>>Collibra is a company that was founded in 2008 right before the so-called modern big data era kicked into high gear. The company was one of the first to focus its business on data governance. Now, historically, data governance and data quality initiatives, they were back office functions and they were largely confined to regulatory regulated industries that had to comply with public policy mandates. But as the cloud went mainstream, the tech giants showed us how valuable data could become and the value proposition for data quality and trust. It evolved from primarily a compliance driven issue to becoming a lynchpin of competitive advantage. But data in the decade of the 2010s was largely about getting the technology to work. You had these highly centralized technical teams that were formed and they had hyper specialized skills to develop data architectures and processes to serve the myriad data needs of organizations. >>And it resulted in a lot of frustration with data initiatives for most organizations that didn't have the resources of the cloud guys and the social media giants to really attack their data problems and turn data into gold. This is why today for example, this quite a bit of momentum to rethinking monolithic data architectures. You see, you hear about initiatives like data mesh and the idea of data as a product. They're gaining traction as a way to better serve the the data needs of decentralized business Uni users, you hear a lot about data democratization. So these decentralization efforts around data, they're great, but they create a new set of problems. Specifically, how do you deliver like a self-service infrastructure to business users and domain experts? Now the cloud is definitely helping with that, but also how do you automate governance? This becomes especially tricky as protecting data privacy has become more and more important. >>In other words, while it's enticing to experiment and run fast and loose with data initiatives kinda like the Wild West, to find new veins of gold, it has to be done responsibly. As such, the idea of data governance has had to evolve to become more automated. And intelligence governance and data lineage is still fundamental to ensuring trust as data. It moves like water through an organization. No one is gonna use data that isn't trusted. Metadata has become increasingly important for data discovery and data classification. As data flows through an organization, the continuously ability to check for data flaws and automating that data quality, they become a functional requirement of any modern data management platform. And finally, data privacy has become a critical adjacency to cyber security. So you can see how data governance has evolved into a much richer set of capabilities than it was 10 or 15 years ago. >>Hello and welcome to the Cube's coverage of Data Citizens made possible by Calibra, a leader in so-called Data intelligence and the host of Data Citizens 2022, which is taking place in San Diego. My name is Dave Ante and I'm one of the hosts of our program, which is running in parallel to data citizens. Now at the Cube we like to say we extract the signal from the noise, and over the, the next couple of days, we're gonna feature some of the themes from the keynote speakers at Data Citizens and we'll hear from several of the executives. Felix Von Dala, who is the co-founder and CEO of Collibra, will join us along with one of the other founders of Collibra, Stan Christians, who's gonna join my colleague Lisa Martin. I'm gonna also sit down with Laura Sellers, she's the Chief Product Officer at Collibra. We'll talk about some of the, the announcements and innovations they're making at the event, and then we'll dig in further to data quality with Kirk Hasselbeck. >>He's the vice president of Data quality at Collibra. He's an amazingly smart dude who founded Owl dq, a company that he sold to Col to Collibra last year. Now many companies, they didn't make it through the Hado era, you know, they missed the industry waves and they became Driftwood. Collibra, on the other hand, has evolved its business. They've leveraged the cloud, expanded its product portfolio, and leaned in heavily to some major partnerships with cloud providers, as well as receiving a strategic investment from Snowflake earlier this year. So it's a really interesting story that we're thrilled to be sharing with you. Thanks for watching and I hope you enjoy the program. >>Last year, the Cube Covered Data Citizens Collibra's customer event. And the premise that we put forth prior to that event was that despite all the innovation that's gone on over the last decade or more with data, you know, starting with the Hado movement, we had data lakes, we'd spark the ascendancy of programming languages like Python, the introduction of frameworks like TensorFlow, the rise of ai, low code, no code, et cetera. Businesses still find it's too difficult to get more value from their data initiatives. And we said at the time, you know, maybe it's time to rethink data innovation. While a lot of the effort has been focused on, you know, more efficiently storing and processing data, perhaps more energy needs to go into thinking about the people and the process side of the equation, meaning making it easier for domain experts to both gain insights for data, trust the data, and begin to use that data in new ways, fueling data, products, monetization and insights data citizens 2022 is back and we're pleased to have Felix Van Dema, who is the founder and CEO of Collibra. He's on the cube or excited to have you, Felix. Good to see you again. >>Likewise Dave. Thanks for having me again. >>You bet. All right, we're gonna get the update from Felix on the current data landscape, how he sees it, why data intelligence is more important now than ever and get current on what Collibra has been up to over the past year and what's changed since Data Citizens 2021. And we may even touch on some of the product news. So Felix, we're living in a very different world today with businesses and consumers. They're struggling with things like supply chains, uncertain economic trends, and we're not just snapping back to the 2010s. That's clear, and that's really true as well in the world of data. So what's different in your mind, in the data landscape of the 2020s from the previous decade, and what challenges does that bring for your customers? >>Yeah, absolutely. And, and I think you said it well, Dave, and and the intro that that rising complexity and fragmentation in the broader data landscape, that hasn't gotten any better over the last couple of years. When when we talk to our customers, that level of fragmentation, the complexity, how do we find data that we can trust, that we know we can use has only gotten kinda more, more difficult. So that trend that's continuing, I think what is changing is that trend has become much more acute. Well, the other thing we've seen over the last couple of years is that the level of scrutiny that organizations are under respect to data, as data becomes more mission critical, as data becomes more impactful than important, the level of scrutiny with respect to privacy, security, regulatory compliance, as only increasing as well, which again, is really difficult in this environment of continuous innovation, continuous change, continuous growing complexity and fragmentation. >>So it's become much more acute. And, and to your earlier point, we do live in a different world and and the the past couple of years we could probably just kind of brute for it, right? We could focus on, on the top line. There was enough kind of investments to be, to be had. I think nowadays organizations are focused or are, are, are, are, are, are in a very different environment where there's much more focus on cost control, productivity, efficiency, How do we truly get value from that data? So again, I think it just another incentive for organization to now truly look at data and to scale it data, not just from a a technology and infrastructure perspective, but how do you actually scale data from an organizational perspective, right? You said at the the people and process, how do we do that at scale? And that's only, only only becoming much more important. And we do believe that the, the economic environment that we find ourselves in today is gonna be catalyst for organizations to really dig out more seriously if, if, if, if you will, than they maybe have in the have in the best. >>You know, I don't know when you guys founded Collibra, if, if you had a sense as to how complicated it was gonna get, but you've been on a mission to really address these problems from the beginning. How would you describe your, your, your mission and what are you doing to address these challenges? >>Yeah, absolutely. We, we started Colli in 2008. So in some sense and the, the last kind of financial crisis, and that was really the, the start of Colli where we found product market fit, working with large finance institutions to help them cope with the increasing compliance requirements that they were faced with because of the, of the financial crisis and kind of here we are again in a very different environment, of course 15 years, almost 15 years later. But data only becoming more important. But our mission to deliver trusted data for every user, every use case and across every source, frankly, has only become more important. So what has been an incredible journey over the last 14, 15 years, I think we're still relatively early in our mission to again, be able to provide everyone, and that's why we call it data citizens. We truly believe that everyone in the organization should be able to use trusted data in an easy, easy matter. That mission is is only becoming more important, more relevant. We definitely have a lot more work ahead of us because we are still relatively early in that, in that journey. >>Well, that's interesting because, you know, in my observation it takes seven to 10 years to actually build a company and then the fact that you're still in the early days is kind of interesting. I mean, you, Collibra's had a good 12 months or so since we last spoke at Data Citizens. Give us the latest update on your business. What do people need to know about your, your current momentum? >>Yeah, absolutely. Again, there's, there's a lot of tail organizations that are only maturing the data practices and we've seen it kind of transform or, or, or influence a lot of our business growth that we've seen, broader adoption of the platform. We work at some of the largest organizations in the world where it's Adobe, Heineken, Bank of America, and many more. We have now over 600 enterprise customers, all industry leaders and every single vertical. So it's, it's really exciting to see that and continue to partner with those organizations. On the partnership side, again, a lot of momentum in the org in, in the, in the markets with some of the cloud partners like Google, Amazon, Snowflake, data bricks and, and others, right? As those kind of new modern data infrastructures, modern data architectures that are definitely all moving to the cloud, a great opportunity for us, our partners and of course our customers to help them kind of transition to the cloud even faster. >>And so we see a lot of excitement and momentum there within an acquisition about 18 months ago around data quality, data observability, which we believe is an enormous opportunity. Of course, data quality isn't new, but I think there's a lot of reasons why we're so excited about quality and observability now. One is around leveraging ai, machine learning, again to drive more automation. And the second is that those data pipelines that are now being created in the cloud, in these modern data architecture arch architectures, they've become mission critical. They've become real time. And so monitoring, observing those data pipelines continuously has become absolutely critical so that they're really excited about about that as well. And on the organizational side, I'm sure you've heard a term around kind of data mesh, something that's gaining a lot of momentum, rightfully so. It's really the type of governance that we always believe. Then federated focused on domains, giving a lot of ownership to different teams. I think that's the way to scale data organizations. And so that aligns really well with our vision and, and from a product perspective, we've seen a lot of momentum with our customers there as well. >>Yeah, you know, a couple things there. I mean, the acquisition of i l dq, you know, Kirk Hasselbeck and, and their team, it's interesting, you know, the whole data quality used to be this back office function and, and really confined to highly regulated industries. It's come to the front office, it's top of mind for chief data officers, data mesh. You mentioned you guys are a connective tissue for all these different nodes on the data mesh. That's key. And of course we see you at all the shows. You're, you're a critical part of many ecosystems and you're developing your own ecosystem. So let's chat a little bit about the, the products. We're gonna go deeper in into products later on at, at Data Citizens 22, but we know you're debuting some, some new innovations, you know, whether it's, you know, the, the the under the covers in security, sort of making data more accessible for people just dealing with workflows and processes as you talked about earlier. Tell us a little bit about what you're introducing. >>Yeah, absolutely. We're super excited, a ton of innovation. And if we think about the big theme and like, like I said, we're still relatively early in this, in this journey towards kind of that mission of data intelligence that really bolts and compelling mission, either customers are still start, are just starting on that, on that journey. We wanna make it as easy as possible for the, for our organization to actually get started because we know that's important that they do. And for our organization and customers that have been with us for some time, there's still a tremendous amount of opportunity to kind of expand the platform further. And again, to make it easier for really to, to accomplish that mission and vision around that data citizen that everyone has access to trustworthy data in a very easy, easy way. So that's really the theme of a lot of the innovation that we're driving. >>A lot of kind of ease of adoption, ease of use, but also then how do we make sure that lio becomes this kind of mission critical enterprise platform from a security performance architecture scale supportability that we're truly able to deliver that kind of an enterprise mission critical platform. And so that's the big theme from an innovation perspective, From a product perspective, a lot of new innovation that we're really excited about. A couple of highlights. One is around data marketplace. Again, a lot of our customers have plans in that direction, how to make it easy. How do we make, how do we make available to true kind of shopping experience that anybody in your organization can, in a very easy search first way, find the right data product, find the right dataset, that data can then consume usage analytics. How do you, how do we help organizations drive adoption, tell them where they're working really well and where they have opportunities homepages again to, to make things easy for, for people, for anyone in your organization to kind of get started with ppia, you mentioned workflow designer, again, we have a very powerful enterprise platform. >>One of our key differentiators is the ability to really drive a lot of automation through workflows. And now we provided a new low code, no code kind of workflow designer experience. So, so really customers can take it to the next level. There's a lot more new product around K Bear Protect, which in partnership with Snowflake, which has been a strategic investor in kib, focused on how do we make access governance easier? How do we, how do we, how are we able to make sure that as you move to the cloud, things like access management, masking around sensitive data, PII data is managed as much more effective, effective rate, really excited about that product. There's more around data quality. Again, how do we, how do we get that deployed as easily and quickly and widely as we can? Moving that to the cloud has been a big part of our strategy. >>So we launch more data quality cloud product as well as making use of those, those native compute capabilities in platforms like Snowflake, Data, Bricks, Google, Amazon, and others. And so we are bettering a capability, a capability that we call push down. So actually pushing down the computer and data quality, the monitoring into the underlying platform, which again, from a scale performance and ease of use perspective is gonna make a massive difference. And then more broadly, we, we talked a little bit about the ecosystem. Again, integrations, we talk about being able to connect to every source. Integrations are absolutely critical and we're really excited to deliver new integrations with Snowflake, Azure and Google Cloud storage as well. So there's a lot coming out. The, the team has been work at work really hard and we are really, really excited about what we are coming, what we're bringing to markets. >>Yeah, a lot going on there. I wonder if you could give us your, your closing thoughts. I mean, you, you talked about, you know, the marketplace, you know, you think about data mesh, you think of data as product, one of the key principles you think about monetization. This is really different than what we've been used to in data, which is just getting the technology to work has been been so hard. So how do you see sort of the future and, you know, give us the, your closing thoughts please? >>Yeah, absolutely. And I, and I think we we're really at this pivotal moment, and I think you said it well. We, we all know the constraint and the challenges with data, how to actually do data at scale. And while we've seen a ton of innovation on the infrastructure side, we fundamentally believe that just getting a faster database is important, but it's not gonna fully solve the challenges and truly kind of deliver on the opportunity. And that's why now is really the time to deliver this data intelligence vision, this data intelligence platform. We are still early, making it as easy as we can. It's kind of, of our, it's our mission. And so I'm really, really excited to see what we, what we are gonna, how the marks gonna evolve over the next, next few quarters and years. I think the trend is clearly there when we talk about data mesh, this kind of federated approach folks on data products is just another signal that we believe that a lot of our organization are now at the time. >>The understanding need to go beyond just the technology. I really, really think about how do we actually scale data as a business function, just like we've done with it, with, with hr, with, with sales and marketing, with finance. That's how we need to think about data. I think now is the time given the economic environment that we are in much more focus on control, much more focused on productivity efficiency and now's the time. We need to look beyond just the technology and infrastructure to think of how to scale data, how to manage data at scale. >>Yeah, it's a new era. The next 10 years of data won't be like the last, as I always say. Felix, thanks so much and good luck in, in San Diego. I know you're gonna crush it out there. >>Thank you Dave. >>Yeah, it's a great spot for an in-person event and, and of course the content post event is gonna be available@collibra.com and you can of course catch the cube coverage@thecube.net and all the news@siliconangle.com. This is Dave Valante for the cube, your leader in enterprise and emerging tech coverage. >>Hi, I'm Jay from Collibra's Data Office. Today I want to talk to you about Collibra's data intelligence cloud. We often say Collibra is a single system of engagement for all of your data. Now, when I say data, I mean data in the broadest sense of the word, including reference and metadata. Think of metrics, reports, APIs, systems, policies, and even business processes that produce or consume data. Now, the beauty of this platform is that it ensures all of your users have an easy way to find, understand, trust, and access data. But how do you get started? Well, here are seven steps to help you get going. One, start with the data. What's data intelligence? Without data leverage the Collibra data catalog to automatically profile and classify your enterprise data wherever that data lives, databases, data lakes or data warehouses, whether on the cloud or on premise. >>Two, you'll then wanna organize the data and you'll do that with data communities. This can be by department, find a business or functional team, however your organization organizes work and accountability. And for that you'll establish community owners, communities, make it easy for people to navigate through the platform, find the data and will help create a sense of belonging for users. An important and related side note here, we find it's typical in many organizations that data is thought of is just an asset and IT and data offices are viewed as the owners of it and who are really the central teams performing analytics as a service provider to the enterprise. We believe data is more than an asset, it's a true product that can be converted to value. And that also means establishing business ownership of data where that strategy and ROI come together with subject matter expertise. >>Okay, three. Next, back to those communities there, the data owners should explain and define their data, not just the tables and columns, but also the related business terms, metrics and KPIs. These objects we call these assets are typically organized into business glossaries and data dictionaries. I definitely recommend starting with the topics that are most important to the business. Four, those steps that enable you and your users to have some fun with it. Linking everything together builds your knowledge graph and also known as a metadata graph by linking or relating these assets together. For example, a data set to a KPI to a report now enables your users to see what we call the lineage diagram that visualizes where the data in your dashboards actually came from and what the data means and who's responsible for it. Speaking of which, here's five. Leverage the calibra trusted business reporting solution on the marketplace, which comes with workflows for those owners to certify their reports, KPIs, and data sets. >>This helps them force their trust in their data. Six, easy to navigate dashboards or landing pages right in your platform for your company's business processes are the most effective way for everyone to better understand and take action on data. Here's a pro tip, use the dashboard design kit on the marketplace to help you build compelling dashboards. Finally, seven, promote the value of this to your users and be sure to schedule enablement office hours and new employee onboarding sessions to get folks excited about what you've built and implemented. Better yet, invite all of those community and data owners to these sessions so that they can show off the value that they've created. Those are my seven tips to get going with Collibra. I hope these have been useful. For more information, be sure to visit collibra.com. >>Welcome to the Cube's coverage of Data Citizens 2022 Collibra's customer event. My name is Dave Valante. With us is Kirk Hasselbeck, who's the vice president of Data Quality of Collibra Kirk, good to see you. Welcome. >>Thanks for having me, Dave. Excited to be here. >>You bet. Okay, we're gonna discuss data quality observability. It's a hot trend right now. You founded a data quality company, OWL dq, and it was acquired by Collibra last year. Congratulations. And now you lead data quality at Collibra. So we're hearing a lot about data quality right now. Why is it such a priority? Take us through your thoughts on that. >>Yeah, absolutely. It's, it's definitely exciting times for data quality, which you're right, has been around for a long time. So why now and why is it so much more exciting than it used to be? I think it's a bit stale, but we all know that companies use more data than ever before and the variety has changed and the volume has grown. And, and while I think that remains true, there are a couple other hidden factors at play that everyone's so interested in as, as to why this is becoming so important now. And, and I guess you could kind of break this down simply and think about if Dave, you and I were gonna build, you know, a new healthcare application and monitor the heartbeat of individuals, imagine if we get that wrong, you know, what the ramifications could be, what, what those incidents would look like, or maybe better yet, we try to build a, a new trading algorithm with a crossover strategy where the 50 day crosses the, the 10 day average. >>And imagine if the data underlying the inputs to that is incorrect. We will probably have major financial ramifications in that sense. So, you know, it kind of starts there where everybody's realizing that we're all data companies and if we are using bad data, we're likely making incorrect business decisions. But I think there's kind of two other things at play. You know, I, I bought a car not too long ago and my dad called and said, How many cylinders does it have? And I realized in that moment, you know, I might have failed him because, cause I didn't know. And, and I used to ask those types of questions about any lock brakes and cylinders and, and you know, if it's manual or, or automatic and, and I realized I now just buy a car that I hope works. And it's so complicated with all the computer chips, I, I really don't know that much about it. >>And, and that's what's happening with data. We're just loading so much of it. And it's so complex that the way companies consume them in the IT function is that they bring in a lot of data and then they syndicate it out to the business. And it turns out that the, the individuals loading and consuming all of this data for the company actually may not know that much about the data itself, and that's not even their job anymore. So we'll talk more about that in a minute, but that's really what's setting the foreground for this observability play and why everybody's so interested. It, it's because we're becoming less close to the intricacies of the data and we just expect it to always be there and be correct. >>You know, the other thing too about data quality, and for years we did the MIT CDO IQ event, we didn't do it last year, Covid messed everything up. But the observation I would make there thoughts is, is it data quality? Used to be information quality used to be this back office function, and then it became sort of front office with financial services and government and healthcare, these highly regulated industries. And then the whole chief data officer thing happened and people were realizing, well, they sort of flipped the bit from sort of a data as a, a risk to data as a, as an asset. And now as we say, we're gonna talk about observability. And so it's really become front and center just the whole quality issue because data's so fundamental, hasn't it? >>Yeah, absolutely. I mean, let's imagine we pull up our phones right now and I go to my, my favorite stock ticker app and I check out the NASDAQ market cap. I really have no idea if that's the correct number. I know it's a number, it looks large, it's in a numeric field. And, and that's kind of what's going on. There's, there's so many numbers and they're coming from all of these different sources and data providers and they're getting consumed and passed along. But there isn't really a way to tactically put controls on every number and metric across every field we plan to monitor, but with the scale that we've achieved in early days, even before calibra. And what's been so exciting is we have these types of observation techniques, these data monitors that can actually track past performance of every field at scale. And why that's so interesting and why I think the CDO is, is listening right intently nowadays to this topic is, so maybe we could surface all of these problems with the right solution of data observability and with the right scale and then just be alerted on breaking trends. So we're sort of shifting away from this world of must write a condition and then when that condition breaks, that was always known as a break record. But what about breaking trends and root cause analysis? And is it possible to do that, you know, with less human intervention? And so I think most people are seeing now that it's going to have to be a software tool and a computer system. It's, it's not ever going to be based on one or two domain experts anymore. >>So, So how does data observability relate to data quality? Are they sort of two sides of the same coin? Are they, are they cousins? What's your perspective on that? >>Yeah, it's, it's super interesting. It's an emerging market. So the language is changing a lot of the topic and areas changing the way that I like to say it or break it down because the, the lingo is constantly moving is, you know, as a target on this space is really breaking records versus breaking trends. And I could write a condition when this thing happens, it's wrong and when it doesn't it's correct. Or I could look for a trend and I'll give you a good example. You know, everybody's talking about fresh data and stale data and, and why would that matter? Well, if your data never arrived or only part of it arrived or didn't arrive on time, it's likely stale and there will not be a condition that you could write that would show you all the good in the bads. That was kind of your, your traditional approach of data quality break records. But your modern day approach is you lost a significant portion of your data, or it did not arrive on time to make that decision accurately on time. And that's a hidden concern. Some people call this freshness, we call it stale data, but it all points to the same idea of the thing that you're observing may not be a data quality condition anymore. It may be a breakdown in the data pipeline. And with thousands of data pipelines in play for every company out there there, there's more than a couple of these happening every day. >>So what's the Collibra angle on all this stuff made the acquisition, you got data quality observability coming together, you guys have a lot of expertise in, in this area, but you hear providence of data, you just talked about, you know, stale data, you know, the, the whole trend toward real time. How is Calibra approaching the problem and what's unique about your approach? >>Well, I think where we're fortunate is with our background, myself and team, we sort of lived this problem for a long time, you know, in, in the Wall Street days about a decade ago. And we saw it from many different angles. And what we came up with before it was called data observability or reliability was basically the, the underpinnings of that. So we're a little bit ahead of the curve there when most people evaluate our solution, it's more advanced than some of the observation techniques that that currently exist. But we've also always covered data quality and we believe that people want to know more, they need more insights, and they want to see break records and breaking trends together so they can correlate the root cause. And we hear that all the time. I have so many things going wrong, just show me the big picture, help me find the thing that if I were to fix it today would make the most impact. So we're really focused on root cause analysis, business impact, connecting it with lineage and catalog metadata. And as that grows, you can actually achieve total data governance at this point with the acquisition of what was a Lineage company years ago, and then my company Ldq now Collibra, Data quality Collibra may be the best positioned for total data governance and intelligence in the space. >>Well, you mentioned financial services a couple of times and some examples, remember the flash crash in 2010. Nobody had any idea what that was, you know, they just said, Oh, it's a glitch, you know, so they didn't understand the root cause of it. So this is a really interesting topic to me. So we know at Data Citizens 22 that you're announcing, you gotta announce new products, right? You're yearly event what's, what's new. Give us a sense as to what products are coming out, but specifically around data quality and observability. >>Absolutely. There's this, you know, there's always a next thing on the forefront. And the one right now is these hyperscalers in the cloud. So you have databases like Snowflake and Big Query and Data Bricks is Delta Lake and SQL Pushdown. And ultimately what that means is a lot of people are storing in loading data even faster in a SaaS like model. And we've started to hook in to these databases. And while we've always worked with the the same databases in the past, they're supported today we're doing something called Native Database pushdown, where the entire compute and data activity happens in the database. And why that is so interesting and powerful now is everyone's concerned with something called Egress. Did your, my data that I've spent all this time and money with my security team securing ever leave my hands, did it ever leave my secure VPC as they call it? >>And with these native integrations that we're building and about to unveil, here's kind of a sneak peek for, for next week at Data Citizens. We're now doing all compute and data operations in databases like Snowflake. And what that means is with no install and no configuration, you could log into the Collibra data quality app and have all of your data quality running inside the database that you've probably already picked as your your go forward team selection secured database of choice. So we're really excited about that. And I think if you look at the whole landscape of network cost, egress, cost, data storage and compute, what people are realizing is it's extremely efficient to do it in the way that we're about to release here next week. >>So this is interesting because what you just described, you know, you mentioned Snowflake, you mentioned Google, Oh actually you mentioned yeah, data bricks. You know, Snowflake has the data cloud. If you put everything in the data cloud, okay, you're cool, but then Google's got the open data cloud. If you heard, you know, Google next and now data bricks doesn't call it the data cloud, but they have like the open source data cloud. So you have all these different approaches and there's really no way up until now I'm, I'm hearing to, to really understand the relationships between all those and have confidence across, you know, it's like Jak Dani, you should just be a note on the mesh. And I don't care if it's a data warehouse or a data lake or where it comes from, but it's a point on that mesh and I need tooling to be able to have confidence that my data is governed and has the proper lineage, providence. And, and, and that's what you're bringing to the table, Is that right? Did I get that right? >>Yeah, that's right. And it's, for us, it's, it's not that we haven't been working with those great cloud databases, but it's the fact that we can send them the instructions now, we can send them the, the operating ability to crunch all of the calculations, the governance, the quality, and get the answers. And what that's doing, it's basically zero network costs, zero egress cost, zero latency of time. And so when you were to log into Big Query tomorrow using our tool or like, or say Snowflake for example, you have instant data quality metrics, instant profiling, instant lineage and access privacy controls, things of that nature that just become less onerous. What we're seeing is there's so much technology out there, just like all of the major brands that you mentioned, but how do we make it easier? The future is about less clicks, faster time to value, faster scale, and eventually lower cost. And, and we think that this positions us to be the leader there. >>I love this example because, you know, Barry talks about, wow, the cloud guys are gonna own the world and, and of course now we're seeing that the ecosystem is finding so much white space to add value, connect across cloud. Sometimes we call it super cloud and so, or inter clouding. All right, Kirk, give us your, your final thoughts and on on the trends that we've talked about and Data Citizens 22. >>Absolutely. Well, I think, you know, one big trend is discovery and classification. Seeing that across the board, people used to know it was a zip code and nowadays with the amount of data that's out there, they wanna know where everything is, where their sensitive data is. If it's redundant, tell me everything inside of three to five seconds. And with that comes, they want to know in all of these hyperscale databases how fast they can get controls and insights out of their tools. So I think we're gonna see more one click solutions, more SAS based solutions and solutions that hopefully prove faster time to value on, on all of these modern cloud platforms. >>Excellent. All right, Kurt Hasselbeck, thanks so much for coming on the Cube and previewing Data Citizens 22. Appreciate it. >>Thanks for having me, Dave. >>You're welcome. Right, and thank you for watching. Keep it right there for more coverage from the Cube. Welcome to the Cube's virtual Coverage of Data Citizens 2022. My name is Dave Valante and I'm here with Laura Sellers, who's the Chief Product Officer at Collibra, the host of Data Citizens. Laura, welcome. Good to see you. >>Thank you. Nice to be here. >>Yeah, your keynote at Data Citizens this year focused on, you know, your mission to drive ease of use and scale. Now when I think about historically fast access to the right data at the right time in a form that's really easily consumable, it's been kind of challenging, especially for business users. Can can you explain to our audience why this matters so much and what's actually different today in the data ecosystem to make this a reality? >>Yeah, definitely. So I think what we really need and what I hear from customers every single day is that we need a new approach to data management and our product teams. What inspired me to come to Calibra a little bit a over a year ago was really the fact that they're very focused on bringing trusted data to more users across more sources for more use cases. And so as we look at what we're announcing with these innovations of ease of use and scale, it's really about making teams more productive in getting started with and the ability to manage data across the entire organization. So we've been very focused on richer experiences, a broader ecosystem of partners, as well as a platform that delivers performance, scale and security that our users and teams need and demand. So as we look at, Oh, go ahead. >>I was gonna say, you know, when I look back at like the last 10 years, it was all about getting the technology to work and it was just so complicated. But, but please carry on. I'd love to hear more about this. >>Yeah, I, I really, you know, Collibra is a system of engagement for data and we really are working on bringing that entire system of engagement to life for everyone to leverage here and now. So what we're announcing from our ease of use side of the world is first our data marketplace. This is the ability for all users to discover and access data quickly and easily shop for it, if you will. The next thing that we're also introducing is the new homepage. It's really about the ability to drive adoption and have users find data more quickly. And then the two more areas of the ease of use side of the world is our world of usage analytics. And one of the big pushes and passions we have at Collibra is to help with this data driven culture that all companies are trying to create. And also helping with data literacy, with something like usage analytics, it's really about driving adoption of the CLE platform, understanding what's working, who's accessing it, what's not. And then finally we're also introducing what's called workflow designer. And we love our workflows at Libra, it's a big differentiator to be able to automate business processes. The designer is really about a way for more people to be able to create those workflows, collaborate on those workflow flows, as well as people to be able to easily interact with them. So a lot of exciting things when it comes to ease of use to make it easier for all users to find data. >>Y yes, there's definitely a lot to unpack there. I I, you know, you mentioned this idea of, of of, of shopping for the data. That's interesting to me. Why this analogy, metaphor or analogy, I always get those confused. I let's go with analogy. Why is it so important to data consumers? >>I think when you look at the world of data, and I talked about this system of engagement, it's really about making it more accessible to the masses. And what users are used to is a shopping experience like your Amazon, if you will. And so having a consumer grade experience where users can quickly go in and find the data, trust that data, understand where the data's coming from, and then be able to quickly access it, is the idea of being able to shop for it, just making it as simple as possible and really speeding the time to value for any of the business analysts, data analysts out there. >>Yeah, I think when you, you, you see a lot of discussion about rethinking data architectures, putting data in the hands of the users and business people, decentralized data and of course that's awesome. I love that. But of course then you have to have self-service infrastructure and you have to have governance. And those are really challenging. And I think so many organizations, they're facing adoption challenges, you know, when it comes to enabling teams generally, especially domain experts to adopt new data technologies, you know, like the, the tech comes fast and furious. You got all these open source projects and get really confusing. Of course it risks security, governance and all that good stuff. You got all this jargon. So where do you see, you know, the friction in adopting new data technologies? What's your point of view and how can organizations overcome these challenges? >>You're, you're dead on. There's so much technology and there's so much to stay on top of, which is part of the friction, right? It's just being able to stay ahead of, of and understand all the technologies that are coming. You also look at as there's so many more sources of data and people are migrating data to the cloud and they're migrating to new sources. Where the friction comes is really that ability to understand where the data came from, where it's moving to, and then also to be able to put the access controls on top of it. So people are only getting access to the data that they should be getting access to. So one of the other things we're announcing with, with all of the innovations that are coming is what we're doing around performance and scale. So with all of the data movement, with all of the data that's out there, the first thing we're launching in the world of performance and scale is our world of data quality. >>It's something that Collibra has been working on for the past year and a half, but we're launching the ability to have data quality in the cloud. So it's currently an on-premise offering, but we'll now be able to carry that over into the cloud for us to manage that way. We're also introducing the ability to push down data quality into Snowflake. So this is, again, one of those challenges is making sure that that data that you have is d is is high quality as you move forward. And so really another, we're just reducing friction. You already have Snowflake stood up. It's not another machine for you to manage, it's just push down capabilities into Snowflake to be able to track that quality. Another thing that we're launching with that is what we call Collibra Protect. And this is that ability for users to be able to ingest metadata, understand where the PII data is, and then set policies up on top of it. So very quickly be able to set policies and have them enforced at the data level. So anybody in the organization is only getting access to the data they should have access to. >>Here's Topica data quality is interesting. It's something that I've followed for a number of years. It used to be a back office function, you know, and really confined only to highly regulated industries like financial services and healthcare and government. You know, you look back over a decade ago, you didn't have this worry about personal information, g gdpr, and, you know, California Consumer Privacy Act all becomes, becomes so much important. The cloud is really changed things in terms of performance and scale and of course partnering for, for, with Snowflake it's all about sharing data and monetization, anything but a back office function. So it was kind of smart that you guys were early on and of course attracting them and as a, as an investor as well was very strong validation. What can you tell us about the nature of the relationship with Snowflake and specifically inter interested in sort of joint engineering or, and product innovation efforts, you know, beyond the standard go to market stuff? >>Definitely. So you mentioned there were a strategic investor in Calibra about a year ago. A little less than that I guess. We've been working with them though for over a year really tightly with their product and engineering teams to make sure that Collibra is adding real value. Our unified platform is touching pieces of our unified platform or touching all pieces of Snowflake. And when I say that, what I mean is we're first, you know, able to ingest data with Snowflake, which, which has always existed. We're able to profile and classify that data we're announcing with Calibra Protect this week that you're now able to create those policies on top of Snowflake and have them enforce. So again, people can get more value out of their snowflake more quickly as far as time to value with, with our policies for all business users to be able to create. >>We're also announcing Snowflake Lineage 2.0. So this is the ability to take stored procedures in Snowflake and understand the lineage of where did the data come from, how was it transformed with within Snowflake as well as the data quality. Pushdown, as I mentioned, data quality, you brought it up. It is a new, it is a, a big industry push and you know, one of the things I think Gartner mentioned is people are losing up to $15 million without having great data quality. So this push down capability for Snowflake really is again, a big ease of use push for us at Collibra of that ability to, to push it into snowflake, take advantage of the data, the data source, and the engine that already lives there and get the right and make sure you have the right quality. >>I mean, the nice thing about Snowflake, if you play in the Snowflake sandbox, you, you, you, you can get sort of a, you know, high degree of confidence that the data sharing can be done in a safe way. Bringing, you know, Collibra into the, into the story allows me to have that data quality and, and that governance that I, that I need. You know, we've said many times on the cube that one of the notable differences in cloud this decade versus last decade, I mean ob there are obvious differences just in terms of scale and scope, but it's shaping up to be about the strength of the ecosystems. That's really a hallmark of these big cloud players. I mean they're, it's a key factor for innovating, accelerating product delivery, filling gaps in, in the hyperscale offerings cuz you got more stack, you know, mature stack capabilities and you know, it creates this flywheel momentum as we often say. But, so my question is, how do you work with the hyperscalers? Like whether it's AWS or Google, whomever, and what do you see as your role and what's the Collibra sweet spot? >>Yeah, definitely. So, you know, one of the things I mentioned early on is the broader ecosystem of partners is what it's all about. And so we have that strong partnership with Snowflake. We also are doing more with Google around, you know, GCP and kbra protect there, but also tighter data plex integration. So similar to what you've seen with our strategic moves around Snowflake and, and really covering the broad ecosystem of what Collibra can do on top of that data source. We're extending that to the world of Google as well and the world of data plex. We also have great partners in SI's Infosys is somebody we spoke with at the conference who's done a lot of great work with Levi's as they're really important to help people with their whole data strategy and driving that data driven culture and, and Collibra being the core of it. >>Hi Laura, we're gonna, we're gonna end it there, but I wonder if you could kind of put a bow on, you know, this year, the event your, your perspectives. So just give us your closing thoughts. >>Yeah, definitely. So I, I wanna say this is one of the biggest releases Collibra's ever had. Definitely the biggest one since I've been with the company a little over a year. We have all these great new product innovations coming to really drive the ease of use to make data more valuable for users everywhere and, and companies everywhere. And so it's all about everybody being able to easily find, understand, and trust and get access to that data going forward. >>Well congratulations on all the pro progress. It was great to have you on the cube first time I believe, and really appreciate you, you taking the time with us. >>Yes, thank you for your time. >>You're very welcome. Okay, you're watching the coverage of Data Citizens 2022 on the cube, your leader in enterprise and emerging tech coverage. >>So data modernization oftentimes means moving some of your storage and computer to the cloud where you get the benefit of scale and security and so on. But ultimately it doesn't take away the silos that you have. We have more locations, more tools and more processes with which we try to get value from this data. To do that at scale in an organization, people involved in this process, they have to understand each other. So you need to unite those people across those tools, processes, and systems with a shared language. When I say customer, do you understand the same thing as you hearing customer? Are we counting them in the same way so that shared language unites us and that gives the opportunity for the organization as a whole to get the maximum value out of their data assets and then they can democratize data so everyone can properly use that shared language to find, understand, and trust the data asset that's available. >>And that's where Collibra comes in. We provide a centralized system of engagement that works across all of those locations and combines all of those different user types across the whole business. At Collibra, we say United by data and that also means that we're united by data with our customers. So here is some data about some of our customers. There was the case of an online do it yourself platform who grew their revenue almost three times from a marketing campaign that provided the right product in the right hands of the right people. In other case that comes to mind is from a financial services organization who saved over 800 K every year because they were able to reuse the same data in different kinds of reports and before there was spread out over different tools and processes and silos, and now the platform brought them together so they realized, oh, we're actually using the same data, let's find a way to make this more efficient. And the last example that comes to mind is that of a large home loan, home mortgage, mortgage loan provider where they have a very complex landscape, a very complex architecture legacy in the cloud, et cetera. And they're using our software, they're using our platform to unite all the people and those processes and tools to get a common view of data to manage their compliance at scale. >>Hey everyone, I'm Lisa Martin covering Data Citizens 22, brought to you by Collibra. This next conversation is gonna focus on the importance of data culture. One of our Cube alumni is back, Stan Christians is Collibra's co-founder and it's Chief Data citizens. Stan, it's great to have you back on the cube. >>Hey Lisa, nice to be. >>So we're gonna be talking about the importance of data culture, data intelligence, maturity, all those great things. When we think about the data revolution that every business is going through, you know, it's so much more than technology innovation. It also really re requires cultural transformation, community transformation. Those are challenging for customers to undertake. Talk to us about what you mean by data citizenship and the role that creating a data culture plays in that journey. >>Right. So as you know, our event is called Data Citizens because we believe that in the end, a data citizen is anyone who uses data to do their job. And we believe that today's organizations, you have a lot of people, most of the employees in an organization are somehow gonna to be a data citizen, right? So you need to make sure that these people are aware of it. You need that. People have skills and competencies to do with data what necessary and that's on, all right? So what does it mean to have a good data culture? It means that if you're building a beautiful dashboard to try and convince your boss, we need to make this decision that your boss is also open to and able to interpret, you know, the data presented in dashboard to actually make that decision and take that action. Right? >>And once you have that why to the organization, that's when you have a good data culture. Now that's continuous effort for most organizations because they're always moving, somehow they're hiring new people and it has to be continuous effort because we've seen that on the hand. Organizations continue challenged their data sources and where all the data is flowing, right? Which in itself creates a lot of risk. But also on the other set hand of the equation, you have the benefit. You know, you might look at regulatory drivers like, we have to do this, right? But it's, it's much better right now to consider the competitive drivers, for example, and we did an IDC study earlier this year, quite interesting. I can recommend anyone to it. And one of the conclusions they found as they surveyed over a thousand people across organizations worldwide is that the ones who are higher in maturity. >>So the, the organizations that really look at data as an asset, look at data as a product and actively try to be better at it, don't have three times as good a business outcome as the ones who are lower on the maturity scale, right? So you can say, ok, I'm doing this, you know, data culture for everyone, awakening them up as data citizens. I'm doing this for competitive reasons, I'm doing this re reasons you're trying to bring both of those together and the ones that get data intelligence right, are successful and competitive. That's, and that's what we're seeing out there in the market. >>Absolutely. We know that just generally stand right, the organizations that are, are really creating a, a data culture and enabling everybody within the organization to become data citizens are, We know that in theory they're more competitive, they're more successful. But the IDC study that you just mentioned demonstrates they're three times more successful and competitive than their peers. Talk about how Collibra advises customers to create that community, that culture of data when it might be challenging for an organization to adapt culturally. >>Of course, of course it's difficult for an organization to adapt but it's also necessary, as you just said, imagine that, you know, you're a modern day organization, laptops, what have you, you're not using those, right? Or you know, you're delivering them throughout organization, but not enabling your colleagues to actually do something with that asset. Same thing as through with data today, right? If you're not properly using the data asset and competitors are, they're gonna to get more advantage. So as to how you get this done, establish this. There's angles to look at, Lisa. So one angle is obviously the leadership whereby whoever is the boss of data in the organization, you typically have multiple bosses there, like achieve data officers. Sometimes there's, there's multiple, but they may have a different title, right? So I'm just gonna summarize it as a data leader for a second. >>So whoever that is, they need to make sure that there's a clear vision, a clear strategy for data. And that strategy needs to include the monetization aspect. How are you going to get value from data? Yes. Now that's one part because then you can leadership in the organization and also the business value. And that's important. Cause those people, their job in essence really is to make everyone in the organization think about data as an asset. And I think that's the second part of the equation of getting that right, is it's not enough to just have that leadership out there, but you also have to get the hearts and minds of the data champions across the organization. You, I really have to win them over. And if you have those two combined and obviously a good technology to, you know, connect those people and have them execute on their responsibilities such as a data intelligence platform like s then the in place to really start upgrading that culture inch by inch if you'll, >>Yes, I like that. The recipe for success. So you are the co-founder of Collibra. You've worn many different hats along this journey. Now you're building Collibra's own data office. I like how before we went live, we were talking about Calibra is drinking its own champagne. I always loved to hear stories about that. You're speaking at Data Citizens 2022. Talk to us about how you are building a data culture within Collibra and what maybe some of the specific projects are that Collibra's data office is working on. >>Yes, and it is indeed data citizens. There are a ton of speaks here, are very excited. You know, we have Barb from m MIT speaking about data monetization. We have Dilla at the last minute. So really exciting agen agenda. Can't wait to get back out there essentially. So over the years at, we've doing this since two and eight, so a good years and I think we have another decade of work ahead in the market, just to be very clear. Data is here to stick around as are we. And myself, you know, when you start a company, we were for people in a, if you, so everybody's wearing all sorts of hat at time. But over the years I've run, you know, presales that sales partnerships, product cetera. And as our company got a little bit biggish, we're now thousand two. Something like people in the company. >>I believe systems and processes become a lot important. So we said you CBRA isn't the size our customers we're getting there in of organization structure, process systems, et cetera. So we said it's really time for us to put our money where is and to our own data office, which is what we were seeing customers', organizations worldwide. And they organizations have HR units, they have a finance unit and over time they'll all have a department if you'll, that is responsible somehow for the data. So we said, ok, let's try to set an examples that other people can take away with it, right? Can take away from it. So we set up a data strategy, we started building data products, took care of the data infrastructure. That's sort of good stuff. And in doing all of that, ISA exactly as you said, we said, okay, we need to also use our product and our own practices and from that use, learn how we can make the product better, learn how we make, can make the practice better and share that learning with all the, and on, on the Monday mornings, we sometimes refer to eating our dog foods on Friday evenings. >>We referred to that drinking our own champagne. I like it. So we, we had a, we had the driver to do this. You know, there's a clear business reason. So we involved, we included that in the data strategy and that's a little bit of our origin. Now how, how do we organize this? We have three pillars, and by no means is this a template that everyone should, this is just the organization that works at our company, but it can serve as an inspiration. So we have a pillar, which is data science. The data product builders, if you'll or the people who help the business build data products. We have the data engineers who help keep the lights on for that data platform to make sure that the products, the data products can run, the data can flow and you know, the quality can be checked. >>And then we have a data intelligence or data governance builders where we have those data governance, data intelligence stakeholders who help the business as a sort of data partner to the business stakeholders. So that's how we've organized it. And then we started following the CBRA approach, which is, well, what are the challenges that our business stakeholders have in hr, finance, sales, marketing all over? And how can data help overcome those challenges? And from those use cases, we then just started to build a map and started execution use of the use case. And a important ones are very simple. We them with our, our customers as well, people talking about the cata, right? The catalog for the data scientists to know what's in their data lake, for example, and for the people in and privacy. So they have their process registry and they can see how the data flows. >>So that's a starting place and that turns into a marketplace so that if new analysts and data citizens join kbra, they immediately have a place to go to, to look at, see, ok, what data is out there for me as an analyst or a data scientist or whatever to do my job, right? So they can immediately get access data. And another one that we is around trusted business. We're seeing that since, you know, self-service BI allowed everyone to make beautiful dashboards, you know, pie, pie charts. I always, my pet pee is the pie chart because I love buy and you shouldn't always be using pie charts. But essentially there's become proliferation of those reports. And now executives don't really know, okay, should I trust this report or that report the reporting on the same thing. But the numbers seem different, right? So that's why we have trusted this reporting. So we know if a, the dashboard, a data product essentially is built, we not that all the right steps are being followed and that whoever is consuming that can be quite confident in the result either, Right. And that silver browser, right? Absolutely >>Decay. >>Exactly. Yes, >>Absolutely. Talk a little bit about some of the, the key performance indicators that you're using to measure the success of the data office. What are some of those KPIs? >>KPIs and measuring is a big topic in the, in the data chief data officer profession, I would say, and again, it always varies with to your organization, but there's a few that we use that might be of interest. Use those pillars, right? And we have metrics across those pillars. So for example, a pillar on the data engineering side is gonna be more related to that uptime, right? Are the, is the data platform up and running? Are the data products up and running? Is the quality in them good enough? Is it going up? Is it going down? What's the usage? But also, and especially if you're in the cloud and if consumption's a big thing, you have metrics around cost, for example, right? So that's one set of examples. Another one is around the data sciences and products. Are people using them? Are they getting value from it? >>Can we calculate that value in ay perspective, right? Yeah. So that we can to the rest of the business continue to say we're tracking all those numbers and those numbers indicate that value is generated and how much value estimated in that region. And then you have some data intelligence, data governance metrics, which is, for example, you have a number of domains in a data mesh. People talk about being the owner of a data domain, for example, like product or, or customer. So how many of those domains do you have covered? How many of them are already part of the program? How many of them have owners assigned? How well are these owners organized, executing on their responsibilities? How many tickets are open closed? How many data products are built according to process? And so and so forth. So these are an set of examples of, of KPIs. There's a, there's a lot more, but hopefully those can already inspire the audience. >>Absolutely. So we've, we've talked about the rise cheap data offices, it's only accelerating. You mentioned this is like a 10 year journey. So if you were to look into a crystal ball, what do you see in terms of the maturation of data offices over the next decade? >>So we, we've seen indeed the, the role sort of grow up, I think in, in thousand 10 there may have been like 10 achieve data officers or something. Gartner has exact numbers on them, but then they grew, you know, industries and the number is estimated to be about 20,000 right now. Wow. And they evolved in a sort of stack of competencies, defensive data strategy, because the first chief data officers were more regulatory driven, offensive data strategy support for the digital program. And now all about data products, right? So as a data leader, you now need all of those competences and need to include them in, in your strategy. >>How is that going to evolve for the next couple of years? I wish I had one of those balls, right? But essentially I think for the next couple of years there's gonna be a lot of people, you know, still moving along with those four levels of the stack. A lot of people I see are still in version one and version two of the chief data. So you'll see over the years that's gonna evolve more digital and more data products. So for next years, my, my prediction is it's all products because it's an immediate link between data and, and the essentially, right? Right. So that's gonna be important and quite likely a new, some new things will be added on, which nobody can predict yet. But we'll see those pop up in a few years. I think there's gonna be a continued challenge for the chief officer role to become a real executive role as opposed to, you know, somebody who claims that they're executive, but then they're not, right? >>So the real reporting level into the board, into the CEO for example, will continue to be a challenging point. But the ones who do get that done will be the ones that are successful and the ones who get that will the ones that do it on the basis of data monetization, right? Connecting value to the data and making that value clear to all the data citizens in the organization, right? And in that sense, they'll need to have both, you know, technical audiences and non-technical audiences aligned of course. And they'll need to focus on adoption. Again, it's not enough to just have your data office be involved in this. It's really important that you're waking up data citizens across the organization and you make everyone in the organization think about data as an asset. >>Absolutely. Because there's so much value that can be extracted. Organizations really strategically build that data office and democratize access across all those data citizens. Stan, this is an exciting arena. We're definitely gonna keep our eyes on this. Sounds like a lot of evolution and maturation coming from the data office perspective. From the data citizen perspective. And as the data show that you mentioned in that IDC study, you mentioned Gartner as well, organizations have so much more likelihood of being successful and being competitive. So we're gonna watch this space. Stan, thank you so much for joining me on the cube at Data Citizens 22. We appreciate it. >>Thanks for having me over >>From Data Citizens 22, I'm Lisa Martin, you're watching The Cube, the leader in live tech coverage. >>Okay, this concludes our coverage of Data Citizens 2022, brought to you by Collibra. Remember, all these videos are available on demand@thecube.net. And don't forget to check out silicon angle.com for all the news and wiki bod.com for our weekly breaking analysis series where we cover many data topics and share survey research from our partner ETR Enterprise Technology Research. If you want more information on the products announced at Data Citizens, go to collibra.com. There are tons of resources there. You'll find analyst reports, product demos. It's really worthwhile to check those out. Thanks for watching our program and digging into Data Citizens 2022 on the Cube, your leader in enterprise and emerging tech coverage. We'll see you soon.

>> Narrator: From around the globe its "theCUBE" presenting Adaptive Data Governance, brought to you by Io-Tahoe. >> And we're back with the Data Automation series. In this episode we're going to learn more about what Io-Tahoe is doing in the field of adaptive data governance, how can help achieve business outcomes and mitigate data security risks. I'm Lisa Martin and I'm joined by Ajay Vohora the CEO of Io-Tahoe, and Lester Waters the CTO of Io-Tahoe. Gentlemen it's great to have you on the program. >> Thank you Lisa is good to be back. >> Great to see you Lisa. >> Likewise, very seriously this isn't cautious as we are. Lester were going to start with you, what's going on at Io-Tahoe, what's new? >> Well, I've been with Io-Tahoe for a little over the year, and one thing I've learned is every customer needs are just a bit different. So we've been working on our next major release of the Io-Tahoe product and to really try to address these customer concerns because we want to be flexible enough in order to come in and not just profile the data and not just understand data quality and lineage, but also to address the unique needs of each and every customer that we have. And so that required a platform rewrite of our product so that we could extend the product without building a new version of the product, we wanted to be able to have pluggable modules. We are also focused a lot on performance, that's very important with the bulk of data that we deal with and we're able to pass through that data in a single pass and do the analytics that are needed whether it's a lineage data quality or just identifying the underlying data. And we're incorporating all that we've learned, we're tuning up our machine learning, we're analyzing on more dimensions than we've ever done before, we're able to do data quality without doing an initial reggie expert for example, just out of the box. So I think it's all of these things are coming together to form our next version of our product and We're really excited about. >> Sounds exciting, Ajay from the CEOs level what's going on? >> Wow, I think just building on that, what Lester just mentioned now it's we're growing pretty quickly with our partners, and today here with Oracle we're excited to explain how that's shaping up lots of collaboration already with Oracle, and government in insurance and in banking. And we're excited because we get to have an impact, it's really satisfying to see how we're able to help businesses transform and redefine what's possible with their data. And having Oracle there as a partner to lean in with is definitely helping. >> Excellent, we're going to dig into that a little bit later. Lester let's go back over to you, explain adaptive data governance, help us understand that. >> Really adaptive data governance is about achieving business outcomes through automation. It's really also about establishing a data-driven culture and pushing what's traditionally managed in IT out to the business. And to do that, you've got to enable an environment where people can actually access and look at the information about the data, not necessarily access the underlying data because we've got privacy concern system, but they need to understand what kind of data they have, what shape it's in, what's dependent on it upstream and downstream, and so that they can make their educated decisions on what they need to do to achieve those business outcomes. A lot of frameworks these days are hardwired, so you can set up a set of business rules, and that set of business rules works for a very specific database and a specific schema. But imagine a world where you could just say, you know, (tapping) the start date of a loan must always be before the end date of a loan, and having that generic rule regardless of the underlying database, and applying it even when a new database comes online and having those rules applied, that's what adaptive data governance about. I like to think of it as the intersection of three circles, really it's the technical metadata coming together with policies and rules, and coming together with the business ontologies that are unique to that particular business. And bringing this all together allows you to enable rapid change in your environment, so, it's a mouthful adaptive data governance, but that's what it kind of comes down to. >> So Ajay help me understand this, is this what enterprise companies are doing now or are they not quite there yet? >> Well, you know Lisa I think every organization is going at his pace, but markets are changing economy and the speed at which some of the changes in the economy happening is compelling more businesses to look at being more digital in how they serve their own customers. So what we're saying is a number of trends here from heads of data, chief data officers, CIO stepping back from a one size fits all approach because they've tried that before and it just hasn't worked. They've spent millions of dollars on IT programs trying to drive value from that data, and they've ended up with large teams of manual processing around data to try and hard-wire these policies to fit with the context and each line of business, and that hasn't worked. So, the trends that we're seeing emerge really relate to how do I as a chief data officer, as a CIO, inject more automation and to allow these common tasks. And we've been able to see that impact, I think the news here is if you're trying to create a knowledge graph, a data catalog, or a business glossary, and you're trying to do that manually, well stop, you don't have to do that manual anymore. I think best example I can give is Lester and I we like Chinese food and Japanese food, and if you were sitting there with your chopsticks you wouldn't eat a bowl of rice with the chopsticks one grain at a time, what you'd want to do is to find a more productive way to enjoy that meal before it gets cold. And that's similar to how we're able to help organizations to digest their data is to get through it faster, enjoy the benefits of putting that data to work. >> And if it was me eating that food with you guys I would be not using chopsticks I would be using a fork and probably a spoon. So Lester how then does Io-Tahoe go about doing this and enabling customers to achieve this? >> Let me show you a little story here. So if you take a look at the challenges that most customers have they're very similar, but every customer is on a different data journey, so, but it all starts with what data do I have, what shape is that data in, how is it structured, what's dependent on it upstream and downstream, what insights can I derive from that data, and how can I answer all of those questions automatically? So if you look at the challenges for these data professionals, you know, they're either on a journey to the cloud, maybe they're doing a migration to Oracle, maybe they're doing some data governance changes, and it's about enabling this. So if you look at these challenges, I'm going to take you through a story here, and I want to introduce Amanda. Amanda is not Latin like anyone in any large organizations, she is looking around and she just sees stacks of data, I mean, different databases the one she knows about, the ones she doesn't know about but should know about, various different kinds of databases, and Amanda is this tasking with understanding all of this so that they can embark on her data journey program. So Amanda goes through and she's great, (snaps finger) "I've got some handy tools, I can start looking at these databases and getting an idea of what we've got." But when she digs into the databases she starts to see that not everything is as clear as she might've hoped it would be. Property names or column names have ambiguous names like Attribute one and Attribute two, or maybe Date one and Date two, so Amanda is starting to struggle even though she's got tools to visualize and look at these databases, she's still knows she's got a long road ahead, and with 2000 databases in her large enterprise, yes it's going to be a long journey. But Amanda is smart, so she pulls out her trusty spreadsheet to track all of her findings, and what she doesn't know about she raises a ticket or maybe tries to track down in order to find what that data means, and she's tracking all this information, but clearly this doesn't scale that well for Amanda. So maybe the organization will get 10 Amanda's to sort of divide and conquer that work. But even that doesn't work that well 'cause there's still ambiguities in the data. With Io-Tahoe what we do is we actually profile the underlying data. By looking at the underlying data, we can quickly see that Attribute one looks very much like a US social security number, and Attribute two looks like a ICD 10 medical code. And we do this by using ontologies, and dictionaries, and algorithms to help identify the underlying data and then tag it. Key to doing this automation is really being able to normalize things across different databases so that where there's differences in column names, I know that in fact they contain the same data. And by going through this exercise with Io-Tahoe, not only can we identify the data, but we also can gain insights about the data. So for example, we can see that 97% of that time, that column named Attribute one that's got US social security numbers, has something that looks like a social security number. But 3% of the time it doesn't quite look right, maybe there's a dash missing, maybe there's a digit dropped, or maybe there's even characters embedded in it, that may be indicative of a data quality issues, so we try to find those kinds of things. Going a step further, we also try to identify data quality relationships. So for example we have two columns, one date one date two, through observation we can see the date one 99% of the time is less than date two, 1% of the time it's not, probably indicative of the data quality issue, but going a step further we can also build a business rule that says date one is actually than date two, and so then when it pops up again we can quickly identify and remediate that problem. So these are the kinds of things that we can do with Io-Tahoe. Going even a step further, we can take your favorite data science solution, productionize it, and incorporate it into our next version as what we call a worker process to do your own bespoke analytics. >> Bespoke analytics, excellent, Lester thank you. So Ajay, talk us through some examples of where you're putting this to use, and also what is some of the feedback from some customers. >> Yeah, what I'm thinking how do you bring into life a little bit Lisa lets just talk through a case study. We put something together, I know it's available for download, but in a well-known telecommunications media company, they have a lot of the issues that lasted just spoke about lots of teams of Amanda's, super bright data practitioners, and are maybe looking to get more productivity out of their day, and deliver a good result for their own customers, for cell phone subscribers and broadband users. So, there are so many examples that we can see here is how we went about auto generating a lot of that old understanding of that data within hours. So, Amanda had her data catalog populated automatically, a business glossary built up, and maybe I would start to say, "Okay, where do I want to apply some policies to the data to set in place some controls, whether I want to adapt how different lines of business maybe tasks versus customer operations have different access or permissions to that data." And what we've been able to do that is to build up that picture to see how does data move across the entire organization, across the state, and monitor that over time for improvement. So we've taken it from being like reactive, let's do something to fix something to now more proactive. We can see what's happening with our data, who's using it, who's accessing it, how it's being used, how it's being combined, and from there taking a proactive approach is a real smart use of the tanons in that telco organization and the folks that work there with data. >> Okay Ajay, so digging into that a little bit deeper, and one of the things I was thinking when you were talking through some of those outcomes that you're helping customers achieve is ROI. How do customers measure ROI, What are they seeing with Io-Tahoe solution? >> Yeah, right now the big ticket item is time to value. And I think in data a lot of the upfront investment costs are quite expensive, they happen today with a lot of the larger vendors and technologies. Well, a CIO, an economic buyer really needs to be certain about this, how quickly can I get that ROI? And I think we've got something that we can show just pull up a before and after, and it really comes down to hours, days, and weeks where we've been able to have that impact. And in this playbook that we put together the before and after picture really shows those savings that committed a bit through providing data into some actionable form within hours and days to drive agility. But at the same time being able to enforce the controls to protect the use of that data and who has access to it, so atleast the number one thing I'd have to say is time, and we can see that on the graphic that we've just pulled up here. >> Excellent, so ostensible measurable outcomes that time to value. We talk about achieving adaptive data governance. Lester, you guys talk about automation, you talk about machine learning, how are you seeing those technologies being a facilitator of organizations adopting adaptive data governance? >> Well, as we see the manual date, the days of manual effort are out, so I think this is a multi-step process, but the very first step is understanding what you have in normalizing that across your data estate. So, you couple this with the ontologies that are unique to your business and algorithms, and you basically go across it and you identify and tag that data, that allows for the next steps to happen. So now I can write business rules not in terms of named columns, but I can write them in terms of the tags. Using that automated pattern recognition where we observed the loan starts should be before the loan (indistinct), being able to automate that is a huge time saver, and the fact that we can suggest that as a rule rather than waiting for a person to come along and say, "Oh wow, okay, I need this rule, I need this rule." These are steps that increase, or I should say decrease that time to value that Ajay talked about. And then lastly, a couple of machine learning, because even with great automation and being able to profile all your data and getting a good understanding, that brings you to a certain point, but there's still ambiguity in the data. So for example I might have two columns date one and date two, I may have even observed that date one should be less than date two, but I don't really know what date one and date two are other than a date. So, this is where it comes in and I'm like, "As the user said, can you help me identify what date one and day two are in this table?" It turns out they're a start date and an end date for a loan, that gets remembered, cycled into machine learning step by step to see this pattern of date one date two. Elsewhere I'm going to say, "Is it start date and end date?" Bringing all these things together with all this automation is really what's key to enable this data database, your data governance program. >> Great, thanks Lester. And Ajay I do want to wrap things up with something that you mentioned in the beginning about what you guys are doing with Oracle, take us out by telling us what you're doing there, how are you guys working together? >> Yeah, I think those of us who worked in IT for many years we've learned to trust Oracle's technology that they're shifting now to a hybrid on-prem cloud generation 2 platform which is exciting, and their existing customers and new customers moving to Oracle are on a journey. So Oracle came to us and said, "Now, we can see how quickly you're able to help us change mindsets," and as mindsets are locked in a way of thinking around operating models of IT that are maybe not agile or more siloed, and they're wanting to break free of that and adopt a more agile API driven approach with their data. So, a lot of the work that we're doing with Oracle is around accelerating what customers can do with understanding their data and to build digital apps by identifying the underlying data that has value. And the time we're able to do that in hours, days, and weeks, rather than many months is opening up the eyes to chief data officers, CIO is to say, "Well, maybe we can do this whole digital transformation this year, maybe we can bring that forward and transform who we are as a company." And that's driving innovation which we're excited about, and I know Oracle keen to drive through. >> And helping businesses transform digitally is so incredibly important in this time as we look to things changing in 2021. Ajay and Lester thank you so much for joining me on this segment, explaining adaptive data governance, how organizations can use it, benefit from it, and achieve ROI, thanks so much guys. >> Thanks you. >> Thanks again Lisa. (bright music)

(upbeat music) >> Announcer: From New York, it's The Cube. Covering ESCAPE/19. (upbeat music) >> Welcome back to The Cube coverage New York City for the inaugural multi-cloud conference. The first one ever in the industry. It's called Escape 2019. We're in New York so escaping from New York, escaping from cloud, that's the conversation. All the thought leaders are here and executives. People thinking about the next generation architecture and talk tracks are all here. Fritz Wetschnig who's the Chief Information Security Officer for Flextronics. >> Flex, yes. >> Flex, thank you for coming on. Love to have CISOs on because security seems to be always the top conversation. You got a very busy job. >> I do yes. (laughing) >> You're under a lot of pressure all the time >> It's fun, it's still fun for me. So, yeah, a CISO, it's always like security's top in mind, right, of everyone now these days. But it's still one of the most interesting jobs. The most interesting for my job is, I learn so much about our business and to have insight into so many things that's actually really great. >> You know, one of the things I was just talking about on a Cube conversation was, you know, how data is a really important part of it and how data backup and recovery was built on old thinking around, you know, data centers failing, floods, hurricanes, electricity gets outages, but the biggest disruption in business today is security, security threats and so that's cybersecurity pressure is causing CISOs to be mindful of the best architecture the best platform. Do we have the right tools? So I want to get your thoughts. How are you thinking about that as an organization, because are you building in-house developers? Are you, how are you organizing, how are you gearing up to fight the battles that need to be fought? >> So, I am with the company, So Flex is a big manufacturing company, right. 26 billion, so we have a lot of P2P business not consumer business, which is I believe a different perspective of security versus actually like a consumer company facing, so and I'm in a security team for 15 years, so we built it up like security operations and all those kind of things we do, right. >> You're old school. >> I am old school learned everything and that, right? >> But you're lot are IOT, I mean, you're Industrial IOT. >> Oh yeah, Industrial IOT it's one of the topics but coming back to you, you're right, data is actually the center even for our business, data is getting more and more center, right. You collect data from the machine, you collect data actually for the business actually to do make more decisions, right. And it could be predictive maintenance, could be inventory management. There could be a lot of things, right. You have to think about it. So, and the funny thing is, I'm real, I'm the CISO now for 5 years, 15 years with the security team, 20 years with the company, So I rebuilt the team always like every three, four years like as a kind of rebirth of the team. We renew, we add new skills, right. And cloud is one of the things, which I think it's a fundamental change and the change is actually, it's actually on the development side. What it means with that is the security team has to move to serve the developers. And the problem with the old school was always like it's afterthought. So why is security such an issue? Because we had to do patching after we found vulnerabilities, right. And then old network is not secure you need to wrap something around it like we did firewalls. So it was always an afterthought. Now with the cloud, it's changing because you have a lot of different things to do but basically we need to enable developers to be very quick and deploy their software very quickly, so I think it's a fundamental change in the way you have to think about security. >> And yeah, that brings up the good question I would love to ask you 'cause you've given, again you're not a consumer, like Capital One with in-house, they had their own channel, they weren't hacked. Amazon, actually the firewall was misconfigured, on an SV Bucket but that's a consumer company. You have data though, you're an industrial company, got a lot of industrial IOT. Ransomware folks are targeting data. >> Yes. >> And everyone's a target. Your service area is large. But you probably lock that down in the past. So how are you thinking about all this new stuff? >> So yeah, I mean, IOT it's, I mean, IOT's a problem, as you said, the industrial right. And it's not solved yet completely, right. Because they still have to rethink a lot of the vendors providing this machinery, which you purchase for twenty five, thirty years, right. They still are old school, right, sometimes, like, the one on Windows you can't upgrade or whatever. So it's basic things they're lacking actually in terms of security. There's still, has to be a shift in this, not just in industry but in a general thinking, how you do that. Yes, I have a big environment, so we locked it down, we use a lot of innovative technologies, actually preventive measurements plus also detective measurements. And you need to create kind of mightily a concept where you actually start, okay, what is if this fails? How we test it? Okay, this fails, do we have other measurements where we can try to prevent, stop those kind of things, right. But ransom is a big one. There's other things, as you know, like hacking, I mean, like Capitol One. >> Malware's a big problem. >> The Capital One was an interesting one in my belief and that's for the cloud is configuration issues, right, which I think it comes with cloud security. It's about policy and configuration management, right. How you manage that and how you think about it, but it's not, it's was not that. >> Automation could have solve that, I mean, that's an open S3 bucket, that's trivial. It wasn't a big, technical. >> Yes and no, if you look at that it was a little bit more in detail, >> Okay. >> So it was actually, their back firewall was misconfigured, which is about security running on a back check, but the misconfiguration was actually is, as (mumbles) force request issue, which means, like, you tricked this firewall into giving you information you shouldn't give information, right. >> John: Okay, so it was a little bit more. So, it was a little bit more granular as people think it was, right. Just as 3-pocket configuration. So it was a little bit more granular, but I think that's the really difficultly comes about whichever security. It's a complex program, right. It's mainly things you have. >> But it was a configuration error? >> It was a configuration. >> It wasn't as dumb as an S3 bucket. >> No, it wasn't dumb. >> But it was a bit more sophisticated, but not that sophisticated, was it? On a scale of 1 to 10. >> It was not sophisticated, but something, it's not easy to solve. So you have to think about it, but you're right, it's still something. >> John: It's an exploit from a corner case. >> Yeah, it's still something you could have. I mean, I'm careful to say you could have avoided it, yes you could, because that's for sure, but I know it's a complex environment, right. >> It's a human, there's humans involved. >> And I don't know the details exactly, we only know that what was published, right, so it's very hard to check. >> Well, it brings up cloud security, so let me ask you, on multi-cloud, this is a multi-cloud conference. What's your definition of multi-cloud? How do you look at the multiple-clouds? >> For me, multiple-cloud is, actually it doesn't matter. We had a good keynote words, it's a bunch of servers, right. That's how I see multi-cloud. It's a bunch of servers. Could be my data centers in a public cloud data centers with different vendors, that's what a cloud is. Where I move my services should be actually independent from the public hyper on premise, whatever it is, right. That's basically how I see it. >> So it doesn't matter, it's infrastructure. >> Yeah. >> On demand, leverage it. >> Leverage it, it could be say, hey today, I spin of this test server, but you know what, today it seems to be a bit cheaper running on (mumbles) verses GBC, let's do it here. Next day, next week we might do it somewhere else, whatever you trigger, whatever what is your requirements. >> So if going to look at that resource at like that, how do you think about the cloud security then, because the configurations, compliance, how do you, how do you stay on top of that? >> So, that's an interesting thing because we have begun to prioritize but we, as you said, no consumer business, so our problem is to find the right skill set, to attract the right people to our company to do that right because this is our, we have some cloud, but it's not yet, there's a journey we are trying to do, as most of the enterprise, so we're looking into startups, manage services, We say, okay what are gaps that we have to maybe have to outsource some of the things and gaps where we need to get internal source of supply. >> What's you're advice to other CISOs out there that are in the B2B space of don't have to deal with the consumer but have to get serious, that is now becoming more industrialized on the IOT side because you guys have been, you know, been there, done that, you have a big footprint on the IOT, 'cause you have a history. But as people get more facilities and they have more virtual offices, more people working, the edge is extending. What's your advice to those CISOs who have to deal with this industrial end IOT edge? >> I think you have to, visibility is the key ingredient is first, right. If you don't know what you have, it's very hard to understand what's a risk portfolio, right. So, you need to find the right toolset, and don't believe you know what you have. It's fantastic what you see when you use the right tool what distance everything is connected. I mean, basically even, like, I found like, this coffee mug, you know. I connect it to devices, right. It's like, not like everyone, not just that they don't understand my coffee mug is connected to (laughing). >> That light bulb's got multithreaded processor. What is that doing? >> So, so there's concerns, I may, but visibility is a key ingredient you have to understand. And then you have to look into how you mitigate a risk. What is a risk about it, right. I mean, if the government goes down, I don't really care, but if my testos goes down and does shut down the production, I really care about that. So you need to understand that the risk and say, how can I mitigate the risk? >> So while I got you here, what's you final question? What's your message to suppliers out there that all want to sell you something? Want to sell you another tool, you know. Want another tool? You know, I got a platform. I got a tool. Buy from me. >> You mean, to sell 750 watches (drowned out by laughter) If you go to ISA conferences, unbelievable, right. >> I want to sell you something. You're the top dog, I promise. >> Don't send me an email. >> Don't send them an email. Are you shrinking suppliers down? Are you looking at some kind of standard API way to deal with them? >> Yes. >> Because, you know, you're probably thinking about platforming, and date of visibility's critical. >> Yes. >> What's you philosophy on how to support video suppliers? >> So usually, honestly, the most time I really go it so for in the weight of technology we built in our company is called the Strategic Partnership Program where we can get for startups, and most of the time we engage, we startups overseas, or as through other channels, right. Where you get introduced, and you review, with the proof of work concept or value, the technology, and we try to keep it like a mini product, very short time, and say, okay, let's show what you can, where your gaps are, and can we get with you guys and can we get you. But don't send me an email, don't call me because I usually not react. I have a job to do. (laughing) >> Yeah, exactly. >> So that's most of the time, whatever we sees, what comes or if, a guy said hey, I found another CISOs tell me there's great technology, you should leap into that. >> And what shows do you go to? What events do you hang out in? What are good events for you in the space, RSA, Red Hat, Black Defcon? Are there certain events you go to that you think are valuable? >> I mean, as a CISO, I go to the RSA Conference, which I should because it's actually very close to me as well, and being part, being out of San Jose, I recommend the BSides, actually. I like the BSides. >> John: The BSides are great. >> The BSides are great. I think they are real, really. And then I try to smaller circles, right. We have our personal round tables. >> BSides for folks watching is an alternative group of community, industry participants, they have kind of a B-side, an A-side, like an album. But it's such a community event. They do hacker funds and a variety of other cool things where people get together, very unstructured kind of, cool conference, in addition to bigger conferences. >> I can recommend this. >> Yeah, awesome. Fritz, thanks for coming on and sharing your insights. >> Thanks. >> Been a pleasure. The Cube coverage in New York City, we're not escaping from New York but this is the Escape Conference, the first multi-cloud conference in the industry, we'll see how it goes. If they're successful, they might be back next year. If not, they won't be. But I think multi-cloud's going to stay. What do you think? >> I am think so too, yes. >> Okay, Fritz, thanks for coming on. I'm John Furrier, thanks for watching. (upbeat music)

>> Live from Orlando, Florida It's the Cube covering Enterprise Connect. Twenty nineteen. Brought to you by five nine. >> Yeah, good afternoon. Welcome to Orlando, Florida The Cube is here at Enterprise Connect. Twenty ninety nine. Lisa Martin with my co host to Minuteman Stew and I have been Here's starting on Day two stew. Good afternoon, >> Lisa. Great to see Yeah. Day two of three. Enterprise Connect. >> It's not that sunny >> here in the Sunshine State, but the nice thing about the Gaylord is it's a nice controlled environment. Walk by. I saw the alligator for bid. They've got nice planning. They've got I love in the atrium there. There's great branding of thie E c. Nineteen. Everybody's taken photos of it. I saw some drone footage in the keynote this morning showing some of the setting here. So >> it's a It's a nice >> event way said sixty five hundred intended, which is nice. It's not one of these, you know, twenty thirty thousand. You're just buried by people toe big Expo Hall. But, you know, you could really get to talk to some people and enjoy the size of the show. >> Yeah, I agree. The size is great. It does no pun intended. Facilitate that collaboration and communication. You mentioned a number of attendees about one hundred forty vendors, and you can hear the noise behind soon. MIAs were in the ex ball in the booth of five nine and lots of conversations going on. This is an event that I find very interesting state because we talk about the contact center were all consumers every day. And we talked about this with a lot of our guests yesterday that the customer experience is absolutely table stakes for an organization, that it's essential to deliver an Omni Channel customer experience meeting with the consumer wherever they want to be and also facilitating a connected conversation so that if a shot is initiated and then the consumer goes to social or makes a phone call, that problem resolution is actually moving forward before we get into. Today's key knows a couple of really interesting things that you and I learned yesterday with some of the guests that we had on when we were talking with Blair Pleasant. One of the things that she and five nine uncovered with some research is that an employee's satisfaction was lower on the ratings for a lot of corporate decision makers, which was surprising from a collab and communications perspective that if employees, especially those agents on the front line, are having some challenges, it's going to be directly relating Tio customer Lifetime Value. >> Yeah, it was a little bit surprising, you know, if you think about just in general, you know, often the admin is not the key focus there. It's I need to get business outcomes. I need to get R. A Y. You know what I care about is, you know, how is my customer doing? But at the end of the day, you talk about the contact centers. If I don't have an agent that's engaged, really, how is that conversation going to go with the customer? So they need to think about that, You know? How will the technology help them do their job? Better help them game mastery faster? There were some things that I saw really parallel toe conversation we're having about cloud in general, which is, you know, there's lots of technologies out there, but it's often it's not the technology issue it is, you know, the organization and the people issue in the keynote this morning there was a big customer panel and that was definitely something we heard. I love one of the customers actually said We're going to make all these changes And they had the Don't panic towels, which, of course, harkens back to The Hitchhiker's Guide to the Galaxy S O. You know, we know things are going to change. There might be some things you need to work through. But don't worry, we're there to help on. We will get through this and at the end, it should be better. >> No, I like that. You brought that up. I love that Tabal. Don't panic because, you know, we were talking yesterday a lot about the customer experience, the expectations of this rising, empowered consumer also the agent experience. But then, of course, there's the internal collaboration that's essential to all of this. And as I think, the gentleman that you're referring to was from Continental G talking about Hey, we don't have all the answers. But adoption of these tools internally is critical, but it's also a cultural sort of stepwise process. I thought that was very cool, that they actually were very transparent with their people. We identify this is not going to be smooth sailing, but it's an essential part of our business growth. >> Yeah, I tell you, it was really interesting. Listen, the panel there was one of the companies up there. They're pretty large and they said, Look, we're going to standardize on a single tool and everybody's going to get on board. And I actually bristled a little bit when I heard that because, you know, the engineering group versus the marketing group versus you know, the Contact Centre. There's certain things that they need to be able to collaborate. But thing like, you know, one tool to rule them all. You know, it sounds a little bit tough out there. Yes, there needs to be some standardization, but, you know, we see that in the cloud world. You know, it turns out customers are using multiple clouds out there because there should be a main one that we focus on. But if I need a best of breed piece for here, or if there's ah, feature functionality, they can't get elsewhere. I need tohave that, and we see that at this show there's just such a diverse ecosystem meant, and there's one hundred forty there's people that make device. There's all these software pieces, there's some big hubs. And then there are all the ancillary things that help plug and enhance and do this because there is some great innovation going on here. Some cool software, things that we're hoping toe, you know, take everything from, you know, White Board and voice two speech and globalization to the next phase. >> Yeah, that was very interesting. Especially the Microsoft teams demo. That Lori writing team this morning, The panel Now that you talked about, there were seven, uh, customers from a variety of industries. Kurtz was their continental. We mentioned, I think, paychecks. I'm curious to get your thoughts on when they were talking about their plans to migrate to cloud, all in some percentage, considering the numbers that we heard yesterday stew in terms of the cloud penetration for the contact center market, what were your thoughts? They're about those things. All in Depends on what makes sense. >> Yeah, It reminds me of what we were talking about in the public loud discussion two years ago. Way No cloud is growing at a very fast pace. Look at our friend here at five. Nine they were growing at a much faster pace, then the contact center. Overall, I believe they're growing somewhere twenty five percent as opposed The industry as a whole is growing at about nine percent. So we understand that cloud is growing faster than the market overall. And it was one of moderated. The panel said that today is about a third, a third, a third on premises hybrid in public and where that kind of steady state will be. I think it's still too early to tell in this industry, just as it is in cloud overall. But absolutely I burst a little bit when it's like, Well, you will never do this one this way. Well, you know, never is not something that we like to say in it because you never know when when that will be possible. You know, my background I worked on virtual ization, started out in test Devon. It reached a point where really there was no technical reasons that it couldn't do it when he rolled. The really large companies will never use cloud for it. Really. Who is better it scaling and updating and making sure you can manage an environment then those hyper scale players. You know, Microsoft got a big present here. You don't ask him. Like her soft customer. Uh oh. You're running off his three sixty five. You're living on Azure. What version of that are you running? And do you have the latest security patch as opposed to? If I have a Windows desktop and I'm not doing up a weight, have I done my patron? If I Donald this stuff and you amplify that by thousands of you know of agents and Contact Center, we know that Cloud has certain speed, agility and being up to get new features and updates in there that I just can't do nearly as well if it is something that I am installing and having to maintain myself or with a service organization, >> right? And so we talked yesterday with the number of guests about what are some of the imperatives to move to cloud in the end, the sum of the non obvious ones cost obviously, is one that we talk about all the time rights to it. Any show that we're at, but also the opportunity for businesses to leverage the burgeoning power of a I. Of course, every show we go Teo Isa Buzzword Machine learning. And of course, the cloud provides the opportunity for there to be more data to train the machines to be better at context and her overall. And, of course, internal communications. >> Right. And something that I like to hear at this show is start talking about a PC compatibility. You talk about the partnerships that are going on, It is not one software stack we're talking about platforms. We're talking about how integrations can happen so that if somebody has the cool new thing that does, you know, a real time engagement better than what I had before. Well, I could probably plug that in, and it's going to work on my platform. You know, everybody here talks about Well, whether you're, you know, a web, acts of Microsoft teams a zoom shop O r. You know any of those various environment, other? Everybody's working across those environments. We've had some standardisation here s O so that whichever one I've chosen, I'm not locked into one environment. And you know, I can help modernized the pieces as a need and take advantage of those new innovations when they come >> Absolutely all right. So, stew, you're a man on the street last night. Tell us some of the interesting things that you heard in some of the folks that you met Way. >> It's interesting. We think we talked about it in our open yesterday. There are a number of companies that have been around for a while And what are they doing today? What is their focus? And couple of companies have done rebranding. So the big party there was a line and I managed to get myself in. Is Polly So Polly has rebranded? Of course it was Polycom and Plantronics coming together. How many times we hear it on the keynote stage that they mentioned that everywhere you go, they're branding is there, So look kudos to their branding and messaging team. We're going to have their CEO on the programme tomorrow, but, you know, you know, the CEO talked about, you know, their new logo. It's like the meaning behind it. Of course, Polly means many, but there's three piece, and if you look at it, it looks like the iconic conference phone. So, you know the room was in there. Everybody is enjoying the appetizers and the open bar. But, you know, there was people, people, no polycom. I'm back in our conference room. We've got one of those speaker phones in there in the nineties. I usedto, you know, sell their conference phones in their video conferencing When I worked for was now a via but was lucid at the time. So there's a lot of intersections. Thie. Other thing I've really found is it feels like everybody here, you know, at one point in their career either work for Cisco or worked for, you know, the Lucent family. You know, of course, T back in the day had the whole telecom space, but it is like many other shows. We go to a rather interconnected community here on DH. You know, we'd guess on It's like, Oh, yeah, Cisco, Skype. And now at five nines. Yeah, it is friendly. You don't see some of the, you know, some of the places we go There's bitter rivalries between, you know, key competitors, and yeah, while you know, all the contact centers don't love, you know that they're there. Brothers and sisters, a two competitors there. Chances are they've worked with half the people there on, you know, Sometimes the future will be working with again. So it's it's a it's a good atmosphere. The people I've talked to really enjoy coming to the show, a Zoe said at the top. >> And this show has evolved over the last night. We were talking about yesterday twenty eight, twenty nine years, starting out as being called PBX and then re branding to Voice Con and then in about twenty eleven to Enterprise Connect. And it was interesting that because the word innovation comes up all the time, as does evolution of communications and collaborations. But when the king it was his kicked off this morning they talked about This is the biggest ever enterprise connect that they've had. So you can feel and you can hear it behind us the momentum, the excitement he talked about. There's a lot of cover artery here. There's a lot of two degrees of separation and tech, but the opportunities for every business, whether yours selling a small particles service on the Amazon marketplace or you're a big a global enterprise, the opportunity to connect and deliver a superior a competitive advantage to your customer experience. This table stakes these days if you don't have that opportunity. Those capabilities. There's going to be something that's going to come and replace you in a heartbeat. >> Yeah, absolutely. At least I have a background in space. But there were places where our walk Drano said, Wow, there's applicability for our business. I mean, we use a number of the collaboration Sweets, You know, I mentioned, I've got I've got maps for, you know, not just the Google sweet, but all the collaboration tools on there's technology that I'm like Gucci. I want to understand that a lot of them are downloaded an app. You can start using them for free. And then there's a Freeman model and and others arm or enterprise licenses on. It's been interesting to watch some of that dynamic as to, you know, it is the pricing. Is Mohr built for the mobile and cloud world than the traditional? You know, I'm going to buy boxes and have a huge capital expense up front. So >> what do you think if you look back to your early days in the call center when you were just a young pup, how much easier your job have been? If you had had some of the capabilities that we're talking about >> now least I wish, you know, back in the nineties, you know, if I just had linked in alone, I could have supercharged s o much of what I did. But all these other tools, right? Putting at my fingertips information. It was like, you know, Lisa tell you date myself in the nineties and taking a call where everybody that works in the call center You knew the area code of every single environment that it didn't tell you where it wass you would be like, Oh, yeah, I, too want to hide in New York. How you doing? You could be whether you're saying good morning or good afternoon based on what part it was like. Oh, wait, I'm talking Arizona. They don't follow daylight savings time. We'd remember all that stuff today. There's too many exchanges. Everybody takes their phone numbers wherever they go. S o it was It was a smaller country back then. But in the other hand, the technology is actually going to give us the opportunity to be ableto imbue that allow humans to focus on the empathy and connectedness that today's digital age sometimes tries to tear away from us. >> Exactly. We need that empathy in that connectedness. So, stew, we have a great program today. Stick around. We've got some folks from Selah Jin we've got. It's now on the programme within communications Fuse. Tetra VX five nine, of course. And there in that little and zoom this afternoon. Yes, thank you. Five O'Clock for student a man. I'm Lisa Martin. You're watching the Cube.

>> It's a pleasure to be here with you and to tell you about something I've been dreaming about and working for for many years and now is coming to the surface quite powerfully and quite usefully in many areas. I apologize, sometimes this flickers for some reason but I hope it doesn't disturb the story. I'd like to give you a little touch of history since I was there at the beginning of this journey and give you a brief introduction to what we mean for Fog Computing. And then go quickly to three powerful application spaces for this technology, together with industrial internet and one is industrial automation. That's the focus of our activity as Nebbiolo Technologies. The other one is one of my favorite ones and we'll get there is the automotive that caught fire here in Silicone Valley in the last years, the autonomous car, the connected vehicle and so on. And this is related to also to intelligent transportation and Smart Cities. And then a little touch on what Fog Computing means for Smart grid energy but many, many other sectors will find the same usefulness, the same architecture dimensions of Fog Computing applicable. So this is the story that comes back hopefully, here, the day in 2010 when Fog Computing, the word started here, oh God, is this jumping around? I think it's the connector, this is the age of the connector, this is the age of the Dongles. This is not an Apple Dongle and so we are having troubles. And this is not yet one of the last machines that are out. Let's hope for, I never had this problem, okay. Alright, this date 2010 at the Aquarium Research Center in Monterey where I gave a talk about robots going down deep in the bottom of those big valleys under the ocean and when I finished, the lady, Ginny in the middle approached me and told me, look, why don't you call what you're talking about fog computing? Because it's cloud computing brought too close to the ground and I protested for about 15 minutes. And on the drive home, I thought that's really a good name for what we are doing, what we have been doing in the last years and I started trying it out and using it and more and more I found good response and so seven years later, I'm still here talking about the same thing. What's happening is Fog, the edge of the metric zone was very important but it was always very important in IT, is still very important in IT in mobile, in content distribution but when IOT came to the surface, it became even more relevant to understand the need of resources, virtualized real time capable, secure, trusted with storage computing and networking coming together at the edge. At the edge of the IT network, now they are calling this mobile edge, they realize we are realizing that mobile can benefit from local resources at the edge, powerful real time capable resources but also and more importantly for what we are doing in this space of operational technologies, this is the space, the other and the other side of the boundary between information technologies and operational technologies and here is where we are living with Fog Computing these days so, apologize, I apologize for this behavior that is, maybe I have another Dongle, Apple Dongle. Maybe I could look at that, maybe Morris can help me out here, anyway, so what is Fog Computing? Fog Computing is really the platform that brings modern, Cloud inspired Computing storage here is important here for our friends at Western Digital and networking functions closer to the data producing sources. In our case, machines, things, but not just bringing Cloud down, it's also bringing functions up from the machine world, the real time, the safety functions, the trusting and reliability functions required in that area and this is a unified solution at the edge that really brings together communication, device management, data harvesting, analysis and control. So this is kind of new except for our friends in Wall Street. The real time part was not as sensitive. Now we are realizing how important it is and how important the position of resources is in the future of solutions in this space and so it's not boxes. It's a distributed layer of resources, well managed at the edge of the network and really has a lot of potential across multiple industries. Here we see the progress also in the awareness of this topic with the open fog control room that is now a very active and even the Vcs. Peter Levine here is talking about the importance of the edge. What is really happening is the the convergence. I think we should probably stop and use a different Dongle. Is this the one, no, no, this is not the right Dongle. The world of Dongles, sorry. Oh boy. Oh you have the computer with the, okay, is the right Dongle with the right computer, okay. Here we are, okay. Alright, we're getting back there. This is the new Apple. Okay, we are here, this looks better, thank you. Alright, so this is to be understood. This is the convergence of IT functionality, the modern IT functionality with the OT requirements and this is fundamentally the powerful angle that Fog Computing brings to IOT and machine world so all the nice things that happened in the Cloud come down but meet the requirements of resources, the needs and the timing of the Edge. And so when you look at what is brought into particularly the world of operations, you see these kind of functions that are not usually there. In fact, when you meet this operational world, you find microprocessors, you find Windows machines, industrial Pcs and so on, not so much Linux, not so much the modern approaches to computing. These are the type of dimensions that you'll see have a particular impact on the pain points seen in the wold of applications. So now we go to the Use cases in, use cases in the internet of things. I think it's on your side, I'm sorry. Because it's the second machine. Okay, well, maybe here's the solution. So we have seen this picture of IOT multiple times. A lot of verticals, we are concentrating on this tree, one is the industrial, the second one is the autonomous vehicle in intelligent transportation, the third one, just touched upon is the Smart Grid. This is the area of activity for Nebbiolo Technologies. Those kind of body shops and industrial floors with large robots with a lot of activity around those robots with cells protecting the activities within each working space, this is the world PLCs, industrial Pcs controlling robots, very fragmented. Here we are really finding even more critical this boundary between operational and informational technologies. This is a fire wall, also a mental fire wall between the two worlds and best practice is very different in one place than the other particularly also in the way we handle data, security, and many other areas. In this space, which is also a little more characterized here with this kind of machines that you see in this ISA 99 or ISA 95 type of picture, you see the boundary between the two spaces, once more when we come back. And alright, so the key message here, very tough to go across, it's very complex, the interaction between the two worlds. And there is where deeply we find a number of pain points at the security level, at the Hardware architecture level, at the data analytics and storage level, at the networking, software technologies and control architecture. There's a lot happening there that is old, 1980's time frame, very stable but in need of new approaches. And this is where Fog Computing has a very strong impact And we'll see, sorry, this is a disaster here. Alright, what do we do, alright. Maybe I should go around with this computer and show it to you. Okay, now it's there for a moment. Now, this is, maybe you have to remember one picture of all this talk, look at this, what is this? This is a graphical image of a body shop of a an important car company, you see the dots represent computers within boxes, industrial Pcs, PLCs, controllers for welding machines, tools and so on. That is, if you sum up the numbers, it's thousands of computers, each one of them is updated through a UPC, USB stick, sorry and is not managed remotely. It's not secure because there's a trust that the whole area is enclosed and protected through a fire wall on the other side but it's very stable but very rigid. So this is the world that we are finding with dedicated, isolated, not secure computing, this is Edge Computing. But it's not what we hope to be seeing soon as Fog Computing in action there so this is the situation. Very delicate, very powerful and very motivating. And now comes IOT and this is not the solution. It's helping, IOT tries to connect this big region, the operational region to the back end to the Clouds, to the power of computing that is there, very important, predicting maintenance, many other things can be done from there but it's still not solving the problem. Because now you have to put little machines, gateways into that region, one more machine to manage, one more machine to secure and now you're taking the data out. You are not solving a lot of the pain points. There's some important benefits, this is very, very good. But it's not the story, the story is sold once you really go one step deeper, in fact, from connectivity between information technologies and informational technologies to really Convergence and you see it here where you're starting to replace those machines supporting each cell with a fog node, with a powerful convergent point of computing, real time computing that can allow control, analytics and storage and networking in the same nodes so now these nodes are starting to replace all the objects controlling a cell. And offer more functions to the cell itself. And now, you can imagine where this goes, to a convergent architecture, much more compact, much more homogeneous, much more like Cloud. Much more like Cloud brought down to the Edge. When this comes back, okay, almost there. So this is okay, this is now the image that you can image leads to this final picture that is now even not, okay, do you see it, okay. Now you're seeing the operational space with the fabric of computing storage and networking that is modern, that is virtualized, that supports an application store, now you have containers there. You can imagine virtual machines and dockers living the operational space. At the same time, you have it continuing from the Cloud to the network, the modern network, moving to the Edge into the operational space. This is where we are going and this is where the world wants us to go and the picture representing this transition and this application of Fog Computing in this area is the following, the triangle, the pyramid is now showing a layer of modern computing that allows communications analysis control application hosting and orchestration in a new way. This is cataclysmic, really is a powerful shift, still not fully understood but with immense consequences. And now you can do control, tight, close to the machines, a little slower through the Fog and a little slower through the Cloud, this is where we are going. And there's many, many used cases, I don't dwell on those. But we are proceeding with some of our partners exactly in this direction. Now the exciting topics if I can have five more minutes making up the time wasted. What's going on here, the connected vehicle, the autonomous vehicle, the electrification of automobile are all converging and I think it's very clear that the para dime of Fog Computing is fundamental here. And in fact, imagine the equivalent of a manufacturing cell with a converging capabilities into the Fog and compare it with what's going on with the autonomous vehicle. This is a picture we used a Sysco seven years ago. But this is now, a car is a set of little control loops, ECUs, little dispersed, totally connected computers. Very difficult to program, same as the manufacturing cell. And now where are we going, we are going towards a Fog node on wheels, data center on wheels but better a Fog node on wheels with much better networking between, with a convergence of the intelligence, the control, the analytics, the communications in the middle and a modern network deterministic internet called TSN is going to replace all these CAN boxes and all these flakey things of the past. Same movement in industrial and in the automobile and then you look at what's going on in the intelligent transportation, you can imagine Fog Computing at the edge, controlling the junctions, the traffic lights, the interactions with cars, cars to cars and you see it here, this is the image, again where you have the operational space of transportation connected to the Clouds in a seamless way which these nodes of computing storage and networking at the junctions inside the cars talking to each other, so this is the beautiful movement coming to us and it requires the distribution of resources with real time capabilities, here you see it. And now, the Smart Grid, again, it cannot continue to go the same way with a utility data center controlling everything one way, it has to have and this is from Duke and a standardization body, you can see that there's a need of intelligence in the middle, Fog nodes, distributed computing that are allowing local decisions. Energy coming from a microcell into the grid and out, a car that wants to sell it's energy or buy energy doesn't need to go slowly to a utility data center to make decisions so again, same architecture, same technologies needed, very, very, very powerful. And we could go on and on and on, so what are we doing? We won't advertise here but the name has to be remembered. The name comes from a grape that grows in the Fog in Northern Italy, it's in Piedmont, my home town is behind that 13th century castle you see there. Out there is Northern Italy close to Switzerland. That vineyard is from my cousin, it's a good Nebbiolo, starting to be sold in California too. So this is the name Nebbia Fog comes to, Nebbiolo Technologies, we are building a platform for this space with all the features that we feel are required and we are applying it to industrial automation. And our funders are not so much from here, are from Germany, Austria, KUKA Robotics, TTTech, GiTV from Japan and a few bullets to complete my presentation. Fog Computing is really happening. There's a deep need for this converged infrastructure for IOT including Fog or Edge as someone calls it. But we need to continue to learn, demonstrate, validate through pilots and POCs and we need to continue to converge with each other and with the integrators because these solutions are big and they are not from a little start up. They are from integrators, customers, big customers at the other end, an ecosystem of creative companies. No body has all the pieces, no Sisco, no GE and so on. In fact, they are all trying to create the ecosystem. And so let's play, let's enjoy the Cloud, the Fog and the machines and try to solve some of the big problems of this world. >> Okay, Flavio, well done. >> Sorry for that. Sorry for the hiccups. >> Now we do that on purpose to see how you'd react and you're a pro, thank you so much for the great presentation. >> Alright. >> Alright, now we're going to get into panel one, looking at the data models and putting data to work.

Las Vegas expensing the signal from the noise it's the kue covering you interconnect 2016 brought to you by IBM now your host John hurry and Dave vellante okay welcome back everyone we are live here in Las Vegas this is silicon angles the cube our flagship program when we go out to the events and extract the signal annoys we are at IBM interconnect 2016 it's our fifth year now doing all the IV meds now interconnecting out the cloud show I'm John furrier with my coach Dave vellante our next guest is Eric Herzog vice president of storage and software-defined at IBM welcome back you belong great to see you great thank you very much always loved helping guys out of the cube thank you very much for including us pleasure we are very cognitive today we get cognition going on the cube we have all kinds of real-time we've got api's and notifications or and we're going to stract some insight and predictive and prescriptive analytics from you right first what's going on with storage and software obviously storage right now you're seeing huge change Dell buying EMC which you know a lot about emc IBM buys the weather company two contrasting strategies but Stewart still it's the center of the value proposition we also heard Robert de Blanc say on stage today cheap compute he didn't say cheap storage storage visited it did he didn't say so long about cheap storage okay I stand corrected but you talk about a commoditization of resource still valuable I always said what's wrong with cheap compute want more of it I want more and more compute so storage does he changing the software values their last time we spoke about that what's the update in context to cloud what's the storage equation was a storage angle well for us there's a huge value proposition when both the cognitive side and in the cloud infrastructure side obviously with the tumultuous change in storage both from just where the world is going we believe that you ride the wave a flash and software-defined and that is our mantra as you know one of the industry analyst firms who tracks the numbers we were number one in flash capacity shift and number one in flash units last year are all flash and we've been number one several years in row and software-defined storage so while the storage envelope is changing if you open up that envelope we're writing the change inside that omelet which is flash software to find converged infrastructure with our pure power product and also with our partnership with Cisco on the verses stack that's two years in a row for flash leadership right yes charge same thing with software to bunt well the good thing is well the other guy leads in revenue we believe in a fair price for an outstanding award-winning product line on the software value now the cell where that fits in we had multiple guests on today we had you know Jamie Thomas former GM and storage now thinking a more systems view its horizontally composable infrastructure now our dead loss infrastructure as code how does that change the equation certainly we want storage but now you've got software driving the change where's the wisdom value points there well when you look at the software-defined infrastructure the magic fairy dust is in the software so we can work with our own hardware we can work with our competitors hardware over 300 different raise from our competitors are completely compatible with our software to find solutions for storage and we can use with white box if one of our channel partners our end users would rather have a white box storage bear hard drives from seagate OWD and some some flash and just a wrapper of metal we are software provides the value add for integration into hybrid cloud configurations in the cognitive configurations into the oceans of data and big data and into analytic environments all powered by software-defined storage ok so you've been on less than a year now all right you came on last summer right yes mid year so what nine months roughly yes inland what are the big learnings that you've encountered and then we'll start from there and then we're going to get into result are you going to transfer yeah I think the big learning is the world is evolving and a lot of the customer base hasn't gotten there yet so we're going to take them on that journey with flash software-defined converged infrastructure so we're going to lead that charge we're going to ride the wave not fight the wave sometimes iBM has fought the wave we've changed that in the storage world so we're going to be a leader we're are a leader in flash we're leader and software-defined are converged infrastructure particularly with Cisco had an incredible year last year you know for our first year we had over 250 customers over 400 units sold and while there are others who are bigger in our first year that was one of the best first years in the converging instructor of any vendor and that's the power of our software to find portfolio our flash portfolio and the things we deliver from a storage perspective that helps customers they convert either the software-defined infrastructure or converged infrastructure so that case so that sort of answers the question as to how you're going to deal with immediate it's not unique you got old stuff that's declining you got new stuff that's growing like crazy but still not big enough to offset the decline of the old stuff you got currency headwinds but the there's light at the end of the tunnel in terms of that transformation to those newer architectures is that fair yes absolutely last year if you look whether it was in the channel with our award from computer reseller news as the best enterprise storage provider in the world and that was in the fall of 2015 so when you look at the channel and what they're looking for from their provider unlike the guys in hopkinton in Austin who are merging they didn't win that IBM one that so great solution for our Channel Partner base we've won awards for software-defined for all flash we did very well in the hybrid or a category last year with several product of the Year awards so again yes we have an older installed base one of our big goals this year is to refresh that installed base with software-defined with all flash with a comprehensive family of hybrid raise to make sure that people understand this is where the market is going this is where you need to go to drive cognitive value hybrid cloud value quite honestly it's all about applications workloads and use cases and even though I've done storage for 31 years let's face it most CEOs can't stand storage have to put it in the language that they understand which is software value-add and how it can enhance their ability to meet the business SLA s that the CIO is under pressure from the VP of Operations the VP of Marketing the finance side and of course ultimately the CEO so in this business I've been in the business maybe not 31 years but maybe 35 okay so the product portfolio is very very important one of the criticisms I've had of IBM over the years has been just not enough product innovation coming out great R&D but doesn't hit the pipeline so when you came to see us in Boston you showed us a little you know glimpse of the roadmap and it's very clear that's accelerating I wonder if you could talk about that what can you share with our audience sure we've done it we've done a couple things first of all we have the flash religion we acquired a flash company get started but so did several of our competitors in addition to spending money on that acquisition we've invested over a billion dollars in engineering resources on the flash site software-defined we're spending a billion dollars in that as you know we recently bought the award-winning and market-leading object storage technology with clever safe and we spent money on that so IBM is putting its money where its mouth is its focus is on storage and how storage enhances hybrid clouds cognitive big data analytics and you know deals with these oceans of data that our customers are facing and how do you manage that and how do you make the data more valuable and more productive to the business because that's what about it's not about storage it's about the management that data to optimize our customers business and how we can deliver that with effective cost so clever save was mentioned in the keynote in context to LeBlanc's reference to the digital transport transit of you know new stream the video stuff interesting how he plugged in clever see how it is that relate I mean honestly I know it's a recent acquisition is it's just the objects towards an unstructured data why is clever stay plugged into that kind of portfolio of those four companies you mentioned around you know is when you develop that type of technology you end up with incredible amounts of data and an object store is designed to handle exabytes of capacity and exabytes of information it doesn't necessarily have to be fast for example video surveillance data and all kinds of other data may be hot for a while and one of the values of clever say for example is on our spectrum scale product which is our scale out network attached storage actually will automatically cheer too clever safe we're in a public beta right now our spectrum protect product we've also talked about is going to support clever safe either as an source so you could back it up but more importantly as a target so you could take gobs of data and back it up into a clever safe repository when you've got oceans of data and people are generating exabytes and exabytes of data what you can get with clever safe on premises or in a cloud configuration allows you to handle this extensive data growth cost-effectively and in an easy to manage and configure way about the end where relationship with storage obviously there in an announcement today with IBM EMC recently had an announcement with VMware and VX rail rom and the big debate was I see his hybrid cloud was deposition using their software stack to be a glue and into the hybrid cloud journey but one of the comments that we made note of that we captured on the prowl chat was from Keith Townsend one of our members of our community he wrote it took Netflix seven years to move to the public cloud meaning everything all flash they had one of the first all flesh implementations that Amazon ever rolled out what does that mean for the average VMware customer in this case IBM customer from a product perspective so you got you know your relationship VMware you have this notion of hybrid cloud right it took Netflix seven years there in the cutting edge what does that mean for the average customer this whole notion of using software in storage plugging the hybrid cloud it took them seven years was it 70 years for an average company well you've got to remember that that started a while ago and the move to the hybrid cloud is just accelerated dramatically so our spectrum scale product our spectrum accelerate product our spectrum protect product all are designed for hybrid cloud configurations right this minute they're easy to employ they're easy to use they're all available in softlayer they're also filled with other cloud providers spectrum protect as close to a hundred different msps and csps who provide backup and archive services with award-winning spectrum protect so our specialist families and I've different than it was seven years ago today actually its accelerated easy-to-deploy it's easy to use you have a wide choice of msps and csps to use whether it's soft layer or other providers in the industry and our software-defined storage supports all of that vendor base regardless of whether it's IBM SoftLayer or other cloud providers as well well you could argue to Netflix did it at a time when it was early days right it was near the Pioneer they were they were final trees hack and you know right they're the ones with the arrows in motion tracking chaos monkeys everywhere so so Tommy you guys okay all right sorry John I want to talk about the state of the industry it's a lot of interesting stuff going on even in the business for four decades you understand some of the trends you've seen a lot of the ebb and the flow how would you describe where we're at right now seems like an uncertain time so storage is incredibly tumultuous right now one of the good things about storage it's constantly filled with innovation as you know from my past I've done seven startups thank God five have been acquired so I can wear a Hawaiian shirt they're expensive these days ISA why insurance so every five six years you have a wave of startups of the storage business that's not common in most other segments of the IT market space but in storage it is so you have a constant wave of startups that happens on a normal basis and we're in one of those phases right now at the same time you have massive change in the Tier one vendor base EMC and Dell emerging HP splits into two network appliance which had been an incredibly great company it's fast has now missed their numbers almost eight quarters in Rowan just last week announced they're laying off 1500 people so the world is changing dramatically also the applications workloads and use cases are changing dramatically so you've gone to a cognitive ear you don't have cereal management of data you now have parallel management of data you don't want databases that react or let's say a data warehouse it takes 30 hours to run a report you want the report to run in one so if you will real-time cognitive data availability and ability to analyze that data and that is dramatically changing what startups are out how successful they'll be how the tier 1 vendors are reacting you know for example one of the great things about IBM is we are focused on flash which is the fastest grain storage systems market and software to find which was one of the fastest growing storage software markets and we're leaders in both market spaces so when you open up the envelope of what's inside storage it's a slow growth market three to four percent per year is all it's growing but certain segments are growing rapidly and IBM focuses on those rapid growth segments now but the cloud piece right so you make you guys are talking about clever safe before I was thought that was a cloud acquisition which it was in part right but it's also something that falls into the storage portfolio right and that's because clever safe can be configured in a number of different ways on-premises only cloud only or hybrid configuration we can have an on-premises clever safe configuration talking to a cloud-based configuration so again part of IBM strategy to make sure that from a storage perspective all of our software to find infrastructure and what we acquired with clever safe are designed for hybrid cloud configurations or private cloud configurations or public again our spectrum family is used by hundreds of public cloud service writers to deliver a backup service for example a spectrum protect so the reason my question was this very clearly in effect on that you talked about three percent or whatever you know the the latest numbers are it's flat Marcus gases and flat is flat but the cloud market of course is growing like that from a smaller base but it's clearly having an impact on demand is that a fair statement yeah I think what's happening when you look at it from a storage perspective where you're really having the biggest impact on cloud is in the lower end in the entry space yes the capacity is growing exponentially but whether it's the department level of a giant at global fortune 500 whether it's Herzog's bar and grill or a midsize company when they need a small array a lot of times are going to a public cloud configuration so that low end of the market is shrinking at the same time when you do software-defined if you're one of the tier 1 vendors the storage could come from off-the-shelf hard drives so the values in the software but that also delivers a revenue hit to the vendor base and Ashley when you think about how would you get incredible performance five or six years ago you would have bought an array that was five to eight million dollars best case if not closer to 10 you'd be lucky if you could get 200,000 I ops maybe you could get five milliseconds latency today at an average sale place of 300,000 dollars we can deliver over a million I ops and sub hundred micro center and latency so you don't need to buy your big iron at five eight 10 million you can do it with something for three hundred thousand dollars huge the bottleneck John okay I mean this is back to our kena brian.krall from Apple was on stage another great company leaders in the delivering great value but he made a comment I want to get your reaction to because I know it's a phone analogy but I want to bring into storage if the values and the software and all flash is the bet you guys are making the numbers are impressive in terms of performance in terms of I ops throughput and and cost per puss per megabyte he said you got to get closer to the hardware to write your native apps and he's referring to the iphone software app using Swift and xcode to the hardware so in storage look different how does the software piece take advantage of the hardware and is that built-in is an obstacle the customer because we're seeing this notion of okay take care of it take advantage of the hardware so what was how do you reconcile the we've done some very strong things there so let's take for example our spectrum virtualized software spectrum virtualize allows enterprise class data services across heterogeneous storage environments hours our competitors and anything that's white box over 300 arrays we have taken the spectrum virtualized platform and integrate it into our v nine thousand flash systems all-flash array into our mid tier storwize v7000 and our mid tier storwize v5000 which we just launched last week three new configurations we also have the sand volume controller but what we've done is integrate that spectrum virtualized software which rides a virtual back end of all storage not just our own provides a single way to replicate a single way to snapshot transparent block migration on the fly and integrate that right into flash systems and storwize as a software comes as a hard annick Stauffer comes with it exactly it's built into the size of Jeff managed as a code or estructuras code like an apple programa billion native app to the iphone what does that develop or doing with you guys is it through that software layer or how they could be right i mean the key thing when you look from a DevOps perspective they want to quickly be able to provision storage okay and with things like all the spectrum family and with the gooeys we've implemented into our store wise our XIV and all of our storage products it's very easy to deploy storage you can do it in minutes so whether the DevOps guy does or where the deadlock flight calls the storage guy the bottom line is they can get the storage up and running in a virtual environment a containerized environment in a matter of minutes and from a DevOps perspective that's what they want so we're able to meet the needs of the DevOps guy but also the traditional storage vendor as well don't get one last question for me for the henna we've run out of time they might have one more but I want to get your take on this because it's really been an interesting industry chess game with VCE and VMware and EMC doing the hyper converged x4 star calling it this hyper conversion without Cisco right this is because no longer you mentioned you in partnership with Cisco so VCC and bx rails was talked about last week what's going on with VCE is it still going to be around you see you're taking multiple forms is the increased breadth of solution is going to be multi-vendor what's your in it what you're taking on so you were at IBM cell you have relationship with cisco has that how does that what a customer's deal and what does the customer do because they're like okay who do I so I think there's a couple things that customers to look at first of all there's going to be a transformation VCE as it was originally constructed a partnership with cisco EMC and VMware will not exist after the acquisition this is my theory what will happen this distinctive sorry Cisco is go in there's no luck involved so all happen is those Cisco servers will be transitioned now and dell servers will be tradition did it's exactly what's going to happen so cisco is aware of this and cisco has been engaging with other partners like i mentioned the vs. tak had the best first year of any converged infrastructure in the history within its first year why well in the middle of last year what happened Dell an EMC an announced a merger so a lot of the business partners a lot of the end users there's cause for concern and EMC is already taken Cisco out of a number of configurations and there's a number of things for an end-user to think about one look at the development budgets what was the EMC development budget what's the dell development budget and substantially lower EMC did an outstanding job of acquiring startups with the debt load that's been written about publicly not just in the storage fresh but really in the financial press will be able to afford to buy a bunch of cool startups like EMC used to do the old days hard to say an EMC well I thought of stata domain was a great acquisition for uniting isilon same thing will they be able to continue to do that and like IBM EMC has a pretty good reputation for support and service that's not really reputation of the guys in Austin their reputation is cost-effective rapid delivery not necessarily the best important service the enterprise side people looking for that enterprise-class important service so those the questions that a customer needs to ask at the end user level where a channel partner use a civ as this merger goes for how's it going to impact the roadmap for the future the development expense my support capability those are things that have different models in those two companies so being should see how it pans out unfortunately we're out of time because we could do a whole cube second just on that area thanks for coming by give you the last word what does the digital transformation for the customer of IBM the buyer when they talked to you in the elevator and they say hey what's the storage angle on this digital treasure where the stores fit into my digital transformation what's the what's the bumper sticker what's the value proposition well the key thing digital transformation is a different sort of data it's been data for years and years and years data has to sit on storage the better the storage is your better the digital environment is the faster it is things like flash systems or our spectrum scale for cognitive the better that date is going to be so the digital era is powered by storage underneath it's like the foundation of a home good foundation great home good foundation great digital data great foundation the cube day one here more foundational coverage tomorrow the cube conversation will continue tomorrow day two we had more interviews today but tomorrow a lot of big names the biggest names in tech most powerful people here IBM interconnect is the cube we right back with more coverage here on day ones wrap up after the short break