(bright music) >> Welcome back to HPE Discover 2021. My name is Dave Vellante and you're watching theCUBE's virtual coverage of HPE's big customer event. Of course, the virtual edition and we're going to dig into transformations, the role of technology and the role of senior technology leadership. Look, let's face it, HPE has gone through a pretty dramatic transformation itself in the past few years so it makes a great example in case study and with me is Rashmi Kumar who is the senior vice president and CIO at HPE, Rashmi welcome come on inside theCUBE. >> Hi Dave nice to be here. >> Well it's been almost a year since COVID you know changed the world as we know it. How would you say the role of the CIO specifically in generally IT has changed? I mean you got digital, zero trust has gone from buzzword to mandate, digital, everybody was you know complacent about digital in many ways and now it's really accelerated, remote work, hybrid, how do you see it? >> Absolutely, as I said in the last Discover that COVID has been the biggest reason to accelerate digital transformation in the companies. I see CIO's role has changed tremendously in the last 15 months. It's no more just keep the operations running, that's become a table stake. Our roles have become not only to create digital customer experience, engage with our customers in different ways, but also to transform the company operations from inside out to be able to give that digital experience from beginning to end of the customer engagement going forward. We have also become responsible for switching our strategies around the companies as the COVID hit in different parts of the world at different times and how companies structured their operations to go from one region to another, a global company like HPE had to look into its supply chain differently, had to look into strategies to mitigate the risk that was created because of the supply chain disruptions, as well as you go to taking care of our employees. How do you create this digital collaboration experience where teams can still come together and make the work happen for our end customers? How do we think about future employee engagement when people are not coming into these big buildings and offices and working together, but how do you create the same level of collaboration, coordination, as well as delivery of faster, good and services which is enabled by technology going forward. So CIO and IT's role has gone from giving a different level of customer experience to different level of employee experience, as well as enabling day-to-day operations of the companies. CEOs have realized that digital is the way to go forward, it does not matter what industry you are in and now CIOs have their seat at the table to define what the future of every company now which is a technology company irrespective you are in oil and gas, or mining, or a technical product, or a car or a mobility company, end of the day you have to act and behave like a technology company. >> So I want to ask you about that because you've been a CIO at a leading technology provider now for the last three years and you've had previous roles and were, you know non-technical, technology, you know, selling to IT companies and as you point out those worlds are coming together. Everybody's a technology company today. How do you think that changes the role of the CIO because it would always seem to me that there was a difference between a CIO at a tech company you know what I mean by that and a CIO at sort of every other company is, are those two worlds converging? >> Absolutely and it's interesting you pointed out that I have worked in many different industries from healthcare and pharma, to entertainment, to utilities and now at a technology company. End of the day the issues that IT deals with are pretty similar across the organization. What is different here is now my customers are people like me in other industries and I have little bit of an advantage because just having the experience across various ecosystem even that HPE look I was fortunate at HPE because of Antonio's leadership we had top-down mandate to transform how we did business and I talked about my NextGEN IT program in last year's CUBE interview. But at the same time while we were changing our customer, partner's experience from ordering, to order processing, to supply chain, to finance, we decided this pivot of becoming as a service company. And if you think about that pivot, it's pretty common. If it was a technology company or non-technology company. At HPE we were very used to selling a product and coming back three years later at the time of refresh of infrastructure or hardware. That's no more true for us. Now we are becoming an as a service or a subscription company and IT played a major role to enable that quote-to-cash experience which is very different than the traditional experience, around how we stay connected with our customer, how we proactively understand their behavior. I always talk about this term digital exhaust which results into data, which can result into better insight and you can not only upsell, cross-sell because now you have more data about your product usage, but first and foremost give what your customer wants in a much better way because you can proactively understand their needs and wants because you are providing a digital product versus a physical product. So this is the change that most of the companies are now going through. If you look at Domino's transition, they are pizza sellers but they did better because they had better digital experience. If you look at Chipotle, these are food service companies. Ikea which is a furniture manufacturer, across the board we have helped our customers and industries to understand how to become a more digital provider. And remember when HPE says edge to cloud platform as a service, edge is the product, the customers is what we deal with and how do we get that, help them get that data, understand how the product is behaving and then get the information to cloud for further analysis and understanding from the data that comes out of the products that they sell. >> I think you've been at HPE now I think around three years and I've been watching of course for decades, you know HPE, well HP then HPE is, I feel like it's entering now that sort of third phase of its transformation, your phase one was okay we got to figure out how to deal or operate as separate companies, okay, that took some time and then it was okay, now how do we align our resources? And you know what are the waves that we're going to ride? And how do we take our human capital, our investments and what bets do we place? And you're all in on as a service and now it's like okay, you know how do we deliver on all those promises? So pretty massive transformations. You talked about edge to cloud as a service so you've got this huge pivot in your business. What's the technology strategy to support that transformation? >> Yeah, that's a great question. So as I mentioned first, your second phase which was becoming a stand-alone company was the NextGEN IT program where we brought in S4 and 60 related ecosystem application where even in the traditional business there was a realization that we were 120 billion company, we are a 30 billion company, we need different types of technologies as well as more integrated across our product line, across the globe and we, I'm very happy to report that we are the last leg of NextGEN IT transformation. Where we have brought in new customer experience through low-touch or no-touch order processing, a very strong S4 capabilities where we are now able to run all global orders across all our hardware and services business together and I'm happy to report that we have been able to successfully run through the transformation which a typical company of our size would take five or six years to do in around close to three years. But at the same time while we were building this foundation and the capabilities to be able to do order management supply chain and data and analytics platforms, we also made the pivot to go to as a service. Now for as a service and subscription selling, it needs a very different quote-to-cash experience for our customers. And that's where we had bring in platforms like BRIM to do subscription billing, convergent charging and a whole different way to address. But we were lucky to have this transformation completed on which we could bolt on this new capability and we had the data analytics platform built which now these as a service products can also use to drive better insight into our customer behavior as well as how they're using our product real time for our operations teams. >> Well they say follow the money, in theCUBE we love to say follow the data. I mean data is obviously a crucial component of competitive advantage, business value, so talk a little bit more about the role of data, I'm interested in where IT fits. You know a lot of companies they'll have a chief data officer, or a CIO, sometimes they're separate sometimes they work, you know for each other, or CDO works for CIO, how do you guys approach the whole data conversation? >> Yeah that's a great question and has been top of the mind of a lot of CEOs, CIOs, chief digital officers in many different companies. The way we have set it up here is we do have a chief data officer and we do have a head of technology and platform and data lake within IT. Look the way I see is that I call the term data torture. If they have multiple data lakes, if they have multiple data locations and the data is not coming together at one place at the first time that it comes out to the source system, we end up with data swamps and it's very difficult to drive insights, it's very difficult to have single version of truth. So HPE had two-pronged approach. First one was as part of this NextGEN IT transformation we embarked upon the journey first of all to define our customers and products in a very uniform way across the globe. It's called entity master data and product master data program. These were very, very difficult program. We are now happy to report that we can understand the customer from cold stage to servicing stage beginning to end across all our system. It's been a tough journey but it was effort well spent. At the same time while we were building this master data capability we also invested time in our analytics platform. Because we are generating so much data now globally as one footprint, how do we link our data lake to our SAP and Salesforce and all these systems where our customer data flows through and create analytics and insight from it from our customers or our operations team. At the same time we also created a chief data officer role where the responsibility is really to drive business from understanding what decision making and analytics they need around product, around customer, around their usage around their experience to be able to drive better alignment with our customers and products going forward. So this creates efficiencies in the organization. If you have a leader who is taking care of your platforms and data, building single source of truth and you have a leader who is propagating this mature notion of handling data as enterprise data and driving that focus on understanding the metrics and the insight that the businesses need to drive better customer alignment, that's when we gain those efficiencies and behind the scenes the chief data officer and the data leader within my organization work very, very closely to understand each other needs, sometimes art of the possible, where do we need the data processing? Is it at the edge? Is it in the cloud? What's the best way to drive the technology and the platform forward? And they kind of rely on each other's knowledge and intelligence to give us superior results. And I have done data analytics in many different companies, this model works. Where you have focus on insight and analytics without, because data without insight is of no value. But at the same time you need clean data, you need efficient, fast platforms to process that insight at the functional non-functional requirement that our business partners have. And that's how we have established in here and we have seen many successes recently as of now. >> I want to ask you a kind of a harder, maybe it's not a harder question it's a weird question around single version of the truth. 'Cause it's clearly a challenge for organizations and there's many applications, workloads that require that single version of the truth, the operational systems, the transaction systems, the HR, the Salesforce and clearly you have to have a single version of the truth. I feel like, however we're on the cusp of a new era where business lines see an opportunity for whatever, their own truth to work with a partner to create some kind of new data product. And it's early days in that but I wonder, maybe not the right question for HPE but I wonder if you see it with in your ecosystems where it's yes, single version of truth is sort of one class of data and analytics got to have that nailed down, data quality, everything else. But then there's this sort of artistic version of the data where business people need more freedom, they need more latitude to create. Are you seeing that? Maybe you can help me put that into context. >> That's a great question Dave and I'm glad you asked it so. I think Tom Davenport, who is known in the data space talks about the offensive and the defensive use cases of leveraging data. I think the piece that you talked about where it's clean, it's pristine, it's quality, it's all that, most of those offer the offensive use cases where you are improving companies' operations incrementally because you have very clean data, you have very good understanding of how my territories are doing, how my customers are doing, how my products are doing, how am I meeting my SLAs or how my financials are looking, there's no room for failure in that area. The other area is though which works on the same set of data. It's not a different set of data but the need is more around finding needles in the haystack to come up with new needs, new wants in customers or new business models that we go with. The way we have done it is we do take this data, take out what's not allowed for everybody to be seen and then what we call is a private space but that's this entire data available to our business leader not real time, because the need is not as real time because they are doing more, what we call this predictive analytics to be able to leverage the same data set and run their analytics. And we work very closely with business units, we educate them, we tell them how to leverage this data set and use it and gather their feedback to understand what they need in that space to continue to run with their analytics. I think as we talk about hindsight, insight and foresight, hindsight and insight happens more from this clean data lakes where you have authenticity, you have quality and then most of the foresight happens in a different space where the users have more leverage to use data in many different ways to drive analytics and insights which is not readily available. >> Great thank you for that. That's an interesting discussion. You know digital transformation it's a journey and it's going to take you know many years. I know a lot of ways, not a lot of ways, 2020 was a forced march to digital you know. If you weren't a digital business you were out of business and so you really didn't have much time to plan. So now organizations are stepping back saying, okay, let's really lean into our strategy, the journey and along the way, there's going to be blind spots, there's bumps in the road, when you look out what are the potential disruptions that you see maybe in terms of how companies are currently approaching their digital transformations? >> That's a great question Dave and I'm going to take a little bit more longer-term view on this topic, right? And what's top of my mind recently is the whole topic of ESG, environmental, social and governance. Most of the companies have governance in place right? Because they are either public companies, or they're under some kind of scrutiny from different regulatory bodies or whatnot even if you're a startup you need to do things with our customers and whatnot. It has been there for companies, it continues to be there. We the public companies are very good at making sure that we have the right compliance, right privacy, right governance in place. Now we'll talk about cybersecurity I think that creates a whole new challenge in that governance space, however we have the setup within our companies to be able to handle that challenge. Now, when we go to social, what happened last year was really important. And now as each and every company we need to think about what are we doing from our perspective to play our part in that and not only the bigger companies, leaders at our level I would say that between last March and this year I have hired more than 400 people during pandemic which was all virtual, but me and my team have made sure that we are doing the right thing to drive inclusion and diversity which is also very big objective for HPE and Antonio himself has been very active in various round tables in US at the World Economic Forum level and I think it's really important for companies to create that opportunity, remove that disparity that's there for the underserved communities. If we want to continue to be successful in this world to create innovative product and services we need to sell it to the broader cross section of populations and to be able to do that we need to bring them in our fold and enable them to create that equal consumption capabilities across different sets of people. HPE has taken many initiatives and so are many companies. I feel like the momentum that companies have now created around the topic of equality is very important. I'm also very excited to see that a lot of startups are now coming up to serve that 99% versus just the shiny ones as you know in the Bay Area to create better delivery methods of food or products right? But the third piece which is environmental is extremely important as well. As we have seen recently in many companies and where even the dollar or the economic value is flowing are around the companies which are serious about environmental. HPE recently published it's a Living Progress Report, we have been in the forefront of innovation to reduce carbon emissions, we help our customers through those processes. Again, if we don't, if our planet is on fire none of us will exist right? So we all have to do that every little part to be able to do better. And I'm happy to report I myself as a person solar panels, battery, electric cars, whatever I can do. But I think something more needs to happen right? Where as an individual I need to pitch in but maybe utilities will be so green in the future that I don't need to put panels on my roof which again creates a different kind of race going forward. So when you ask me about disruptions, I personally feel that successful company like ours have to have ESG top of their mind and think of product and services from that perspective, which creates equal opportunity for people, which creates better environment sustainability going forward and you know our customers, our investors are very interested in seeing what we are doing to be able to serve that cause for bigger cross section of companies. And I'm most of the time very happy to share with my CIO cohort around how our HPEFS capabilities creates or feeds into the circular economy, how much e-waste we have recycled or kept it off of landfills, our green lake capabilities, how it reduces the e-waste going forward, as well as our sustainability initiatives which can help other CIOs to be more carbon neutral going forward as well. >> You know that's a great answer Rashmi thank you for that 'cause I got to tell you I hear a lot of mumbo jumbo about ESG but that was a very substantive, thoughtful response that I think tech companies in particular are, have to lead and are leading in this area. So I really appreciate that sentiment. I want to end with a very important topic which is cyber it's, obviously you know escalated in the news the last several months, it's always in the news but, you know 10 or 15 years ago there was this mentality of failure equals fire. And now we realize, hey they're going to get in, it's how you handle it. Cyber has become a board-level topic. You know years ago there was a lot of discussion, oh you can't have the SecOps team working for the CIO because that's like the fox watching the hen house that's changed. It's been a real awakening, a kind of a rude awakening so the world is now more virtual, you've got a secure physical assets. I mean any knucklehead can now become a ransomware attacker, they can buy ransomware as a service in the dark web so that's something we've never seen before. You're seeing supply chains get hacked and self-forming malware I mean it's a really scary time. So you've got these intellectual assets it's a top priority for organizations. Are you seeing a convergence of the CISO role, the CIO role, the line of business roles relative to sort of prior years in terms of driving security throughout organizations? >> Yeah this is a great question and this was a big discussion at my public board meeting a couple of days ago. It's, as I talk about many topics, if you think digital, if you think data, if you think ESG, it's no more one organization's business, it's now everybody's responsibility. I saw a Wall Street Journal article a couple of days ago where somebody has compared cyber to 9/11 type scenario that if it happens for a company that's the level of impact you feel on your operations. So, you know all models are going to change where CISO reports to CIO, at HPE we are also into product security and that's why CISO is a peer of mine who I work with very closely, who also worked with product teams where we are saving our customers from lot of pain in this space going forward and HPE itself is investing enormous amount of efforts and time in coming out of products which are secure and are not vulnerable to these types of attacks. The way I see it is CISO role has become extremely critical in every company and a big part of that role is to make people understand that cybersecurity is also everybody's responsibility. That's why an IT we propagate DevSecOps, as we talk about it we are very, very careful about picking the right products and services. This is one area where companies cannot shy away from investing. You have to continuously looking at cybersecurity architecture, you have to continuously look at and understand where the gaps are and how do we switch our product or service that we use from the providers to make sure our companies stay secure. The training not only for individual employees around anti-phishing or what does cybersecurity mean, but also to the executive committee and to the board around what cyber security means, what zero trust means, but at the same time doing drive-ins. We did it for business continuity and disaster recovery before, now it is time we do it for a ransomware attack and stay prepared. As you mentioned and we all say in tech community, it's always if not when. No company can take them their chest and say, "oh we are fully secure," because something can happen going forward. But what is the readiness for something that can happen? It has to be handled at the same risk level as a pandemic, or a earthquake, or a natural disaster and assume that it's going to happen and how as a company we will behave when something like this happens. So I'm huge believer in the framework of protect, detect, govern and respond as these things happen. So we need to have exercises within the company to ensure that everybody's aware of the part that they play day to day but at the same time when some event happen and making sure we do very periodic reviews of IT and cyber practices across the company, there is no more differentiation between IT and OT. That was 10 years ago. I remember working with different industries where OT was totally out of reach of IT and guess what happened? WannaCry and Petya and XP machines were still running your supply chains and they were not protected. So, if it's a technology it needs to be protected. That's the mindset people need to go with. Invest in education, training, awareness of your employees, your management committee, your board and do frequent exercises to understand how to respond when something like this happen. See it's a big responsibility to protect our customer data, our customer's operations and we all need to be responsible and accountable to be able to provide all our product and services to our customers when something unforeseen like this happens. >> Rashmi you're very generous with your time thank you so much for coming back in theCUBE it was great to have you again. >> Thank you Dave, it was really nice chatting with you. >> And thanks for being with us for our ongoing coverage of HPE Discover '21. This is Dave Vellante you're watching the virtual CUBE, the leader in digital tech coverage we'll be right back. (bright music)

>>Welcome back to HPD discovered 2021 the cubes virtual coverage, continuous coverage of H P. S H. P. S. Annual customer event. My name is Dave Volonte and we're going to dive into the intersection of high performance computing data and AI with DR Eng limb go who is the senior vice president and CTO for AI at Hewlett Packard enterprise Doctor go great to see you again. Welcome back to the cube. >>Hello Dave, Great to talk to you again. >>You might remember last year we talked a lot about swarm intelligence and how AI is evolving. Of course you hosted the day two keynotes here at discover you talked about thriving in the age of insights and how to craft a data centric strategy and you addressed you know some of the biggest problems I think organizations face with data that's You got a data is plentiful but insights they're harder to come by. And you really dug into some great examples in retail banking and medicine and health care and media. But stepping back a little bit with zoom out on discovered 21, what do you make of the events so far? And some of your big takeaways? >>Mm Well you started with the insightful question, right? Yeah. Data is everywhere then. But we like the insight. Right? That's also part of the reason why that's the main reason why you know Antonio on day one focused and talked about that. The fact that we are now in the age of insight. Right? Uh and and uh and and how to thrive thrive in that in this new age. What I then did on the day to kino following Antonio is to talk about the challenges that we need to overcome in order in order to thrive in this new age. >>So maybe we could talk a little bit about some of the things that you took away in terms I'm specifically interested in some of the barriers to achieving insights when you know customers are drowning in data. What do you hear from customers? What we take away from some of the ones you talked about today? >>Oh, very pertinent question. Dave you know the two challenges I spoke about right now that we need to overcome in order to thrive in this new age. The first one is is the current challenge and that current challenge is uh you know stated is you know, barriers to insight, you know when we are awash with data. So that's a statement right? How to overcome those barriers. What are the barriers of these two insight when we are awash in data? Um I in the data keynote I spoke about three main things. Three main areas that received from customers. The first one, the first barrier is in many with many of our customers. A data is siloed. All right. You know, like in a big corporation you've got data siloed by sales, finance, engineering, manufacturing, and so on, uh supply chain and so on. And uh, there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the that was the first barrier we spoke about barriers to incite when we are washed with data. The second barrier is uh, that we see amongst our customers is that uh data is raw and dispersed when they are stored and and uh and you know, it's tough to get tough to to get value out of them. Right? And I in that case I I used the example of uh you know the May 6 2010 event where the stock market dropped a trillion dollars in in tens of ministerial. We we all know those who are financially attuned with know about this uh incident But this is not the only incident. There are many of them out there and for for that particular May six event uh you know, it took a long time to get insight months. Yeah before we for months we had no insight as to what happened, why it happened, right. Um and and there were many other incidences like this. And the regulators were looking for that one rule that could, that could mitigate many of these incidences. Um one of our customers decided to take the hard road go with the tough data right? Because data is rolling dispersed. So they went into all the different feeds of financial transaction information. Uh took the took the tough uh took the tough road and analyze that data took a long time to assemble and they discovered that there was court stuffing right? That uh people were sending a lot of traits in and then cancelling them almost immediately. You have to manipulate the market. Um And why why why didn't we see it immediately? Well the reason is the process reports that everybody sees uh rule in there that says all trades. Less than 100 shares don't need to report in there. And so what people did was sending a lot of less than 103 100 100 shares trades uh to fly under the radar to do this manipulation. So here is here the second barrier right? Data could be raw and dispersed. Um Sometimes you just have to take the hard road and um and to get insight And this is 1 1 great example. And then the last barrier is uh is has to do with sometimes when you start a project to to get insight to get uh to get answers and insight. You you realize that all the datas around you but you don't you don't seem to find the right ones To get what you need. You don't you don't seem to get the right ones. Yeah. Um here we have three quick examples of customers. 111 was it was a great example right? Where uh they were trying to build a language translator, a machine language translator between two languages. Right? But not do that. They need to get hundreds of millions of word pairs, you know, of one language compared uh with the corresponding other hundreds of millions of them. They say we are going to get all these word pairs. Someone creative thought of a willing source and a huge, so it was a United Nations you see. So sometimes you think you don't have the right data with you, but there might be another source and a willing one that could give you that data right. The second one has to do with uh there was uh the uh sometimes you you may just have to generate that data, interesting one. We had an autonomous car customer that collects all these data from their cars, right, massive amounts of data, loss of senses, collect loss of data. And uh you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car uh in in um in fine weather and collected the car driving on this highway in rain and also in stone, but never had the opportunity to collect the car in hale because that's a rare occurrence. So instead of waiting for a time where the car can dr inhale, they build a simulation you by having the car collector in snow and simulated him. So these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated the fact that data is silo Federated, it various associated with data. That's tough to get that. They just took the hard road, right? And sometimes, thirdly, you just have to be creative to get the right data you need, >>wow, I tell you, I have about 100 questions based on what you just said. Uh, there's a great example, the flash crash. In fact, Michael Lewis wrote about this in his book, The Flash Boys and essentially right. It was high frequency traders trying to front run the market and sending in small block trades trying to get on the front end it. So that's and they, and they chalked it up to a glitch like you said, for months, nobody really knew what it was. So technology got us into this problem. I guess my question is, can technology help us get out of the problem? And that maybe is where AI fits in. >>Yes, yes. Uh, in fact, a lot of analytics, we went in, uh, to go back to the raw data that is highly dispersed from different sources, right, assemble them to see if you can find a material trend, right? You can see lots of trends right? Like, uh, you know, we, if if humans look at things right, we tend to see patterns in clouds, right? So sometimes you need to apply statistical analysis, um math to be sure that what the model is seeing is is real. Right? And and that required work. That's one area. The second area is uh you know, when um uh there are times when you you just need to to go through that uh that tough approach to to find the answer. Now, the issue comes to mind now is is that humans put in the rules to decide what goes into a report that everybody sees in this case uh before the change in the rules. Right? But by the way, after the discovery, the authorities change the rules and all all shares, all traits of different any sizes. It has to be reported. No. Yeah. Right. But the rule was applied uh you know, to say earlier that shares under 100 trades under 100 shares need not be reported. So sometimes you just have to understand that reports were decided by humans and and under for understandable reasons. I mean they probably didn't want that for various reasons not to put everything in there so that people could still read it uh in a reasonable amount of time. But uh we need to understand that rules were being put in by humans for the reports we read. And as such, there are times you just need to go back to the raw data. >>I want to ask, >>albeit that it's gonna be tough. >>Yeah. So I want to ask a question about AI is obviously it's in your title and it's something you know a lot about but and I want to make a statement, you tell me if it's on point or off point. So it seems that most of the Ai going on in the enterprise is modeling data science applied to troves of data >>but >>but there's also a lot of ai going on in consumer whether it's you know, fingerprint technology or facial recognition or natural language processing. Will a two part question will the consumer market has so often in the enterprise sort of inform us uh the first part and then will there be a shift from sort of modeling if you will to more you mentioned autonomous vehicles more ai influencing in real time. Especially with the edge. She can help us understand that better. >>Yeah, it's a great question. Right. Uh there are three stages to just simplify, I mean, you know, it's probably more sophisticated than that but let's simplify three stages. All right. To to building an Ai system that ultimately can predict, make a prediction right or to to assist you in decision making, have an outcome. So you start with the data massive amounts data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data and the machine uh starts to evolve a model based on all the data is seeing. It starts to evolve right to the point that using a test set of data that you have separately campus site that you know the answer for. Then you test the model uh you know after you trained it with all that data to see whether it's prediction accuracy is high enough and once you are satisfied with it, you you then deploy the model to make the decision and that's the influence. Right? So a lot of times depend on what what we are focusing on. We we um in data science are we working hard on assembling the right data to feed the machine with, That's the data preparation organization work. And then after which you build your models, you have to pick the right models for the decisions and prediction you wanted to make. You pick the right models and then you start feeding the data with it. Sometimes you you pick one model and the prediction isn't that robust, it is good but then it is not consistent right now what you do is uh you try another model so sometimes it's just keep trying different models until you get the right kind. Yeah, that gives you a good robust decision making and prediction after which It is tested well Q eight. You would then take that model and deploy it at the edge. Yeah. And then at the edges is essentially just looking at new data, applying it to the model, you're you're trained and then that model will give you a prediction decision. Right? So uh it is these three stages. Yeah, but more and more uh you know, your question reminds me that more and more people are thinking as the edge become more and more powerful. Can you also do learning at the edge? Right. That's the reason why we spoke about swarm learning the last time, learning at the edge as a swamp, right? Because maybe individually they may not have enough power to do so. But as a swampy me, >>is that learning from the edge or learning at the edge? In other words? Yes. Yeah. Question Yeah. >>That's a great question. That's a great question. Right? So uh the quick answer is learning at the edge, right? Uh and also from the edge, but the main goal, right? The goal is to learn at the edge so that you don't have to move the data that the Edge sees first back to the cloud or the core to do the learning because that would be the reason. One of the main reasons why you want to learn at the edge, right? Uh So so that you don't need to have to send all that data back and assemble it back from all the different edge devices, assemble it back to the cloud side to to do the learning right? With swampland. You can learn it and keep the data at the edge and learn at that point. >>And then maybe only selectively send the autonomous vehicle example you gave us. Great because maybe there, you know, there may be only persisting, they're not persisting data that is inclement weather or when a deer runs across the front and then maybe they they do that and then they send that smaller data set back and maybe that's where it's modelling done. But the rest can be done at the edges. It's a new world that's coming down. Let me ask you a question, is there a limit to what data should be collected and how it should be collected? >>That's a great question again. You know uh wow today, full of these uh insightful questions that actually touches on the second challenge. Right? How do we uh in order to thrive in this new age of inside? The second challenge is are you know the is our future challenge, right? What do we do for our future? And and in there is uh the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that I talk about what to collect right? When to organize it when you collect and then where will your data be, you know going forward that you are collecting from? So what, when and where for the what data for the what data to collect? That? That was the question you ask. Um it's it's a question that different industries have to ask themselves because it will vary, right? Um let me give you the you use the autonomous car example, let me use that. And you have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from the fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars collecting data so they can train and eventually deploy commercial cars, right? Um so this data collection cars they collect as a fleet of them collect temporal bikes a day. And when it came to us building a storage system to store all of that data, they realized they don't want to afford to store all of it. Now, here comes the dilemma, right? What should I after I spent so much effort building all these cars and sensors and collecting data, I've now decide what to delete. That's a dilemma right now in working with them on this process of trimming down what they collected. You know, I'm constantly reminded of the sixties and seventies, right? To remind myself 60 and seventies, we call a large part of our D. N. A junk DNA. Today. We realize that a large part of that what we call john has function as valuable function. They are not jeans, but they regulate the function of jeans, you know, So, so what's jump in the yesterday could be valuable today or what's junk today could be valuable tomorrow, Right? So, so there's this tension going on right between you decided not wanting to afford to store everything that you can get your hands on. But on the other hand, you you know, you worry you you you ignore the wrong ones, right? You can see this tension in our customers, right? And it depends on industry here, right? In health care, they say I have no choice. I I want it. All right. One very insightful point brought up by one health care provider that really touched me was, you know, we are not we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But you also care for the people were not caring for. How do we find them? Mhm. Right. And that therefore, they did not just need to collect data. That is that they have with from their patients. They also need to reach out right to outside data so that they can figure out who they are not caring for, right? So they want it all. So I tell us them, so what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and find that. Of course they also come back to us rightfully that you know, we have to then work out a way to help them build that system, you know? So that's health care, right? And and if you go to other industries like banking, they say they can't afford to keep them off, but they are regulated, seems like healthcare, they are regulated as to uh privacy and such. Like so many examples different industries having different needs, but different approaches to how what they collect. But there is this constant tension between um you perhaps deciding not wanting to fund all of that uh all that you can store, right? But on the other hand, you know, if you if you kind of don't want to afford it and decide not to store some uh if he does some become highly valuable in the future, right? Yeah. >>We can make some assumptions about the future, can't we? I mean, we know there's gonna be a lot more data than than we've ever seen before. We know that we know well notwithstanding supply constraints on things like nand. We know the prices of storage is going to continue to decline. We also know, and not a lot of people are really talking about this but the processing power but he says moore's law is dead okay. It's waning. But the processing power when you combine the Cpus and NP US and GPUS and accelerators and and so forth actually is is increasing. And so when you think about these use cases at the edge, you're going to have much more processing power, you're gonna have cheaper storage and it's going to be less expensive processing And so as an ai practitioner, what can you do with that? >>Yeah, it's highly again, another insightful questions that we touched on our keynote and that that goes up to the why I do the where? Right, When will your data be? Right. We have one estimate that says that by next year there will be 55 billion connected devices out there. Right. 55 billion. Right. What's the population of the world? Of the other? Of 10 billion? But this thing is 55 billion. Right? Uh and many of them, most of them can collect data. So what do you what do you do? Right. Um So the amount of data that's gonna come in, it's gonna weigh exceed right? Our drop in storage costs are increasing computer power. Right? So what's the answer? Right. So, so the the answer must be knowing that we don't and and even the drop in price and increase in bandwidth, it will overwhelm the increased five G will overwhelm five G. Right? Given amount 55 billion of them collecting. Right? So, the answer must be that there might need to be a balance between you needing to bring all that data from the 55 billion devices of data back to a central as a bunch of central Cause because you may not be able to afford to do that firstly band with even with five G. M and and SD when you'll still be too expensive given the number of devices out there. Were you given storage cause dropping will still be too expensive to try and store them all. So the answer must be to start at least to mitigate the problem to some leave both a lot of the data out there. Right? And only send back the pertinent ones as you said before. But then if you did that, then how are we gonna do machine learning at the core and the cloud side? If you don't have all the data you want rich data to train with. Right? Some sometimes you want a mix of the uh positive type data and the negative type data so you can train the machine in a more balanced way. So the answer must be eventually right. As we move forward with these huge number of devices out of the edge to do machine learning at the edge. Today, we don't have enough power. Right? The edge typically is characterized by a lower uh, energy capability and therefore lower compute power. But soon, you know, even with lower energy, they can do more with compute power improving in energy efficiency, Right? Uh, so learning at the edge today, we do influence at the edge. So we data model deploy and you do influence at the age, that's what we do today. But more and more, I believe, given a massive amount of data at the edge, you you have to have to start doing machine learning at the edge. And and if when you don't have enough power, then you aggregate multiple devices, compute power into a swamp and learn as a swan, >>interesting. So now, of course, if I were sitting and fly on the wall in HP board meeting, I said, okay, HP is as a leading provider of compute, how do you take advantage of that? I mean, we're going, I know it's future, but you must be thinking about that and participating in those markets. I know today you are you have, you know, edge line and other products. But there's it seems to me that it's it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that >>opportunity for your customers? Uh the world will have to have a balance right? Where today the default, Well, the more common mode is to collect the data from the edge and train at uh at some centralized location or a number of centralized location um going forward. Given the proliferation of the edge devices, we'll need a balance. We need both. We need capability at the cloud side. Right. And it has to be hybrid. And then we need capability on the edge side. Yeah. That they want to build systems that that on one hand, uh is uh edge adapted, right? Meaning the environmentally adapted because the edge different they are on a lot of times on the outside. Uh They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery powered. Right? Um so you have to build systems that adapt to it, but at the same time they must not be custom. That's my belief. They must be using standard processes and standard operating system so that they can run rich a set of applications. So yes. Um that's that's also the insightful for that Antonio announced in 2018, Uh the next four years from 2018, right, $4 billion dollars invested to strengthen our edge portfolio, edge product lines, right Edge solutions. >>I get a doctor go. I could go on for hours with you. You're you're just such a great guest. Let's close what are you most excited about in the future of of of it? Certainly H. P. E. But the industry in general. >>Yeah I think the excitement is uh the customers right? The diversity of customers and and the diversity in a way they have approached their different problems with data strategy. So the excitement is around data strategy right? Just like you know uh you know the the statement made was was so was profound. Right? Um And Antonio said we are in the age of insight powered by data. That's the first line right? The line that comes after that is as such were becoming more and more data centric with data the currency. Now the next step is even more profound. That is um you know we are going as far as saying that you know um data should not be treated as cost anymore. No right. But instead as an investment in a new asset class called data with value on our balance sheet, this is a this is a step change right in thinking that is going to change the way we look at data the way we value it. So that's a statement that this is the exciting thing because because for for me a city of AI right uh machine is only as intelligent as the data you feed it with. Data is a source of the machine learning to be intelligent. So so that's that's why when when people start to value data right? And and and say that it is an investment when we collect it. It is very positive for ai because an Ai system gets intelligent, more intelligence because it has a huge amounts of data and the diversity of data. So it'd be great if the community values values data. Well >>you certainly see it in the valuations of many companies these days. Um and I think increasingly you see it on the income statement, you know data products and people monetizing data services and maybe eventually you'll see it in the in the balance. You know Doug Laney when he was a gardener group wrote a book about this and a lot of people are thinking about it. That's a big change isn't it? Dr >>yeah. Question is is the process and methods evaluation. Right. But uh I believe we'll get there, we need to get started then we'll get their belief >>doctor goes on and >>pleasure. And yeah and then the yeah I will will will will benefit greatly from it. >>Oh yeah, no doubt people will better understand how to align you know, some of these technology investments, Doctor goes great to see you again. Thanks so much for coming back in the cube. It's been a real pleasure. >>Yes. A system. It's only as smart as the data you feed it with. >>Excellent. We'll leave it there. Thank you for spending some time with us and keep it right there for more great interviews from HP discover 21. This is dave a lot for the cube. The leader in enterprise tech coverage right back.

(upbeat music) >> Welcome back to HPE Discover 2021, theCUBE's virtual coverage, continuous coverage of HPE's Annual Customer Event. My name is Dave Vellante, and we're going to dive into the intersection of high-performance computing, data and AI with Doctor Eng Lim Goh, who's a Senior Vice President and CTO for AI at Hewlett Packard Enterprise. Doctor Goh, great to see you again. Welcome back to theCUBE. >> Hello, Dave, great to talk to you again. >> You might remember last year we talked a lot about Swarm intelligence and how AI is evolving. Of course, you hosted the Day 2 Keynotes here at Discover. And you talked about thriving in the age of insights, and how to craft a data-centric strategy. And you addressed some of the biggest problems, I think organizations face with data. That's, you've got a, data is plentiful, but insights, they're harder to come by. >> Yeah. >> And you really dug into some great examples in retail, banking, in medicine, healthcare and media. But stepping back a little bit we zoomed out on Discover '21. What do you make of the events so far and some of your big takeaways? >> Hmm, well, we started with the insightful question, right, yeah? Data is everywhere then, but we lack the insight. That's also part of the reason why, that's a main reason why Antonio on day one focused and talked about the fact that we are in the now in the age of insight, right? And how to try thrive in that age, in this new age? What I then did on a Day 2 Keynote following Antonio is to talk about the challenges that we need to overcome in order to thrive in this new age. >> So, maybe we could talk a little bit about some of the things that you took away in terms of, I'm specifically interested in some of the barriers to achieving insights. You know customers are drowning in data. What do you hear from customers? What were your takeaway from some of the ones you talked about today? >> Oh, very pertinent question, Dave. You know the two challenges I spoke about, that we need to overcome in order to thrive in this new age. The first one is the current challenge. And that current challenge is, you know, stated is now barriers to insight, when we are awash with data. So that's a statement on how do you overcome those barriers? What are the barriers to insight when we are awash in data? In the Day 2 Keynote, I spoke about three main things. Three main areas that we receive from customers. The first one, the first barrier is in many, with many of our customers, data is siloed, all right. You know, like in a big corporation, you've got data siloed by sales, finance, engineering, manufacturing and so on supply chain and so on. And there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above, they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the first barrier we spoke about, you know? Barriers to insight when we are awash with data. The second barrier is that we see amongst our customers is that data is raw and disperse when they are stored. And you know, it's tough to get at, to tough to get a value out of them, right? And in that case, I use the example of, you know, the May 6, 2010 event where the stock market dropped a trillion dollars in terms of minutes. We all know those who are financially attuned with know about this incident but that this is not the only incident. There are many of them out there. And for that particular May 6 event, you know, it took a long time to get insight. Months, yeah, before we, for months we had no insight as to what happened. Why it happened? Right, and there were many other incidences like this and the regulators were looking for that one rule that could mitigate many of these incidences. One of our customers decided to take the hard road they go with the tough data, right? Because data is raw and dispersed. So they went into all the different feeds of financial transaction information, took the tough, you know, took a tough road. And analyze that data took a long time to assemble. And they discovered that there was caught stuffing, right? That people were sending a lot of trades in and then canceling them almost immediately. You have to manipulate the market. And why didn't we see it immediately? Well, the reason is the process reports that everybody sees, the rule in there that says, all trades less than a hundred shares don't need to report in there. And so what people did was sending a lot of less than a hundred shares trades to fly under the radar to do this manipulation. So here is the second barrier, right? Data could be raw and dispersed. Sometimes it's just have to take the hard road and to get insight. And this is one great example. And then the last barrier has to do with sometimes when you start a project to get insight, to get answers and insight, you realize that all the data's around you, but you don't seem to find the right ones to get what you need. You don't seem to get the right ones, yeah? Here we have three quick examples of customers. One was a great example, right? Where they were trying to build a language translator or machine language translator between two languages, right? By not do that, they need to get hundreds of millions of word pairs. You know of one language compare with the corresponding other. Hundreds of millions of them. They say, well, I'm going to get all these word pairs. Someone creative thought of a willing source and a huge, it was a United Nations. You see? So sometimes you think you don't have the right data with you, but there might be another source and a willing one that could give you that data, right? The second one has to do with, there was the sometimes you may just have to generate that data. Interesting one, we had an autonomous car customer that collects all these data from their their cars, right? Massive amounts of data, lots of sensors, collect lots of data. And, you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car in fine weather and collected the car driving on this highway in rain and also in snow. But never had the opportunity to collect the car in hill because that's a rare occurrence. So instead of waiting for a time where the car can drive in hill, they build a simulation by having the car collected in snow and simulated him. So these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated. In fact, that data silo, they federated it. Virus associated with data, that's tough to get at. They just took the hard road, right? And sometimes thirdly, you just have to be creative to get the right data you need. >> Wow! I tell you, I have about a hundred questions based on what you just said, you know? (Dave chuckles) And as a great example, the Flash Crash. In fact, Michael Lewis, wrote about this in his book, the Flash Boys. And essentially, right, it was high frequency traders trying to front run the market and sending into small block trades (Dave chuckles) trying to get sort of front ended. So that's, and they chalked it up to a glitch. Like you said, for months, nobody really knew what it was. So technology got us into this problem. (Dave chuckles) I guess my question is can technology help us get out of the problem? And that maybe is where AI fits in? >> Yes, yes. In fact, a lot of analytics work went in to go back to the raw data that is highly dispersed from different sources, right? Assembled them to see if you can find a material trend, right? You can see lots of trends, right? Like, no, we, if humans look at things that we tend to see patterns in Clouds, right? So sometimes you need to apply statistical analysis math to be sure that what the model is seeing is real, right? And that required, well, that's one area. The second area is you know, when this, there are times when you just need to go through that tough approach to find the answer. Now, the issue comes to mind now is that humans put in the rules to decide what goes into a report that everybody sees. Now, in this case, before the change in the rules, right? But by the way, after the discovery, the authorities changed the rules and all shares, all trades of different any sizes it has to be reported. >> Right. >> Right, yeah? But the rule was applied, you know, I say earlier that shares under a hundred, trades under a hundred shares need not be reported. So, sometimes you just have to understand that reports were decided by humans and for understandable reasons. I mean, they probably didn't wanted a various reasons not to put everything in there. So that people could still read it in a reasonable amount of time. But we need to understand that rules were being put in by humans for the reports we read. And as such, there are times we just need to go back to the raw data. >> I want to ask you... >> Oh, it could be, that it's going to be tough, yeah. >> Yeah, I want to ask you a question about AI as obviously it's in your title and it's something you know a lot about but. And I'm going to make a statement, you tell me if it's on point or off point. So seems that most of the AI going on in the enterprise is modeling data science applied to, you know, troves of data. But there's also a lot of AI going on in consumer. Whether it's, you know, fingerprint technology or facial recognition or natural language processing. Well, two part question will the consumer market, as it has so often in the enterprise sort of inform us is sort of first part. And then, there'll be a shift from sort of modeling if you will to more, you mentioned the autonomous vehicles, more AI inferencing in real time, especially with the Edge. Could you help us understand that better? >> Yeah, this is a great question, right? There are three stages to just simplify. I mean, you know, it's probably more sophisticated than that. But let's just simplify that three stages, right? To building an AI system that ultimately can predict, make a prediction, right? Or to assist you in decision-making. I have an outcome. So you start with the data, massive amounts of data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data, and the machine starts to evolve a model based on all the data it's seeing. It starts to evolve, right? To a point that using a test set of data that you have separately kept aside that you know the answer for. Then you test the model, you know? After you've trained it with all that data to see whether its prediction accuracy is high enough. And once you are satisfied with it, you then deploy the model to make the decision. And that's the inference, right? So a lot of times, depending on what we are focusing on, we in data science are, are we working hard on assembling the right data to feed the machine with? That's the data preparation organization work. And then after which you build your models you have to pick the right models for the decisions and prediction you need to make. You pick the right models. And then you start feeding the data with it. Sometimes you pick one model and a prediction isn't that robust. It is good, but then it is not consistent, right? Now what you do is you try another model. So sometimes it gets keep trying different models until you get the right kind, yeah? That gives you a good robust decision-making and prediction. Now, after which, if it's tested well, QA, you will then take that model and deploy it at the Edge. Yeah, and then at the Edge is essentially just looking at new data, applying it to the model that you have trained. And then that model will give you a prediction or a decision, right? So it is these three stages, yeah. But more and more, your question reminds me that more and more people are thinking as the Edge become more and more powerful. Can you also do learning at the Edge? >> Right. >> That's the reason why we spoke about Swarm Learning the last time. Learning at the Edge as a Swarm, right? Because maybe individually, they may not have enough power to do so. But as a Swarm, they may. >> Is that learning from the Edge or learning at the Edge? In other words, is that... >> Yes. >> Yeah. You do understand my question. >> Yes. >> Yeah. (Dave chuckles) >> That's a great question. That's a great question, right? So the quick answer is learning at the Edge, right? And also from the Edge, but the main goal, right? The goal is to learn at the Edge so that you don't have to move the data that Edge sees first back to the Cloud or the Call to do the learning. Because that would be the reason, one of the main reasons why you want to learn at the Edge. Right? So that you don't need to have to send all that data back and assemble it back from all the different Edge devices. Assemble it back to the Cloud Site to do the learning, right? Some on you can learn it and keep the data at the Edge and learn at that point, yeah. >> And then maybe only selectively send. >> Yeah. >> The autonomous vehicle, example you gave is great. 'Cause maybe they're, you know, there may be only persisting. They're not persisting data that is an inclement weather, or when a deer runs across the front. And then maybe they do that and then they send that smaller data setback and maybe that's where it's modeling done but the rest can be done at the Edge. It's a new world that's coming through. Let me ask you a question. Is there a limit to what data should be collected and how it should be collected? >> That's a great question again, yeah. Well, today full of these insightful questions. (Dr. Eng chuckles) That actually touches on the the second challenge, right? How do we, in order to thrive in this new age of insight? The second challenge is our future challenge, right? What do we do for our future? And in there is the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that, I talked about what to collect, right? When to organize it when you collect? And then where will your data be going forward that you are collecting from? So what, when, and where? For what data to collect? That was the question you asked, it's a question that different industries have to ask themselves because it will vary, right? Let me give you the, you use the autonomous car example. Let me use that. And we do have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from a fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars, collecting data so they can train and eventually deploy commercial cars, right? Also this data collection cars, they collect 10, as a fleet of them collect 10 petabytes a day. And then when they came to us, building a storage system you know, to store all of that data, they realized they don't want to afford to store all of it. Now here comes the dilemma, right? What should I, after I spent so much effort building all this cars and sensors and collecting data, I've now decide what to delete. That's a dilemma, right? Now in working with them on this process of trimming down what they collected, you know, I'm constantly reminded of the 60s and 70s, right? To remind myself 60s and 70s, we called a large part of our DNA, junk DNA. >> Yeah. (Dave chuckles) >> Ah! Today, we realized that a large part of that what we call junk has function as valuable function. They are not genes but they regulate the function of genes. You know? So what's junk in yesterday could be valuable today. Or what's junk today could be valuable tomorrow, right? So, there's this tension going on, right? Between you deciding not wanting to afford to store everything that you can get your hands on. But on the other hand, you worry, you ignore the wrong ones, right? You can see this tension in our customers, right? And then it depends on industry here, right? In healthcare they say, I have no choice. I want it all, right? Oh, one very insightful point brought up by one healthcare provider that really touched me was you know, we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But who also care for the people we are not caring for? How do we find them? >> Uh-huh. >> Right, and that definitely, they did not just need to collect data that they have with from their patients. They also need to reach out, right? To outside data so that they can figure out who they are not caring for, right? So they want it all. So I asked them, so what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and fund that. Of course, they also come back to us rightfully, that you know we have to then work out a way to help them build a system, you know? So that's healthcare, right? And if you go to other industries like banking, they say they can afford to keep them all. >> Yeah. >> But they are regulated, seemed like healthcare, they are regulated as to privacy and such like. So many examples different industries having different needs but different approaches to what they collect. But there is this constant tension between you perhaps deciding not wanting to fund all of that, all that you can install, right? But on the other hand, you know if you kind of don't want to afford it and decide not to start some. Maybe those some become highly valuable in the future, right? (Dr. Eng chuckles) You worry. >> Well, we can make some assumptions about the future. Can't we? I mean, we know there's going to be a lot more data than we've ever seen before. We know that. We know, well, not withstanding supply constraints and things like NAND. We know the prices of storage is going to continue to decline. We also know and not a lot of people are really talking about this, but the processing power, but the says, Moore's law is dead. Okay, it's waning, but the processing power when you combine the CPUs and NPUs, and GPUs and accelerators and so forth actually is increasing. And so when you think about these use cases at the Edge you're going to have much more processing power. You're going to have cheaper storage and it's going to be less expensive processing. And so as an AI practitioner, what can you do with that? >> Yeah, it's a highly, again, another insightful question that we touched on our Keynote. And that goes up to the why, uh, to the where? Where will your data be? Right? We have one estimate that says that by next year there will be 55 billion connected devices out there, right? 55 billion, right? What's the population of the world? Well, of the other 10 billion? But this thing is 55 billion. (Dave chuckles) Right? And many of them, most of them can collect data. So what do you do? Right? So the amount of data that's going to come in, it's going to way exceed, right? Drop in storage costs are increasing compute power. >> Right. >> Right. So what's the answer, right? So the answer must be knowing that we don't, and even a drop in price and increase in bandwidth, it will overwhelm the, 5G, it will overwhelm 5G, right? Given the amount of 55 billion of them collecting. So the answer must be that there needs to be a balance between you needing to bring all of that data from the 55 billion devices of the data back to a central, as a bunch of central cost. Because you may not be able to afford to do that. Firstly bandwidth, even with 5G and as the, when you'll still be too expensive given the number of devices out there. You know given storage costs dropping is still be too expensive to try and install them all. So the answer must be to start, at least to mitigate from to, some leave most a lot of the data out there, right? And only send back the pertinent ones, as you said before. But then if you did that then how are we going to do machine learning at the Core and the Cloud Site, if you don't have all the data? You want rich data to train with, right? Sometimes you want to mix up the positive type data and the negative type data. So you can train the machine in a more balanced way. So the answer must be eventually, right? As we move forward with these huge number of devices all at the Edge to do machine learning at the Edge. Today we don't even have power, right? The Edge typically is characterized by a lower energy capability and therefore lower compute power. But soon, you know? Even with low energy, they can do more with compute power improving in energy efficiency, right? So learning at the Edge, today we do inference at the Edge. So we data, model, deploy and you do inference there is. That's what we do today. But more and more, I believe given a massive amount of data at the Edge, you have to start doing machine learning at the Edge. And when you don't have enough power then you aggregate multiple devices, compute power into a Swarm and learn as a Swarm, yeah. >> Oh, interesting. So now of course, if I were sitting and fly on the wall and the HPE board meeting I said, okay, HPE is a leading provider of compute. How do you take advantage of that? I mean, we're going, I know it's future but you must be thinking about that and participating in those markets. I know today you are, you have, you know, Edge line and other products. But there's, it seems to me that it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that opportunity for the customers? >> Hmm, the wall will have to have a balance, right? Where today the default, well, the more common mode is to collect the data from the Edge and train at some centralized location or number of centralized location. Going forward, given the proliferation of the Edge devices, we'll need a balance, we need both. We need capability at the Cloud Site, right? And it has to be hybrid. And then we need capability on the Edge side that we need to build systems that on one hand is an Edge adapter, right? Meaning they environmentally adapted because the Edge differently are on it, a lot of times on the outside. They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery powered. Right? So you have to build systems that adapts to it. But at the same time, they must not be custom. That's my belief. It must be using standard processes and standard operating system so that they can run a rich set of applications. So yes, that's also the insight for that Antonio announced in 2018. For the next four years from 2018, right? $4 billion invested to strengthen our Edge portfolio. >> Uh-huh. >> Edge product lines. >> Right. >> Uh-huh, Edge solutions. >> I could, Doctor Goh, I could go on for hours with you. You're just such a great guest. Let's close. What are you most excited about in the future of, certainly HPE, but the industry in general? >> Yeah, I think the excitement is the customers, right? The diversity of customers and the diversity in the way they have approached different problems of data strategy. So the excitement is around data strategy, right? Just like, you know, the statement made for us was so was profound, right? And Antonio said, we are in the age of insight powered by data. That's the first line, right? The line that comes after that is as such we are becoming more and more data centric with data that currency. Now the next step is even more profound. That is, you know, we are going as far as saying that, you know, data should not be treated as cost anymore. No, right? But instead as an investment in a new asset class called data with value on our balance sheet. This is a step change, right? Right, in thinking that is going to change the way we look at data, the way we value it. So that's a statement. (Dr. Eng chuckles) This is the exciting thing, because for me a CTO of AI, right? A machine is only as intelligent as the data you feed it with. Data is a source of the machine learning to be intelligent. Right? (Dr. Eng chuckles) So, that's why when the people start to value data, right? And say that it is an investment when we collect it it is very positive for AI. Because an AI system gets intelligent, get more intelligence because it has huge amounts of data and a diversity of data. >> Yeah. >> So it'd be great, if the community values data. >> Well, you certainly see it in the valuations of many companies these days. And I think increasingly you see it on the income statement. You know data products and people monetizing data services. And yeah, maybe eventually you'll see it in the balance sheet. I know Doug Laney, when he was at Gartner Group, wrote a book about this and a lot of people are thinking about it. That's a big change, isn't it? >> Yeah, yeah. >> Dr. Goh... (Dave chuckles) >> The question is the process and methods in valuation. Right? >> Yeah, right. >> But I believe we will get there. We need to get started. And then we'll get there. I believe, yeah. >> Doctor Goh, it's always my pleasure. >> And then the AI will benefit greatly from it. >> Oh, yeah, no doubt. People will better understand how to align, you know some of these technology investments. Dr. Goh, great to see you again. Thanks so much for coming back in theCUBE. It's been a real pleasure. >> Yes, a system is only as smart as the data you feed it with. (Dave chuckles) (Dr. Eng laughs) >> Excellent. We'll leave it there. Thank you for spending some time with us and keep it right there for more great interviews from HPE Discover 21. This is Dave Vellante for theCUBE, the leader in Enterprise Tech Coverage. We'll be right back. (upbeat music)

(bright music) >> Welcome back to HPE Discover 2021. My name is Dave Vellante and you're watching theCUBE's virtual coverage of Discover. We're going to dig into the most pressing topic, not only for IT, but entire organizations. And that's cyber security. With me is Sunil James, senior director of security engineering at Hewlett Packard Enterprise. Sunil, welcome to theCUBE. Come on in. >> Dave, thank you for having me. I appreciate it. >> Hey, you talked about project Aurora today. Tell us about project Aurora, what is that? >> So I'm glad you asked. Project Aurora is a new framework that we're working on that attempts to provide the underpinnings for Zero Trust architectures inside of everything that we build at HPE. Zero Trust is a way of providing a mechanism for enterprises to allow for everything in their enterprise, whether it's a server, a human, or anything in between, to be verified and attested to before they're allowed to access or transact in certain ways. That's what we announced today. >> Well, so in response to a spate of damaging cyber attacks last month, President Biden issued an executive order designed to improve the United States' security posture. And in that order, he essentially issued a Zero Trust mandate. You know, it's interesting, Sunil. Zero Trust has gone from a buzzword to a critical part of a security strategy. So in thinking about a Zero Trust architecture, how do you think about that, and how does project Aurora fit in? >> Yeah, so Zero Trust architecture, as a concept, has been around for quite some time now. And over the last few years, we've seen many a company attempting to provide technologies that they purport to be Zero Trust. Zero Trust is a framework. It's not one technology, it's not one tool, it's not one product. It is an entire framework of thinking and applying cybersecurity principles to everything that we just talked about beforehand. Project Aurora, as I said beforehand, is designed to provide a way for ourselves and our customers to be able to measure, attest, and verify every single piece of technology that we sell to them. Whether it's a server or everything else in between. Now, we've got a long way to go before we're able to cover everything that HPE sells. But for us, these capabilities are the root of Zero Trust architectures. You need to be able to, at any given moment's notice, verify, measure, and attest, and this is what we're doing with project Aurora. >> So you founded a company called Scytale and sold that to HPE last year. And my understanding is you were really the driving force behind the secure production identity framework, but you said Zero Trust is really a framework. That's an open source project. Maybe you can explain what that is. I mean, people talk about the NIST Framework for cybersecurity. How does that relate? Why is this important and how does Aurora fit into it? >> Yeah, so that's a good question. The NIST Framework is a broader framework for cybersecurity that couples and covers many aspects of thinking about the security posture of an enterprise, whether it's network security, host based intrusion detection capabilities, incident response, things of that sort. SPIFFE, which you're referring to, Secure Production Identity Framework For Everyone, is an open source framework and technology base that we did work on when I was the CEO of Scytale, that was designed to provide a platform agnostic way to assign identity to anything that runs in a network. And so think about yourself or myself. We have identities in our back pocket, driver's license, passports, things of that sort. They provide a unique assertion of who we are, and what we're allowed to do. That does not exist in the world of software. And what SPIFFE does is it provides that mechanism so that you can actually use frameworks like project Aurora that can verify the underpinning infrastructure on top of which software workloads run to be able to verify those SPIFFE identities even better than before. >> Is the intent to productize this capability, you know, within this framework? How do you approach this from HPE's standpoint? >> So SPIFFE and SPIRE will and always will be, as far as I'm concerned, remain an open source project held by the Cloud Native Computing Foundation. It's for the world, all right. And we want that to be the case because we think that more of our Enterprise customers are not living in the world of one vendor or two vendors. They have multiple vendors. And so we need to give them the tools and the flexibility to be able to allow for open source capabilities like SPIFFE and SPIRE to provide a way for them to assign these identities and assign policies and control, regardless of the infrastructure choices they make today or tomorrow. HPE recognizes that this is a key differentiating capability for our customers. And our goal is to be able to look at our offerings that power the next generation of workloads. Kubernetes instances, containers, serverless, and anything that comes after that. And our responsibility is to say, "How can we actually take what we have and be able to provide those kinds of assertions, those underpinnings for Zero Trust that are going to be necessary to distribute those identities to those workloads, and to do so in a scalable, effective, and automated manner?" Which is one of the most important things that project Aurora does. >> So a lot of companies, Sunil, will set up a security division. But is the HPE strategy to essentially embed security across its entire portfolio? How should we think about HPE strategy in cyber? >> Yeah, so it's a great question. HPE has a long history in security and other domains, networking, and servers, and storage, and beyond. The way we think about what we're building with project Aurora, this is plumbing. This is plumbing that must be in everything we build. Customers don't buy one product from us and they think it's one company, and something else from us, and they think it's another company. They're buying HPE products. And our goal with project Aurora is to ensure that this plumbing is widely and uniformly distributed and made available. So whether you're buying an Aruba device, a Primera storage device, or a ProLiant server, project Aurora's capabilities are going to provide a consistent way to do the things that I've mentioned beforehand to allow for those Zero Trust architectures to become real. >> So, as I alluded to President Biden's executive order previously. I mean, you're a security practitioner, you're an expert in this area. It just seems as though, and I'd love to get your comments on this. I mean, the adversaries are well-funded, you know, they're either organized crime, they're nation states. They're extracting a lot of very valuable information, they're monetizing that. You've seen things like ransomware as a service now. So any knucklehead can be in the ransomware business. So it's just this endless escalation game. How do you see the industry approaching this? What needs to happen? So obviously I like what you're saying about the plumbing. You're not trying to attack this with a bunch of point tools, which is part of the problem. How do you see the industry coming together to solve this problem? >> Yeah. If you operate in the world of security, you have to operate from the standpoint of humility. And the reason why you have to operate from a standpoint of humility is because the attack landscape is constantly changing. The things, and tools, and investments, and techniques that you thought were going to thwart an attacker today, they're quickly outdated within a week, a month, a quarter, whatever it might be. And so you have to be able to consistently and continuously evolve and adapt towards what customers are facing on any given moment's notice. I think to be able to, as an industry, tackle these issues more and moreso, you need to be able to have all of us start to abide, not abide, but start to adopt these open-source patterns. We recognize that every company, HPE included, is here to serve customers and to make money for its shareholders as well. But in order for us to do that, we have to also recognize that they've got other technologies in their infrastructure as well. And so it's our belief, it's my belief, that allowing for us to support open standards with SPIFFE and SPIRE, and perhaps with some of the aspects of what we're doing with project Aurora, I think allows for other people to be able to kind of deliver the same underpinning capabilities, the plumbing, if you will, regardless of whether it's an HPE product or something else along those lines as well. We need more of that generally across our industry, and I think we're far from it. >> I mean, this sounds like a war. I mean, it's more than a battle, it's a war that actually is never going to end. And I don't think there is an end in sight. And you hear CESOs talk about the shortage of talent, they're getting inundated with point products and tools, and then that just creates more technical debt. It's been interesting to watch. Interesting maybe is not the right word. But the pivot to Zero Trust, endpoint security, cloud security, and the exposure that we've now seen as a result of the pandemic was sort of rushed. And then of course, we've seen, you know, the adversaries really take advantage of that. So, I mean what you're describing is this ongoing never-ending battle, isn't it? >> Yeah, yeah, no, it's going to be ongoing. And by the way, Zero Trust is not the end state, right? I mean, there was things that we called the final nail in the coffin five years ago, 10 years ago, and yet the attackers persevered. And that's because there's a lot of innovation out there. There's a lot of infrastructure moving to dynamic architectures like cloud and others that are going to be poorly configured, and are going to not have necessarily the best and brightest providing security around them. So we have to remain vigilant. We have to work as hard as we can to help customers deploy Zero Trust architectures. But we have to be thinking about what's next. We have to be watching, studying, and evolving to be able to prepare ourselves, to be able to go after whatever the next capabilities are. >> What I like about what you're saying is, you're right. You have to have humility. I don't want to say, I mean, it's hard because I do feel like a lot of times the vendor community says, "Okay, we have the answer," to your point. "Okay, we have a Zero Trust solution." Or, "We have a solution." And there is no silver bullet in this game. And I think what I'm hearing from you is, look we're providing infrastructure, plumbing, the substrate, but it's an open system. It's got to evolve. And the thing you didn't say, but I'd love your thoughts on this is we've got to collaborate with somebody you might think is your competitor. 'Cause they're the good guys. >> Yeah. Our customers don't care that we're competitors with anybody. They care that we're helping them solve their problems for their business. So our responsibility is to figure out what we need to do to work together to provide the basic capabilities that allow for our customers to remain in business, right? If cybersecurity issues plague any of our customers that doesn't affect just HPE, that affects all of the companies that are serving that customer. And so, I think we have a shared responsibility to be able to protect our customers. >> And you've been in cyber for much, if not most of your career, right? >> Correct. >> So I got to ask you, did you have a superhero when you were a kid? Did you have a sort of a, you know, save the world thing going? >> Did I have a, you know, I didn't have a save the world thing going, but I had, I had two parents that cared for the world in many, many ways. They were both in the world of healthcare. And so everyday I saw them taking care of other people. And I think that probably rubbed off in some of the decisions that I make too. >> Well it's awesome. You're doing great work, really appreciate you coming on theCUBE, and thank you so much for your insights. >> I appreciate that, thanks. >> And thank you for being with us for our ongoing coverage of HPE Discover 21. This is Dave Vellante. You're watching theCUBE. The leader in digital tech coverage. We'll be right back. (bright music)

(lively music) >> Welcome back to HPE Discover 2021. My name is Dave Vellante and you're watching theCUBE's virtual coverage of Discover '21, and we're excited to welcome back Arwa Kaddoura, she's a vice president and world-wide go-to market leader for HPE's smoking hot GreenLake Cloud Services. Arwa, welcome back to theCUBE, good to see you again. >> Thank you for having me, it's good to be with you. >> So, talk about how your products and services are supporting customer transformations. I'm interested in the experience that everybody's been dreaming about. Describe how you're giving your customer that competitive advantage. And if you've got an examples, that would be awesome. >> Yeah, you got it. I think as we heard Antonio say, that cloud is an experience, not a destination, right? And what we're doing with GreenLake is bringing those cloud capabilities and the cloud experience to our customers. You know, we like to say, colocations, data center and edge of course. So this is the cloud on prem. And so rather than forcing customers to only have to go up to cloud, to get modern cloud capabilities or the benefits of things like, pay as you go for consumption, etc, cloud native capabilities, like containers, leveraging Kubernetes, we now bring all of that to GreenLake and to our customers, edge locations, and Colocation and data centers. We've been able to dramatically transform many of our customers businesses, right, and you'll probably see it discover some of those examples come to life, for example, Carestream, who is in the electronic medical imaging world, right, they have all of the X-Ray equipment that capture X-rays and different sort of diagnostics for patients. And we worked with them to not only craft a ML solution to better read and diagnose these images, but also all of the underlying infrastructure with the HPE GreenLake ML Ops platform that allows them to instantly leverage the capabilities of machine learning and the infrastructure to go with it. >> And so tell me, so how is it resonating with customers? They're talking to customers all the time? What do they tell you? >> Sure, you know I think what our customers appreciate about HP GreenLake is, it's not sort of look, it's either all on prem in my data center, and I have to fully manage it, build it, implement it, take care of it, or it's fully public cloud, I have little control and basically, I get whatever the public cloud gives me, right? HPE GreenLake gives our customers the flexibility and control that they require, right? And so you can think of many use cases where customers have a need to have the compute storage sort of processing need to happen closer to where their data and apps live. And so for that exact reason, our customers love the flexibility, right. Cloud One Dotto was public cloud, Cloud Two Dotto I think is the cloud that comes to our customers at their convenience. And to me, what I tell CIOs and CTOs and sort of other lines of business leaders when I meet with them, is you shouldn't be forced to have to take your data and apps elsewhere to get the transformation that you need. We want to be able to bring that directly to our customers. >> 'Cause a lot of the transformation is around data, we love talking about data on theCUBE. It's funny, I mean, we talked about big data last decade, we don't use that term much anymore. It was kind of overhyped, but as oftentimes is the case may be in the early days it's overhyped, but then it's underhyped. When it actually starts to kick in, and I feel like we're entering a new age of data and insights with the ascendancy of machine learning and AI. What does this mean from HPEs perspective and what are customers telling you that it means for them? >> Yeah, now, data I think, we often hear data is the new currency, right? It's the new gold. we've heard Antonio even say things like, data can even become something that maybe over time companies start to put some kind of value on their balance sheet behind, right, the same way that maybe brands represented this value on a balance sheet. Effectively, what's happened with data is, a lot of people have a lot of data. But there's not been a lot of ability to extract insights from data, right. And I think this is the new revolution that we're all undergoing is we finally have the modern analytics tools to actually turn the data into insights. And what we bring to the table from an HPE perspective is the fact that we have the best infrastructure, we obviously now have the cloud capabilities mixed in with our data fabric or container platform, or machine learning operations platform, to then be able to process that data, again, integrated with many of the great ISV partners that we have on the data side allow our customers to turn that into real insights for their business. And effectively data is becoming a huge competitive advantage, right? I think many of us are leveraging some pretty interesting tools or gadgets these days, right? Like, I wear one of those sleep rings. You can imagine a company like that in the future that's able to collect so much data from the folks that purchase their products, then being able to give us insights about, where's the best ZIP Code that people get the most amount of sleep and which ZIP Codes are the healthiest in the United States or countries, et cetera? But data really is becoming a competitive advantage. And one of the things that we care most about at HPE is also using it as a force for good and making sure that there is a sort of ethical AI capability. >> That's a great message and very important one. It's interesting what you're saying about data and the value, how we value, it's clearly being valued in terms of companies' market caps, but maybe it's not in the balance sheet yet, but it's on the income statement in terms of data products and data services that that's happening. So, maybe we'll see if Antonia is right in the next several years. But so, let's talk more about the specific data challenges that you're solving for your customers, they talk about silos, they talk about, they haven't gotten as much value out of their data initiatives as they wanted to. What are they telling you are their challenges and how are you approaching it? >> Yeah, I think data is everywhere, right? The ability for customers to store the right amount of data is a huge challenge. Because obviously, there's a huge cost associated with collecting, keeping, cleansing, processing, all the way to sort of analyzing your data. There tends to be a ton of data silos, right. So customers are looking for a common data fabric that they can then process their data sources across, and then be able to sort of tap into that data from an analytics perspective. So much of the technology, again, that we're focused on is be able to store the data, right, our Data Fabric layer with Ezmeral, right, being able to process that data, capture that data, and then allow the analytics tools to then harness the power of that data and turn that into real business insights for our customers. Every customer that I spoken to whether their financial services, you can imagine the big financial services, I mean, they've got just bazillions of pockets of data everywhere. And the real sort of challenge for them is how do I build a common data platform that allows me to tap into that data in effective ways for my business users? >> Can you talk a little bit about how you're changing the way you're providing solutions, maybe you could contrast it with the way HPE has done in the past? Because I think that's important when you think about, you talk a lot about GreenLake and as a service. But if the products are still kind of boxes and lands and gigahertz and ports, then that's a discontinuity. So, what's changed from the past and how are you feeding into the way customers are transforming their business and supporting their outcomes? >> That's exactly right. At some point in time, right, if you think maybe 10 or 20 years back, it used to be very much about the infrastructure for HPE. What's exciting about what we're doing differently for our customers, is, look, we have the best infrastructure in the business, right? HPE has been doing this longer than anyone has probably almost 60 years now. But being able to vertically integrate right, move up in that value chain so that our customers can get more complete solutions, is the more interesting part for our customers. Our customers love our technology yes, the gigahertz and the speeds and feeds, all of that do matter because they make for some very powerful infrastructure. However, what makes it easier is the fact that we are building platform stacks on top of that hardware, that help abstract away the complexity of that infrastructure and the ability to use it far more seamlessly. And then, if you think about it we of course have also one of the most advanced services organizations. So being able to leverage our services capabilities, our platform capabilities, on top of that hardware, again, deliver it back to our customers in a consumption model, which they've come to expect from a cloud model. And then surrounded by a very rich ecosystem of partners, and we're talking about system integrators that now have capabilities on helping our customers run their GreenLake environments. We're talking about ISVs, right, so software stacks and platforms that fully integrate with the GreenLake platform for completely seamless solutions, as well as channel partners and global distributors. So I think that's where we can truly deliver the ultimate end-to-end solution. It's not just the hardware, right? But it's being complemented with the right services, being complemented with the right platform capabilities, the software integrations to deliver that workload that the customer expects. >> So customers and partners, they got to place bets, they've got to put resources, time, money, and align their resources with their partners and their suppliers like HPE. So when they ask you, hey, okay, "HPE, tell me what's your overall strategy? "Why is it compelling? "And why do you give me competitive advantage relative to some of your peers in the industry?" >> Yeah, I think what partners are going to be most excited about is the openness of the platform, right? Being able to allow our partners to leverage GreenLake Central with open API, so that they can integrate some of their own technologies into our platform, the ability to allow them to also layer in their own managed services on top of the platform is key. And, of course, being able to build sort of these win-win solutions with the system integrators, right? The system integrators have some fantastic capabilities all the way from an application development, all the way down to the infrastructure management, and data center delivery centers that they have. And so leveraging HPE GreenLake really helps them have access to the core technologies that they need to deliver these solutions. >> I wonder if I could take a little sort of side road here and ask you because so many changes going on, HPE itself is transforming, your customers are transforming, the pandemic has accelerated all these transformations. Can you talk a little bit about how you've transformed go-to-market specifically in the context of as a service? I mean, that had to be quite a change for you guys. >> Yeah, now go-to-market transformations in support of sort of moving from traditional go-to-markets, right, to cloud go-to-markets are significant. They required us to really think through what does delivering as a service solutions mean for our direct Salesforce? What does it mean for our partners and their transformations and being able to support as a service solutions? For HPE specifically, it also means thinking about our customer outcomes, not just our ability to ship the requisite hardware and say, look, once it's left our dock, our job is done, right. It really takes our obligation all the way to the customer using the technology on a day by day basis, as well as supporting them in making sure that everything from implementation to set up to the ongoing monitoring operations of the technology is working for them in the way that they'd expect in an as a service way, right? We don't expect them to operate it, we don't expect them to do anything more than pick up the phone and call us if something doesn't go as planned. >> Then how about your sellers and your partners? How did they respond? I mean, you wake up one day is Okay guys, here we go. New compensation scheme, new way to sell, new way to market. That took some thought and some time and where are you in that journey? >> That's right. And I always say, if you expect people to wake up one day and be transformed, right, you're kidding yourself. So everything from sort of the way that we think about our customers use cases, right, and empowering our sellers to understand the outcomes that our customers expect and demand from us to things like compensation to the partner rebate program that we leverage through the channel partners in order to give them the right incentives to also allow them to make the right investments to support GreenLake. HPE has a fairly significant field, sales and solution team. And so not thinking about this only as a single person that represents GreenLake, but looking at our capabilities across the board, right, we have fantastic advisory consultants on the ground with PhDs and data science, we have folks that understand high performance computing. So making sure that we're embedding the expertise in all of the right personas that support our customers, not just from a comp perspective, but also from an understanding of the end-to-end solutions that we're bringing to those markets. >> So what gets you stoked in the morning, you get out of bed, you're like, "Okay, I'm going to go attack the world." What are you most excited about for HPE and its future? >> There's so much happening right now in this sort of cloud world, right? To me, the most exciting portion is the fact that given that we've now introduced on prem cloud to the world, our ability to ship new services and new capabilities, but also do that via a very rich partner ecosystem, honestly is what probably has me most excited. This is no longer the age of go-at-it-alone, right. So not only are our engineering and product teams hard at work in the engine room producing capabilities at sort of lightning fast speeds, but it's also our ability to partner, whether it's with platform providers, software providers, or system integrators and services providers. That ecosystem is starting to come together to deliver highly meaningful solutions to our customers and all in a very open way. The number one thing that I personally care about is that our customers never feel like they are being locked in, or that they are sort of being forced, have to give up certain levels of capabilities, we want to give them the best of what's out there and allow them to then have that flexibility in their solution. >> And one of the challenges, of course, with virtual events is you don't have the hallway track, somebody can say, "Hey, have you seen that IoT zone? It's amazing, they got all these robots going around." So what would you say that people should be focused on at discover maybe things that you want to call out specific highlights or segments that you think are relevant? >> Yeah, there's going to be a ton of fantastic stuff. I think, really looking for that edge to cloud strategy, that we're going to be spending a lot of time talking about looking at some of our vertical workload solutions, right? We're going to be talking about quite a few from electronic healthcare records, to payment solutions and many more. I think, depending on what folks are interested in there's going to be something for everyone. Project Aurora, which now starts to announce our new security capabilities, the zero trust capabilities that we're delivering is probably interesting to a lot of our customers. So lots of exciting things coming and I'm excited for our customers to check those out. >> No doubt, that's a hot topic, especially given what's been happening in the news these past several months. Arwa, thanks so much for coming back in theCUBE. It's great to see you hopefully face-to-face next time. >> Thank you, I sure hope so. Thanks so much for having me. >> It was our pleasure. And thank you for watching and thank you for being with us in our ongoing coverage of HPE Discover 2021. This is Dave Vellante. You're watching theCUBE, the leader in digital tech coverage. >> Thank you. (soft music)