>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to Cambridge, Massachusetts. Everybody watching the Cuban leader live tech coverage. We here covering the M I t CDO conference M I t CEO Day to wrapping up. Bob Parr is here. He's a partner in principle at KPMG, and he's joined by Streetcar Krishna, who is the managing director of data science. Aye, aye. And innovation at KPMG. Gents, welcome to the Cube. Thank >> thank you. Let's start with your >> roles. So, Bob, where do you focus >> my focus? Ah, within KPMG, we've got three main business lines audit tax, an advisory. And so I'm the advisory chief date officer. So I'm more focused on how we use data competitively in the market. More the offense side of our focus. So, you know, how do we make sure that our teams have the data they need to deliver value? Uh, much as possible working concert with the enterprise? CDO uh, who's more focused on our infrastructure, Our standards, security, privacy and those >> you've focused on making KPMG better A >> supposed exactly clients. OK, >> I also have a second hat, and I also serve financial service is si Dios as well. So Okay, so >> get her out of a dual role. I got sales guys in >> streetcar. What was your role? >> Yeah, You know, I focus a lot on data science, artificial intelligence and overall innovation s o my reaction. I actually represent a centre of >> excellence within KPMG that focuses on the I machine learning natural language processing. And I work with Bob's Division to actually advance the data site off the store because all the eye needs data. And without data, there's no algorithms, So we're focusing a lot on How do we use a I to make data Better think about their equality. Think about data lineage. Think about all of the problems that data has. How can we make it better using algorithms? And I focused a lot on that working with Bob, But no, it's it's customers and internal. I mean, you know, I were a horizontal within the form, So we help customers. We help internal, we focus a lot on the market. >> So, Bob, you mentioned used data offensively. So 10 12 years ago, it was data was a liability. You had to get rid of it. Keep it no longer than you had to, because you're gonna get soon. So email archives came in and obviously thinks flipped after the big data. But so what do you What are you seeing in terms of that shift from From the defense data to the offensive? >> Yeah, and it's it's really you know, when you think about it and let me define sort of offense versus defense. Who on the defense side, historically, that's where most of CEOs have played. That's risk regulatory reporting, privacy, um, even litigation support those types of activities today. Uh, and really, until about a year and 1/2 ago, we really saw most CEOs still really anchored in that I run a forum with a number of studios and financial service is, and every year we get them together and asked him the same set of questions. This was the first year where they said that you know what my primary focus now is. Growth. It's bringing efficiency is trying to generate value on the offensive side. It's not like the regulatory work's going away, certainly in the face of some of the pending privacy regulation. But you know, it's It's a sign that the volume of use cases as the investments in their digital transformations are starting to kick out, as well as the volumes of data that are available. The raw material that's available to them in terms of third party data in terms of the the just the general volumes that that exist that are streaming into the organization and the overall literacy in the business units are creating this, this massive demand. And so they're having to >> respond because of getting a handle on the data they're actually finding. Word is, they're categorizing it there, there, >> yeah, organizing that. That is still still a challenge. Um, I think it's better with when you have a very narrow scope of critical data elements going back to the structure data that we're talking it with the regulatory reporting when you start to get into the three offense, the generating value, getting the customer experience, you know, really exploring. You know that side of it. There's there's a ton of new muscle that has to be built new muscle in terms of data quality, new muscle in terms of um, really more scalable operating model. I think that's a big issue right now with Si Dios is, you know, we've got ah, we're used to that limited swath of CDs and they've got Stewardship Network. That's very labor intensive. A lot of manual processes still, um, and and they have some good basic technology, but it's a lot of its rules based. And when you do you think about those how that constraints going to scale when you have all of this demand. You know, when you look at the customer experience analytics that they want to do when you look at, you know, just a I applied to things like operations. The demand on the focus there is is is gonna start to create a fundamental shift >> this week are one of things that I >> have scene, and maybe it's just my small observation space. But I wonder, if you could comment Is that seems like many CBO's air not directly involved in the aye aye initiatives. Clearly, the chief digital officer is involved, but the CDO zehr kind of, you know, in the background still, you see that? >> That's a fantastic question, and I think this is where we're seeing some off the cutting it change that is happening in the industry. And when Barbara presenter idea that we can often civilly look at data, this is what it is that studios for a long time have become more reactive in their roles. And that is that is starting to come forefront now. So a lot of institutions were working with are asking What's the next generation Roll off a CDO and why are they in the background and why are they not in the foreground? And this is when you become more often they were proactive with data and the digital officers are obviously focused on, you know, the transformation that has to happen. But the studios are their backbone in order to make the transformation. Really. And if the CDO started, think about their data as an asset did as a product did us a service. The judicial officers are right there because those are the real, you know, like the data data they're living so CDO can really become from my back office to really become a business line. We've >> seen taking the reins in machine learning in machine learning projects and cos you work with. Who >> was driving that? Yeah. Great question. So we are seeing, like, you know, different. I would put them in buckets, right? There is no one mortal fits all. We're seeing different generations within the company's. Some off. The ones were just testing out the market. There's two keeping it in their technology space in their back office. Take idea and, you know, in in forward I d let me call them where they are starting to experiment with this. But you see, the mature organizations on the other end of the spectrum, they are integrating action, learning and a I right into the business line because they want to see ex souls having the technology right by their side so they can lead leverage. Aye, aye. And machine learning spot right for the business right there. And that is where we're seeing know some of the new models. Come on. >> I think the big shift from a CDO perspective is using a i to prep data for a That's that's fundamentally where you know, where the data science was distributed. Some of that data science has to come back and free the integration for equality for data prepping because you've got all this data third party and other from customer streaming into the organization. And you know, the work that you're doing around, um, anomaly detection is it transcends developing the rules, doing the profiling, doing the rules. You know, the very manual, the very labor intensive process you've got to get away from that >> is used in order for this to be scale goes and a I to figure out which out goes to apply t >> clean to prepare the data toe, see what algorithms we can use. So it's basically what we're calling a eye for data rather than just data leading into a I. So it's I mean, you know, you developed a technology for one off our clients and pretty large financial service. They were getting closer, like 1,000,000,000 data points every day. And there was no way manually, you could go through the same quality controls and all of those processes. So we automated it through algorithms, and these algorithms are learning the behavior of data as they flow into the organization, and they're able to proactively tell their problems are starting very much. And this is the new face that we see in in the industry, you cannot scale the traditional data governance using manual processes, we have to go to the next generation where a i natural language processing and think about on structure data, right? I mean, that is, like 90% off. The organization is unstructured data, and we have not talked about data quality. We have not talked about data governance. For a lot of these sources of information, now is the time. Hey, I can do it. >> And I think that raised a great question. If you look at unstructured and a lot of the data sources, as you start to take more of an offensive stance will be unstructured. And the data quality, what it means to apply data quality isn't the the profiling and the rules generation the way you would with standard data. So the teams, the skills that CEOs have in their organizations, have to change. You have to start to, and, you know, it's a great example where, you know, you guys were ingesting documents and there was handwriting all over the documents, you know, and >> yeah, you know, you're a great example, Bob. Like you no way would ask the client, like, you know, is this document gonna scanned into the system so my algorithm can run and they're like, Yeah, everything is good. I mean, the deal is there, but when you then start scanning it, you realize there's handwriting and the information is in the handwriting. So all the algorithms breakdown now >> tribal knowledge striving Exactly. >> Exactly. So that's what we're seeing. You know, if I if we talk about the digital transformation in data in the city organization, it is this idea dart. Nothing is left unseen. Some algorithm or some technology, has seen everything that is coming into. The organization has has has a para 500. So you can tell you where the problems are. And this is what algorithms do. This scale beautifully. >> So the data quality approaches are evolving, sort of changing. So rather than heavy, heavy emphasis on masking or duplication and things like that, you would traditionally think of participating the difficult not that that goes away. But it's got to evolve to use machine >> intelligence. Exactly what kind of >> skill sets people need thio achieve that Is it Is it the same people or do we need to retrain them or bring in new skills. >> Yeah, great question. And I can talk from the inspector off. Where is disrupting every industry now that we know, right? But we knew when you look at what skills are >> required, all of the eye, including natural language processing, machine learning, still require human in the loop. And >> that is the training that goes in there. And who do you who are the >> people who have that knowledge? It is the business analyst. It's the data analyst who are the knowledge betters the C suite and the studios. They are able to make decisions. But the day today is still with the data analyst. >> Those s Emmys. Those sm >> means So we have to obscure them to really start >> interacting with these new technologies where they are the leaders, rather than just waiting for answers to come through. And >> when that happens now being as a data scientist, my job is easy because they're Siamese, are there? I deploy the technology. They're semi's trained algorithms on a regular basis. Then it is a fully fungible model which is evolving with the business. And no longer am I spending time re architect ing my rules. And like my, you know, what are the masking capabilities I need to have? It is evolving us. >> Does that change the >> number one problem that you hear from data scientists, which is the 80% of the time >> spent on wrangling cleaning data 10 15 20% run into sm. He's being concerned that they're gonna be replaced by the machine. Their training. >> I actually see them being really enabled now where they're spending 80% of the time doing boring job off, looking at data. Now they're spending 90% of their time looking at the elements future creative in which requires human intelligence to say, Hey, this is different because off X, >> y and Z so let's let's go out. It sounds like a lot of what machine learning is being used for now in your domain is clean things up its plumbing. It's basic foundation work. So go out. Three years after all that work has been done and the data is clean. Where are your clients talking about going next with machine learning? Bob, did you want? >> I mean, it's a whole. It varies by by industry, obviously, but, um but it covers the gamut from, you know, and it's generally tied to what's driving their strategies. So if you look at a financial service is organization as an example today, you're gonna have, you know, really a I driving a lot of the behind the scenes on the customer experience. It's, you know, today with your credit card company. It's behind the scenes doing fraud detection. You know, that's that's going to continue. So it's take the critical functions that were more data. It makes better models that, you know, that that's just going to explode. And I think they're really you can look across all the functions, from finance to to marketing to operations. I mean, it's it's gonna be pervasive across, you know all of that. >> So if I may, I don't top award. While Bob was saying, I think what's gonna what What our clients are asking is, how can I exhilarate the decision making? Because at the end of the day on Lee, all our leaders are focused on making decisions, and all of this data science is leading up to their decision, and today you see like you know what you brought up, like 80% of the time is wasted in cleaning the data. So only 20% time was spent in riel experimentation and analytics. So your decision making time was reduced to 20% off the effort that I put in the pipeline. What if now I can make it 80% of the time? They're I put in the pipeline, better decisions are gonna come on the train. So when I go into a meeting and I'm saying like, Hey, can you show me what happened in this particular region or in this particular part of the country? Previously, it would have been like, Oh, can you come back in two weeks? I will have the data ready, and I will tell you the answer. But in two weeks, the business has ran away and the CDO know or the C Street doesn't require the same answer. But where we're headed as as the data quality improves, you can get to really time questions and decisions. >> So decision, sport, business, intelligence. Well, we're getting better. Isn't interesting to me. Six months to build a cube, we'd still still not good enough. Moving too fast. As the saying goes, data is plentiful. Insights aren't Yes, you know, in your view, well, machine intelligence. Finally, close that gap. Get us closer to real time decision >> making. It will eventually. But there's there's so much that we need to. Our industry needs to understand first, and it really ingrained. And, you know, today there is still a fundamental trust issues with a I you know, it's we've done a lot of work >> watch Black box or a part of >> it. Part of it. I think you know, the research we've done. And some of this is nine countries, 2400 senior executives. And we asked some, ah, a lot of questions around their data and trusted analytics, and 92% of them came back with. They have some fundamental trust issues with their data and their analytics and and they feel like there's reputational risk material reputational risk. This isn't getting one little number wrong on one of the >> reports about some more of an >> issue, you know, we also do a CEO study, and we've done this many years in a row going back to 2017. We started asked them okay, making a lot of companies their data driven right. When it comes to >> what they say they're doing well, They say they're day driven. That's the >> point. At the end of the day, they making strategic decisions where you have an insight that's not intuitive. Do you trust your gut? Go with the analytics back then. You know, 67% said they go with their gut, So okay, this is 2017. This industry's moving quickly. There's tons and tons of investment. Look at it. 2018 go down. No, went up 78%. So it's not aware this issue there is something We're fundamentally wrong and you hit it on. It's a part of its black box, and part of it's the date equality and part of its bias. And there's there's all of these things flowing around it. And so when we dug into that, we said, Well, okay, if that exists, how are we going to help organizations get their arms around this issue and start digging into that that trust issue and really it's the front part is, is exactly what we're talking about in terms of data quality, both structured more traditional approaches and unstructured, using the handwriting example in those types of techniques. But then you get into the models themselves, and it's, you know, the critical thing she had to worry about is, you know, lineage. So from an integrity perspective, where's the data coming from? Whether the sources for the change controls on some of that, they need to look at explain ability, gain at the black box part where you can you tell me the inferences decisions are those documented. And this is important for this me, the human in the loop to get confidence in the algorithm as well as you know, that executive group. So they understand there's a structure set of processes around >> Moneyball. Problem is actually pretty confined. It's pretty straightforward. Dono 32 teams are throwing minor leagues, but the data models pretty consistent through the problem with organizations is I didn't know data model is consistent with the organization you mentioned, Risk Bob. The >> other problem is organizational inertia. If they don't trust it, what is it? What is a P and l manage to do when he or she wants to preserve? Yeah, you know, their exit position. They attacked the data. You know, I don't believe that well, which which is >> a fundamental point, which is culture. Yes. I mean, you can you can have all the data, science and all the governance that you want. But if you don't work culture in parallel with all this, it's it's not gonna stick. And and that's, I think the lot of the leading organisations, they're starting to really dig into this. We hear a lot of it literacy. We hear a lot about, you know, top down support. What does that really mean? It means, you know, senior executives are placing bats around and linking demonstrably linking the data and the role of data days an asset into their strategies and then messaging it out and being specific around the types of investments that are going to reinforce that business strategy. So that's absolutely critical. And then literacy absolutely fundamental is well, because it's not just the executives and the data scientists that have to get this. It's the guy in ops that you're trying to get you. They need to understand, you know, not only tools, but it's less about the tools. But it's the techniques, so it's not. The approach is being used, are more transparent and and that you know they're starting to also understand, you know, the issues of privacy and data usage rights. That's that's also something that we can't leave it the curb. With all this >> innovation, it's also believing that there's an imperative. I mean, there's a lot of for all the talk about digital transformation hear it everywhere. Everybody's trying to get digital, right? But there's still a lot of complacency in the organization in the lines of business in operation to save. We're actually doing really well. You know, we're in financial service is health care really hasn't been disrupted. This is Oh, it's coming, it's coming. But there's still a lot of I'll be retired by then or hanging. Actually, it's >> also it's also the fact that, you know, like in the previous generation, like, you know, if I had to go to a shopping, I would go into a shop and if I wanted by an insurance product, I would call my insurance agent. But today the New world, it's just a top off my screen. I have to go from Amazon, so some other some other app, and this is really this is what is happening to all of our kind. Previously that they start their customers, pocketed them in different experience. Buckets. It's not anymore that's real in front of them. So if you don't get into their digital transformation, a customer is not going to discount you by saying, Oh, you're not Amazon. So I'm not going to expect that you're still on my phone and you're only two types of here, so you have to become really digital >> little surprises that you said you see the next. The next stage is being decision support rather than customer experience, because we hear that for CEOs, customer experience is top of mind right now. >> No natural profile. There are two differences, right? One is external facing is absolutely the customer internal facing. It's absolutely the decision making, because that's how they're separating. The internal were, says the external, and you know most of the meetings that we goto Customer insight is the first place where analytics is starting where data is being cleaned up. Their questions are being asked about. Can I master my customer records? Can I do a good master off my vendor list? That is where they start. But all of that leads to good decision making to support the customers. So it's like that external towards internal view well, back >> to the offense versus defense and the shift. I mean, it absolutely is on the offense side. So it is with the customer, and that's a more directly to the business strategy. So it's get That's the area that's getting the money, the support and people feel like it's they're making an impact with it there. When it's it's down here in some admin area, it's below the water line, and, you know, even though it's important and it flows up here, it doesn't get the VIN visibility. So >> that's great conversation. You coming on? You got to leave it there. Thank you for watching right back with our next guest, Dave Lot. Paul Gillen from M I t CDO I Q Right back. You're watching the Cube

(upbeat music) >> Hi, I'm Peter Burris. Welcome to another Cube Conversation from our beautiful studio here in Palo Alto, California, and once again I'm joined by Eric Herzog. Eric is the CMO and vice president of channels in IBM Sports Group, Eric? >> Well thank you, we love coming to see theCUBE. And by the way, I'm a Palo Alto native! >> Hey, I've heard that! You know, I don't think we're going to get into that today. Maybe some other time we can talk about that. But what I do want to talk about is, storage has historically been way down in the food chain of strategy, of business strategy. >> Eric: Right. >> Now we're talking about digital business. And digital business at least from our perspective, and I think you would agree, is predicated on the idea that you use data as an asset. In fact, Wikibon says that the difference between business and digital business is that digital businesses use data as an asset. Now, since storage is where you put that data, and is responsible for availability, throughput, reliability, flexibility, in how you do things differently with data, that kind of elevates storage's position as part of a strategic digital business capability. But nobody talks about it. So, as a smart guy in the storage community, I'd like to talk to you about that. First off, do you agree with my proposition? >> Ah yeah, I would posit that in today's digital business world build around private clouds, storage is, if not the critical foundation, one of the top two critical foundations you have to rely for your digital business. So if you're the CEO or you're the CFO and you know you're gone digital business, if the storage goes down or the storage is slow, your digital business and the value of that data you're driving both internally and for your customers just dramatically shrank. So it's critical, critical. Just as when you're building a giant building in downtown San Francisco or downtown New York or downtown Singapore, if you don't have that foundation right, the building literally can fall right down. >> And in San Francisco, one of the big buildings is actually starting to lean about two or three degrees. It's got a bunch of people pretty freaked out. But let's talk about, therefore what are the strategic digital business capabilities that are associated with storage that CXOs have to start thinking about? You mentioned one, we call it true private cloud. >> Right. >> The idea that you're still going to need some capacity where the data has to be. Because you're not going to be able to put everything in the public cloud. So a true private cloud is going to be one of them. >> Eric: Right. >> What's another one? >> So I think the big thing here is modern data protection. In the modern data protection scheme, because you're a digital business, which means you probably have test guys, you have some DevOps guys, inside, that are constantly playing with your own code or commercial code that you've bought. To optimize for your digital business, you've got to be able to give them real data sets to work with. Now that doesn't mean you can't track it in case there's a data leak or whatever, but the bottom-line is you want those developers to be A, always be able to get it, B, have it to be self service, because the IT guys are keeping your digital business up and going, they can't be bothered with hey can you get me real data set? The quality of the data that those developers produce for you goes way up, so you have less downtime which by the way, if you're a digital business you don't want to have any downtime. >> Peter: Right. >> And it gets it faster. So if you're playing with some commercial software writing your own that you're using in a digital business, you want to get there before your competitors do. >> Peter: Right. >> So you want those DevOps guys constantly working, and you want to be able to do that. Yet at the same time the IT guy can focus on what they need to do. And by the way, they can track where all those copies of the real data sets are. So everybody wins and it makes it work well. >> So in other respects, what you're saying is that availability, which used to be associated with the performance of an individual application, is now associated with digital business strategy. Because you need to be able to test new business ideas, pursue new business ideas, be fast in bringing things to market. And the whole notion of availability extends beyond just making sure the data is where you need it to be, or where the application says it needs to be, to now using availability as a metric for ensuring that the data can be where it needs to be within the business, so the business can make the appropriate changes and adjustments and extensions to what its strategy is. >> Well, think of this as you've got a data set. The more you use that data set, not just for the primary storage that's used by the end users that are coming to your digital business, but for everything else. Back it up, okay great. Check box. Okay, interface with common API so the DevOps guys can use it. Another check box. Making sure that the test guys can test real data sets, not faux ones, guess what? Faster test time, more accurate test time, boom, better impact for your digital business. So extending the value of the data from just primary storage throughout your entire digital software development process is critical. And in today's world, that's what you can do with the modern data protection that, for example, we have with our IBM solutions. >> So let me build on top of that, because I think another capability might be increasing the association with speed of development. It used to be, again, that storage was very closely aligned with what your server needed. Increasingly, we're starting to see the concept of data protection and the concept of data availability actually start to mean something very, very different to developers. Are we now seeing developers and the developer ecosystem start to drive more of what's required in storage? >> Well, you've got to look at it, the developer wants to be able to spin things up quickly on their own. So in the old world, you do a ticket, you give it to the VMaura guy, the HyperV guy, the IT guy, takes a week or so. You don't want to do that. So with our stuff, for example, you could check in and check out, it integrates with all their APIs, they can quickly do their work. It's a real data set, not using fake data, so that makes it better from a reliability perspective, and it gets done faster and they know how it really is going to work when you deploy that in your digital business. They can check it in and check it out themselves, so it happens way faster. So all that means for you as a digital business guy is better time and faster time to market with more reliable products for your end users and your clients. >> And that's kind of a key, that's kind of what the goal is. So we've got three thus far. This notion of true private cloud, where we need resources where the data demands. This notion of modern data protection, which is to say that the notion of availability is beyond just backup and restore, >> Right. >> Peter: So now the multiple ways to use it. New communities are going to be more closely associated with storage capabilities like the developer world. >> Eric: Right, the DevOps. >> Are there multi-cloud? Are there other kind of strategic business capabilities that CXOs have to think about as they envision their digital business strategy and the role that storage is going to play in either constraining it or facilitating it? >> Well, I think there's a couple things. First of all, you can look at it in three buckets. Item one is that your storage infrastructure, you still may have some of your older tuff. You still may be using Oracle, not yet using Hadoop. And using an Oracle data warehouse versus Hadoop big data analytic workload. >> I hear there's some customers out there that still have z-series installed, running things. >> So, you've got to be able to take the thing and cut costs on your traditional infrastructure, and your traditional applications workloads and use cases, while you're going to the next generation and modernizing. So you want to be able to handle the older workloads and cut their costs, at the same time invest more in things like Hadoop and Spark and Mango. And Cassandra. You want to be able to do both. At the same time, as you create your private cloud infrastructure, you want to be able to use new paragons, such as containers. And if you're going to do that, you want a set of storage solutions, both software and array infrastructure that can support that. And that's a critical element, is being able to A, modernize and cut costs of your older while you're moving to the new. For all your new stuff you want to be out that it's optimized and the DevOps guys can work it and you've got all the right APIs. And then, for the true private cloud, you're going to containerize model just like the public cloud guys do, because you want every advantage for your own digital business that the public does, and we can do all of that, but it's critical that you do all of that continuum, from the newest of the new, to the application in the middle, but you still have the old stuff while you're getting the new stuff up and running. So it's critical, if I'm the CEO, to make sure that my storage does all of that. 'Cuz if I fall down on the old stuff, well that's a problem. I can't bill, I can't invoice, I can't ship things. >> Right. >> If I fall down on the new stuff, guess what? I'm completely uncompetitive. >> Mm hm. >> Right? If I fail on the container world, what I'll call the refactoring of my infrastructure, I've totally lost the game because I'm not making it fast, I'm not making it resilient, I'm not cutting my costs, because everything is cost competitive. If data is the value, everything is built around that data, that doesn't mean you want your data to be super expensive, got to figure a way to do it cost-effectively, yet still deliver the value in your digital business that the end user wants. >> And it's got to be flexibility across the board. Okay, so we've got some strategic capabilities that CXOs have to think about that storage is going to enable. It's February 2018, we're looking at say, October 2019, next 18 months. What's going to be the one or two biggest changes in the storage world do you think? >> Ah, okay. First thing is going to be the automation of storage software across the board. Not just for the storage guy, not that all storage companies don't love the storage guy, but increasingly there's a move to DevOps and other functions. So, while each company is managing eons and eons and eons of more storage capacity, they're not adding eons and eons of storage admins. So you've got to have the docker guy, you've got to have the application guy, be able to backup, be able to optimize their workloads, be able to go ahead and spin up a new container without calling the IT guy, because the IT guys are overworked. So that's item one, is integration with all the coming APIs, automation, self-service are critical. The more it's automated the more self-serve is is, the more you can factor in the non-storage guys into creating a true digital business. That's on number one. Second thing you've got is a new technology known as NVME. This is a very high performance storage interface, it's new to the market, all the big storage vendors are working on it including IMB. We did an announcement on February 20th all about NVME. The value there is more and more applications and workloads, because the performance of the system itself, which is already highly resistant, highly available, and highly capable of handling any failure mode, is it's super fast, which means you can put more and more applications and workloads on a physical infrastructure, which saves you time and saves you money. So those are two critical things. The rise of this automation paradime, and the self-service paired across all of your storage software, and integrating it with your application layer, being able to use real data sets, right, as modern did, and then this new high-performance storage interface that will dramatically allow more workloads for every ounce of storage you buy, saving you money, and also making highly performing to meet your digital business SLAs. >> I think those are two great ones. I'm going to add one more and I'm sure we're going to be talking more about this. I think over the course of the next year and a half we're going to see even greater understanding of the relationship between storage and data, and new rules, new conventions, new approaches to how to think about that relationship so that all this great stuff that's happening when the storage actually does become a strategic business capability. >> Yeah, you could say that storage managing software will morph into data management, or at least a hybrid of partially managing the storage but actually also managing the data. And things like what is the metadata and how can you use metadata to more effectively manage your business. And we're going a whole bunch of that with IBM and we've already announced several things around that, so that's an actually great observation. >> And it's not too far from there to say digital asset management. Not in a traditional marketing sense, but overall how it works. Eric Herzog, CMO, vice president of channels, IMB storage, once again thanks for coming theCUBE and talking to us about some of the things that are going to happen over the next 18 months in the storage world. >> Great, thank you very much, we always apprecite being with you, and thanks again. >> I'm Peter Burris, once again this has been a Cube conversation from our Palo Alto studios with Eric Herzog of IBM. Until next time. (upbeat music)