driver will get digitized. Autonomous cars. The engine is definitely gone, it's a different kind of an engine. In fact, we'll re-skill a lot of automotive engineers who actually used to work in mechanical things to look at real chemical things like battery technologies and so on. A lot of those things that used to be physical are now in software in the car itself. Media itself got digitized. Think about a physical newspaper, or physical ads in newspapers. Now we talk about virtual ads, the digital ads, they're all over on websites and so on is our digital experience now. Education is no different, you know, we look back at the kind of things we used to do physically with physical things. Their now all digital. The experience has become that digital. And I can go on and on. You look at retail, you look at healthcare, look at a lot of these industries, they all are at the cusp of a digital disruption. And in fact, if you look at the data, everybody wants it. We all want a digital transformation for industries, for companies around us. In fact, the whole idea of a cloud is a highly digitized data center, basically. It's not just about digitizing servers and storage and networks and security, it's about virtualizing, digitizing the entire data center itself. That's what cloud is all about. So we all know that it's a very natural phenomenon, because it's happening around us and that's the obviousness of Nutanix, actually. Why is it actually a good thing? Because obviously it makes anything that we digitize and we work in the digital world, bring 10X more productivity and decision making efficiencies as well. And there are challenges, obviously there are challenges, but before I talk about the challenges of digitization, think about why are things moving this fast? Why are things becoming digitally disrupted quicker than we ever imagined? There are some reasons for it. One of the big reasons is obviously we all know about Moore's Law. The fact that a lot of hardware's been commoditized, and we have really miniaturized hardware. Nutanix today runs on a palm-sized server. Obviously it runs on the other end of the spectrum with high-end IBM power systems, but it also runs on palm-sized servers. Moore's Law has made a tremendous difference in the way we actually think about consuming software itself. Of course, the internet is also a big part of this. The fact that there's a bandwidth glut, there's Trans-Pacific cables and Trans-Atlantic cables and so on, has really connected us a lot faster than we ever imagined, actually, and a lot of this was also the telecom revolution of the '90s where we really produced a ton of glut for the internet itself. There's obviously a more subtle reason as well, because software development is democratizing. There's consumer-grade programming languages that we never imagined 10, 15, 20 years ago, that's making it so much faster to write- >> Speaker 1: 15-20 years ago that's making it so much faster to write code, with this crowdsourcing that never existed before with Githubs and things like that, open source. There's a lot more stuff that's happening that's outside the boundary of a corporation itself, which is making things so much faster in terms of going getting disrupted and writing things at 10x the speed it used to be 20 years ago. There is obviously this technology at the tip of our fingers, and we all want it in our mobile experience while we're driving, while we're in a coffee shop, and so on; and there's a tremendous focus on design on consumer-grade simplicity, that's making digital disruption that much more compressed in some of sense of this whole cycle of creative disruption that we talk about, is compressed because of mobility, because of design, because of API, the fact that machines are talking to machines, developers are talking to developers. We are going and miniaturizing the experience of organizations because we talk about micro-services and small two-pizza teams, and they all want to talk about each other using APIs and so on. Massive influence on this digital disruption itself. Of course, one of the reasons why this is also happening is because we want it faster, we want to consume it faster than ever before. And our attention spans are reducing. I like the fact that not many people are watching their cell phones right now, but you can imagine the multi-tasking mode that we are all in today in our lives, makes us want to consume things at a faster pace, which is one of the big drivers of digital disruption. But most importantly, and this is a very dear slide to me, a lot of this is happening because of infrastructure. And I can't overemphasize the importance of infrastructure. If you look at why did Google succeed, it was the ninth search engine, after eight of them before, and if you take a step back at why Facebook succeeded over MySpace and so on, a big reason was infrastructure. They believed in scale, they believed in low latency, they believed in being able to crunch information, at 10x, 100x, bigger scale than anyone else before. Even in our geopolitical lives, look at why is China succeeding? Because they've made infrastructure seamless. They've basically said look, governance is about making infrastructure seamless and invisible, and then let the businesses flourish. So for all you CIOs out there who actually believe in governance, you have to think about what's my first role? What's my primary responsibility? It's to provide such a seamless infrastructure, that lines of business can flourish with their applications, with their developers that can write code 10x faster than ever before. And a lot of these tenets of infrastructure, the fact of the matter is you need to have this always-on philosophy. The fact that it's breach-safe culture. Or the fact that operating systems are hardware agnostic. A lot of these tenets basically embody what Nutanix really stands for. And that's the core of what we really have achieved in the last eight years and want to achieve in the coming five to ten years as well. There's a nuance, and obviously we talk about digital, we talk about cloud, we talk about everything actually going to the cloud and so on. What are the things that could slow us down? What are the things that challenge us today? Which is the reason for Nutanix? Again, I go back to this very important point that the reason why we think enterprise cloud is a nuanced term, because the word "cloud" itself doesn't solve for a lot of the problems. The public cloud itself doesn't solve for a lot of the problems. One of the big ones, and obviously we face it here in Europe as well, is laws of the land. We have bureaucracy, which we need to deal with and respect; we have data sovereignty and computing sovereignty needs that we need to actually fulfill as well, while we think about going at breakneck speed in terms of disrupting our competitors and so on. So there's laws of the land, there's laws of physics. This is probably one of the big ones for what the architecture of cloud will look like itself, over the coming five to ten years. Our take is that cloud will need to be more dispersed than they have ever imagined, because computing has to be local to business operations. Computing has to be in hospitals and factories and shop floors and power plants and on and on and on... That's where you really can have operations and computing really co-exist together, cause speed is important there as well. Data locality is one of our favorite things; the fact that computing and data have to be local, at least the most relevant data has to be local as well. And the fact that electrons travel way faster when it's actually local, versus when you have to have them go over a Wide Area Network itself; it's one of the big reasons why we think that the cloud will actually be more nuanced than just some large data centers. You need to disperse them, you need to actually think about software (cloud is about software). Whether data plane itself could be dispersed and even miniaturized in small factories and shop floors and hospitals. But the control plane of the cloud is centralized. And that's the way you can have the best of both worlds; the control plane is centralized. You think as if you're managing one massive data center, but it's not because you're really managing hundreds or thousands of these sites. Especially if you think about edge-based computing and IoT where you really have your tentacles in tens of thousands of smaller devices and so on. We've talked about laws of the land, which is going to really make this digital transformation nuanced; laws of physics; and the third one, which is really laws of entropy. These are hackers that do this for adrenaline. These are parochial rogue states. These are parochial geo-politicians, you know, good thing I actually left the torture sign there, because apparently for our creative designer, geo-politics is equal to torture as well. So imagine one bad tweet can actually result in big changes to the way we actually live in this world today. And it's important. Geo-politics itself is digitized to a point where you don't need a ton of media people to go and talk about your principles and what you stand for and what you strategy for, for running a country itself is, and so on. And these are all human reasons, political reasons, bureaucratic reasons, compliance and regulations reasons, that, and of course, laws of physics is yet another one. So laws of physics, laws of the land, and laws of entropy really make us take a step back and say, "What does cloud really mean, then?" Cause obviously we want to digitize everything, and it all should appear like it's invisible, but then you have to nuance it for the Global 5000, the Global 10000. There's lots of companies out there that need to really think about GDPR and Brexit and a lot of the things that you all deal with on an everyday basis, actually. And that's what Nutanix is all about. Balancing what we think is all about technology and balancing that with things that are more real and practical. To deal with, grapple with these laws of the land and laws of physics and laws of entropy. And that's where we believe we need to go and balance the private and the public. That's the architecture, that's the why of Nutanix. To be able to really think about frictionless control. You want things to be frictionless, but you also realize that you are a responsible citizen of this continent, of your countries, and you need to actually do governance of things around you, which is computing governance, and data governance, and so on. So this idea of melding the public and the private is really about melding control and frictionless together. I know these are paradoxical things to talk about like how do you really have frictionless control, but that's the life you all lead, and as leaders we have to think about this series of paradoxes itself. And that's what Nutanix strategy, the roadmap, the definition of enterprise cloud is really thinking about frictionless control. And in fact, if anything, it's one of the things is also very interesting; think about what's disrupting Nutanix as a company? We will be getting disrupted along the way as well. It's this idea of true invisibility, the public cloud itself. I'd like to actually bring on board somebody who I have a ton of respect for, this leader of a massive company; which itself is undergoing disruption. Which is helping a lot of its customers undergo disruption as well, and which is thinking about how the life of a business analyst is getting digitized. And what about the laws of the land, the laws of physics, and laws of entropy, and so on. And we're learning a lot from this partner, massively giant company, called IBM. So without further ado, Bob Picciano. >> Bob Picciano: Thanks, >> Speaker 1: Thank you so much, Bob, for being here. I really appreciate your presence here- >> Bob Picciano: My pleasure! >> Speaker 1: And for those of you who actually don't know Bob, Bob is a Senior VP and General Manager at IBM, and is all things cognitive and obviously- >> Speaker 1: IBM is all things cognitive. Obviously, I learn a lot from a lot of leaders that have spent decades really looking at digital disruption. >> Bob: Did you just call me old? >> Speaker 1: No. (laughing) I want to talk about experience and talking about the meaning of history, because I love history, actually, you know, and I don't want to make you look old actually, you're too young right now. When you talk about digital disruption, we look at ourselves and say, "Look we are not extremely invisible, we are invisible, but we have not made something as invisible as the public clouds itself." And hence as I. But what's digital disruption mean for IBM itself? Now, obviously a lot of hardware is being digitized into software and cloud services. >> Bob: Yep. >> Speaker 1: What does it mean for IBM itself? >> Bob: Yeah, if you allow me to take a step back for a moment, I think there is some good foundational understanding that'll come from a particular point of view. And, you talked about it with the number of these dimensions that are affecting the way businesses need to consider their competitiveness. How they offer their capabilities into the market place. And as you reflected upon IBM, you know, we've had decades of involvement in information technology. And there's a big disruption going on in the information technology space. But it's what I call an accretive disruption. It's a disruption that can add value. If you were to take a step back and look at that digital trajectory at IBM you'd see our involvement with information technology in a space where it was all oriented around adding value and capability to how organizations managed inscale processes. Thinking about the way they were going to represent their businesses in a digital form. We came to call them applications. But it was how do you open an account, how do you process a claim, how do you transfer money, how do you hire an employee? All the policies of a company, the way the people used to do it mechanically, became digital representations. And that foundation of the digital business process is something that IBM helped define. We invented the role of the CIO to help really sponsor and enter in this notion that businesses could re represent themselves in a digital way and that allowed them to scale predictably with the qualities of their brand, from local operations, to regional operations, to international operations, and show up the same way. And, that added a lot of value to business for many decades. And we thrived. Many companies, SAP all thrived during that span. But now we're in a new space where the value of information technology is hitting a new inflection point. Which is not about how you scale process, but how you scale insight, and how you scale wisdom, and how you scale knowledge and learning from those operational systems and the data that's in those operational systems. >> Speaker 1: How's it different from 1993? We're talking about disruption. There was a time when IBM reinvented itself, 20-25 years ago. >> Bob: Right. >> Speaker 1: And you said it's bigger than 25 years ago. Tell us more. >> Bob: You know, it gets down. Everything we know about that process space right down to the very foundation, the very architecture of the CPU itself and the computer architecture, the von Neumann architecture, was all optimized on those relatively static scaled business processes. When you move into the notion where you're going to scale insight, scale knowledge, you enter the era that we call the cognitive era, or the era of intelligence. The algorithms are very different. You know the data semantically doesn't integrate well across those traditional process based pools and reformation. So, new capabilities like deep learning, machine learning, the whole field of artificial intelligence, allows us to reach into that data. Much of it unstructured, much of it dark, because it hasn't been indexed and brought into the space where it is directly affecting decision making processes in a business. And you have to be able to apply that capability to those business processes. You have to rethink the computer, the circuitry itself. You have to think about how the infrastructure is designed and organized, the network that is required to do that, the experience of the applications as you talked about have to be very natural, very engaging. So IBM does all of those things. So as a function of our transformation that we're on now, is that we've had to reach back, all the way back from rethinking the CPU, and what we dedicate our time and attention to. To our services organization, which is over 130,000 people on the consulting side helping organizations add digital intelligence to this notion of a digital business. Because, the two things are really a confluence of what will make this vision successful. >> Speaker 1: It looks like massive amounts of change for half a million people who work with the company. >> Bob: That's right. >> Speaker 1: I'm sure there are a lot of large customers out here, who will also read into this and say, "If IBM feels disrupted ... >> Bob: Uh hm >> Speaker 1: How can we actually stay not vulnerable? Actually there is massive amounts of change around their own competitive landscape as well. >> Bob: Look, I think every company should feel vulnerable right. If you're at this age, this cognitive era, the age of digital intelligence, and you're not making a move into being able to exploit the capabilities of cognition into the business process. You are vulnerable. If you're at that intersection, and your competitor is passing through it, and you're not taking action to be able to deploy cognitive infrastructure in conjunction with the business processes. You're going to have a hard time keeping up, because it's about using the machines to do the training to augment the intelligence of our employees of our professionals. Whether that's a lawyer, or a doctor, an educator or whether that's somebody in a business function, who's trying to make a critical business decision about risk or about opportunity. >> Speaker 1: Interesting, very interesting. You used the word cognitive infrastructure. >> Bob: Uh hm >> Speaker 1: There's obviously computer infrastructure, data infrastructure, storage infrastructure, network infrastructure, security infrastructure, and the core of cognition has to be infrastructure as well. >> Bob: Right >> Speaker 1: Which is one of the two things that the two companies are working together on. Tell us more about the collaboration that we are actually doing. >> Bob: We are so excited about our opportunity to add value in this space, so we do think very differently about the cognitive infrastructure that's required for this next generation of computing. You know I mentioned the original CPU was built for very deterministic, very finite operations; large precision floating point capabilities to be able to accurately calculate the exact balance, the exact amount of transfer. When you're working in the field of AI in cognition. You actually want variable precision. Right. The data is very sparse, as opposed to the way that deterministic or scorecastic operations work, which is very dense or very structured. So the algorithms are redefining the processes that the circuitry actually has to run. About five years ago, we dedicated a huge effort to rethink everything about the chip and what we made to facilitate an orchestra of participation to solve that problem. We all know the GPU has a great benefit for deep learning. But the GPU in many cases, in many architectures, specifically intel architectures, it's dramatically confined by a very small amount of IO bandwidth that intel allows to go on and off the chip. At IBM, we looked at all 686 roughly square millimeters of our chip and said how do we reuse that square area to open up that IO bandwidth? So the innovation of a GPU or a FPGA could really be utilized to it's maximum extent. And we could be an orchestrator of all of the diverse compute that's going to be necessary for AI to really compel these new capabilities. >> Speaker 1: It's interesting that you mentioned the fact that you know power chips have been redefined for the cognitive era. >> Bob: Right, for Lennox for the cognitive era. >> Speaker 1: Exactly, and now the question is how do you make it simple to use as well? How do you bring simplicity which is where ... >> Bob: That's why we're so thrilled with our partnership. Because you talked about the why of Nutanix. And it really is about that empowerment. Doing what's natural. You talked about the benefits of calm and being able to really create that liberation of an information technology professional, whether it's in operations or in development. Having the freedom of action to make good decisions about defining the infrastructure and deploying that infrastructure and not having to second guess the physical limitations of what they're going to have to be dealing with. >> Speaker 1: That's why I feel really excited about the fact that you have the power of software, to really meld the two forms together. The intel form and the power form comes together. And we have some interesting use cases that our CIO Randy Phiffer is also really exploring, is how can a power form serve as a storage form for our intel form. >> Bob: Sure. >> Speaker 1: It can serve files and mocks and things like that. >> Bob: Any data intensive application where we have seen massive growth in our Lennox business, now for our business, Lennox is 20% of the revenue of our power systems. You know, we started enabling native Lennox distributions on top of little Indian ones, on top of the power capabilities just a few years ago, and it's rocketed. And the reason for that if for any data intensive application like a data base, a no sequel database or a structured data base, a dupe in the unstructured space, they typically run about three to four times better price performance on top of Lennox on power, than they will on top of an intel alternative. >> Speaker 1: Fascinating. >> Bob: So all of these applications that we're talking about either create or consume a lot of data, have to manage a lot of flexibility in that space, and power is a tremendous architecture for that. And you mentioned also the cohabitation, if you will, between intel and power. What we want is that optionality, for you to utilize those benefits of the 3X better price performance where they apply and utilize the commodity base where it applies. So you get the cost benefits in that space and the depth and capability in the space for power. >> Speaker 1: Your tongue in cheek remark about commodity intel is not lost on people actually. But tell us about... >> Speaker 1: Intel is not lost on people actually. Tell us about ... Obviously we digitized Linux 10, 15 years ago with [inaudible 00:40:07]. Have you tried to talk about digitizing AIX? That is the core of IBM's business for the last 20, 25, 30 years. >> Bob: Again, it's about this ability to compliment and extend the investments that businesses have made during their previous generations of decision making. This industry loves to talk about shifts. We talked about this earlier. That was old, this is new. That was hard, this is easy. It's not about shift, it's about using the inflection point, the new capability to extend what you already have to make it better. And that's one thing that I must compliment you, and the entire Nutanix organization. It's really empowering those applications as a catalog to be deployed, managed, and integrated in a new way, and to have seamless interoperability into the cloud. We see the AIX workload just having that same benefit for those businesses. And there are many, many 10's of thousands around the world that are critically dependent on every element of their daily operations and productivity of that operating platform. But to introduce that into that network effect as well. >> Speaker 1: Yeah. I think we're looking forward to how we bring the same cloud experience on AIX as well because as a company it keeps us honest when we don't scoff at legacy. We look at these applications the last 10, 15, 20 years and say, "Can we bring them into the new world as well?" >> Bob: Right. >> Speaker 1: That's what design is all about. >> Bob: Right. >> Speaker 1: That's what Apple did with musics. We'll take an old world thing and make it really new world. >> Bob: Right. >> Speaker 1: The way we consume things. >> Bob: That governance. The capability to help protect against the bad actors, the nefarious entropy players, as you will. That's what it's all about. That's really what it takes to do this for the enterprise. It's okay, and possibly easier to do it in smaller islands of containment, but when you think about bringing these class of capabilities into an enterprise, and really helping an organization drive both the flexibility and empowerment benefits of that, but really be able to depend upon it for international operations. You need that level of support. You need that level of capability. >> Speaker 1: Awesome. Thank you so much Bob. Really appreciate you coming. [crosstalk 00:42:14] Look forward to your [crosstalk 00:42:14]. >> Bob: Cheers. Thank you. >> Speaker 1: Thanks again for all of you. I know that people are sitting all the way up there as well, which is remarkable. I hope you can actually see some of the things that Sunil and the team will actually bring about, talk about live demos. We do real stuff here, which is truly live. I think one of the requests that I have is help us help you navigate the digital disruption that's upon you and your competitive landscape that's around you that's really creating that disruption. Thank you again for being here, and welcome again to Acropolis. >> Speaker 3: Ladies and gentlemen, please welcome Chief Product and Development Officer, Nutanix Sunil Potti. >> Sunil Potti: Okay, so I'm going to just jump right in because I know a bunch of you guys are here to see the product as well. We are a lot of demos lined up for you guys, and we'll try to mix in the slides, and the demos as well. Here's just an example of the things I always bring up in these conferences to look around, and say in the last few months, are we making progress in simplifying infrastructure? You guys have heard this again and again, this has been our mantra from the beginning, that the hotter things get, the more differentiated a company like Nutanix can be if we can make things simple, or keep things simple. Even though I like this a lot, we found something a little bit more interesting, I thought, by our European marketing team. If you guys need these tea bags, which you will need pretty soon. It's a new tagline for the company, not really. I thought it was apropos. But before I get into the product and the demos, to give you an idea. Every time I go to an event you find ways to memorialize the event. You meet people, you build relationships, you see something new. Last night, nothing to do with the product, I sat beside someone. It was a customer event. I had no idea who I was sitting beside. He was a speaker. How many of you guys know him, by the way? Sir Ranulph Fiennes. Few hands. Good for you. I had no idea who I was sitting beside. I said, "Oh, somebody called Sir. I should be respectful." It's kind of hard for me to be respectful, but I tried. He says, "No, I didn't do anything in the sense. My grandfather was knighted about 100 years ago because he was the governor of Antigua. And when he dies, his son becomes." And apparently Sir Ranulph's dad also died in the war, and so that's how he is a sir. But then I started looking it up because he's obviously getting ready to present. And the background for him is, in my opinion, even though the term goes he's the World's Greatest Living Explorer. I would have actually called it the World's Number One Stag, and I'll tell you why. Really, you should go look it up. So this guy, at the age of 21, gets admitted to Special Forces. If you're from the UK, this is as good as it gets, SAS. Six, seven years into it, he rebels, helps out his local partner because he doesn't like a movie who's building a dam inside this pretty village. And he goes and blows up a dam, and he's thrown out of that Special Forces. Obviously he's in demolitions. Goes all the way. This is the '60's, by the way. Remember he's 74 right now. The '60's he goes to Oman, all by himself, as the only guy, only white guy there. And then around the '70's, he starts truly exploring, truly exploring. And this is where he becomes really, really famous. You have to go see this in real life, when he sees these videos to really appreciate the impact of this guy. All by himself, he's gone across the world. He's actually gone across Antarctica. Now he tells me that Antarctica is the size of China and India put together, and he was prepared for -50 to 60 degrees, and obviously he got -130 degrees. Again, you have to see the videos, see his frostbite. Two of his fingers are cut off, by the way. He hacksawed them himself. True story. And then as he, obviously, aged, his body couldn't keep up with him, but his will kept up with him. So after a recent heart attack, he actually ran seven marathons. But most importantly, he was telling me this story, at 65 he wanted to do something different because his body was letting him down. He said, "Let me do something easy." So he climbed Mount Everest. My point being, what is this related to Nutanix? Is that if Nutanix is a company, without technology, allows to spend more time on life, then we've accomplished a piece of our vision. So keep that in mind. Keep that in mind. Now comes the boring part, which is the product. The why, what, how of Nutanix. Neeris talked about this. We have two acts in this company. Invisible Infrastructure was what we started off. You heard us talk about it. How did we do it? Using one-click technologies by converging infrastructure, computer storage, virtualization, et cetera, et cetera. What we are now about is about changing the game. Saying that just like we'd applicated what powers Google and Amazon inside the data center, could we now make them all invisible? Whether it be inside or outside, could we now make clouds invisible? Clouds could be made invisible by a new level of convergence, not about computer storage, but converging public and private, converging CAPEX and OPEX, converging consumption models. And there, beyond our core products, Acropolis and Prism, are these new products. As you know, we have this core thesis, right? The core thesis says what? Predictable workloads will stay inside the data center, elastic workloads will go outside, as long as the experience on both sides is the same. So if you can genuinely have a cloud-like experience delivered inside a data center, then that's the right a- >> Speaker 1: Genuinely have a cloud like experience developed inside the data center. And that's the right answer of predictable workloads. Absolutely the answer of elastic workloads, doesn't matter whether security or compliance. Eventually a public cloud will have a data center right beside your region, whether through local partner or a top three cloud partner. And you should use it as your public cloud of choice. And so, our goal is to ensure that those two worlds are converged. And that's what Calm does, and we'll talk about that. But at the same time, what we found in late 2015, we had a bunch of customers come to us and said "Look, I love this, I love the fact that you're going to converge public and private and all that good stuff. But I have these environments and these apps that I want to be delivered as a service but I want the same operational tooling. I don't want to have two different environments but I don't want to manage my data centers. Especially my secondary data centers, DR data centers." And that's why we created Xi, right? And you'll hear a lot more about this, obviously it's going to start off in the U.S but very rapidly launch in Europe, APJ globally in the next 9-12 months. And so we'll spend some quality time on those products as well today. So, from the journey that we're at, we're starting with the score cloud that essentially says "Look, your public and private needs to be the same" We call that the first instantiation of your cloud architectures and we're essentially as a company, want to build this enterprise cloud operating system as a fabric across public and private. But that's just the starting point. The starting point evolves to the score architecture that we believe that the cloud is being dispersed. Just like you have a public and a private cloud in the core data centers and so forth, you'll need a similar experience inside your remote office branch office, inside your DR data centers, inside your branches, and it won't stop there. It'll go all the way to the edge. All we're already seeing this right? Not just in the army where your forward operating bases in Afghanistan having a three note cluster sitting inside a tent. But we're seeing this in a variety of enterprise scenarios. And here's an example. So, here's a customer, global oil and gas company, has couple of primary data centers running Nutanix, uses GCP as a core public cloud platform, has a whole bunch of remote offices, but it also has this interesting new edge locations in the form of these small, medium, large size rigs. And today, they're in the process of building a next generation cloud architecture that's completely dispersed. They're using one node, coming out on version 5.5 with Nutanix. They're going to use two nodes, they're going to throw us three nods, multicultural architectures. Day one, they're going to centrally manage it using Prism, with one click upgrades, right? And then on top of that, they're also now provisioning using Calm, purpose built apps for the various locations. So, for example, there will be a re control app at the edge, there's an exploration data lag in Google and so forth. My point being that increasingly this architecture that we're talking about is happening in real time. It's no longer just an existing cellular civilization data center that's being replatformed to look like a private cloud and so forth, or a hybrid cloud. But the fact that you're going into this multi cloud era is getting excel bated, the more someone consumes AWL's GCP or any public cloud, the more they're excel bating their internal transformation to this multi cloud architecture. And so that's what we're going to talk about today, is this construct of ONE OS and ONE Click, and when you think about it, every company has a standard stack. So, this is the only slide you're going to see from me today that's a stack, okay? And if you look at the new release coming out, version 5.5, it's coming out imminently, easiest way to say it is that it's got a ton of functionality. We've jammed as much as we can onto one slide and then build a product basically, okay? But I would encourage you guys to check out the release, it's coming out shortly. And we can go into each and every feature here, we'd be spending a lot of time but the way that we look at building Nutanix products as many of you know, it is not feature at a time. It's experience at a time. And so, when you really look at Nutanix using a lateral view, and that's how we approach problems with our customers and partners. We think about it as a life cycle, all the way from learning to using, operating, and then getting support and experiences. And today, we're going to go through each of these stages with you. And who better to talk about it than our local version of an architect, Steven Poitras please come up on stage. I don't know where you are, Steven come on up. You tucked your shirt in? >> Speaker 2: Just for you guys today. >> Speaker 1: Okay. Alright. He's sort of putting on his weight. I know you used a couple of tight buckles there. But, okay so Steven so I know we're looking for the demo here. So, what we're going to do is, the first step most of you guys know this, is we've been quite successful with CE, it's been a great product. How many of you guys like CE? Come on. Alright. I know you had a hard time downloading it yesterday apparently, there's a bunch of guys had a hard time downloading it. But it's been a great way for us not just to get you guys to experience it, there's more than 25,000 downloads and so forth. But it's also a great way for us to see new features like IEME and so forth. So, keep an eye on CE because we're going to if anything, explode the way that we actually use as a way to get new features out in the next 12 months. Now, one thing beyond CE that we did, and this was something that we did about ... It took us about 12 months to get it out. While people were using CE to learn a lot, a lot of customers were actually getting into full blown competitive evals, right? Especially with hit CI being so popular and so forth. So, we came up with our own version called X-Ray. >> Speaker 2: Yup. >> Speaker 1: What does X-Ray do before we show it? >> Speaker 2: Yeah. Absolutely. So, if we think about back in the day we were really the only ACI platform out there on the market. Now there are a few others. So, to basically enable the customer to objectively test these, we came out with X-Ray. And rather than talking about the slide let's go ahead and take a look. Okay, I think it's ready. Perfect. So, here's our X-Ray user interface. And essentially what you do is you specify your targets. So, in this case we have a Nutanix 80150 as well as some of our competitors products which we've actually tested. Now we can see on the left hand side here we see a series of tests. So, what we do is we go through and specify certain workloads like OLTP workloads, database colocation, and while we do that we actually inject certain test cases or scenarios. So, this can be snapshot or component failures. Now one of the key things is having the ability to test these against each other. So, what we see here is we're actually taking a OLTP workload where we're running two virtual machines, and then we can see the IOPS OLTP VM's are actually performing here on the left hand side. Now as we're actually go through this test we perform a series of snapshots, which are identified by these red lines here. Now as you can see, the Nutanix platform, which is shown by this blue line, is purely consistent as we go through this test. However, our competitor's product actually degrades performance overtime as these snapshots are taken. >> Speaker 1: Gotcha. And some of these tests by the way are just not about failure or benchmarking, right? It's a variety of tests that we have that makes real life production workloads. So, every couple of months we actually look at our production workloads out there, subset those two cases and put it into X-Ray. So, X-Ray's one of those that has been more recently announced into the public. But it's already gotten a lot of update. I would strongly encourage you, even if you an existing Nutanix customer. It's a great way to keep us honest, it's a great way for you to actually expand your usage of Nutanix by putting a lot of these real life tests into production, and as and when you look at new alternatives as well, there'll be certain situations that we don't do as well and that's a great way to give us feedback on it. And so, X-Ray is there, the other one, which is more recent by the way is a fact that most of you has spent many days if not weeks, after you've chosen Nutanix, moving non-Nutanix workloads. I.e. VMware, on three tier architectures to Atrio Nutanix. And to do that, we took a hard look and came out with a new product called Xtract. >> Speaker 2: Yeah. So essentially if we think about what Nutanix has done for the data center really enables that iPhone like experience, really bringing it simplicity and intuitiveness to the data center. Now what we wanted to do is to provide that same experience for migrating existing workloads to us. So, with Xtract essentially what we've done is we've scanned your existing environment, we've created design spec, we handled the migration process ... >> Steven: ... environment, we create a design spec. We handle for the migration process as well as the cut over. Now, let's go ahead and take a look in our extract user interface here. What we can see is we have a source environment. In this case, this is a VC environment. This can be any VC, whether it's traditional three tier or hypherconverged. We also see our Nutanix target environments. Essentially, these are our AHV target clusters where we're going to be migrating the data and performing the cut over to you. >> Speaker 2: Gotcha. Steven: The first thing that we do here is we go ahead and create a new migration plan. Here, I'm just going to specify this as DB Wave 2. I'll click okay. What I'm doing here is I'm selecting my target Nutanix cluster, as well as my target Nutanix container. Once I'll do that, I'll click next. Now in this case, we actually like to do it big. We're actually going to migrate some production virtual machines over to this target environment. Here, I'm going to select a few windows instances, which are in our database cluster. I'll click next. At this point, essentially what's occurring is it's going through taking a look at these virtual machines as well as taking a look at the target environment. It takes a look at the resources to ensure that we actually have enough, an ample capacity to facilitate the workload. The next thing we'll do is we'll go ahead and type in our credentials here. This is actually going to be used for logging into the virtual machine. We can do a new device driver installation, as well as get any static IP configuration. Well specify our network mapping. Then from there, we'll click next. What we'll do is we'll actually save and start. This will go through create the migration plan. It'll do some analysis on these virtual machines to ensure that we can actually log in before we actually start migrating data. Here we have a migration, which has been in progress. We can see we have a few virtual machines, obviously some Linux, some Windows here. We've cut over a few. What we do to actually cut over these VMS, is go ahead select the VMS- Speaker 2: This is the actual task of actually doing the final stage of cut over. Steven: Yeah, exactly. That's one of the nice things. Essentially, we can migrate the data whenever we want. We actually hook into the VADP API's to do this. Then every 10 minutes, we send over a delta to sync the data. Speaker 2: Gotcha, gotcha. That's how one click migration can now be possible. This is something that if you guys haven't used this, this has been out in the wild, just for a month or so. Its been probably one of our bestselling, because it's free, bestselling features of the recent product release. I've had customers come to me and say, "Look, there are situations where its taken us weeks to move data." That is now minutes from the operator perspective. Forget where the director, or the VP, it's the line architecture and operator that really loves these tools, which is essentially the core of Nutanix. That's one of our core things, is to make sure that if we can keep the engineer and the architect truly happy, then everything else will be fine for us, right? That's extract. Then we have a lot of things, right? We've done the usual things, there's a tunnel functionality on day zero, day one, day two, kind of capabilities. Why don't we start with something around Prism Central, now that we can do one click PC installs? We can do PC scale outs, we can go from managing thousands of VMS, tens of thousands of VMS, while doing all the one click operations, right? Steven: Yep. Speaker 2: Why don't we take a quick look at what's new in Prism Central? Steven: Yep. Absolutely. Here, we can see our Prism element interface. As you mentioned, one of the key things we added here was the ability to deploy Prism Central very simply just with a few clicks. We'll actually go through a distributed PC scale of deployment here. Here, we're actually going to deploy, as this is a new instance. We're going to select our 5.5 version. In this case, we're going to deploy a scale out Prism Central cluster. Obviously, availability and up-time's very critical for us, as we're mainly distributed systems. In this case we're going to deploy a scale-out PC cluster. Here we'll select our number of PC virtual machines. Based upon the number of VMS, we can actually select our size of VM that we'd deploy. If we want to deploy 25K's report, we can do that as well. Speaker 2: Basically a thousand to tens of thousands of VM's are possible now. Steven: Yep. That's a nice thing is you can start small, and then scale out as necessary. We'll select our PC network. Go ahead and input our IP address. Now, we'll go to deploy. Now, here we can see it's actually kicked off the deployment, so it'll go provision these virtual machines to apply the configuration. In a few minutes, we'll be up and running. Speaker 2: Right. While Steven's doing that, one of the things that we've obviously invested in is a ton of making VM operations invisible. Now with Calm's, what we've done is to up level that abstraction. Two applications. At the end of the day, more and more ... when you go to AWS, when you go to GCP, you go to [inaudible 01:04:56], right? The level of abstractions now at an app level, it's cloud formations, and so forth. Essentially, what Calm's able to do is to give you this marketplace that you can go in and self-service [inaudible 01:05:05], create this internal cloud like environment for your end users, whether it be business owners, technology users to self-serve themselves. The process is pretty straightforward. You, as an operator, or an architect, or [inaudible 01:05:16] create these blueprints. Consumers within the enterprise, whether they be self-service users, whether they'll be end business users, are able to consume them for a simple marketplace, and deploy them on whether it be a private cloud using Nutanix, or public clouds using anything with public choices. Then, as a single frame of glass, as operators you're doing conversed operations, at an application centric level between [inaudible 01:05:41] across any of these clouds. It's this combination of producer, consumer, operator in a curated sense. Much like an iPhone with an app store. It's the core construct that we're trying to get with Calm to up level the abstraction interface across multiple clouds. Maybe we'll do a quick demo of this, and then get into the rest of the stuff, right? Steven: Sure. Let's check it out. Here we have our Prism Central user interface. We can see we have two Nutanix clusters, our cloudy04 as well as our Power8 cluster. One of the key things here that we've added is this apps tab. I'm clicking on this apps tab, we can see that we have a few [inaudible 01:06:19] solutions, we have a TensorFlow solution, a [inaudible 01:06:22] et cetera. The nice thing about this is, this is essentially a marketplace where vendors as well as developers could produce these blueprints for consumption by the public. Now, let's actually go ahead and deploy one of these blueprints. Here we have a HR employment engagement app. We can see we have three different tiers of services part of this. Speaker 2: You need a lot of engagement at HR, you know that. Okay, keep going. Steven: Then the next thing we'll do here is we'll go and click on. Based upon this, we'll specify our blueprint name, HR app. The nice thing when I'm deploying is I can actually put in back doors. We'll click clone. Now what we can see here is our blueprint editor. As a developer, I could actually go make modifications, or even as an in-user given the simple intuitive user interface. Speaker 2: This is the consumers side right here, but it's also the [inaudible 01:07:11]. Steven: Yep, absolutely. Yeah, if I wanted to make any modifications, I could select the tier, I could scale out the number of instances, I could modify the packages. Then to actually deploy, all I do is click launch, specify HR app, and click create. Speaker 2: Awesome. Again, this is coming in 5.5. There's one other feature, by the way, that is coming in 5.5 that's surrounding Calm, and Prism Pro, and everything else. That seems to be a much awaited feature for us. What was that? Steven: Yeah. Obviously when we think about multi-tenant, multi-cloud role based access control is a very critical piece of that. Obviously within the organization, we're going to have multiple business groups, multiple units. Our back's a very critical piece. Now, if we go over here to our projects, we can see in this scenario we just have a single project. What we've added is if you want to specify certain roles, in this case we're going to add our good friend John Doe. We can add them, it could be a user or group, but then we specify their role. We can give a developer the ability to edit and create these blueprints, or consumer the ability to actually provision based upon. Speaker 2: Gotcha. Basically in 5.5, you'll have role based access control now in Prism and Calm burned into that, that I believe it'll support custom role shortly after. Steven: Yep, okay. Speaker 2: Good stuff, good stuff. I think this is where the Nutanix guys are supposed to clap, by the way, so that the rest of the guys can clap. Steven: Thank you, thank you. Okay. What do we have? Speaker 2: We have day one stuff, obviously there's a ton of stuff that's coming in core data path capabilities that most of you guys use. One of the most popular things is synchronous replication, especially in Europe. Everybody wants to do [Metro 01:08:49] for whatever reason. But we've got something new, something even more enhanced than Metro, right? Steven: Yep. Speaker 2: Do you want to talk a little bit about it? Steven: Yeah, let's talk about it. If we think about what we had previously, we started out with a synchronous replication. This is essentially going to be your higher RPO. Then we moved into Metro cluster, which was RPO zero. Those are two ins of the gamete. What we did is we introduced new synchronous replication, which really gives you the best of both worlds where you have very, very decreased RPO's, but zero impact in line mainstream performance. Speaker 2: That's it. Let's show something. Steven: Yeah, yeah. Let's do it. Here, we're back at our Prism Element interface. We'll go over here. At this point, we provisioned our HR app, the next thing we need to do is to protect that data. Let's go here to protection domain. We'll create a new PD for our HR app. Speaker 2: You clearly love HR. Steven: Spent a lot of time there. Speaker 2: Yeah, yeah, yeah. Steven: Here, you can see we have our production lamp DBVM. We'll go ahead and protect that entity. We can see that's protected. The next thing we'll do is create a schedule. Now, what would you say would be a good schedule we should actually shoot for? Speaker 2: I don't know, 15 minutes? Steven: 15 minutes is not bad. But I ... Section 7 of 13 [01:00:00 - 01:10:04] Section 8 of 13 [01:10:00 - 01:20:04] (NOTE: speaker names may be different in each section) Speaker 1: ... 15 minutes. Speaker 2: 15 minutes is not bad, but I think the people here deserve much better than that, so I say let's shoot for ... what about 15 seconds? Speaker 1: Yeah. They definitely need a bathroom break, so let's do 15 seconds. Speaker 2: Alright, let's do 15 seconds. Speaker 1: Okay, sounds good. Speaker 2: K. Then we'll select our retention policy and remote cluster replicate to you, which in this case is wedge. And we'll go ahead and create the schedule here. Now at this point we can see our protection domain. Let's go ahead and look at our entities. We can see our database virtual machine. We can see our 15 second schedule, our local snapshots, as well as we'll start seeing our remote snapshots. Now essentially what occurs is we take two very quick snapshots to essentially see the initial data, and then based upon that then we'll start taking our continuous 15 second snaps. Speaker 1: 15 seconds snaps, and obviously near sync has less of impact than synchronous, right? From an architectural perspective. Speaker 2: Yeah, and that's a nice thing is essentially within the cluster it's truly pure synchronous, but externally it's just a lagged a-sync. Speaker 1: Gotcha. So there you see some 15 second snapshots. So near sync is also built into five-five, it's a long-awaited feature. So then, when we expand in the rest of capabilities, I would say, operations. There's a lot of you guys obviously, have started using Prism Pro. Okay, okay, you can clap. You can clap. It's okay. It was a lot of work, by the way, by the core data pad team, it was a lot of time. So Prism Pro ... I don't know if you guys know this, Prism Central now run from zero percent to more than 50 percent attach on install base, within 18 months. And normally that's a sign of true usage, and true value being supported. And so, many things are new in five-five out on Prism Pro starting with the fact that you can do data[inaudible 01:11:49] base lining, alerting, so that you're not capturing a ton of false positives and tons of alerts. We go beyond that, because we have this core machine-learning technology power, we call it cross fit. And, what we've done is we've used that as a foundation now for pretty much all kinds of operations benefits such as auto RCA, where you're able to actually map to particular [inaudible 01:12:12] crosses back to who's actually causing it whether it's the network, a computer, and so forth. But then the last thing that we've also done in five-five now that's quite different shading, is the fact that you can now have a lot of these one-click recommendations and remediations, such as right-sizing, the fact that you can actually move around [inaudible 01:12:28] VMs, constrained VMs, and so forth. So, I now we've packed a lot of functionality in Prism Pro, so why don't we spend a couple of minutes quickly giving a sneak peak into a few of those things. Speaker 2: Yep, definitely. So here we're back at our Prism Central interface and one of the things we've added here, if we take a look at one of our clusters, we can see we have this new anomalies portion here. So, let's go ahead and select that and hop into this. Now let's click on one of these anomaly events. Now, essentially what the system does is we monitor all the entities and everything running within the system, and then based upon that, we can actually determine what we expect the band of values for these metrics to be. So in this scenario, we can see we have a CPU usage anomaly event. So, normal time, we expect this to be right around 86 to 100 percent utilization, but at this point we can see this is drastically dropped from 99 percent to near zero. So, this might be a point as an administrator that I want to go check out this virtual machine, ensure that certain services and applications are still up and running. Speaker 1: Gotcha, and then also it changes the baseline based on- Speaker 2: Yep. Yeah, so essentially we apply machine-learning techniques to this, so the system will dynamically adjust based upon the value adjustment. Speaker 1: Gotcha. What else? Speaker 2: Yep. So the other thing here that we mentioned was capacity planning. So if we go over here, we can take a look at our runway. So in this scenario we have about 30 days worth of runway, which is most constrained by memory. Now, obviously, more nodes is all good for everyone, but we also want to ensure that you get the maximum value on your investment. So here we can actually see a few recommendations. We have 11 overprovision virtual machines. These are essentially VMs which have more resources than are necessary. As well as 19 inactives, so these are dead VMs essentially that haven't been powered on and not utilized. We can also see we have six constrained, as well as one bully. So, constrained VMs are essentially VMs which are requesting more resources than they actually have access to. This could be running at 100 percent CPU utilization, or 100 percent memory, or storage utilization. So we could actually go in and modify these. Speaker 1: Gotcha. So these are all part of the auto remediation capabilities that are now possible? Speaker 2: Yeah. Speaker 1: What else, do you want to take reporting? Speaker 2: Yeah. Yeah, so I know reporting is a very big thing, so if we think about it, we can't rely on an administrator to constantly go into Prism. We need to provide some mechanism to allow them to get emailed reports. So what we've done is we actually autogenerate reports which can be sent via email. So we'll go ahead and add one of these sample reports which was created today. And here we can actually get specific detailed information about our cluster without actually having to go into Prism to get this. Speaker 1: And you can customize these reports and all? Speaker 2: Yep. Yeah, if we hop over here and click on our new report, we can actually see a list of views we could add to these reports, and we can mix and match and customize as needed. Speaker 1: Yeah, so that's the operational side. Now we also have new services like AFS which has been quite popular with many of you folks. We've had hundreds of customers already on it live with SMB functionality. You want to show a couple of things that is new in five-five? Speaker 2: Yeah. Yep, definitely. So ... let's wait for my screen here. So one of the key things is if we looked at that runway tab, what we saw is we had over a year's worth of storage capacity. So, what we saw is customers had the requirement for filers, they had some excess storage, so why not actually build a software featured natively into the cluster. And that's essentially what we've done with AFS. So here we can see we have our AFS cluster, and one of the key things is the ability to scale. So, this particular cluster has around 3.1 or 3.16 billion files, which are running on this AFS cluster, as well as around 3,000 active concurrent sessions. Speaker 1: So basically thousands of concurrent sessions with billions of files? Speaker 2: Yeah, and the nice thing with this is this is actually only a four node Nutanix cluster, so as the cluster actually scales, these numbers will actually scale linearly as a function of those nodes. Speaker 1: Gotcha, gotcha. There's got to be one more bullet here on this slide so what's it about? Speaker 2: Yeah so, obviously the initial use case was realistically for home folders as well as user profiles. That was a good start, but it wasn't the only thing. So what we've done is we've actually also introduced important and upcoming release of NFS. So now you can now use NFS to also interface with our [crosstalk 01:16:44]. Speaker 1: NFS coming soon with AFS by the way, it's a big deal. Big deal. So one last thing obviously, as you go operationalize it, we've talked a lot of things on features and functions but one of the cool things that's always been seminal to this company is the fact that we all for really good customer service and support experience. Right now a lot of it is around the product, the people, the support guys, and so forth. So fundamentally to the product we have found ways using Pulse to instrument everything. With Pulse HD that has been allowed for a little bit longer now. We have fine grain [inaudible 01:17:20] around everything that's being done, so if you turn on this functionality you get a lot of information now that we built, we've used when you make a phone call, or an email, and so forth. There's a ton of context now available to support you guys. What we've now done is taken that and are now externalizing it for your own consumption, so that you don't have to necessarily call support. You can log in, look at your entire profile across your own alerts, your own advisories, your own recommendations. You can look at collective intelligence now that's coming soon which is the fact that look, here are 50 other customers just like you. These are the kinds of customers that are using workloads like you, what are their configuration profiles? Through this centralized customer insights portal you going to get a lot more insight, not just about your own operations, but also how everybody else is also using it. So let's take a quick look at that upcoming functionality. Speaker 2: Yep. Absolutely. So this is our customer 360 portal, so as [inaudible 01:18:18] mentioned, as a customer I can actually log in here, I can get a high-level overview of my existing environment, my cases, the status of those cases, as well as any relevant announcements. So, here based upon my cluster version, if there's any updates which are available, I can then see that here immediately. And then one of the other things that we've added here is this insights page. So essentially this is information that previously support would leverage to essentially proactively look out to the cluster, but now we've exposed this to you as the customer. So, clicking on this insights tab we can see an overview of our environment, in this case we have three Nutanix clusters, right around 550 virtual machines, and over here what's critical is we can actually see our cases. And one of the nice things about this is these area all autogenerated by the cluster itself, so no human interaction, no manual intervention was required to actually create these alerts. The cluster itself will actually facilitate that, send it over to support, and then support can get back out to you automatically. Speaker 1: K, so look for customer insights coming soon. And obviously that's the full life cycle. One cool thing though that's always been unique to Nutanix was the fact that we had [inaudible 01:19:28] security from day one built-in. And [inaudible 01:19:31] chunk of functionality coming in five-five just around this, because every release we try to insert more and more security capabilities, and the first one is around data. What are we doing? Speaker 2: Yeah, absolutely. So previously we had support for data at rest encryption, but this did have the requirement to leverage self-encrypting drives. These can be very expensive, so what we've done, typical to our fashion is we've actually built this in natively via software. So, here within Prism Element, I can go to data at rest encryption, and then I can go and edit this configuration here. Section 8 of 13 [01:10:00 - 01:20:04] Section 9 of 13 [01:20:00 - 01:30:04] (NOTE: speaker names may be different in each section) Steve: Encryption and then I can go and edit this configuration here. From here I could add my CSR's. I can specify KMS server and leverage native software base encryption without the requirement of SED's. Sunil: Awesome. So data address encryption [inaudible 01:20:15] coming soon, five five. Now data security is only one element, the other element was around network security obviously. We've always had this request about what are we doing about networking, what are we doing about network, and our philosophy has always been simple and clear, right. It is that the problem in networking is not the data plan. Problem in networking is the control plan. As in, if a packing loss happens to the top of an ax switch, what do we do? If there's a misconfigured board, what do we do? So we've invested a lot in full blown new network visualization that we'll show you a preview of that's all new in five five, but then once you can visualize you can take action, so you can actually using our netscape API's now in five five. You can optovision re lands on the switch, you can update reps on your load balancing pools. You can update obviously rules on your firewall. And then we've taken that to the next level, which is beyond all that, just let you go to AWS right now, what do you do? You take 100 VM's, you put it in an AWS security group, boom. That's how you get micro segmentation. You don't need to buy expensive products, you don't need to virtualize your network to get micro segmentation. That's what we're doing with five five, is built in one click micro segmentation. That's part of the core product, so why don't we just quickly show that. Okay? Steve: Yeah, let's take a look. So if we think about where we've been so far, we've done the comparison test, we've done a migration over to a Nutanix. We've deployed our new HR app. We've protected it's data, now we need to protect the network's. So one of the things you'll see that's new here is this security policies. What we'll do is we'll actually go ahead and create a new security policy and we'll just say this is HR security policy. We'll specify the application type, which in this case is HR. Sunil: HR of course. Steve: Yep and we can see our app instance is automatically populated, so based upon the number of running instances of that blueprint, that would populate that drop-down. Now we'll go ahead and click next here and what we can see in the middle is essentially those three tiers that composed that app blueprint. Now one of the important things is actually figuring out what's trying to communicate with this within my existing environment. So if I take a look over here on my left hand side, I can essentially see a few things. I can see a Ha Proxy load balancer is trying to communicate with my app here, that's all good. I want to allow that. I can see some sort of monitoring service is trying to communicate with all three of the tiers. That's good as well. Now the last thing I can see here is this IP address which is trying to access my database. Now, that's not designed and that's not supposed to happen, so what we'll do is we'll actually take a look and see what it's doing. Now hopping over to this database virtual machine or the hack VM, what we can see is it's trying to perform a brute force log in attempt to my MySQL database. This is not good. We can see obviously it can connect on the socket, however, it hasn't guessed the right password. In order to lock that down, we'll go back to our policies here and we're going to click deny. Once we've done that, we'll click next and now we'll go to Apply Now. Now we can see our newly created security policy and if we hop back over to this VM, we can now see it's actually timing out and what this means is that it's not able to communicate with that database virtual machine due to micro segmentation actively blocking that request. Sunil: Gotcha and when you go back to the Prism site, essentially what we're saying now is, it's as simple as that, to set up micro segmentation now inside your existing clusters. So that's one click micro segmentation, right. Good stuff. One other thing before we let Steve walk off the stage and then go to the bathroom, but is you guys know Steve, you know he spends a lot time in the gym, you do. Right. He and I share cubes right beside each other by the way just if you ever come to San Jose Nutanix corporate headquarters, you're always welcome. Come to the fourth floor and you'll see Steve and Sunil beside each other, most of the time I'm not in the cube, most of the time he's in the gym. If you go to his cube, you'll see all kinds of stuff. Okay. It's true, it's true, but the reason why I brought this up, was Steve recently became a father, his first kid. Oh by the way this is, clicker, this is how his cube looks like by the way but he left his wife and his new born kid to come over here to show us a demo, so give him a round of applause. Thank you, sir. Steve: Cool, thanks, Sunil. That was fun. Sunil: Thank you. Okay, so lots of good stuff. Please try out five five, give us feedback as you always do. A lot of sessions, a lot of details, have fun hopefully for the rest of the day. To talk about how their using Nutanix, you know here's one of our favorite customers and partners. He normally comes with sunglasses, I've asked him that I have to be the best looking guy on stage in my keynotes, so he's going to try to reduce his charm a little bit. Please come on up, Alessandro. Thank you. Alessandro R.: I'm delighted to be here, thank you so much. Sunil: Maybe we can stand here, tell us a little bit about Leonardo. Alessandro R.: About Leonardo, Leonardo is a key actor of the aerospace defense and security systems. Helicopters, aircraft, the fancy systems, the fancy electronics, weapons unfortunately, but it's also a global actor in high technology field. The security information systems division that is the division I belong to, 3,000 people located in Italy and in UK and there's several other countries in Europe and the U.S. $1 billion dollar of revenue. It has a long a deep experience in information technology, communications, automation, logical and physical security, so we have quite a long experience to expand. I'm in charge of the security infrastructure business side. That is devoted to designing, delivering, managing, secure infrastructures services and secure by design solutions and platforms. Sunil: Gotcha. Alessandro R.: That is. Sunil: Gotcha. Some of your focus obviously in recent times has been delivering secure cloud services obviously. Alessandro R.: Yeah, obviously. Sunil: Versus traditional infrastructure, right. How did Nutanix help you in some of that? Alessandro R.: I can tell something about our recent experience about that. At the end of two thousand ... well, not so recent. Sunil: Yeah, yeah. Alessandro R.: At the end of 2014, we realized and understood that we had to move a step forward, a big step and a fast step, otherwise we would drown. At that time, our newly appointed CEO confirmed that the IT would be a core business to Leonardo and had to be developed and grow. So we decided to start our digital transformation journey and decided to do it in a structured and organized way. Having clear in mind our targets. We launched two programs. One analysis program and one deployments programs that were essentially transformation programs. We had to renew ourselves in terms of service models, in terms of organization, in terms of skills to invest upon and in terms of technologies to adopt. We were stacking a certification of technologies that adopted, companies merged in the years before and we have to move forward and to rationalize all these things. So we spent a lot of time analyzing, comparing technologies, and evaluating what would fit to us. We had two main targets. The first one to consolidate and centralize the huge amount of services and infrastructure that were spread over 52 data centers in Italy, for Leonardo itself. The second one, to update our service catalog with a bunch of cloud services, so we decided to update our data centers. One of our building block of our new data center architecture was Nutanix. We evaluated a lot, we had spent a lot of time in analysis, so that wasn't a bet, but you are quite pioneers at those times. Sunil: Yeah, you took a lot of risk right as an Italian company- Alessandro R.: At this time, my colleague used to say, "Hey, Alessandro, think it over, remember that not a CEO has ever been fired for having chose IBM." I apologize, Bob, but at that time, when Nutanix didn't run on [inaudible 01:29:27]. We have still a good bunch of [inaudible 01:29:31] in our data center, so that will be the chance to ... Audience Member: [inaudible 01:29:37] Alessandro R.: So much you must [inaudible 01:29:37] what you announced it. Sunil: So you took a risk and you got into it. Alessandro R.: Yes, we got into, we are very satisfied with the results we have reached. Sunil: Gotcha. Alessandro R.: Most of the targets we expected to fulfill have come and so we are satisfied, but that doesn't mean that we won't go on asking you a big discount ... Sunil: Sure, sure, sure, sure. Alessandro R.: On price list. Sunil: Sure, sure, so what's next in terms of I know there are some interesting stuff that you're thinking. Alessandro R.: The next- Section 9 of 13 [01:20:00 - 01:30:04] Section 10 of 13 [01:30:00 - 01:40:04] (NOTE: speaker names may be different in each section) Speaker 1: So what's next, in terms of I know you have some interesting stuff that you're thinking of. Speaker 2: The next, we have to move forward obviously. The name Leonardo is inspired to Leonardo da Vinci, it was a guy that in terms of innovation and technology innovation had some good ideas. And so, I think, that Leonardo with Nutanix could go on in following an innovation target and following really mutual ... Speaker 1: Partnership. Speaker 2: Useful partnership, yes. We surely want to investigate the micro segmentation technologies you showed a minute ago because we have some looking, particularly by the economical point of view ... Speaker 1: Yeah, the costs and expenses. Speaker 2: And we have to give an alternative to the technology we are using. We want to use more intensively AHV, again as an alternative solution we are using. We are selecting a couple of services, a couple of quite big projects to build using AHV talking of Calm we are very eager to understand the announcement that they are going to show to all of us because the solution we are currently using is quite[crosstalk 01:31:30] Speaker 1: Complicated. Speaker 2: Complicated, yeah. To move a step of automation to elaborate and implement[inaudible 01:31:36] you spend 500 hours of manual activities that's nonsense so ... Speaker 1: Manual automation. Speaker 2: (laughs) Yes, and in the end we are very interested also in the prism features, mostly the new features that you ... Speaker 1: Talked about. Speaker 2: You showed yesterday in the preview because one bit of benefit that we received from the solution in the operations field means a bit plus, plus to our customer and a distinctive plus to our customs so we are very interested in that ... Speaker 1: Gotcha, gotcha. Thanks for taking the risk, thanks for being a customer and partner. Speaker 2: It has been a pleasure. Speaker 1: Appreciate it. Speaker 2: Bless you, bless you. Speaker 1: Thank you. So, you know obviously one OS, one click was one of our core things, as you can see the tagline doesn't stop there, it also says "any cloud". So, that's the rest of the presentation right now it's about; what are we doing, to now fulfill on that mission of one OS, one cloud, one click with one support experience across any cloud right? And there you know, we talked about Calm. Calm is not only just an operational experience for your private cloud but as you can see it's a one-click experience where you can actually up level your apps, set up blueprints, put SLA's and policies, push them down to either your AWS, GCP all your [inaudible 01:33:00] environments and then on day one while you can do one click provisioning, day two and so forth you will see new and new capabilities such as, one-click migration and mobility seeping into the product. Because, that's the end game for Calm, is to actually be your cloud autonomy platform right? So, you can choose the right cloud for the right workload. And talk about how they're building a multi cloud architecture using Nutanix and partnership a great pleasure to introduce my other good Italian friend Daniele, come up on stage please. From Telecom Italia Sparkle. How are you sir? Daniele: Not too bad thank you. Speaker 1: You want an espresso, cappuccino? Daniele: No, no later. Speaker 1: You all good? Okay, tell us a little about Sparkle. Daniele: Yeah, Sparkle is a fully owned subsidy of Telecom Italia group. Speaker 1: Mm-hmm (affirmative) Daniele: Spinned off in 2003 with the mission to develop the wholesale and multinational corporate and enterprise business abroad. Huge network, as you can see, hundreds of thousands of kilometers of fiber optics spread between; south east Asia to Europe to the U.S. Most of it proprietary part of it realized on some running cables. Part of them proprietary part of them bilateral part of them[inaudible 01:34:21] with other operators. 37 countries in which we have offices in the world, 700 employees, lean and clean company ... Speaker 1: Wow, just 700 employees for all of this. Daniele: Yep, 1.4 billion revenues per year more or less. Speaker 1: Wow, are you a public company? Daniele: No, fully owned by TIM so far. Speaker 1: So, what is your experience with Nutanix so far? Daniele: Well, in a way similar to what Alessandro was describing. To operate such a huge network as you can see before, and to keep on bringing revenues for the wholesale market, while trying to turn the bar toward the enterprise in a serious way. Couple of years ago the management team realized that we had to go through a serious transformation, not just technological but in terms of the way we build the services to our customers. In terms of how we let our customer feel the Sparkle experience. So, we are moving towards cloud but we are moving towards cloud with connectivity attached to it because it's in our cord as a provider of Telecom services. The paradigm that is driving today is the on-demand, is the dynamic and in order to get these things we need to move to software. Most of the network must become invisible as the Nutanix way. So, we decided instead of creating patchworks onto our existing systems, infrastructure, OSS, BSS and network systems, to build a new data center from scratch. And the paradigm being this new data center, the mantra was; everything is software designed, everything must be easy to manage, performance capacity planning, everything must be predictable and everything to be managed by few people. Nutanix is at the moment the baseline of this data center for what concern, let's say all the new networking tools, meaning as the end controllers that are taking care of automation and programmability of the network. Lifecycle service orchestrator, network orchestrator, cloud automation and brokerage platform and everything at the moment runs on AHV because we are forcing our vendors to certify their application on AHV. The only stack that is not at the moment AHV based is on a specific cloud platform because there we were really looking for the multi[inaudible 01:37:05]things that you are announcing today. So, we hope to do the migration as soon as possible. Speaker 1: Gotcha, gotcha. And then looking forward you're going to build out some more data center space, expose these services Daniele: Yeah. Speaker 1: For the customers as well as your internal[crosstalk 01:37:21] Daniele: Yeah, basically yes for sure we are going to consolidate, to invest more in the data centers in the markets on where we are leader. Italy, Turkey and Greece we are big data centers for [inaudible 01:37:33] and cloud, but we believe that the cloud with all the issues discussed this morning by Diraj, that our locality, customer proximity ... we think as a global player having more than 120 pops all over the world, which becomes more than 1000 in partnerships, that the pop can easily be transformed in a data center, so that we want to push the customer experience of what we develop in our main data centers closer to them. So, that we can combine traditional infrastructure as a service with the new connectivity services every single[inaudible 01:38:18] possibly everything running. Speaker 1: I mean, it makes sense, I mean I think essentially in some ways to summarize it's the example of an edge cloud where you're pushing a micro-cloud closer to the customers edge. Daniele: Absolutely. Speaker 1: Great stuff man, thank you so much, thank you so much. Daniele: Pleasure, pleasure. Thank you. Speaker 1: So, you know a couple of other things before we get in the next demo is the fact that in addition to Calm from multi-cloud management we have Zai, we talked about for extended enterprise capabilities and something for you guys to quickly understand why we have done this. In a very simple way is if you think about your enterprise data center, clearly you have a bunch of apps there, a bunch of public clouds and when you look at the paradigm you currently deploy traditional apps, we call them mode one apps, SAP, Exchange and so forth on your enterprise. Then you have next generation apps whether it be [inaudible 01:39:11] space, whether it be Doob or whatever you want to call it, lets call them mode two apps right? And when you look at these two types of apps, which are the predominant set, most enterprises have a combination of mode one and mode two apps, most public clouds primarily are focused, initially these days on mode two apps right? And when people talk about app mobility, when people talk about cloud migration, they talk about lift and shift, forklift [inaudible 01:39:41]. And that's a hard problem I mean, it's happening but it's a hard problem and ends up that its just not a one time thing. Once you've forklift, once you move you have different tooling, different operation support experience, different stacks. What if for some of your applications that mattered ... Section 10 of 13 [01:30:00 - 01:40:04] Section 11 of 13 [01:40:00 - 01:50:04] (NOTE: speaker names may be different in each section) Speaker 1: What if, for some of your applications that matter to you, that are your core enterprise apps that you can retain the same toolimg, the same operational experience and so forth. And that is what we achieve to do with Xi. It is truly making hybrid invisible, which is a next act for this company. It'll take us a few years to really fulfill the vision here, but the idea here is that you shouldn't think about public cloud as a different silo. You should think of it as an extension of your enterprise data centers. And for any services such as DR, whether it would be dev test, whether it be back-up, and so-forth. You can use the same tooling, same experience, get a public cloud-like capability without lift and shift, right? So it's making this lift and shift invisible by, soft of, homogenizing the data plan, the network plan, the control plan is what we really want to do with Xi. Okay? And we'll show you some more details here. But the simplest way to understand this is, think of it as the iPhone, right? D has mentioned this a little bit. This is how we built this experience. Views IOS as the core, IP, we wrap it up with a great package called the iPhone. But then, a few years into the iPhone era, came iTunes and iCloud. There's no apps, per se. That's fused into IOS. And similarly, think about Xi that way. The more you move VMs, into an internet-x environment, stuff like DR comes burnt into the fabric. And to give us a sneak peek into a bunch of the com and Xi cable days, let me bring back Binny who's always a popular guys on stage. Come on up, Binny. I'd be surprised in Binny untucked his shirt. He's always tucking in his shirt. Binny Gill: Okay, yeah. Let's go. Speaker 1: So first thing is com. And to show how we can actually deploy apps, not just across private and public clouds, but across multiple public clouds as well. Right? Binny Gill: Yeah, basically, you know com is about simplifying the disparity between various public clouds out there. So it's very important for us to be able to take one application blueprint and then quickly deploy in whatever cloud of your choice. Without understanding how one cloud is different. Speaker 1: Yeah, that's the goal. Binny Gill: So here, if you can see, I have market list. And by the way, this market list is a great partner community interest. And every single sort of apps come up here. Let me take a sample app here, Hadoop. And click launch. And now where do you want me to deploy? Speaker 1: Let's start at GCP. Binny Gill: GCP, okay. So I click on GCP, and let me give it a name. Hadoop. GCP. Say 30, right. Clear. So this is one click deployment of anything from our marketplace on to a cloud of your choice. Right now, what the system is doing, is taking the intent-filled description of what the application should look like. Not just the infrastructure level but also within the merchant machines. And it's creating a set of work flows that it needs to go deploy. So as you can see, while we were talking, it's loading the application. Making sure that the provisioning workflows are all set up. Speaker 1: And so this is actually, in real time it's actually extracting out some of the GCP requirements. It's actually talking to GCP. Setting up the constructs so that we can actually push it up on the GCP personally. Binny Gill: Right. So it takes a couple of minutes. It'll provision. Let me go back and show you. Say you worked with deploying AWS. So you Hadoop. Hit address. And that's it. So again, the same work flow. Speaker 1: Same process, I see. Binny Gill: It's going to now deploy in AWS. Speaker 1: See one of the keys things is that we actually extracted out all the isms of each of these clouds into this logical substrate. Binny Gill: Yep. Speaker 1: That you can now piggy-back off of. Binny Gill: Absolutely. And it makes it extremely simple for the average consumer. And you know we like more cloud support here over time. Speaker 1: Sounds good. Binny Gill: Now let me go back and show you an app that I had already deployed. Now 13 days ago. It's on GCP. And essentially what I want to show you is what is the view of the application. Firstly, it shows you the cost summary. Hourly, daily, and how the cost is going to look like. The other is how you manage it. So you know one click ways of upgrading, scaling out, starting, deleting, and so on. Speaker 1: So common actions, but independent of the type of clouds. Binny Gill: Independent. And also you can act with these actions over time. Right? Then services. It's learning two services, Hadoop slave and Hadoop master. Hadoop slave runs fast right now. And auditing. It shows you what are the important actions you've taken on this app. Not just, for example, on the IS front. This is, you know how the VMs were created. But also if you scroll down, you know how the application was deployed and brought up. You know the slaves have to discover each other, and so on. Speaker 1: Yeah got you. So find game invisibility into whatever you were doing with clouds because that's been one of the complaints in general. Is that the cloud abstractions have been pretty high level. Binny Gill: Yeah. Speaker 1: Yeah. Binny Gill: Yeah. So that's how we make the differences between the public clouds. All go away for the Indias of ... Speaker 1: Got you. So why don't we now give folks ... Now a lot of this stuff is coming in five, five so you'll see that pretty soon. You'll get your hands around it with AWS and tree support and so forth. What we wanted to show you was emerging alpha version that is being baked. So is a real production code for Xi. And why don't we just jump right in to it. Because we're running short of time. Binny Gill: Yep. Speaker 1: Give folks a flavor for what the production level code is already being baked around. Binny Gill: Right. So the idea of the design is make sure it's not ... the public cloud is no longer any different from your private cloud. It's a true seamless extension of your private cloud. Here I have my test environment. As you can see I'm running the HR app. It has the DB tier and the Web tier. Yeah. Alright? And the DB tier is running Oracle DB. Employee payroll is the Web tier. And if you look at the availability zones that I have, this is my data center. Now I want to protect this application, right? From disaster. What do I do? I need another data center. Speaker 1: Sure. Binny Gill: Right? With Xi, what we are doing is ... You go here and click on Xi Cloud Services. Speaker 1: And essentially as the slide says, you are adding AZs with one click. Binny Gill: Yeps so this is what I'm going to do. Essentially, you log in using your existing my.nutanix.com credentials. So here I'm going to use my guest credentials and log in. Now while I'm logging in what's happening is we are creating a seamless network between the two sides. And then making the Xi cloud availability zone appear. As if it was my own. Right? Speaker 1: Gotcha. Binny Gill: So in a couple of seconds what you'll notice this list is here now I don't have just one availability zone, but another one appears. Speaker 1: So you have essentially, real time now, paid a one data center doing an availability zone. Binny Gill: Yep. Speaker 1: Cool. Okay. Let's see what else we can do. Binny Gill: So now you think about VR setup. Now I'm armed with another data center, let's do DR Center. Now DR set-up is going to be extremely simple. Speaker 1: Okay but it's also based because on the fact that it is the same stack on both sides. Right? Binny Gill: It's the same stack on both sides. We have a secure network lane connecting the two sides, on top of the secure network plane. Now data can flow back and forth. So now applications can go back and forth, securely. Speaker 1: Gotcha, okay. Let's look at one-click DR. Binny Gill: So for one-click DR set-up. A couple of things we need to know. One is a protection rule. This is the RPO, where does it apply to? Right? And the connection of the replication. The other one is recovery plans, in case disaster happens. You know, how do I bring up my machines and application work-order and so on. So let me first show you, Protection Rule. Right? So here's the protection rule. I'll create one right now. Let me call it Platinum. Alright, and source is my own data center. Destination, you know Xi appears now. Recovery point objective, so maybe in a one hour these snapshots going to the public cloud. I want to retain three in the public side, three locally. And now I select what are the entities that I want to protect. Now instead of giving VMs my name, what I can do is app type employee payroll, app type article database. It covers both the categories of the application tiers that I have. And save. Speaker 1: So one of the things here, by the way I don't know if you guys have noticed this, more and more of Nutanix's constructs are being eliminated to become app-centric. Of course is VM centric. And essentially what that allows one to do is to create that as the new service-level API/abstraction. So that under the cover over a period of time, you may be VMs today, maybe containers tomorrow. Or functions, the day after. Binny Gill: Yep. What I just did was all that needs to be done to set up replication from your own data center to Xi. So we started off with no data center to actually replication happening. Speaker 1: Gotcha. Binny Gill: Okay? Speaker 1: No, no. You want to set up some recovery plans? Binny Gill: Yeah so now set up recovery plan. Recovery plans are going to be extremely simple. You select a bunch of VMs or apps, and then there you can say what are the scripts you want to run. What order in which you want to boot things. And you know, you can set up access these things with one click monthly or weekly and so on. Speaker 1: Gotcha. And that sets up the IPs as well as subnets and everything. Binny Gill: So you have the option. You can maintain the same IPs on frame as the move to Xi. Or you can make them- Speaker 1: Remember, you can maintain your own IPs when you actually use the Xi service. There was a lot of things getting done to actually accommodate that capability. Binny Gill: Yeah. Speaker 1: So let's take a look at some of- Binny Gill: You know, the same thing as VPC, for example. Speaker 1: Yeah. Binny Gill: You need to possess on Xi. So, let's create a recovery plan. A recovery plan you select the destination. Where does the recovery happen. Now, after that Section 11 of 13 [01:40:00 - 01:50:04] Section 12 of 13 [01:50:00 - 02:00:04] (NOTE: speaker names may be different in each section) Speaker 1: ... does the recovery happen. Now, after that you have to think of what is the runbook that you want to run when disaster happens, right? So you're preparing for that, so let me call "HR App Recovery." The next thing is the first stage. We're doing the first stage, let me add some entities by categories. I want to bring up my database first, right? Let's click on the database and that's it. Speaker 2: So essentially, you're building the script now. Speaker 1: Building the script- Speaker 2: ... on the [inaudible 01:50:30] Speaker 1: ... but in a visual way. It's simple for folks to understand. You can add custom script, add delay and so on. Let me add another stage and this stage is about bringing up the web tier after the database is up. Speaker 2: So basically, bring up the database first, then bring up the web tier, et cetera, et cetera, right? Speaker 1: That's it. I've created a recovery plan. I mean usually it's complicated stuff, but we made it extremely simple. Now if you click on "Recovery Points," these are snapshots. Snapshots of your applications. As you can see, already the system has taken three snapshots in response to the protection rule that we had created just a couple minutes ago. And these are now being seeded to Xi data centers. Of course this takes time for seeding, so what I have is a setup already and that's the production environment. I'll cut over to that. This is my production environment. Click "Explore," now you see the same application running in production and I have a few other VMs that are not protected. Let's go to "Recovery Points." It has been running for sometime, these recover points are there and they have been replicated to Xi. Speaker 2: So let's do the failover then. Speaker 1: Yeah, so to failover, you'll have to go to Xi so let me login to Xi. This time I'll use my production account for logging into Xi. I'm logging in. The first thing that you'll see in Xi is a dashboard that gives you a quick summary of what your DR testing has been so far, if there are any issues with the replication that you have and most importantly the monthly charges. So right now I've spent with my own credit card about close to 1,000 bucks. You'll have to refund it quickly. Speaker 2: It depends. If the- Speaker 1: If this works- Speaker 2: IF the demo works. Speaker 1: Yeah, if it works, okay. As you see, there are no VMs right now here. If I go to the recovery points, they are there. I can click on the recovery plan that I had created and let's see how hard it's going to be. I click "Failover." It says three entities that, based on the snapshots, it knows that it can recovery from source to destination, which is Xi. And one click for the failover. Now we'll see what happens. Speaker 2: So this is essentially failing over my production now. Speaker 1: Failing over your production now. [crosstalk 01:52:53] If you click on the "HR App Recovery," here you see now it started the recovery plan. The simple recovery plan that we had created, it actually gets converted to a series of tasks that the system has to do. Each VM has to be hydrated, powered on in the right order and so on and so forth. You don't have to worry about any of that. You can keep an eye on it. But in the meantime, let's talk about something else. We are doing failover, but after you failover, you run in Xi as if it was your own setup and environment. Maybe I want to create a new VM. I create a VM and I want to maybe extend my HR app's web tier. Let me name it as "HR_Web_3." It's going to boot from that disk. Production network, I want to run it on production network. We have production and test categories. This one, I want to give it employee payroll category. Now it applies the same policies as it's peers will. Here, I'm going to create the VM. As you can see, I can already see some VMs coming up. There you go. So three VMs from on-prem are now being filled over here while the fourth VM that I created is already being powered. Speaker 2: So this is basically realtime, one-click failover, while you're using Xi for your [inaudible 01:54:13] operations as well. Speaker 1: Exactly. Speaker 2: Wow. Okay. Good stuff. What about- Speaker 1: Let me add here. As the other cloud vendors, they'll ask you to make your apps ready for their clouds. Well we tell our engineers is make our cloud ready for your apps. So as you can see, this failover is working. Speaker 2: So what about failback? Speaker 1: All of them are up and you can see the protection rule "platinum" has been applied to all four. Now let's look at this recovery plan points "HR_Web_3" right here, it's already there. Now assume the on-prem was already up. Let's go back to on-prem- Speaker 2: So now the scenario is, while Binny's coming up, is that the on-prem has come back up and we're going to do live migration back as in a failback scenario between the data centers. Speaker 1: And how hard is it going to be. "HR App Recovery" the same "HR App Recovery", I click failover and the system is smart enough to understand the direction is reversed. It's also smart enough to figure out "Hey, there are now the four VMs are there instead of three." Xi to on-prem, one-click failover again. Speaker 2: And it's rerunning obviously the same runbook but in- Speaker 1: Same runbook but the details are different. But it's hidden from the customer. Let me go to the VMs view and do something interesting here. I'll group them by availability zone. Here you go. As you can see, this is a hybrid cloud view. Same management plane for both sides public and private. There are two availability zones, the Xi availability zone is in the cloud- Speaker 2: So essentially you're moving from the top- Speaker 1: Yeah, top- Speaker 2: ... to the bottom. Speaker 1: ... to the bottom. Speaker 2: That's happening in the background. While this is happening, let me take the time to go and look at billing in Xi. Speaker 1: Sure, some of the common operations that you can now see in a hybrid view. Speaker 2: So you go to "Billing" here and first let me look at my account. And account is a simple page, I have set up active directory and you can add your own XML file, upload it. You can also add multi-factor authentication, all those things are simple. On the billing side, you can see more details about how did I rack up $966. Here's my credit card. Detailed description of where the cost is coming from. I can also download previous versions, builds. Speaker 1: It's actually Nutanix as a service essentially, right? Speaker 2: Yep. Speaker 1: As a subscription service. Speaker 2: Not only do we go to on-prem as you can see, while we were talking, two VMs have already come back on-prem. They are powered off right now. The other two are on the wire. Oh, there they are. Speaker 1: Wow. Speaker 2: So now four VMs are there. Speaker 1: Okay. Perfect. Sometimes it works, sometimes it doesn't work, but it's good. Speaker 2: It always works. Speaker 1: Always works. All right. Speaker 2: As you can see the platinum protection rule is now already applied to them and now it has reversed the direction of [inaudible 01:57:12]- Speaker 1: Remember, we showed one-click DR, failover, failback, built into the product when Xi ships to any Nutanix fabric. You can start with DSX on premise, obviously when you failover to Xi. You can start with AHV, things that are going to take the same paradigm of one-click operations into this hybrid view. Speaker 2: Let's stop doing lift and shift. The era has come for click and shift. Speaker 1: Binny's now been promoted to the Chief Marketing Officer, too by the way. Right? So, one more thing. Speaker 2: Okay. Speaker 1: You know we don't stop any conferences without a couple of things that are new. The first one is something that we should have done, I guess, a couple of years ago. Speaker 2: It depends how you look at it. Essentially, if you look at the cloud vendors, one of the key things they have done is they've built services as building blocks for the apps that run on top of them. What we have done at Nutanix, we've built core services like block services, file services, now with Calm, a marketplace. Now if you look at [inaudible 01:58:14] applications, one of the core building pieces is the object store. I'm happy to announce that we have the object store service coming up. Again, in true Nutanix fashion, it's going to be elastic. Speaker 1: Let's- Speaker 2: Let me show you. Speaker 1: Yeah, let's show it. It's something that is an object store service by the way that's not just for your primary, but for your secondary. It's obviously not just for on-prem, it's hybrid. So this is being built as a next gen object service, as an extension of the core fabric, but accommodating a bunch of these new paradigms. Speaker 2: Here is the object browser. I've created a bunch of buckets here. Again, object stores can be used in various ways: as primary object store, or for secondary use cases. I'll show you both. I'll show you a Hadoop use case where Hadoop is using this as a primary store and a backup use case. Let's just jump right in. This is a Hadoop bucket. AS you can see, there's a temp directory, there's nothing interesting there. Let me go to my Hadoop VM. There it is. And let me run a Hadoop job. So this Hadoop job essentially is going to create a bunch of files, write them out and after that do map radius on top. Let's wait for the job to start. It's running now. If we go back to the object store, refresh the page, now you see it's writing from benchmarks. Directory, there's a bunch of files that will write here over time. This is going to take time. Let's not wait for it, but essentially, it is showing Hadoop that uses AWS 3 compatible API, that can run with our object store because our object store exposes AWS 3 compatible APIs. The other use case is the HYCU backup. As you can see, that's a- Section 12 of 13 [01:50:00 - 02:00:04] Section 13 of 13 [02:00:00 - 02:13:42] (NOTE: speaker names may be different in each section) Vineet: This is the hycu back up ... As you can see, that's a back-up software that can back-up WSS3. If you point it to Nutanix objects or it can back-up there as well. There are a bunch of back-up files in there. Now, object stores, it's very important for us to be able to view what's going on there and make sure there's no objects sprawled because once it's easy to write objects, you just accumulate a lot of them. So what we wanted to do, in true Nutanix style, is give you a quick overview of what's happening with your object store. So here, as you can see, you can look at the buckets, where the load is, you can look at the bucket sizes, where the data is, and also what kind of data is there. Now this is a dashboard that you can optimize, and customize, for yourself as well, right? So that's the object store. Then we go back here, and I have one more thing for you as well. Speaker 2: Okay. Sounds good. I already clicked through a slide, by the way, by mistake, but keep going. Vineet: That's okay. That's okay. It is actually a quiz, so it's good for people- Speaker 2: Okay. Sounds good. Vineet: It's good for people to have some clues. So the quiz is, how big is my SAP HANA VM, right? I have to show it to you before you can answer so you don't leak the question. Okay. So here it is. So the SAP HANA VM here vCPU is 96. Pretty beefy. Memory is 1.5 terabytes. The question to all of you is, what's different in this screen? Speaker 2: Who's a real Prism user here, by the way? Come on, it's got to be at least a few. Those guys. Let's see if they'll notice something. Vineet: What's different here? Speaker 3: There's zero CVM. Vineet: Zero CVM. Speaker 2: That's right. Yeah. Yeah, go ahead. Vineet: So, essentially, in the Nutanix fabric, every server has to run a [inaudible 02:01:48] machine, right? That's where the storage comes from. I am happy to announce the Acropolis Compute Cloud, where you will be able to run the HV on servers that are storage-less, and add it to your existing cluster. So it's a compute cloud that now can be managed from Prism Central, and that way you can preserve your investments on your existing server farms, and add them to the Nutanix fabric. Speaker 2: Gotcha. So, essentially ... I mean, essentially, imagine, now that you have the equivalent of S3 and EC2 for the enterprise now on Premisis, like you have the equivalent compute and storage services on JCP and AWS, and so forth, right? So the full flexibility for any kind of workload is now surely being available on the same Nutanix fabric. Thanks a lot, Vineet. Before we wrap up, I'd sort of like to bring this home. We've announced a pretty strategic partnership with someone that has always inspired us for many years. In fact, one would argue that the genesis of Nutanix actually was inspired by Google and to talk more about what we're actually doing here because we've spent a lot of time now in the last few months to really get into the product capabilities. You're going to see some upcoming capabilities and 55X release time frame. To talk more about that stuff as well as some of the long-term synergies, let me invite Bill onstage. C'mon up Bill. Tell us a little bit about Google's view in the cloud. Bill: First of all, I want to compliment the demo people and what you did. Phenomenal work that you're doing to make very complex things look really simple. I actually started several years ago as a product manager in high availability and disaster recovery and I remember, as a product manager, my engineers coming to me and saying "we have a shortage of our engineers and we want you to write the fail-over routines for the SAP instance that we're supporting." And so here's the PERL handbook, you know, I haven't written in PERL yet, go and do all that work to include all the network setup and all that work, that's amazing, what you are doing right there and I think that's the spirit of the partnership that we have. From a Google perspective, obviously what we believe is that it's time now to harness the power of scale security and these innovations that are coming out. At Google we've spent a lot of time in trying to solve these really large problems at scale and a lot of the technology that's been inserted into the industry right now. Things like MapReduce, things like TenserFlow algorithms for AI and things like Kubernetes and Docker were first invented at Google to solve problems because we had to do it to be able to support the business we have. You think about search, alright? When you type in search terms within the search box, you see a white screen, what I see is all the data-center work that's happening behind that and the MapReduction to be able to give you a search result back in seconds. Think about that work, think about that process. Taking and pursing those search terms, dividing that over thousands of [inaudible 02:05:01], being able to then search segments of the index of the internet and to be able to intelligent reduce that to be able to get you an answer within seconds that is prioritized, that is sorted. How many of you, out there, have to go to page two and page three to get the results you want, today? You don't because of the power of that technology. We think it's time to bring that to the consumer of the data center enterprise space and that's what we're doing at Google. Speaker 2: Gotcha, man. So I know we've done a lot of things now over the last year worth of collaboration. Why don't we spend a few minutes talking through a couple things that we're started on, starting with [inaudible 02:05:36] going into com and then we'll talk a little bit about XI. Bill: I think one of the advantages here, as we start to move up the stack and virtualize things to your point, right, is virtual machines and the work required of that still takes a fair amount of effort of which you're doing a lot to reduce, right, you're making that a lot simpler and seamless across both On-Prem and the cloud. The next step in the journey is to really leverage the power of containers. Lightweight objects that allow you to be able to head and surface functionality without being dependent upon the operating system or the VM to be able to do that work. And then having the orchestration layer to be able to run that in the context of cloud and On-Prem We've been very successful in building out the Kubernetes and Docker infrastructure for everyone to use. The challenge that you're solving is how to we actually bridge the gap. How do we actually make that work seamlessly between the On-Premise world and the cloud and that's where our partnership, I think, is so valuable. It's cuz you're bringing the secret sauce to be able to make that happen. Speaker 2: Gotcha, gotcha. One last thing. We talked about Xi and the two companies are working really closely where, essentially the Nutanix fabric can seamlessly seep into every Google platform as infrastructure worldwide. Xi, as a service, could be delivered natively with GCP, leading to some additional benefits, right? Bill: Absolutely. I think, first and foremost, the infrastructure we're building at scale opens up all sorts of possibilities. I'll just use, maybe, two examples. The first one is network. If you think about building out a global network, there's a lot of effort to do that. Google is doing that as a byproduct of serving our consumers. So, if you think about YouTube, if you think about there's approximately a billion hours of YouTube that's watched every single day. If you think about search, we have approximately two trillion searches done in a year and if you think about the number of containers that we run in a given week, we run about two billion containers per week. So the advantage of being able to move these workloads through Xi in a disaster recovery scenario first is that you get to take advantage of the scale. Secondly, it's because of the network that we've built out, we had to push the network out to the edge. So every single one of our consumers are using YouTube and search and Google Play and all those services, by the way we have over eight services today that have more than a billion simultaneous users, you get to take advantage of that network capacity and capability just by moving to the cloud. And then the last piece, which is a real advantage, we believe, is that it's not just about the workloads you're moving but it's about getting access to new services that cloud preventers, like Google, provide. For example, are you taking advantage like the next generation Hadoop, which is our big query capability? Are you taking advantage of the artificial intelligence derivative APIs that we have around, the video API, the image API, the speech-to-text API, mapping technology, all those additional capabilities are now exposed to you in the availability of Google cloud that you can now leverage directly from systems that are failing over and systems that running in our combined environment. Speaker 2: A true converged fabric across public and private. Bill: Absolutely. Speaker 2: Great stuff Bill. Thank you, sir. Bill: Thank you, appreciate it. Speaker 2: Good to have you. So, the last few slides. You know we've talked about, obviously One OS, One Click and eCloud. At the end of the day, it's pretty obvious that we're evaluating the move from a form factor perspective, where it's not just an OS across multiple platforms but it's also being distributed genuinely from consuming itself as an appliance to a software form factor, to subscription form factor. What you saw today, obviously, is the fact that, look you know we're still continuing, the velocity has not slowed down. In fact, in some cases it's accelerated. If you ask my quality guys, if you ask some of our customers, we're coming out fast and furious with a lot of these capabilities. And some of this directly reflects, not just in features, but also in performance, just like a public cloud, where our performance curve is going up while our price-performance curve is being more attractive over a period of time. And this is balancing it with quality, it is what differentiates great companies from good companies, right? So when you look at the number of nodes that have been shipping, it was around ten more nodes than where we were a few years ago. But, if you look at the number of customer-found defects, as a percentage of number of nodes shipped it is not only stabilized, it has actually been coming down. And that's directly reflected in the NPS part. That most of you guys love. How many of you guys love your Customer Support engineers? Give them a round of applause. Great support. So this balance of velocity, plus quality, is what differentiates a company. And, before we call it a wrap, I just want to leave you with one thing. You know, obviously, we've talked a lot about technology, innovation, inspiration, and so forth. But, as I mentioned, from last night's discussion with Sir Ranulph, let's think about a few things tonight. Don't take technology too seriously. I'll give you a simple story that he shared with me, that puts things into perspective. The year was 1971. He had come back from Aman, from his service. He was figuring out what to do. This was before he became a world-class explorer. 1971, he had a job interview, came down from Scotland and applied for a role in a movie. And he failed that job interview. But he was selected from thousands of applicants, came down to a short list, he was a ... that's a hint ... he was a good looking guy and he lost out that role. And the reason why I say this is, if he had gotten that job, first of all I wouldn't have met him, but most importantly the world wouldn't have had an explorer like him. The guy that he lost out to was Roger Moore and the role was for James Bond. And so, when you go out tonight, enjoy with your friends [inaudible 02:12:06] or otherwise, try to take life a little bit once upon a time or more than once upon a time. Have fun guys, thank you. Speaker 5: Ladies and gentlemen please make your way to the coffee break, your breakout sessions will begin shortly. Don't forget about the women's lunch today, everyone is welcome. Please join us. You can find the details in the mobile app. Please share your feedback on all sessions in the mobile app. There will be prizes. We will see you back here and 5:30, doors will open at 5, after your last breakout session. Breakout sessions will start sharply at 11:10. Thank you and have a great day. Section 13 of 13 [02:00:00 - 02:13:42]

>> Welcome back to the Cuban Peterborough's chief research officer of Silicon Angle and general manager of Wicked Bond. We're as part of our continuing coverage of the arse a show. We have a great guest Z scaler amid sin. Ha! Welcome to the Cube. >> Thank you for having me here. It's a pleasure to be here. >> So, um, it what exactly does Z scaler? D'oh >> Z's killer is in the business of providing the entire security stack as a service for large enterprises. We sit in between enterprise users and the Internet and various destinations they want to goto, and we want to make sure that they have a fast, nimble Internet experience without compromising any security. >> So if I can interpret what that means, that means that as Maur companies are trying to serve their employees that Air Mobile or customers who aren't part of their corporate network they're moving more. That communication in the Cloud Z scale is making it possible for them to get the same quality of security on that communication in the cloud is he would get on premise. >> Absolutely. If you look at some of the big business transformations that are happening, work lords for enterprises are moving to the cloud. For example, enterprises are adopting Office 3 65 instead, off traditional exchange based email and on your desktop applications. They might be adopting sales force for CR M Net suite for finance box for storage. So as these workloads are moving to the cloud and employees are becoming more and more mobile, you know they might be at a coffee shop. They might be on an iPad. Um, and they might be anywhere in the world. That begs the basic security question. Where should that enterprise DMC the security stack be sitting back in the day? Enterprises had a hub and spokes model, right? They might have 50 branch offices across the world. A few mobile workers, all of them, came back over private networks to a central hub, and that hub was where racks and racks of security appliances were deployed. Maybe they started off with a firewall. Later on, they added a proxy. You are l filtering some d e l P er down the road. People realized that you need to inspect us to sell. So they added some SSL offload devices. Someone said, Hey, we need to do some sand boxing for behavioral analysis. People started adding sandboxes. And so, over time the D. M. Z got cluttered and complicated and fast forward to Today. Users have become mobile. Workloads have moved to the cloud. So if I'm sitting in a San Francisco office on my laptop trying to do my regular work, my email is in the cloud. My my court applications are sitting in the cloud. Why should I have to vpn back to my headquarters in Cincinnati over a private network, you know, incurring all the Leighton see and the delays just so that I can get inspected by some legacy appliances that are sitting in that DMC, right? So we looked at that network transformation on We started this journey at Ze scale or eight years ago, and we said, Look, if users are going to be mobile and workloads are going to be in the cloud, the entire security stack should be as close as possible to where the users are. In that example, I described, I'm sitting here. I'm going to Salesforce. We're probably going to the same data center in San Francisco. Shouldn't my entire security stag be available right where I am, um, and my administrators should have full visibility, full control from a single pane of glass. I get a fast, nimble user experience. The enterprise doesn't have to compromise in any security, and that's sort of the vision that we have executing towards. >> But it's not just for some of the newer applications or some of the newer were close. We're also seeing businesses acknowledge that the least secure member of their community has an impact on overall security. So the whole concept of even the legacy has to become increasingly a part of this broad story. So if anybody accesses anything from anywhere through the cloud that those other workloads increasing, they're gonna have to come under the scrutiny of a cloud based security option. >> Absolutely. I mean, that's a brilliant point, Peter. >> I >> think of >> it this way. Despite all those security appliances that have been deployed over time, they're still security breach is happening. And why is that? That is because users are the weakest link, right? If I'm a mobile work user, I'm sitting in a branch office. It's just painful for me to go back to those headquarter facilities just for additional scanning so two things happen either I have a painful user experience. What? I bypassed security, right? Um, and more and more of the attacks that we see leverage the user as the weakest link. I send you a phishing email. It looks like it came from HR. It has a excel sheet attached to it to update some information. But, you know, inside is lurking a macro, right? You open it. It is from a squatter domain that looks very similar to the company you work for. You click on it and your machine is infected. And then that leads to further malware being downloaded, data being expatriated out. So the Z scaler solution is very, very simple. Conceptually, we want to sit between users and the destinations they goto all across the world. And we built this network of 100 data centers. Why? Because you cannot travel faster than the speed of light. So if you're in San Francisco, you better go through our San Francisco facility. All your policies will show up here. All the latest and greatest security protections will be available. We serve 5000 large enterprises. So if we discover a new security threat because of an employee from, let's say, a General Electric. Then someone from United Airlines automatically gets protection simply because the cloud is live all the time. You're not waiting for your security boxes to get, you know, the weekly patch updates for new malware indicators and so on. Right, So, um, you get your stack right where you are. It's always up to date. User experience is not compromised. Your security administrators get a global view off things. And one >> of the >> things that that I that we haven't talked about here it is the dramatic cost savings that this sort of network transformation brings for enterprises. To put that in perspective, let's say you're a Fortune 100 organization with 100,000 employees worldwide in that, huh? Been spoke model. You are forcing all those workloads to come toe a few choke points, right? That is coming over. Very expensive. NPLs circuits private circuits from service providers. You're double trombone in traffic, back and forth. You know, you and I are in a branch. We might be on. Ah, Skype session. Ah, Google Hangout session. All our traffic goes to H Q. Goes to the cloud comeback comes back to h. Q comes back to you, there's this is too much back and forth, and you're paying for those expensive circuits and getting a poor user experience. Wouldn't it be great if you and I could go straight to the Internet? And that can only be enabled if we can provide that pervasive security stack wherever you are? And for that, we built this network of 100 data centers worldwide. Always live, always up to date you. You get routed to the closest the scaler facility. All your policy show up. They're automatically and you get the latest and greatest protection. >> So it seems as though you end up with three basic benefits. One is you get the cost benefit of being able to, uh, have being able to leverage a broader network of talent, skills and resources You reduce. Your risk is not the least of which is that the cost and the challenges configuring a whole bunch of appliances has not gotten any easier over the last. No, it hasn't cheaters. And so not only do you have user error, but you also Administrator Erin, absolutely benign, but nonetheless it's there, and then finally and this is what I want to talk about. Increasingly, the clot is acknowledged as the way that companies are going to improve their portfolio through digital assets. Absolutely. Which means new opportunities, new competition, new ways of improving customer experience. But security has become the function of no within a lot of organizations. Absolutely. So How does how does AE scaler facilitate the introduction of new business capabilities that can attack these opportunities in a much more timely way by reducing doesn't reduce some of those some of those traditional security constraints. >> Absolutely right, and we call it the Department of No right. We've talked to most people in the industry. They view their I t folks there, security forces, the department of Know Why? Because there's this big push from users to adopt newer, nimble, faster cloud based ah solutions that that improved productivity. But often I t comes in the way. No, If you look at what Izzy's killer is doing, it's trying to transform the adoption of these Cloud service. Is that do improve business productivity? In fact, there is no debate now because there are many, many industries that ever doubt adopted a cloud first strategy. Well, that means is, as they think of the network and their security, they want to make sure that cloud is front and center. Words E scaler does is it enables that cloud for a strategy without any security compromise. I'll give you some specific examples. Eight out of 10 c I ose that we talk to our thinking about office 3 65 or they have already deployed it right. One of the first challenge is that happens when you try to adopt office. 3 65 is that your legacy network and security infrastructure starts to come crumble. Very simple things happen. You have your laptop. Suddenly, that laptop has many, many persistent SSL connections to the clothes. Because exchange is moved to the cloudy directory, service is are moving to the cloud. If you have a small branch office with 2000 users, each of them having 30 40 persistent connections to the cloud will your edge firewall chokes. Why? Because it cannot maintain so many active ports at the same time, we talked about the double trombone ing of traffic back and forth. If you try to not go direct to the Internet but force everyone to go through a couple of hubs. So you pay for all the excessive band with your traditional network infrastructure, and your security infrastructure might need a forklift upgrades. So a cloud transformation project quickly becomes a network in a security transformation project. And this is where you nosy scaler helps tremendously because we were born and bred in the cloud. Many of these traditional limitations that you have with appliance based security or networking, you know, in the traditional sense don't exist for the scaler, right? We can enable your branch officers to go directly to the cloud. In fact, we've started doing some very clever things. For example, we peer with Microsoft in about 20 sites worldwide. So what that means is, when you come to the scaler for security, there's a very high likelihood that Microsoft has a presence in the same data center. We might be one or two or three millisecond hops away because we're in the same equinox facility in New York or San Jose. And so not only are you getting your full security stack where you are, you're getting the superfast peered connections to the end Cloud service is that you want to goto. You don't have to work. Worry about you know your edge Firewalls not keeping up. You don't have to worry about a massive 30 40% increase in back hole costs because you were now shipping all this extra traffic to those couple of hubs. And more importantly, you know, you've adopted these transformative technologies on your users don't have to complain about how slow they are because you know, most of the millennials hitting the workforce. I used to a very fast, nimble experience on their mobile phones with consumer APS. And then they come into the enterprise and they quickly realize that, well, this is all cumbersome and old and legacy stuff >> in me s. So let's talk a little bit about Let's talk a bit about this notion of security being everywhere and increasingly is removed to a digital business or digital orientation. With digital assets being the basis for the value proposition, which is certainly happening on a broad scale right now, it means it's security going back to the idea of security being department. No security has to move from an orientation of limiting access to appropriately sharing. Security becomes the basis for defining the digital brand. So talk to us a little bit about how the how you look out, how you see the world, that you think security's gonna be playing in ultimately defining this notion of digital brand digital perimeters from a not a iittie standpoint. But from a business value standpoint, >> absolutely. I would love to talk about that. So Izzy's killer Our cloud today sees about 30,000,000,000 transactions a day from about 5000 enterprises. So we have a very, very good pulse on what is happening in large enterprises, from from a cloud at perspective or just what users are doing on the Internet. So here are some of the things that we see. Number one. We see that about 50 60% of the threats are coming inside SSL, so it's very important to inspect SSL. The second thing that we observe is without visibility. It is very different, very difficult for your security guys to come up with a Chris policy, right? If you cannot see what is happening inside an SSL connection, how are you going to have a date? A leakage policy, right? Maybe your policy is no P I information should leak out. No source code should leak out. How can you make sure that an engineer is not dropping something in this folder, which is sinking to Google Drive or drop box in an in an SSL tano, Right. How do you prioritize mission Critical business applications like office 3 65 over streaming media, Right. So for step two, crafting good policy is 100% real time visibility. And that's what happens when you adopt the Siskel a network. You can see what any user is doing anywhere in the world within seconds. And once you have that kind of visibility, you can start formulating policies, both security and otherwise that strike a good balance between business productivity that you want to achieve without compromising security. >> That's the policy's been 10 more net. You can also end that decisions. >> Yes, right. So, for example, you can you can have a more relaxed social media policy, right? You can say Well, you know, everyone is allowed access, but they can. Maybe streaming media is restricted to one hour a day. You know, after hours, or you can say, I want to adopt um, storage applications in the clothes here are some sanctioned APS These other raps were not going to allow right. You can do policies by users, by locations by departments, right? And once you have the visibility, you can. You can be very, very precise and say, Well, boxes, my sanction story, Jap other APS are not allowed right and hear other things that a particular group of users can do on box. Or they cannot do because we were seeing every transaction between the user on going to the destination and as a result, begin, you know, we can enable the enterprise administrator to come up with very, very specific policies that are tailored for that. >> You said something really interesting. I'm gonna ask you one more question, but I'm gonna make a common here. And that common is that the power of digital technology is that it can be configured and copied and changed, and it's very mutable. It's very plastic, but at the end of the day it has to be precise, and I've never heard anybody talk about the idea of precise and security, and I think it's a very, very powerful concept. But what are what's What's the scale are talking about in our say this year. >> Well, we're going to talk about a bunch of very interesting things. First, we'll talk about the scale of private access. This is a new offering on the scale of platform. We believe that VP ends have become irrelevant because of all the discussions we just had, um, Enterprises are treating their Internet as though it was the Internet, right? You know, sort of a zero trust model. They're moving the crown jewel applications to either private cloud offerings are, you know, sort of restricting that in a very micro segmented way. And the question is, how do you access those applications? Right? And the sea skill immortal is very straightforward. You have a pervasive cloud users authenticate to the cloud and based on policies, we can allow them to go to the Internet to sites that have been sanctioned and allowed. We make sure nothing good is leaking out. Nothing bad is coming in, and that same cloud model can be leveraged for private access to crown jewel applications that traditionally would have required a full blown vpn right. And the difference between a VPN and the skill of private access is VP ends basically give you full network access keys to the kingdom, right? Whether it's a contractor with, it's an employee just so that you could access, you know, Internet application. You allow full network access, and we're just gonna getting rid of that whole notion. That's one thing we're gonna stroke ISS lots of cloud white analytics, As I mentioned, you know, we process 30,000,000,000 transactions a day. To put that in perspective, Salesforce reports about four and 1 30,000,000,000 4 1/2 to 5,000,000,000 transactions. They're about three and 1/2 1,000,000,000 Google searches done daily, right? So it is truly a tin Internet scale. We're blocking over 100,000,000 threats every day for, ah, for all our enterprise user. So we have a very good pulse on you know what's what's an average enterprise user doing? And you're going to see some interesting cloud? Wait, Analytics. Just where we talk about a one of the top prevalent Claude APs, what are the top threats? You know, by vertical buy by geography, ese? And then, you know, we as a platform has emerged. We started off as a as a sort of a proxy in the cloud, and we've added sand boxing capabilities. Firewall capabilities, you know, in our overall vision, as I said, is to be that entire security stack that sits in your inbound and outbound gateway in that DMC as a pure service. So everything from firewall at layer three to a proxy at Layer seven, everything from inline navy scanning right to full sand. Boxing everything from DLP to cloud application control. Right? And all of that is possible because, you know, we have this very scalable architecture that allows you to to do sort of single scan multiple action right in that appliance model that I describe. What ends up happening is that you have many bumps in the wire. One of the examples we use is if you wanted to build a utility company, you don't start off with small portable generators and stack them in a warehouse, right? That's inefficient. It requires individual maintenance. It doesn't scale properly. Imagine if you build a turbine and ah, and then started your utility company. You can scale better. You can do things that traditional appliance vendors cannot think about. So we build this scalable, elastic security platform, and on that platform it's very easy for us to add. You know, here's a firewall. Here's a sandbox. And what does it mean for end users? You know, you don't need to deploy new boxes. You just go and say, I want to add sand boxing capabilities or I want to add private access or I want to add DLP. And it is as simple as enabling askew, which is what a cloud service offering should be. >> Right. So we're >> hardly know software. >> So we're talking about we're talking about lower cost, less likelihood of human error, which improves the quality, security, greater plasticity and ultimately, better experience, especially for your non employees. Absolutely. All right, so we are closing up this particular moment I want Thank you very much for coming down to our Pallotta studio is part of our coverage on Peter Boris. And we've been talking to the scanner amidst, huh? Thank you very much. And back to Dio Cube.