>>From San Francisco. It's the cube covering get lab commit 20, 20 Raji you buy get lab. >>Hi, I'm Stu Miniman and this is the cubes coverage of get lab commit 2020. We're here in San Francisco. It's a little bit chilly but uh, my first guests, uh, on the program are used to the weather cause they're coming to us from Wisconsin. Uh, both from Northwestern mutual, uh, sitting to my left here is Kyle person who is a senior engineer and sitting to his left is Sean who is also a senior engineer. Gentlemen, thanks so much for joining us. Thanks for having us. Alright. We thought, you know, both of us coming from colder climates that may be coming to San Francisco would be a little warmer, but they have hand warmers, they have warm drinks and it is the warmth of the community that will warm our innards. I'm short right there. It says get warm. That's what we're here to do. All right, Kyle, let's start with you. Northwestern mutual. I think most people are familiar with the organization, but give us a little bit of a, you know, how people should think of Northwestern mutual in 2020 and, uh, your roles. >>Yeah. So obviously we mean we're a large insurance company but also into financial services and products and we're really trying to become more of a digital company as well. We think that that's going to be a differentiator in the marketplace. You know, having apps that our customers can interact with, um, trying to speed up underwriting, things like that. So we're really just trying to be a technology company as much of an insurance company. Okay, >>great. And Sean, I understand you're, you're on the same team as Kyle helping you along with that digital transformation that that's been all the buzz for the last couple of years. Yeah. He can't get rid of me. We flew, you know, 1200 miles and I'm still sitting next to, uh, but yeah, at Northwestern mutual, I mean, going back a number of years now, the, the company started down this path of doing a digital transformation where we wanted to be, you know, a software company that is providing financial service and financial stability for our clients. So it was a big shift that we, we started, we knew we needed to modernize everything. So we started down that path. Great. So we had that. So Kyle, maybe if you, it can, you know, when did get lab enter the picture, what was kind of the initial use case and, uh, let's, let's go from there. >>Yeah, it was before my time. I'm, Chad has been there for a long time. Most of the ride, but uh, yeah, it's been several years and it's been, uh, you know, starting out with SCM, moving into CEI and then, you know, adopted sustainer journey that you hear about even in the keynote today. That's pretty much how we charted our course. Okay. >>So Sean, you've been there since the beginning of a, uh, to get lab usage? Pretty much it, it showed up a couple months before I got there. But, uh, going back to your early mid 22, yeah, 2015, uh, we had kind of a more of a pilot group of engineers that were, were starting out, you know, to get us down this path to where we wanted to go and they needed a new tool, something that worked better than what we currently had at an M and a, they settled on, on get lab because it provided, you know, one being open source was a huge selling point for us. Um, and it was just ever-growing. So it allowed our developers to really get going and get going much faster. Okay, great. And in the keynote, okay, Kyle, they were talking about how it's not just about the dev, the second the ops, but really not allowing everybody into the same tooling, even marketing and finance. What's kind of the breadth of the organization is it is mostly devs that dev and ops does security, you know, who, who's involved in using this tooling. >>It's everybody. We're a, we're figuring out our, you know, everyone's kinda got their own spin on things. So we're in that, um, classic position where I think we have the tooling sprawl that everyone talks about and we're, we're constantly evaluating, you know, how does Gilad fit into that picture? What do we bolt on? You know, we have the luxury of being able to integrate with other things as well. But then if certainly if we can get an economy of scale where we can just use get lab, it's to provide that seamless interface. That's something we always look to do too. All right. >>So Sean, my understanding is a NM is also using Coubernetties and that's something that you're involved in. So very money you bring us in people, you know, sometimes get misconstrued as to the scope and the purpose of, of Kubernetes. We've been at the cube con cloud date of con for a number of years, but uh, why don't you set the stage for us and kind of walk us through the, the what and the why of Kubernetes? Yeah. For us at least being able to leverage something like Kubernetes, which when you really back out and you know, do the 10,000 foot view, it's container management and being able to go into a more modern architecture. We're leveraging containers for pretty much whatever we can, or at least what makes sense. Um, and that's kind of how we started down the path with get lab moving into Kubernetes. You know, we were, we were trying to figure out like, where do we want to go so, you know, let's not just push the boat out a little, let's drop, kick the boat off the end of the pier and see where we end up. >>So we started working down that path of deploying get lab into Kubernetes cause it allowed us to easily expand and make the application highly available. So even if some easies go down in AWS, which knock on wood never happens. Uh, we're still good to go. Our users are, wouldn't even notice. Okay. Um, so you mentioned AWS. Is that your primary cloud, your only cloud? What, what is your cloud situation? Yeah, that's, that's a Northwestern mutual is public cloud. Okay, great. And speak a little bit to, you know, Amazon does have plenty of its own tooling. Uh, maybe a little bit about how get lab and, how those fit together for you. Um, well, I mean, we use get lab CIS specifically to be able to provision different services in one, not that we need as long as they fit near within our security requirements. And, you know, everything we do, we get vetted out through our internal processes, but it's really allowed our developers to move forward so much faster. >>You know, it's kind of gone are the days of, let me fill out a request for, you know, X, Y, Z and, you know, wait two as it goes through somebody's work queue and they eventually get around to it. Um, allowing our developers to just, you know, do their commits, get their, you know, peer review and just deploy and provision right away, allows us to get our applications to market just so much faster than even a few years ago. Alright. So Kyle, the two of you are presenting here at the show, uh, you know, we, we love, we heard GitLab talking on stages. You know, customers don't just use it, they commit, they add feedback in and they speak at the show. So maybe speak a little bit of, uh, you know, NMS, you know, involvement as to uh, you know, are you committing code and what, what are you speaking about? >>So we're here to speak about our journey on Kubernetes. I'm trans covering the application side and I'm going to talk about our, our dabble in Kubernetes CII. So we're, we're really looking to, um, we're looking for efficiencies I guess in, in density. That's a primary driver behind trying to explore the trail. But also, um, one of the things I'll talk about in the talk is, is boosting our security posture using Kubernetes. So a lot of times people are using API keys and they're getting stale and not being rotated. Uh, we can do some neat things with Kubernetes and it's native. I am offerings to boost our security posture by moving towards role based access and getting those credentials that are rotating and providing us, uh, you know, non stale sort of authentication credentials, things like that. >>Sean, yeah, pretty much covers it. Uh, uh, and beyond with the CIA, you know, being able to run and get lab itself within Qube and having the different components broken out and we're alive. It's, it, it's enabling us to limit any kind of attack plane that could exist. You know, it's, you have to get through a lot to even get to it. So it's really just been a huge, a huge plus for us. OK. I, I'd love to hear both. Both of you have been to these events a number of times. You're speaking to event. What, what, what's the value of coming to get loud commit? I mean, for me it's a, a huge networking thing and being able to relay our experiences that we've gone through to other people that are out in the community. I mean, I know lots of, you know, everyone wants to see, you know, what can you do in Kubernetes and like, here's some of the things that we've done. >>Here's some of the things that you know, works that didn't work. You know, we would recommend you going this kind of route if we were to start it over again. And beyond that, you know, meeting people from all over the world, like, uh, we were talking with some, uh, some guy, gentleman Nico from white duck who is from Germany. It's not something you get to do, you know, face to face all the time. Alright. Sean, can you share with our audience any of those? You know, if we could do it over again, we'd change something. Is it an organizational thing or technical piece or until don't don't use EFS for getting repo data. It will not end well for you can take away. All right. Kyle, how about you? You've been to a number of these shows, uh, you know, the networking, the piece or you know, what else, what, what, what, what for you personally and for NM, uh, you know, brings you back. >>Yeah. Networking is a big thing. I think it's also getting feedback on, you know, what we're doing. Does it make sense? Just like get lab is throwing things out there early, trying to tighten up that contribution loop. We want to get our ideas out there and then this is an opportunity for people to ask questions about our presentation. If maybe we're off in the wrong direction, maybe we can get that steered back on course. Um, you know, we're just really here to get the feedback. Yeah. I always love talking to people in the financial industry and you talk about open source, you know, if, if you went back, you know, five years ago, you'd probably get a little bit of sideways looks as to wait, you know, you're doing what, um, are we past that? Do do you feel are most people, uh, you know, really understanding where we are with, with cloud and open source in general that it, you know, it makes perfect sense for a financial institution to be part of it. >>I'd say at NM we, we've finally gotten past that curve and now we're, we're trying to, you know, make it even easier for our internal developers to easier participate in open source, you know, their internal products and contribute more to the community. Uh, we've completely done an about face from, you know, probably 15 years ago where it was open source. You wanted to, what to, yeah, let's go. How can we make things better? It's, it's all about, you know, our, our customers. So we want to make sure we create the best product and experience for them. That's awesome. Yeah, there's still some barriers there. I mean, it's all about managing risk, right? So you have to do things diligently and make sure that your bases are covered. And so it's not like it can be a free for all. We have to do our due diligence, but we, you know, we love to contribute. And we love to get up without their there as we can. All right. Well, Kyle and Sean, thank you so much for sharing with our audience. Best of luck with your presentations and, uh, have a great time at the show. Thank you. All right. Uh, thank you to, to NM for joining us. I'm Stu Miniman and thank you for watching the cube.

>> Announcer: Live from Orlando, Florida, it's theCUBE covering Grace Hopper Celebration of Women in Computing, brought to you by SiliconANGLE Media. >> Welcome back to theCUBE's coverage of the Grace Hopper Conference here at the Orange County Convention Center, I'm your host Rebecca Knight. We are joined today by Emilia Sherifova, she is the VP of Architecture and Engineering at Northwestern Mutual, thanks so much for joining us Emilia. >> Thank you for having me. >> So I want to start off by talking about how you got to Northwestern Mutual. You came via an acquisition, you were CTO of LearnVest. What is LearnVest? >> LearnVest is a financial planning start up, it's a company that is bringing financial planning to the masses, it's a very mission driven organization. When Northwestern Mutual came as an interest to acquire us, we saw an incredible opportunity to partner with a Fortune 100 company, and tap into its client base of five million people, and bring sort of best in class digital experience and innovation, with best in class financial services products. >> Talk about that problem a little bit, in terms of bringing financial planning to the masses. Why don't the masses have financial planning? What's the disconnect? >> I think it's not easy, often it's a human driven problem. Often humans do not want to deal with their finances, as I know personally for myself, historically when I met with my financial advisor in the past I would get a plan and I wouldn't follow up on that. So building delightful experiences that engage our clients, with the combination of a financial planner that's prodding you, and giving you guidance. >> So there is a human there? >> Absolutely, there's no way to avoid a human. So it was that regional model of LearnVest to have the human help the robot part of it, and we are doing the same thing with Northwestern Mutual, where we're leveraging Northwestern Mutual best in class distribution work force and providing them tools to help them do their work best. >> I love the idea of a delightful experience when dealing with your finances, it seems antithetical. Give me some examples of what you mean by this. >> I think ability to give you a 360 view of your life, and give you a financial wellness score, for instance, after we've gotten a couple of data points about you, but also gathered some of the predictive data points that we know are probably true about you, and give you a score, one score, that gives you an idea what's the probability of you reaching your financial goal, or you retiring, or you going broke. So there is a way to do that in an easy, digestible and kind of delightful way where we're able to leverage technology and predictive capabilities to really push for financial security of our clients. >> And what is the customer response here? >> Customer response, it's been great. Now that we've rolled out a lot of these experiences for the customer base of Northwestern Mutual we have massive engagement with our customers, our traffic has gone dramatically up. >> So people are hungering for this? >> Absolutely, it's a much needed thing, and we're here to help them. >> So you've now been with Northwestern Mutual for a few years now, dividing your time between New York and Milwaukee, you're in both technology and financial services, both male dominated fields, can you describe a little bit about your career path, and how you got into it, and what you've learned along the way. >> Oh, absolutely. I'm originally from Russia, and I come from a family of engineers, so it was a somewhat natural path for me. I got into software engineering in the late 90s. My go to language initially was C programming, and I participated in the Y2K Challenge on Wall Street. >> Which seems so quaint! (laughs) >> And I've spent over a decade on Wall Street, building electronic trading systems, market data feeds. So I feel honored to have been able to pursue and have these possibilities, but I know how not easy it is, given what a male dominated world this is. >> Is it as bad as the headlines make it out to be? I mean, it really does, when you read it, it's sickening. The sexism, the biases, what's your experience been? >> I think I've been lucky enough to work in very supportive places, but I can tell you majority of teams that I've been part of are majority male, and whether my team mates want to be inclusive and engaging, when the majority is someone else that doesn't look like you, act like you, lean on similar defaults as you, it does not make for a very welcome environment. So I recognize that, and a big part of that, I feel, is having proper on boarding practices. Because on boarding often can happen, if you don't have a formal on boarding process, on boarding can happen in informal ways, and when it happens in informal ways, you tend to be attracted to the people who are like you, and you hang out with. So if you look at the technology world, it's dominated by mostly male. If you are in a start up world, it's mostly young males. And so I am determined to bring operational excellence and sustainability and diversity through strong operational practices, like ensuring that there is proper on boarding. Where for instance, a young mother who has a child has the potential to go home at 4 p.m., and cannot hang out with the guys and drink coke or beer at 7 p.m., to really understand the culture of the group that she joined. We want to make sure that she has sustainable, thoughtful on boarding practices, feeling like she's part of the organization. This is just one way of doing it. >> In terms of the on boarding, and I think you're absolutely right in a sense, that we do gravitate towards people who are just like us, look like us, talk like us, think like us, so are you pairing the new people with people who are not like them? >> Absolutely, but also actually I am pairing them with people who also recently just went through on boarding, that just join also fairly recently. That way they can explain the pitfalls that they gone through, and so we're definitely making sure we have these co pilots, but also rigorous processes to get people comfortable, whatever their background is. >> Now how many Grace Hoppers have you been to, Emilia? >> I have to say this is my first one. >> Your first one, you're a newbie! So what is your experience been so far. >> I am incredibly moved by the experience, actually. I have to say I've never seen so much energy before. I am moved by the stories that I have heard, incredibly inspired. I am inspired to keep pushing. I felt I could relate to a lot of presenters' backgrounds, I also came from a small town, that actually is not on the map, because it was a military town in the former Soviet Union, and a lot of stories of overcoming, and persisting, and ending up here, is what I can relate to. So I'm very excited, and very grateful, and I want to be here every year. >> So you'll be back? >> Totally! >> Great! Well, Emilia, thanks so much for joining us, it's been really fun talking to you. >> Thanks for having me. >> We'll be back with more from Grace Hopper just after this.

(upbeat intro music) >> Good afternoon, ladies and gentlemen and welcome back to fabulous Las Vegas, Nevada where we are theCUBE covering AWS re:Invent for the 10th year in a row. John Furrier, you've been here for all 10. How does this one stack up? >> It's feeling great. It's just back into the saddle of more people. Everyone's getting bigger and growing up. The companies that were originally on are getting stronger, bigger. They're doing takeovers in restaurants and still new players are coming in. More startups are coming in and taking care of what I call the (indistinct) on classic, all the primitives. And then you starting to see a lot more ecosystem platforms building on top of AWS. I call that NextGen Cloud, NextGen AWS. It's happening. It's happening right now. >> Best thing about all of these startups is they grow up, they mature, and we stay the same age, John. (John laughing) All right. All right. All right. Very excited to introduce you our next guest, he wears a lot of hats as the CEO, founder, and chairman at Reltio, please welcome Manish. Manish, welcome to the show. How is your show going so far? >> Well, thank you so much. You know, this is amazing. Just the energy, the number of people. You know, I was here last year, just after the pandemic, and I think it's almost double, if not more the number of people this year. >> John: Pushing 50,000. The high water mark was 65,000 in 2019. >> We should be doing like a Price Is Right sort of thing here on the show and figure out. >> Yeah, $1. >> Savannah: Yeah, yeah. (laughing) One guest, 80,000 guests. How many guests are here? Just in case the audience is not familiar, we know you're fast growing, very exciting business. Tell us what Reltio does. >> So, Reltio is a SaaS platform for data unification and we started Reltio in 2011. We have been serving some of the largest customers across industries like life sciences, healthcare, financial services, insurance, high tech, and retail. Those are, you know, some of the areas that we are focused on. The product capabilities are horizontal because we see the same data problem across every industry. Highly fragmented, highly siloed data that is slowing down the business for every organization out there. And that's the problem that we are solving. We are breaking down these silos, you know, one profile or one record, or one customer product supplier information record at a time, and bringing the acceleration of this unified data to every organization. >> This is the show Steam this year, Adam Celeste is going to be on stage talking about data end to end. Okay. Integrating in all aspects of a company. The word data analyst probably goes away pretty shortly. Everyone was going to be using data. This has been, and he talks about horizontal and vertical use cases. We've been saying that in theCUBE, I think it was about seven years ago, we first said we're going to start to see horizontally scalable data not just compute and cloud. This is now primetime conversation. Making that all work with governance is a real hard problem. Understanding the data. Companies have to put this horizontal and vertical capabilities in place together. >> Absolutely. You know, the data problem may be a horizontal problem, but every industry or vertical that you go into adds its own nuance or flavor to it. And that's why, you know, this has to be a combination of the horizontal and vertical. And we at Reltio thought about this for a while, where, you know, every time we enter a conversation, we are talking about patient data or physician data or client data and financial services or policy and customer information and insurance. But every time it's the number of silos that we encounter that is just an increasing number of applications, increasing number of third party data sources, and bringing that together in a manner where you can understand the semantics of it. Because, you know, every record is not created equal. Every piece of information is not created equal. But at the same time, you have to stitch it together in order to create that holistic, you know, the so-called 360 degree view. Because without that, the types of problems that you're trying to solve are not possible. Right? It's not possible to make those breakthroughs. And that's where I think the problem may be horizontal, but the application of the capabilities has to be verticalized. >> John: I'm smiling because, you know, when you're a founder like you are, and Dave, a lot here are at theCUBE, you're often misunderstood before people figure out what you do and why you started the company. And I can imagine, and knowing you and covering your company, that this is not just yesterday you came up with this idea that now everyone's talking about. There was probably moments in your history when you started, you're scratching it, "Hey the future's going to be this horizontal and vertical, especially where machine learning needs to know the data, the linguistics, whatever the data is, it's got to be very particular for the vertical, but you need to expand it." So when did you have the moment where people finally figured out like, what you guys doing is, like, relevant? I mean, now the whole world now sees- >> Savannah: Overnight success 11 years later. >> John: This shows the first time I've heard Amazon and the industry generally agree that horizontally scalable data systems with vertical value, that it's natural. We've been saying it for seven years on theCUBE. You've been doing the startup. >> Yeah. >> As a founder, you were there early. Now people are getting it. What's it like? Tell, take us through. When did you have the moment? When did you tipping point for the world getting it? >> Yeah, and you know, the key thing to remember is that, you know, not only have I been in this space for a long time but the experiences that we have gone through starting in 2011, there was a lot of focus on, you know, even AWS was at that point in time in the infancy stages. >> Yeah. >> And we said that we are going to set up a software as a service capability that runs only on public cloud because we had seen what customers had tried to do behind their firewalls and the types of hurdles that they had run into before. And while the concept was still in its nascent stages, but the directional signals, the fact that number of applications that you see in use today across any organization, that's growing. It used to be a case when in early 2000s, you know, this is early part of my career, where having six different applications across the enterprise landscape was considered complex. But now those same organizations are talking about 400, 500, a thousand different applications that they're using to run their business end to end. So, you know, this direction was clear. The need for digital transformation was becoming clear. And the fact that, you know, cloud was the only vehicle that you could use to solve these types of ad scale problems was also becoming clear. But what wasn't yet mainstream was this notion that, you know, if you're doing digital transformation, you need access to clean, consistent, trusted information. Or if you're doing machine learning or any kind of data analytics, you need similar kinds of trusted information. It wasn't a mainstream concept, but people were struggling with it because, you know, the whole notion of garbage in garbage out was becoming clearer to them as they started running into hurdles. And it's great to see that now, you know, after having gone through the transformation of, yes, we have provided the compute and the storage, but now we really need to unlock the value out of data that goes on this compute and storage. You know, it's great to see that even Amazon or AWS is talking about it. >> Well, as a founder, it's satisfying, and congratulations, we've been covering that. I got to ask, you mention this end to end. I like the example of in the 2006 applications considered complex, now hundreds and thousands of workloads are on an enterprise. Today we're going to hear more end to end data services on AWS and off AWS, hybrid or edge or whatever, that's happens. Now cross, it sounds like it's going to get more complex still. >> I mean... >> John: Right. I mean, that's not easy. >> Savannah: The gentle understatement of the century. I love that. Yes. >> If Adam's message is end to end, it's going to be more complex. How does it get easier? Because the enterprise, you know, the enterprise vendors love solving complexity with more complexity. That's the wrong answer. >> Well, you're absolutely right that things are going to get more complex. But you know, this is where, whether it is Amazon or you know, us, Reltio as a vendor coming in, the goal should always be what are we going to simplify for the customer? Because they are going to end up with a complex landscape on their hands anyway. Right? >> Savannah: Right. >> So that is where, what can be below the surface and simplified for the customers to use versus bringing their focus to the business value that they can get out of it. Unlocking that business value has to be the key aspect that we have to bring to the front. And, you know, that is where, yes, the landscape complexity may grow, but how is the solution making it simpler, easier, faster for you to get value out of the data that you're trying to work with? >> As a mission, that seems very clear and clean cut, but I'm curious, I can imagine there's so many different things that you're prioritizing when you're thinking about how to solve those problems. What is that decision matrix like for you? >> For us, it goes back to the core focus and the core problem that we are in the business of solving which is in a siloed, fragmented landscape, how can we create a single source of truth orientation that your business can depend on? If you're looking for the unified view of the customer, the product, the supplier, the location, the asset, all these are elements that are critical or crucial for you to run your business end to end. And we are there to provide that solution as Reltio to our customers. So, you know, we always, for our decision matrix have to go back to are we simplifying that problem for our customers and how much faster, easier, nimbler can it be, you know, both as a solution and also the time to value that it brings to the equation for the customer. >> Super important, end of the equation. Clearly you are on to something. You are not only a unicorn company, unicorn company being evaluated at over $1 billion latest evaluation, correct me if I'm wrong, is $1.7 billion as of last year. But you are also a centaur, which is seven times more rare than a unicorn, which for the audience maybe not familiar with the mythical creatures that define the Silicon Valley nomenclature in Lexicon. A centaur is a company with a hundred million in annual reoccurring revenue. How does it feel to be able to say that as a CEO or to hear me say that to you? >> Well, as a CEO, it's, you know, something that we have been working towards. the goal that we can deliver value to our customers, help every industry, you know, you just think about the types of products that you touch in a day, whether it's, you know, any healthcare related products that you're looking at. We are working with customers who are solving for the patient record to be unified with our platform. We are working with financial services companies who are helping you simplify how you do banking with them. We are working with retailers who are working in the area of, you know, leisure apparel or athletic goods and they are using our capabilities to simplify how they deliver better experience to you. So as I go across these industries, being able to influence and touch and simplify things overall for the customers that these companies are serving, that's an amazing feeling. And, you know, doing this while we are also making sure that we can build a durable business that has substantial revenue behind it- >> Savannah: Substantial. >> Gives us a lot of legs to stand on and talk about how we can change how the companies should run their entire data stack. >> And you're obviously a very efficient team practicing what you teach. You told me how many employees that you have? >> We have 450 employees across the globe. >> 450 employees and a hundred million in reoccurring revenue. It's pretty strong. It's pretty strong. >> Thank you. >> That's a quarter million in rev per employee. They're doing a pretty good job. That's absolutely fantastic. >> The cloud has been very successful, partnering with the cloud, a lot of leverage for the cloud. >> And that's been a part of our thesis from the very beginning that, you know, the capabilities that we build and bring to life have to be built on public cloud infrastructure. That's something that has been core to our innovation cycle because we look at it as a layer cake of innovation that we sit on and we can continue to drive faster value for our customers. >> John: Okay, so normally we do a bumper sticker. Tell me the bumper sticker for the show. We changed it to kind of modernize it called the Insta Challenge, Instagram challenge. Instagram has reels, short videos. What's the Instagram reel from your perspective? You have to do an Instagram reel right now about why this time in history, this time in for Amazon web services, this point for Reltio. Why is this moment in time important in the computer industry? Because, you know, we've reported, I put a story out, NextGen Clouds here. People are seeing their status go from ISV to ecosystem platforms on top of AWS. Your success has continued to grow. Something's going on. What's the Instagram reel about why this year's so important in the history of the cloud? >> Well, you know, just think about the overall macroeconomic conditions. You know, everybody's trying to think about where the next, you know, the set of growth is going to come from or how we are going to tackle, you know, what we have as challenges in front of us. And at the end of the day, most of the efficiency that came from applying new applications or, you know, buying new products in the application space has delivered its value. The next unlock is going to come from data. And that is the key that we have to think about because the traditional model of going across 500 different applications to run your business is no longer going to be a scalable model to work with. If you really want to move faster with your business, you have to think about how to use data as a strategic asset and think about things differently. And we are talking about delivering experience at the edge, delivering, you know, real time type of engagement with the customers that we work with. And that is where the entire data value proposition starts to deliver a whole new set of options to the customers. And that's something that we all have to think about differently. It's going to require a fundamentally different architecture, innovation, leading with data instead of thinking about the traditional landscape that we have been running with. >> Leading with data and transforming architecture. A couple themes we've had on the show lately already. >> John: Well I think there's been a great, I mean this is a great leadership example of what's going on in the industry. As young people are looking at their careers. I've talked with a lot of folks under 30, they're trying to figure out what's a good career path and they're looking at all this change in front of them. >> That's a great point, John. >> Whether it's a computer science student or someone in healthcare, these industries are being reinvented with data. What's your advice to those young, this up and coming generation that might not take the traditional path traveled 'cause it might not be there. What's your advice for those people making these career decisions? >> I think there are two things that are relevant to every career option out there. Knowledge and awareness of data and how to apply computing techniques to the data is key and relevant. It's the language that we all have to learn and be familiar with. Without that, you know, you'll be missing a key part of your arsenal that you will be required to bring to work but won't have access to if you're not well-versed or familiar with those two areas. So this is lingua franca that we all have to get used to. >> Data and computer technology applied to business or some application or some problem. >> Manish: Applied to business. You know, figuring out how to apply it to deliver business outcomes is the key thing to keep in mind. >> Okay. >> Yeah. Last question for you to wrap us up. It's obviously an exciting, thrilling, vibrant moment here on the show floor, but I'm curious because I can imagine some of your customers, especially given the scale that they're at, I mean we're talking about some Fortune 100s here, how are you delivering value in this uncertain market? I mean, I know you solved this baseline problem but I can imagine there's a little bit of frantic energy within your customer base. >> Manish: Yeah. You know, with data this has been a traditional challenge. Everybody talks about the motherhood and apple pie. If you have better data, you can drive better outcomes. But some of the work that we have been doing is quantifying, measuring those outcomes and translating what the dollar impact of that value is for each one of the customers. And this is where the work that we have done with large, you know, let's say life sciences companies like AstraZeneca or GSK or in financial services with companies like Northwestern Mutual or Fidelity or, you know, common household names like McDonald's where they're delivering their digital transformation with the data capabilities that we are helping build with them. That's the key part that's been, you know, extremely valuable. And that is where in each one of these situations, we are helping them measure what the ROI is at every turn. So being able to go into these discussions with the hard dollar ROI that you can expect out of it is the key thing that we are focused on. >> And that's so mission critical now and at any economic juncture. Just to echo that, I noticed that Forrester did an independent study looking at customers that invested in your MDM solution. 366% ROI and a total net present value of 13 million over three years. So you clearly deliver on what you just promised there with customers and brands that we touch in all of our everyday lives. Manish, thank you so much for being on the show with us today. You and Reltio are clearly crushing it. We can't wait to have you back hopefully for some more exciting updates at next year's AWS re:Invent. John, thanks for- >> Or sooner. >> Yeah, yeah. Or sooner or maybe in the studios or who knows, at one of the other fabulous events we'll all be at. I'm sure you'll be traveling around given the success that the company is seeing. And John, thanks for bringing the young folks into the conversation, was a really nice touch. >> We got skill gaps, we might as well solve that right now. >> Yeah. And I like to think that there are young minds watching theCUBE or at least watching, maybe their parents are- >> We're streaming to Twitch. All the gamers are watching this right now. Stop playing the video games. >> We have the hottest stream on Twitch right now if you're not already ready for it. John Furrier, Manish Sood, thank you so much for being on the show with us. Thank all of you at home or at the office or in outer space or wherever you happen to be tuned in to this fabulous live stream. You are watching theCUBE, the leader in high tech coverage. My name is Savannah Peterson. We're at AWS re:Invent here in Las Vegas where we'll have our head in the clouds all week.

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi buddy welcome to this special noob digital event where we're focusing in on data ops data ops in Acton with generous support from friends at IBM let me set up the situation here there's a real problem going on in the industry and that's that people are not getting the most out of their data data is plentiful but insights perhaps aren't what's the reason for that well it's really a pretty complicated situation for a lot of organizations there's data silos there's challenges with skill sets and lack of skills there's tons of tools out there sort of a tools brief the data pipeline is not automated the business lines oftentimes don't feel as though they own the data so that creates some real concerns around data quality and a lot of finger-point quality the opportunity here is to really operationalize the data pipeline and infuse AI into that equation and really attack their cost-cutting and revenue generation opportunities that are there in front of you think about this virtually every application this decade is going to be infused with AI if it's not it's not going to be competitive and so we have organized a panel of great practitioners to really dig in to these issues first I want to introduce Victoria Stassi with who's an industry expert in a top at Northwestern you two'll very great to see you again thanks for coming on excellent nice to see you as well and Caitlin Alfre is the director of AI a vai accelerator and also part of the peak data officers organization at IBM who has actually eaten some of it his own practice what a creep let me say it that way Caitlin great to see you again and Steve Lewis good to see you again see vice president director of management associated a bank and Thompson thanks for coming on thanks Dave make speaker alright guys so you heard my authority with in terms of operationalizing getting the most insight hey data is wonderful insights aren't but getting insight in real time is critical in this decade each of you is a sense as to where you are on that journey or Victoria your taste because you're brand new to Northwestern Mutual but you have a lot of deep expertise in in health care and manufacturing financial services but where you see just the general industry climate and we'll talk about the journeys that you are on both personally and professionally so it's all fair sure I think right now right again just me going is you need to have speech insight right so as I experienced going through many organizations are all facing the same challenges today and a lot of those pounds is hard where do my to live is my data trust meaning has a bank curated has been Clinton's visit qualified has a big a lot of that is ready what we see often happen is businesses right they know their KPIs they know their business metrics but they can't find where that data Linda Barragan asked there's abundant data disparity all over the place but it is replicated because it's not well managed it's a lot of what governance in the platform of pools that governance to speak right offer fact it organizations pay is just that piece of it I can tell you where data is I can tell you what's trusted that when you can quickly access information and bring back answers to business questions that is one answer not many answers leaving the business to question what's the right path right which is the correct answer which which way do I go at the executive level that's the biggest challenge where we want the industry to go moving forward right is one breaking that down along that information to be published quickly and to an emailing data virtualization a lot of what you see today is most businesses right it takes time to build out large warehouses at an enterprise level we need to pivot quicker so a lot of what businesses are doing is we're leaning them towards taking advantage of data virtualization allowing them to connect to these data sources right to bring that information back quickly so they don't have to replicate that information across different systems or different applications right and then to be able to provide that those answers back quickly also allowing for seamless access to from the analysts that are running running full speed right try and find the answers as quickly as they find great okay and I want to get into that sort of how news Steve let me go to you one of the things that we talked about earlier was just infusing this this mindset of a data cult and thinking about data as a service so talk a little bit about how you got started what was the starting NICUs through that sure I think the biggest thing for us there is to change that mindset from data being just for reporting or things that have happened in the past to do some insights on us and some data that already existed well we've tried to shift the mentality there is to start to use data and use that into our actual applications so that we're providing those insight in real time through the applications as they're consumed helping with customer experience helping with our personalization and an optimization of our application the way we've started down that path or kind of the journey that we're still on was to get the foundation laid birch so part of that has been making sure we have access to all that data whether it's through virtualization like vic talked about or whether it's through having more of the the data selected in a data like that that where we have all of that foundational data available as opposed to waiting for people to ask for it that's been the biggest culture shift for us is having that availability of data to be ready to be able to provide those insights as opposed to having to make the businesses or the application or asked for that day Oh Kailyn when I first met into pulp andari the idea wobble he paid up there yeah I was asking him okay where does a what's the role of that at CBO and and he mentioned a number of things but two of the things that stood out is you got to understand how data affect the monetization of your company that doesn't mean you know selling the data what role does it play and help cut cost or ink revenue or productivity or no customer service etc the other thing he said was you've got a align with the lines of piss a little sounded good and this is several years ago and IBM took it upon itself Greek its own champagne I was gonna say you know dogfooding whatever but it's not easy just flip a switch and an infuse a I and automate the data pipeline you guys had to go you know some real of pain to get there and you did you were early on you took some arrows and now you're helping your customers better on thin debt but talk about some of the use cases that where you guys have applied this obviously the biggest organization you know one of the biggest in the world the real challenge is they're sure I'm happy today you know we've been on this journey for about four years now so we stood up our first book to get office 2016 and you're right it was all about getting what data strategy offered and executed internally and we want to be very transparent because as you've mentioned you know a lot of challenges possible think differently about the value and so as we wrote that data strategy at that time about coming to enterprise and then we quickly of pivoted to see the real opportunity and value of infusing AI across all of our needs were close to your question on a couple of specific use cases I'd say you know we invested that time getting that platform built and implemented and then we were able to take advantage of that one particular example that I've been really excited about I have a practitioner on my team who's a supply chain expert and a couple of years ago he started building out supply chain solution so that we can better mitigate our risk in the event of a natural disaster like the earthquake hurricane anywhere around the world and be cuz we invest at the time and getting the date of pipelines right getting that all of that were created and cleaned and the quality of it we were able to recently in recent weeks add the really critical Kovach 19 data and deliver that out to our employees internally for their preparation purposes make that available to our nonprofit partners and now we're starting to see our first customers take advantage too with the health and well-being of their employees mine so that's you know an example I think where and I'm seeing a lot of you know my clients I work with they invest in the data and AI readiness and then they're able to take advantage of all of that work work very quickly in an agile fashion just spin up those out well I think one of the keys there who Kaelin is that you know we can talk about that in a covet 19 contact but it's that's gonna carry through that that notion of of business resiliency is it's gonna live on you know in this post pivot world isn't it absolutely I think for all of us the importance of investing in the business continuity and resiliency type work so that we know what to do in the event of either natural disaster or something beyond you know it'll be grounded in that and I think it'll only become more important for us to be able to act quickly and so the investment in those platforms and approach that we're taking and you know I see many of us taking will really be grounded in that resiliency so Vic and Steve I want to dig into this a little bit because you know we use this concept of data op we're stealing from DevOps and there are similarities but there are also differences now let's talk about the data pipeline if you think about the data pipeline as a sort of quasi linear process where you're investing data and you might be using you know tools but whether it's Kafka or you know we have a favorite who will you have and then you're transforming that that data and then you got a you know discovery you got to do some some exploration you got to figure out your metadata catalog and then you're trying to analyze that data to get some insights and then you ultimately you want to operationalize it so you know and and you could come up with your own data pipeline but generally that sort of concept is is I think well accepted there's different roles and unlike DevOps where it might be the same developer who's actually implementing security policies picking it the operations in in data ops there might be different roles and fact very often are there's data science there's may be an IT role there's data engineering there's analysts etc so Vic I wonder if you could you could talk about the challenges in in managing and automating that data pipeline applying data ops and how practitioners can overcome them yeah I would say a perfect example would be a client that I was just recently working for where we actually took a team and we built up a team using agile methodologies that framework right we're rapidly ingesting data and then proving out data's fit for purpose right so often now we talk a lot about big data and that is really where a lot of industries are going they're trying to add an enrichment to their own data sources so what they're doing is they're purchasing these third-party data sets so in doing so right you make that initial purchase but what many companies are doing today is they have no real way to vet that so they'll purchase the information they aren't going to vet it upfront they're going to bring it into an environment there it's going to take them time to understand if the data is of quality or not and by the time they do typically the sales gone and done and they're not going to ask for anything back but we were able to do it the most recent claim was use an instructure data source right bring that and ingest that with modelers using this agile team right and within two weeks we were able to bring the data in from the third-party vendor what we considered rapid prototyping right be able to profile the data understand if the data is of quality or not and then quickly figure out that you know what the data's not so in doing that we were able to then contact the vendor back tell them you know it sorry the data set up to snuff we'd like our money back we're not gonna go forward with it that's enabling businesses to be smarter with what they're doing with 30 new purchases today as many businesses right now um as much as they want to rely on their own data right they actually want to rely on cross the data from third-party sources and that's really what data Ops is allowing us to do it's allowing us to think at a broader a higher level right what to bring the information what structures can we store them in that they don't necessarily have to be modeled because a modeler is great right but if we have to take time to model all the information before we even know we want to use it that's gonna slow the process now and that's slowing the business down the business is looking for us to speed up all of our processes a lot of what we heard in the past raised that IP tends to slow us down and that's where we're trying to change that perception in the industry is no we're actually here to speed you up we have all the tools and technologies to do so and they're only getting better I would say also on data scientists right that's another piece of the pie for us if we can bring the information in and we can quickly catalog it in a metadata and burn it bring in the information in the backend data data assets right and then supply that information back to scientists gone are the days where scientists are going and asking for connections to all these different data sources waiting days for access requests to be approved just to find out that once they figure out how it with them the relationship diagram right the design looks like in that back-end database how to get to it write the code to get to it and then figure out this is not the information I need that Sally next to me right fold me the wrong information that's where the catalog comes in that's where due to absent data governance having that catalog that metadata management platform available to you they can go into a catalog without having to request access to anything quickly and within five minutes they can see the structures what if the tables look like what did the fields look like are these are these the metrics I need to bring back answers to the business that's data apps it's allowing us to speed up all of that information you know taking stuff that took months now down two weeks down two days down two hours so Steve I wonder if you could pick up on that and just help us understand what data means you we talked about earlier in our previous conversation I mentioned it upfront is this notion of you know the demand for for data access is it was through the roof and and you've gone from that to sort of more of a self-service environment where it's not IT owning the data it's really the businesses owning the data but what what is what is all this data op stuff meaning in your world sure I think it's very similar it's it's how do we enable and get access to that clicker showing the right controls showing the right processes and and building that scalability and agility and into all of it so that we're we're doing this at scale it's much more rapidly available we can discover new data separately determine if it's right or or more importantly if it's wrong similar to what what Vic described it's it's how do we enable the business to make those right decisions on whether or not they're going down the right path whether they're not the catalog is a big part of that we've also introduced a lot of frameworks around scale so just the ability to rapidly ingest data and make that available has been a key for us we've also focused on a prototyping environment so that sandbox mentality of how do we rapidly stand those up for users and and still provide some controls but have provide that ability for people to do that that exploration what we're finding is that by providing the platform and and the foundational layers that were we're getting the use cases to sort of evolve and come out of that as opposed to having the use cases prior to then go build things from we're shifting the mentality within the organization to say we don't know what we need yet let's let's start to explore that's kind of that data scientist mentality and culture it more of a way of thinking as opposed to you know an actual project or implement well I think that that cultural aspect is important of course Caitlin you guys are an AI company or at least that you know part of what you do but you know you've you for four decades maybe centuries you've been organized around different things by factoring plant but sales channel or whatever it is but-but-but-but how has the chief data officer organization within IBM been able to transform itself and and really infuse a data culture across the entire company one of the approaches you know we've taken and we talk about sort of the blueprint to drive AI transformation so that we can achieve and deliver these really high value use cases we talked about the data the technology which we've just pressed on with organizational piece of it duration are so important the change management enabling and equipping our data stewards I'll give one a civic example that I've been really excited about when we were building our platform and starting to pull districting structured unstructured pull it in our ADA stewards are spending a lot of time manually tagging and creating business metadata about that data and we identified that that was a real pain point costing us a lot of money valuable resources so we started to automate the metadata and doing that in partnership with our deep learning practitioners and some of the models that they were able to build that capability we pushed out into our contacts our product last year and one of the really exciting things for me to see is our data stewards who be so value exporters and the skills that they bring have reported that you know it's really changed the way they're able to work it's really sped up their process it's enabled them to then move on to higher value to abilities and and business benefits so they're very happy from an organizational you know completion point of view so I think there's ways to identify those use cases particularly for taste you know we drove some significant productivity savings we also really empowered and hold our data stewards we really value to make their job you know easier more efficient and and help them move on to things that they are more you know excited about doing so I think that's that you know another example of approaching taken yes so the cultural piece the people piece is key we talked a little bit about the process I want to get into a little bit into the tech Steve I wonder if you could tell us you know what's it what's the tech we have this bevy of tools I mentioned a number of them upfront you've got different data stores you've got open source pooling you've got IBM tooling what are the critical components of the technology that people should be thinking about tapping in architecture from ingestion perspective we're trying to do a lot of and a Python framework and scaleable ingestion pipe frameworks on the catalog side I think what we've done is gone with IBM PAC which provides a platform for a lot of these tools to stay integrated together so things from the discovery of data sources the cataloging the documentation of those data sources and then all the way through the actual advanced analytics and Python models and our our models and the open source ID combined with the ability to do some data prep and refinery work having that all in an integrated platform was a key to us for us that the rollout and of more of these tools in bulk as opposed to having the point solutions so that's been a big focus area for us and then on the analytic side and the web versus IDE there's a lot of different components you can go into whether it's meal soft whether it's AWS and some of the native functionalities out there you mentioned before Kafka and Anissa streams and different streaming technologies those are all the ones that are kind of in our Ketil box that we're starting to look at so and one of the keys here is we're trying to make decisions in as close to real time as possible as opposed to the business having to wait you know weeks or months and then by the time they get insights it's late and really rearview mirror so Vic your focus you know in your career has been a lot on data data quality governance master data management data from a data quality standpoint as well what are some of the key tools that you're familiar with that you've used that really have enabled you operationalize that data pipeline you know I would say I'm definitely the IBM tools I have the most experience with that also informatica though as well those are to me the two top players IBM definitely has come to the table with a suite right like Steve said cloud pack for data is really a one-stop shop so that's allowing that quick seamless access for business user versus them having to go into some of the previous versions that IBM had rolled out where you're going into different user interfaces right to find your information and that can become clunky it can add the process it can also create almost like a bad taste and if in most people's mouths because they don't want to navigate from system to system to system just to get their information so cloud pack to me definitely brings everything to the table in one in a one-stop shop type of environment in for me also though is working on the same thing and I would tell you that they haven't come up with a solution that really comes close to what IBM is done with cloud pack for data I'd be interested to see if they can bring that on the horizon but really IBM suite of tools allows for profiling follow the analytics write metadata management access to db2 warehouse on cloud those are the tools that I've worked in my past to implement as well as cloud object store to bring all that together to provide that one stop that at Northwestern right we're working right now with belieber I think calibra is a great set it pool are great garments catalog right but that's really what it's truly made for is it's a governance catalog you have to bring some other pieces to the table in order for it to serve up all the cloud pack does today which is the advanced profiling the data virtualization that cloud pack enables today the machine learning at the level where you can actually work with our and Python code and you put our notebooks inside of pack that's some of this the pieces right that are missing in some of the under vent other vendor schools today so one of the things that you're hearing here is the theme of openness others addition we've talked about a lot of tools and not IBM tools all IBM tools there there are many but but people want to use what they want to use so Kaitlin from an IBM perspective what's your commitment the openness number one but also to you know we talked a lot about cloud packs but to simplify the experience for your client well and I thank Stephen Victoria for you know speaking to their experience I really appreciate feedback and part of our approach has been to really take one the challenges that we've had I mentioned some of the capabilities that we brought forward in our cloud platform data product one being you know automating metadata generation and that was something we had to solve for our own data challenges in need so we will continue to source you know our use cases from and grounded from a practitioner perspective of what we're trying to do and solve and build and the approach we've really been taking is co-creation line and that we roll these capability about the product and work with our customers like Stephen light victorious you really solicit feedback to product route our dev teams push that out and just be very open and transparent I mean we want to deliver a seamless experience we want to do it in partnership and continue to solicit feedback and improve and roll out so no I think that will that has been our approach will continue to be and really appreciate the partnerships that we've been able to foster so we don't have a ton of time but I want to go to practitioners on the panel and ask you about key key performance indicators when I think about DevOps one of the things that we're measuring is the elapsed time the deploy applications start finished where we're measuring the amount of rework that has to be done the the quality of the deliverable what are the KPIs Victoria that are indicators of success in operationalizing date the data pipeline well I would definitely say your ability to deliver quickly right so how fast can you deliver is that is that quicker than what you've been able to do in the past right what is the user experience like right so have you been able to measure what what the amount of time was right that users are spending to bring information to the table in the past versus have you been able to reduce that time to delivery right of information business answers to business questions those are the key performance indicators to me that tell you that the suite that we've put in place today right it's providing information quickly I can get my business answers quickly but quicker than I could before and the information is accurate so being able to measure is it quality that I've been giving that I've given back or is this not is it the wrong information and yet I've got to go back to the table and find where I need to gather that from from somewhere else that to me tells us okay you know what the tools we've put in place today my teams are working quicker they're answering the questions they need to accurately that is when we know we're on the right path Steve anything you add to that I think she covered a lot of the people components the around the data quality scoring right for all the different data attributes coming up with a metric around how to measure that and and then showing that trend over time to show that it's getting better the other one that we're doing is just around overall date availability how how much data are we providing to our users and and showing that trend so when I first started you know we had somewhere in the neighborhood of 500 files that had been brought into the warehouse and and had been published and available in the neighborhood of a couple thousand fields we've grown that into weave we have thousands of cables now available so it's it's been you know hundreds of percent in scale as far as just the availability of that data how much is out there how much is is ready and available for for people to just dig in and put into their their analytics and their models and get those back into the other application so that's another key metric that we're starting to track as well so last question so I said at the top that every application is gonna need to be infused with AI this decade otherwise that application not going to be as competitive as it could be and so for those that are maybe stuck in their journey don't really know where to get started I'll start with with Caitlin and go to Victoria and then and then even bring us home what advice would you give the people that need to get going on this my advice is I think you pull the folks that are either producing or accessing your data and figure out what the rate is between I mentioned some of the data management challenges we were seeing this these processes were taking weeks and prone to error highly manual so part was ripe for AI project so identifying those use cases I think that are really causing you know the most free work and and manual effort you can move really quickly and as you build this platform out you're able to spin those up on an accelerated fashion I think identifying that and figuring out the business impact are able to drive very early on you can get going and start really seeing the value great yeah I would actually say kids I hit it on the head but I would probably add to that right is the first and foremost in my opinion right the importance around this is data governance you need to implement a data governance at an enterprise level many organizations will do it but they'll have silos of governance you really need an interface I did a government's platform that consists of a true framework of an operational model model charters right you have data domain owners data domain stewards data custodians all that needs to be defined and while that may take some work in in the beginning right the payoff down the line is that much more it's it it's allowing your business to truly own the data once they own the data and they take part in classifying the data assets for technologists and for analysts right you can start to eliminate some of the technical debt that most organizations have acquired today they can start to look at what are some of the systems that we can turn off what are some of the systems that we see valium truly build out a capability matrix we can start mapping systems right to capabilities and start to say where do we have wares or redundancy right what can we get rid of that's the first piece of it and then the second piece of it is really leveraging the tools that are out there today the IBM tools some of the other tools out there as well that enable some of the newer next-generation capabilities like unit nai right for example allowing automation for automation which right for all of us means that a lot of the analysts that are in place today they can access the information quicker they can deliver the information accurately like we've been talking about because it's been classified that pre works being done it's never too late to start but once you start that it just really acts as a domino effect to everything else where you start to see everything else fall into place all right thank you and Steve bring us on but advice for your your peers that want to get started sure I think the key for me too is like like those guys have talked about I think all everything they said is valid and accurate thing I would add is is from a starting perspective if you haven't started start right don't don't try to overthink that over plan it it started just do something and and and start the show that progress and value the use cases will come even if you think you're not there yet it's amazing once you have the national components there how some of these things start to come out of the woodwork so so it started it going may have it have that iterative approach to this and an open mindset it's encourage exploration and enablement look your organization in the eye to say why are their silos why do these things like this what are our problem what are the things getting in our way and and focus and tackle those those areas as opposed to trying to put up more rails and more boundaries and kind of encourage that silo mentality really really look at how do you how do you focus on that enablement and then the last comment would just be on scale everything should be focused on scale what you think is a one-time process today you're gonna do it again we've all been there you're gonna do it a thousand times again so prepare for that prepare forever that you're gonna do everything a thousand times and and start to instill that culture within your organization a great advice guys data bringing machine intelligence an AI to really drive insights and scaling with a cloud operating model no matter where that data live it's really great to have have three such knowledgeable practitioners Caitlyn Toria and Steve thanks so much for coming on the cube and helping support this panel all right and thank you for watching everybody now remember this panel was part of the raw material that went into a crowd chat that we hosted on May 27th Crouch at net slash data ops so go check that out this is Dave Volante for the cube thanks for watching [Music]