(upbeat music) >> Hey, welcome back to Las Vegas. theCUBE is live at AWS re:Invent 2021. I'm Lisa Martin. We have two live sets, theCUBE. We are running one of the largest hybrid tech events, most important events of the year with AWS and its massive ecosystem of partners like as I said. Two live sets, two remote sets. Over a hundred guests on the program talking about the next generation of cloud innovation. I'm pleased to welcome a first timer to theCUBE. Seth Rao, the CEO of FirstEigen joins me. Seth, nice to have you on the program. >> Thank you nice to be here. >> Talk to me about FirstEigen. Also explain to me the name. >> So FirstEigen is a startup company based out of Chicago. The name Eigen is a German word. It's a mathematical term. It comes from eigenvectors and eigenvalues which is used and what it's called is principal component analysis, which is used to detect anomalies, which is related to what we do. So we look for errors in data and hence our name FirstEigen. >> Got it. That's excellent. So talk to me. One of the things that has been a resounding theme of this year's re:Invent is that especially in today's age, every company needs to be a data company. >> Yeah. >> It's all one thing to say it's as a whole other thing to be able to put that into practice with reliable data, with trustworthy data. Talk to me about some of the challenges that you help customers solve 'cause some of the theme about not just being a data company but if you're not a data company you're probably not going to be around much longer. >> Yeah, absolutely .So what we have seen across the board across all verticals, the customers we work with is data governance teams and data management teams are constantly firefighting to find errors in data and fix it. So what we have done is we have created the software DataBuck that autonomously looks at every data set and it will discover errors that are hidden to the human eye. They're hard to find out, hard to detect. Our machine learning algorithms figure out those errors before those errors impact the business. In the usual way, things are sorted out, things are done. It's very laborious, time-consuming and expensive. You have taken a process that takes man-years or even man-months and compressed it to a few hours. >> So dramatic time-savings there. >> Absolutely. >> So six years ago when you guys were founded, you realize this gap in the market, thought it's taking way too long. We don't have this amount of time. Gosh, can you imagine if you guys weren't around the last 22 months when certainly time was of the essence? >> Absolutely. Yeah. Six years ago when we founded the company, my co-founder who's also the CTO. He has extensive experience in validating data and data quality. And my own background and my own experiences in AI and ML. And what we saw was that people are spending an enormous amount of time and yet errors were getting down through to the business side. And at that point it comes back and people are still firefighting. So it was a waste of time, waste of money, waste of effort. >> Right. But also there's the potential for brand damage, brand reputation. Whatever products and services you're producing, if your employees don't have the right data, if there's errors there of what's going out to the consumers is wrong then you've got a big problem. >> Absolutely. Interesting you should mention that because over the summer there was a Danish bank, a very big name Danish bank that had to send apology letters to its customers because they overcharged them on the mortgage because the data in the backend had some errors in it and didn't realize it was inadvertent. But somebody ultimately caught it and did the right thing. Absolutely correct. If the data is incorrect and then you're doing analytics or you're doing reporting or you're sending people a bill that they need to pay it better be very accurate. Otherwise it's a serious brand damage. It has real implications and it has a whole bunch of other issues as well. >> It does and those things can snowball very quickly. >> Yeah. >> So talk to me about one of the things that we've seen in the recent months and years is this explosion of data. And then when the pandemic struck we had this scattering of people and data sources or so much data. The edge is persistent. We've got this work from anywhere environment. What are some of the risks for organizations? They come to you and saying help us ensure that our data is trustworthy. I mean that the trust is key but how do you help organizations that are in somewhat a flux figure out how to solve that problem? >> Yeah. So you're absolutely correct. There is an explosion of data, number one. And along with that, there is also an explosion of analytical tools to mine that data. So as a consequence, there is a big growth. It's exponential growth of microservices, how people are consuming that data. Now in the old world when there were a few consumers of data, it was a lot easier to validate the data. You had few people who are the gatekeepers or the data stewards. But with an explosion of data consumers within a company, you have to take a completely different approach. You cannot now have people manually looking and creating rules to validate data. So there has to be a change in the process. You start validating the data. As soon as the data comes into your system, you start validating if the data is reliable at point zero. >> Okay. >> And then it goes downstream. And every stage the data hops that is a chance that data can get corrupted. And these are called systems risks. Because there are multiple systems and data comes from multiple systems onto the cloud, errors creep in. So you validate the data from the beginning all the way to the end and the kinds of checks you do also increase in complexity as the data is going downstream. You don't want to boil the ocean upfront. You want to do the essential checks. Is my water drinkable at this point, right? I'm not trying to cook as soon as it comes out of the tap. Is it drinkable? - Right. >> Good enough quality. If not then we go back to the source and say, guys, send me better quality data. So sequence, the right process and check every step along the way. >> How much of a cultural shift is FirstEigen helping to facilitate within organizations that now don't... There isn't time to, like we talked about if an error gets in, there's so many downstream effects that can happen, but how do you help organizations shift their mindset? 'Cause that's hard thing to change. >> Fantastic point. In fact, what we see is the mindset change is the biggest wall for companies to have good data. People have been living in the old world where there is a team that is a group, much downstream that is responsible for accurate data. But the volume of data, the complexity of data has gone up so much that that team cannot handle it anymore. It's just beyond their scope. It's not fair for us to expect them to save the world. So the mindshift has to come from an organization leadership that says guys, the data engineers who are upfront who are getting the data into the organization, who are taking care of the data assets have to start thinking of trustable data. Because if they stopped doing it, everything downstream becomes easy. Otherwise it's much, much more complex for these guys. And that's what we do. Our tool provides autonomous solution to monitor the data. It comes out with a data trust score with zero human input. Our software will be able to validate the data and give an objective trust score. Right now it's a popularity contest. People are saying they vote. Yeah, I think I like this. I like this and I like that. That's okay. Maybe it's acceptable. But the reason they do it is because there is no way to objectively say the data is trustable. If there is a small error somewhere, it's a needle in the haystack. It's hard to find out, but we can. With machine learning algorithms our software can detect the errors, the minutest errors, and to give an objective score from zero to a hundred, trust or no trust. So along with a mindset, now they have the tool to implement that mindset and we can make it happen. >> Talk to me about some of the things that you've seen from a data governance perspective, as we've seen, the explosion, the edge, people working from anywhere. This hybrid environment that we're going to be in for quite some time. >> Yeah. >> From a data governance perspective and Dave Vellante did his residency. We're seeing so many more things pop up, you know different regulations. How do you help facilitate data governance for organizations as the data volume is just going to continue to proliferate? >> Absolutely correct. So data governance. So we are a key component of data governance and data quality and data trustworthiness, reliability is a key component of it. And one of the central, one of the central pillars of data governance is the data catalog. Just like a catalog in the library. It's cataloging every data asset. But right now the catalogs, which are the mainstay are not as good as they can be. A key information that is missing is I know where my data is what I don't know is how good is my data? How usable is it? If I'm using it for an accounts receivable or an accounts payable, for example, the data better be very, very accurate. So what our software will do is it'll help data governance by linking with any data governance tool and giving an important component which is data quality, reliability, trustability score, which is objective to every data asset. So imagine I open the catalog. I see where my book is in the library. I also know if there are pages missing in the book is the book readable? So it's not good enough to know that I have a book somewhere but it's how good is it? >> Right >> So DataBuck will make that happen. >> So when customers come to you, how do you help them start? 'Cause obviously the data, the volume it's intimidating. >> Yeah. >> Where do they start? >> Great. This is interestingly enough a challenge that every customer has. >> Right. >> Everybody is ambitious enough to say, no, I want to make the change. But the previous point was, if you want to do such a big change, it's an organizational change management problem. So the way we recommend customers is start with the small problem. Get some early victories. And this software is very easy. Just bring it in, automate a small part. You have your sales data or transactional data, or operational data. Take a small portion of it, automate it. Get reliable data, get good analytics, get the results and start expanding to other places. Trying to do everything at one time, it's just too much inertia, organizations don't move. You don't get anywhere. Data initiatives will fail. >> Right. So you're helping customers identify where are those quick wins? >> Yes. And where are the landmines that we need to be able to find out where they are so we can navigate around them? >> Yeah. We have enough expedience over 20 years of working with different customers. And I know if something can go wrong we know where it'll go wrong and we can help them steer them away from the landmines and take them to areas where they'll get quick wins. 'Cause we want the customer to win. We want them to go back and say, look, because of this, we were able to do better analytics. We are able to do better reporting and so on and so forth. We can help them navigate this area. >> Do you have a favorite example, customer example that you think really articulates that value there, that we're helping customers. We can't boil the ocean like you said. It doesn't make any sense, but customer that you helped with small quick wins that really just opened up the opportunity to unlock the value of trustable data. >> Absolutely. So we're working with a fortune 50 company in the US and it's a manufacturing company. Their CFO is a little in a concern whether the data that she's reporting to the Wall Street is acceptable, does it have any errors? And ultimately she signing off on it. So she had a large team in the technology side that was supporting her and they were doing their best. But in spite of that, she's a very sharp woman. She was able to look and find errors and saying, "Something does not look right here guys. Go back and check". Then it goes back to the IT team and they go, "Oh yeah, actually, there was an error". Some errors had slipped through. So they brought us in and we were able to automate the process, What they could do. They could do a few checks within that audit window. We were able to do an enormous number of checks more. More detailed, more accurate. And we were able to reduce the number of errors that were slipping through by over 98%. >> Big number. >> So, absolutely. Really fast. Really good. Now that this has gone through they feel a lot more comfortable than the question is, okay. In addition to financial reporting, can I use it to iron out my supply chain data? 'Cause they have thousands of vendors. They have hundreds of distributors. They have products all over the globe. Now they want to validate all the data because even if your data is off in a one or 2%, if you're a hundred plus billion dollar company, it has an enormous impact on your balance sheet and your income statement. >> Absolutely. Yeah. >> So we are slowly expanding as soon as they allow us. They like us now they're taking it to other areas from beyond finance. >> Well it sounds like you have not only great technology, Seth but a great plan for helping customers with those quick wins and then learning and expanding within and really developing that trusted relationship between FirstEigen and your customers. Thank you so much for joining me on the program today. Introducing the company, what you guys are doing really cool stuff. Appreciate your time. >> Thank you very much. >> All right. >> Pleasure to be here. >> For Seth Rao, I'm Lisa Martin. You're watching theCUBE. 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hey everyone this is thecube's live coverage from los angeles of kubecon and cloud native con north america 21 lisa martin with dave nicholson we're going to be talking with the founder and ceo next of cloudnatics rohit seth rohit welcome to the program thank you very much lisa pleasure to meet you good to meet you too welcome so tell the the audience about cloudnatics what you guys do when you were founded and what was the gap in the market that you saw that said we need a solution so just to start uh cloud9x was started in 2019 by me and the reason for starting cloud netex was as i was starting to look at the cloud adoption and how enterprises are kind of almost blindly jumping on this cloud bandwagon i started reading what are the key challenges the market is facing and it started resonating with what i saw in google 15 years before when i joined google the first thing i noticed was of course the scale would just overwhelm anyone but at the same time how good they are utilized at that scale was the key that i was starting to look for and over the next couple of months i did all the scripting and such with my teams and found out that lower teens is the utilization of their computers servers and uh lower utilization means if you're spending a billion dollars you're basically wasting the major portion of that and a tech savvy company like google if that's a state of affair you can imagine what would be happening in other companies so in any case we actually now started work at that time started working on a technology so that more groups more business units could share the same machine in a efficient fashion and that's what led to the invention of containers over the next six years we rolled out containers across the whole google fleet the utilization went up at least three times right fast forward 15 years and you start reading 125 billion dollars are spent on a cloud and 60 billion dollars of waste someone would say 90 billion dollars a waste you know what i don't care whether 60 or 90 billion is a very large number and if tech savvy company google couldn't fix it on its own i bet you it it's not an easy problem for enterprises to fix it so we i started talking to several executives in the valley about is this problem for real or not the worst thing that i found was not only they didn't know how bad the problem was they actually didn't have any means to find out how bad the problem could be right one cfo just ran like headless chicken for about two months to figure out okay i know i'm spending this much but where is that spend going so i started kind of trading those waters and i started saying okay visibility is the first thing that we need to provide to the end customer saying that listen it doesn't need to be rocket science for you to figure out how much is your marketing spending how much your different business units so the first line of action is basically give them the visibility that they need to make the educated business decisions about how good or how bad they are doing their operations once they have the visibility the next thing is what to do if there is a waste there are a thousand different type of vms on aws alone people talk about complexity on multi-cloud hybrid cloud and that's all right but even on a single cloud you have thousand vms the heterogeneity of the vms with dynamic pricing that changes every so often is a killer and so and so rohit when you talk about driving levels of efficiency you're not just you're not just talking about abstraction versus bare metal utilization you're talking about even in environments that have used sort of traditional virtualization yes okay absolutely i think all clouds run in vms but within vms sometimes you have containers sometimes you don't have containers if you don't have containers there is no way for you to securely have a protagonist and antagonist job running on the same machines so containers basically came to the world just so that different applications could share the same resources in a meaningful fashion we are basically extending that landscape to to the enterprises so that that utilization benefit exists for everyone right so first of first order business for cloud natick is basically provide them the visibility on how well or bad they are doing the second is to give them the recommendation if you are not doing well what to do about it to do well and we can actually slice and dice the data based on what is important for you okay we don't tell you that these are the dimensions that you should be looking at of course we have our recommendations but we actually want you to figure out basically do you want to look at your marketing organization or your engineering organization or your product organization to see where they are spending money and you can slice and match that data according and we'll give you recommendations for those organizations but now you have the visibility now you have the recommendations but then what right if you ask a cubernities administrator to go and apply those recommendations i bet you the moment you have more than five cluster which is a kind of a very ordinary thing it'll take at least two hours just to figure out how to go from where you are to be able to log in and to be able to apply those recommendations and then changing back the ci cd pipelines and asking your developers to be cognizant about your resources next time is a month-long ordeal no one follows it that's why those recommendations falls on deaf ears most of the time what we do is we give you the choice you want to apply those recommendations manually or you can put the whole system on autopilot in which case once you have enough confidence in cloud native platform we will actually apply those recommendations for you dynamically on the fly as your workloads are increasing or decreasing in utilization and where are your customer conversations happening you mentioned the cfl you mentioned the billions in cloud waste where do you start having these conversations within an organization because clearly you mentioned marketing services you can give them that visibility across the organization who are you talking to within these customers so we start with mostly the cios ctos vp of engineering but it's very interesting we say it's a waste and i think the waste is most more of an effect than a cause the real cause is the complexity and who is having the complexity is the devops and the developers so in 99 of our customer interactions we basically start from cios and ctos but very soon we have these conversations over a week with developers and devops leads also sitting in the room saying that but this is a challenge on why i cannot do this so what we have done is to address the real cause and waste aspect of cloud computing we have we have what we call the management console through which we reduce the complexity of kubernetes operations themselves so think about how you can log into a crashing pod within two minutes rather than two hours right and this is where cloud native start differentiating from the rest of the competition out there because we provide you not only or do this recommendation do this right sizing of vm here or there but this is how you structurally fix the issue going forward right i'm not going to tell you that your containers are not going to crash loop their failures are regular part of distributed systems how you deal with them how you debug them and how you get it back up and running is a core integral part of how businesses get run that's what we provide in cloud natives platform a lot of this learning that we have is actually coming from our experience in hyperscalers we have a chief architect who is also from google he was a dl of a technology called borg and then we have sonic who was the head of products at mesosphere before so we understand what it takes for an enterprise who's primarily coming from on-prem or even the companies that are starting from cloud to scale in cloud often you hear this trillion dollar paradoxes that hey you're stupid if you don't start from cloud and you're stupid if you scale at cloud we are saying that if you're really careful about how you function on cloud it has a value prop that can actually take you to the web scalar heights without even blinking twice can you share an example of one of your favorite customer stories absolutely even by industry only where you've really shown them tremendous value in savings absolutely so a couple of discussions that happened that led like oh but we are we have already spent a team of four people trying to optimize our operations over the last year and we said that's fine uh you know what our onboarding exercise takes only 20 minutes right let's do the onboarding in about a week we will tell you if we could save you any money or not and put your best devops on this pov prove a value exercise to see if it actually help their daily life in terms of operations or not this particular customer only has 30 clusters so it's not very small but it's not very big in terms of what we are seeing in the market first thing the maximum benefit or the cost optimization that they could do over the past year using some of the tools and using their own top-class engineering shots were about seven to ten percent within a week we told them 38 without even having those engineers spend more than two hours in that week we gave them the recommendations right another two weeks because they did not want to put it on autopilot just because it's a new platform in production within next two hours they were able to apply i think at least close to 16 recommendations to their platform to get that 37 improvement in cost what are some examples of of recommendations um obviously you don't want to reveal too much of the secret sauce behind the scene but but but you know what are some what are some classic recommendations that are made so some of them could be as low-hanging fruit as or you have not right sized your vms right this is what i call a lot of companies you would find that oh you have not right side but for us that's the lowest hanging code you go in and you can tell them that whether you have right size that thing or not but in kubernetes in particular if you really look at how auto scaling up and how auto scaling down happens and particularly when you get a global federated view of the number of losses that's where our secret sources start coming and that's where we know how to load balance and how to scale vertically up or how to scale horizontally within the cluster right those kind of optimization we have not seen anywhere in the market so far and that's where the most of the value prop that our customers are seeing kind of comes out and it doesn't take uh too much time i think within a week we have enough data to to say that this service that has thousands of containers could benefit by about this much and just to kind of give you i wouldn't be able to go into the specific dollar numbers here but we are talking in at least a 5 million ish kind of a range of a spend for this cluster and think about it 37 of that if we could save that that kind of money is a real money that not only helps you save your bottom line but at that level you're actually impacting your top line of the business as well sure right that's our uh value crop that we are going to go in and completely automate you're not going to look for devops that don't exist anymore to hire one of the key challenges i'm pretty sure that you must have already heard 86 percent of businesses are not able to hire the devops and they want to hire 86 percent what happens when you don't have that devops that you want to have your existing devops want to move as fast cutting corners sometimes not because they don't know anywhere but just because there's so much pressure to do so much more they don't scale when things become brittle that's when um the fragility of the system comes up and when the demand goes up that's when the systems break but you're not prepared for that breakage just because you have not really done the all the things that you would have done if you had all the time that you needed to do the right thing it sounds like some of the microservices that are in containers that are that run the convention center here have just crashed i think it's gone hopefully the background noise didn't get picked up too much yeah but you're the so the the time to value the roi that you're able to deliver to customers is significant yes you talked about that great customer use case are there any kind of news or announcements anything that you want to kind of share here that folks can can be like looking forward to without the index absolutely so two things even though this is kubecon and everyone is focused on kubernetes kubernetes is still only about three to five percent of enterprise market okay we differentiate ourselves by saying that it doesn't matter whether you're running kubernetes or you're in running legacy vms we will come on board in your environment without you making a single line of change in less than 20 minutes and either we give you the value prop in one week or we don't all right that's number one number two we have a webinar coming on november 3rd uh please go to cloudnetix.com and subscribe or sign up for that webinar sonic and i will be presenting that webinar giving you the value proposition going through some use cases that oh we have seen with our customers so far so that we can actually educate the broader audience and let them know about this beautiful platform i think that my team has built up here all right cloudnatics.com rohit thank you for joining us sharing with us what you're doing at cloud natives why you founded the company and the tremendous impact and roi that you're able to give to your customers we appreciate learning more about the technology thank you so much and i really believe that cloud is here for stay for a long long time it's a trillion dollar market out there and if we do it right i do believe we will accelerate the adoption of cloud even further than what we have seen so far so thanks a lot lisa it's been a pleasure nice to meet you it's a pleasure we want to thank you for watching for dave nicholson lisa martin coming to you live from los angeles we are at kubecon cloudnativecon north america 21. dave and i will be right back with our next guest thank you you

>>From around the globe. It's the queue with digital coverage of IBM. Think 20, 21 brought to you by IBM. Okay. We're back with our coverage of IBM. Think 2021. This is Dave Vellante and this is the cube. We're not going to dig into AI and explore the issue of trust in machine intelligence. And we're very excited to welcome. Long-time cube alum, Seth Dobrin, who is the global chief AI officer at IBM. Seth. Always good to see you. Thanks so much for making time for us. >>Yeah, always good to see you, Dave. Thanks for having me on again. >>It's our pleasure. Um, look, language matters and IBM has been talking about language automation and, and trust. And I know you're doing a session on trustworthy AI. Can you talk about trust in the world of machine intelligence and AI, what we should, what do we need to know? >>Yeah. So, you know, as you mentioned, you know, language, language matters, automation matters and to do either of those well, you need to really trust, uh, the AI that's coming out of, of your models and trusting the AI really means five things. There's I think of it as there's five pillars, uh, the AI needs to be transparent that AI needs to be fair. It needs to be explainable. It needs to be robust, robust, and it needs to ensure privacy. And without those five things, all combined, you don't really have the ability to trust your AI either as a consumer or even as an end user. So imagine I'm a business user that's supposed to use this cool new AI, that's going to help me make a decision if I don't understand it. And I can't figure out how it got to a decision, I'm going to be less likely to consume it and use it in my day-to-day work. Whereas if I can really understand how and why it got to a decision and know that it's, you know, protecting the, the ultimate end users data, it's gonna be a lot more easy, a lot more likely that I'll, I'll end up using that AI, >>But there is the black box problem with, with, with AI and, and, uh, you know, how is that a technical issue? Uh, I mean, how do you get around that? I mean, this is, it seems non-trivial. >>Yeah. So, you know, I think solving the black box problem of AI specifically of either complex traditional machine learning models or what we think of as deep learning models is not a trivial problem, but, you know, IBM and others over the years have been really trying to tackle this, this problem of explainable AI. And we've come to a point in the world now where we've, we really believe that we have a good handle on how to, how to explain these black box models. How do you basically interpret it, interpret their results and explain them from, from the end point and understand what went into each decision at each layer in the model, if it's deep learning model to kind of be able to extract why and how it's making a certain decision. So I don't, you know, three years ago it was, you know, we thought of it as an intractable problem, but we knew we'd be able to solve it in the future. I think we're at that future today, where unless you get into something incredibly complex, we can, we can explain how and why it got to a decision. And we do this through various sets of tooling that we have, some are open source, you know, we're, we're so committed to, to explainable and fair and fair and trust when it comes to AI. That probably half of what we do is that the open source community in the form of what we call our AI fairness three 60 tool kits. Right. >>Great. Thank you for that. So let me ask you another sort of probing question here is, is there a risk, I mean, people talk about that. There's maybe a risk of, of putting too much attention on trust mean early days in the AI journey. And people are worried that it's going to stifle projects maybe slow down innovation, or maybe even be a headwind to AI adoption, adoption and scale. What are your thoughts on that? >>You know, I, I, I, I think it's a slippery slope to say it's to serve soon to ever be concerned about fairness, trust, privacy, bias, explainability, you know, what we think of as trustworthy AI. Um, I think if you, you can do very interesting and very exciting and very innovative and game changing things in the context of doing what's right. Um, and it is right, especially when you're building AIS or anything that actually impacts people's lives to make sure that it's trustworthy to make sure that it's, you know, it's, it's transparent, it's fair, it's explainable, it's robust. And it ensures the privacy of the underlying data in that model. Otherwise, you know, you get into a point where you may be able to do cool things, but those cool things get undermined by previous missteps that have caused the industry or the tools or the technology to get a bad rap. >>I mean, a great example of that is, I mean, look at the conversational AI that were released in the wild and Twitter and Facebook without any kind of thought about how do you keep them trustworthy. Um, you know, that, that, you know, that went bad really quick. And we want to make sure that, you know, our, we don't, we don't, you know, IBM's a consumer facing company we're kind of the, you know, the IBM inside, if you will, right. We want to make sure that when, you know, when the, the world's largest companies are deploying, IBM's AI, we're using IBM's pools to deploy their own AI that it's done in a way that gives them the ability to make sure that things don't go off the rails quickly, because we're not talking about a conversational Twitter bot we're talking about potentially, you know, an AI that's going to help make a life changing decision. Like, do you, you know, do we keep Dave a mortgage? Uh, do we let Dave at a jail? Is he likely to recidivate? Um, you know, is he, you know, things like that that are actually life-changing and not just going to be embarrassment for the company, it's important to keep trucking >>Great points you make. I mean, you're right. And it did turn bad, uh, very quickly. And it's not resolved. I mean, a lot of the social companies are saying, well, government, you figure it out. We can't. So let's bring it back to the enterprise. That's what we're kind of interested in where IBM's main focuses right now. And where do you see it going? I mean, you mentioned, you know, things like recidivism and mortgages. I mean, these, these really are events that you can predict with very high probability. You know, maybe you don't get it a hundred percent. Right. Uh, but it really is world changing in, in many ways. Where's the focus now? Where do you see it headed? >>Yeah. So, so I think the focus is now, or has been for a little while and continues to be, and probably will be in the future on augmenting intelligence. So especially when it comes to life-changing decisions, we don't really want an AI making that decision independent of the human. Uh, we want that AI guiding the decisions that humans make. Um, and, you know, but reducing the, the, the, the universe of those decisions, something down to something that's digestible, uh, by, by a human and also at the same time using the AI to help eliminate biases, cognitive biases that may exist within, within us as humans. So, and when we think about things like bias, uh, we have to remember too, that the, by the math itself is not where the bias is coming from, right? The bias is coming from the data. And the bias in the data comes from prior decisions of humans that were themselves done for, for bias reasons, right? >>And so by leveraging AI to remove the bias from the decisions that are surface to humans, it helps eliminate some of these things. So for instance, you know, back to the mortgage example, right, if we look at the impact of redlining, right, where people in certain zip codes or certain addresses, certain areas didn't get mortgages that red lining still exists in the data. I don't know of a single mortgage provider today that wants to have that in part of their day-to-day practices helps remove that from them and surface a decision that's based on the context of the individual, based on their, you know, their, their ability to repay in the case of a mortgage, as opposed to what they look like or where they live. >>I mean, I liked the concept of the common editorial power of machines and humans. Um, but, but I think there's, well, there's all, I wonder if you could sort of educate me on them. And there seems to be a lot of potential use cases for many companies, but IBM as well for inference, you know, at the edge. I mean, everybody's talking about the edge OpenShift obviously is a big play there hybrid cloud. Uh, so how do you see that, that kind of real-time inference playing it is that date, certain data comes back to the model and that's where the humans come in. How do you see that? >>Yeah, so, so I often get asked, what do you, you know, what do you see as the future of AI? And my response is the future of AI is edge. And, and the reason for that is if I can solve for an edge use case, I can solve for every use case between the edge and the data center. Right. And some of the challenges that you brought up, uh, as far as being on the edge, get back to, you know, and it actually helps address other problems too, such as how do I handle data sovereignty regulations when it comes to, to training models, even models themselves. Right. Um, but when you think about, you know, I have 50 models deployed around the world, there are 50 50 versions of the same model deployed around the world at different scoring end points or different places where I'm inferencing. >>Um, how do I, without having to bring the model back or all the data back, how do I keep all those models in sync? How do I make sure that, you know, back to the social media example, that one of them doesn't go completely off the rails. And we do this through federated learning, right? And this is, or distributed, distributed learning, federated learning, whatever you want to call it. It's this concept of you have models running at discrete edge locations or discrete distributed locations, those models over time, learn from the data that they're, that they're scoring on. Um, and instead of sending the data back to retrain, or even the model back to retrain, you send back the, the height, the changes in the parameters, uh, that have been updated in that model. You can then pull, put all those together. So you have 50 different distributions that you're managing. You pull all those together, and you can even assign weights to the different ones based on bias that might exist, you know, uh, not biopsies, but different distributions that may exist. That one node or another, you can do it based on the amount of data that's been going into econ into changing those weights. And so you, you combine these models back into a single model, then you push them back out to the edge. >>So I just thinking about, I mean, cause Mo most of the work in AI today, correct me if I'm wrong is in, in, in, I would say modeling versus influencing. And, but, but if you're laying out a future where that's going to change, and if I think about some of the things that we're familiar with today, things like fraud detection and maybe weather, um, supply chains and, and, and that's just going to get better and better and better. But I think about some of the areas, and I'm curious if you could maybe talk to some of the use cases you're seeing in examples both today and the future, but I think of things like, you know, smart power grid, smart factories, automated retail, I mean, he seemed like wheelhouses for, for IBM. So maybe you could share with us your thoughts on that. And some other examples. Yeah. >>So you brought up, you know, fraud fraud is a really good example of an edge use case that might not seem like an edge use case. Um, and so, you know, as you're swiping a credit card and let's just focus on credit card transactions, most of those transactions occur on a mainframe and, you know, they, they need a response time that's, you know, less than a millisecond. And so if I'm, if I'm, if I'm responsible for making sure that my bank doesn't have any credit card fraud, and I have a model that's going to do it, I can't have, in this case, the mainframe call-out to someplace else to score the model and then come back and this gets back to the power of hybrid cloud, right? And so if I can deploy that model on my main frame, where those transactions are happening, you know, half a millisecond at a time, I can then score every single transaction that comes back and on the mainframe directly without having to go out, which enables me to keep my SLA enables me to keep that less than half a millisecond response time while also preventing any fraudulent transactions from happening. >>That's a great example of what I would call let's. Let's fall into my, everything is edge bucket, right? Where, yeah, you're training the model somewhere else where you don't have the cost of running it, training it on the mainframe, but I want to score it back there. And we've actually done this with a couple of banks where we've trained models in the cloud, on GPS and done the influencing and scoring on the mainframe for just exactly that for fraud >>Edge. If you can make it there, you can make it anywhere. Uh, Seth, we gotta leave it there. Uh, really, really appreciate your time. >>All right. Great to have great for having me. Thanks, Dave. I appreciate it as always. >>Great to see you hope we can see you later this year, face to face, or at least in 22. And, uh, and thank you. I hope so. Yeah. Let's, let's make that happen. So that's where those virtual shake on it. Thanks everybody for watching our ongoing coverage of IBM think 2021, the virtual edition is Dave Volante for the cube. Keep it right there for more great content from the show.

(Upbeat music) >> Narrator: From around the globe. It's The Cube with digital coverage of IBM Think 2021, brought to you by IBM. >> Okay. We're back with our coverage of IBM Think 2021. This is Dave Vellante and this is The Cube. We're now going to dig into AI and explore the issue of trust in machine intelligence. And we're very excited to welcome longtime Cube alum Seth Dobrin, who is a global chief AI officer at IBM. Seth, always good to see you. Thanks so much for making time for us. >> Yeah, always good to see you, Dave. Thanks for having me on again. >> It's our pleasure. Look, language matters and IBM has been talking about language, automation, and-- and trust. And I know you're doing a session on trustworthy AI. Can you talk about trust in the world of machine intelligence and AI, what we should-- what do we need to know? >> Yeah. So, you know as you mentioned, you know, language, language matters. Automation matters. And to do either of those well, you need to really trust the AI that's coming out of-- of your models. And trusting the AI really means five things. There's-- I think of it as there's five pillars. The AI needs to be transparent. The AI needs to be fair. It needs to be explainable. It needs to be robust. And it needs to ensure privacy. And without those five things all combined, you don't really have the ability to trust your AI either as a consumer or even as an end user. So if I imagine I'm a business user that's supposed to use this cool, new AI that's going to help me make a decision, if I don't understand it and I can't figure out how it got to a decision, I'm going to be less likely to consume it and use it in my day-to-day work. Whereas if I can really understand how and why it got to a decision and know that it's, you know, protecting the ultimate end user's data, it's going to be a lot more easy a lot more likely that I'll end up using that AI. >> But there is the black box problem with, with, with AI and, and, you know, how-- is that a technical issue? I mean, how do you get around that? I mean, this is, it seems non-trivial. >> Yeah. So, you know I think solving the black box problem of AI specifically of either complex traditional machine learning models or what we think of as deep learning models is not a trivial problem, but, you know, IBM and others over the years have been really trying to tackle this this problem of explainable AI. And we've come to a point in the world now where we really believe that we have a good handle on how to, how to explain these black box models. How do you basically interpret it-- interpret their results and explain them from the end point and understand what went into each decision at each layer in the model, if it's deep learning model, to kind of be able to extract why and how it's making a certain decision. So I don't, you know, three years ago it was, you know we thought of it as an intractable problem but we knew we'd be able to solve it in the future. I think we're at that future today, where unless you get into something incredibly complex, we can, we can explain how and why it got to a decision. >> Awesome >> And we do this through various sets of tooling that we have. Some are open source, you know, we're so committed to, to explainable and fair and trust when it comes to AI, that probably half of what we do is at the open source community in the form of what we call our AI fairness 360 tool kits. >> Great. Thank you for that. So let me ask you another sort of probing question here. Is there a risk-- I mean, people talk about that there's maybe a risk of putting too much attention on trust and these are early days in the AI journey and people are worried that it's going to stifle projects, maybe slow down innovation, or maybe even be a headwind to AI adoption and scale. What are your thoughts on that? >> You know, I, I, I-- I think it's a slippery slope to say it's too soon to ever be concerned about fairness, trust, privacy, bias, explainability, you know, what we think of as trustworthy AI. I think if you-- you can do very interesting and very exciting and very innovative and game changing things in the context of doing what's right. And it is right, especially when you're building AIs or anything that actually impacts people's lives to make sure that it's trustworthy. To make sure that it's, you know, it's, it's transparent, it's fair, it's explainable, it's robust. And it ensures the privacy of the underlying data in that model. Otherwise, you know, you get into a point where you may be able to do cool things but those cool things get undermined by previous missteps that have caused the industry or the tools or the technology to get a bad rap. I mean, a great example of that is, I mean, look at the conversational AI that were released in the wild in Twitter and Facebook without any kind of thought about how do you keep them trustworthy? You know, that-- you know, that went bad really quick. And we want to make sure that, you know, our-- we don't-- we don't, you know, IBM's not a consumer facing company, we're kind of the, you know, the IBM inside, if you will. >> Dave: Right >> Right? We want to make sure that when, you know, when the-- the world's largest companies are deploying, IBM's AI, or using IBM's tools to deploy their own AI, that it's done in a way that gives them the ability to make sure that things don't go off the rail quickly. 'Cause we're not talking about a conversational Twitter bot. We're talking about potentially, you know, an AI that's going to help make a life-changing decision. Like, do you, you know, do we give Dave a mortgage? Do we let Dave out of jail? Is he likely to recidivate? You know, is he, you know-- things like that that are actually life changing and not just going to be embarrassment for the company. So it's important to keep trust upfront. >> Great points you make. I mean, you're right. And it did turn bad very quickly and it's not resolved. I mean, a lot of the social companies are saying, "Oh, government you figure it out." We can't. (chuckles) So let's bring it back to the enterprise. That's what we're kind of interested in where IBM's main focus is right now. And where do you see it going? I mean, you mentioned, you know, things like recidivism and mortgages. I mean, these, these really are events that you can predict with very high probability. You know, maybe you don't get it a hundred percent right. But it really is world changing in, in many ways. Where's the focus now? And where do you see it headed? >> Yeah, so, so I think the focus is now, or has been for a little while and continues to be, and probably will be in the future on augmenting intelligence. >> Dave: Mmm hmm >> So especially when it comes to life-changing decisions, we don't really want an AI making that decision independent of the human. We want that AI guiding the decisions that humans make and you know-- but reducing the universe of those decisions, something-- down to something that's digestible by, by a human, and also at the same time using the AI to help eliminate biases, cognitive biases that that may exist within us as humans. So, and when we think about things like bias, we have to remember too, that the bi- the math itself is not where the bias is coming from, right? The bias is coming from the data. And the bias in the data comes from prior decisions of humans that were themselves done for bias reasons, right? And so by leveraging AI to remove the bias from the decisions that are surface to humans, it helps eliminate some of these things. So for instance, you know, back to the mortgage example, right? If we look at the impact of redlining, right? Where people in certain zip codes or certain addresses, certain areas didn't get mortgages, that redlining still exists in the data. I don't know of a single mortgage provider today that wants to have that in part of their day-to-day practice. This helps remove that from them and surface a decision that's based on the context of the individual, based on their-- you know, their ability to repay, in the case of a mortgage, as opposed to what they look like or where they live. >> So, I mean, I liked the concept of the common and pictorial power of machines and humans, but I think there's-- well, there's all-- I wonder if you could sort of educate me on them. There seems to be a lot of potential use cases for many companies, but IBM as well, for inference, you know, at the edge. I mean, everybody's talking about the edge. Open shift obviously is a big play there. Hybrid cloud... So how do you see-- that-- that kind of real time inference playing in? Does that-- certain data comes back to the model and that's where the humans come in? W-- H-- How do you see that? >> Yeah, so, so-- I often get asked, what do you, you know what do you see as the future of AI? And my response is the future of AI is edge. And, and the reason for that is if I can solve for an edge use case, I can solve for every use case between the edge and the data center. >> Dave: Uh huh. >> Right? And some of the challenges that you brought up as far as being on the edge, get back to, you know, and it actually helps address other problems too, such as how do I handle data sovereignty regulations when it comes to training models, and even models themselves. >> Dave: Right. >> But when you think about, you know, I have 50 models deployed around the world, there are 50 versions of the same model deployed around the world at different scoring end points or different places where I'm inferencing. How do I-- without having to bring the model back or all the data back, how do I keep all those models in sync? How do I make sure that, you know, back to the social media example, that one of them doesn't go completely off the rails. And we do this through federated learning, right? And this is-- or distributed, distributed learning, federated learning, whatever you want to call it. It's this concept of you have models running at the discrete edge locations or discrete distributed locations, those models over time learn from the data that they're, that they're scoring on. And instead of sending the data back to retrain or even the model back to retrain you send back the the-- the changes in the parameters that have been updated in that model. You can then pull-- put all those together. So you have 50 different distributions that you're managing. You pull all those together and you can even assign weights to the different ones based on bias that might, you know, not biases but different distributions that may exist at one node or another, you can do it based on the amount of data that's been going into-- gone into changing those weights. And so you-- you combine these models back into a single model and then you push them back out to the edge. >> So I just thinking about, I mean, cause mm-mo-- most of the work in AI today, correct me if I'm wrong is in, in, in-- I would say modeling versus inferencing. And, but, but if you're laying out a future where that's going to change, and if I think about some of the things that we're familiar with today, things like fraud detection, maybe weather... supply chains and, and-- and that's just going to get better and better and better. But I think about some of the areas and I'm curious if you could maybe talk to some of the use cases you're seeing in examples, both today and the future, but I think of things like, you know, smart power grid, smart factories, automated retail, I mean these seemed like wheelhouses for IBM. So maybe you could share with us your thoughts on that. And some other examples. >> Yeah, so you brought up, you know, fraud. Fraud is a really good example of an edge use case that might not seem like an edge use case. >> Dave: Yeah. >> And so, you know, as you're swiping a credit card, and let's just focus on credit card transactions, most of those transactions occur on a mainframe and you know, they, they need a response time that's, you know, less than a millisecond. And so if I'm, if I'm, you know, if I'm responsible for making sure that my bank doesn't have any credit card fraud and I have a model that's going to do it, I can't have, in this case, the mainframe call-out to someplace else to score the model and then come back. And this gets back to the power of hybrid cloud, right? And so if I can deploy that model on my mainframe, where those transactions are happening, you know, half a millisecond at a time, I can then score every single transaction that comes back and on the mainframe directly without having to go out, which enables me to keep my SLA, enables me to keep that less than half a millisecond response time, while also preventing any fraudulent transactions from happening. That's a great example of what I would call-- That falls into my "everything is edge bucket," right? Where, yeah, you're training the model somewhere else where you don't have the cost of running and training it on the mainframe, but I want to score it back there. And we've actually done this with a couple of banks, where we've trained models in the cloud on GPUs and done the inferencing and scoring on the mainframe for just exactly that, for fraud. >> Edge: if you can make it there, you can make it anywhere. (chuckles) Seth, we-- we got to leave it there. Really, really appreciate your time. >> All right. Great to have-- great for having me. Thanks, Dave, I appreciate it, as always. >> Oh, it was great to see you. Hope we can see you later this year, face to face, or at least in '22. And, and thank you! >> I hope so. >> Yeah, let's, let's make that happen Seth. We'll virtual shake on it. (chuckles) Thanks everybody for watching our ongoing coverage of IBM Think 2021, the virtual edition. This is Dave Vellante for The Cube. Keep it right there for more great content from the show. (light-hearted music) (light-hearted music continues) (light-hearted music fades) (upbeat music)

>>Live from Orlando, Florida. It's the cube covering Microsoft ignite brought to you by Cohesity. >>Good afternoon everyone and welcome back to the cubes live coverage of Microsoft ignite 26,000 people here at this conference at the orange County convention center. I'm your host, Rebecca Knight, alongside my cohost Stu Miniman. We are joined by Seth Juarez. He is the cloud developer advocate at Microsoft. Thank you so much for coming on the show. >>Glad to be here. You have such a lovely sad and you're lovely people. We just met up. You don't know any better? No. Well maybe after after the end of the 15 minutes we'll have another discussion. >>You're starting off on the right foot, so tell us a little bit about what you do. You're also a host on channel nine tell us about your role as a, as a cloud developer. >>So a cloud advocate's job is primarily to help developers be successful on Azure. My particular expertise lies in AI and machine learning and so my job is to help developers be successful with AI in the cloud, whether it be developers, data scientists, machine learning engineers or whatever it is that people call it nowadays. Because you know how the titles change a lot, but my job is to help them be successful and sometimes what's interesting is that sometimes our customers can't find success in the cloud. That's actually a win for me too because then I have a deep integration with the product group and my job is to help them understand from a customer perspective what it is they need and why. So I'm like the ombudsman so to speak because the product groups are the product groups. I don't report up to them. So I usually go in there and I'm like, Hey, I don't report to any of you, but this is what the customers are saying. >>We are very keen on being customer centered and that's why I do what I do. >> Seth, I have to imagine when you're dealing with customers, some of that skills gap and learning is something that they need to deal with. You know, we've been hearing for a long time, you know, there's not enough data scientists, you know, we need to learn these environments. Satya Nadella spent a lot of time talking about the citizen developers out there. So you know H bring us inside the customers you're talking to, you know, kind of, where do you usually start and you know, how do they pull the right people in there or are they bringing in outside people a little bit? Great organization, great question. It turns out that for us at Microsoft we have our product groups and then right outside we have our advocates that are very closely aligned to the product groups. >>And so anytime we do have an interaction with a customer, it's for the benefit of all the other customers. And so I meet with a lot of customers and I don't, I'm to get to talk about them too much. But the thing is I go in there, I see what they're doing. For example, one time I went to the touring Institute in the UK. I went in there and because I'm not there to sell, I'm there to figure out like what are you trying to do and does this actually match up? It's a very different kind of conversation and they'd tell me about what they're working on. I tell them about how we can help them and then they tell me where the gaps are or where they're very excited and I take both of those pieces of feedback to the, to the product group and they, they just love being able to have someone on the ground to talk to people because sometimes you know, when work on stuff you get a little siloed and it's good to have an ombudsman so to speak, to make sure that we're doing the right thing for our customers. >>As somebody that works on AI. You must've been geeking out working, working with the Turing Institute though. Oh yeah. Those people are absolutely wonderful and it was like as I was walking in, a little giddy, but the problems that they're facing in AI are very similar. The problems that people at the other people doing and that are in big organizations, other organizations are trying to onboard to AI and try to figure out, everyone says I need to be using this hammer and they're trying to hammer some screws in with the hammer. So it's good to figure out when it's appropriate to use AI and when it isn't. And I also have customers with that >>and I'm sure the answer is it depends in terms of when it's appropriate, but do you have any sort of broad brush advice for helping an organization determine is is this a job for AI? Absolutely. >>That's uh, it's a question I get often and developers, we have this thing called the smell that tells us if a code smell, we have a code smell tells us, maybe we should refactor, maybe we should. For me, there's this AI smell where if you can't precisely figure out the series of steps to execute an algorithm and you're having a hard time writing code, or for example, if every week you need to change your if L statements or if you're changing numbers from 0.5 to 0.7 and now it works, that's the smell that you should think about using AI or machine learning, right? There's also a set of a class of algorithms that, for example, AI, it's not that we've solved, solved them, but they're pretty much solved. Like for example, detecting what's in an image, understanding sentiment and text, right? Those kinds of problems we have solutions for that are just done. >>But if you have a code smell where you have a lot of data and you don't want to write an algorithm to solve that problem, machine learning and AI might be the solution. Alright, a lot of announcements this week. Uh, any of the highlights for from your area. We last year, AI was mentioned specifically many times now with you know, autonomous systems and you know it feels like AI is in there not necessarily just you know, rubbing AI on everything. >> I think it's because we have such a good solution for people building custom machine learning that now it's time to talk about the things you can do with it. So we're talking about autonomous systems. It's because it's based upon the foundation of the AI that we've already built. We released something called Azure machine learning, a set of tools called in a studio where you can do end and machine learning. >>Because what what's happening is most data scientists nowadays, and I'm guilty of this myself, we put stuff in things called Jupiter notebooks. We release models, we email them to each other, we're emailing Python files and that's kinda like how programming was in 1995 and now we're doing is we're building a set of tools to allow machine learning developers to go end to end, be able to see how data scientists are working and et cetera. For example, let's just say you're a data scientist. Bill. Did an awesome job, but then he goes somewhere else and Sally who was absolutely amazing, comes in and now she's the data scientist. Usually Sally starts from zero and all of the stuff that bill did is lost with Azure machine learning. You're able to see all of your experiments, see what bill tried, see what he learned and Sally can pick right up and go on. And that's just doing the experiments. Now if you want to get machine learning models into production, we also have the ability to take these models, version them, put them into a CIC, D similar process with Azure dev ops and machine learning. So you can go from data all the way to machine learning in production very easily, very quickly and in a team environment, you know? And that's what I'm excited about mostly. >>So at a time when AI and big and technology companies in general are under fire and not, Oh considered to not always have their users best interests at heart. I'd like you to talk about the Microsoft approach to ethical AI and responsible AI. >>Yeah, I was a part of the keynote. Scott Hanselman is a very famous dab and he did a keynote and I got to form part of it and one of the things that we're very careful even on a dumb demo or where he was like doing rock paper, scissors. I said, and Scott, we were watching you with your permission to see like what sequence of throws you were doing. We believe that through and through all the way we will never use our customers' data to enhance any of our models. In fact, there was a time when we were doing like a machine learning model for NLP and I saw the email thread and it's like we don't have language food. I don't remember what it was. We don't have enough language food. Let's pay some people to ethically source this particular language data. We will never use any of our customer's data and I've had this question asked a lot. >>Like for example, our cognitive services which have built in AI, we will never use any of our customer's data to build that neither. For example, if we have, for example, we have a custom vision where you upload your own pictures, those are your pictures. We're never going to use them for anything. And anything that we do, there's always consent and we want to make sure that everyone understands that AI is a powerful tool, but it also needs to be used ethically. And that's just on how we use data for people that are our customers. We also have tools inside of Azure machine learning to get them to use AI. Ethically. We have tools to explain models. So for example, if you very gender does the model changes prediction or if you've very class or race, is your model being a little iffy? We allow, we have those tools and Azure machine learning, so our customers can also be ethical with the AI they build on our platform. So we have ethics built into how we build our models and we have ethics build into how our customers can build their models too, which is to me very. >>And is that a selling point? Are customers gravitating? I mean we've talked a lot about it on the show. About the, the trust that customers have in Microsoft and the image that Microsoft has in the industry right now. But the idea that it is also trying to perpetuate this idea of making everyone else more ethical. Do you think that that is one of the reasons customers are gravitate? >>I hope so. And as far as a selling point, I absolutely think it's a selling point, but we've just released it and so I'm going to go out there and evangelize the fact that not only are we as tickle with what we do in AI, but we want our customers to be ethical as well. Because you know, trust pays, as Satya said in his keynote, tra trust the enhancer in the exponent that allows tech intensity to actually be tech intensity. And we believe that through and through not only do believe it for ourselves, but we want our customers to also believe it and see the benefits of having trust with our customers. One of the things we, we talked to Scott Hanselman a little bit yesterday about that demo is the Microsoft of today isn't just use all the Microsoft products, right? To allow you to use, you know, any tool, any platform, you know, your own environment, uh, to tell us how that, that, that plays into your world. >>It's, you know, like in my opinion, and I don't know if it's the official opinion, but we are in the business of renting computer cycles. We don't care how you use them, just come into our house and use them. You wanna use Java. We've recently announced a tons of things with spraying. We're become an open JDK contributor. You know, one of my colleagues, we're very hard on that. I work primarily in Python because it's machine learning. I have a friend might call a friend and colleague, David Smith who works in our, I have other colleagues that work in a number of different languages. We don't care. What we are doing is we're trying to empower every organization and every person on the planet to achieve more where they are, how they are, and hopefully bring a little bit of of it to our cloud. >>What are you doing that, that's really exciting to you right now? I know you're doing a new.net library. Any other projects that are sparking your end? >>Yeah, so next week I'm going to France and this is before anyone's going to see this and there is a, there is a company, I think it's called surf, I'll have to look it up and we'll put it in the notes, but they are basically trying to use AI to be more environmentally conscious and they're taking pictures of trash and rivers and they're using AI to figure out where it's coming from so they can clean up environment. I get to go over there and see what they're doing, see how I can help them improvement and promote this kind of ethical way of doing AI. We also do stuff with snow leopards. I was watching some Netflix thing with my kids and we were watching snow leopards and there was like two of them. Like this is impressive because as I'm watching this with my kids, I'm like, Hey we are at Microsoft, we're helping this population, you know, perpetuate with AI. >>And so those are the things it's actually a had had I've seen on TV is, you know, rather than spending thousands of hours of people out there, the AI can identify the shape, um, you know, through the cameras. So they're on a, I love that powerful story to explain some of those pieces as opposed to it. It's tough to get the nuance of what's happening here. Absolutely. With this technology, these models are incredibly easy to build on our platform. And, and I and I st fairly easy to build with what you have. We love people use TensorFlow, use TensorFlow, people use pie torch. That's great cafe on it. Whatever you want to use. We are happy to let you use a rent out our computer cycles because we want you to be successful. Maybe speak a little bit of that when you talk about, you know, the, the cloud, one of the things is to democratize, uh, availability of this. >>There's usually free tiers out there, especially in the emerging areas. Uh, you know, how, how is Microsoft helping to get that, that compute and that world technology to people that might not have had it in the past? I was in, I was in Peru a number of years ago and I and I had a discussion with someone on the channel nine show and it was absolutely imp. Like I under suddenly understood the value of this. He said, Seth, if I wanted to do a startup here in Peru, right, and it was a capital Peru, like a very industrialized city, I would have to buy a server. It would come from California on a boat. It would take a couple of months to get here and then it would be in a warehouse for another month as it goes through customs. And then I would have to put it into a building that has a C and then I could start now sat with a click of a button. >>I can provision an entire cluster of machines on Azure and start right now. That's what, that's what the cloud is doing in places like Peru and places that maybe don't have a lot of infrastructure. Now infrastructure is for everyone and maybe someone even in the United States, you know, in a rural area that doesn't, they can start up their own business right now anywhere. And it's not just because it's Peru, it's not just because it's some other place that's becoming industrialized. It's everywhere. Because any kid with a dream can spin up an app service and have a website done in like five minutes. >>So what does this mean? I mean, as you said, any, any kid, any person or rural area, any developing country, what does this mean in five or 10 years from now in terms of the future of commerce and work and business? >>Honestly, some people feel like computers are art, stealing, you know, human engineering. I think they are really augmenting it. Like for example, I don't have to, if I want to know something for her. Back when, when I was a kid, I had to, if I want to know something, sometimes I had to go without knowing where like I guess we'll never know. Right? And then five years later we're like, okay, we found out it was that a character on that show, you know? And now we just look at our phone. It's like, Oh, you were wrong. And I like not knowing that I'm wrong for a lot longer, you know what I'm saying? But nowadays with our, with our phones and with other devices, we have information readily available so that we can make appropriate response, appropriate answers to questions that we have. AI is going to help us with that by augmenting human ingenuity, by looking at the underlying structure. >>We can't, for example, if you look at, if you look at an Excel spreadsheet, if it's like five rows and maybe five columns, you and I as humans can look at and see a trend. But what if it's 10 million rows and 5,000 columns? Our ingenuity has been stretched too far, but with computers now we can aggregate, we can do some machine learning models, and then we can see the patterns that the computer found aggregated, and now we can make the decisions we could make with five columns, five rows, but it's not taking our jobs. It's augmenting our capacity to do the right thing. >>Excellent. We'll assess that. Thank you so much for coming on the Cuba. Really fun conversation. >>Glad to be here. Thanks for having me. >>Alright, I'm Rebecca Knight for Stu minimun. Stay tuned for more of the cubes live coverage of Microsoft ignite.

>>live from Miami, Florida It's the Q covering. IBM is data in a I forum brought to you by IBM. >>Welcome back to the port of Miami, everybody. We're here at the Intercontinental Hotel. You're watching the Cube? The leader and I live tech covered set. Daubert is here. He's the vice president of data and I and a I and the chief data officer of cloud and cognitive software. And I'd be upset too. Good to see you again. >>Good. See, Dave, thanks for having me >>here. The data in a I form hashtag data. I I It's amazing here. 1700 people. Everybody's gonna hands on appetite for learning. Yeah. What do you see out in the marketplace? You know what's new since we last talked. >>Well, so I think if you look at some of the things that are really need in the marketplace, it's really been around filling the skill shortage. And how do you operationalize and and industrialize? You're a I. And so there's been a real need for things ways to get more productivity out of your data. Scientists not necessarily replace them. But how do you get more productivity? And we just released a few months ago, something called Auto A I, which really is, is probably the only tool out there that automates the end end pipeline automates 80% of the work on the Indian pipeline, but isn't a black box. It actually kicks out code. So your data scientists can then take it, optimize it further and understand it, and really feel more comfortable about it. >>He's got a eye for a eyes. That's >>exactly what is a eye for an eye. >>So how's that work? So you're applying machine intelligence Two data to make? Aye. Aye, more productive pick algorithms. Best fit. >>Yeah, So it does. Basically, you feed it your data and it identifies the features that are important. It does feature engineering for you. It does model selection for you. It does hyper parameter tuning and optimization, and it does deployment and also met monitors for bias. >>So what's the date of scientists do? >>Data scientist takes the code out the back end. And really, there's some tweaks that you know, the model, maybe the auto. Aye, aye. Maybe not. Get it perfect, Um, and really customize it for the business and the needs of the business. that the that the auto A I so they not understand >>the data scientist, then can can he or she can apply it in a way that is unique to their business that essentially becomes their I p. It's not like generic. Aye, aye for everybody. It's it's customized by And that's where data science to complain that I have the time to do this. Wrangling data >>exactly. And it was built in a combination from IBM Research since a great assets at IBM Research plus some cattle masters at work here at IBM that really designed and optimize the algorithm selection and things like that. And then at the keynote today, uh, wonderment Thompson was up there talking, and this is probably one of the most impactful use cases of auto. Aye, aye to date. And it was also, you know, my former team, the data science elite team, was engaged, but wonderment Thompson had this problem where they had, like, 17,000 features in their data sets, and what they wanted to do was they wanted to be able to have a custom solution for their customers. And so every time they get a customer that have to have a data scientist that would sit down and figure out what the right features and how the engineer for this customer. It was an intractable problem for them. You know, the person from wonderment Thompson have prevented presented today said he's been trying to solve this problem for eight years. Auto Way I, plus the data science elite team solve the form in two months, and after that two months, it went right into production. So in this case, oughta way. I isn't doing the whole pipeline. It's helping them identify the features and engineering the features that are important and giving them a head start on the model. >>What's the, uh, what's the acquisition bottle for all the way as a It's a license software product. Is it assassin part >>of Cloudpack for data, and it's available on IBM Cloud. So it's on IBM Cloud. You can use it paper use so you get a license as part of watching studio on IBM Cloud. If you invest in Cloudpack for data, it could be a perpetual license or committed term license, which essentially assassin, >>it's essentially a feature at dawn of Cloudpack for data. >>It's part of Cloudpack per day and you're >>saying it can be usage based. So that's key. >>Consumption based hot pack for data is all consumption based, >>so people want to use a eye for competitive advantage. I said by my open that you know, we're not marching to the cadence of Moore's Law in this industry anymore. It's a combination of data and then cloud for scale. So so people want competitive advantage. You've talked about some things that folks are doing to gain that competitive advantage. But the same time we heard from Rob Thomas that only about 4 to 10% penetration for a I. What? What are the key blockers that you see and how you're knocking them >>down? Well, I think there's. There's a number of key blockers, so one is of access to data, right? Cos have tons of data, but being able to even know what data is, they're being able to pull it all together and being able to do it in a way that is compliant with regulation because you got you can't do a I in a vacuum. You have to do it in the context of ever increasing regulation like GDP R and C, C, P A and all these other regulator privacy regulations that are popping up. So so that's that's really too so access to data and regulation can be blockers. The 2nd 1 or the 3rd 1 is really access to appropriate skills, which we talked a little bit about. Andi, how do you retrain, or how do you up skill, the talent you have? And then how do you actually bring in new talent that can execute what you want on then? Sometimes in some cos it's a lack of strategy with appropriate measurement, right? So what is your A II strategy, and how are you gonna measure success? And you and I have talked about this on Cuban on Cube before, where it's gotta measure your success in dollars and cents right cost savings, net new revenue. That's really all your CFO is care about. That's how you have to be able to measure and monitor your success. >>Yes. Oh, it's so that's that Last one is probably were where most organizations start. Let's prioritize the use cases of the give us the best bang for the buck, and then business guys probably get really excited and say Okay, let's go. But to up to truly operationalize that you gotta worry about these other things. You know, the compliance issues and you gotta have the skill sets. Yeah, it's a scale. >>And sometimes that's actually the first thing you said is sometimes a mistake. So focusing on the one that's got the most bang for the buck is not necessarily the best place to start for a couple of reasons. So one is you may not have the right data. It may not be available. It may not be governed properly. Number one, number two the business that you're building it for, may not be ready to consume it right. They may not be either bought in or the processes need to change so much or something like that, that it's not gonna get used. And you can build the best a I in the world. If it doesn't get used, it creates zero value, right? And so you really want to focus on for the first couple of projects? What are the one that we can deliver the best value, not Sarah, the most value, but the best value in the shortest amount of time and ensure that it gets into production because especially when you're starting off, if you don't show adoption, people are gonna lose interest. >>What are you >>seeing in terms of experimentation now in the customer base? You know, when you talk to buyers and you talk about, you know, you look at the I T. Spending service. People are concerned about tariffs. The trade will hurt the 2020 election. They're being a little bit cautious. But in the last two or three years have been a lot of experimentation going on. And a big part of that is a I and machine learning. What are you seeing in terms of that experimentation turning into actually production project that we can learn from and maybe do some new experiments? >>Yeah, and I think it depends on how you're doing the experiments. There's, I think there's kind of academic experimentation where you have data science, Sistine Data science teams that come work on cool stuff that may or may not have business value and may or may not be implemented right. They just kind of latch on. The business isn't really involved. They latch on, they do projects, and that's I think that's actually bad experimentation if you let it that run your program. The good experimentation is when you start identity having a strategy. You identify the use cases you want to go after and you experiment by leveraging, agile to deliver these methodologies. You deliver value in two weeks prints, and you can start delivering value quickly. You know, in the case of wonderment, Thompson again 88 weeks, four sprints. They got value. That was an experiment, right? That was an experiment because it was done. Agile methodologies using good coding practices using good, you know, kind of design up front practices. They were able to take that and put it right into production. If you're doing experimentation, you have to rewrite your code at the end. And it's a waste of time >>T to your earlier point. The moon shots are oftentimes could be too risky. And if you blow it on a moon shot, it could set you back years. So you got to be careful. Pick your spots, picked ones that maybe representative, but our lower maybe, maybe lower risk. Apply agile methodologies, get a quick return, learn, develop those skills, and then then build up to the moon ship >>or you break that moon shot down its consumable pieces. Right, Because the moon shot may take you two years to get to. But maybe there are sub components of that moon shot that you could deliver in 34 months and you start delivering knows, and you work up to the moon shot. >>I always like to ask the dog food in people. And I said, like that. Call it sipping your own champagne. What do you guys done internally? When we first met, it was and I think, a snowy day in Boston, right at the spark. Some it years ago. And you did a big career switch, and it's obviously working out for you, But But what are some of the things? And you were in part, brought in to help IBM internally as well as Interpol Help IBM really become data driven internally? Yeah. How has that gone? What have you learned? And how are you taking that to customers? >>Yeah, so I was hired three years ago now believe it was that long toe lead. Our internal transformation over the last couple of years, I got I don't want to say distracted there were really important business things I need to focus on, like gpr and helping our customers get up and running with with data science, and I build a data science elite team. So as of a couple months ago, I'm back, you know, almost entirely focused on her internal transformation. And, you know, it's really about making sure that we use data and a I to make appropriate decisions on DSO. Now we have. You know, we have an app on her phone that leverages Cognos analytics, where at any point, Ginny Rometty or Rob Thomas or Arvin Krishna can pull up and look in what we call E P M. Which is enterprise performance management and understand where the business is, right? What what do we do in third quarter, which just wrapped up what was what's the pipeline for fourth quarter? And it's at your fingertips. We're working on revamping our planning cycle. So today planning has been done in Excel. We're leveraging Planning Analytics, which is a great planning and scenario planning tool that with the tip of a button, really let a click of a button really let you understand how your business can perform in the future and what things need to do to get it perform. We're also looking across all of cloud and cognitive software, which data and A I sits in and within each business unit and cloud and cognitive software. The sales teams do a great job of cross sell upsell. But there's a huge opportunity of how do we cross sell up sell across the five different businesses that live inside of cloud and cognitive software. So did an aye aye hybrid cloud integration, IBM Cloud cognitive Applications and IBM Security. There's a lot of potential interplay that our customers do across there and providing a I that helps the sales people understand when they can create more value. Excuse me for our customers. >>It's interesting. This is the 10th year of doing the Cube, and when we first started, it was sort of the beginning of the the big data craze, and a lot of people said, Oh, okay, here's the disruption, crossing the chasm. Innovator's dilemma. All that old stuff going away, all the new stuff coming in. But you mentioned Cognos on mobile, and that's this is the thing we learned is that the key ingredients to data strategies. Comprised the existing systems. Yes. Throw those out. Those of the systems of record that were the single version of the truth, if you will, that people trusted you, go back to trust and all this other stuff built up around it. Which kind of created dissidents. Yeah. And so it sounds like one of the initiatives that you you're an IBM I've been working on is really bringing in the new pieces, modernizing sort of the existing so that you've got sort of consistent data sets that people could work. And one of the >>capabilities that really has enabled this transformation in the last six months for us internally and for our clients inside a cloud pack for data, we have this capability called IBM data virtualization, which we have all these independent sources of truth to stomach, you know? And then we have all these other data sources that may or may not be as trusted, but to be able to bring them together literally. With the click of a button, you drop your data sources in the Aye. Aye, within data. Virtualization actually identifies keys across the different things so you can link your data. You look at it, you check it, and it really enables you to do this at scale. And all you need to do is say, pointed out the data. Here's the I. P. Address of where the data lives, and it will bring that in and help you connect it. >>So you mentioned variances in data quality and consumer of the data has to have trust in that data. Can you use machine intelligence and a I to sort of give you a data confidence meter, if you will. Yeah. So there's two things >>that we use for data confidence. I call it dodging this factor, right. Understanding what the dodging this factor is of the data. So we definitely leverage. Aye. Aye. So a I If you have a date, a dictionary and you have metadata, the I can understand eight equality. And it can also look at what your data stewards do, and it can do some of the remediation of the data quality issues. But we all in Watson Knowledge catalog, which again is an in cloudpack for data. We also have the ability to vote up and vote down data. So as much as the team is using data internally. If there's a data set that had a you know, we had a hive data quality score, but it wasn't really valuable. It'll get voted down, and it will help. When you search for data in the system, it will sort it kind of like you do a search on the Internet and it'll it'll down rank that one, depending on how many down votes they got. >>So it's a wisdom of the crowd type of. >>It's a crowd sourcing combined with the I >>as that, in your experience at all, changed the dynamics of politics within organizations. In other words, I'm sure we've all been a lot of meetings where somebody puts foursome data. And if the most senior person in the room doesn't like the data, it doesn't like the implication he or she will attack the data source, and then the meeting's over and it might not necessarily be the best decision for the organization. So So I think it's maybe >>not the up, voting down voting that does that, but it's things like the E PM tool that I said we have here. You know there is a single source of truth for our finance data. It's on everyone's phone. Who needs access to it? Right? When you have a conversation about how the company or the division or the business unit is performing financially, it comes from E. P M. Whether it's in the Cognos app or whether it's in a dashboard, a separate dashboard and Cognos or is being fed into an aye aye, that we're building. This is the source of truth. Similarly, for product data, our individual products before me it comes from here's so the conversation at the senior senior meetings are no longer your data is different from my data. I don't believe it. You've eliminated that conversation. This is the data. This is the only data. Now you can have a conversation about what's really important >>in adult conversation. Okay, Now what are we going to do? It? It's >>not a bickering about my data versus your data. >>So what's next for you on? You know, you're you've been pulled in a lot of different places again. You started at IBM as an internal transformation change agent. You got pulled into a lot of customer situations because yeah, you know, you're doing so. Sales guys want to drag you along and help facilitate activity with clients. What's new? What's what's next for you. >>So really, you know, I've only been refocused on the internal transformation for a couple months now. So really extending IBM struck our cloud and cognitive software a data and a I strategy and starting to quickly implement some of these products, just like project. So, like, just like I just said, you know, we're starting project without even knowing what the prioritized list is. Intuitively, this one's important. The team's going to start working on it, and one of them is an aye aye project, which is around cross sell upsell that I mentioned across the portfolio and the other one we just got done talking about how in the senior leadership meeting for Claude Incognito software, how do we all work from a Cognos dashboard instead of Excel data data that's been exported put into Excel? The challenge with that is not that people don't trust the data. It's that if there's a question you can't drill down. So if there's a question about an Excel document or a power point that's up there, you will get back next meeting in a month or in two weeks, we'll have an e mail conversation about it. If it's presented in a really live dashboard, you can drill down and you can actually answer questions in real time. The value of that is immense, because now you as a leadership team, you can make a decision at that point and decide what direction you're going to do. Based on data, >>I said last time I have one more questions. You're CDO but you're a polymath on. So my question is, what should people look for in a chief data officer? What sort of the characteristics in the attributes, given your >>experience, that's kind of a loaded question, because there is. There is no good job, single job description for a chief date officer. I think there's a good solid set of skill sets, the fine for a cheap date officer and actually, as part of the chief data officer summits that you you know, you guys attend. We had were having sessions with the chief date officers, kind of defining a curriculum for cheap date officers with our clients so that we can help build the chief. That officer in the future. But if you look a quality so cheap, date officer is also a chief disruption officer. So it needs to be someone who is really good at and really good at driving change and really good at disrupting processes and getting people excited about it changes hard. People don't like change. How do you do? You need someone who can get people excited about change. So that's one thing. On depending on what industry you're in, it's got to be. It could be if you're in financial or heavy regulated industry, you want someone that understands governance. And that's kind of what Gardner and other analysts call a defensive CDO very governance Focus. And then you also have some CDOs, which I I fit into this bucket, which is, um, or offensive CDO, which is how do you create value from data? How do you caught save money? How do you create net new revenue? How do you create new business models, leveraging data and a I? And now there's kind of 1/3 type of CDO emerging, which is CDO not as a cost center but a studio as a p N l. How do you generate revenue for the business directly from your CDO office. >>I like that framework, right? >>I can't take credit for it. That's Gartner. >>Its governance, they call it. We say he called defensive and offensive. And then first time I met Interpol. He said, Look, you start with how does data affect the monetization of my organization? And that means making money or saving money. Seth, thanks so much for coming on. The Cube is great to see you >>again. Thanks for having me >>again. All right, Keep it right to everybody. We'll be back at the IBM data in a I form from Miami. You're watching the Cube?

>> Live from San Francisco, California, it's the theCUBE, covering the IBM Chief Data Officer Summit, brought to you by IBM. >> Welcome back to San Francisco everybody. You're watching theCUBE, the leader in live tech coverage. We go out to the events, we extract the signal from the noise and we're here at the IBM Chief Data Officer Summit, 10th anniversary. Seth Dobrin is here, he's the Vice President and Chief Data Officer of the IBM Analytics Group. Seth, always a pleasure to have you on. Good to see you again. >> Yeah, thanks for having me back Dave. >> You're very welcome. So I love these events you get a chance to interact with chief data officers, guys like yourself. We've been talking a lot today about IBM's internal transformation, how IBM itself is operationalizing AI and maybe we can talk about that, but I'm most interested in how you're pointing that at customers. What have you learned from your internal experiences and what are you bringing to customers? >> Yeah, so, you know, I was hired at IBM to lead part of our internal transformation, so I spent a lot of time doing that. >> Right. >> I've also, you know, when I came over to IBM I had just left Monsanto where I led part of their transformation. So I spent the better part of the first year or so at IBM not only focusing on our internal efforts, but helping our clients transform. And out of that I found that many of our clients needed help and guidance on how to do this. And so I started a team we call, The Data Science an AI Elite Team, and really what we do is we sit down with clients, we share not only our experience, but the methodology that we use internally at IBM so leveraging things like design thinking, DevOps, Agile, and how you implement that in the context of data science and AI. >> I've got a question, so Monsanto, obviously completely different business than IBM-- >> Yeah. >> But when we talk about digital transformation and then talk about the difference between a business and a digital business, it comes down to the data. And you've seen a lot of examples where you see companies traversing industries which never used to happen before. You know, Apple getting into music, there are many, many examples, and the theory is, well, it's 'cause it's data. So when you think about your experiences of a completely different industry bringing now the expertise to IBM, were there similarities that you're able to draw upon, or was it a completely different experience? >> No, I think there's tons of similarities which is, which is part of why I was excited about this and I think IBM was excited to have me. >> Because the chances for success were quite high in your mind? >> Yeah, yeah, because the chance for success were quite high, and also, you know, if you think about it there's on the, how you implement, how you execute, the differences are really cultural more than they're anything to do with the business, right? So it's, the whole role of a Chief Data Officer, or Chief Digital Officer, or a Chief Analytics Officer, is to drive fundamental change in the business, right? So it's how do you manage that cultural change, how do you build bridges, how do you make people, how do you make people a little uncomfortable, but at the same time get them excited about how to leverage things like data, and analytics, and AI, to change how they do business. And really this concept of a digital transformation is about moving away from traditional products and services, more towards outcome-based services and not selling things, but selling, as a Service, right? And it's the same whether it's IBM, you know, moving away from fully transactional to Cloud and subscription-based offerings. Or it's a bank reimagining how they interact with their customers, or it's oil and gas company, or it's a company like Monsanto really thinking about how do we provide outcomes. >> But how do you make sure that every, as a Service, is not a snowflake and it can scale so that you can actually, you know, make it a business? >> So underneath the, as a Service, is a few things. One is, data, one is, machine learning and AI, the other is really understanding your customer, right, because truly digital companies do everything through the eyes of their customer and so every company has many, many versions of their customer until they go through an exercise of creating a single version, right, a customer or a Client 360, if you will, and we went through that exercise at IBM. And those are all very consistent things, right? They're all pieces that kind of happen the same way in every company regardless of the industry and then you get into understanding what the desires of your customer are to do business with you differently. >> So you were talking before about the Chief Digital Officer, a Chief Data Officer, Chief Analytics Officer, as a change agent making people feel a little bit uncomfortable, explore that a little bit what's that, asking them questions that intuitively they, they know they need to have the answer to, but they don't through data? What did you mean by that? >> Yeah so here's the conversations that usually happen, right? You go and you talk to you peers in the organization and you start having conversations with them about what decisions are they trying to make, right? And you're the Chief Data Officer, you're responsible for that, and inevitably the conversation goes something like this, and I'm going to paraphrase. Give me the data I need to support my preconceived notions. >> (laughing) Yeah. >> Right? >> Right. >> And that's what they want to (voice covers voice). >> Here's the answer give me the data that-- >> That's right. So I want a Dashboard that helps me support this. And the uncomfortableness comes in a couple of things in that. It's getting them to let go of that and allow the data to provide some inkling of things that they didn't know were going on, that's one piece. The other is, then you start leveraging machine learning, or AI, to actually help start driving some decisions, so limiting the scope from infinity down to two or three things and surfacing those two or three things and telling people in your business your choices are one of these three things, right? That starts to make people feel uncomfortable and really is a challenge for that cultural change getting people used to trusting the machine, or in some instances even, trusting the machine to make the decision for you, or part of the decision for you. >> That's got to be one of the biggest cultural challenges because you've got somebody who's, let's say they run a big business, it's a profitable business, it's the engine of cashflow at the company, and you're saying, well, that's not what the data says. And you're, say okay, here's a future path-- >> Yeah. >> For success, but it's going to be disruptive, there's going to be a change and I can see people not wanting to go there. >> Yeah, and if you look at, to the point about, even businesses that are making the most money, or parts of a business that are making the most money, if you look at what the business journals say you start leveraging data and AI, you get double-digit increases in your productivity, in your, you know, in differentiation from your competitors. That happens inside of businesses too. So the conversation even with the most profitable parts of the business, or highly, contributing the most revenue is really what we could do better, right? You could get better margins on this revenue you're driving, you could, you know, that's the whole point is to get better leveraging data and AI to increase your margins, increase your revenue, all through data and AI. And then things like moving to, as a Service, from single point to transaction, that's a whole different business model and that leads from once every two or three or five years, getting revenue, to you get revenue every month, right? That's highly profitable for companies because you don't have to go in and send your sales force in every time to sell something, they buy something once, and they continue to pay as long as you keep 'em happy. >> But I can see that scaring people because if the incentives don't shift to go from a, you know, pay all up front, right, there's so many parts of the organization that have to align with that in order for that culture to actually occur. So can you give some examples of how you've, I mean obviously you ran through that at IBM, you saw-- >> Yeah. >> I'm sure a lot of that, got a lot of learnings and then took that to clients. Maybe some examples of client successes that you've had, or even not so successes that you've learned from. >> Yeah, so in terms of client success, I think many of our clients are just beginning this journey, certainly the ones I work with are beginning their journey so it's hard for me to say, client X has successfully done this. But I can certainly talk about how we've gone in, and some of the use cases we've done-- >> Great. >> With certain clients to think about how they transformed their business. So maybe the biggest bang for the buck one is in the oil and gas industry. So ExxonMobile was on stage with me at, Think, talking about-- >> Great. >> Some of the work that we've done with them in their upstream business, right? So every time they drop a well it costs them not thousands of dollars, but hundreds of millions of dollars. And in the oil and gas industry you're talking massive data, right, tens or hundreds of petabytes of data that constantly changes. And no one in that industry really had a data platform that could handle this dynamically. And it takes them months to get, to even start to be able to make a decision. So they really want us to help them figure out, well, how do we build a data platform on this massive scale that enables us to be able to make decisions more rapidly? And so the aim was really to cut this down from 90 days to less than a month. And through leveraging some of our tools, as well as some open-source technology, and teaching them new ways of working, we were able to lay down this foundation. Now this is before, we haven't even started thinking about helping them with AI, oil and gas industry has been doing this type of thing for decades, but they really were struggling with this platform. So that's a big success where, at least for the pilot, which was a small subset of their fields, we were able to help them reduce that timeframe by a lot to be able to start making a decision. >> So an example of a decision might be where to drill next? >> That's exactly the decision they're trying to make. >> Because for years, in that industry, it was boop, oh, no oil, boop, oh, no oil. >> Yeah, well. >> And they got more sophisticated, they started to use data, but I think what you're saying is, the time it took for that analysis was quite long. >> So the time it took to even overlay things like seismic data, topography data, what's happened in wells, and core as they've drilled around that, was really protracted just to pull the data together, right? And then once they got the data together there were some really, really smart people looking at it going, well, my experience says here, and it was driven by the data, but it was not driven by an algorithm. >> A little bit of art. >> True, a lot of art, right, and it still is. So now they want some AI, or some machine learning, to help guide those geophysicists to help determine where, based on the data, they should be dropping wells. And these are hundred million and billion dollar decisions they're making so it's really about how do we help them. >> And that's just one example, I mean-- >> Yeah. >> Every industry has it's own use cases, or-- >> Yeah, and so that's on the front end, right, about the data foundation, and then if you go to a company that was really advanced in leveraging analytics, or machine learning, JPMorgan Chase, in their, they have a division, and also they were on stage with me at, Think, that they had, basically everything is driven by a model, so they give traders a series of models and they make decisions. And now they need to monitor those models, those hundreds of models they have for misuse of those models, right? And so they needed to build a series of models to manage, to monitor their models. >> Right. >> And this was a tremendous deep-learning use case and they had just bought a power AI box from us so they wanted to start leveraging GPUs. And we really helped them figure out how do you navigate and what's the difference between building a model leveraging GPUs, compared to CPUs? How do you use it to accelerate the output, and again, this was really a cost-avoidance play because if people misuse these models they can get in a lot of trouble. But they also need to make these decisions very quickly because a trader goes to make a trade they need to make a decision, was this used properly or not before that trade is kicked off and milliseconds make a difference in the stock market so they needed a model. And one of the things about, you know, when you start leveraging GPUs and deep learning is sometimes you need these GPUs to do training and sometimes you need 'em to do training and scoring. And this was a case where you need to also build a pipeline that can leverage the GPUs for scoring as well which is actually quite complicated and not as straight forward as you might think. In near real time, in real time. >> Pretty close to real time. >> You can't get much more real time then those things, potentially to stop a trade before it occurs to protect the firm. >> Yeah. >> Right, or RELug it. >> Yeah, and don't quote, I think this is right, I think they actually don't do trades until it's confirmed and so-- >> Right. >> Or that's the desire as to not (voice covers voice). >> Well, and then now you're in a competitive situation where, you know. >> Yeah, I mean people put these trading floors as close to the stock exchange as they can-- >> Physically. >> Physically to (voice covers voice)-- >> To the speed of light right? >> Right, so every millisecond counts. >> Yeah, read Flash Boys-- >> Right, yeah. >> So, what's the biggest challenge you're finding, both at IBM and in your clients, in terms of operationalizing AI. Is it technology? Is it culture? Is it process? Is it-- >> Yeah, so culture is always hard, but I think as we start getting to really think about integrating AI and data into our operations, right? As you look at what software development did with this whole concept of DevOps, right, and really rapidly iterating, but getting things into a production-ready pipeline, looking at continuous integration, continuous development, what does that mean for data and AI? And these concept of DataOps and AIOps, right? And I think DataOps is very similar to DevOps in that things don't change that rapidly, right? You build your data pipeline, you build your data assets, you integrate them. They may change on the weeks, or months timeframe, but they're not changing on the hours, or days timeframe. As you get into some of these AI models some of them need to be retrained within a day, right, because the data changes, they fall out of parameters, or the parameters are very narrow and you need to keep 'em in there, what does that mean? How do you integrate this for your, into your CI/CD pipeline? How do you know when you need to do regression testing on the whole thing again? Does your data science and AI pipeline even allow for you to integrate into your current CI/CD pipeline? So this is actually an IBM-wide effort that my team is leading to start thinking about, how do we incorporate what we're doing into people's CI/CD pipeline so we can enable AIOps, if you will, or MLOps, and really, really IBM is the only company that's positioned to do that for so many reasons. One is, we're the only one with an end-to-end toolchain. So we do everything from data, feature development, feature engineering, generating models, whether selecting models, whether it's auto AI, or hand coding or visual modeling into things like trust and transparency. And so we're the only one with that entire toolchain. Secondly, we've got IBM research, we've got decades of industry experience, we've got our IBM Services Organization, all of us have been tackling with this with large enterprises so we're uniquely positioned to really be able to tackle this in a very enterprised-grade manner. >> Well, and the leverage that you can get within IBM and for your customers. >> And leveraging our clients, right? >> It's off the charts. >> We have six clients that are our most advanced clients that are working with us on this so it's not just us in a box, it's us with our clients working on this. >> So what are you hoping to have happen today? We're just about to get started with the keynotes. >> Yeah. >> We're going to take a break and then come back after the keynotes and we've got some great guests, but what are you hoping to get out of today? >> Yeah, so I've been with IBM for 2 1/2 years and I, and this is my eighth CEO Summit, so I've been to many more of these than I've been at IBM. And I went to these religiously before I joined IBM really for two reasons. One, there's no sales pitch, right, it's not a trade show. The second is it's the only place where I get the opportunity to listen to my peers and really have open and candid conversations about the challenges they're facing and how they're addressing them and really giving me insights into what other industries are doing and being able to benchmark me and my organization against the leading edge of what's going on in this space. >> I love it and that's why I love coming to these events. It's practitioners talking to practitioners. Seth Dobrin thanks so much for coming to theCUBE. >> Yeah, thanks always, Dave. >> Always a pleasure. All right, keep it right there everybody we'll be right back right after this short break. You're watching, theCUBE, live from San Francisco. Be right back.

(inspiring music) >> Hey welcome back everybody, Jeff Rick here with theCUBE. We're in our Palo Alto studios for a Cube Conversation. 2018 is winding down, I think we're at our last big show of the week, this week at KubeCon. It's always nice to get back into the studio, things are a little bit calmer, a little bit less hectic, and learn about new businesses, new companies. So we're excited to have, I think first time to theCUBE, Seth Ravin, he is the CEO and co-founder of Rimini Street. Seth, great to see you. >> Thank you very much, good to be here. >> Yeah, welcome. So for the folks that aren't familiar with Rimini Street, give us a quick overview. >> Sure, Rimini Street is a 13 year old company. We went public last year on the NASDAQ, RMNI. >> Congratulations. >> Thank you. We have 1,100 people or so, operating in 18 countries, and we're servicing nearly 2,700 companies that have moved and used Rimini Street services around the world, including about 150 of the Fortune 500. >> You've got a pretty interesting business model. Very, kind of, innovative, but it's one of those so simple and so obvious, why didn't anybody see it ahead of time? So, tell the folks what your basic, core business model is. >> Sure, the enterprise software space has about $160 billion that's spent every year on annual maintenance fees. And, of that, Oracle and SAP have about $32 billion in annual fees. But this market has not been a competitive market. Those companies drive north of 90% profit margins, and their customers are not all that happy with the service. So we came in and offered a service at 50% off that provides a better service overall, for customers, and makes them very happy. >> Right, so the core components of a maintenance contract, so they pay their licensing fee and they pay, whatever, 15, 20% of that licensing fee for the maintenance. They're getting patches, they're getting upgrades. What are some of the other things that should be included in that maintenance fee? >> Well what they get is, they would get upgrades, they would get updates, which includes tax, legal, and reg updates, which everybody needs when you're running a global company, for, whether it's payroll taxes or financial taxes. You also need, when things break, you need to get them fixed. You also need advice and counsel in these very complex, large systems. And Rimini Street comes in, and what we don't do is we don't offer upgrades. We don't offer new versions of the product. What we do do is extend the life of these existing products for 15 to 20 years, beyond what the vendor would consider their normal support life. >> Right. So there's a whole bunch of things that work into that. One of 'em is, they want it to be supported, or they want you to have the latest patches and stuff so that they'll continue to support them. You've basically, per our earlier conversation, just basically taken over that whole responsibility, so not only the software patches and changes, but also then the support on top of that. >> That's right. In fact, we have support with less than five minute turnaround time, with a senior engineer, 24 by seven. So we've really offered a concierge level of service at half the annual fees. So our customers can save up to 90% total operating costs on these large, complex systems. >> So it's pretty, (laughs) it's kind of hard to grasp at first, but I think you gave a great analogy before we went on air, which is kind of like getting your car fixed. Take it to the dealer, or take it to Bob's Mechanic. And if you can get the quality of service, customer service, same parts, it's actually for a lot of people a better alternative. And that's kind of what you're doing, right? >> That's right. Think of it as, you could take your car to the dealer, you could take it to your local mechanic, who you might think is better at fixing that system. We have hundreds of engineers around the world, and we think that we are very very good at fixing these core systems and providing the updates that are needed to keep them moving forward. And I think one of the other parts that's really important here, is this is a difference between two different directions that every single licensee of someone like an Oracle or SAP product has to make a decision on. Number one, do you go down the vendor's road map, which includes, if you follow their road map, that's upgrades and updates and costs that are very expensive, that are designed around what the vendor wants to do, and the vendor's needs. Or, you choose to go down a business-driven road map, which is the focus around the company, and focus around competitive advantage and growth. And we are the company that works on the business-driven road maps. >> And you made a good point earlier, 'cause we talked about updates on our mobile phones, and DevOps, and we're at KubeCon, it's all about DevOps, and patches are coming out all the time, and updates are coming all the time. But the systems you're talking about are big, nasty, hairy ERP systems. These are not things that you want to be changing all the time, and in fact for a lot of cases, you probably don't, I would imagine the biggest value, one of the biggest values for your customers is extending that lifetime of that current install, and continuing to get the support which is threatened if they don't continue to pay the tax to the big red machine. >> That's correct. Instead of paying a 20% annual fee just for the maintenance on it, they can focus at saving half of that money, putting it into new innovation into their environment. And the kind of changes you're talking about, they're systems of engagement. So on the front end, where we interface with customers, and vendors, on the front end that's constantly changing, that's a dynamic system. On the back end where we work, these are big core transaction systems where change introduces risk into the system. We want to run these systems for a long time. We don't have competitive advantage on the back end of our financial system. Competitive advantage is done on the front end, where we compete against other people in the industry. >> So how'd you come up with the idea? I mean, it seems so obvious in hindsight, again, with the car repair analogy, which is just dead dumb simple. But what did you see, you were in the business, and what was your kind of experience in kind of the other side that got you to think, hmm, here's an opportunity that I think a lot of people would like to take advantage of? >> So I was part of the management team building out PeopleSoft for many years, and I was in a business where I was part of the team that had to try and force customers to take these upgrades. That was one of my jobs, was to move people forward onto these new releases. And I had an epiphany one day, that said, really, I am tired of selling people things they don't want, and let's focus on selling people what they really need. And this is a function of the maturity of the products that we deal with. They are so mature that they don't need to be changed out that frequently. So we want to move away from what the vendor wants, and we want to focus in on what is right for the company to allow them to shift more spending into these systems of innovation, that they have to do because the CIO world, IT is changing. The mission of IT is to support competitive advantage and growth now. It's not just to run a data center. >> Right, and as you said, taking a patch is not just like a quick update on your phone. You got to bring systems down, you've got labor components, you've got, again, complex APIs and connections that have to be managed, so, so these are pretty disruptive processes that people had to do, you had no choice if you wanted to keep your support active, right? You had to do it. >> And thousands of Rimini Street customers don't have to worry about all of those risks being introduced into their environment. And when things do need to be changed, proactively, like a tax, legal, and regulatory update, they get those. And if they need support, they have a very fast turnaround with an assigned engineer. So we've really changed the dynamics of the support model into one that people rave about, because it works very well compared to your typical call center model. We have no call center. So, our customers call their engineers directly, which allows them to get support from senior people very quickly. >> Right. So the other part you touched on, is then that frees up the CIO, and the inside team, to worry about front end innovation, to worry about some of these other more dynamic processes, where you do have to be a little more active, you do have to be on the cutting edge, you do have to be more responsive to competitive threats, which is not an ERP upgrade, but it's a new app, it's a new, you know, whatever. Versus (laughs) the unplanned, unwanted, and unanticipated forced process on a back end that you didn't even, maybe, want to, or see the benefit of. >> That's correct, and CIOs have to decide how they're going to distribute their budget between, what we say, keeping the lights on, a day to day operating cost, and then how much they're going to spend in innovation. And many customers wind up spending, just like a federal budget deficit issue, they spend 90% of their budget keeping the lights on, paying maintenance bills, running a data center, and that leaves very little money for innovation, which they need for that competitive advantage and growth. We are helping them shift money from the side of keeping the lights on, into innovation. >> So where do you guys go next? Is it just more of the same, the big giant TAM, obviously a whole lot of Oracle, SAP, and other enterprise applications, is that really your mission going forward, freeing up people to do the more innovative and creative, you're basically kind of offloading a big headache. >> Sure, but I think what you're going to watch is we expand the services that we cover. Today, we replace the vendor's maintenance. Tomorrow, we may do more of the work inside the IT organization. All support, but expanding the definition of support so that we can provide freed-up capital, time, and resources, to focus on innovation. As you know, in today's world, you're either growing or you're dyin'. There is no status quo left in this world. It's too competitive. And so we are helping companies make sure they keep their competitive edge, and gain new ones. >> Well it's a great story, and now that you're public, we can all watch it unfold and it looks like you've paid off a bunch of debt recently, I was goin' through some of the financial information, so congratulations, and, really interesting model. I know I use my local car repairman Bob. As long as he keeps deliverin', I'll keep goin' back, and so I imagine once you get seated in, it's probably a good long term relationship. >> Yes, that's the thing, it's a recurring revenue business. We're a subscription, just like a SAS business, only we're subscription revenue on maintenance. >> Alright, well Seth, thanks for taking a few minutes of your day and sharin' your story. >> Thank you much. >> Alright, he's Seth, I'm Jeff, you're watchin' theCUBE, we're at our Palo Alto studio havin' Cube Conversations. Thanks for watchin', we'll see you next time. (inspiring music)

>> Live from Las Vegas, it's theCUBE, covering AWS re:Invent 2018, brought to you by Amazon Web Services, Intel, and their ecosystem partners. >> And welcome back here to Las Vegas. We're in the Sands expo, we're in Hall D. If you happen to be at the show or dropping in just to watch, come on by and say hi to us. Love to see you here on theCUBE, as we continue our coverage, day two. And along with Justin Warren, I'm John Walls. And now we're joined by a couple of gents from HUB International, Seth Morrell, who's the vice president of enterprise, architecture and design. Seth, good morning to you. >> Good morning. >> And Jeremy Embalabala, who is the director of security architecture and engineering, also at HUB International. Good morning, Jeremy. >> Good morning. >> Seth, by the way, playing hurt, broken finger with a snowblower in Chicago on Monday. >> On Monday. >> Yeah, good luck though with the winter. >> Yeah, yeah, yeah, it started off well. >> Sorry to see that, but thanks for coming regardless. >> No problem. >> All right, tell us about HUB International a little bit, about primary mission and then the two of you, what you're doing for them primarily. >> Right, right, so HUB International is an insurance brokerage. Personal, commercial, we do employee benefits, retirement as well. We're based in the US in Chicago, operate in US and Canada. 500 plus locations, 12,000 employees. >> Okay, and then primary responsibilities between the two of you? >> Well, I'm the director of security architecture. I'm responsible for all things technical with regards to security, both on the architecture side, engineering and operations. >> All right, so yesterday we were talking about this early, you did a session, you're big Splunk guys, right? So let's talk about what you're doing with that, how that's working for you in general, if you would. >> Yeah, yeah, go ahead. >> Yeah, the reason Splunk Enterprise Security, the on-premise version we actually, people always ask me, are you using Splunk Cloud or Splunk On Prem? And I always joke, well we're using Splunk On Prem in the cloud in AWS. But for us, we're really focused on Splunk as a SIEM, to enable our security operations center to provide insights into our environment and help us detect and understand threats that are going on in the environment. So we have a manage partner that runs our security operations center for us. They also manage our Splunk environment. It helps us keep an eye on both our AWS environment that we have, our Azure environment, and our on-premise data center as well. >> A few people have sort of gotten wary of the idea of a SIEM. People have tried to use SIEMs and they haven't been very successful and they go, "Oh SIEM's a bit of a dirty word." But it sounds like SIEM's actually working for you really well. >> Yeah, I really view a SIEM as a cornerstone of security program. Specifically if you have a mature security operation center, it's really hard to operate that without a SIEM. SIEMs are tricky, they're tricky to implement, they're generally very costly and they require a lot of tuning, a lot of love, care, and feeding in order to be effective. Quite frankly, if you don't get that right, it can actually be detrimental to your security program. But if you put the proper care and feeding into a SIEM, it will be very beneficial to your organization. >> Okay, so what's some of the things that you've been able to do now that you've got Splunk in there and it's helping you manage the security? Because I saw some statistics earlier this morning, where security is basically the second biggest, most popular term here at AWS and at re:Invent. It's clearly front of mind for a lot of enterprises. So what is it that Splunk in helping you to achieve that you wouldn't have been able to go otherwise? >> The biggest thing for us is the aggregation of all of our logs, our data sources in AWS, data sources on prem, our Windows file servers, our network traffic flow data, all of that's aggregated into Splunk. And that allows us to do some correlation with third-party threat intelligence feeds. Take indicators of compromise that are streamed, that are observed out there in the real world, and apply those to data that we're seeing on our actual data sources in our environment. It allows us to detect threats that we wouldn't have been able to detect otherwise. >> Right, how does that translate through to what you're actually doing as a business? I mean, this is a very sort of technology-centric thing, but you're an insurance agent. So how does this investment in security translate into the business value? >> One, it just gives us visibility into the environment, and we can proactively identify potential threats and remediate them before they actually cause an impact to the business. Without these tools and without these capabilities, it'd be a much riskier endeavor. And so it's helped us throughout, and we've been good partners with Splunk, they're been good partners with us. And coupled with all the other things that we're doing in the security space and in the cloud space, we're able to build a nice secure environment for our customers and ourselves. >> We're also a very highly regulated industry, so we have regulations that we have to comply with for security. And our customers also care about security very, very deeply. So it allows us to be able to protect our customers' data and really assure our customers that their data is safe with us, whether that data is hosted on-prem or it's in the cloud. >> What about that battle? There's often a battle between private enterprise and regulation, just in general, right? It's making sure the policy makers understand capabilities and real threats as opposed to maybe perceptions or whatever. What do you see in terms of the federal regulatory environment and what you deal with in a Balkanized system where you're dealing with 50 states and Canada. So you've got your hands full, I assume. >> So at HUB, we view security and compliance a little differently. Instead of trying to build security programs and achieve compliance by abiding by all the regulations, we do the right thing from a security perspective. We make the right investments. We put the right controls into our environment. When those new regulations come out for provincial law in Canada or different states or GDPR in Europe, that we'll be 95% of the way there, by just building the right controls into our environment at a foundational level. Then we have to just spend our efforts just kind of aligning ourselves with the other 5% that vary from regulation to regulation. >> Was that a shift in management philosophy at all? Because quite often or maybe in the past, it's like, I'm only going to do something. I'm not saying HUB, but in general, when I have to. As opposed to you appear to be preemptive. Right, you're doing things because you should. So there's a different mindset there, right? >> It sounds like a much more strategic view of security rather than a tactical reactive kind of security. How long has that been the philosophy at HUB? >> So we really built out our a security program starting the beginning of last year. There's all new leadership that came in, Seth came in, myself came in, all new leadership across the organization. And that's really where that mindset came from. And the need and recognition to make an investment in security. We view security as a driver of business, not just a cost center. It's a way we can add to the bottom line and be able to generate revenue for the business by being able to show our customers that we really care about their data, and we're going to do our best to take of them. So with that mindset, we can actually help market, and use that as a marketing tool to be able to help drive business. >> So what are some of the things that you've seen here at the show that you're thinking about, well actually that will support my strategy? Some of the more longer term things. Is there anything that's sort of stuck out to you as sort of going, ooh, that's something that we should actually take back? >> Yeah, well, there's some tactical announcements that are very important to us. The announcement of Windows File Server support. File Server support is big deal for us. We're a heavy File Server organization. And having that native within AWS is very interesting. There's been some other announcements with SFTP. Other items that we're going to be trying to take advantage of in a fairly quick fashion. And we're excited about that. We've been on our journey to cloud since essentially the summer of 2017 through now. And we're kind of ready for the next steps, the next set of capabilities. And so, a conference like this and all these announcements, we're excited to take a look at the menu and start picking out what we want to eat. >> It's a great buffet. >> Yeah, yeah. >> In a city that's famous for it. >> That's true, that's true. >> All you can eat. >> Yeah. >> All right, so let's talk about the journey then. You said 2017, so it's been a year, year plus into that. And you're excited about what's coming, but what do you need? So I know you got this great buffet that you're looking at, but maybe you don't want the pork. Maybe you want the turkey. What do you need, what do you want the most, you think, to service your clients? >> Right, so, we spent most of our migration just essentially moving what we had over to the cloud. And so, what our next steps are, let's really understand our workloads, let's be smarter about how we're running them, let's take advantage of the appropriate technology, the menu items that are out there, per work load, just to be smarter. We're going to be spending much more time this year looking at more automation, orchestration, and basically maturing our cloud capabilities so that we're ready for the next big thing. And as we acquire another company or there's a new business need, we're working to be more proactive and being able to anticipate those needs with building a platform that we can really extend and build upon. >> I'm sorry, go ahead. >> I have a question on the choosing of workloads then. So are you going to be moving everything to the cloud? Or do you think that there'll be some things that will actually remain on-prem or is it going to be a hybrid cloud? >> Our goal is to go from a data center to a network closet. >> Right. >> So we have moved almost all of our application workloads out of our data center right now. We have a large VDI environment we're looking to move as well. Once that's done, we'll be down to our phone system and a couple other legacy applications that we're trying to determine what we actually want to do with strategically. >> Right, okay. That's a pretty common sort of story. There's a lot of people who are moving as much as they possibly can, and then there's a few little bits that just sort of sit there that you need to decide, do we rewrite this, do we actually need this at all, maybe we just turn it off. >> Right. >> Yeah. >> Are there any capabilities specific to your industry that you need or that you'd like to have refined? Something that would allow you to do your job, specifically in the insurance space, that would be unique to you? Anything floating out there that you say, if we had that, that'll fine-tune this to a better degree or a greater degree? >> So for us, it's all about flexibility. We grow very, very rapidly through our mergers and acquisitions. We bought 52 companies last year and we're on pace to do almost 70 companies this year. So for us, the cloud really enables us to be able to absorb those organizations that we acquire, bring them in much, much faster. Part of the story of our cloud migration, we were able to move the integration time for mergers and acquisitions from six months down to under 90 days. Because we're now able to move those workloads in much, much quicker with the clouds. For us that's really a key capability. >> Well you guys are used to writing checks, dinner's on them tonight, right? >> Definitely. >> Seth, Jeremy, thanks for being with us. >> Thank you. >> Glad to be here. >> We appreciate the time. Good luck with the winter, I think you might need it. >> Yeah, yeah, exactly. >> All right, we'll be back with more from AWS re:Invent. You're watching theCUBE from Las Vegas. (snappy techno music)

>> Live, from Times Square, in New York City, it's theCUBE covering IBM's Change the Game: Winning with AI. Brought to you by IBM. >> Welcome back to the Big Apple, everybody. I'm Dave Vellante, and you're watching theCUBE, the leader in live tech coverage, and we're here covering a special presentation of IBM's Change the Game: Winning with AI. IBM's got an analyst event going on here at the Westin today in the theater district. They've got 50-60 analysts here. They've got a partner summit going on, and then tonight, at Terminal 5 of the West Side Highway, they've got a customer event, a lot of customers there. We've talked earlier today about the hard news. Seth Dobern is here. He's the Chief Data Officer of IBM Analytics, and he's joined by Shreesha Rao who is the Senior Manager of IT Applications at California-based Niagara Bottling. Gentlemen, welcome to theCUBE. Thanks so much for coming on. >> Thank you, Dave. >> Well, thanks Dave for having us. >> Yes, always a pleasure Seth. We've known each other for a while now. I think we met in the snowstorm in Boston, sparked something a couple years ago. >> Yep. When we were both trapped there. >> Yep, and at that time, we spent a lot of time talking about your internal role as the Chief Data Officer, working closely with Inderpal Bhandari, and you guys are doing inside of IBM. I want to talk a little bit more about your other half which is working with clients and the Data Science Elite Team, and we'll get into what you're doing with Niagara Bottling, but let's start there, in terms of that side of your role, give us the update. >> Yeah, like you said, we spent a lot of time talking about how IBM is implementing the CTO role. While we were doing that internally, I spent quite a bit of time flying around the world, talking to our clients over the last 18 months since I joined IBM, and we found a consistent theme with all the clients, in that, they needed help learning how to implement data science, AI, machine learning, whatever you want to call it, in their enterprise. There's a fundamental difference between doing these things at a university or as part of a Kaggle competition than in an enterprise, so we felt really strongly that it was important for the future of IBM that all of our clients become successful at it because what we don't want to do is we don't want in two years for them to go "Oh my God, this whole data science thing was a scam. We haven't made any money from it." And it's not because the data science thing is a scam. It's because the way they're doing it is not conducive to business, and so we set up this team we call the Data Science Elite Team, and what this team does is we sit with clients around a specific use case for 30, 60, 90 days, it's really about 3 or 4 sprints, depending on the material, the client, and how long it takes, and we help them learn through this use case, how to use Python, R, Scala in our platform obviously, because we're here to make money too, to implement these projects in their enterprise. Now, because it's written in completely open-source, if they're not happy with what the product looks like, they can take their toys and go home afterwards. It's on us to prove the value as part of this, but there's a key point here. My team is not measured on sales. They're measured on adoption of AI in the enterprise, and so it creates a different behavior for them. So they're really about "Make the enterprise successful," right, not "Sell this software." >> Yeah, compensation drives behavior. >> Yeah, yeah. >> So, at this point, I ask, "Well, do you have any examples?" so Shreesha, let's turn to you. (laughing softly) Niagara Bottling -- >> As a matter of fact, Dave, we do. (laughing) >> Yeah, so you're not a bank with a trillion dollars in assets under management. Tell us about Niagara Bottling and your role. >> Well, Niagara Bottling is the biggest private label bottled water manufacturing company in the U.S. We make bottled water for Costcos, Walmarts, major national grocery retailers. These are our customers whom we service, and as with all large customers, they're demanding, and we provide bottled water at relatively low cost and high quality. >> Yeah, so I used to have a CIO consultancy. We worked with every CIO up and down the East Coast. I always observed, really got into a lot of organizations. I was always observed that it was really the heads of Application that drove AI because they were the glue between the business and IT, and that's really where you sit in the organization, right? >> Yes. My role is to support the business and business analytics as well as I support some of the distribution technologies and planning technologies at Niagara Bottling. >> So take us the through the project if you will. What were the drivers? What were the outcomes you envisioned? And we can kind of go through the case study. >> So the current project that we leveraged IBM's help was with a stretch wrapper project. Each pallet that we produce--- we produce obviously cases of bottled water. These are stacked into pallets and then shrink wrapped or stretch wrapped with a stretch wrapper, and this project is to be able to save money by trying to optimize the amount of stretch wrap that goes around a pallet. We need to be able to maintain the structural stability of the pallet while it's transported from the manufacturing location to our customer's location where it's unwrapped and then the cases are used. >> And over breakfast we were talking. You guys produce 2833 bottles of water per second. >> Wow. (everyone laughs) >> It's enormous. The manufacturing line is a high speed manufacturing line, and we have a lights-out policy where everything runs in an automated fashion with raw materials coming in from one end and the finished goods, pallets of water, going out. It's called pellets to pallets. Pellets of plastic coming in through one end and pallets of water going out through the other end. >> Are you sitting on top of an aquifer? Or are you guys using sort of some other techniques? >> Yes, in fact, we do bore wells and extract water from the aquifer. >> Okay, so the goal was to minimize the amount of material that you used but maintain its stability? Is that right? >> Yes, during transportation, yes. So if we use too much plastic, we're not optimally, I mean, we're wasting material, and cost goes up. We produce almost 16 million pallets of water every single year, so that's a lot of shrink wrap that goes around those, so what we can save in terms of maybe 15-20% of shrink wrap costs will amount to quite a bit. >> So, how does machine learning fit into all of this? >> So, machine learning is way to understand what kind of profile, if we can measure what is happening as we wrap the pallets, whether we are wrapping it too tight or by stretching it, that results in either a conservative way of wrapping the pallets or an aggressive way of wrapping the pallets. >> I.e. too much material, right? >> Too much material is conservative, and aggressive is too little material, and so we can achieve some savings if we were to alternate between the profiles. >> So, too little material means you lose product, right? >> Yes, and there's a risk of breakage, so essentially, while the pallet is being wrapped, if you are stretching it too much there's a breakage, and then it interrupts production, so we want to try and avoid that. We want a continuous production, at the same time, we want the pallet to be stable while saving material costs. >> Okay, so you're trying to find that ideal balance, and how much variability is in there? Is it a function of distance and how many touches it has? Maybe you can share with that. >> Yes, so each pallet takes about 16-18 wraps of the stretch wrapper going around it, and that's how much material is laid out. About 250 grams of plastic that goes on there. So we're trying to optimize the gram weight which is the amount of plastic that goes around each of the pallet. >> So it's about predicting how much plastic is enough without having breakage and disrupting your line. So they had labeled data that was, "if we stretch it this much, it breaks. If we don't stretch it this much, it doesn't break, but then it was about predicting what's good enough, avoiding both of those extremes, right? >> Yes. >> So it's a truly predictive and iterative model that we've built with them. >> And, you're obviously injecting data in terms of the trip to the store as well, right? You're taking that into consideration in the model, right? >> Yeah that's mainly to make sure that the pallets are stable during transportation. >> Right. >> And that is already determined how much containment force is required when your stretch and wrap each pallet. So that's one of the variables that is measured, but the inputs and outputs are-- the input is the amount of material that is being used in terms of gram weight. We are trying to minimize that. So that's what the whole machine learning exercise was. >> And the data comes from where? Is it observation, maybe instrumented? >> Yeah, the instruments. Our stretch-wrapper machines have an ignition platform, which is a Scada platform that allows us to measure all of these variables. We would be able to get machine variable information from those machines and then be able to hopefully, one day, automate that process, so the feedback loop that says "On this profile, we've not had any breaks. We can continue," or if there have been frequent breaks on a certain profile or machine setting, then we can change that dynamically as the product is moving through the manufacturing process. >> Yeah, so think of it as, it's kind of a traditional manufacturing production line optimization and prediction problem right? It's minimizing waste, right, while maximizing the output and then throughput of the production line. When you optimize a production line, the first step is to predict what's going to go wrong, and then the next step would be to include precision optimization to say "How do we maximize? Using the constraints that the predictive models give us, how do we maximize the output of the production line?" This is not a unique situation. It's a unique material that we haven't really worked with, but they had some really good data on this material, how it behaves, and that's key, as you know, Dave, and probable most of the people watching this know, labeled data is the hardest part of doing machine learning, and building those features from that labeled data, and they had some great data for us to start with. >> Okay, so you're collecting data at the edge essentially, then you're using that to feed the models, which is running, I don't know, where's it running, your data center? Your cloud? >> Yeah, in our data center, there's an instance of DSX Local. >> Okay. >> That we stood up. Most of the data is running through that. We build the models there. And then our goal is to be able to deploy to the edge where we can complete the loop in terms of the feedback that happens. >> And iterate. (Shreesha nods) >> And DSX Local, is Data Science Experience Local? >> Yes. >> Slash Watson Studio, so they're the same thing. >> Okay now, what role did IBM and the Data Science Elite Team play? You could take us through that. >> So, as we discussed earlier, adopting data science is not that easy. It requires subject matter, expertise. It requires understanding of data science itself, the tools and techniques, and IBM brought that as a part of the Data Science Elite Team. They brought both the tools and the expertise so that we could get on that journey towards AI. >> And it's not a "do the work for them." It's a "teach to fish," and so my team sat side by side with the Niagara Bottling team, and we walked them through the process, so it's not a consulting engagement in the traditional sense. It's how do we help them learn how to do it? So it's side by side with their team. Our team sat there and walked them through it. >> For how many weeks? >> We've had about two sprints already, and we're entering the third sprint. It's been about 30-45 days between sprints. >> And you have your own data science team. >> Yes. Our team is coming up to speed using this project. They've been trained but they needed help with people who have done this, been there, and have handled some of the challenges of modeling and data science. >> So it accelerates that time to --- >> Value. >> Outcome and value and is a knowledge transfer component -- >> Yes, absolutely. >> It's occurring now, and I guess it's ongoing, right? >> Yes. The engagement is unique in the sense that IBM's team came to our factory, understood what that process, the stretch-wrap process looks like so they had an understanding of the physical process and how it's modeled with the help of the variables and understand the data science modeling piece as well. Once they know both side of the equation, they can help put the physical problem and the digital equivalent together, and then be able to correlate why things are happening with the appropriate data that supports the behavior. >> Yeah and then the constraints of the one use case and up to 90 days, there's no charge for those two. Like I said, it's paramount that our clients like Niagara know how to do this successfully in their enterprise. >> It's a freebie? >> No, it's no charge. Free makes it sound too cheap. (everybody laughs) >> But it's part of obviously a broader arrangement with buying hardware and software, or whatever it is. >> Yeah, its a strategy for us to help make sure our clients are successful, and I want it to minimize the activation energy to do that, so there's no charge, and the only requirements from the client is it's a real use case, they at least match the resources I put on the ground, and they sit with us and do things like this and act as a reference and talk about the team and our offerings and their experiences. >> So you've got to have skin in the game obviously, an IBM customer. There's got to be some commitment for some kind of business relationship. How big was the collective team for each, if you will? >> So IBM had 2-3 data scientists. (Dave takes notes) Niagara matched that, 2-3 analysts. There were some working with the machines who were familiar with the machines and others who were more familiar with the data acquisition and data modeling. >> So each of these engagements, they cost us about $250,000 all in, so they're quite an investment we're making in our clients. >> I bet. I mean, 2-3 weeks over many, many weeks of super geeks time. So you're bringing in hardcore data scientists, math wizzes, stat wiz, data hackers, developer--- >> Data viz people, yeah, the whole stack. >> And the level of skills that Niagara has? >> We've got actual employees who are responsible for production, our manufacturing analysts who help aid in troubleshooting problems. If there are breakages, they go analyze why that's happening. Now they have data to tell them what to do about it, and that's the whole journey that we are in, in trying to quantify with the help of data, and be able to connect our systems with data, systems and models that help us analyze what happened and why it happened and what to do before it happens. >> Your team must love this because they're sort of elevating their skills. They're working with rock star data scientists. >> Yes. >> And we've talked about this before. A point that was made here is that it's really important in these projects to have people acting as product owners if you will, subject matter experts, that are on the front line, that do this everyday, not just for the subject matter expertise. I'm sure there's executives that understand it, but when you're done with the model, bringing it to the floor, and talking to their peers about it, there's no better way to drive this cultural change of adopting these things and having one of your peers that you respect talk about it instead of some guy or lady sitting up in the ivory tower saying "thou shalt." >> Now you don't know the outcome yet. It's still early days, but you've got a model built that you've got confidence in, and then you can iterate that model. What's your expectation for the outcome? >> We're hoping that preliminary results help us get up the learning curve of data science and how to leverage data to be able to make decisions. So that's our idea. There are obviously optimal settings that we can use, but it's going to be a trial and error process. And through that, as we collect data, we can understand what settings are optimal and what should we be using in each of the plants. And if the plants decide, hey they have a subjective preference for one profile versus another with the data we are capturing we can measure when they deviated from what we specified. We have a lot of learning coming from the approach that we're taking. You can't control things if you don't measure it first. >> Well, your objectives are to transcend this one project and to do the same thing across. >> And to do the same thing across, yes. >> Essentially pay for it, with a quick return. That's the way to do things these days, right? >> Yes. >> You've got more narrow, small projects that'll give you a quick hit, and then leverage that expertise across the organization to drive more value. >> Yes. >> Love it. What a great story, guys. Thanks so much for coming to theCUBE and sharing. >> Thank you. >> Congratulations. You must be really excited. >> No. It's a fun project. I appreciate it. >> Thanks for having us, Dave. I appreciate it. >> Pleasure, Seth. Always great talking to you, and keep it right there everybody. You're watching theCUBE. We're live from New York City here at the Westin Hotel. cubenyc #cubenyc Check out the ibm.com/winwithai Change the Game: Winning with AI Tonight. We'll be right back after a short break. (minimal upbeat music)

>> Narrator: Live from downtown San Francisco, it's The Cube, covering IBM chief data officer strategy summit 2018, brought to you by IBM. (playful music) >> Welcome back to the IBM chief data officer strategy summit in San Francisco. We're here at the Parc 55. My name is Dave Vellante, and you're watching The Cube, the leader in live tech coverage, #IBMCDO. Seth Dobrin is here. He's the chief data officer for IBM analytics. Seth, good to see you again. >> Good to see you again, Dave. >> Many time Cube alum; thanks for coming back on. Asim Tewary, Tewary? Tewary; sorry. >> Tewary, yes. >> Asim Tewary; I can't read my own writing. Head of data science and advanced analytics at Verizon, and from Jersey. Two east coast boys, three east coast boys. >> Three east coast boys. >> Yeah. >> Welcome, gentlemen. >> Thank you. >> Asim, you guys had a panel earlier today. Let's start with you. What's your role? I mean, we talked you're the defacto chief data officer at Verizon. >> Yes, I'm responsible for all the data ingestion platform, big data, and the data science for Verizon, for wireless, wire line, and enterprise businesses. >> It's a relatively new role at Verizon? You were saying previously you were CDO at a financial services organization. Common that a financial service organization would have a chief data officer. How did the role come about at Verizon? Are you Verizon's first CDO or-- >> I was actually brought in to really pull together the analytics and data across the enterprise, because there was a realization that data only creates value when you're able to get it from all the difference sources. We had separate teams in the past. My role was to bring it all together, to have a common platform, common data science team to drive revenue across the businesses. >> Seth, this is a big challenge, obviously. We heard Caitlyn this morning, talking about the organizational challenges. You got data in silos. Inderpal and your team are basically, I call it dog-fooding. You're drinking your own champagne. >> Champagne-ing, yeah. >> Yeah, okay, but you have a similar challenge. You have big company, complex, a lot of data silos coming. Yeah, I mean, IBM is really, think of it as five companies, right? Any one of them would be a fortune 500 company in and of themselves. Even within each of those, there were silos, and then Inderpal trying to bring them across, you know, the data from across all of them is really challenging. Honestly, the technology part, the bringing it together is the easy part. It's the cultural change that goes along with it that's really, really hard, to get people to think about it as IBM's or Verizon's data, and not their data. That's really how you start getting value from it. >> That's a cultural challenge you face is, "Okay, I've got my data; I don't want to share." How do you address that? >> Absolutely. Governance and ownership of data, having clear roles and responsibilities, ensuring there's this culture where people realize that data is an asset of the firm. It is not your data or my data; it is firm's data, and the value you create for the business is from that data. It is a transformation. It's changing the people culture aspect, so there's a lot of education. You know, you have to be an evangelist. You wear multiple hats to show people the value, why they should do. Obviously, I had an advantage because coming in, Verizon management was completely sold to the idea that the data has to be managed as an enterprise asset. Business was ready and willing to own data as an enterprise asset, and so it was relatively easier. However, it was a journey to try to get everyone on the same page in terms of ensuring that it wasn't the siloed mentality. This was a enterprise asset that we need to manage together. >> A lot of organizations tell me that, first of all, you got to have top-down buy-in. Clearly, you had that, but a lot of the times I hear that the C-suite says, "Okay, we're going to do this," but the middle management is sort of, they got to PNL, they've got to make their plan, and it takes them longer to catch up. Did you face that challenge, and how do you ... How were you addressing it? >> Absolutely. What we had to do was really make sure that we were not trying to boil the ocean, that we were trying to show the values. We found champions. For example, finance, you know, was a good champion for us, where we used the data and analytics to really actually launch some very critical initiatives for the firm, asset-backed securities. For the first time, Verizon launched ABS, and we actually enabled that. That created the momentum, if you will, as to, "Okay, there's value in this." That then created the opportunity for all the other business to jump on and start leveraging data. Then we all are willing to help and be part of the journey. >> Seth, before you joined IBM, obviously the company was embarking on this cognitive journey. You know, Watson, the evolution of Watson, the kind of betting a lot on cognitive, but internally you must have said, "Well, if we're going to market this externally, "we'd better become a cognitive enterprise." One of the questions that came up on the panel was, "What is a cognitive enterprise?" You guys, have you defined it? Love to ask Asim the same question. >> Yeah, so I mean, a cognitive enterprise is really about an enterprise that uses data and analytics, and cognition to run their business, right? You can't just jump to being a cognitive enterprise, right? It's a journey or a ladder, right? Where you got to get that foundation data in order. Then you've got to start even being able to do basic analytics. Then you can start doing things like machine learning, and deep learning, and then you can get into cognition. It's not a, just jump to the top of the ladder, because there's just a lot of work that's required to do it. You can do that within a business unit. The whole company doesn't need to get there, and in fact, you'll see within a company, different part of the company will be at different stages. Kind of to Asim's point about partnering with finance, and that's my experience both at IBM and before I joined. You find a partner that's going to be a champion for you. You make them immensely successful, and everyone else will follow because of shame, because they don't want to be out-competed by their peers. >> So, similar definition of a cognitive enterprise? >> Absolutely. In fact, what I would say is cognitive is a spectrum, right? Where most companies are at the low end of that spectrum where using data for decision-making, but those are reports, BI reports, and stuff like that. As you evolve to become smarter and more AI machine learning, that's when you get into predictive, where you're using the data to predict what might happen based on prior historical information. Then that evolution goes all the way to being prescriptive, where you're not only looking back and being able to predict, but you're actually able to recommend action that you want to take. Obviously, with the human involvement, because governance is an important aspect to all of this, right? Completely agree that the cognitive is really covering the spectrum of prescriptive, predictive, and using data for all your decision making. >> This actually gets into a good point, right? I mean, I think Asim has implemented some deep learning models at Verizon, but you really need to think about what's the right technology or the right, you know, the right use case for that. There's some use cases where descriptive analytics is the right answer, right? There's no reason to apply machine learning or deep learning. You just need to put that in front of someone. Then there are use cases where you do want deep learning, either because the problem is so complex, or because the accuracy needs to be there. I go into a lot of companies to talk to senior executives, and they're like, "We want to do deep learning." You ask them what the use case is, and you're like, "Really, that's rules," right? It gets back to Occam's razor, right? The simplest solution is always the answer, is always the best answer. Really understanding from your perspective, having done this at a couple of companies now, kind of when do you know when to use deep learning versus machine learning, versus just basic statistics? >> How about that? >> Yeah. >> How do you parse that? >> Absolutely. You know, like anything else, it's very important to understand what problem you're trying to solve. When you have a hammer, everything looks like a nail, and deep learning might be one of those hammers. What we do is make sure that any problem that requires explain-ability, interpret-ability, you cannot use deep learning, because you cannot explain when you're using deep learning. It's a multi-layered neural network algorithm. You can't really explain why the outcome was what it was. For that, you have to use more simpler algorithms, like decision tree, like regression, classification. By the way, 70 to 80% of the problem that you have in the company, can be solved by those algorithms. You don't always use deep learning, but deep learning is a great use case algorithm to use when you're solving complex problems. For example, when you're looking at doing friction analysis as to customer journey path analysis, that tends to be very noisy. You know, you have billions of data points that you have to go through for an algorithm. That is, you know, good for deep learning, so we're using that today, but you know, those are a narrow set of use cases where it is required, so it's important to understand what problem you're trying to solve and where you want to use deep learning. >> To use deep learning, you need a lot of label data, right? >> Yes. >> And that's-- >> A lot of what? Label data? >> Label data. So, and that's often a hurdle to companies using deep learning, even when they have a legitimate deep learning use cases. Just the massive amount of label data you need for that use case. >> As well as scale, right? >> Yeah. >> The whole idea is that when you have massive amounts of data with a lot of different variables, you need deep learning to be able to make that decision. That means you've got to have scale and real time capability within the platform, that has the elasticity and compute, to be able to crunch all that data. >> Yeah. >> Initially, when we started on this journey, our infrastructure was not able to handle that. You know, we had a lot of failures, and so obviously we had to enhance our infrastructure to-- >> You spoke to Samit Gupta and Ed earlier, about, you know, GPUs, and flash storage, and the need for those types of things to do these complex, you know, deep learning problems. We struggled with that even inside of IBM when we first started building this platform as, how do we get the best performance of ingesting the data, getting it labeled, and putting it into these models, these deep learning models, and some of the instance we use that. >> Yeah, my takeaway is that infrastructure for AI has to be flexible, you got to be great granularity. It's got to not only be elastic, but it's got to be, sometimes we call it plastic. It's got to sometimes retain its form. >> Yes. >> Right? Then when you bring in some new unknown workload, you've got to be able to adjust it without ripping down the entire infrastructure. You have to purpose built a whole next set of infrastructure, which is kind of how we built IT over the years. >> Exactly. >> I think, Dave, too, When you and I first spoke four or five years ago, it was all about commodity hardware, right? It was going to Hadoop ecosystem, minimizing, you know, getting onto commodity hardware, and now you're seeing a shift away from commodity hardware, in some instances, toward specialized hardware, because you need it for these use cases. So we're kind of making that. We shifted to one extreme, and now we're kind of shifting, and I think we're going to get to a good equilibrium where it's a balance of commodity and specialized hardware for big data, as much as I hate that word, and advanced analytics. >> Well, yeah, even your cloud guys, all the big cloud guys, they used to, you know, five, six years ago, say, "Oh, it's all commodity stuff," and now it's a lot of custom, because they're solving problems that you can't solve with a commodity. I want to ask you guys about this notion of digital business. To us, the difference between a business and a digital business is how you use data. As you become a digital business, which is essentially what you're doing with cognitive and AI, historically, you may have organized around, I don't know, your network, and certain you've got human skills that are involved, and your customers. I mean, IBM in your case, it's your products, your services, your portfolio, your clients. Increasingly, you're organizing around your data, aren't you? Which brings back to cultural change, but what about the data model? I presume you're trying to get to a data model where the customer service, and the sales, and the marketing aren't separate entities. I don't have to deal with them when I talk to Verizon. I deal with just Verizon, right? That's not easy when the data's all inside. How are you dealing with that challenge? >> Customer is at the center of the business model. Our motto and out goal is to provide the best products to the customers, but even more important, provide the best experience. It is all about the customer, agnostic of the channel, which channel the customer is interacting with. The customer, for the customer, it's one Verizon. The way we are organizing our data platform is, first of all, breaking all the silos. You know, we need to have data from all interactions with the customer, that is all digital, that's coming through, and creating one unified model, essentially, that essentially teaches all the journeys, and all the information about the customer, their events, their behavior, their propensities, and stuff like that. Then that information, using algorithms, like predictive, prescriptive, and all of that, make it available in all channels of engagement. Essentially, you have common intelligence that is made available across all channels. Whether the customer goes to point of sale in a retail store, or calls a call center, talks to a rep, or is on the digital channel, is the same intelligence driving the experience. Whether a customer is trying to buy a phone, or has an issue with a service related aspect of it, and that's the key, which is centralized intelligence from common data lake, and then deliver a seamless experience across all channels for that customer-- >> Independent of where I bought that phone, for example, right? >> Exactly. Maintaining the context is critical. If you went to the store and you know, you're looking for a phone, and you know, you didn't find what you're looking for, you want to do some research, if you go to the digital channel, you should be able to have a seamless experience where we should know that you went, that you're looking for the phone, or you called care and you asked the agent about something. Having that context be transferred across channel and be available, so the customer feels that we know who the customer is, and provide them with a good experience, is the key. >> We have limited time, but I want to talk about skills. It's hard to come by; we talked about that. It's number five on Inderpal's sort of, list of things you've got to do as a CDO. Sometimes you can do MNA, by the weather company. You've got a lot of skills, but that's not always so practical. How have you been dealing with the skills gap? >> Look, skill is hard to find, data scientists are hard to find. The way we are envisioning our talent management is two things we need to take care of. One, we need solid big data engineers, because having a solid platform that has real trans-streaming capability is very critical. Second, data scientists, it's hard to get. However, our plan is to really take the domain experts, who really understand the business, who understand the business process and the data, and give them the tools, automation tools for data science, that essentially, you know, will put it in a box for them, in terms of which algorithm to use, and enable them to create more value. While we will continue to hire specialized data scientists who are going to work on much more of the complex problems, the skill will come from empowering and enabling the domain experts with data science capabilities that automates choosing model development and algorithm development. >> Presumably grooming people in house, right? >> Grooming people in house, and I actually break it down a little more granular. I even say there's data engineers, there's machine learning engineers, there's optimization engineers, then there's data journalists. They're the ones that tell the story. I think we were talking earlier, Asim, about you know, it's not just PhDs, right? You're not just looking for PhDs to fill these rolls anymore. You're looking for people with masters degrees, and even in some cases, bachelors degrees. With IBM's new collar job initiative, we're even bringing on some, what we call P-TECH students, which are five year high school students, and we're building a data science program for them. We're building apprenticeships, which is, you know, you've had a couple years of college, building a data science program, and people look at me like I'm crazy when I say that, but the bulk of the work of a data science program, of executing data science, is not implementing machine learning models. It's engineering features, it's cleaning data. With basic Python skills, this is something that you can very easily teach these people to do, and then under the supervision of a principal data scientist or someone with a PhD or a masters degree, they can start learning how to implement models, but they can start contributing right away with just some basic Python skills. >> Then five, seven years in, they're-- >> Yeah. >> domain experts. All right, guys, got to jump, but thanks very much, Asim, for coming on and sharing your story. Seth, always a pleasure. >> Yeah, good to see you again, Dave. >> All right. >> Thank you, Dave. >> You're welcome. Keep it right there, buddy. >> Thanks. >> We'll be back with our next guest. This is The Cube, live from IBM CDO strategy summit in San Francisco. We'll be right back. (playful music) (phone dialing)

>> Announcer: Live from San Jose, it's theCUBE. Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and it's ecosystem partners. >> Welcome back to theCUBE's continuing coverage of our own event, Big Data SV. I'm Lisa Martin, with my cohost Dave Vellante. We're in downtown San Jose at this really cool place, Forager Eatery. Come by, check us out. We're here tomorrow as well. We're joined by, next, one of our CUBE alumni, Seth Dobrin, the Vice President and Chief Data Officer at IBM Analytics. Hey, Seth, welcome back to theCUBE. >> Hey, thanks for having again. Always fun being with you guys. >> Good to see you, Seth. >> Good to see you. >> Yeah, so last time you were chatting with Dave and company was about in the fall at the Chief Data Officers Summit. What's kind of new with you in IBM Analytics since then? >> Yeah, so the Chief Data Officers Summit, I was talking with one of the data governance people from TD Bank and we spent a lot of time talking about governance. Still doing a lot with governance, especially with GDPR coming up. But really started to ramp up my team to focus on data science, machine learning. How do you do data science in the enterprise? How is it different from doing a Kaggle competition, or someone getting their PhD or Masters in Data Science? >> Just quickly, who is your team composed of in IBM Analytics? >> So IBM Analytics represents, think of it as our software umbrella, so it's everything that's not pure cloud or Watson or services. So it's all of our software franchise. >> But in terms of roles and responsibilities, data scientists, analysts. What's the mixture of-- >> Yeah. So on my team I have a small group of people that do governance, and so they're really managing our GDPR readiness inside of IBM in our business unit. And then the rest of my team is really focused on this data science space. And so this is set up from the perspective of we have machine-learning engineers, we have predictive-analytics engineers, we have data engineers, and we have data journalists. And that's really focus on helping IBM and other companies do data science in the enterprise. >> So what's the dynamic amongst those roles that you just mentioned? Is it really a team sport? I mean, initially it was the data science on a pedestal. Have you been able to attack that problem? >> So I know a total of two people that can do that all themselves. So I think it absolutely is a team sport. And it really takes a data engineer or someone with deep expertise in there, that also understands machine-learning, to really build out the data assets, engineer the features appropriately, provide access to the model, and ultimately to what you're going to deploy, right? Because the way you do it as a research project or an activity is different than using it in real life, right? And so you need to make sure the data pipes are there. And when I look for people, I actually look for a differentiation between machine-learning engineers and optimization. I don't even post for data scientists because then you get a lot of data scientists, right? People who aren't really data scientists, and so if you're specific and ask for machine-learning engineers or decision optimization, OR-type people, you really get a whole different crowd in. But the interplay is really important because most machine-learning use cases you want to be able to give information about what you should do next. What's the next best action? And to do that, you need decision optimization. >> So in the early days of when we, I mean, data science has been around forever, right? We always hear that. But in the, sort of, more modern use of the term, you never heard much about machine learning. It was more like stats, math, some programming, data hacking, creativity. And then now, machine learning sounds fundamental. Is that a new skillset that the data scientists had to learn? Did they get them from other parts of the organization? >> I mean, when we talk about math and stats, what we call machine learning today has been what we've been doing since the first statistics for years, right? I mean, a lot of the same things we apply in what we call machine learning today I did during my PhD 20 years ago, right? It was just with a different perspective. And you applied those types of, they were more static, right? So I would build a model to predict something, and it was only for that. It really didn't apply it beyond, so it was very static. Now, when we're talking about machine learning, I want to understand Dave, right? And I want to be able to predict Dave's behavior in the future, and learn how you're changing your behavior over time, right? So one of the things that a lot of people don't realize, especially senior executives, is that machine learning creates a self-fulfilling prophecy. You're going to drive a behavior so your data is going to change, right? So your model needs to change. And so that's really the difference between what you think of as stats and what we think of as machine learning today. So what we were looking for years ago is all the same we just described it a little differently. >> So how fine is the line between a statistician and a data scientist? >> I think any good statistician can really become a data scientist. There's some issues around data engineering and things like that but if it's a team sport, I think any really good, pure mathematician or statistician could certainly become a data scientist. Or machine-learning engineer. Sorry. >> I'm interested in it from a skillset standpoint. You were saying how you're advertising to bring on these roles. I was at the Women in Data Science Conference with theCUBE just a couple of days ago, and we hear so much excitement about the role of data scientists. It's so horizontal. People have the opportunity to make impact in policy change, healthcare, etc. So the hard skills, the soft skills, mathematician, what are some of the other elements that you would look for or that companies, enterprises that need to learn how to embrace data science, should look for? Someone that's not just a mathematician but someone that has communication skills, collaboration, empathy, what are some of those, openness, to not lead data down a certain, what do you see as the right mix there of a data scientist? >> Yeah, so I think that's a really good point, right? It's not just the hard skills. When my team goes out, because part of what we do is we go out and sit with clients and teach them our philosophy on how you should integrate data science in the enterprise. A good part of that is sitting down and understanding the use case. And working with people to tease out, how do you get to this ultimate use case because any problem worth solving is not one model, any use case is not one model, it's many models. How do you work with the people in the business to understand, okay, what's the most important thing for us to deliver first? And it's almost a negotiation, right? Talking them back. Okay, we can't solve the whole problem. We need to break it down in discreet pieces. Even when we break it down into discreet pieces, there's going to be a series of sprints to deliver that. Right? And so having these soft skills to be able to tease that in a way, and really help people understand that their way of thinking about this may or may not be right. And doing that in a way that's not offensive. And there's a lot of really smart people that can say that, but they can come across at being offensive, so those soft skills are really important. >> I'm going to talk about GDPR in the time we have remaining. We talked about in the past, the clocks ticking, May the fines go into effect. The relationship between data science, machine learning, GDPR, is it going to help us solve this problem? This is a nightmare for people. And many organizations aren't ready. Your thoughts. >> Yeah, so I think there's some aspects that we've talked about before. How important it's going to be to apply machine learning to your data to get ready for GDPR. But I think there's some aspects that we haven't talked about before here, and that's around what impact does GDPR have on being able to do data science, and being able to implement data science. So one of the aspects of the GDPR is this concept of consent, right? So it really requires consent to be understandable and very explicit. And it allows people to be able to retract that consent at any time. And so what does that mean when you build a model that's trained on someone's data? If you haven't anonymized it properly, do I have to rebuild the model without their data? And then it also brings up some points around explainability. So you need to be able to explain your decision, how you used analytics, how you got to that decision, to someone if they request it. To an auditor if they request it. Traditional machine learning, that's not too much of a problem. You can look at the features and say these features, this contributed 20%, this contributed 50%. But as you get into things like deep learning, this concept of explainable or XAI becomes really, really important. And there were some talks earlier today at Strata about how you apply machine learning, traditional machine learning to interpret your deep learning or black box AI. So that's really going to be important, those two things, in terms of how they effect data science. >> Well, you mentioned the black box. I mean, do you think we'll ever resolve the black box challenge? Or is it really that people are just going to be comfortable that what happens inside the box, how you got to that decision is okay? >> So I'm inherently both cynical and optimistic. (chuckles) But I think there's a lot of things we looked at five years ago and we said there's no way we'll ever be able to do them that we can do today. And so while I don't know how we're going to get to be able to explain this black box as a XAI, I'm fairly confident that in five years, this won't even be a conversation anymore. >> Yeah, I kind of agree. I mean, somebody said to me the other day, well, it's really hard to explain how you know it's a dog. >> Seth: Right (chuckles). But you know it's a dog. >> But you know it's a dog. And so, we'll get over this. >> Yeah. >> I love that you just brought up dogs as we're ending. That's my favorite thing in the world, thank you. Yes, you knew that. Well, Seth, I wish we had more time, and thanks so much for stopping by theCUBE and sharing some of your insights. Look forward to the next update in the next few months from you. >> Yeah, thanks for having me. Good seeing you again. >> Pleasure. >> Nice meeting you. >> Likewise. We want to thank you for watching theCUBE live from our event Big Data SV down the street from the Strata Data Conference. I'm Lisa Martin, for Dave Vellante. Thanks for watching, stick around, we'll be rick back after a short break.

>> This is George Gilbert, we're on the ground at Demandbase, the B2B CRM company, based on AI, one of uh, a very special company that's got some really unique technology. We have the privilege to be with Seth Myers today, Senior Data Scientist and resident wizard, and who's going to take us on a journey through some of the technology Demandbase is built on, and some of the technology coming down the road. So Seth, welcome. >> Thank you very much for having me. >> So, we talked earlier with Aman Naimat, Senior VP of Technology, and we talked about some of the functionality in Demandbase, and how it's very flexible, and reactive, and adaptive in helping guide, or react to a customer's journey, through the buying process. Tell us about what that journey might look like, how it's different, and the touchpoints, and the participants, and then how your technology rationalizes that, because we know, old CRM packages were really just lists of contact points. So this is something very different. How's it work? >> Yeah, absolutely, so at the highest level, each customer's going to be different, each customer's going to make decisions and look at different marketing collateral, and respond to different marketing collateral in different ways, you know, as the companies get bigger, and their products they're offering become more sophisticated, that's certainly the case, and also, sales cycles take a long time. You're engaged with an opportunity over many months, and so there's a lot of touchpoints, there's a lot of planning that has to be done, so that actually offers a huge opportunity to be solved with AI, especially in light of recent developments in this thing called reinforcement learning. So reinforcement learning is basically machine learning that can think strategically, they can actually plan ahead in a series of decisions, and it's actually technology behind AlphaGo which is the Google technology that beat the best Go players in the world. And what we basically do is we say, "Okay, if we understand "you're a customer, we understand the company you work at, "we understand the things they've been researching elsewhere "on third party sites, then we can actually start to predict "about content they will be likely to engage with." But more importantly, we can start to predict content they're more likely to engage with next, and after that, and after that, and after that, and so what our technology does is it looks at all possible paths that your potential customer can take, all the different content you could ever suggest to them, all the different routes they will take, and it looks at ones that they're likely to follow, but also ones they're likely to turn them into an opportunity. And so we basically, in the same way Google Maps considers all possible routes to get you from your office to home, we do the same, and we choose the one that's most likely to convert the opportunity, the same way Google chooses the quickest road home. >> Okay, this is really, that's a great example, because people can picture that, but how do you, how do you know what's the best path, is it based on learning from previous journeys from customers? >> Yes. >> And then, if you make a wrong guess, you sort of penalize the engine and say, "Pick the next best, "what you thought was the next best path." >> Absolutely, so the way, the nuts and bolts of how it works is we start working with our clients, and they have all this data of different customers, and how they've engaged with different pieces of content throughout their journey, and so the machine learning model, what it's really doing at any moment in time, given any customer in any stage of the opportunity that they find themselves in, it says, what piece of content are they likely to engage with next, and that's based on historical training data, if you will. And then once we make that decision on a step-by-step basis, then we kind of extrapolate, and we basically say, "Okay, if we showed them this page, or if they engage with "this material, what would that do, what situation would "we find them in at the next step, and then what would "we recommend from there, and then from there, "and then from there," and so it's really kind of learning the right move to make at each time, and then extrapolating that all the way to the opportunity being closed. >> The picture that's in my mind is like, the Deep Blue, I think it was chess, where it would map out all the potential moves. >> Very similar, yeah. >> To the end game. >> Very similar idea. >> So, what about if you're trying to engage with a customer across different channels, and it's not just web content? How is that done? >> Well, that's something that we're very excited about, and that's something that we're currently really starting to devote resources to. Right now, we already have a product live that's focused on web content specifically, but yeah, we're working on kind of a multi-channel type solution, and we're all pretty excited about it. >> Okay so, obviously you can't talk too much about it. Can you tell us what channels that might touch? >> I might have to play my cards a little close to my chest on this one, but I'll just say we're excited. >> Alright. Well I guess that means I'll have to come back. >> Please, please. >> So, um, tell us about the personalized conversations. Is the conversation just another way of saying, this is how we're personalizing the journey? Or is there more to it than that? >> Yeah, it really is about personalizing the journey, right? Like you know, a lot of our clients now have a lot of sophisticated marketing collateral, and a lot of time and energy has gone into developing content that different people find engaging, that kind of positions products towards pain points, and all that stuff, and so really there's so much low-hanging fruit by just organizing and leveraging all of this material, and actually forming the conversation through a series of journeys through that material. >> Okay, so, Aman was telling us earlier that we have so many sort of algorithms, they're all open source, or they're all published, and they're only as good as the data you can apply them to. So, tell us, where do companies, startups, you know, not the Googles, Microsofts, Amazons, where do they get their proprietary information? Is it that you have algorithms that now are so advanced that you can refine raw information into proprietary information that others don't have? >> Really I think it comes down to, our competitive advantage I think is largely in the source of our data, and so, yes, you can build more and more sophisticated algorithms, but again, you're starting with a public data set, you'll be able to derive some insights, but there will always be a path to those datasets for, say, a competitor. For example, we're currently tracking about 700 billion web interactions a year, and then we're also able to attribute those web interactions to companies, meaning the employees at those companies involved in those web interactions, and so that's able to give us an insight that no amount of public data or processing would ever really be able to achieve. >> How do you, Aman started to talk to us about how, like there were DNS, reverse DNS registries. >> Reverse IP lookups, yes. >> Yeah, so how are those, if they're individuals within companies, and then the companies themselves, how do you identify them reliably? >> Right, so reverse IP lookup is, we've been doing this for years now, and so we've kind of developed a multi-source solution, so reverse IP lookups is a big one. Also machine learning, you can look at traffic coming from an IP address, and you can start to make some very informed decisions about what the IP address is actually doing, who they are, and so if you're looking at, at the account level, which is what we're tracking at, there's a lot of information to be gleaned from that kind of information. >> Sort of the way, and this may be a weird-sounding analogy, but the way a virus or some piece of malware has a signature in terms of its behavior, you find signatures in terms of users associated with an IP address. >> And we certainly don't de-anonymize individual users, but if we're looking at things at the account level, then you know, the bigger the data, the more signal you can infer, and so if we're looking at a company-wide usage of an IP address, then you can start to make some very educated guesses as to who that company is, the things that they're researching, what they're in market for, that type of thing. >> And how do you find out, if they're not coming to your site, and they're not coming to one of your customer's sites, how do you find out what they're touching? >> Right, I mean, I can't really go into too much detail, but a lot of it comes from working with publishers, and a lot of this data is just raw, and it's only because we can identify the companies behind these IP addresses, that we're able to actually turn these web interactions into insights about specific companies. >> George: Sort of like how advertisers or publishers would track visitors across many, many sites, by having agreements. >> Yes. Along those lines, yeah. >> Okay. So, tell us a little more about natural language processing, I think where most people have assumed or have become familiar with it is with the B2C capabilities, with the big internet giants, where they're trying to understand all language. You have a more well-scoped problem, tell us how that changes your approach. >> So a lot of really exciting things are happening in natural language processing in general, and the research, and right now in general, it's being measured against this yardstick of, can it understand languages as good as a human can, obviously we're not there yet, but that doesn't necessarily mean you can't derive a lot of meaningful insights from it, and the way we're able to do that is, instead of trying to understand all of human language, let's understand very specific language associated with the things that we're trying to learn. So obviously we're a B2B marketing company, so it's very important to us to understand what companies are investing in other companies, what companies are buying from other companies, what companies are suing other companies, and so if we said, okay, we only want to be able to infer a competitive relationship between two businesses in an actual document, that becomes a much more solvable and manageable problem, as opposed to, let's understand all of human language. And so we actually started off with these kind of open source solutions, with some of these proprietary solutions that we paid for, and they didn't work because their scope was this broad, and so we said, okay, we can do better by just focusing in on the types of insights we're trying to learn, and then work backwards from them. >> So tell us, how much of the algorithms that we would call building blocks for what you're doing, and others, how much of those are all published or open source, and then how much is your secret sauce? Because we talk about data being a key part of the secret sauce, what about the algorithms? >> I mean yeah, you can treat the algorithms as tools, but you know, a bag of tools a product does not make, right? So our secret sauce becomes how we use these tools, how we deploy them, and the datasets we put them again. So as mentioned before, we're not trying to understand all of human language, actually the exact opposite. So we actually have a single machine learning algorithm that all it does is it learns to recognize when Amazon, the company, is being mentioned in a document. So if you see the word Amazon, is it talking about the river, is it talking about the company? So we have a classifier that all it does is it fires whenever Amazon is being mentioned in a document. And that's a much easier problem to solve than understanding, than Siri basically. >> Okay. I still get rather irritated with Siri. So let's talk about, um, broadly this topic that sort of everyone lays claim to as their great higher calling, which is democratizing machine learning and AI, and opening it up to a much greater audience. Help set some context, just the way you did by saying, "Hey, if we narrow the scope of a problem, "it's easier to solve." What are some of the different approaches people are taking to that problem, and what are their sweet spots? >> Right, so the the talk of the data science community, talking machinery right now, is some of the work that's coming out of DeepMind, which is a subsidiary of Google, they just built AlphaGo, which solved the strategy game that we thought we were decades away from actually solving, and their approach of restricting the problem to a game, with well-defined rules, with a limited scope, I think that's how they're able to propel the field forward so significantly. They started off by playing Atari games, then they moved to long term strategy games, and now they're doing video games, like video strategy games, and I think the idea of, again, narrowing the scope to well-defined rules and well-defined limited settings is how they're actually able to advance the field. >> Let me ask just about playing the video games. I can't remember Star... >> Starcraft. >> Starcraft. Would you call that, like, where the video game is a model, and you're training a model against that other model, so it's almost like they're interacting with each other. >> Right, so it really comes down, you can think of it as pulling levers, so you have a very complex machine, and there's certain levers you can pull, and the machine will respond in different ways. If you're trying to, for example, build a robot that can walk amongst a factory and pick out boxes, like how you move each joint, what you look around, all the different things you can see and sense, those are all levers to pull, and that gets very complicated very quickly, but if you narrow it down to, okay, there's certain places on the screen I can click, there's certain things I can do, there's certain inputs I can provide in the video game, you basically limit the number of levers, and then optimizing and learning how to work those levers is a much more scoped and reasonable problem, as opposed to learn everything all at once. >> Okay, that's interesting, now, let me switch gears a little bit. We've done a lot of work at WikiBound about IOT and increasingly edge-based intelligence, because you can't go back to the cloud for your analytics for everything, but one of the things that's becoming apparent is, it's not just the training that might go on in a cloud, but there might be simulations, and then the sort of low-latency response is based on a model that's at the edge. Help elaborate where that applies and how that works. >> Well in general, when you're working with machine learning, in almost every situation, training the model is, that's really the data-intensive process that requires a lot of extensive computation, and that's something that makes sense to have localized in a single location which you can leverage resources and you can optimize it. Then you can say, alright, now that I have this model that understands the problem that's trained, it becomes a much simpler endeavor to basically put that as close to the device as possible. And so that really is how they're able to say, okay, let's take this really complicated billion-parameter neural network that took days and weeks to train, and let's actually derive insights at the level, right at the device level. Recent technology though, like I mentioned deep learning, that in itself, just the actual deploying the technology creates new challenges as well, to the point that actually Google invented a new type of chip to just run... >> The tensor processing. >> Yeah, the TPU. The tensor processing unit, just to handle what is now a machine learning algorithm so sophisticated that even deploying it after it's been trained is still a challenge. >> Is there a difference in the hardware that you need for training vs. inferencing? >> So they initially deployed the TPU just for the sake of inference. In general, the way it actually works is that, when you're building a neural network, there is a type of mathematical operation to do a whole bunch, and it's based on the idea of working with matrices and it's like that, that's still absolutely the case with training as well as inference, where actually, querying the model, but so if you can solve that one mathematical operation, then you can deploy it everywhere. >> Okay. So, one of our CTOs was talking about how, in his view, what's going to happen in the cloud is richer and richer simulations, and as you say, the querying the model, getting an answer in realtime or near realtime, is out on the edge. What exactly is the role of the simulation? Is that just a model that understands time, and not just time, but many multiple parameters that it's playing with? >> Right, so simulations are particularly important in taking us back to reinforcement learning, where you basically have many decisions to make before you actually see some sort of desirable or undesirable outcome, and so, for example, the way AlphaGo trained itself is basically by running simulations of the game being played against itself, and really what that simulations are doing is allowing the artificial intelligence to explore the entire possibilities of all games. >> Sort of like WarGames, if you remember that movie. >> Yes, with uh... >> Matthew Broderick, and it actually showed all the war game scenarios on the screen, and then figured out, you couldn't really win. >> Right, yes, it's a similar idea where they, for example in Go, there's more board configurations than there are atoms in the observable universe, and so the way Deep Blue won chess is basically, more or less explore the vast majority of chess moves, that's really not the same option, you can't really play that same strategy with AlphaGo, and so, this constant simulation is how they explore the meaningful game configurations that it needed to win. >> So in other words, they were scoped down, so the problem space was smaller. >> Right, and in fact, basically one of the reasons, like AlphaGo was really kind of two different artificial intelligences working together, one that decided which solutions to explore, like which possibilities it should pursue more, and which ones not to, to ignore, and then the second piece was, okay, given the certain board configuration, what's the likely outcome? And so those two working in concert, one that narrows and focuses, and one that comes up with the answer, given that focus, is how it was actually able to work so well. >> Okay. Seth, on that note, that was a very, very enlightening 20 minutes. >> Okay. I'm glad to hear that. >> We'll have to come back and get an update from you soon. >> Alright, absolutely. >> This is George Gilbert, I'm with Seth Myers, Senior Data Scientist at Demandbase, a company I expect we'll be hearing a lot more about, and we're on the ground, and we'll be back shortly.

>> Live from Boston, Massachusetts. It's The Cube! Covering IBM Chief Data Officer's Summit. Brought to you by IBM. (techno music) >> Welcome back to The Cube's live coverage of the IBM CDO Strategy Summit here in Boston, Massachusetts. I'm your host Rebecca Knight along with my Co-host Dave Vellante. We're joined by Jennifer Gibbs, the VP Enterprise Data Management of TD Bank, and Seth Dobrin who is VP and Chief Data Officer of IBM Analytics. Thanks for joining us Seth and Jennifer. >> Thanks for having us. >> Thank you. >> So Jennifer, I want to start with you can you tell our viewers a little about TD Bank, America's Most Convenient Bank. Based, of course, in Toronto. (laughs). >> Go figure. (laughs) >> So tell us a little bit about your business. >> So TD is a, um, very old bank, headquartered in Toronto. We do have, ah, a lot of business as well in the U.S. Through acquisition we've built quite a big business on the Eastern seaboard of the United States. We've got about 85 thousand employees and we're servicing 42 lines of business when it comes to our Data Management and our Analytics programs, bank wide. >> So talk about your Data Management and Analytics programs a little bit. Tell our viewers a little bit about those. >> So, we split up our office of the Chief Data Officer, about 3 to 4 years ago and so we've been maturing. >> That's relatively new. >> Relatively new, probably, not unlike peers of ours as well. We started off with a strong focus on Data Governance. Setting up roles and responsibilities, data storage organization and councils from which we can drive consensus and discussion. And then we started rolling out some of our Data Management programs with a focus on Data Quality Management and Meta Data Management, across the business. So setting standards and policies and supporting business processes and tooling for those programs. >> Seth when we first met, now you're a long timer at IBM. (laughs) When we first met you were a newbie. But we heard today, about,it used to be the Data Warehouse was king but now Process is king. Can you unpack that a little bit? What does that mean? >> So, you know, to make value of data, it's more than just having it in one place, right? It's what you do with the data, how you ingest the data, how you make it available for other uses. And so it's really, you know, data is not for the sake of data. Data is not a digital dropping of applications, right? The whole purpose of having and collecting data is to use it to generate new value for the company. And that new value could be cost savings, it could be a cost avoidance, or it could be net new revenue. Um, and so, to do that right, you need processes. And the processes are everything from business processes, to technical processes, to implementation processes. And so it's the whole, you need all of it. >> And so Jennifer, I don't know if you've seen kind of a similar evolution from data warehouse to data everywhere, I'm sure you have. >> Yeah. >> But the data quality problem was hard enough when you had this sort of central master data management approach. How are you dealing with it? Is there less of a single version of the truth now than there ever was, and how do you deal with the data quality challenge? >> I think it's important to scope out the work effort in a way that you can get the business moving in the right direction without overwhelming and focusing on the areas that are most important to the bank. So, we've identified and scoped out what we call critical data. So each line of business has to identify what's critical to them. Does relate very strongly to what Seth said around what are your core business processes and what data are you leveraging to provide value to that, to the bank. So, um, data quality for us is about a consistent approach, to ensure the most critical elements of data that used for business processes are where they need to be from a quality perspective. >> You can go down a huge rabbit whole with data quality too, right? >> Yeah. >> Data quality is about what's good enough, and defining, you know. >> Right. >> Mm-hmm (affirmative) >> It's not, I liked your, someone, I think you said, it's not about data quality, it's about, you know it's, you got to understand what good enough is, and it's really about, you know, what is the state of the data and under, it's really about understanding the data, right? Than it is perfection. There are some cases, especially in banking, where you need perfection, but there's tons of cases where you don't. And you shouldn't spend a lot of resources on something that's not value added. And I think it's important to do, even things like, data quality, around a specific use case so that you do it right. >> And what you were saying too, it that it's good enough but then that, that standard is changing too, all the time. >> Yeah and that changes over time and it's, you know, if you drive it by use case and not just, we have get this boil the ocean kind of approach where all data needs to be perfect. And all data will never be perfect. And back to your question about processes, usually, a data quality issue, is not a data issue, it's a process issue. You get bad data quality because a process is broken or it's not working for a business or it's changed and no one's documented it so there's a work around, right? And so that's really where your data quality issues come from. Um, and I think that's important to remember. >> Yeah, and I think also coming out of the data quality efforts that we're making, to your point, is it central wise or is it cross business? It's really driving important conversations around who's the producer of this data, who's the consumer of this data? What does data quality mean to you? So it's really generating a lot of conversation across lines of business so that we can start talking about data in more of a shared way versus more of a business by business point of view. So those conversations are important by-products I would say of the individual data quality efforts that we're doing across the bank. >> Well, and of course, you're in a regulated business so you can have the big hammer of hey, we've got regulations, so if somebody spins up a Hadoop Cluster in some line of business you can reel 'em in, presumably, more easily, maybe not always. Seth you operate in an unregulated business. You consult with clients that are in unregulated businesses, is that a bigger challenge for you to reel in? >> So, I think, um, I think that's changing. >> Mm-hmm (affirmative) >> You know, there's new regulations coming out in Europe that basically have global impact, right? This whole GDPR thing. It's not just if you're based in Europe. It's if you have a subject in Europe and that's an employee, a contractor, a customer. And so everyone is subject to regulations now, whether they like it or not. And, in fact, there was some level of regulation even in the U.S., which is kind of the wild, wild, west when it comes to regulations. But I think, um, you should, even doing it because of regulation is not the right answer. I mean it's a great stick to hold up. It's great to be able to go to your board and say, "Hey if we don't do this, we need to spend this money 'cause it's going to cost us, in the case of GDPR, four percent of our revenue per instance.". Yikes, right? But really it's about what's the value and how do you use that information to drive value. A lot of these regulation are about lineage, right? Understanding where your data came from, how it's being processed, who's doing what with it. A lot of it is around quality, right? >> Yep. >> And so these are all good things, even if you're not in a regulated industry. And they help you build a better connection with your customer, right? I think lots of people are scared of GDPR. I think it's a really good thing because it forces companies to build a personal relationship with each of their clients. Because you need to get consent to do things with their data, very explicitly. No more of these 30 pages, two point font, you know ... >> Click a box. >> Click a box. >> Yeah. >> It's, I am going to use your data for X. Are you okay with that? Yes or no. >> So I'm interested from, to hear from both of you, what are you hearing from customers on this? Because this is such a sensitive topic and, in particularly, financial data, which is so private. What are you, what are you hearing from customers on this? >> Um, I think customers are, um, are, especially us in our industry, and us as a bank. Our relationship with our customer is top priority and so maintaining that trust and confidence is always a top priority. So whenever we leverage data or look for use cases to leverage data, making sure that that trust will not be compromised is critically important. So finding that balance between innovating with data while also maintaining that trust and frankly being very transparent with customers around what we're using it for, why we're using it, and what value it brings to them, is something that we're focused on with, with all of our data initiatives. >> So, big part of your job is understanding how data can affect and contribute to the monetization, you know, of your businesses. Um, at the simplest level, two ways, cut costs, increase revenue. Where do you each see the emphasis? I'm sure both, but is there a greater emphasis on cutting costs 'cause you're both established, you know, businesses, with hundreds of thousands, well in your case, 85 thousand employees. Where do you see the emphasis? Is it greater on cutting costs or not necessarily? >> I think for us, I don't necessarily separate the two. Anything we can do to drive more efficiency within our business processes is going to help us focus our efforts on innovative use of data, innovative ways to interact with our customers, innovative ways to understand more about out customers. So, I see them both as, um, I don't see them mutually exclusive, I see them as contributing to each. >> Mm-hmm (affirmative) >> So our business cases tend to have an efficiency slant to them or a productivity slant to them and that helps us redirect effort to other, other things that provide extra value to our clients. So I'd say it's a mix. >> I mean I think, I think you have to do the cost savings and cost avoidance ones first. Um, you learn a lot about your data when you do that. You learn a lot about the gaps. You learn about how would I even think about bringing external data in to generate that new revenue if I don't understand my own data? How am I going to tie 'em all together? Um, and there's a whole lot of cultural change that needs to happen before you can even start generating revenue from data. And you kind of cut your teeth on that by doing the really, simple cost savings, cost avoidance ones first, right? Inevitably, maybe not in the bank, but inevitably most company's supply chain. Let's go find money we can take out of your supply chain. Most companies, if you take out one percent of the supply chain budget, you're talking a lot of money for the company, right? And so you can generate a lot of money to free up to spend on some of these other things. >> So it's a proof of concept to bring everyone along. >> Well it's a proof of concept but it's also, it's more of a cultural change, right? >> Mm-hmm (affirmative) It's not even, you don't even frame it up as a proof of concept for data or analytics, you just frame it up, we're going to save the company, you know, one percent of our supply chain, right? We're going to save the company a billion dollars. >> Yes. >> And then there's gain share there 'cause we're going to put that thing there. >> And then there's a gain share and then other people are like, "Well, how do I do that?". And how do I do that, and how do I do that? And it kind of picks up. >> Mm-hmm (affirmative) But I don't think you can jump just to making new revenue. You got to kind of get there iteratively. >> And it becomes a virtuous circle. >> It becomes a virtuous circle and you kind of change the culture as you do it. But you got to start with, I don't, I don't think they're mutually exclusive, but I think you got to start with the cost avoidance and cost savings. >> Mm-hmm (affirmative) >> Great. Well, Seth, Jennifer thanks so much for coming on The Cube. We've had a great conversation. >> Thanks for having us. >> Thanks. >> Thanks you guys. >> We will have more from the IBM CDO Summit in Boston, Massachusetts, just after this. (techno music)

(switch clicking) >> Hey, welcome back, everybody. Jeff Frick, here with theCUBE. We're at the Mission Bay Conference Center in downtown San Francisco at Node Summit 2017. TheCUBE's been coming here for a number of years. In fact, Ryan Dahl's one of our most popular interviews in the history of the show, talking about Node. And, the community's growing, the performance is going up and there's a lot of good energy here, so we're excited to be here and there's a lot of big companies that maybe you would or wouldn't expect to be involved. And, we're excited to have Gaurav Seth. He is the Product Manager for Several Things JavaScript. I think that's the first time we've ever had that title on. He's from Microsoft. Thanks for stopping by. >> Yeah, hey, Jeff, nice to be here. Thanks for having me over. >> Absolutely, >> Yes. >> so let's just jump right into it. What is Microsoft doing here in such a big way? >> So, one of the things that Microsoft is, like, I think we really are, now, committed and, you know, we have the mantra that we are trying to follow which is any app, any developer, any platform. You know, Node actually is a great growing community and we've been getting soaked more and more and trying to help the community and build the community and play along and contribute and that's the reason that brings us here, like, it's great to see the energy, the passion with people around here. It's great to get those connections going, have those conversations, hear from the customers as to what they really need, hear from developers about their needs and then having, you know, a close set of collaboration with the Core community members to see how we can even evolve the project further. >> Right, right, and specifically on Azure, which is interesting. You know, it's been interesting to watch Microsoft really go full bore into cloud, via Azure. >> Right. >> I just talked to somebody the other day, I was talking about 365 being >> Uh huh. >> such a game-changer in terms of cloud implementation, as a big company. There was a report that came out about, you know, the path at 20 billion, >> Right. >> so, clearly, Microsoft is not only all-in, but really successfully >> Right. >> executing on that strategy >> Yeah, I mean-- >> and you're a big piece of that. >> Yes, I mean, I think one of the big, big, big pieces, really, is as the developer paradigms are changing, as the app paradigms are changing, you know, how do you really help make developers this transition to a cloud-native world? >> Right, right. >> How do you make sure that the app platforms, the underlying infrastructure, the cloud, the tools that developer use, how do you combine all of them and make sure that you're making it a much easier experience for developers to move on >> Right. >> from their existing paradigms to these new cloud-native paradigms? You know, one of the things we've been doing on the Azure side of the house and when, especially when we look at Node.js as a platform, we've been working on making sure that Node.js has a great story across all the different compute models that we support on Azure, starting from, like, hey, if you you want to do server list of functions, if you want to do BasS, if you want to go the container way, if you want to just use WEAMS, and, in fact, we just announced the Azure container instances, today, >> Right. >> so it's, one of the work, some of the work we are doing is really focused on making sure that the developer experiences as you migrate your workloads from old traditional, monolithic apps are also getting ready to move to this cloud native era. >> Right, so it's an interesting point of view from Microsoft 'cause some people, again, people in-the-know already know, but a lot of people maybe don't know, kind of, Microsoft's heritage in open source. We think, you know, that I used to buy my Office CD, >> Right. >> and my Outlook CD >> Right. >> you know, it's different, especially as you guys go more heavily into cloud, >> Right. >> you need to be more open to the various tools of the developer community. >> That's absolutely true and one of the focus areas for us, really, has been, you know, as we think through the cloud-native transition, what are the big pieces, the main open source tools, the frameworks that are available and how do we provide great experiences for those on Azure? >> Right, right. >> Right, because, at times, people come with the notion that, hey, Azure probably might just be good for dot NET or might just be good for Windows, but, you know, the actual fact, today, is really that Azure has great supporting story for Linux, Azure has great story for a lot of these open source tools and we are continuing to grow our story in that perspective. >> Right. >> So, we really want to make sure that open source developers who come and work on our platform are successful. >> And then, specifically for Node, and you're actually on the Board, so you've got >> Right. >> a leadership position, >> Yep. >> when you look at Node.js within the ecosystem of opensource projects and the growth that we keep hearing about in the sessions, >> Yep. >> you know, how are you, and you specifically and Microsoft generally, kind of helping to guide the growth of this community and the development of this community as it gets bigger and bigger and bigger? >> Right, I think that's a great question. I think from my perspective, and also Microsoft's perspective, there are a bunch of things we are actually doing to engage with the community, so I'll kind of list out three or four things that we are doing. I think the first and foremost is, you know, we are a participant in the Node.js Foundation. >> Right. >> You know, that's where like, hey, we kind of look at the administrative stuff. We are a sponsor of, you know, at the needed levels, et cetera, so that's just the initial monetary support, but then it gets to really being a part of the Node Core Committee, like, as we work on some of the Core pieces, as we evolve Node, how can we actually bring more perspectives, more value, into the actual project? So, that's, you know, we have many set of engineers who are, right now, working across different working groups with Node and helping evolve Node. You know, you might have heard about the NAPI effort. We are working with the Diagnostics Working Group, we are working with the Benchmarking Working Group and, you know, bringing the thing. The third thing that we did, a while back, was we also did this integration of bringing Chakra which is the JavaScript Runtime from Microsoft that powers Microsoft Edge. We made Node work with Chakra because we wanted to bring the power of Node to this new platform called Windows IoT >> Right, right. >> and, you know, the existing Node could not get there because some of the platform limitations. So, those are like some of the few examples that we've, and how we've been actually communicating and contributing. And then, I think the biggest and the foremost for me, really, are the two pillars, like when I think about Microsoft's contribution, it's really, like, you know, the big story or the big pivot for us is, we kind of go create developer tools and help make developer live's easier by giving them the right set of tools to achieve what they want to achieve in less time, be more productive >> Right, right. >> and the second thing is, really, like the cloud platforms, as things are moving. I think across both of those areas, our focus really had been to make sure that Node as a language, Node as a platform has great first-class experiences that we can help define. >> Right. Well, you guys are so fortunate. You have such a huge install base of developers, >> Right. >> but, again, traditionally, it wasn't necessarily cloud application developers and that's been changing >> Yep. >> over time >> Yep. >> and there's such a fierce competition for that guy, >> Yep. >> or gal, who wakes up >> Yep. >> in the morning or not, maybe, the morning, at 10:00, >> Yep. >> has a cup of coffee >> Yep. >> and has to figure out what they're going to develop today >> Right. >> and there's so many options >> Right. >> and it's a fierce competition, >> Right. >> so you need to have an easy solution, you need to have a nice environment, you need to have everything that they want, so they're coding on your stuff and not on somebody else's. >> That's true, I mean I, you know, somehow, I kind of instead of calling it competition, I have started using this term coopetition because between a lot of the companies and vendors that we talk about, right, it's more about, for all of us, it's working together to grow the community. >> Right. >> It's working together to grow the pie. You know, with open source, it's not really one over the other. It's like the more players you have and the more players who engage with great ideas, I think better things come out of that, so it's all about that coopetition, >> rather than competition, >> Right. >> I would say. >> Well, certainly, around and open source project, here, >> Yes, exactly. >> and we see a lot of big names, >> Exactly. >> but I can tell you, I've been to a lot of big shows where they are desperately trying to attract >> Right, right, yes. >> the developer ecosystem. "Come develop on our platforms." >> Yes, yes. >> So, you're in a fortunate spot, you started, >> Yes, I mean that-- >> not from zero, but, but open source is different >> Yes. >> and it's an important ethos because it is much more community >> Exactly, exactly. >> and people look at the name, they don't necessarily look at the title >> Exactly. >> or even the company >> Yep, exactly. >> that people work for. >> Exactly, and I think having more players involved also means, like, it's going to be great for the developer ecosystem, right, because everybody's going to keep pushing for making it better and better, >> Right. >> so, you know, as we grow from a smaller stage to, like, hey, there's actually a lot of enterprised option of these use case scenarios that people are coming up with, et cetera, it's always great to have more parties involved and more people involved. >> Gaurav, thank you very much >> Yeah. >> and, again, congratulations on your work here in Node. Keep this community strong. >> Sure. >> It looks like you guys are well on your way. >> Yeah. Thanks, Jeff. >> All right. >> Thanks for your time, take care, yeah. >> Guarav Seth, he's a Project Lead at Microsoft. I'm Jeff Frick. You're watching theCUBE from Node Summit 2017. Thanks for watching. (upbeat synthpop music)