>> Welcome back to the program. My name is Dave Vellante and in this session we're going to explore one of the more interesting topics of the day. IoT for smart factories and with me are Todd Edmunds, the global CTO of Smart Manufacturing, Edge and Digital Twins, at Dell Technologies. That is such a cool title. (Todd laughs) I want to be you. And Dr. Aditi Banerjee, who's the Vice President General Manager for Aerospace Defense and Manufacturing at DXC Technology. Another really cool title. Folks, welcome to the program. Thanks for coming on. >> Thanks Dave. >> Thank you. Great to be here. >> Well- >> Nice to be here. >> Todd, let's start with you. We hear a lot about Industry 4.0, smart factories, IIoT. Can you briefly explain, like, what is Industry 4.0 all about and why is it important for the manufacturing industry? >> Yeah, sure Dave. You know, it's been around for quite a while and it's got, it's gone by multiple different names. As you said, Industry 4.0, smart manufacturing, industrial IoT, smart factory. But it all really means the same thing. It's really applying technology to get more out of the factories and the facilities that you have to do your manufacturing. So being much more efficient. Implementing really good sustainability initiatives. And so we really look at that by saying, "Okay, what are we going to do with technology to really accelerate what we've been doing for a long, long time"? So it's really not, it's not new. It's been around for a long time. What's new is that manufacturers are looking at this, not as a one-off, two off individual use case point of view, but instead they're saying, "We really need to look at this holistically, thinking about a strategic investment in how we do this." Not to just enable one or two use cases, but enable many, many use cases across the spectrum. I mean, there's tons of 'em out there. There's predictive maintenance and there's OEE, overall equipment effectiveness, and there's computer vision. And all of these things are starting to percolate down to the factory floor, but it needs to be done in a little bit different way. And really to to really get those outcomes that they're looking for in smart factory, or Industry 4.0, or however you want to call it. And truly transform. Not just throw an Industry 4.0 use case out there, but to do the digital transformation that's really necessary and to be able to stay relevant for the future. You know, I heard it once said that you have three options. Either you digitally transform and stay relevant for the future or you don't and fade into history like 52% of the companies that used to be on the Fortune 500 since 2000, right. And so really that's a key thing and we're seeing that really, really being adopted by manufacturers all across the globe. >> Yeah, so Aditi, that's like digital transformation is almost synonymous with business transformation. So is there anything you'd add to what Todd just said? >> Absolutely, though, I would really add that what really drives Industry 4.0 is the business transformation. What we are able to deliver in terms of improving the manufacturing KPIs and the KPIs for customer satisfaction, right. For example, improving the downtime, you know, or decreasing the maintenance cycle of the equipments or improving the quality of products, right. So I think these are lot of business outcomes that our customers are looking at while using Industry 4.0 and the technologies of Industry 4.0 to deliver these outcomes. >> So Aditi, one, if I could stay with you and maybe this is a bit esoteric, but when I first started researching IoT and Industrial IoT 4.0, et cetera, I felt, you know, while there could be some disruptions in the ecosystem, I kind of came to the conclusion that large manufacturing firms, aerospace defense companies, the firms building out critical infrastructure, actually had kind of an incumbent advantage and a great opportunity. Of course, then I saw on TV, somebody now, they're building homes with 3D printers. It like blows your mind. So that's pretty disruptive. But. So, but they got to continue, the incumbents have to continue to invest in the future. They're well capitalized. They're pretty good businesses. Very good businesses. But there's a lot of complexities involved in kind of connecting the old house to the new addition that's being built, if you will. Or there's transformation that we're talking about. So my question is how are your customers preparing for this new era? What are the key challenges that they're facing in the blockers, if you will? >> Yeah, I mean the customers are looking at Industry 4.0 for greenfield factories, right. That is where the investments are going directly into building the factories with the new technologies with the new connectivities, right, for the machines, for example. Industry IoT, Having the right type of data platforms to drive computational analytics and outcomes, as well as looking at edge versus cloud type of technologies, right. Those are all getting built in the greenfield factories. However, for the install-based factories, right, that is where our customers are looking at how do I modernize, right. These factories. How do I connect the existing machine? And that is where some of the challenges come in on, you know, the legacy system connectivity that they need to think about. Also, they need to start thinking about cybersecurity and operation technology security, right, because now you are connecting the factories to each other, right. So cybersecurity becomes top of mind, right. So there is definitely investment that is involved. Clients are creating roadmaps for digitizing and modernizing these factories and investments in a very strategic way, right. So perhaps they start with the innovation program. And then they look at the business case and they scale it up, right. >> Todd, I'm glad Aditi brought up security because if you think about the operations technology, you know folks, historically they air gapped, you know, the systems. That's how they created security. That's changed. The business came in and said, "Hey, we got to connect. We got to make it intelligent." So that's got to be a big challenge as well. >> It absolutely is Dave. And, you know, you can no longer just segment that because really to get all of those efficiencies that we talk about, that IOT and industrial IoT and Industry 4.0 promise, you have to get data out of the factory but then you got to put data back in the factory. So no longer is it just firewalling everything is really the answer. So you really have to have a comprehensive approach to security, but you also have to have a comprehensive approach to the cloud and what that means. And does it mean a continuum of cloud all the way down to the edge, right down to the factory? It absolutely does because no one approach has the answer to everything. The more you go to the cloud, the broader the attack surface is. So what we're seeing is a lot of our customers approaching this from, kind of, that hybrid, you know, write once, run anywhere on the factory floor down to the edge. And one of things we're seeing too is to help distinguish between what is the edge and that. And bridge that gap between, like Dave, you talked about IT and OT, and also help that what Aditi talked about is the greenfield plants versus the brownfield plants, that they call it, that are the legacy ones and modernizing those, is it's great to kind of start to delineate. What does that mean? Where's the edge? Where's the IT and the OT? We see that from a couple of different ways. We start to think about, really, two edges in a manufacturing floor. We talk about an industrial edge that sits, or some people call it a far edge or a thin edge, sits way down on that plant. Consists of industrial hardened devices that do that connectivity, the hard stuff, about how do I connect to this obsolete legacy protocol and what do I do with it? And create that next generation of data that has context. And then we see another edge evolving above that which is much more of a data and analytics and enterprise grade application layer that sits down in the factory itself that helps figure out where we're going to run this. Is... Does it connect to the cloud? Do we run applications on-prem? Because a lot of times that on-prem application is needs to be done because that's the only way it's going to work. Because of security requirements. Because of latency requirements, performance, and a lot of times, cost. It's really helpful to build that multiple edge strategy because then you consolidate all of those resources, applications, infrastructure, hardware, into a centralized location. Makes it much, much easier to really deploy and manage that security. But it also makes it easier to deploy new applications, new use cases, and become the foundation for DXC's expertise in applications that they deliver to our customers as well. >> Todd, how complex are these projects? I mean, I feel like it's kind of the digital equivalent of building the Hoover Dam. I mean, it... So, yeah, how long does a typical project take? I know it varies, but what, you know, what are the critical success factors in terms of delivering business value quickly? >> Yeah, that's a great question in that we're, you know, like I said at the beginning, this is not new smart factory and Industry 4.0 is not new. It's been... It's people have been trying to implement the holy grail of smart factory for a long time. And what we're seeing is a switch, a little bit of a switch or quite a bit of a switch, to where the enterprise and the IT folks are having a much bigger say and have a lot to offer to be able to help that complexity. So instead of deploying a computer here and a gateway there and a server there. I mean, you go walk into any manufacturing plant and you can see servers sitting underneath someone's desk or a PC in a closet somewhere running a a critical production application. So we're seeing the enterprise have a much bigger say at the table. Much louder voice at the table to say, "We've been doing this enterprise all the time. We know how to really consolidate, bring hyper-converged applications, hyper-converged infrastructure, to really accelerate these kind of applications. Really accelerate the outcomes that are needed to really drive that smart factory." And start to bring that same capabilities down into the Mac on the factory floor. That way, if you do it once to make it easier to implement you can repeat that. You can scale that. You can manage it much easily. And you can then bring that all together because you have the security in one centralized location. So we're seeing manufacturers... Yeah, that first use case may be fairly difficult to implement and we got to go down in and see exactly what their problems are. But when the infrastructure is done the correct way, when that... Think about how you're going to run that and how are you going to optimize the engineering. Well, let's take that what you've done in that one factory and then set. Let's that, make that across all the factories including the factory that we're in, but across the globe. That makes it much, much easier. You really do the hard work once and then repeat almost like a cookie cutter. >> Got it, thank you. Aditi, what about the skillsets available to apply these to these projects? You got to have knowledge of digital, AI, data, integration. Is there a talent shortage to get all this stuff done? >> Yeah, I mean, definitely. Different types of skillsets are needed from a traditional manufacturing skillset, right. Of course, the basic knowledge of manufacturing is important. But the digital skillsets, like, you know, IoT. Having a skillset in different protocols for connecting the machines, right. That experience that comes with it. Data and analytics, security, augmented virtual reality, programming. You know, again, looking at robotics and the digital twin. So, you know, it's a lot more connectivity software data-driven skillsets that are needed to smart factory to life at scale. And, you know, lots of firms are, you know, recruiting these types of resources with these skillsets to, you know, accelerate their smart factory implementation as well as consulting firms like DXC technology and others. We recruit. We train our talent to provide these services. >> Got it. Aditi, I wonder if we could stay on you. Let's talk about the partnership between DXC and Dell. What are you doing specifically to simplify the move to industry 4.0 for customers? What solutions are you offering? How are you working together, Dell and DXC, to bring these to market? >> Yeah, I... Dell and DXC have a very strong partnership, you know, and we work very closely together to create solutions, to create strategies, and how we are going to jointly help our clients, right. So. Areas that we have worked closely together is edge compute, right. How that impacts the smart factory. So we have worked pretty closely in that area. We're also looked at vision technologies, you know. How do we use that at the edge to improve the quality of products, right. So we have several areas that we collaborate in and our approach is that we want to bring solutions to our client and as well as help them scale those solutions with the right infrastructure, the right talent, and the right level of security. So we bring a comprehensive solution to our clients. >> So, Todd, last question. Kind of similar but different. You know, why Dell DXC? Pitch me. What's different about this partnership? You know, where are you confident that, you know, you're going to deliver the best value to customers? >> Absolutely, great question. You know, there's no shortage of bespoke solutions that are out there. There's hundreds of people that can come in and do individual use cases and do these things and just... And that's where it ends. What Dell and DXC Technology together bring to the table is we do the optimization of the engineering of those previously bespoke solutions upfront, together. Right. The power of our scalables, enterprise grade, structured, you know, industry standard infrastructure as well as our expertise in delivering package solutions that really accelerate with DXC's expertise and reputation as a global trusted advisor. Be able to really scale and repeat those solutions that DXC is so really, really good at. And Dell's infrastructure and our, what, 30,000 people across the globe that are really, really good at that scalable infrastructure to be able to repeat. And then it really lessens the risk that our customers have and really accelerates those solutions. So it's, again, not just one individual solutions. It's all of the solutions that not just drive use cases but drive outcomes with those solutions. >> Yeah, you're right. The partnership has gone... I mean, I first encountered it back in, I think, it was 2010, May of 2010. We had you guys both on the queue... I think we were talking about converged infrastructure and I had a customer on, and it was actually manufacturing customer. Was quite interesting. And back then it was how do we kind of replicate what's coming in the cloud? And you guys have obviously taken it into the digital world. Really want to thank you for your time today. Great conversation. And love to have you back. >> Thank you so much. >> Absolutely. >> It was a pleasure speaking with you. >> I agree. >> All right, keep it right there for more discussions that educate and inspire on theCUBE.

(electronica music) >> Hello everybody. This is Dave Volante and we are covering HPE's big GreenLake announcements. We've got wall-to-wall coverage, a ton of content. We've been watching GreenLake since the beginning. And of one of the things we said early on was let's watch and see how frequently, what the cadence of innovations that HPE brings to the market. Because that's what a cloud company does. So, we're here to welcome you. Keith White is here as the Senior Vice President General Manager of GreenLake cloud services. He runs the commercial business. Keith, thanks for coming on. Help me kick off. >> Thanks for having me. It's awesome to be here. >> So you guys got some momentum orders, 40% growth a year to year on year. You got a lot of momentum, customer growth. >> Yeah, it's fantastic. It's 46%. >> Kyle, thank you for that clarification. And in 46. Big different from 40 to 46. >> No, I think what we're seeing is we're seeing the momentum happen in the marketplace, right? We have a scenario where we're bringing the cloud experience to the customer on their premises. They get to have it automated. Self-serve, easy to consume. They pay for what they use. They can have it in their data center. They can have it at the edge. They can have it at the colo, and, we can manage it all for them. And so they're really getting that true cloud experience and we're seeing it manifest itself in a variety of different customer scenarios. You know, we talked about at Discover, a lot of work that we're doing on the hybrid cloud side of the house, and a lot of work that we're doing on the edge side of things with our partners. But you know, it's exciting to see the explosion of data and how now we're providing this data capability for our customers. >> What are the big trends you're hearing from customers? And how is that informing what you're doing with Green? I mean, I feel like in a lot of ways, Keith, what happened last year, you guys were, were in a better position maybe than most. But what are you hearing and how is that informing your go forward? >> Yeah, I think it's really three things with customers, right? First off, Hey, we're trying to accelerate our digital transformation and it's all becoming about the data. So help us monetize the data, help us protect that data. Help us analyze it to make decisions. And so, you know, number one, it's all about data. Number two is wow, this pandemic, you know, we need to look for cost savings. So, we still need to move our business forward. We've got to accelerate our business, but help me find some cost savings with respect to what I can do. And third, what we're hearing is, hey, we're in a situation, where there's a lot of different capabilities happening with our workforce. They're working from home. They're working hybrid. Help us make sure that we can stay connected to those folks, but also in a secure way, making sure that they have all the tools and resources they need. So those are sort of three of the big themes that we're seeing that GreenLake really helps manifest itself, with the data we're doing now. With all the hybrid cloud capabilities. With the cost savings that we get with respect to our platform, as well as with solutions such as VDI or workforce enablements that we've, we create from a solution standpoint. . >> So, what's the customer reaction, I mean, I mean, everybody now, who's has a big on-premise state, has an as a service capability. A customer saying, oh yeah, oh yeah, how do you make it not me too? In the customer conversations? >> Yeah. I think it turns into, you know, you have to bring the holistic solution to the customer. So yes, there's technology there and we're hearing from, you know, some of the competitors out there. Yeah, we're doing as a service as well, but maybe it's a little bit of storage here. Maybe it's a little bit of networking there. Customers need that end to end solution. And so as you've seen us announce over time, we've got the building blocks, of course, compute storage and networking, but everything runs in a virtual machine. Everything runs in a container or everything runs on the bare metal itself. And that package that we've created for customers means that they can do whatever solution, or whatever workload they want So, if you're a hospital and you're running Epic for your electronic medical records, you can go that route. If you're upgrading SAP and you're using virtual machines at a very large scale, you can use this, use a GreenLake for that as well. So, as you go down the list, there's just so many opportunities with respect to bring those solutions to our customers. And then you bring in our point-next capabilities to support that. You bring in our advisory and professional services, along with our ecosystem to help enable that. You bring in our HPE financial services to help fund that digital transformation. And you've got the complete package. And that's why customers are saying, hey, you guys are now partners of us. You're not just a hardware provider, you're a partner you're helping us solve our business problems and helping us accelerate our business. >> So what should people expect today? You guys got some announcements. What should people look for? >> Well, I think this is, as we've talked about, you know, now we're sort of providing much more capabilities around the data side of the house. Because data is so such, it's the gold, if you will, of a customer's environment. So first off we want to do analytics. So we want an open platform that provides really a unified set of analytics capabilities. And this is where we have a real strong, sweet spot with respect to some of the, the software that we've built around Esperal. But also with the hardware capabilities. As you know, we have all the way up to the Cray supercomputers that, that are doing all of the analytics for whether this or, or financial data that. So, I think that's one of the key things. The second is you got to protect that data. And, and so if it's going to be on prem, I want to know that it's protected and secured. So how do I back it up? How do I have a disaster recovery plan? How do I watch out for ransomware attacks, as well? So we're providing some capabilities there. And then I'd say, lastly, because of all the experience we have with our customers now implementing these hybrid solutions, they're saying, hey, help me with this edge to cloud framework and how do I go and implement that on my own? And so we've taken all the experience and we've bucketed that into our edge to cloud adoption framework to provide that capability for our customers. So we, you know, we're really excited about, again, talking about solutions, talking about accelerating your business, not just talking about technology. >> I said up the top, Keith, that one of the ways I was evaluating you as the pace and the cadence of the innovations. And, and is that, is that fair? How do you guys think about that internally? Are you, you know, you're pushing yourself to go faster, I'm sure you are, but what's that conversation like? >> I think it's a great question because in essence, we're now pivoting the company holistically to being a cloud services and a software company. And that's really exciting and we're seeing that happen internally. But this pace of innovation is really built on what customers are asking us for us. So now that we've grown over 1200 customers worldwide. You know, over $5 billion of total contract value. You know, signing some, some large deals in a variety of solutions and workloads and verticals, et cetera. What we're now seeing is, hey, this is what we need. Help me with my internal IT out to my business groups. Help me with my edge strategy as I build the factory of the future, or, you know, help me with my data and analytics that I'm trying to accomplish for my, you know, diagnosis of, of x-rays and, and capabilities such as Carestream, if you will. So it's, it's exciting to see them come to us and say, this is the capabilities that we're requiring, and we've got our foot on the gas to provide that innovation. And we're miles ahead of the competition. >> All right, we've got an exciting day ahead. We got all kinds of technology discussions, solution discussions. We got, we got, we're going to hear from the analyst community. Really bringing you the, the full package of announcements here. Keith, thanks for helping me set this up. >> Always. Yeah. Thanks so much for having me. >> I look forward today. And thank you for watching. Keep it right there. Tons of content coming your way. You're watching The Cubes coverage of HP's big GreenLake announcement. Right back. (electronica music)

>> Announcer: Live from San Francisco, it's The Cube covering Informatica World 2017. Brought to you by Informatica. >> Okay, welcome back everyone. We are here live in San Francisco for The Cube's exclusive coverage of Informatica World 2017. I'm John Furrier, this is Siliconangle's flagship program, we go out to the events and (he mumbles). My next guest is Jitesh Ghai who's the Vice President General Manager of data quality and governance for Informatica. Welcome to The Cube, thanks for joining us today. >> Happy to be here, John. Pleasure. >> So, two things right out of the gate. One, data quality and governance, two of the hottest topics in the industry, never mind within Informatica. You guys are announcing a lot of stuff, customers are pretty happy, you got a solid customer base. >> That's right. >> Product's been blooming, you got a big brand behind you now. This is important. There's laws now in place coming online in 2018, I think it's the GDPR. >> That's right. >> And there's a variety of other things, but more importantly customers got to get hold of their data. >> That's right. >> What's your take and what are you announcing here at the show? >> Well, you know, from a data governance and compliance and overall quality standpoint, data governance started off as a stick, a threat of regulatory pressure, but really the heart of what it is is effective access to and consumption of data, trusted data. And through that exercise of the threat of a stick, healthy practices have been implemented and that's resulted in an appreciation for data governance as a carrot, as an opportunity to innovate, innovate with your data to develop new business models. The challenge is as this maturation in the practice of data governance has happened there's been a realization that there's a lot of manual work, there's a lot of collaboration that's required across a cross-functional matrixed organization of stakeholders. And there's the concept of ... >> There's some dogma too, let's just face it, within organizations. I got all this data, I did it this way before. >> Right. >> And now, whoa, the pressure's on to make data work, right, I mean that's the big thing. >> That's exactly right. So, you collaborate, you align, and you agree on what data matters and how you govern it. But then you ultimately have to stop documenting your policies but actually make it real, implement it, and that's where the underlying data management stack comes into place. That could be making it real for regulatory, financial regulations, like BCBS 239 and CCAR, where data quality is essential. It could be making it real for security related regulations where protection is essential, like GDPR, the data protection regulation in the EU. And that's where, Informatica is launching a holistic enterprise data governance offering that enables you to not just document it, or as one CDO said to me, "You know, at some point you've got to stop talking about it, "you actually have to do it." To connecting the conceptual, the policies, with the underlying physical systems, which is where intelligent automation with the underlying data management portfolio, the industry-leading data management portfolio that we have, really delivers significant productivity benefits, it's really redefining the practice of data governance. >> Yeah, most people think of data as being one of those things, it's been kind of like, whether it's healthcare, HIPAA old models, it's always been an excuse to say no. "Whoa, we don't do it that way." Or, "Hey." It's kind of become a no-op kind of thing where, "No, we don't want to do any more than data." But you guys introduced CLAIR which is the acronym for the clairvoyant or AI, it's kind of a clever way to brand. >> That's right. >> That's going to bring in machine learning augmented intelligence and cool things. That only, to me, feels like you're speeding things up. >> That's exactly right. >> When in reality governance is more of a slowdown, so how do you blend the innovation strategy of making data freely available ... >> Right. >> ..and yet managing the control layer of governance, because governance wants to go slow, CLAIR wants to go fast, you know. Help me explain that. >> Well, in short, sometimes you have to go slow to go fast. And that's the heart of what our automated intelligence that CLAIR provides in the practice of data governance, is to ensure that people are getting access to, efficient access to trusted data and consuming it in the right context. And that's where you can set, you can define a set of policies, but ultimately you need those policies to connect to the right data assets within the enterprise. And to do that you need to be able to scan an entire enterprise's data sets to understand where all the data is and understand what that data is. >> Talk about the silver bullet that everyone just wants to buy, the answer to the test, which is ungettable, by the way, I believe, we just had Allegis on, one of your customers, and their differentiation to their competition is that they're using data as an asset but they're not going all algorithmic. There's the human data relationship. >> Absolutely. >> So there's really no silver bullet in data. You could use algorithms like machine learning to speed things up and work on things that are repeatal tasks. >> Right. >> Talk about that dynamic because governance can be accelerated with machine learning, I would imagine, right? >> Absolutely, absolutely. Governance is a practice of ensuring an understanding across people, processes and systems. And to do that you need to collaborate and define who are the people, what are your processes, and what are the systems that are most critical to you. Once you've defined that it's, well, how do we connect that to the underlying data assets that matter, and that's where machine learning really helps. Machine learning tells you that if you define customer id as a critical data element, through machine learning, through CLAIR, we are able to surface up everywhere in your organization where customer id resides. It could be cmd id, it could be customer_id, could be customer space id, cust id. Those are all the inferences we can make, the relationships we can make, and surface all of that up so that people have a clear understanding of where all these data assets reside. >> Jitesh, let's take a step back. I want to get your thoughts on this, I really want you to take a minute to explain something for the folks watching. So, there's a couple of different use cases, at least I've observed in a row and the wikibon team has certainly observed. Some people have an older definition of governance. >> Right. >> What's the current definition from your standpoint? What should people know about governance today that's different than just last year or even a few years ago, what's the new picture, what's the new narrative for governance and the impact to business? >> You know, it's a great question. I held a CDO summit in February, we had about 20 Chief Data Officers in New York and I just held an informal survey. "Who implements data governance programs "for regulatory reasons?" Everybody put their hand up. >> Yeah. >> And then I followed that up with, "Who implements data governance programs "to positively affect the top line?" and everybody put their hand up. That's the big transition that's happened in the industry is a realization that data governance is not just about compliance, it's also about effective policies to better understand your data, work with your data, and innovate with your data. Develop new business models, support your business in developing those new business models so that you can positively affect the top line. >> Another question we get up on The Cube all the time, and we also observe, and we've heard this here from other folks at Informatica and your customers have said, getting to know what you actually have is the first step. >> Right. >> Which sounds counter-intuitive but the reality is that a lot of folks realize there's an asset opportunity, they raise their, hey, top line revenue. I mean, who's not going to raise their hand on that one, right, you get fired. I mean, the reality is this train's coming down the tracks pretty fast, data as an input into value creation. >> That's exactly right. >> So now the first step is oh boy, just signed up for that, raise my hand, now what the hell do I have? >> Right. >> How do you react to that? What's your perspective on that? >> That's where you need to be able to, google indexed the internet to make it more consumable. Actually, a few search engines indexed the internet. Google came up with sophistication through its page-ranking algorithm. Similarly, we are cataloging the enterprise and through CLAIR we're making it so that the right relevant information is surfaced to the right practitioner. >> And that's the key. >> That is the key. >> Accelerating the access method, so increase the surface area of data, have the control catalog for the enterprise. >> That's right. >> Which is like your google search analogy. A little harder than searching the internet, but even google's not doing a great job these days, in my opinion, I should say that. But there's so many new data points coming in. >> That's right. >> So now the followup question is, okay, it's really hard when you start having IOT come in. >> That's right. >> Or gesture data or any kind of data coming in. How do you guys deal with that? How does that rock your world, as they say? >> And that's where effective consumption of data permeates across big data, cloud, as well as streaming data. We have implemented, in service to governance, we've implemented in-stream data quality rules to filter out the noise from the signal in sensor data coming in from aircraft subsystems, as an example. That's a means of, well, first you need to understand what are the events that matter, and that's a policy definition exercise which is a governance exercise. And then there's the implementation of filtering events in realtime so that you're only getting the signal and avoiding the noise, that's another IOT example. >> What's your big, take your Informatica hat off, put your kind of industry citizen hat on. >> Mm-hm. >> What's your view of the marketplace right now? What's the big wave that people are riding? Obviously, data, you could say data, don't say data 'cause we know that already. >> Sure. >> What should people, what do you observe out there in the marketplace that's different, that's changing very rapidly? Obviously we see Amazon stock going up like a hockey stick, obviously cloud is there. What are you getting excited about these days? >> You know, what I'm excited about is bringing broad-based access of data to the right users in the right context, and why that's exciting is because there's an appreciation that it's not the analytics that are important, it's the data that fuels those analytics that's important. 'Cause if you're not delivering trusted, accurate data it's effectively a garbage in, garbage out analytics problem. >> Hence the argument, data or algorithms, which one's more important? >> Right. >> I mean data is more important than algorithms 'cause algorithms need data. >> That's exactly right and that's even more true when you get into non-deterministic algorithms and when you get into machine learning. Your machine learning algorithm is only as good as the data you train it with. >> I mean look, machine learning is not a new thing. Unsupervised machine learning's getting better. >> Right. >> But that's really where the compute comes in, and the more data you have the more modeling you can do. These are new areas that are kind of coming online, so the question is, to you, what new exciting areas are energizing some of these old paradigms? We hear neural nets, I mean, google's just announced neural nets that teach neural nets to make machine learning easier for humans. >> Right. >> Okay. I mean, it has a little bit of computer science baseball but you're seeing machine learning now hitting mainstream. >> Right. >> What's the driver for all this? >> The driver for all this comes down to productivity and automation. It's productivity and automation in autonomous vehicles, it's productivity and automation that's now coming into smart homes, it's productivity and automation that is being introduced through data-driven transformation in the enterprise as well, right, that's the driver. >> It's so funny, one of my undergraduate computer science degrees was databases. And in the '80s it wasn't like you went out to the tub, "Hey, I'm a databaser." (He mimics uncertain mumbling) And now it's like the hottest thing, being a data guy. >> Right. >> And what's also interesting is a lot of the computer science programs have been energized by this whole software defined with cloud data because now they have unlimited, potentially, compute power. >> Right. >> What's your view on the young generation coming in as you look to hire and you look to interview people? What are some of the disciplines that are coming out of the universities and the masters programs that are different than it was even five years ago? What are some trends you're seeing in the young kids coming in, what are they gravitating towards? >> Well, you know, there's always an appreciation of, a greater appreciation for, you know, the phrase I love is, "In god we trust, all others must have data." There's an increasing growing culture around being data-driven. But from a background of young people, it's from a variety of backgrounds, of course computer science but philosophy majors, arts majors in general, all in service to the larger cause of making information more accessible, democratizing data, making it more consumable. >> I think AI, I agree, by the way, I would just add, I think AI, although it is hyped and I don't really want to burst that bubble because it's really promoting software. >> Right. >> I mean, AI's giving people a mental model of, "Oh my god, some pretty amazing things are happening." >> Sure. >> I mean, autonomous vehicles is what most people point to and say, "Hey, wow, that's pretty cool." A Tesla's much different than a classic car. I mean, you test-drive a TESLA you go, "Why am I buying BMW, Audi, Mercedes?" >> Right, exactly. >> It's a no brainer. >> Right. >> Except it's like (he mumbles), you got to get it installed. But, again, that's going to change pretty quickly. >> At this point it's becoming a table sticks exercise. If you're not innovating, if you're not applying intelligence and AI, you're not doing it right. >> Right, final question. What's your advice to your customers who are in the trenches, they raise their hand, they're committed to the mandate, they're going down the digital business transformation route, they recognize that data's the center of the value proposition, and they have to rethink and reimagine their businesses. >> Right. >> What advice do you give them in respect to how to think architecturally about data? >> Well, you know, it all starts with your data-driven transformations are only as good as the data that you're driving your transformations with. So, ensure that that's trusted data. Ensure that that's data you agree as an organization upon, not as a functional group, right. The definition of a customer in support is different from the definition of a customer in sales versus marketing. It's incredibly important to have a shared understanding, an alignment on what you are defining and what you're reporting against, because that's how you're running your business. >> So, the old schema concept, the old database world, know your types. >> Right. >> But then you got the unstructured data coming in as well, that's a tsunami IOT coming in. >> Sure, sure. >> That's going to be undefined, right? >> And the goal and the power of AI is to infer and extract metadata and meaning from this whole landscape of semi-structured and unstructured data. >> So you're of the opinion, I'm sure you're biased with being Informatica, but I'm just saying, I'm sure you're in favor of collect everything and connect the dots as you see fit. >> Well ... >> Or is that ...? >> It's a nuance, you can't collect everything but you can collect the metadata of everything. >> Metadata's important. >> Data that describes the data is what makes this achievable and doable, practically implementable. >> Jitesh Ghai here sharing the metadata, we're getting all the metadata from the industry, sharing it with you here on The Cube. I'm John Furrier here live at Informatica World 2017, exclusive Cube coverage, this is our third year. Go to siliconangle.com, check us out there, and also wikibon.com for our great research. Youtube.com/siliconangle for all the videos. More live coverage here at Informatica World in San Francisco after this short break, stay with us.