(techno music) >> From Cambridge, Massachusetts, it's theCUBE. Covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. >> Welcome back to Cambridge, Massachusetts everybody, we're here with theCUBE at the MIT Chief Data Officer Information Quality Conference. I'm Dave Vellante with my co-host Paul Gillin. Susan Wilson is here, she's the vice president of data governance and she's the leader at Informatica. Blake Anders is the corporate vice president of data governance at New York Life. Folks, welcome to theCUBE, thanks for coming on. >> Thank you. >> Thank you. >> So, Susan, interesting title; VP, data governance leader, Informatica. So, what are you leading at Informatica? >> We're helping our customers realize their business outcomes and objectives. Prior to joining Informatica about 7 years ago, I was actually a customer myself, and so often times I'm working with our customers to understand where they are, where they going, and how to best help them; because we recognize data governance is more than just a tool, it's a capability that represents people, the processes, the culture, as well as the technology. >> Yeah so you've walked the walk, and you can empathize with what your customers are going through. And Blake, your role, as the corporate VP, but more specifically the data governance lead. >> Right, so I lead the data governance capabilities and execution group at New York Life. We're focused on providing skills and tools that enable government's activities across the enterprise at the company. >> How long has that function been in place? >> We've been in place for about two and half years now. >> So, I don't know if you guys heard Mark Ramsey this morning, the key-note, but basically he said, okay, we started with enterprise data warehouse, we went to master data management, then we kind of did this top-down enterprise data model; that all failed. So we said, all right, let's pump the governance. Here you go guys, you fix our corporate data problem. Now, right tool for the right job but, and so, we were sort of joking, did data governance fail? No, you always have to have data governance. It's like brushing your teeth. But so, like I said, I don't know if you heard that, but what are your thoughts on that sort of evolution that he described? As sort of, failures of things like EDW to live up to expectations and then, okay guys over to you. Is that a common theme? >> It is a common theme, and what we're finding with many of our customers is that they had tried many of the, if you will, the methodologies around data governance, right? Around policies and structures. And we describe this as the Data 1.0 journey, which was more application-centric reporting to Data 2.0 to data warehousing. And a lot of the failed attempts, if you will, at centralizing, if you will, all of your data, to now Data 3.0, where we look at the explosion of data, the volumes of data, the number of data consumers, the expectations of the chief data officer to solve business outcomes; crushing under the scale of, I can't fit all of this into a centralized data at repository, I need something that will help me scale and to become more agile. And so, that message does resonate with us, but we're not saying data warehouses don't exist. They absolutely do for trusted data sources, but the ability to be agile and to address many of your organizations needs and to be able to service multiple consumers is top-of-mind for many of our customers. >> And the mind set from 1.0 to 2.0 to 3.0 has changed. From, you know, data as a liability, to now data as this massive asset. It's sort of-- >> Value, yeah. >> Yeah, and the pendulum is swung. It's almost like a see-saw. Where, and I'm not sure it's ever going to flip back, but it is to a certain extent; people are starting to realize, wow, we have to be careful about what we do with our data. But still, it's go, go, go. But, what's the experience at New York Life? I mean, you know. A company that's been around for a long time, conservative, wants to make sure risk averse, obviously. >> Right. >> But at the same time, you want to keep moving as the market moves. >> Right, and we look at data governance as really an enabler and a value-add activity. We're not a governance practice for the sake of governance. We're not there to create a lot of policies and restrictions. We're there to add value and to enable innovation in our business and really drive that execution, that efficiency. >> So how do you do that? Square that circle for me, because a lot of people think, when people think security and governance and compliance they think, oh, that stifles innovation. How do you make governance an engine of innovation? >> You provide transparency around your data. So, it's transparency around, what does the data mean? What data assets do we have? Where can I find that? Where are my most trusted sources of data? What does the quality of that data look like? So all those things together really enable your data consumers to take that information and create new value for the company. So it's really about enabling your value creators throughout the organization. >> So data is an ingredient. I can tell you where it is, I can give you some kind of rating as to the quality of that data and it's usefulness. And then you can take it and do what you need to do with it in your specific line of business. >> That's right. >> Now you said you've been at this two and half years, so what stages have you gone through since you first began the data governance initiative. >> Sure, so our first year, year and half was really focused on building the foundations, establishing the playbook for data governance and building our processes and understanding how data governance needed to be implemented to fit New York Life in the culture of the company. The last twelve months or so has really been focused on operationalizing governance. So we've got the foundations in place, now it's about implementing tools to further augment those capabilities and help assist our data stewards and give them a better skill set and a better tool set to do their jobs. >> Are you, sort of, crowdsourcing the process? I mean, you have a defined set of people who are responsible for governance, or is everyone taking a role? >> So, it is a two-pronged approach, we do have dedicated data stewards. There's approximately 15 across various lines of business throughout the company. But, we are building towards a data democratization aspect. So, we want people to be self-sufficient in finding the data that they need and understanding the data. And then, when they have questions, relying on our stewards as a network of subject matter experts who also have some authorizations to make changes and adapt the data as needed. >> Susan, one of the challenges that we see is that the chief data officers often times are not involved in some of these skunkworks AI projects. They're sort of either hidden, maybe not even hidden, but they're in the line of business, they're moving. You know, there's a mentality of move fast and break things. The challenge with AI is, if you start operationalizing AI and you're breaking things without data quality, without data governance, you can really affect lives. We've seen it. In one of these unintended consequences. I mean, Facebook is the obvious example and there are many, many others. But, are you seeing that? How are you seeing organizations dealing with that problem? >> As Blake was mentioning often times what it is about, you've got to start with transparency, and you got to start with collaborating across your lines of businesses, including the data scientists, and including in terms of what they are doing. And actually provide that level of transparency, provide a level of collaboration. And a lot of that is through the use of our technology enablers to basically go out and find where the data is and what people are using and to be able to provide a mechanism for them to collaborate in terms of, hey, how do I get access to that? I didn't realize you were the SME for that particular component. And then also, did you realize that there is a policy associated to the data that you're managing and it can't be shared externally or with certain consumer data sets. So, the objective really is around how to create a platform to ensure that any one in your organization, whether I'm in the line of business, that I don't have a technical background, or someone who does have a technical background, they can come and access and understand that information and connect with their peers. >> So you're helping them to discover the data. What do you do at that stage? >> What we do at that stage is, creating insights for anyone in the organization to understand it from an impact analysis perspective. So, for example, if I'm going to make changes, to as well as discovery. Where exactly is my information? And so we have-- >> Right. How do you help your customers discover that data? >> Through machine learning and artificial intelligence capabilities of our, specifically, our data catalog, that allows us to do that. So we use such things like similarity based matching which help us to identify. It doesn't have to be named, in miscellaneous text one, it could be named in that particular column name. But, in our ability to scan and discover we can identify in that column what is potentially social security number. It might have resided over years of having this data, but you may not realize that it's still stored there. Our ability to identify that and report that out to the data stewards as well as the data analysts, as well as to the privacy individuals is critical. So, with that being said, then they can actually identify the appropriate policies that need to be adhered to, alongside with it in terms of quality, in terms of, is there something that we need to archive. So that's where we're helping our customers in that aspect. >> So you can infer from the data, the meta data, and then, with a fair degree of accuracy, categorize it and automate that. >> Exactly. We've got a customer that actually ran this and they said that, you know, we took three people, three months to actually physically tag where all this information existed across something like 7,000 critical data elements. And, basically, after the set up and the scanning procedures, within seconds we were able to get within 90% precision. Because, again, we've dealt a lot with meta data. It's core to our artificial intelligence and machine learning. And it's core to how we built out our platforms to share that meta data, to do something with that meta data. It's not just about sharing the glossary and the definition information. We also want to automate and reduce the manual burden. Because we recognize with that scale, manual documentation, manual cataloging and tagging just, >> It doesn't work. >> It doesn't work. It doesn't scale. >> Humans are bad at it. >> They're horrible at it. >> So I presume you have a chief data officer at New York Life, is that correct? >> We have a chief data and analytics officer, yes. >> Okay, and you work within that group? >> Yes, that is correct. >> Do you report it to that? >> Yes, so-- >> And that individual, yeah, describe the organization. >> So that sits in our lines of business. Originally, our data governance office sat in technology. And then, our early 2018 we actually re-orged into the business under the chief data and analytics officer when that role was formed. So we sit under that group along with a data solutions and governance team that includes several of our data stewards and also some others, some data engineer-type roles. And then, our center for data science and analytics as well that contains a lot of our data science teams in that type of work. >> So in thinking about some of these, I was describing to Susan, as these skunkworks projects, is the data team, the chief data officer's team involved in those projects or is it sort of a, go run water through the pipes, get an MVP and then you guys come in. How does that all work? >> We're working to try to centralize that function as much as we can, because we do believe there's value in the left hand knowing what the right hand is doing in those types of things. So we're trying to build those communications channels and build that network of data consumers across the organization. >> It's hard right? >> It is. >> Because the line of business wants to move fast, and you're saying, hey, we can help. And they think you're going to slow them down, but in fact, you got to make the case and show the success because you're actually not going to slow them down to terms of the ultimate outcome. I think that's the case that you're trying to make, right? >> And that's one of the things that we try to really focus on and I think that's one of the advantages to us being embedded in the business under the CDAO role, is that we can then say our objectives are your objectives. We are here to add value and to align with what you're working on. We're not trying to slow you down or hinder you, we're really trying to bring more to the table and augment what you're already trying to achieve. >> Sometimes getting that organization right means everything, as we've seen. >> Absolutely. >> That's right. >> How are you applying governance discipline to unstructured data? >> That's actually something that's a little bit further down our road map, but one of the things that we have started doing is looking at our taxonomy's for structured data and aligning those with the taxonomy's that we're using to classify unstructured data. So, that's something we're in the early stages with, so that when we get to that process of looking at more of our unstructured content, we can, we already have a good feel for there's alignment between the way that we think about and organize those concepts. >> Have you identified automation tools that can help to bring structure to that unstructured data? >> Yes, we have. And there are several tools out there that we're continuing to investigate and look at. But, that's one of the key things that we're trying to achieve through this process is bringing structure to unstructured content. >> So, the conference. First year at the conference. >> Yes. >> Kind of key take aways, things that interesting to you, learnings? >> Oh, yes, well the number of CDO's that are here and what's top of mind for them. I mean, it ranges from, how do I stand up my operating model? We just had a session just about 30 minutes ago. A lot of questions around, how do I set up my organization structure? How do I stand up my operating model so that I could be flexible? To, right, the data scientists, to the folks that are more traditional in structured and trusted data. So, still these things are top-of-mind and because they're recognizing the market is also changing too. And the growing amount of expectations, not only solving business outcomes, but also regulatory compliance, privacy is also top-of-mind for a lot of customers. In terms of, how would I get started? And what's the appropriate structure and mechanism for doing so? So we're getting a lot of those types of questions as well. So, the good thing is many of us have had years of experience in this phase and the convergence of us being able to support our customers, not only in our principles around how we implement the framework, but also the technology is really coming together very nicely. >> Anything you'd add, Blake? >> I think it's really impressive to see the level of engagement with thought leaders and decision makers in the data space. You know, as Susan mentioned, we just got out of our session and really, by the end of it, it turned into more of an open discussion. There was just this kind of back and forth between the participants. And so it's really engaging to see that level of passion from such a distinguished group of individuals who are all kind of here to share thoughts and ideas. >> Well anytime you come to a conference, it's sort of any open forum like this, you learn a lot. When you're at MIT, it's like super-charged. With the big brains. >> Exactly, you feel it when you come on the campus. >> You feel smarter when you walk out of here. >> Exactly, I know. >> Well, guys, thanks so much for coming to theCUBE. It was great to have you. >> Thank you for having us. We appreciate it, thank you. >> You're welcome. All right, keep it right there everybody. Paul and I will be back with our next guest. You're watching theCUBE from MIT in Cambridge. We'll be right back. (techno music)

>> Announcer: Live from Las Vegas, it's theCUBE, covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel along with its ecosystem partners. >> Welcome back to the eighth year of AWS re:Invent. It's 2019. There's over 60,000 in attendance. Seventh year of theCUBE. Wall-to-wall coverage, covering all the angles of this broad and massively-growing ecosystem. I am Stu Miniman. My co-host is Justin Warren, and one of our Cube alumni are back on the program. Ramin Sayar, who is the president and CEO of Sumo Logic. >> Stu: Booth always at the front of the expo hall. I think anybody that's come to this show has one of the Sumo-- >> Squishies. >> Stu: Squish dolls there. I remember a number of years you actually had live sumos-- >> Again this year. >> At the event, so you know, bring us, the sixth year you've been at the show, give us a little bit of the vibe and your experience so far. >> Yeah, I mean, naturally when you've been here so many times, it's interesting to be back, not only as a practitioner who's attended this many years ago, but now as a partner of AWS, and seeing not only our own community growth in terms of Sumo Logic, but also the community in general that we're here to see. You know, it's a good mix of practitioners and business folks from DevOps to security and much, much more, and as we were talking right before the show, the vendors here are so different now then it was three years go, let alone six years ago. So, it's nice to see. >> All right, a lot of news from Amazon. Anything specific jump out from you from their side, or I know Sumo Logic has had some announcements this week. >> Yeah, I mean, like, true to Amazon, there's always a lot of announcements, and, you know, what we see is customers need time to understand and digest that. There's a lot of confusion, but, you know, selfishly speaking from the Sumo side, you know, we continue to be a strong AWS partner. We announced another set of services along with AWS at this event. We've got some new competencies for container, because that's a big aspect of what customers are doing today with microservices, and obviously we announced some new capabilities around our security intelligence capabilities, specifically for CloudTrail, because that's becoming a really important aspect of a lot of customers maturation of cloud and also operating in the cloud in this new world. >> Justin: So walk us through what customers are using CloudTrail to do, and how the Sumo Logic connection to CloudTrail actually helps them with what they're trying to do. >> Well, first and foremost, it's important to understand what Sumo does and then the context of CloudTrail and other services. You know, we started roughly a decade ago with AWS, and we built and intelligence platform on top of AWS that allows us to deal with the vast amount of unstructured data in specific use cases. So one very common use case, very applicable to the users here, is around the DevOps teams. And so, the DevOps teams are having a much more complicated and difficult time today understanding, ascertaining, where trouble, where problems reside, and how to go troubleshoot those. It's not just about a siloed monitoring tool. That's just not enough. It doesn't the analytics or intelligence. It's about understanding all the data, from CloudTrail, from EC2, and non-AWS services, so you can appropriately understand these new modern apps that are dependent on these microservices and architectures, and what's really causing the performance issue, the availability issue, and, God forbid, a security or breach issue, and that's a unique thing that Sumo provides unlike others here. >> Justin: Yeah, now I believe you've actually extended the Sumo support beyond CloudTrail and into some of the Kubernetes services that Amazon offers like AKS, and you also, I believe it's ESC FireLens support? >> Ramin: Yeah, so, and that's just a continuation of a lot of stuff we've done with respect to our analytics platform, and, you know, we introduced some things earlier this year at re:Inforce with AWS as well so, around VPC Flow Logs and the like, and this is a continuation now for CloudTrail. And really what it helps our customers and end users do is better better and more proactively be able to detect potential issues, respond to those security issues, and more importantly, automate the resolution process, and that's what's really key for our users, because they're inundated with false positives all the time whether it's on the ops side let alone the security side. So Sumo Logic is very unique back to our value prop, but providing a horizontal platform across all these different use cases. One being ops, two being cybersecurity and threat, and three being line-of-business users who are trying to understand what their own users on their digital apps are doing with their services and how to better deliver value. >> Justin: Now, automation is so important when you've got this scope and scale of cloud and the pace of innovation that's happening with all the technology that's around us here at the show, so the automation side of things I think is a little bit underappreciated this year. We're talking about transformation and we're talking about AI and ML. I think, with the automation piece, is one thing that's a little bit underestimated from this year's show. What do you think about that? >> Yeah, I mean, our philosophy all along has been, you can't automate without AI and ML, and it's proven fact that, you know, by next year the machine data growth is going to be 16 zettabytes. By 2025, it's going to be 75 zettabytes of data. Okay, while that's really impressive in terms of volume of data, the challenge is, the tsunami of data that's being generated, how to go decipher what's an important aspect and what's not an important aspect, so you first have to understand from the streaming data services, how to be able to dynamically and schema on read, be able to analyze that data, and then be able to put in context to those use cases I talked about, and then to drive automation remediation, so it's a multifaceted problem that we've been solving for nearly a decade. In a given day, we're analyzing several hundred petabytes of data, right? And we're trying to distill it down to the most important aspects for you, for your particular role and your responsibility. >> Stu: Yeah, um, we've talked a lot about transformation at this show, and one of the big challenges for customers is, they're going through that application modernization journey. I wonder if you could bring us inside some of your customers, you know, where are they having success, where are some of the bottlenecks slowing them down from moving along on this transformation journey? >> Yeah, so, it's interesting because, whether you're a cloud-native company like Sumo Logic or you're aspiring to be a cloud-native company or a cloud-first project going through migration, you have similar problems. It's now become a machine-scale problem, not a human-scale problem, back to the data growth, right? And so, some of our customers, regardless of their maturation, are really trying to understand, you know, as they embark on these digital transformations, how do they solve, what we call, the intelligence gap? And that is, because there's so much silos across the enterprise organizations today, across development, operations, IT, security, lines of business, in its context, in its completeness, it's creating more complexity for our customers. So, what Sumo tries to help solve, do, is, solve that intelligence gap in this new intelligence economy by providing an intelligence platform we call "continuous intelligence". So what do customers do? So, some of our customers use Sumo to monitor and troubleshoot their cloud workloads. So whether it's, you know, the Netflix team themselves, right, because they're born and bred in the cloud or it's Hudl, who's trying to provide, you know, analytics and intelligence for players and coaches, right, to insurance companies that are going through the migration journey to the cloud, Hartford Insurance, New York Life, to sports and media companies, Major League Baseball, with the whole cyber SOC, and what they're trying to do there on the backs of Sumo, to even trucking companies like Packard, who's trying to do driverless, autonomous cars. It doesn't matter what industry you're in, everyone is trying to do through the digital transformation or be disrupted. Everyone's trying to gain that intelligence or not just be left behind but be lapped, and so what Sumo really helps them do is provide one single intelligence platform across dev, sec, and ops, bringing these teams together to be able to collaborate much more efficiently and effectively through the true multi-tenant SaaS platform that we've optimized for 10 years on AWS. >> Justin: So we heard from Andy yesterday that one of the important ways to drive that transformational change is to actually have the top-down support for that. So you mentioned that you're able to provide that one layer across multiple different teams who traditionally haven't worked that well together, so what are you seeing with customers around, when they put in Sumo Logic, where does that transformational change come from? Are we seeing the top-down driven change? Is that were customers come from, or is it a little bit more bottom-up, were you have developers and operations and security all trying to work together, and then that bubbles up to the rest of the organization? >> Ramin: Well, it's interesting, it's both for us because a lot of times, it depends on the size of the organization, where the responsibilities reside, so naturally, in a larger enterprise where there's a lot of forces of mass because of the different siloed organizations, you have to, often times, start with the CISO, and we make sure the CISO is a transformation agent, and if they are the transformation agent, then we partner with them to really help get a handle and control on their cybersecurity and threat, and then he or she typically sponsors us into other parts of the line of business, the DevOps teams, like, for example, we've seen with Hartford Insurance, right, or that we saw with F5 Networks and many more. But then, there's a flip side of that where we actually start in, let's use another example, uh, you know, with, for example, Hearst Media, right. They actually started because they were doing a lift-and-shift to the cloud and their DevOps team, in one line of business, started with Sumo, and expanded the usage and growth. They migrated 32 applications over to AWS, and then suddenly the security teams got wind of it and then we went top-down. Great example of starting, you know, bottom-up in the case of Hearst or top-down in the case of other examples. So, the trick here is, as we look at embarking upon these journeys with our customers, we try to figure out which technology partners are they using. It's not only in the cloud provider, but it's also which traditional on-premise tools versus potentially cloud-native services and SaaS applications they're adopting. Second is, which sort of organizational models are they adopting? So, a lot of people talk about DevOps. They don't practice DevOps, and then you can understand that very quickly by asking them, "What tools are you using?" "Are you using GitHub, Jenkins, Artifactory?" "Are you using all these other tools, "and how are you actually getting visibility "into your pipeline, and is that actually speeding "the delivery of services and digital applications, "yes or no?" It's a very binary answer, and if they can't answer that, you know they're aspiring to be. So therefore, it's a consultative sale for us in that mode. If they're already embarking upon that, however, then we use a different approach, where we're trying to understand how they're challenged, what they're challenged with, and show other customers, and then it's really more of a partnership. Does that makes sense? >> Justin: Yeah, makes perfect sense to me. >> So, one of the debates we had coming into this show is, a lot of discussion at multicloud around the industry. Of course, Amazon doesn't talk specifically about multicloud all that well. If you look historically, attempts to manage lots of different environments under a single pane of glass, we always say, "pane is spelled P-I-A-N", when you try to do that. There's been great success. If you look at VMware in the data center, VMware didn't cover the entire environment, but vCenter was the center of your, you know, admin's world, and you would edge cases to manage some of the other environments here. Feels that AWS is extending their footprint with thing like Outposts and the environments, but there are lots of things that won't be on Amazon, whether it be a second cloud provider, my legacy data center pieces, or anything else there. Sounds like you touch many of the pieces, so I'm curious if you, just, weigh in on what you hear from customers, how they get their arms around the heterogeneous mess that IT traditionally is, and what we need to do as an industry to make things better. >> You know, for a long time, many companies have been bi-modal, and now they're tri-modal, right, meaning that, you know, they have their traditional and their new aspects of IT. Now they're tri-modal in the sense of, now they have a third leg of that complexity in stool, which is public cloud, and so, it's a reality regardless of Amazon or GCP or Azure, that customers want flexibility and choice, and if fact, we see that with our own data. Every year, as you guys well know, we put out an intelligence report that actually shows year-over-year, the adoption of not only various technologies, but adoption of technologies used across one cloud provider versus multicloud providers, and earlier this year in September when we put the new release of the report out, we saw that year-over-year, there was more than 2x growth in the user of Kubernetes in production, and it was almost three times growth year-over-year in use of Kubernetes across multiple cloud providers. That tells you something. That tells you that they don't want lock-in. That tells you that they also want choice. That tells you that they're trying to abstract away from the IaaS layer, infrastructure-as-a-service layer, so they have portability, so to speak, across different types of providers for the different types of workload needs as well as the data sovereignty needs they have to constantly manage because of regulatory requirements, compliance requirements and the like. And so, this is actually it benefits someone like Sumo to provide that agnostic platform to customers so they can have the choice, but also most importantly, the value, and this is something that we announced also at this event where we introduced editions to our Cloud Flex licensing model that allows you to not only address multi-tiers of data, but also allows you to have choice of where you run those workloads and have choice for different types of data for different types of use cases at different cost models. So again, delivering on that need for customers to have flexibility and choice, as well as, you know, the promise of options to move workloads from provider to provider without having to worry about the headache of compliance and audit and security requirements, 'cause that's what Sumo uniquely does versus point tools. >> Well, Ramin, I think that's a perfect point to end on. Thank you so much for joining us again. >> Thanks for having me. >> Stu: And looking forward to catching up with Sumo in the future. >> Great to be here. >> All right, we're at the midway point of three days, wall-to-wall coverage here in Las Vegas. AWS re:Invent 2019. He's Justin Warren, I'm Stu Miniman, and you're watching theCUBE. (upbeat music)