>> live from San Francisco, California. It's the key. You covering the IBM chief Data officer? Someone brought to you by IBM. >> We're back at the IBM CDO conference. Fisherman's Worf Worf in San Francisco. You're watching the Cube, the leader in life tech coverage. My name is David Dante. Glenn Finches. Here's the global leader of Big Data Analytics and IBM, and we're pleased to have Mark Clare. He's the head of data enablement at AstraZeneca. Gentlemen, welcome to the Cube. Thanks for coming on my mark. I'm gonna start with this head of data Data Enablement. That's a title that I've never heard before. And I've heard many thousands of titles in the Cube. What is that all about? >> Well, I think it's the credit goes to some of the executives at AstraZeneca when they recruited me. I've been a cheap date officer. Several the major financial institutions, both in the U. S. And in Europe. Um, AstraZeneca wanted to focus on how we actually enable our business is our science areas in our business is so it's not unlike a traditional CDO role, but we focus a lot more on what the enabling functions or processes would be >> So it sounds like driving business value is really the me and then throw. Sorry. >> I've always looked at this role in three functions value, risk and cost. So I think that in any CDO role, you have to look at all three. I think the you'd slide it if you didn't. This one with the title. Obviously, we're looking at quite a bit at the value we will drive across the the firm on how to leverage our date in a different way. >> I love that because you can quantify all three. All right, Glenn. So you're the host of this event. So awesome. I love that little presentation that you gave. So for those you didn't see it, you gave us pay stubs and then you gave us a website and said, Take a picture of the paste up, uploaded, and then you showed how you're working with your clients. Toe. Actually digitize that and compress all kinds of things. Time to mortgage origination. Time to decision. So explain that a little bit. And what's that? What's the tech behind that? And how are people using it? You know, >> for three decades, we've had this OCR technology where you take a piece of paper, you tell the machine what's on the paper. What longitudinal Enter the coordinates are and you feed it into the hope and pray to God that it isn't in there wrong. The form didn't change anything like that. That's what that's way. We've lived for three decades with cognitive and a I, but I read things like the human eye reads things. And so you put the page in and the machine comes back and says, Hey, is this invoice number? Hey, is this so security number? That's how you train it as compared to saying, Here's what it So we use this cognitive digitization capability to grab data that's locked in documents, and then you bring it back to the process so that you can digitally re imagine the process. Now there's been a lot of use of robotics and things like that. I'm kind of taken existing processes, and I'm making them incrementally. Better write This says look, you now have the data of the process. You can re imagine it. However, in fact, the CEO of our client ADP said, Look, I want you to make me a Netflix, not a blood Urbach Blockbuster, right? So So it's a mind shift right to say we'll use this data will read it with a I will digitally re imagine the process. And it usually cuts like 70 or 80% of the cycle time, 50 to 75% of the cost. I mean, it's it's pretty groundbreaking when you see it. >> So markets ahead of data neighborhood. You hear something like that and you're not. You're not myopically focused on one little use case. You're taking a big picture of you doing strategies and trying to develop a broader business cases for the organization. But when you see an example like that and many examples out there, I'm sure the light bulbs go off. So >> I wrote probably 10 years cases down while >> Glenn was talking about you. You do get tactical, Okay, but but But where do you start when you're trying to solve these problems? >> Well, I look att, Glenn's example, And about five and 1/2 years ago, Glenn was one I went to had gone to a global financial service, firms on obviously having scale across dozens of countries, and I had one simple request. Thio Glenn's team as well as a number of other technology companies. I want cognitive intelligence for on data in Just because the process is we've had done for 20 years just wouldn't scale not not its speed across many different languages and cultures. And I now look five and 1/2 years later, and we have beginning of, I would say technology opportunities. When I asked Glenn that question, he was probably the only one that didn't think I had horns coming out of my head, that I was crazy. I mean, some of the leading technology firms thought I was crazy asking for cognitive data management capabilities, and we are five and 1/2 years later and we're seeing a I applied not just on the front end of analytics, but back in the back end of the data management processes themselves started automate. So So I look, you know, there's a concept now coming out day tops on date offices. You think of what Dev Ops is. It's bringing within our data management processes. It's bringing cognitive capabilities to every process step, And what level of automation can we do? Because the, you know, for typical data science experiment 80 to 90% of that work Estate engineering. If I can automate that, then through a date office process, then I could get to incite much faster, but not in scale it and scale a lot more opportunities and have to manually do it. So I I look at presentations and I think, you know, in every aspect of our business, where we clear could we apply >> what you talk about date engineering? You talk about data scientist spending his or her time just cleaning the wrangling data, All the all the not fun stuff exactly plugging in cables back in the infrastructure date. >> You're seeing horror stories right now. I heard from a major academic institution. A client came to them and their data scientists. They had spent several years building. We're spending 99% of their time trying to cleanse and prep data. They were spend 90% cleansing and prepping, and of the remaining 10% 90% of that fixing it where they fix it wrong and the first time so they had 1% of their job doing their job. So this is a huge opportunity. You can start automating more of that and actually refocusing data science on data >> science. So you've been a chief data officer number of financial institutions. You've got this kind of cool title now, which touches on some of the things a CDO might do and your technical. We got a technical background. So when you look a lot of the what Ginny Rometty calls incumbents, call them incumbent Disruptors two years ago at Ivy and think they've got data that has been hardened, you know, in all these projects and use cases and it's locked and people talk about the silos, part of your role is to figure out Okay, how do we get that data out? Leverage. It put it at the core. Is that is that fair? >> Well, and I'm gonna stay away from the word core cause to make core Kenan for kind of legacy processes of building a single repositories single warehouse, which is very time consuming. So I think I can I leave it where it is, but find a wayto to unify it. >> Not physically, exactly what I say. Corny, but actually the court, that's what we need >> to think about is how to do this logically and cream or of Ah unification approach that has speed and agility with it versus the old physical approaches, which took time. And resource is >> so That's a that's a computer science problem that people have been trying to solve for years. Decentralized, distributed, dark detectors, right? And why is it that we're now able Thio Tap your I think it's >> a perfect storm of a I of Cloud, the cloud native of Io ti, because when you think of I o. T, it's a I ot to be successful fabric that can connect millions of devices or millions of sensors. So you'd be paired those three with the investment big data brought in the last seven or eight years and big data to me. Initially, when I started talking to companies in the Valley 10 years ago, the early days of, um, apparatus, what I saw or companies and I could get almost any of the digital companies in the valley they were not. They were using technology to be more agile. They were finding agile data science. Before we call the data signs the map produce and Hadoop, we're just and after almost not an afterthought. But it was just a mechanism to facilitate agility and speed. And so if you look at how we built out all the way up today and all the convergence of all these new technologies, it's a perfect storm to actually innovate differently. >> Well, what was profound about my producing in the dupe? It was like leave the data where it is and shipped five megabytes a code two upended by the data and that you bring up a good point. We've now, we spent 10 years leveraging that at a much lower cost. And you've got the cloud now for scale. And now machine intelligence comes in that you can apply in the data causes. Bob Pityana once told me, Data's plentiful insights aren't Amen to that. So Okay, so this is really interesting discussion. You guys have known each other for a couple of couple of decades. How do you work together toe to solve problems Where what is that conversation like, Do >> you want to start that? >> So, um, first of all, we've never worked together on solving small problems, not commodity problems. We would usually tackle something that someone would say would not be possible. So normally Mark is a change agent wherever he goes. And so he usually goes to a place that wants to fix something or change something in an abnormally short amount of time for an abnormally small amount of money. Right? So what's strange is that we always find that space together. Mark is very judicious about using us as a service is firm toe help accelerate those things. But then also, we build in a plan to transition us away in transition, in him into full ownership. Right. But we usually work together to jump start one of these wicked, hard, wicked, cool things that nobody else >> was. People hate you. At first. They love you. I would end the one >> institution and on I said, OK, we're going to a four step plan. I'm gonna bring the consultants in day one while we find Thailand internally and recruit talent External. That's kind of phases one and two in parallel. And then we're gonna train our talent as we find them, and and Glenn's team will knowledge transfer, and by face for where, Rayna. And you know, that's a model I've done successfully in several organizations. People can. I hated it first because they're not doing it themselves, but they may not have the experience and the skills, and I think as soon as you show your staff you're willing to invest in them and give them the time and exposure. The conversation changes, but it's always a little awkward. At first, I've run heavy attrition, and some organizations at first build the organizations. But the one instance that Glen was referring to, we came in there and they had a 4 1 1 2 1 12 to 15 year plan and the C I O. Looked at me, he says. I'll give you two years. I'm a bad negotiator. I got three years out of it and I got a business case approved by the CEO a week later. It was a significant size business case in five minutes. I didn't have to go back a second or third time, but we said We're gonna do it in three years. Here's how we're gonna scale an organization. We scaled more than 1000 person organization in three years of talent, but we did it in a planned way and in that particular organization, probably a year and 1/2 in, I had a global map of every data and analytics role I need and I could tell you were in the US they set and with what competitors earning what industry and where in India they set and in what industry And when we needed them. We went out and recruited, but it's time to build that. But you know, in any really period, I've worked because I've done this 20 plus years. The talent changes. The location changes someone, but it's always been a challenge to find him. >> I guess it's good to have a deadline. I guess you did not take the chief data officer role in your current position. Explain that. What's what. What's your point of view on on that role and how it's evolved and how it's maybe being used in ways that don't I >> mean, I think that a CDO, um on during the early days, there wasn't a definition of a matter of fact. Every time I get a recruiter, call me all. We have a great CDO row for first time I first thing I asked him, How would you define what you mean by CDO? Because I've never seen it defined the same way into cos it's just that way But I think that the CDO, regardless of institutions, responsibility end in to make sure there's an Indian framework from strategy execution, including all of the governance and compliance components, and that you have ownership of each piece in the organization. CDO most companies doesn't own all of that, but I think they have a responsibility and too many organizations that hasn't occurred. So you always find gaps and each organization somewhere between risk costs and value, in terms of how how they're, how the how the organization's driving data and in my current role. Like I said, I wanted to focus. We want the focus to really be on how we're enabling, and I may be enabling from a risk and compliance standpoint, Justus greatly as I'm enabling a gross perspective on the business or or cost management and cost reductions. We have been successful in several programs for self funding data programs for multi gears. By finding and costs, I've gone in tow several organizations that it had a decade of merger after merger and Data's afterthought in almost any merger. I mean, there's a Data Silas section session tomorrow. It'd be interesting to sit through that because I've found that data data is the afterthought in a lot of mergers. But yet I knew of one large health care company. They've made data core to all of their acquisitions, and they was one the first places they consolidated. And they grew faster by acquisition than any of their competitors. So I think there's a There's a way to do it correctly. But in most companies you go in, you'll find all kinds of legacy silos on duplication, and those are opportunities to, uh, to find really reduce costs and self fund. All the improvements, all the strategic programs you wanted, >> a number inferring from the Indian in the data roll overlaps or maybe better than gaps and data is that thread between cost risk. And it is >> it is. And I've been lucky in my career. I've report toe CEOs. I reported to see Yellows, and I've reported to CEO, so I've I've kind of reported in three different ways, and each of those executives really looked at it a little bit differently. Value obviously is in a CEO's office, you know, compliance. Maurizio owes office and costs was more in the c i o domain, but you know, we had to build a program looking >> at all three. >> You know, I think this topic, though, that we were just talking about how these rules are evolving. I think it's it's natural, because were about 5 2.0. to 7 years into the evolution of the CDO, it might be time for a CDO Um, and you see Maur CEOs moving away from pure policy and compliance Tomb or value enablement. It's a really hard change, and that's why you're starting to Seymour turnover of some of the studios because people who are really good CEOs at policy and risk and things like that might not be the best enablers, right? So I think it's pretty natural evolution. >> Great discussion, guys. We've got to leave it there, They say. Data is the new oil date is more valuable than oil because you could use data to reduce costs to reduce risk. The same data right toe to drive revenue, and you can't put a gallon of oil in your car and a quart of oil in the car quarter in your house of data. We think it's even more valuable. Gentlemen, thank you so much for coming on the cues. Thanks so much. Lot of fun. Thanks. Keep right, everybody. We'll be back with our next guest. You're watching the Cube from IBM CDO 2019 right back.

>>Hey everyone. Welcome back to the cube. Live in Las Vegas, Lisa Martin, with Dave Nicholson, we are covering AWS reinvent 2021. This was probably one of the most important and largest hybrid tech events this year with AWS and its enormous ecosystem of partners. We're going to be talking with a hundred guests in the next couple of days. We started a couple of days ago and about really the innovation that's going to be going on in the cloud and tech in the next decade. We're pleased to welcome Rick Tam Daniel's as our next guest VP of strategic ecosystems at Informatica. Rick. Welcome to >>The program. Thank you for having me. It's a, it's a pleasure to be back. >>Isn't it nice to be back in person? Oh, it's amazing. All these conversations you just can't replicate by video conferencing. Absolutely >>Great to reconnect with folks haven't seen in a few years as well. >>Absolutely. That's been the sentiment. I think one of the, one of the sentiments that we've heard the last three days, so one of the things thematically that we've also been hearing about in, in between all of the plethora of AWS announcements, typical reinvent is that every company has to become a data company, public sector, private sector, small business, large business. Talk to us about how Informatica and AWS are helping companies become data companies so that they don't get left behind. >>But one of the biggest things that we're hearing at reinvent is that customers are really concerned with data, fragmentation, data silos, access to trusted data, and how do they, how do they get that information to really affect data led transformation? In fact, we did a survey earlier in the year of chief, the chief data officers were found that up to 80, almost 80% of organizations had 50% or more of their data in hybrid or multi-cloud environments. And also a 79% are looking to leverage more than 100 data sources. And 30% are looking to leverage more than 1000 data sources. So Informatica we, with our intelligent data management cloud, we're really focused on enabling customers to bring together the data assets, no matter where they live, what format they're in, on-premise cloud, multi-cloud bringing that all together. >>Well, we sold this massive scatter 22 months ago now, right? Of everyone just, and the edge exploded and data exploded and volumes and data sources exploded hard for organizations to get their head around that, to go or that the data is going to be living in all these different places. You talked about a lot of customers and every industry being hybrid multi-cloud because based on strategy, based on acquisition, but to get their arms around that data and to be able to actually extract value from it fast is going to be the difference between those businesses that succeed and those that don't >>Absolutely. And our partnership with AWS, that's a long standing partnership and we're very much focused on addressing the challenges you're talking about. Uh, and in fact, earlier this year we announced our cloud first, our cloud native, uh, data governance and data catalog on AWS, which is really focused on creating that central point of trusted data access and visibility for the organization. And just today, we had an announcement about how we're bringing data democratization and really accelerating data democratization for AWS lake formation. >>What is, when you, when you, we talk about data democratization often, what does that mean to you? What does that mean to Informatica? How do you deliver that to customers so that they can really be able to extract as much value as they can? >>Yeah. So a great question. And really that whole data management journey is a big piece of this. So it starts with data discovery. How do I even begin to find my data assets? How do I get them from where they are to where they need to go in the cloud? How do I make sure they're clean, they're ready to use. I trust them. I understand where they came from. And so the solution that we announced today is really focused on how do we provide a business users with a self-service way of getting access to data lake data, sitting in Amazon S3 with lake formation governance, but doing it in a way that doesn't create an undue burden on those business users, around data compliance and data policies. And so what we've done is we brought our business user-friendly self-service experience an axon data marketplace together with AWS lake formation. >>So Informatica has had a long history in the data world. Um, I think of terms like MDM and ETL. Yes. Where does, where does Informatica is history dovetail with the present day in terms of cloud the con does the concept of extract translate load? I think that's what ETL stood for too many TLAs running as far as trying to transform, uh, w where does that play in today's world? Are you focused separately on cloud from on-premise data center or do you, or do you link the two? Yeah, >>So we focus on, uh, addressing data management, uh, when, no matter where the data lives. So on-premise cloud multi-cloud, uh, on our intelligent data management cloud platform is a, is the industry's first end-to-end cloud native as a service data management platform that delivers all those capabilities. I mentioned before, uh, to customers. So we can manage all those workloads that are distributed from a single cloud-based as a service data management platform. So >>The platform is, is as a service in the cloud, but you could be managing data assets that are in traditional on premises, data centers, the like, absolutely. >>Okay. >>So congratulations on the IPO. Of course we can't, we can't not talk to Informatica without that. I imagined the momentum is probably pretty great right about now when we think of AWS, I, when I think of AWS, I always think of momentum. We, I mean the, the volume of announcements, but also when I think about AWS, I think about their absolute focus on the customer, that working backwards approach from a partnership perspective. Is there alignment there? I imagine, like I said, with the IPO, a lot of momentum right now, probably a lot of excitement are, is infant medical also was focused and customer obsessed as AWS's. >>Yeah. So, um, first of all, thank you so much. Congratulations. Uh, so we had a very successful IPO last month. And in fact, just yesterday, our CEO I'm at Wailea presented our Q3 results, uh, which showcase the continued growth of our subscription revenue or cloud revenue. And in fact, our cloud revenue grew 44% year over year, which is really reflective of our big shift to being a cloud first company and also the success of our intelligent data management cloud platform. Right. And, and that platform, again, as I mentioned, it's spanning all those aspects of data management and we're delivering that for more than 5,000 customers globally. Uh, and just from an adoption perspective, we processed about 23 trillion transactions a month for customers in our cloud platform. And that's doubling every six to 12 months. So it's incredible amount of adoption. Some of the biggest enterprises in the world like Unilever, Sanofi folks like that are using the cloud is their preferred data management platform of choice in the cloud. >>Well, you know, of course, congratulations is in order for the IPO, but also really on what you just mentioned, the trajectory of where Informatica is going, because Informatica wasn't born yesterday. Right. And, uh, we shouldn't overlook the fact that there are challenges associated with moving from the world as it exists on premises for still 80% of it spend at least navigating that transition, going from private to public, getting the right kind of investment where people realize that cloud is a significant barrier to entry, uh, for, for a lot of companies. I think it's, it's, you know, you have a lot of folks cheering for you as you navigate this transition. >>Well, one thing I do I say is, yes, we have it in the business of data for a long time, but we also then the business of cloud quite a long time. So this is true. This is the 10th reinvent. This is also the ten-year anniversary of the Informatica AWS partnership, right? So we've been working in the cloud with AWS for, for that long innovating all of these different, different core services. So, um, and from that perspective, you know, I think we're doing a tremendous amount of innovation together, you know, solutions like when we talked about for lake formation, but we also announced today a couple of key programs that we partnered with AWS around, around modernization and migration, right? So that's a big area of focus as well is how do we help customers modernize and take advantage of all the great services that AWS offers? So that's how we announced our membership and what's called the workload migration program and also the data lead migrations program, which is part of the public sector focus at AWS as well. >>The station perspective that was talked a lot about by Adam yesterday. And we've talked about it a lot today, every organization needs to monitorize, even some of those younger ones that you think, oh, aren't, they already, you know, fairly modern, but where, where are your customer conversations happening from a modernization perspective is that elevated up the, the C stat that we've got to modernize our or our organization get better handle of our data, be able to use it more protected, secure it so that we can be competitive and deliver outstanding customer experiences. >>What happens is the pain points that the legacy infrastructure has from the business perspective really do elevate the conversation to the C-suite. They're looking at saying, Hey, especially with the pandemic, right? We have to transform our business. We have to have data. We have to have trust in data. How do we do that? And we're not going to get there >>On rigid on-premise infrastructure. We need to be in a cloud native footprint. And so we've been focused on helping customers get to those cloud native end points, but also to a truly cloud native data management, we talked about earlier can manage all those different workloads, right? From a single that SAS serverless type experience. Right? What have been some of the interesting conversations that you've had here? Again, we are in person yep. Fresh off the IPO, lots of announcements coming out. You guys made announcements today. What's been the sentiment from the, those customers and partners that you've talked about. >>Well, I'll give you guys actually a little sneak preview of another announcement we have coming tomorrow, uh, with our friends at Databricks. So we, uh, we are announcing a data, data democratization solution with Databricks accelerating some of the same, you know, addressing some of the same challenges we were talking about here, but in the data breaks in the Lakehouse environment. Um, so, so, but around that, and I had a great conversation with some partners here, some of the global system integrators, and they're just so happy to see that, right, because a lot of the infrastructure that's around data lakes are lake formation. It's pretty technical it's for a technical audience. And, and Informatica has really been focused on how do we expand the base of users that are able to tap into data and that's through no code experiences, right? It's through visual experiences. And we bring that tightly coupled together with the performance and the power and scale of platforms like Databricks and the AWS Redshift and S3, it's really transformative for customers. >>What are some of the things that here we are wrapping up the 10th, re-invent almost as tomorrow, but also wrapping up the end of 2021. What are some of the things that th th that there's obviously a lot of momentum with Informatica right now that from a partnership perspective, anything that you, you just gave us some breaking news. Thank you. We always love that. What are some of the things that you're looking forward to in 2022 that you think are really going to help Informatica customers just be incredibly competitive and utilizing data in the cloud on prem to their maximum? >>Well, I think as we go into the next year data complexity data fragmentation, it's gonna continue to grow. It's, it's, it's exploding out there. Uh, and one of the key components of our platform or the IDMC platform is we call it Clare, which is the industry first kind of metadata driven AI engine. And what we've done is we've taken the intelligence of machine learning and AI, and brought that to the business of data management. And we truly believe that the way customers are going to tame that data, they're going to address those problems and continue to scale and keep up is leveraging the power of AI in a cloud native cloud, first data management platform. >>Excellent. Rick, thank you so much for joining us today. Again, congratulations on last month, Informatica IPO, great solid, strong, deep partnership with AWS. We thank you for your insights and best of luck next year. >>Awesome. Thank you so much. Pleasure being here. Our >>Pleasure to have you for my co-host David Nicholson, I'm Martin. You're watching the cube, the global leader in live tech coverage.

>>Thank you and good morning or afternoon, everyone, depending on where you're coming to us from and welcome to today's breakout session, fast data, a retail industry business imperative. My name is Brent Bedell, global managing director of retail, consumer bids here at Cloudera and today's hosts joining today. Joining me today is our feature speaker Brian Hill course managing partner from RSR. We'll be sharing insights and implications from recently completed research across retailers of all sizes in vertical segments. At the end of today's session, I'll share a brief overview on what I personally learned from retailers and how Cloudera continues to support retail data analytic requirements, and specifically around streaming data, ingest analytics, automation for customers around the world. There really is the next step up in terms of what's happening with data analytics today. So let's get started. So I thought it'd be helpful to provide some background first on how Clare to Cloudera is supporting and retail industry leaders specifically how they're leveraging Cloudera for leading practice data analytics use cases primarily across four key business pillars. >>And these will be very familiar to, to those in the industry. Personalize interactions of course, plays heavily into e-commerce and marketing, whether that's developing customer profiles, understanding the OB omni-channel journey, moving into the merchandising line of business focused on localized promotional planning, forecasting demand, forecast accuracy, then into supply chain where inventory visibility is becoming more and more critical today, whether it's around fulfillment or just understanding where your stuff is from a customer perspective. And obviously in and outbound route optimization right now, as retailers are taking control of actual delivery, whether it's to a physical store location or to the consumer. And then finally, uh, which is pretty exciting to me as a former store operator, you know, what's happening with physical brick and mortar right now, especially for traditional retailers. Uh, the whole re-imagining of stores right now is on fire in a lot of focus because, you know, frankly, this is where fulfillment is happening. >>Um, this is where customers, you know, still 80% of revenue is driven through retail, through physical brick and mortar. So right now store operations is getting more focused and I would say it probably is had and decades. Uh, and a lot of has to do for us with IOT data and analytics in the new technologies that really help, uh, drive, uh, benefits for retailers from a brick and mortar standpoint. And then, and then finally, um, you know, to wrap up before handing off to Brian, um, as you'll see, you know, all of these, these lines of businesses are raw, really experiencing the need for speed, uh, you know, fast data. So we're, we're moving beyond just discovery analytics. You don't things that happened five, six years ago with big data, et cetera. And we're really moving into real time capabilities because that's really where the difference makers are. >>That's where the competitive differentiation as across all of these, uh, you know, lines of business and these four key pillars within retail, um, the dependency on fast data is, is evident. Um, and it's something that we all read, you know, you know, in terms of those that are students of the industry, if you will, um, you know, that we're all focused on in terms of bringing value to the individual, uh, lines of business, but more importantly to the overall enterprise. So without further ado, I, I really want to, uh, have Brian speak here as a, as a third party analyst. You know, he, he's close in touch with what's going on, retail talking to all the solution providers, all the key retailers about what's important, what's on their plate. What are they focusing on right now in terms of fast data and how that could potentially make a difference for them going forward? So, Brian, uh, off to you, >>Well, thanks, Brent. I appreciate the introduction. And I was thinking, as you were talking, what is fast data? Well, data is fast. It is fast data it's stuff that comes at you very quickly. When I think about the decision cycles in retail, they were, they were, they were time phased and there was a time when we could only make a decision perhaps once a month and then met once a week and then once a day, and then intraday fast data is data that's coming at you and something approaching real time. And we'll explain why that's important in just a second. But first I want to share with you just a little bit about RSR. We've been in business now for 14 years. And what we do is we studied the business use cases that drive the adoption of technology in retail. We come from the retail industry, I was a retail technologist, my entire working life. >>And so we started this company. So I'm, I have a built in bias, of course, and that is that the difference between the winners in the retail world and in fact, in the entire business world and everybody else is how they value the strategic importance of information, and really that's where the battle is being fought today. We'll talk a little bit about that. So anyway, uh, one other thing about RSR research, our research is free to the entire world. Um, we don't, we don't have a paywall. You have to get behind. All you have to do is sign into our website, uh, identify yourself and all of our research, including these two reports that we're showing on the screen now are available to you. And we'd love to hear your comments. So when we talk about data, there's a lot of business implications to what we're trying to do with fast data and as being driven by the real world. >>Uh, we saw a lot of evidence of that during the COVID pandemic in 2020, when people had to make many decisions very, very quickly, for example, a simple one. Uh, do I redirect my replenishments to store B because store a is impacted by the pandemic, those kinds of things. Uh, these two drawings are actually from a book that came out in 1997. It was a really important book for me personally is by a guy named Steven Hegel. And it was the name of the book was the adaptive enterprise. When you think about your business model, um, and you think about the retail business model, most of those businesses are what you see on the left. First of all, the mission of the business doesn't change much at all. It changes once in a generation or maybe once in a lifetime, um, but it it's established quite early. >>And then from that point on it's, uh, basically a wash rinse and repeat cycle. You do the things that you do over and over and over again, year in and year out season in and season out. And the most important piece of information that you have is the transaction data from the last cycle. So a Brent knows this from his experience as a, as a retailer, the baseline for next year's forecast is last year's performance. And this is transactional in nature. It's typically pulled from your ERP or from your best of breed solution set on the right is where the world is really going. And before we get into the details of this, I'll just use a real example. I'm I'm sure like, like me, you've watched the path of hurricanes as they go up to the Florida coast. And one of the things you might've noticed is that there's several different possible paths. >>These are models, and you'll hear a lot about models. When you talk to people in the AI world, these are models based on lots and lots of information that they're getting from Noah and from the oceanographic people and all those kinds of folks to understand the likely path of the hurricane, based on their analysis, the people who watch these things will choose the most likely paths and they will warn communities to lock down and do whatever they need to do. And then they see as the, as the real hurricane progresses, they will see if it's following that path, or if it's varying, it's going down a different path and based on that, they will adapt to a new model. And that is what I'm talking about here now that not everything is of course is life and death as, as a hurricane. But it's basically the same concept what's happening is you have your internal data that you've had since this, a command and control model that we've mentioned on the left, and you're taking an external data from the world around you, and you're using that to make snap decisions or quick decisions based on what you see, what's observable on the outside, back to my COVID example, um, when people were tracking the path of the pandemic through communities, they learn that customers or consumers would favor certain stores to pick up their, what they needed to get. >>So they would avoid some stores and they would favor other stores. And that would cause smart retailers to redirect the replenishments on very fast cycles to those stores where the consumers are most likely to be. They also did the same thing for employees. Uh, they wanted to know where they could get their employees to service these customers. How far away were they, were they in a community that was impacted or were they relatively safe? These are the decisions that were being made in real time based on the information that they were getting from the marketplace around them. So, first of all, there's a context for these decisions. There's a purpose and the bounds of the adaptive structure, and then there's a coordination of capabilities in real time. And that creates an internal feedback loop, but there's also an external feedback loop. This is more of an ecosystem view. >>And based on those two, those two inputs what's happening internally, what your performance is internally and how your community around you is reacting to what you're providing. You make adjustments as necessary. And this is the essence of the adaptive enterprise. Engineers might call this a sense and respond model. Um, and that's where retail is going. But what's essential to that is information and information, not just about the products that you sell or the stores that you sell it in, or the employees that you have on the sales floor or the number of market baskets you've completed in the day, but something much, much more. Um, if you will, a twin, a digital twin of the physical assets of your business, all of your physical assets, the people, the products, the customers, the buildings, the rolling stock, everything, everything. And if you can create a digital equivalent of a physical thing, you can then analyze it. >>And if you can analyze it, you can make decisions much, much more quickly. So this is what's happening with the predict pivot based on what you see, and then, because it's an intrinsically more complicated model to automate, decision-making where it makes sense to do so. That's pretty complicated. And I talk about new data. And as I said earlier, the old data is all transactional in nature. Mostly about sales. Retail has been a wash in sales data for as long as I can remember throw, they throw most of it away, but they do keep enough to create the forecast the next for the next business cycle. But there's all kinds of new information that they need to be thinking about. And a lot of this is from the outside world. And a lot of this is non-transactional nature. So let's just take a look at some of them, competitive information. >>Those are always interested in what the competitor is up to. What are they promoting? How well are they they doing, where are they? What kind of traffic are they generating sudden and stuff, significant changes in customer behaviors and sentiment COVID is a perfect example of something that would cause this consumers changing their behaviors very quickly. And we have the ability to, to observe this because in a great majority of cases, nowadays retailers have observed that customers start their, uh, shopping journey in the digital space. As a matter of fact, Google recently came out and said, 60%, 63% of all, all sales transactions begin in the digital domain. Even if many of them end up in the store. So we have the ability to observe changes in consumer behavior. What are they looking at? When are they looking at it? How long do they spend looking at it? >>What else are they looking at while they're, while they're doing that? What are the, what is the outcome of that market metrics? Certainly what's going on in the marketplace around you? A good idea. Example of this might be something related to a sporting event. If you've planned based on normal demand and for, for your store. And there's a big sporting event, like a football match or a baseball game, suddenly you're going to see a spike in demand. So understanding what's going on in the market is really important. Location, demographics and psychographics, demographics have always been important to retailers, but now we're talking about dynamic demographics, what customers, or what consumers are, are in your market, in something approaching real time, psychographics has more to do with their attitudes. What kind of folks are, are, are in them in a particular marketplace? What do they think about what do they favor? >>And all those kinds of interesting deep tales, real-time environmental and social incidents. Of course, I mentioned hurricanes. And so that's fairly, self-evident disruptive events, sporting events, et cetera. These are all real. And then we get the real time internet of things. These are, these are RFID sensors, beacons, video, et cetera. There's all kinds of stuff. And this is where, yeah, it's interesting. This is where the supply chain people will start talking about the difference, little twin to their physical world. If you can't say something, you can manage it. And retailers want to be able to manage things in real time. So IOT, along with it, the analytics and the data that's generated is really, really important for them going forward, community health. We've been talking a lot about that, the progression of the flu, et cetera, et cetera, uh, business schedules, commute patterns, school schedules, and whether these are all external data that are interesting to retailers and can help them to make better operational in something approaching real time. >>I mentioned the automation of decision making. This is a chart from Gardner, and I'd love to share with you. It's a really good one because it describes very simply what we're talking about. And it also describes where the inflection of new technology happens. If you look on the left there's data, we have lots and lots of data. We're getting more data all the time, retailers for a long time. Now, since certainly since the seventies or eighties have been using data to describe what happened, this is the retrospective analysis that we're all very familiar with, uh, data cubes and those kinds of things. And based on that, the human makes some decisions about what they're going to do going forward. Um, sometime in the not too distant past, this data was started to be used to make diagnostic decisions, not only what happened, but why did it happen? >>And me might think of this as, for example, if sales were depressed for a certain product, was it because we had another product on sale that day, that's a good example of fairly straightforward diagnostics. We then move forward to what we might think of as predictive analytics. And this was based on what happened in the past and why it happened in the past. This is what's likely to happen in the future. You might think of this as, for example, halo effect or, or the cannibalization effect of your category plans. If you're, if you happen to be a grocer and based on that, the human will make a decision as to what they need to do next then came along AI, and I don't want to oversell AI here. AI is a new way for us to examine lots and lots of data, particularly unstructured data AI. >>If I could simplify it to its maximum extent, it essentially is a data tool that allows you to see patterns in data, which might be interesting. It's very good at sifting through huge data sets of unstructured data and detecting statistically significant patterns. It gets deeper than that, of course, because it uses math instead of rules. So instead of an if then, or else a statement that we might've used with our structured data, we use the math to detect these patterns in unstructured data. And based on those, we can make some models. For example, uh, my guy in my, in my, uh, just turned 70 on my 70 year old man, I'm a white guy. I live in California. I have a certain income and a certain educational level. I'm likely to behave in this way based on a model that's pretty simplistic. But based on that, you can see that. >>And when another person who meets my psychographics, my demographics, my age group, my income level and all the rest, um, you, they might, they might be expected to make a certain action. And so this is where prescriptive really comes into play. Um, AI makes that possible. And then finally, when you start to think about moving closer to the customer on something, approaching a personalized level, a one-to-one level, you, you suddenly find yourself in this situation of having to make not thousands of decisions, but tens of millions of decisions. And that's when the automation of decision-making really gets to be pretty important. So this is all interesting stuff, and I don't want to oversell it. It's exciting. And it's new. It's just the latest turn of the technology screw. And it allows us to use this new data to basically automate decision-making in the business, in something approaching real time so that we can be much, much more responsive to real-time conditions in the marketplace. >>Very exciting. So I hope this is interesting. This is a piece of data from one of our recent pieces of research. Uh, this happens to be from a location analytics study. We just published last week and we asked retailers, what are the big challenges what's been going on in the last 12 months for them? And what's likely to be happening for them in the next few years. And it's just fascinating because it speaks to the need for faster decision-making there. The challenges in the last 12 months were all related to COVID. First of all, fulfilling growing online demand. This is a very, very real time issue that we all had to deal with. But the next one was keeping forecasts in sync with changing demand. And this is one of those areas where retailers are now finding themselves, needing to look at that exoticness for that external data that I mentioned to you last year, sales were not a good predictor of next year of sales. >>They needed to look at sentiment. They needed to look at the path of the disease. They needed to look at the availability of products, alternate sourcing, global political issues. All of these things get to be pretty important and they affect the forecast. And then finally managing a supply them the movement of the supply through the supply chain so that they could identify bottlenecks now, point to one of them, which we can all laugh at now because it's kind of funny. It wasn't funny at the time we ran out of toilet paper, toilet paper was a big problem. Now there is nothing quite as predictable as toilet paper, it's tied directly to the size of the population. And yet we ran out and the thing we didn't expect when the COVID pandemic hit was that people would panic. And when people panic, they do funny things. >>One of the things I do is buy up all the available toilet paper. I'm not quite sure why that happened. Um, but it did happen and it drained the supply chain. So retailers needed to be able to see that they needed to be able to find alternative sources. They needed to be able to do those kinds of things. This gets to the issue of visibility, real time data, fast data tomorrow's challenge. It's kind of interesting because one of the things that they've retailers put at the top of their list is improved inventory productivity. Uh, the reason that they are interested in this is because then we'll never spend as much money, anything as they will on inventory. And they want the inventory to be targeted to those places where it is most likely to be consumed and not to places where it's least likely to be consumed. >>So this is trying to solve the issue of getting the right product at the right place at the right time to the right consumer and retailers want to improve this because the dollars are just so big, but in this complex, fast moving world that we live in today, it's this requires something approaching real-time visibility. They want to be able to monitor the supply chain, the DCS and the warehouses. And they're picking capacity. We're talking about each of us, we're talking about each his level. Decision-making about what's flowing through the supply chain all the way from the, from the manufacturing doctor, the manufacturer through to consumption. There's two sides of the supply chain and retailers want to look at it, you'll hear retailers and, and people like me talk about the digital twin. This is where this really becomes important. And again, the digital twin is, is enabled by IOT and AI analytics. >>And finally they need to re to increase their profitability for online fulfillment. Uh, this is a huge issue, uh, for some grocers, the volume of online orders went from less than 10% to somewhere north of 40%. And retailers did in 2020, what they needed to do to fulfill those customer orders in the, in the year of the pandemic, that now the expectation that consumers have have been raised significantly. They now expect those, those features to be available to them all the time. And many people really liked them. Now retailers need to find out how to do it profitably. And one of the first things they need to do is they need to be able to observe the process so that they can find places to optimize. This is out of our recent research and I encourage you to read it. >>And when we think about the hard one wisdoms that retailers have come up with, we think about these things better visibility has led to better understanding, which increases their reaction time, which increases their profitability. So what are the opportunities? This is the first place that you'll see something that's very common. And in our research, we separate over performers, who we call retail winners from everybody else, average and under-performers, and we've noticed throughout the life of our company, that retail winners, don't just do all the same things that others do. They tend to do other things. And this shows up in this particular graph, this again is from the same study. So what are the opportunities to, to address these challenges? I mentioned to you in the last slide, first of all, strategic placement of inventory throughout the supply chain to better fulfill customer needs. This is all about being able to observe the supply chain, get the inventory into a position where it can be moved quickly to fast changing demand. >>And on the consumer side, a better understanding and reacting to unplanned events that can drive a dramatic change in customer behavior. Again, this is about studying the data, analyzing the data and reacting to the data that comes before the sales transaction. So this is observing the path to purchase observing things that are happening in the marketplace around the retailer, so that they can respond very quickly, a better understanding of the dramatic changes in customer preference and path to purchase. As they engage with us. One of the things we, all we all know about consumers now is that they are in control and the literally the entire planet is the assortment that's available to them. If they don't like the way they're interacting with you, they will drop you like a hot potato and go to somebody else. And what retailers fear justifiably is the default response to that is to just see if they can find it on Amazon. >>You don't want this to happen if you're a retailer. So we want to observe how we are interacting with consumers and how well we are meeting their needs, optimizing omni-channel order fulfillment to improve profitability. We've already mentioned this, uh, retailers did what they needed to do to offer new fulfillment options to consumers. Things like buy online pickup curbside, buy online pickup in store, buy online, pick up at a locker, a direct to consumer all of those things. Retailers offer those in 2020 because the consumers demand it and needed it. So when retailers are trying to do now is to understand how to do that profitably. And finally, this is important. It never goes away. Is the reduction of waste shrink within the supply chain? Um, I'm embarrassed to say that when I was a retail executive in the nineties, uh, we were no more certain of consumer demand than anybody else was, but we, we wanted to commit to very high service levels for some of our key county categories somewhere approaching 95%. >>And we found the best way to do that was to flood the supply chain with inventory. Uh, it sounds irresponsible now, but in those days, that was a sure-fire way to make sure that the customer had what she was looking for when she looked for it. You can't do that in today's world. Money is too tight and we can't have that, uh, inventory sitting around and move to the right places. Once we discovered what the right place is, we have to be able to predict, observe and respond in something much closer to your time. One of the next slide, um, the simple message here, again, a difference between winners and everybody else, the messages, if you can't see it, you can't manage it. And so we asked retailers to identify, to what extent an AI enabled supply chain can help their company address some issues. >>Look at the differences here. They're shocking identifying network bottlenecks. This is the toilet paper story I told you about over half of retail winners, uh, feel that that's very important. Only 19% of average and under performers, no surprise that their average and under-performers visibility into available to sell inventory anywhere within the enterprise, 58% of winners and only 32% of everybody else. And you can go on down the list, but you get the just retail winners, understand that they need to be able to see their assets and something approaching real time so that they can make the best decisions possible going forward in something approaching real time. This is the world that we live in today. And in order to do that, you need to be able to number one, see it. And number two, you need to be able to analyze it. And number three, you have to be able to make decisions based on what you saw, just some closing observations on. >>And I hope this was interesting for you. I love talking about this stuff. You can probably tell I'm very passionate about it, but the rapid pace of change in the world today is really underscoring the importance. For example, of location intelligence, as a key component of helping businesses to achieve sustainable growth, greater operational effectiveness and resilience, and ultimately your success. So this is really, really critical for retailers to understand and successfully evolving businesses need to accommodate these new consumer shopping behaviors and changes in how products are brought to the market. So that, and in order to do that, they need to be able to see people. They need to be able to see their assets, and they need to be able to see their processes in something approaching real time, and then they need to analyze it. And based on what they've uncovered, they need to be able to make strategic and operational decision making very quickly. This is the new world we live in. It's a real-time world. It's a, it's a sense and respond world and it's the way forward. So, Brent, I hope that was interesting for you. I really enjoyed talking about this, as I said, we'd love to hear a little bit more. >>Hey, Brian, that was excellent. You know, I always love me love hearing from RSR because you're so close to what retailers are talking about and the research that your company pulls together. Um, you know, one of the higher level research articles around, uh, fast data frankly, is the whole notion of IOT, right? And he does a lot of work in this space. Um, what I find fascinating based off the recent research is believe it or not, there's $1.2 trillion at stake in retail per year, between now and 2025. Now, how is that possible? Well, part of it is because the Kinsey captures not only traditional retail, but also QSRs and entertainment then use et cetera. That's considered all of retail, but it's a staggering number. And it really plays to the effect that real-time can have on individual enterprises. In this case, we're talking of course, about retail. >>So a staggering number. And if you think about it from streaming video to sensors, to beacons, RFID robotics, autonomous vehicles, retailers are testing today, even pizza delivery, you know, autonomous vehicle. Well, if you think about it, it shouldn't be that shocking. Um, but when they were looking at 12 different industries, retail became like the number three out of 12, and there's a lot of other big industries that will be leveraging IOT in the next four years. So, um, so retailers in the past have been traditionally a little stodgy about their spend in data and analytics. Um, I think retailers in general have got the religion that this is what it's going to take to compete in today's world, especially in a global economy. And in IOT really is the next frontier, which is kind of the definition of fast data. Um, so I, I just wanted to share just a few examples or exemplars of, of retailers that are leveraging Cloudera technology today. >>So now, so now the paid for advertisement at the end of this, right? So, so, you know, so what bringing to market here. So, you know, across all retail, uh, verticals, you know, if we look at, you know, for example, a well-known global mass virtual retailer, you know, they're leveraging Cloudera data flow, which is our solution to move data from point to point in wicked fast space. So it's open source technology that was originally developed by the NSA. So, um, it is best to class movement of data from an ingest standpoint, but we're also able to help the roundtrip. So we'll pull the sensor data off all the refrigeration units for this particular retailer. They'll hit it up against the product lifecycle table. They'll understand, you know, temperature fluctuations of 10, 20 degrees based on, you know, fresh food products that are in the store, what adjustments might need to be made because frankly store operators, they'll never know refrigeration don't know if a cooler goes down and they'll have to react quickly, but they won't know that 10, 20 degree temperature changes have happened overnight. >>So this particular customer leverages father a data flow understand temperature, fluctuations the impact on the product life cycle and the round trip communication back to the individual department manager, let's say a produce department manager, deli manager, meat manager, Hey, you had, you know, a 20 degree drop in temperature. We suggest you lower the price on these products that we know are in that cooler, um, for the next couple of days by 20%. So you don't have to worry, tell me about freshness issues and or potential shrink. So, you know, the grocery with fresh product, if you don't sell it, you smell it, you throw it away. It's lost to the bottom line. So, you know, critically important and, you know, tremendous ROI opportunity that we're helping to enable there, uh, from a, a leading global drugstore retailer. So this is more about data processing and, you know, we're excited to, you know, the recent partnership with the Vidia. >>So fast data, isn't always at the edge of IOT. It's also about workloads. And in retail, if you are processing your customer profiles or segmentation like intra day, you will ever achieve personalization. You will never achieve one-on-one communications with readers killers or with customers. And why is that? Because customers in many cases are touching your brand several times a week. So taking you a week or longer to process your segmentation schemes, you've already lost and you'll never achieve personalization in frack. In fact, you may offend customers by offering. You might push out based on what they just bought yesterday. You had no idea of it. So, you know, that's what we're really excited about. Uh, again, with, with the computation speed, then the video brings to, to Cloudera, we're already doing this today already, you know, been providing levels, exponential speed and processing data. But when the video brings to the party is course GPU's right, which is another exponential improvement, uh, to processing workloads like demand forecast, customer profiles. >>These things need to happen behind the scenes in the back office, much faster than retailers have been doing in the past. Um, that's just the world we all live in today. And then finally, um, you know, proximity marketing standpoint, or just from an in-store operation standpoint, you know, retailers are leveraging Cloudera today, not only data flow, but also of course our compute and storage platform and ML, et cetera, uh, to understand what's happening in store. It's almost like the metrics that we used to look at in the past in terms of conversion and traffic, all those metrics are now moving into the physical world. If you can leverage computer vision in streaming video, to understand how customers are traversing your store, how much time they're standing in front of the display, how much time they're standing in checkout line. Um, you can now start to understand how to better merchandise the store, um, where the hotspots are, how to in real time improve your customer service. >>And from a proximity marketing standpoint, understand how to engage with the customer right at the moment of truth, right? When they're right there, um, in front of a particular department or category, you know, of course leveraging mobile devices. So that's the world of fast data in retail and just kind of a summary in just a few examples of how folks are leveraging Cloudera today. Um, you know, from an overall platform standpoint, of course, father as an enterprise data platform, right? So, you know, we're, we're helping to the entire data life cycle. So we're not a data warehouse. Um, we're much more than that. So we have solutions to ingest data from the edge from IOT leading practice solutions to bring it in. We also have experiences to help, you know, leverage the analytic capabilities of, uh, data engineering, data science, um, analytics and reporting. Uh, we're not, uh, you know, we're not, we're not encroaching upon the legacy solutions that many retailers have today. >>We're providing a platform, this open source that helps weave all of this mess together that existed retail today from legacy systems because no retailer, frankly, is going to rip and replace a lot of stuff that they have today. Right. And the other thing that Cloudera brings to market is this whole notion of on-prem hybrid cloud and multi-cloud right. So our whole, our whole culture has been built around open source technology as the company that provides most of the source code to the Apache network around all these open source technologies. Um, we're kind of religious about open source and lack of vendor lock-in, uh, maybe to our fault. Uh, but as a company, we pull that together from a data platform standpoint. So it's not a rip and replace situation. It's like helping to connect legacy systems, data and analytics, um, you know, weaving that whole story together to be able to solve this whole data life cycle from beginning to end. >>And then finally, you know, just, you know, I want to thank everyone for joining today's session. I hope you found it informative. I can't say Brian killed course enough. Um, you know, he's my trusted friend in terms of what's going on in the industry. He has much broader reach of course, uh, in talking to a lot of our partners in, in, in, in other, uh, technology companies out there as well. But I really appreciate everyone joining the session and Brian, I'm going to kind of leave it open to you to, you know, any closing comments that you might have based on, you know, what we're talking about today in terms of fast data and retail. >>First of all, thank you, Brent. Um, and this is an exciting time to be in this industry. Um, and I'll just leave it with this. The reason that we are talking about these things is because we can, the technology has advanced remarkably in the last five years. Some of this data has been out there for a lot longer than that in it, frankly wasn't even usable. Um, but what we're really talking about is increasing the cycle time for decisions, making them go faster and faster so that we can respond to consumer expectations and delight them in ways that that make us a trusted provider of their life, their lifestyle needs. So this is really a good time to be a retailer, a real great time to be servicing the retail technology community. And I'm glad to be a part of it. And I was glad to be working with you. So thank you, Brian. >>Yeah, of course, Brian, and one of the exciting things for me to not being in the industry, as long as I have and being a former retailer is it's really exciting for me to see retailers actually spending money on data and it for a change, right? They've all kind of come to this final pinnacle of this is what it's going to take to compete. Um, you know, you know, and I talked to, you know, a lot of colleagues, even, even salespeople within Cloudera, I like, oh, retail, very stodgy, you know, slow to move. That's not the case anymore. Um, you know, religion is everyone's, everyone gets the religion of data and analytics and the value of that. And what's exciting for me to see as all this infusion of immense talent within the industry years ago, Brian, I mean, you know, retailers are like, you know, pulling people from some of the, you know, the greatest, uh, tech companies out there, right? From a data science data engineering standpoint, application developers, um, retail is really getting this legs right now in terms of, you know, go to market and in the leverage of data and analytics, which to me is very exciting. Well, >>You're right. I mean, I, I became a CIO around the time that, uh, point of sale and data warehouses were starting to happen data cubes and all those kinds of things. And I never thought I would see a change that dramatic, uh, as the industry experience back in those days, 19 89, 19 90, this changed doors that, but the good news is again, as the technology is capable, uh, it's, it's, we're talking about making technology and information available to, to retail decision-makers that consumers carry around in their pocket purses and pockets is there right now today. Um, so the, the, the question is, are you going to utilize it to win or are you going to get beaten? That's really what it boils down to. Yeah, >>For sure. Uh, Hey, thanks everyone. We'll wrap up. I know we ran a little bit long, but, uh, appreciate, uh, everyone, uh, hanging in there with us. We hope you enjoyed the session. The archive contact information is right there on the screen. Feel free to reach out to either Brian and I. You can go to cloudera.com. Uh, we even have, you know, joint sponsored papers with RSR. You can download there as well as eBooks and other assets that are available if you're interested. So thanks again, everyone for joining and really appreciate you taking the time. >>Hello everyone. And thanks for joining us today. My name is Brent Bedell, managing director retail, consumer goods here at Cloudera. Cloudera is very proud to be partnering with companies like three soft to provide data and analytic capabilities for over 200 retailers across the world and understanding why demand forecasting could be considered the heartbeat of retail. And what's at stake is really no mystery to most, to most retailers. And really just a quick level set before handing this over to my good friend, uh, Camille three soft, um, you know, IDC Gartner. Um, many other analysts have kind of summed up an average, uh, here that I thought would be important to share just to level set the importance of demand forecasting or retail. And what's at stake. I mean the combined business value for retailers leveraging AI and IOT. So this is above and beyond. What demand forecasting has been in the past is a $371 billion opportunity. >>And what's critically important to understand about demand forecasting. Is it directly impacts both the top line and the bottom line of retail. So how does it affect the top line retailers that leverage AI and IOT for demand forecasting are seeing average revenue increases of 2% and think of that as addressing the in stock or out of stock issue in retail and retail is become much more complex now, and that is no longer just brick and mortar, of course, but it's fulfillment centers driven by e-commerce. So inventory is now having to be spread over multiple channels. Being able to leverage AI and IOT is driving 2% average revenue increases. Now, if you think about the size of most retailers or the average retailer that on its face is worth millions of dollars of improvement for any individual retailer on top of that is balancing your inventory, getting the right product in the right place and having productive inventory. >>And that is the bottom line. So the average inventory reduction, leveraging AI and IOT as the analyst have found, and frankly, having spent time in this space myself in the past a 15% average inventory reduction is significant for retailers not being overstocked on product in the wrong place at the wrong time. And it touches everything from replenishment to out-of-stocks labor planning and customer engagement for purposes of today's conversation. We're going to focus on inventory and inventory optimization and reducing out-of-stocks. And of course, even small incremental improvements. I mentioned before in demand forecast accuracy have millions of dollars of direct business impact, especially when it comes to inventory optimization. Okay. So without further ado, I would like to now introduce Dr. Camille Volker to share with you what his team has been up to. And some of the amazing things that are driving at top retailers today. So over to you, Camille, >>Uh, I'm happy to be here and I'm happy to speak to you, uh, about, uh, what we, uh, deliver to our customers. But let me first, uh, introduce three soft. We are a 100 person company based in Europe, in Southern Poland. Uh, and we, uh, with 18 years of experience specialized in providing what we call a data driven business approach, uh, to our customers, our roots are in the solutions in the services. We originally started as a software house. And on top of that, we build our solutions. We've been automation that you get the software for biggest enterprises in Poland, further, we understood the meaning of data and, and data management and how it can be translated into business profits. Adding artificial intelligence on top of that, um, makes our solutions portfolio holistic, which enables us to realize very complex projects, which, uh, leverage all of those three pillars of our business. However, in the recent time, we also understood that services is something which only the best and biggest companies can afford at scale. And we believe that the future of retail, uh, demon forecasting is in the product solutions. So that's why we created occupy our AI platform for data driven retail. That also covers this area that we talked about today. >>I'm personally proud to be responsible for our technology partnerships with other on Microsoft. Uh, it's a great pleasure to work with such great companies and to be able to, uh, delivered a solution store customers together based on the common trust and understanding of the business, uh, which cumulates at customer success at the end. So why, why should you analyze data at retail? Why is it so important? Um, it's kind of obvious that there is a lot of potential in the data per se, but also understanding the different areas where it can be used in retail is very important. We believe that thanks to using data, it's basically easier to the right, uh, the good decisions for the business based on the facts and not intuition anymore. Those four areas that we observe in retail, uh, our online data analysis, that's the fastest growing sector, let's say for those, for those data analytics services, um, which is of course based on the econ and, uh, online channels, uh, availability to the customer. >>Pandemic only speeds up this process of engagement of the customers in that channel, of course, but traditional offline, um, let's say brick and mortar shops. Uh, they still play the biggest role for most of the retailers, especially from the FMCG sector. However, it's also very important to remember that there is plenty of business, uh, related questions that meet that need to be answered from the headquarter perspective. So is it actually, um, good idea to open a store in a certain place? Is it a good idea to optimize a stock with Saturday in producer? Is it a good idea to allocate the goods to online channel in specific way, those kinds of questions they are, they need to be answered in retail every day. And with that massive amount of factors coming into that question, it's really not, not that easy to base, only on the intuition and expert knowledge, of course, uh, as Brent mentioned at the beginning, the supply chain and everything who's relates to that is also super important. We observe our customers to seek for the huge improvements in the revenue, just from that one single area as well. Okay. >>So let me present you a case study of one of our solutions, and that was the lever to a leading global grocery retailer. Uh, the project started with the challenge set of challenges that we had to conquer. And of course the most important was how to limit overstocks and out of stocks. Uh, that's like the holy grail in of course, uh, how to do it without flooding the stores with the goods and in the same time, how to avoid empty shelves, um, from the perspective of the customer, it was obvious that we need to provide a very well, um, a very high quality of sales forecast to be able to ask for, uh, what will be the actual sales of the individual product in each store, uh, every day, um, considering huge role of the perishable goods in the specific grocery retailer, it was a huge challenge, uh, to provide a solution that was able to analyze and provide meaningful information about what's there in the sales data and the other factors we analyzed on daily basis at scale, however, uh, our holistic approach implementing AI with data management, uh, background, and these automation solutions all together created a platform that was able to significantly increase, uh, the sales for our customer just by minimizing out of stocks. >>In the same time we managed to not overflow the stock, the shops with the goods, which actually decreased losses significantly, especially on the fresh fruit. >>Having said that this results of course translate into the increase in revenue, which can be calculated in hundreds of millions of dollars per year. So how the solution actually works well in its principle, it's quite simple. We just collect the data. We do it online. We put that in our data lake, based on the cloud, there are technology, we implement our artificial intelligence models on top of it. And then based on the aggregated information, we create the forecast and we do it every day or every night for every single product in every single store. This information is sent to the warehouses and then the automated replenishment based on the forecast is on the way the huge and most important aspect of that is the use of the good tools to do the right job. Uh, having said that you can be sure that there is too many information in this data, and there is actually two-minute forecast created every night that any expert could ever check. >>This means our solution needs to be, uh, very robust. It needs to provide information with high quality and high porosity. There is plenty of different business process, which is on our forecast, which need to be delivered on time for every product in each individual shop observing the success of this project and having the huge market potential in mind, we decided to create our QB, which can be used by many retailers who don't want to create a dedicated software for that. We'll be solving this kind of problem. Occupy is, uh, our software service offering, which is enabling retailers to go data driven path management. >>We create occupant with retailers, for retailers, uh, implementing artificial intelligence, uh, on top of data science models created by our experts, uh, having data, data analysis in place based on data management tools that we use we've written first, um, attitude. The uncertain times of pandemic clearly shows that it's very important to apply correction factors, which are sometimes required because we need to respond quickly to the changes in the sales characteristics. That's why occupy B is open box solution, which means that you basically can implement that in your organization. We have without changing the process internally, it's all about mapping your process into this into the system, not the other way around the fast trends and products, collection possibilities allow the retailers to react to any changes, which are pure in the sales every day. >>Also, it's worth to mention that really it's not only FMCG. And we believe that different use cases, which we observed in fashion health and beauty, common garden pharmacies and electronics, flavors of retail are also very meaningful. They also have one common thread. That's the growing importance of e-commerce. That's why we didn't want to leave that aside of occupant. And we made everything we can to implement a solution, which covers all of the needs. When you think about the factors that affect sales, there is actually huge variety of data and that we can analyze, of course, the transactional data that every dealer possesses like sales data from sale from, from e-commerce channel also, uh, averaging numbers from weeks, months, and years makes sense, but it's also worth to mention that using the right tool that allows you to collect that data from also internal and external sources makes perfect sense for retail. Uh, it's very hard to imagine a competitive retailer that is not analyzing the competitor's activity, uh, changes in weather or information about some seasonal stores, which can be very important during the summer during the holidays, for example. Uh, but on the other hand, um, having that information in one place makes the actual benefit and environment for the customer. >>Okay. Demon forecasting seems to be like the most important and promising use case. We can talk about when I think about retail, but it's also their whole process of replenishment that can cover with different sets of machine learning models. And they done management tools. We believe that analyzing data from different parts of the retail, uh, replenishment process, uh, can be achieved with implementing a data management solution based on caldera products and with adding some AI on top of it, it makes perfect sense to focus on not only demand forecasting, but also further use cases down the line when it comes to the actual benefits from implementing solutions for demand management, we believe it's really important to analyze them holistically. First is of course, out of stocks, memorization, which can be provided by simply better sales focus, but also reducing overstocks by better inventory management can be achieved in, in the same time. Having said that we believe that analyzing data without any specific new equipment required in point of sales is the low hanging fruit that can be easily achieved in almost every industry in almost every regular customer. >>Hey, thanks, Camille, having worked with retailers in this space for a couple of decades, myself, I was really impressed by a couple of things and they might've been understated, frankly. Um, the results of course, I mean, you, you know, as I kind of set up this session, you doubled the numbers on the statistics that the analysts found. So obviously in customers you're working with, um, you know, you're, you're doubling average numbers that the industry is having and, and most notably how the use of AI or occupy has automated so many manual tasks of the past, like tour tuning, item profiles, adding new items, et cetera. Uh, and also how quickly it felt like, and this is my, my core question. Your team can cover, um, or, or provide the solution to, to not only core center store, for example, in grocery, but you're covering fresh products. >>And frankly, there are, there are solutions out on the market today that only focus on center store non-perishable department. So I was really impressed by the coverage that you're able to provide as well. So can you articulate kind of what it takes to get up and running and your overall process to roll out the solution? I feel like based on what you talked about, um, and how you were approaching this in leveraging AI, um, that you're, you're streamlining processes of legacy demand, forecasting solutions that required more manual intervention, um, how quickly can you get people set up and what is the overall process like to get started with soft? >>Yeah, it's usually it takes three to six months, uh, to onboard a new customer to that kind of solution. And frankly it depends on the data that the customer, uh, has. Uh, usually it's different, uh, for smaller, bigger companies, of course. Uh, but we believe that it's very important to start with a good foundation. The platform needs to be there, the platform that is able to, uh, basically analyze or process different types of data, structured, unstructured, internal, external, and so on. But when you have this platform set, it's all about starting ingesting data there. And usually for a smaller companies, it's easier to start with those, let's say, low hanging fruits. So the internal data, which is there, this data has the highest veracity is already easy to start with, to work with them because everyone in the organization understands this data for the bigger companies. It might be important to ingest also kind of more unstructured data, some kind of external data that need to be acquired. So that may, that may influence the length of the process. But we usually start with the customers. We have, uh, workshops. That's very important to understand their business because not every deal is the same. Of course, we believe that the success of our customers comes also due to the fact that we train those models, those AI models individually to the needs of our >>Totally understand and POS data, every retailer has right in, in one way shape or form. And it is the fundamental, uh, data point, whether it's e-comm or the brick and mortar data, uh, every retailer has that data. So that, that totally makes sense. But what you just described was bunts. Um, there are, there are legacy and other solutions out there that this could be a, a year or longer process to roll out to the number of stores, for example, that you're scaling to. So that's highly impressive. And my guess is a lot of the barriers that have been knocked down with your solution are the fact that you're running this in the cloud, um, you know, on, from a compute standpoint on Cloudera from a public cloud stamp point on Microsoft. So there's, there's no, it intervention, if you will, or hurdles in preparation to get the database set up and in all of the work, I would imagine that part of the time-savings to getting started, would that be an accurate description? >>Yeah, absolutely. Uh, in the same time, this actually lowering the business risks, because we simply take data and put that into the data lake, which is in the cloud. We do not interfere with the existing processes, which are processing this data in the combined. So we just use the same data. We just already in the company, we ask some external data if needed, but it's all aside of the current customers infrastructure. So this is also a huge gain, as you said, right? >>And you're meeting customers where they are. Right. So, as I said, foundationally, every retailer POS data, if they want to add weather data or calendar event data or, you know, want incorporate a course online data with offline data. Um, you have a roadmap and the ability to do that. So it is a building block process. So getting started with, for data, uh, as, as with POS online or offline is the foundational component, which obviously you're very good at. Um, and then having that ability to then incorporate other data sets is critically important because that just improves demand, forecast accuracy, right. By being able to pull in those, those other data sources, if you will. So Camille, I just have one final question for you. Um, you know, there, there are plenty of not plenty, but I mean, there's enough demand forecasting solutions out on the market today for retailers. One of the things that really caught my eye, especially being a former retailer and talking with retailers was the fact that you're, you're promoting an open box solution. And that is a key challenge for a lot of retailers that have, have seen black box solutions come and go. Um, and especially in this space where you really need direct input from the, to continue to fine tune and improve forecast accuracy. Could you give just a little bit more of a description or response to your approach to open box versus black box? >>Yeah, of course. So, you know, we've seen in the past the failures of the projects, um, based on the black box approach, uh, and we believe that this is not the way to go, especially with this kind of, uh, let's say, uh, specialized services that we provide in meaning of understanding the customer's business first and then applying the solution, because what stands behind our concept in occupy is the, basically your process in the organization as a retailer, they have been optimized for years already. That's where retailers put their, uh, focus for many years. We don't want to change that. We are not able to optimize it properly. For sure as it combined, we are able to provide you a tool which can then be used for mapping those very well optimized process and not to change them. That's our idea. And the open box means that in every process that you will map in the solution, you can then in real time monitor the execution of those processes and see what is the result of every step. That way we create truly explainable experience for our customers, then okay, then can easily go for the whole process and see how the forecast, uh, was calculated. And what is the reason for a specific number to be there at the end of the day? >>I think that is, um, invaluable. Um, can be, I really think that is a differentiator and what three soft is bringing to market with that. Thanks. Thanks everyone for joining us today, let's stay in touch. I want to make sure to leave, uh, uh, Camille's information here. Uh, so reach out to him directly or feel free at any, any point in time, obviously to reach out to me, um, again, so glad everyone was able to join today, look forward to talking to you soon.

(upbeat music) >> From around the globe, it's theCUBE with digital coverage of Postgres vision 2021 brought to you by EDB. >> Hello everybody. Welcome back to Postgres Vision 21. My name is Dave Vellante and we're super excited to have Josh Berkus on. He's joining us, he's a leader in the Kubernetes community, extremely well-versed in containerized applications, application development, containerizing databases all things Open-source, CUBE alum, Josh Berkus welcome back to theCUBE. Great to see you again. >> Thank you. I'm glad to be here. >> Just recently, you're coming off KuberCon, we heard some of the themes from that event. There was a lot of focus on inclusion and diversity, which of course, you know, that's the Open-source ethos and a lot of discussion around designing security in, the whole conversation about shift left. That's great to see larger companies giving back, to obviously a lot of the pressure over the years on the big companies that there's a one-way street, they're actually giving back, making some investments. So we love to see that. And just Open-source continues to be the main spring of innovation. I got to say, I got to call-out and a recent Red Hat survey the state of the enterprise Open-source in 2021, 90% of technology leaders said that they're adopting Open-source and made a joke that the other 10% they're doing it they just don't know it. But so what were some of your takeaways from the event and some of the trends you're seeing but specifically as it relates to containers. >> So, I mean, you're right, one thing is this sort of return to security, the security topic again because we've had like a couple of things happen. One was, when we initially got, started doing containers or platform with Docker and with early Kubernetes and that sort of thing we got a lot of container image scan, right? So you have like Clare and Docker has a scanning thing and Amazon and Azure have their own scanning things. And people felt that was kind of good enough for a while but then we both had the solar winds hack. And the thing is like, in the meantime, we've gone from a stage where people were mostly using Kubernetes in dev to people using Kubernetes in production. And there's a lot of extra security issues and vulnerabilities that come up in an actual production environment that people just didn't necessarily think about before. And so now we're looking at adding more pieces to the security stack and making those more standard for everyone who uses Kubernetes. And I've had the chance to work with the StackRox folks since they became part of Red Hat. So it's been very exciting to look at the whole thing and look at things like container supply chain because the solar winds showed us obviously, it's not enough to necessarily just trust the vendor. You need to trust their whole supply chain. And it helps to be able to examine that supply chain. >> Yeah, it's very scary when you look at that you're absolutely right. Multiple components of malware coming into an organization through the supply chain cell forming, different signatures. And so it's great to see the community spending time on that and an emphasis on that. Now I got to cut right to the chase here, in 2018, you wrote a two-part blog series it's called Should I run Postgres in Kubernetes? Obviously it's highly relevant for this community. So I want to talk about your perspective, well, first of all, the thing I love about you is you're tactical and you can go deep, but at the same time, you can speak to a business audience. >> Thanks. >> You're welcome and thank you for writing this and communicating the way you do, but talk about when it makes sense and when it doesn't, I mean, that's kind of... My big three takeaways on the pros were simplify, simplify, simplify, especially if you're running application components and other services on Kubernetes but give us the update three years later, why should you, why shouldn't? >> You know let's actually, why don't we zoom out to an even bigger picture? Which is just honestly like every new platform that we've got, right? So when virtualization and VMware became a thing we had the same sort of decisions about when do I move my database to this, when AWS and the public cloud became a thing. I could have like, like if I had written that 12 years ago I could have written it about AWS and it would have had a lot of the same decision tree 'cause what it really sort of comes down to is the more commodifiable a particular database instance is the better candidate it is to move to an advanced infrastructure platform, and the most advanced, currently being Kubernetes. To the extent that you can describe this particular database, what it does, who needs to use it, what's in it in and a simple one pager then that's probably a really good candidate for hosting on Kubernetes. Whereas if you have a database where it's like, Hey, the entire company uses it and it's so complicated we can't describe it's inputs and outputs. That's possibly the last thing in your company that you're going to migrate to Kubernetes, because both in terms of there's less gain to be made there, because the real advantage of moving stuff to Kubernetes is your ability to automate things. The whole way I got into Kubernetes in the first place was I started out way down the line not using containers at all. I was just looking to solve the problem of how do we automate Postgres high availability. That's what I was looking for. And it started out with something I built using SaltStack called handy rep, that Casey and I built. And mostly that was a problem discovery exercise, we discovered what the hard problems were there. And then we moved from that, and then we moved from that to Docker because containers offered an encapsulation strategy because one of the problems you run into when automating high availability is the database actually down or not. And so the first thing that containers offered us was not packaging, what people usually talk about but instead of encapsulation, right, because it's a lot easier to determine is the container running or not, than is the database down or not? Because an actual Postgres database has multiple components and multiple processes that make it up. And some of those can be down without the others being down which can then make you think a database is down that's not actually shut down. And being able to put that in a container, it gives me more of a binary up or down. And then from there, I got into, okay, well but I need to automate a lot of other components. I need to automate the storage and everything else. And that led to Kubernetes. And so if you look at it in terms of deciding when you're going to migrate the database to Kubernetes you look at, can I take advantage of that automation? Is this something that my application workflow and my team organization allows me to do? And if the answer is yes, particularly, if you're in a company that's doing the full dev ops thing where you have a unified development and infra team that owns the entire stack then those people are going to be a really good candidate for moving that stack to Kubernetes. >> Got it. Okay, so let me ask you, in database especially in critical apps, your recovery's everything, when something goes wrong, you got to recover. So if I understand it correctly, just in reading and listening to you, if you've got Kubernetes expertise and you're building applications in that environment then the application components are in there. And am I inferring correctly that you're going to be able to automate and facilitate high quality recovery with certainty? >> Yeah, there's a bunch of infrastructure involved, and this is why, what enterprises do is they move things like the web front-end to Kubernetes first and is what they should do, right? That is absolutely the right order of things to do because the minute that you're looking at bringing databases in, you're now looking at your whole storage infrastructure. So that direct attack storage that was attached physically to one machine is not going to work once you've moved to a container-based cloud. You suddenly need a way to be able to attach that storage to any of the nodes in your cluster so that you can move the database around and you can have fail-over. But once you build those things up, you can't. I mean, some of the stuff that I've done, I work in the office of the CTO now at Red Hat. So I'm not in production support. So the only Postgres instance I'm supporting are ones for some Open-source projects we support like the Python project. And in those cases, it's not a high criticality database, but I'm not support, I'm not on call on the weekend. I want something where it doesn't require need to be on call in order for it to stay up. And so putting that on open shift with the Patroni fail-over driver was the answer for that. And it has failed over in the Red Hat IT team contacts me and says, "Hey, we need to move those servers. And then we'll just add a node to the cluster and delete the old node and it'll do the right thing." And I don't have to worry about it, which is really what you're going for there. >> The other thing I took away from your writing was that you suggested that a lot of the successes in areas where the Postgres databases were rather small and there were a lots of them. And so to the extent that you can automate that you're going to save yourself a lot of problems. Whereas in the flip side if you're running extremely large databases or there may be performance constraint that might be an area to be a little bit more circumspect. >> Yeah and that's absolutely true because like the other side of this, like I've worked with the dev ops people and the people who are on Heroku and that sort of thing that have one database per application, right. And those people are great candidates for migrating. But then I've also worked with the people who have a one big database for the company, where the database is three terabytes in size, it powers their reporting system and their customer's system and the web portal and everything else in one database. That's the one that's really going to be a hard call and that you might in fact, never physically migrate to Kubernetes because even if it's on Kubernetes you are going to mess with the hardware policy to give it its own dedicated machine. So in that case, what I would honestly tend to do is there's a feature in Kubernetes called service catalog that allows you to expose an external service within Kubernetes as if it were a Kubernetes service. And that's what I tend to do with those kinds of databases because it's, there's not a huge advantage in actually physically moving the database to a container. There's a bunch of steps involved and going via service catalog is a lot easier. >> But essentially you're you're speaking the same language in that example that you just gave. >> Yeah. >> Now, the other thing you pointed out at the time that you wrote this article is there's a lot of pre 1.0 kind of alpha in the Kubernetes stack and it might be prudent to if, not putting your HIPAA compliant, since it evolved. >> Yeah, if I was to update two things in the article I guess that would be one of them the other one I'll get to in a minute. So the first one is that, Kubernetes has progressed along that maturity timeline. Like we recently added the production readiness reviews as part of our feature review process. We've really improved tested adherence, so that we're not releasing with known broken tests, and a bunch of other things to make it more stable. But part of it depends on who I'm talking to because there's still degrees here. So if I'm talking to the context of the world of software then Kubernetes has reached the point of maturity that it is as stable as anything else. And if you use a release, you can assume that any sort of major issues have been worked out. The one difference with it and some other platforms people may have used is it's still young enough that backwards compatibility can be an issue. As in Kubernetes releases now three times a year, we've stepped down from four and within three releases you can find yourself needing to change API calls which means needing to refactor parts of your application. So if you compare that with some other things, like a JVM platform, when's the last time you had a major API change with a JVM platform. But you know the Kubernetes is only six years old, so that's part of that. The other thing is the question is I'm talking to the Postgres community, right? Which is within Postgres, people run the daily Postgres snapshot in production. I would not do that with Kubernetes, I would wait for release. So there's still kind of a difference there if people are coming from the Postgres community, right. Is we're used to this really extreme level of stability that we have with Postgres and Kubernetes as a much younger project isn't quite there yet. >> So that's a process, a change that you would have to be aware of if you want to take the benefits of containers with Postgres, you just have to really understand that and make that process part of your change management. >> The other thing I would say has changed is there are new opportunities in running your data warehouse, your big data databases on Kubernetes. A number of platforms, the one I'm most familiar with is Citus, because I worked with those folks that have taken advantage of Kubernetes as a deployment and management platform for their database, their big data database infrastructure, which makes sense because if you look at a lot of modern data analysis and data mining platforms that are built on top of Postgres part of how they do their work is they actually run a bunch of little Postgres instances that they federate together. And then Kubernetes becomes the tool that allows you to manage all of those little Postgres instances. So that's the sort of exception to the, should I migrate this really big database? That can be a yes, if you are migrating it to a big data platform that supports Kubernetes, then it can be a huge advantage. >> Obviously you've got the practitioner knowledge and you were working in the community. I'm wondering if you can share just thinking about sort of the motivation to move to a container environment if you're one of the Postgres folks in the audience could you share any, either anecdotal or other data on business impact, benchmarks that you've seen, some of the things that you've seen some positives there? >> If you actually look at my history when you talk about performance is one, right? And if you actually look at my history, I actually did, and for that matter of some of the folks from Percona and some of our other folks in the database field did a bunch of benchmarks of running Postgres in MySQL, on Kubernetes versus running it not on Kubernetes. And one of the advantages of containers over VMS is that there isn't any intrinsic, there's not any intrinsic sort of layer gap or virtualization that modifies your performance. In other words, if a container is using storage that's present on the node where the container is running it is using that storage through Linux. And therefore the performance is, with some caveats, performance is going to be identical to if you were running that on the host system. Now, where performance differences creep in is that you might not be able to use the same kind of storage. In that Kubernetes and containers systems in general are organized around the idea that no service is using a majority of the resources on the system, so again, if you're planning on user running a larger Postgres database that really needs all the RAM that a system has you're going to have to do a lot of tinkering with Kubernetes configuration to get the same performance, you would have a running it on a dedicated hardware now. >> Okay, but fundamentally you're saying that overhead is less with caveats, like you said, you just mentioned in the story, right? >> Yeah, well, the overhead is not any different from if you were running under the host system. So a really good example of that was, if you go back to on my lightning talking in, (indistinct) Austin, I think. I showed running a benchmark with Postgres on an AWS instance using EBS storage, both not in Kubernetes and in Kubernetes. And there was no perceptible performance difference between the two of them because it was all metered by how fast was EBS for me. >> Right, and I said less, but I should've been more specific less than say you would expect with virtualization. >> Right, and then it just comes down to a business decision, which is that if you're already on some sort of cloud storage or network storage, and again you have databases that can share hardware systems then you shouldn't really expect substantial performance differences by moving to Kubernetes. That's something that you can eliminate inside of words, but if you're going in the process going to be migrating from direct attached storage to network storage then you are going to see a performance difference but that's caused by the change in storage. Or if you're going to be moving from systems that are not shared to systems that aren't shared again you're going to see a difference from them, but it wouldn't be any different than if you did that without Kubernetes containers being involved. >> If you're using any world-class shared storage device from whatever name of big vendor, you're going to accommodate if you're racking and stacking your own flash drives or worse yet spinning disk drives that's in direct attached, that's maybe a different story, so, okay. That's good. Where would you advise people to get started with Postgres and Kubernetes? >> The nice thing is there are a number of advanced systems now, and advanced systems that are supported by the various Postgres vendors. And that can actually be a great place to get started because the systems are Open-source so you can try them out. This is, as far as I know, they're Open-source you can try them out but then if you decide you like them, you can get support. And so that would include Crunchy data. Enterprise DB has a system, and honestly, I have to admit less familiar with than the ones that Crunchy runs. StackRox is another one out of Europe that has their own system for running cloud native Postgres. And there's one I'm forgetting, and what a lot of these have to do with is taking advantage of the automation. 'Cause you can obviously can put Postgres and container play around, right? But your whole point of moving to Kubernetes in general is going to be take advantage of the automation, so you want to look at the various automation platforms and you can go ahead and do that and the one I'm most familiar with because I develop it as Patroni, is the component for automating Postgres. You do Patroni plus you do operators, it's another word that comes in here. But if you're looking at this as a business you're probably going to want something that supported or that at least there's a potential to buy support and a bunch of the different companies in the Postgres space package up these components for you into a platform. Like I know the Crunchy platform uses Patroni plus some proxy stuff, plus PG back rest plus a couple of other things to give you a sort of full automation platform for running Postgres on Kubernetes. >> Awesome, last question. Where are we in the whole container adoption, we started out kind of you've mentioned this stateless and now you're building stateful applications but still you look at the, we look at spending data with our data partners ETR and containers and container orchestration. It's it's right up there with RPA, with cloud, with AI just in terms of the attention and resource that's going in. So it's exploding. It feels like it's still early days. There's a lot of legs left, what do you see? >> Yeah, well, a lot of it is, I mean you're talking about migrating IT infrastructure, right? So where we are with Kubernetes is we have the early adopters, right? We have all the people who were at the point of building their new infrastructure when Kubernetes came out, right. And people who had major unsolved problems which is a big reason for adopting a new platform was just was no old platform for you. and so we sort of have those people and those people are already on Kubernetes and running their stuff there. And so now we're looking at the really long path of people who are not in one of those camps moving, right. And in a lot of cases, that's a matter of coinciding with other reasons why they have to look at an upgrade because even if, whether it's the gradual replacement of old applications by new ones, where you gradually all the legacy applications get offline and the new applications run in Kubernetes or sometimes it's a, "Hey we're waiting for replacement cycle." We're waiting for, we already had plans to move from on-prem to public cloud, and so we're going to move from on-prem to public cloud on Kubernetes, to make it part of the migration. And that'll be years. I still like, I have fingers into other areas, like I still know a lot of people in the nonprofit space and a lot of nonprofits just got around to adopting virtualization, right? Like they're not even at public cloud yet. I don't even talk to them about Kubernetes. There's this huge long tail in terms of adoption. The nice thing is we don't show any signs of stopping, is that one of the things that we kind of learned from earlier stuff particularly learned from our friends at OpenStack was to really really focus on the APIs, to look at who Kubernetes more as the hub of a system of an infrastructure idea with potentially unbounded growth. If you have a new concept that comes in like service mesh, service mesh is not a successor to Kubernetes. It's not an alternative to Kubernetes. It is a thing you layer on top of Kubernetes because we didn't make it exclusive. >> Right. Great, great example going back to OpenStack and thank you for bringing that in because there's lessons learned. And so Josh, we've got to leave it there. Thanks so much for coming back in theCUBE, great conversation, you're awesome. >> Okay, good to talk to you. >> All right, and thank you for watching everybody, keep it right there for more content from Postgres Vision 21. My name is Dave Vellante, you're watching theCUBE. (upbeat music)

>>From around the globe, it's the cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. >>Oh, welcome back to the cube. As we continue our coverage of AWS reinvent 2020, you know, I know you're familiar with Moneyball, the movie, Brad Pitt, starting as Billy Bean, the Oakland A's general manager, where the A's were all over data, right. With the Billy Bean approach, it was a very, uh, data driven approach to building his team and a very successful team. Well, AWS is taking that to an extraordinary level and with us to talk about that as Matt Hearst, who was the head of global sports marketing and communications at AWS and Matt, thanks for joining us here on the queue. >>John is my pleasure. Thanks so much for having me. You >>Bet. Um, now we've already heard from a couple of folks, NFL folks, uh, at re-invent, uh, about the virtual draft. Um, but for those of our viewers who maybe aren't up to speed on that, or having a chance to see, uh, what those folks had to say, uh, let's just talk about that as an opener, um, about your involvement with the NFL and particularly with, with the draft and, and what that announcement was all about. >>Sure. We, we saw, we've seen a great evolution with our work with the NFL over the past few years. And you mentioned during the infrastructure keynote where Michelle McKenna who's, the CIO for the NFL talks about how they were able to stage the 2020 virtual draft, which was the NFL is much most watched ever, uh, you know, over 55 million viewers over three days and how they were unable to do it without the help and the power of AWS, you know, utilizing AWS is reliability, scalability, security, and network connectivity, where they were able to manage thousands of live feeds to flow to the internet and go to ESPN, to airline. Um, but additionally, Jennifer LinkedIn, who's the SVP of player health and innovation at the NFL spoke during the machine learning keynote during reinvent. And she talked about how we're working with the NFL, uh, to co-develop the digital athlete, which is a computer simulation model of a football player that can replicate infinite scenarios in a game environment to help better foster and understanding of how to treat and rehabilitate injuries in the short term and in the long-term in the future, ultimately prevent, prevent and predict injuries. >>And they're using machine learning to be able to do that. So there's, those are just a couple of examples of, uh, what the NFL talked about during re-invent at a couple of keynotes, but we've seen this work with the NFL really evolve over the past few years, you know, starting with next gen stats. Those are the advanced statistics that, uh, brings a new level of entertainment to football fans. And what we really like to do, uh, with the NFL is to excite, educate, and innovate. And those stats really bring fans closer to the game to allow the broadcasters to go a little bit deeper, to educate the fans better. And we've seen some of those come to life through some of our ads, uh, featuring Deshaun Watson, Christian McCaffrey, um, these visually compelling statistics that, that come to life on screen. Um, and it's not just the NFL. AWS is doing this with some of the top sports leagues around the world, you know, powering F1 insights, Buddhist league, and match facts, six nations, rugby match stats, all of which utilize AWS technology to uncover advanced stats and really help educate and engage fans around the world in the sports that they love. >>Let's talk about that engagement with your different partners then, because you just touched on it. This is a wide array of avenues that you're exploring. You're in football, you're in soccer, you're in sailing, uh, you're uh, racing formula one and NASCAR, for example, all very different animals, right? In terms of their statistics and their data and of their fan interest, what fans ultimately want. So, um, maybe on a holistic basis first, how are you, uh, kind of filtering through your partner's needs and their fans needs and your capabilities and providing that kind of merger of capabilities with desires >>Sports, uh, for AWS and for Amazon are no different than any other industry. And we work backwards from the customer and what their needs are. You know, when we look at the sports partners and customers that we work with and why they're looking to AWS to help innovate and transform their sports, it's really the innovative technologies like machine learning, artificial intelligence, high performance computing, internet of things, for example, that are really transforming the sports world and some of the best teams and leagues that we've talked about, that you touched on, you know, formula one, NASCAR, NFL, Buena, Sligo, six nations, rugby, and so on and so forth are using AWS to really improve the athlete and the team performance transform how fans view and engage with sports and deliver these real-time advanced statistics to give fans, uh, more of that excitement that we're talking about. >>Let me give you a couple of examples on some of these innovative technologies that our customers are using. So the Seattle Seahawks, I built a data Lake on AWS to use it for talent, evaluation and acquisition to improve player health and recovery times, and also for their game planning. And another example is, you know, formula and we talk about the F1 insights, those advanced statistics, but they're also using AWS high-performance computing that helped develop the next generation race car, which will be introduced in the 2022 season. And by using AWS F1 was able to reduce the average time to run simulations by 70% to improve the car's aerodynamics, reducing the downforce loss and create more wheel to wheel racing, to bring about more excitement on the track. And a third example, similar to, uh, F1 using HPC is any of those team UK. So they compete in the America's cup, which is the oldest trophy in international sports. And endosteum UK is using an HPC environment running on Amazon, easy to spot instances to design its boat for the upcoming competition. And they're depending on this computational power on AWS needing 2000 to 3000 simulations to design the dimension of just a single boat. Um, and so the power of the cloud and the power of the AWS innovative technologies are really helping, uh, these teams and leagues and sports organizations around the world transform their sport. >>Well, let's go back. Uh, you mentioned the Seahawks, um, just as, uh, an example of maybe, uh, the kind of insights that that you're providing. Uh, let's pretend I'm there, there's an outstanding running back and his name's Matt Hearst and, uh, and he's at a, you know, a college let's just pretend in California someplace. Um, what kind of inputs, uh, are you now helping them? Uh, and what kind of insights are you trying to, are you helping them glean from those inputs that maybe they didn't have before? And how are they actually applying that then in terms of their player acquisition and thinking about draft, right player development, deciding whether Matt Hertz is a good fit for them, maybe John Wallace is a good fit for them. Um, but what are the kinds of, of, uh, what's that process look like? >>So the way that the Seahawks have built the data Lake, they built it on AWFs to really, as you talk about this talent, evaluation and acquisition, to understand how a player, you know, for example, a John Walls could fit into their scheme, you know, that, that taking this data and putting it in the data Lake and figuring out how it fits into their schemes is really important because you could find out that maybe you played, uh, two different positions in high school or college, and then that could transform into, into the schematics that they're running. Um, and try to find, I don't want to say a diamond in the rough, but maybe somebody that could fit better into their scheme than, uh, maybe the analysts or others could figure out. And that's all based on the power of data that they're using, not only for the talent evaluation and acquisition, but for game planning as well. >>And so the Seahawks building that data Lake is just one of those examples. Um, you know, when, when you talk about a player, health and safety, as well, just using the NFL as the example, too, with that digital athlete, working with them to co-develop that for that composite NFL player, um, where they're able to run those infinite scenarios to ultimately predict and prevent injury and using Amazon SageMaker and AWS machine learning to do so, it's super important, obviously with the Seahawks, for the future of that organization and the success that they, that they see and continue to see, and also for the future of football with the NFL, >>You know, um, Roger Goodell talks about innovation in the national football league. We hear other commissioners talking about the same thing. It's kind of a very popular buzz word right now is, is leagues look to, uh, ways to broaden their, their technological footprint in innovative ways. Again, popular to say, how exactly though, do you see AWS role in that with the national football league, for example, again, or maybe any other league in terms of inspiring innovation and getting them to perhaps look at things differently through different prisms than they might have before? >>I think, again, it's, it's working backwards from the customer and understanding their needs, right? We couldn't have predicted at the beginning of 2020, uh, that, you know, the NFL draft will be virtual. And so working closely with the NFL, how do we bring that to life? How do we make that successful, um, you know, working backwards from the NFL saying, Hey, we'd love to utilize your technology to improve Clare health and safety. How are we able to do that? Right. And using machine learning to do so. So the pace of innovation, these innovative technologies are very important, not only for us, but also for these, uh, leagues and teams that we work with, you know, using F1 is another example. Um, we talked about HPC and how they were able to, uh, run these simulations in the cloud to improve, uh, the race car and redesign the race car for the upcoming seasons. >>But, uh, F1 is also using Amazon SageMaker, um, to develop new F1 insights, to bring fans closer to the action on the track, and really understand through technology, these split-second decisions that these drivers are taking in every lap, every turn, when to pit, when not to pit things of that nature and using the power of the cloud and machine learning to really bring that to life. And one example of that, that we introduced this year with, with F1 was, um, the fastest driver insight and working F1, worked with the Amazon machine learning solutions lab to bring that to life and use a data-driven approach to determine the fastest driver, uh, over the last 40 years, relying on the years of historical data that they store in S3 and the ML algorithms that, that built between AWS and F1 data scientists to produce this result. So John, you and I could sit here and argue, you know, like, like two guys that really love F1 and say, I think Michael Schumacher is the fastest drivers. It's Lewis, Hamilton. Who's great. Well, it turned out it was a arts incentive, you know, and Schumacher was second. And, um, Hamilton's third and it's the power of this data and the technology that brings this to life. So we could still have a fun argument as fans around this, but we actually have a data-driven results through that to say, Hey, this is actually how it, how it ranked based on how everything works. >>You know, this being such a strange year, right? With COVID, uh, being rampant and, and the major influence that it has been in every walk of global life, but certainly in the American sports. Um, how has that factored into, in terms of the kinds of services that you're looking to provide or to help your partners provide in order to increase that fan engagement? Because as you've pointed out, ultimately at the end of the day, it's, it's about the consumer, right? The fan, and giving them info, they need at the time they want it, that they find useful. Um, but has this year been, um, put a different point on that for you? Just because so many eyeballs have been on the screen and not necessarily in person >>Yeah. T 20, 20 as, you know, a year, unlike any other, um, you know, in our lifetimes and hopefully going forward, you know, it's, it's not like that. Um, but we're able to understand that we can still bring fans closer to the sports that they love and working with, uh, these leagues, you know, we talk about NFL draft, but with formula one, we, uh, in the month of may developed the F1 Pro-Am deep racer event that featured F1 driver, uh, Daniel Ricardo, and test driver TA Sianna Calderon in this deep racer league and deep racers, a one 18th scale, fully autonomous car, um, that uses reinforcement learning, learning a type of machine learning. And so we had actual F1 driver and test driver racing against developers from all over the world. And technology is really playing a role in that evolution of F1. Um, but also giving fans a chance to go head to head against the Daniel Ricardo, which I don't know that anyone else could ever say that. >>Yeah, I raced against an F1 driver for head to head, you know, and doing that in the month of may really brought forth, not only an appreciation, I think for the drivers that were involved on the machine learning and the technology involved, but also for the developers on these split second decisions, these drivers have to make through an event like that. You know, it was, it was great and well received. And the drivers had a lot of fun there. Um, you know, and that is the national basketball association. The NBA played in the bubble, uh, down in Orlando, Florida, and we work with second spectrum. They run on AWS. And second spectrum is the official optical provider of the NBA and they provide Clippers court vision. So, uh, it's a mobile live streaming experience for LA Clippers fans that uses artificial intelligence and machine learning to visualize data through on-screen graphic overlays. >>And second spectrum was able to rely on, uh, AWS is reliability, connectivity, scalability, and move all of their equipment to the bubble in Orlando and still produce a great experience for the fans, um, by reducing any latency tied to video and data processing, um, they needed that low latency to encode and compress the media to transfer an edit with the overlays in seconds without losing quality. And they were able to rely on AWS to do that. So a couple of examples that even though 2020 was, uh, was a little different than we all expected it to be, um, of how we worked closely with our sports partners to still deliver, uh, an exceptional fan experience. >>So, um, I mean, first off you have probably the coolest job at AWS. I think it's so, uh, congratulations. I mean, it's just, it's fascinating. What's on your want to do less than in terms of 20, 21 and beyond and about what you don't do now, or, or what you would like to do better down the road, any one area in particular that you're looking at, >>You know, our, our strategy in sports is no different than any other industry. We want to work backwards from our customers to help solve business problems through innovation. Um, and I know we've talked about the NFL a few times, but taking them for, for another example, with the NFL draft, improving player health and safety, working closely with them, we're able to help the NFL advance the game both on and off the field. And that's how we look at doing that with all of our sports partners and really helping them transform their sport, uh, through our innovative technologies. And we're doing this in a variety of ways, uh, with a bunch of engaging content that people can really enjoy with the sports that they love, whether it's, you know, quick explainer videos, um, that are short two minute or less videos explaining what these insights are, these advanced stats. >>So when you see them on the screening and say, Oh yeah, I understand what that is at a, at a conceptual level or having blog posts from a will, Carlin who, uh, has a long storied history in six nations and in rugby or Rob Smedley, along story history and F1 writing blog posts to give fans deeper perspective as subject matter experts, or even for those that want to go deeper under the hood. We've worked with our teams to take a deeper look@howsomeofthesecometolifedetailingthetechnologyjourneyoftheseadvancedstatsthroughsomedeepdiveblogsandallofthiscanbefoundataws.com slash sports. So a lot of great rich content for, uh, for people to dig into >>Great stuff, indeed. Um, congratulations to you and your team, because you really are enriching the fan experience, which I am. One of, you know, hundreds of millions are enjoying that. So thanks for that great work. And we wish you all the continued success down the road here in 2021 and beyond. Thanks, Matt. Thanks so much, Sean.

(upbeat music) >> Hello, everyone, welcome to this CUBE conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We're here with a very special guest, Amit Walia CEO of Informatica. Newly appointed CEO, about a month ago, a little bit over a month ago. Head of product before that. Been with Informatica since 2013. Informatica went private in 2015, and has since been at the center of the digital transformation around data, data transformation, data privacy, data everything around data and value and AI. Amit, great to see you, and congratulations on the new CEO role at Informatica. >> Thank you. Always good to be back here, John. >> It's been great to follow you, and for the folks who don't know you, you've been a very product centric CEO. You're a product set CEO, as they call it. But also now you have a company in the middle of the transformation. CloudScale is really mainstream. Enterprise is looking to multicloud, hybrid cloud. This is something that you've been on for many, many years. We've talked about it. So now that you're in charge, you've got the ship, the wheel in your hands. Where are you taking it? What is the update of Informatica? Give us the update. >> Well, thank you. So look, business couldn't be better. I think to give you a little bit of color where we're coming from the last couple of years Informatica went through a huge amount of transformation. All things trying to transform a business model, pivoting to subscription, all things have really been into Cloud, the new workloads as we talked about and all things new like AI. To give a little bit of color, we basically exited last year with a a billion dollars of ARR, not just revenues. So we had a billion dollar ARR company and as we pivoted to subscription, our subscription business for the last couple of years has been growing North of 55%. So that's the scale at which we are running multimillion dollars and if you look at the other two metrics which we keep very clicked near and dear to heart, one is innovation. So we are participating in five Magic Quadrants and we are the leader in all five Magic Quadrants. Five on five as we like to call it Gartner Magic Quadrants, very critical to us because innovation in the tech is very important. Also customer loyalty, very important to us. So we again, we're the number one in customer sat from a TSI survey and Gartner publishes the vendor ratings. We basically have a very strong positioning in that. And lastly, our market share continues to grow. So last IDC survey, our market share continued to grow and with the number one in all our markets. So business couldn't be at a better place where we are right now. >> I want to get into some of the business discussion. We first on the Magic Quadrant front, it's very difficult for the folks that aren't in the Cloud as to understand that to participate in multiple Magic Quadrants, what many do is hard because Clouds horizontally scalable Magic Quadrants used to be old IT kind of categories but to be in multiple Magic Quadrants is the nature of the beast but to be a leader is very difficult because Magic Quarter doesn't truly capture that if you're just a pure play and then try to be Cloud. So you guys are truly that horizontal brand and technology. We've covered this on theCUBE so it's no secret, but I want to get your comments on to be a leader in today, in these quadrants, you have to be on all the right waves. You've got data warehouses are growing and changing, you got the rise of Snowflake. You guys partner with Databricks, again, machine learning and AI, changing very rapidly and there's a huge growth wave behind it as well as the existing enterprises who were transforming analytics and operational workloads. This is really, really challenging. Can you just share your thoughts on why is it so hard? What are some of the key things behind these trends? We've got analytics, I guess you can do if it's just Analytics and Cloud, great, but this is a, this horizontal data play Is not easy. Can you share why? >> No, so yes, first we are actually I would say a very hidden secret. We're the only software company and I'll say that again, the only software company that was the leader in the traditional workloads legacy on premise and via the leader and the Cloud workloads. Not a single software company can say that they were the leader of and they were started 27 years ago and they're still the leader in the Magic Quadrants today. Our Cloud by the way runs at 10 trillion transactions a month scale and obviously we partnered with all the hyperscalers across the board and our goal is to be the Switzerland of data for our customers. And the question you ask is is a critical one, when you think of the key business drivers, what are customers trying to do? One of them is all things Cloud, all things AI is obviously there but one is all data warehouses are going to Cloud, we just talked about that. Moving workloads to Cloud, whether it is analytical, operational, basically we are front and center helping customers do that. Second, a big trend in the world of digital transformation is helping our customers, customer experience and driving that, fueling that is a master data management business, so on and so products behind that, but driving customer experiences, big, big driver of our growth and the third one is no large enterprise can live without data governance, data privacy. Even this is a thing today. You going to make sure that you would deliver a good governance, whether it's compliance oriented or brand oriented, privacy and risk management. And all three of them basically span the business initiatives that featured into those five Magic Quadrants. Our goal is to play across all of them and that's what we do. >> Pat Gelsinger here said a quote on theCUBE, many years ago. He said, "If you're not on the right wave, your could be driftwood," meaning you're going to get crashed over. >> He said very well. >> A lot of people have, we've seen a lot of companies have a good scale and then get washed away, if you will, by a wave. You're seeing like AI and machine learning. We talked a little bit about that. You guys are in there and I want to get your thoughts on this one. Whenever this executive changes, there's always questions around what's happening with the company. So I want you to talk about the state of Informatica because you're now the CEO, there's been some changes. Has there been a pivot? Has there been a sharpening focus? What is going on with Informatica? >> So I think our goal right now is to scale and hyperscale, that's the word. I mean we are in a very strong position. In fact, we use this phrase internally within the company, the next phase of great. We're at a great place and we are chartering the next phase of great for the company. And the goal that is helping our customers, I talked about these three big, big initiatives that companies are investing in, data warehousing and analytics, going to the Cloud, transforming customer experiences and data governance and privacy. And the fourth one that underpins all of them is all things AI. I mean, as we've talked about it before, right? All of these things are complex, hard to do. Look at the volume and complexity of data and what we're investing in is what we call native AI. AI needs, data, data needs AI, as I always said, right? And we had investing in AI to make these things easy for our customers, to make sure that they can scale and grow into the future. And what we've also been very diligent about is partnering. We partnered very well with the hyperscalers, like whether it's AWS, Microsoft, whether it's GCP, Snowflake, great partner of ours, Databricks great partner of ours, Tablo, great partners of ours. We have a variety of these partners and our goal is always customer first. Customers are investing in these technologies. Our goal is to help customers adopt these technologies, not for the sake of technologies, but for the sake of transforming those three business initiatives I talked about. >> You brought up, I was going to ask you the next question about Snowflake and Databricks. Databricks has been on theCUBE, Ali, >> And here's a good friend of ours. And he's got chops, I mean Stanford, Berkeley, he'll kill me with that, he's a cowl at Stanford but Databricks is doing well. They made some good bets and it's paying off for them. Snowflake, a rising star, Frank Slootman's over there now, they are clearly a choice for modern data warehouses as is, inhibits Redshift. How are you working with Snowflake? How do you take advantage of that? Can you just unpack your relationship with Snowflake? >> It's a very deep partnership. Our goal is to help our customers as they pick these technology choices for data warehousing as an example where Snowflake comes into play to make sure that the underlying data infrastructure can work seamlessly for them. See, customers build this complex logic sitting in the old technologies. As they move to anything new, they want to make sure that their transition, migration is seamless, as seamless as it can be. And typically they'll start something new before they retire to something old. With us, they can carry all of that business logic for the last 27 years, their business logic seamlessly and run natively in this case, in the Cloud. So basically we allow them this whole from-to and also the ability to have the best of new technology in the context of data management to power up these new infrastructures where they are going. >> Let me ask you the question around the industry trends, what are the top trends, industry trends that are driving your business and your product direction and customer value? >> Look, digital transformation has been a big trend and digital transformation has fueled all things like customer experiences being transformed, so that remains a big vector of growth. I would say Clouded option is still relatively that an early innings. So now you love baseballs, so we can still say what second, third inning as much as we'd like to believe Cloud has been there. Customers more with that analytical workloads first, still happening. The operational workloads are still in its very, very infancy so that is still a big vector of growth and and a big trend to BC for the next five plus years. >> And you guys are in the middle of that because of data? >> Absolutely. Absolutely because if you're running a large operation workload, it's all about the data at the end of the day because you can change the app, but it's the data that you want to carry, the logic that you've written that you want to carry and we participate in that. >> I've asked you before what I want to ask you again because I want to get the modern update because PureCloud, born in the Cloud startups and whatever, it's easy to say that, do that, everyone knows that. Hybrid is clear now, everyone that sees it as an architectural thing. Multicloud is kind of a state of, I have multiple Clouds but being true multicloud a little bit different maybe downstream conversation but certainly relevant. So as Cloud evolves from public Cloud, hybrid and maybe multi or certainly multi, how do you see those things evolving for Informatica? >> Well, we believe in the word hybrid and I define hybrid exactly as these two things. One is hybrid is multicloud. You're going to have hybrid Clouds. Second is hybrid means you're going to have ground and Cloud inter-operate for a period of time. So to us, we in the center of this hybrid Cloud trail and our goal is to help customers go Cloud native but make sure that they can run whatever was the only business that they were running as much possible in the most seamless way before they can at some point contour. And which is why, as I said, I mean our Cloud native business, our Cloud platform, which we call Informatica Intelligent Cloud Services, runs at scale globally across the globe by the way, on all hyperscalers at 10 plus trillion transactions a month. But yet we've allowed customers to run their on-prem technologies as much as they can because they cannot just rip the bandaid over there, right? So multicloud, ground Cloud, our goal is to help customers, large enterprise customers manage that complexity. Then AI plays a big role because these are all very complex environments and our investment in AI, our AI being called Clare is to help them manage that as in an as automated way, as seamless a way and to be honest, the most important with them is, in the most governed way because that's where the biggest risk or risks come into play. That's when our investments are. >> Let's talk about customers for a second. I want to get your thoughts on this 'cause at Amazon reinvent last year in December, there was a meme going around that we starred on theCUBE called, "If you take the T out of Cloud native, it's Cloud naive," and so the point was is to say, hey, doing Cloud native makes sense in certain cases, but if you'd not really thinking about the overall hybrid and the architecture of what's going on, you kind of could get into a naive situation. So I asked Andy this and I want to ask you any chance and I want to ask you the same question is that, what would be naive for a customer to think about Cloud, so they can be Cloud native or operated in a Cloud, what are some of the things they should avoid so they don't fall into that naive category? Now you've being, hi, I am doing Cloud for Cloud's sake. I mean, so there's kind of this perception of you got to do Cloud right, what's your view on Cloud native and how does people avoid the Cloud naive label? >> It's a good question. I think to me when I talk to customers and hundreds of them across the globe as I meet them in a year, is to really think of their Cloud as a reference architecture for at least the next five years, if not 10. I mean technology changes think of a reference architecture for the next five years. In that, you've got to think of multiple best of breed technologies that can help you. I mean, you've got to think of best of breed as much as possible. Now, you're not going to go have hundreds of different technologies running around because you've got to scale them. But think as much as possible that you are best of breed yet settled to what I call a few platforms as much as possible and then make sure that you basically have the right connection points across different workloads will be optimal for different, let's say Cloud environments, analytical workload and operational workload, financial workload, each one of them will have something that will work best in somewhere else, right? So to me, putting the business focus on what the right business outcome is and working your way back to what Cloud environments are best suited for that and building that reference architecture thoughtfully with a five year goal in mind then jumping to the next most exciting thing, hot thing and trying to experiment your way through it that will not scale would be the right way to go. >> It's not naive to be focusing on the business problems and operating it in a Cloud architecture is specifically what you're saying. Okay so let's talk about the customer journey around AI because this has become a big one. You guys been on the AI wave for many, many years, but now that it's become full mainstream enterprise, how are the applications, software guys looking at this because if I'm an enterprise and I want to go Cloud native, I have to make my apps work. Apps are driving everything these days and you guys play a big role. Data is more important than ever for applicants. What's your view on the app developer DevOps market? >> So to me the big chains that we see, in fact we're going to talk a lot about that in a couple of months when we are at Informatica World, our user conference in May is how data is moving to the next phase. And it's what developers today are doing is that they are building the apps with data in mind first, data first apps. I mean if you're building, let's say a great customer service app, you've got to first figure out what all data do you need to service that customer before you go build an app. So that is a very fundamental shift that has happened. And in that context what happens is that in a Cloud native environment, obviously you have a lot of flexibility to begin with that bring data over there and DevOps is getting complimented by what we see is data Ops, having all kinds of data available for you to make those decisions as you're building an application and in that discussion you and me are having before is that, there is so much data that you would not be able to understand that investing in metadata so you can understand data about the data. I call metadata as the intelligent data. If you're an intelligent enterprise, you've got to invest in metadata. Those are the places where we see developers going first and from there ground up building what we call apps that are more intelligent apps on the future not just business process apps. >> Cloud native versus Cloud naive discussion we were just having it's interesting, you talk about best of breed. I want to get your thoughts on some trends we're seeing you seeing even in cybersecurity with RSA coming up, there's been consolidation. You saw Dell just sold RSA to a private equity company. So you starting to see a lot of these shiny new toy type companies being consolidated in because there's too much for companies to deal with. You're seeing also skills gaps, but also skill shortages. There's not enough people. >> That is true. >> So now you have multiple Clouds, you got Amazon, you got Azure, you got Google GCP, you got Oracle, IBM, VMware, now you have a shortage problem. >> True. So this is putting pressure on the customers. So with that in mind, how are the customers reacting to this and what is best of breed really mean? >> So that is actually a really good one. Look, we all live in Silicon Valley, so we get excited about the latest technology and we have the best of skills here, even though we have a skills problem over here, right? Think about as you move up here from Silicon Valley and you start flying and I fly all over the world and you start seeing that if you're in the middle of nowhere, that is not a whole lot of developers who understand the latest cutting edge technology that happens here. Our goal has been to solve that problem for our customers. Look, our goal is to help the developers but as much as possible provide the customers the ability to have a handful of skilled developers but they can still take our offerings and we abstract away that complexity so that they are dealing only at a higher level. The underlying technology comes and goes and it'll come and go a hundred times. They don't have to worry about that. So our goal is abstract of the underlying changes in technology, focus at the business logically and you could move, you can basically run your business for over the course of 20 years. And that's what we've done for customers. Customers have invested with us, have run their businesses seamlessly for two decades, three decades while so much technology has changed over a period of time. >> And the Cloud is right here scaling up. So I want to get your thoughts on the different Clouds, I'll say Amazon Web Services number one in the Cloud, hyperscaler we're talking pure Cloud, they've got more announcements, more capabilities. Then you've got Azure again, hyperscale trying to catch up to Amazon. More enterprise-focused, they're doing very, very well in the enterprise. I said on Twitter, they're mopping up the enterprise because it's easy, they have an install base there. They've been leveraging it very well. So I think Nadella has done the team, has done a great job with that. You had Google try to specialize and figure out where they're going to fit, Oracle, IBM and everyone else. As you'd have to deal with this, you're kind of an arms dealer in a way with data. >> I would love to say I dance with it, not an arms dealer. >> Not an arms dealer, that's a bad analogy, but you get my point. You have to play well, you have to. It's not like an aspiration, your requirement is you have to play and operate with value in all the Clouds. One, how is that going and what are the different Clouds like? >> Well, look, I always begin with the philosophy that it's customer first. You go where the customers are going and customers choose different technologies for different use cases as deems fit for them. Our job is to make sure our customers are successful. So we begin with the customer in mind and we solve from there. Number two, that's a big market. There is plenty of room for everybody to play. Of course there is competition across the board, but plenty of room for everybody to play and our job is to make sure that we assist all of them to help at the end of the day, our joint customers, we have great success stories with all of them. Again, within mind, the end customer. So that has always been Informatica's philosophy, customer first and we partner with a critical strategic partners in that context and we invest and we've invested with all of them, deep partnerships with all of them. They've all been at Informatica well you've seen them. So again, as I said and I think the easiest way we obviously believe that the subset of data, but keep the customer in mind all the time and everything follows from there. >> What is multicloud mean to your customers if your customer century house, we hear people say, yeah, I use this for that and I get that. When I talk to CIOs and CSOs where there's real dollars and impact on the business, there tends to be a gravitational pull towards one Cloud. Why do people are building their own stacks which is why in-house development is shifted to be very DevOps, Cloud native and then we'll have a secondary Cloud, but they recognize that they have multiple Clouds but they're not spreading their staff around for the reasons around skill shortage. Are you seeing that same trend and two, what do you see is multicloud? >> Well, it is multicloud. I think people sometimes don't realize they're already in a multicloud world. I mean you have so many SaaS applications running around, right? Look around that, so whether you have Workday, whether you have Salesforce and I can keep going on and on and on, right. There are multiple, similarly, multi platform Clouds are there, right? I mean people are using Azure for some use cases. They may want to go AWS for certain other native use cases. So quite naturally customers begin with something to begin with and then the scale from there. But they realize as we, as I talked to customers, I realize, hey look, I have use cases and they're optimally set for some things that are multicloud and they'll end up there, but they all have to begin somewhere before they go somewhere. >> So I have multipleclouds, which I agree with you by the way and talking about this on theCUBE a lot. There's multi multiple Clouds and then this interoperability among Clouds. I mean, remember multi-vendor back in the old days, multicloud, it kind of feels like a multi-vendor kind of value proposition. But if I have Salesforce or Workday and these different Clouds and Amazon where I'm developing or Azure, what is the multi-Cloud interoperability? Is it the data control plane? What problems are the customers facing and the challenge that they want to turn into opportunities around multicloud. >> See a good example, one of the biggest areas of growth for us is helping our customers transform their customer experience. Now if you think about an enterprise company that is thinking about having a great understanding of their customer. Now just think about the number of places that customer data sits. One of our big areas of investment for data is a CRM product called salesforce.com right? Good customer data sits there but there could be where ticketing data sits. There could be where marketing data sits. There could be some legacy applications. The customer data sits in so many places. More often than not we realize when we talked to a customer, it sits in at least 20 places within an enterprise and then there is so much customer data sitting outside of the firewalls of an enterprise. Clickstream data where people are social media data partner data. So in that context, bringing that data together becomes extremely important for you to have a full view of your customer and deliver a better customer experience from there. So it is the customer. >> Is that the problem? >> It's a huge problem right now. Huge problem right now across the board where our customer like, hey, I want to serve my customer better but I need to know my customer better before I can serve them better. So we are squarely in the middle of that helping and we being the Switzerland of data, being fully understanding the application layer and the platform layer, we can bring all that stuff together and through the lens of our customer 360 which is fueled by our master data management product, we allow customers to get to see that full view. And from there you can service them better, give them a next best offer or you can understand the full lifetime value for customer, so on and so forth. So that's how we see the world and that's how we help our customers in this really fragmented Cloud world. >> And that's your primary value proposition. >> A huge value proposition and again as I said, always think customer first. >> I mean you got your big event coming up this Spring, so looking forward to seeing you there. I want to get your take as now that you're looking at the next great chapter of Informatica, what is your vision? How do you see that 20 mile stare out in the marketplace? As you execute, again, your product oriented CEO 'cause your product shops, now you're leading the team. What's your vision? What's the 20 mile stare? >> Well as simple as possible, we're going to double the company. Our goal is to double the company across the board. We have a great foundation of innovation we've put together and we remain paranoid all the time as to where and we always try to look where the world is going, serve our customers and as long as we have great customer loyalty, which we have today, have the foundations of great innovation and a great team and culture at the company, which we fundamentally believe in, we basically right now have the vision of doubling the company. >> That's awesome. Well really appreciate you taking the time. One final question I want to get your thoughts on the Silicon Valley and in the industry, is starting to see Indian-American executives become CEO. You now see you have Informatica. Congratulations. >> Amit: Thank you. >> Arvind over at IBM, Satya Nadella. This has been a culture of the technology for generations 'cause I remember when I broke into the business in the late 80s, 90s, this is the pure love of tech and the meritocracy of technology is at play here. This is a historic moment and it's been written about, but I want to get your thoughts on how you see it evolving and advice for young entrepreneurs out there, future CEOs, what's it take to get there? What's it like? What's your personal thoughts? >> Well, first of all, it's been a humbling moment for me to lead Informatica. It's a great company and a great opportunity. I mean I can say it's the true American dream. I mean I came here in 1998. As a lot of the immigrants didn't have much in my pocket. I went to business school, I was deep in loans and I believed in the opportunity. And I think there is something very special about America. And I would say something really special about Silicon Valley where it's all about at the end of the day value, it's all about meritocracy. The color of your skin and your accent and your, those things don't really matter. And I think we are such an embracing culture typically over here. And, and my advice to anybody is that look, believe, and I genuinely used that word and I've gone through stages in my life where you sometimes doubt it, but you have to believe and stay honest on what you want and look, there is no substitute to hard work. Sometimes luck does play a role, but there is no substitute for hard work. And at the end of the day, good things happen. >> As we say, the for the love of the game, love of tech, your tech athlete, loved it, loved to interview and congratulate, been great to follow your career and get to know you and, and Informatica. It's great to see you at the helm. >> Thank you John, pleasure being here. >> I'm John Furrier here at CUBE conversation at Palo Alto, getting the update on the new CEO from Informatica, Amit Walia, a friend of theCUBE and of course a great tech athlete, and now running a great company. I'm John Furrier. Thanks for watching. (upbeat music)

>> live from San Francisco, California. It's the Q covering the IBM Chief Data Officer Summit brought to you by IBM. >> Welcome back to Fisherman's Fisherman's Wharf in San Francisco. Everybody, my name is David wanted. You're watching the Cube, the leader in live tech coverage, you ought to events. We extract the signal from the noise. We're here. The IBM CDO event. This is the 10th anniversary of this event. Caitlin Hallford is here. She's the director of a I Accelerator and client success at IBM. Caitlin, great to see you again. Wow. 10 years. Amazing. They and Carlo Apple Apple Glace e is here. Who is the program director for data and a I at IBM. Because you again, my friend. Thanks for coming on to Cuba. Lums. Wow, this is 10 years, and I think the Cube is covered. Probably eight of these now. Yeah, kind of. We bounce between San Francisco and Boston to great places for CEOs. Good places to have intimate events, but and you're taking it global. I understand. Congratulations. Congratulations on the promotion. Thank you. Going. Thank you so much. >> So we, as you know well are well, no. We started our chief date officer summits in San Francisco here, and it's gone 2014. So this is our 10th 1 We do two a year. We found we really have a unique cohort of clients. The join us about 100 40 in San Francisco on the spring 140 in Boston in the fall, and we're here celebrating the 10th 10 Summit. >> So, Carlo, talk about your role and then let's get into how you guys, you know, work together. How you hand the baton way we'll get to the client piece. >> So I lead the Data Center League team, which is a group within our product development, working side by side with clients really to understand their needs as well developed, use cases on our platform and tools and make sure we are able to deliver on those. And then we work closely with the CDO team, the global CEO team on best practices, what patterns they're seeing from an architecture perspective. Make sure that our platforms really incorporating that stuff. >> And if I recall the data science that lead team is its presales correct and could >> be posted that it could, it really depends on the client, so it could be prior to them buying software or after they bought the software. If they need the help, we can also come in. >> Okay, so? So it can be a for pay service. Is that correct or Yeah, we can >> before pay. Or sometimes we do it based on just our relation with >> It's kind of a mixed then. Right? Okay, so you're learning the client's learning, so they're obviously good, good customers. And so you want to treat him right >> now? How do you guys work >> together? Maybe Caitlin, you can explain. The two organizations >> were often the early testers, early adopters of some of the capabilities. And so what we'll do is we'll test will literally will prove it out of skill internally using IBM itself as an example. And then, as we build out the capability, work with Carlo and his team to really drive that in a product and drive that into market, and we share a lot of client relationships where CEOs come to us, they're want advice and counsel on best practices across the organization. And they're looking for latest applications to deploy deploy known environments and so we can capture a lot of that feedback in some of the market user testing proved that out. Using IBM is an example and then work with you to really commercialized and bring it to market in the most efficient manner. >> You were talking this morning. You had a picture up of the first CDO event. No Internet, no wife in the basement. I love it. So how is this evolved from a theme standpoint? What do you What are the patterns? Sure. So when >> we started this, it was really a response. Thio primarily financial service is sector regulatory requirements, trying to get data right to meet those regulatory compliance initiatives. Defensive posture certainly weren't driving transformation within their enterprises. And what I've seen is a couple of those core elements are still key for us or data governance and data management. And some of those security access controls are always going to be important. But we're finding his videos more and more, have expanded scope of responsibilities with the enterprise they're looked at as a leader. They're no longer sitting within a c i o function there either appear or, you know, working in partnership with, and they're driving enterprise wide, you know, initiatives for the for their enterprises and organizations, which has been great to see. >> So we all remember when you know how very and declared data science was gonna be the number one job, and it actually kind of has become. I think I saw somewhere, maybe in Glass door was anointed that the top job, which is >> kind of cool to see. So what are you seeing >> with customers, Carlo? You guys, you have these these blueprints, you're now applying them, accelerating different industries. You mentioned health care this morning. >> What are some >> of those industry accelerators And how is that actually coming to fruition? Yes. >> So some of the things we're seeing is speaking of financial clients way go into a lot of them. We do these one on one engagements, we build them from custom. We co create these engineering solutions, our platform, and we're seeing patterns, patterns around different use cases that are coming up over and over again. And the one thing about data science Aye, aye. It's difficult to develop a solution because everybody's date is different. Everybody's business is different. So what we're trying to do is build these. We can't just build a widget that's going to solve the problem, because then you have to force your data into that, and we're seeing that that doesn't really work. So building a platform for these clients. But these accelerators, which are a set of core code source code notebooks, industry models in terms a CZ wells dashboards that allow them to quickly build out these use cases around a turn or segmentation on dhe. You know some other models we can grab the box provide the models, provide the know how with the source code, as well as a way for them to train them, deploy them and operationalize them in an organization. That's kind of what we're doing. >> You prime the pump >> prime minute pump, we call them there right now, we're doing client in eights for wealth management, and we're doing that, ref SS. And they come right on the box of our cloudpack for data platform. You could quickly click and install button, and in there you'll get the sample data files. You get no books. You get industry terms, your governance capability, as well as deployed dashboards and models. >> So talk more about >> cloudpack for data. What's inside of that brought back the >> data is a collection of micro Service's Andi. It includes a lot of things that we bring to market to help customers with their journey things from like data ingestion collection to all the way Thio, eh? I model development from building your models to deploying them to actually infusing them in your business process with bias detection or integration way have a lot of capability. Part >> of it's actually tooling. It's not just sort of so how to Pdf >> dualism entire platform eso. So the platform itself has everything you need an organization to kind of go from an idea to data ingestion and governance and management all the way to model training, development, deployment into integration into your business process. >> Now Caitlin, in the early days of the CDO, saw CDO emerging in healthcare, financialservices and government. And now it's kind of gone mainstream to the point where we had Mark Clare on who's the head of data neighborhood AstraZeneca. And he said, I'm not taking the CDO title, you know, because I'm all about data enablement and CDO. You know, title has sort of evolved. What have you seen? It's got clearly gone mainstream Yep. What are you seeing? In terms of adoption of that, that role and its impact on organizations, >> So couple of transit has been interesting both domestically and internationally as well. So we're seeing a lot of growth outside of the U. S. So we did our first inaugural summit in Tokyo. In Japan, there's a number of day leaders in Japan that are really eager to jump start their transformation initiatives. Also did our first Dubai summit. Middle East and Africa will be in South Africa next month at another studio summit. And what I'm seeing is outside of North America a lot of activity and interest in creating an enabling studio light capability. Data Leader, Like, um, and some of these guys, I think we're gonna leapfrog ahead. I think they're going to just absolutely jump jump ahead and in parallel, those traditional industries, you know, there's a new federal legislation coming down by year end for most federal agencies to appoint a chief data officer. So, you know, Washington, D. C. Is is hopping right now, we're getting a number of agencies requesting advice and counsel on how to set up the office how to be successful I think there's some great opportunity in those traditional industries and also seeing it, you know, outside the U. S. And cross nontraditional, >> you say >> Jump ahead. You mean jump ahead of where maybe some of the U. S. >> Absolute best? Absolutely. And I'm >> seeing a trend where you know, a lot of CEOs they're moving. They're really closer to the line of business, right? They're moving outside of technology, but they have to be technology savvy. They have a team of engineers and data scientists. So there is really an important role in every organization that I'm seeing for every client I go to. It's a little different, but you're right, it's it's definitely up and coming. Role is very important for especially for digital transformation. >> This is so good. I was gonna say one of the ways they are teens really, partner Well, together, I think is weaken source some of these in terms of enabling that you know, acceleration and leap frog. What are those pain points or use cases in traditional data management space? You know, the metadata. So I think you talk with Steven earlier about how we're doing some automated meditate a generation and really using a i t. O instead of manually having to label and tag that we're able to generate about 85% of our labels internally and drive that into existing product. Carlos using. And our clients are saying, Hey, we're spending, you know, hundreds of millions of dollars and we've got teams of massive teams of people manual work. And so we're able to recognize it, adopts something like that, press internally and then work with you guys >> actually think of every detail developer out there that has to go figure out what this date is. If you have a tool which we're trying to cooperate the platform based on the guidance from the CDO Global CEO team, we can automatically create that metadata are likely ingested and provide into platform so that data scientists can start to get value out >> of it quickly. So we heard Martin Schroeder talked about digital trade and public policy, and he said there were three things free flow of data. Unless it doesn't make sense like personal information prevent data localization mandates, yeah, and then protect algorithms and source code, which is an I P protection thing. So I'm interested in how your customers air Reacting to that framework, I presume the protect the algorithms and source code I p. That's near and dear right? They want to make sure that you're not taking models and then giving it to their competitors. >> Absolutely. And we talk about that every time we go in there and we work on projects. What's the I p? You know, how do we manage this? And you know, what we bring to the table with the accelerators is to help them jump start them right, even though that it's kind of our a p we created, but we give it to them and then what they derive from that when they incorporate their data, which is their i p, and create new models, that is then their i. P. So those air complicated questions and every company is a little different on what they're worried about with that, so but many banks, we give them all the I P to make sure that they're comfortable and especially in financial service is but some other spaces. It's very competitive. And then I was worried about it because it's, ah, known space. A lot of the algorithm for youse are all open source. They're known algorithms, so there's not a lot of problem there. >> It's how you apply them. That's >> exactly right how you apply them in that boundary of what >> is P, What's not. It's kind of >> fuzzy, >> and we encourage our clients a lot of times to drive that for >> the >> organisation, for us, internally, GDP, our readiness, it was occurring to the business unit level functional area. So it was, you know, we weren't where we needed to be in terms of achieving compliance. And we have the CEO office took ownership of that across the business and got it where we needed to be. And so we often encourage our clients to take ownership of something like that and use it as an opportunity to differentiate. >> And I talked about the whole time of clients. Their data is impor onto them. Them training models with that data for some new making new decisions is their unique value. Prop In there, I'd be so so we encourage them to make sure they're aware that don't just tore their data in any can, um, service out there model because they could be giving away their intellectual property, and it's important. Didn't understand that. >> So that's a complicated one. Write the piece and the other two seem to be even tougher. And some regards, like the free flow of data. I could see a lot of governments not wanting the free flow of data, but and the client is in the middle. OK, d'oh. Government is gonna adjudicate. What's that conversation like? The example that he gave was, maybe was interpolate. If it's if it's information about baggage claims, you can you can use the Blockchain and crypt it and then only see the data at the other end. So that was actually, I thought, a good example. Why do you want to restrict that flow of data? But if it's personal information, keep it in country. But how is that conversation going with clients? >> Leo. Those can involve depending on the country, right and where you're at in the industry. >> But some Western countries are strict about that. >> Absolutely. And this is why we've created a platform that allows for data virtualization. We use Cooper nannies and technologies under the covers so that you can manage that in different locations. You could manage it across. Ah, hybrid of data centers or hybrid of public cloud vendors. And it allows you to still have one business application, and you can kind of do some of the separation and even separation of data. So there's there's, there's, there's an approach there, you know. But you gotta do a balance. Balance it. You gotta balance between innovation, digital transformation and how much you wanna, you know, govern so governs important. And then, you know. But for some projects, we may want to just quickly prototype. So there's a balance there, too. >> Well, that data virtualization tech is interesting because it gets the other piece, which was prevent data localization mandates. But if there is a mandate and we know that some countries aren't going to relax that mandate, you have, ah, a technical solution for that >> architecture that will support that. And that's a big investment for us right now. And where we're doing a lot of work in that space. Obviously, with red hat, you saw partnership or acquisition. So that's been >> really Yeah, I heard something about that's important. That's that's that's a big part of Chapter two. Yeah, all right. We'll give you the final world Caitlyn on the spring. I guess it's not spring it. Secondly, this summer, right? CDO event? >> No, it's been agreed. First day. So we kicked off. Today. We've got a full set of client panel's tomorrow. We've got some announcements around our meta data that I mentioned. Risk insights is a really cool offering. We'll be talking more about. We also have cognitive support. This is another one. Our clients that I really wanted to help with some of their support back in systems. So a lot of exciting announcements, new thought leadership coming out. It's been a great event and looking forward to the next next day. >> Well, I love the fact >> that you guys have have tied data science into the sea. Sweet roll. You guys have done a great job, I think, better than anybody in terms of of, of really advocating for the chief data officer. And this is a great event because it's piers talking. Appears a lot of private conversations going on. So congratulations on all the success and continued success worldwide. >> Thank you so much. Thank you, Dave. >> You welcome. Keep it right there, everybody. We'll be back with our next guest. Ready for this short break. We have a panel coming up. This is David. Dante. You're >> watching the Cube from IBM CDO right back.

>> Live from Las Vegas, it's theCUBE, covering Informatica World 2019. Brought to you by Informatica. >> Welcome back, everyone, to theCUBE's live coverage of Informatica World 2019. I'm your host, Rebecca Knight, along with my cohost, John Furrier. We have two guests for this segment. We have Charlie Emer. He is the senior director data management and governance strategy at Honeywell. Thanks for joining us. >> Thank you. >> And Randy Mickey, senior vice president global professional services at Informatica. Thanks for coming on theCUBE. >> Thank you. >> Charlie, I want to start with you. Honeywell is a household name, but tell us a little bit about the business now and about your role at Honeywell. >> Think about it this way. When I joined Honeywell, even before I knew Honeywell, all I thought was thermostats. That's what people would think about Honeywell. >> That's what I thought. >> But Honeywell's much bigger than that. Look, if you go back to the Industrial Revolution, back in, I think, '20s, we talked about new things. Honeywell was involved from the beginning making things. But we think this year and moving forward in this age, Honeywell is looking at it as the new Industrial Revolution. What is that? Because Honeywell makes things. We make aircraft engines, we make aircraft parts. We make everything, household goods, sensors, all types of sensors. We make things. So when we say the new Industrial Revolution is about the Internet of Things, who best to participate because we make those things. So what we are doing now is what we call IIOT, Industrial Internet of Things. Now, that is what Honeywell is about, and that's the direction we are heading, connecting those things that we make and making them more advancing, sort of making life easier for people, including people's quality of life by making those things that we make more usable for them and durable. >> Now, you're a broad platform customer of Informatica. I'd love to hear a little bit from both of you about the relationship and how it's evolved over the years. >> Look, we look at Informatica as supporting our fundamentals, our data fundamentals. For us to be successful in what we do, we need to have good quality data, well governed, well managed, and secure. Not only that, and also accessible. And we using Informatica almost end to end. We are using Informatica for our data movement ETL platform. We're using Informatica for our data quality. We're using Informatica for our master data management. And we have Informatica beginning now to explore and to use Informatica big data management capabilities. And more to that, we also utilize Informatica professional services to help us realize those values from the platforms that we are deploying. IIoT, Industrial IoT has really been a hot trend. Industrial implies factories building big things, planes, wind farms, we've heard that before. But what's interesting is these are pre-existing physical things, these plants and all this manufacturing. When you add digital connectivity to it and power, it's going to change what they were used to be doing to new things. So how do you see Industrial IoT changing or creating a builder culture of new things? Because this connect first, got to have power and connectivity. 5G's coming around, Wi-Fi 6 is around the corner. This is going to light up all these devices that might have had battery power or older databases. What's the modernization of these industrial environments going to look like in your view? First of all, let me give you an example of the value that is coming with this connectivity. Think of it, if you are an aircraft engineer. Back in the day, a plane landed in Las Vegas. You went and inspected it, physically, and checked in your manual when to replace a part. But now Honeywell is telling you, we're connecting directly to the mechanic who is going to inspect the plane, and there will be sort of in their palms they can see and say wait a minute. This part, one more flight and I should replace this part. Now, we are advising you now, doing some predictive analytics, and telling you when this part could even fail. We're telling you when to replace it. So we're saying okay, the plane is going to fly from here to California. Prepare the mechanics in California when it lands with the part so they can replace it. That's already safety 101. So guaranteeing safety, sort of improving the equity or the viability of the products that we produce. When we're moving away from continue to build things because people still need those things built, safety products, but we're just making them more. We've heard supply chain's a real low-hanging fruit on this, managing the efficiency so there's no waste. Having someone ready at the plane is efficient. That's kind of low-hanging fruit. Any ideas on some of the creativity of new applications that's going to come from the data? Because now you start getting historical data from the connections, that's where I think the thing can get interesting here. Maybe new jobs, new types of planes, new passenger types. >> We are not only using the data to improve on the products and help us improve customer needs, design new products, create new products, but we also monitorizing that data, allowing our partners to also get some insights from that data to develop their own products. So creating sort of an environment where there is a partnership between those who use our products. And guess what, most of the people who use our products, our products actually input into their products. So we are a lot more business-to-business company than a B2C. So I see a lot of value in us being able to share that intelligence, that insight, in our data at a level of scientific discovery for our partners. >> Randy, I want to bring you into the conversation a little bit here (laughs). >> Thanks. >> So you lead Informatica's professional services. I'm interested to hear your work with Honeywell, and then how it translates to the other companies that you engage with. Honeywell is such a unique company, 130 years of innovation, inventor of so many important things that we use in our everyday lives. That's not your average company, but talk a little bit about their journey and how it translates to other clients. >> Sure, well, you could tell, listening to Charlie, how strategic data is, as well as our relationship. And it's not just about evolution from their perspective, but also you mentioned the historicals and taking advantage of where you've been and where you need to go. So Charlie's made it very clear that we need to be more than just a partner with products. We need to be a partner with outcomes for their business. So hence, a professional services relationship with Honeywell and Charlie and the organization started off more straightforward. You mentioned ETL, and we started off 2000, I believe, so 19 years ago. So it's been a journey already, and a lot more to go. But over the years you can kind of tell, using data in different ways within the organization, delivering business outcomes has been at the forefront, and we're viewed strategically, not just with the products, but professional services as well, to make sure that we can continue to be there, both in an advisory capacity, but also in driving the right outcomes. And something that Charlie even said this morning was that we were kind of in the fabric. We have a couple of team members that are just like Honeywell team members. We're in the fabric of the organization. I think that's really critically important for us to really derive the outcomes that Charlie and the business need. >> And data is so critical to their business. You have to be, not only from professional services, but as a platform. Yes. This is kind of where the value comes from. Now, I can't help but just conjure up images of space because I watch my kids that watch, space is now hot. People love space. You see SpaceX landing their rocket boosters to the finest precision. You got Blue Origin out there with Amazon. And they are Honeywell sensors either. Honeywell's in every manned NASA mission. You have a renaissance of activity going on in a modern way. This is exciting, this is critical. Without data, you can't do it. >> Absolutely, I mean, also sometimes we take a break. I'm a fundamentalist. I tell everybody that excitement is great, but let's take a break. Let's make sure the fundamentals are in place. And we actually know what is it, what are those critical data that we need to be tracking and managing? Because you don't just have to manage a whole world of data. There's so much of it, and believe me, there's not all value in everything. You have to be critical about it and strategic about it. What are the critical data that we need to manage, govern, and actually, because it's expensive to manage the critical data. So we look at a value tree as well, and say, okay, if we, as Honeywell, want to be able to be also an efficient business enabler, we have to be efficient inside. So there's looking out, and there's also looking inside to make sure that we are in the right place, we are understanding our data, our people understand data. Talking about our relationship with IPS, Informatica Professional Services, one of the things that we're looking at is getting the right people, the engineers, the people to actually realize that okay, we have the platform, we've heard of Clare, We heard of all those stuff. But where are the people to actually go and do the real stuff, like actually programming, writing the code, connecting things and making it work? It's not easy because the technology's going faster than the capabilities in terms of people, skills. So the partnership we're building with Informatica professional services, and we're beginning to nurture, inside that, we want to be in a position were Honeywell doesn't have to worry so much about the churn in terms of getting people and retraining and retraining and retraining. We want to have a reliable partner who is also moving with the certain development and the progress around the products that we bought so we can have that success. So the partnership with IPS is for the-- >> The skill gaps we've been talking about, I know she's going to ask next, but I'll just jump in because I know there's two threads here. One is there's a new generation coming into the workforce, okay, and they're all data-full. They've been experiencing the digital lifestyle, the engineering programs. To data, it's all changing. What are some of the new expertise that really stand out when evaluating candidates, both from the Informatica side and also Honeywell? What's the ideal candidate look like, because there's no real four-year degree anymore? Well, Berkeley just had their first class of data analytics. That's new two-generation. But what are some of those skills? There's no degree out there. You can't really get a degree in data yet. >> Do you want to talk about that? >> Sure, I can just kick off with what we're looking at and how we're evolving. First of all, the new graduates are extremely innovative and exciting to bring on. We've been in business for 26 years, so we have a lot of folks that have done some great work. Our retention is through the roof, so it's fun to meld the folks that have been doing things for over 10, 15 years, to see what the folks have new ideas about how to leverage data. The thing I can underscore is it's business and technology, and I think the new grads get that really, really well in terms of data. To them, data's not something that's stored somewhere in the cloud or in a box. It's something that's practically applied for business outcomes, and I think they get that right out of school, and I think they're getting that message loud and clear. Lot of hybrid programs. We do hire direct from college, but we also hire experienced hires. And we look for people that have had degrees that are balanced. So the traditional just CS-only degrees, still very relevant, but we're seeing a lot of people do hybrids because they know they want to understand supply chain along with CS and data. And there are programs around just data, how organizations can really capitalize on that. >> And also we're hearing, too, that having domain expertise is actually just as important as having the coding skills because you got to know what an outcome looks like before you collect the data. You got to know what checkmate is if you're going to play chess. That's the old expression, right? >> I think people with the domain, both the hybrid experience or expertise, are more valuable to the company because maybe from the product perspective, from building products, you could be just a scientist, code the code. But when you come to Honeywell, for example, we want you to be able to understand, think about materials. Want you to be able to understand what are the products, what are the materials that we use. What are the inputs that we have to put into these products? Now a simple thing like a data scientist deciding what the right correct value of what an attribute should be, that's not something that because you know code you can determine. You have to understand the domain, the domain you're dealing with. You have to understand the context. So that comes, the question of context management, understanding the context and bringing it together. That is a big challenge, and I can tell you that's a big gap there. >> Big gap indeed, and understand the business and the data too. >> Yes. >> Charles, Randy, thank you both so much for coming on theCUBE. It's been a great conversation. >> Thank you. >> Thank you. >> I'm Rebecca Knight for John Furrier. You are watching theCUBE. (funky techno music)

>> from our studios in the heart of Silicon Valley. HOLLOWAY ALTO, California It is a cube conversation. >> Welcome to this. Keep conversation here in Palo Alto, California. Keep studios. I'm John for the host of the Cube were with Cuba Lum nine. Special gas *** while the president of products and marking it in from Attica. I make great to see you has been a while, but a couple months. How's things good to be >> back has always >> welcome back. Okay, so in dramatic, a world's coming up. We have a whole segment on that, but we've been covering you guys for a long, long time. Data is at the center the value proposition. Again and again, it's Maur amplified. Now the fog is lifting. Show in the world is now seeing what we think we were told about four years ago with data. What's new? What's that? What's the big trends going on that you guys air doubling down on what's new? What's changed? Here's the update. Sure, >> I think we've been talking for the last couple of years. I think you're right. It is becoming more and more important. I think three things we see a lot one is. Obviously you saw this whole world of district transformation. I think that definitely has picked up so much steam. Now. I mean, every company's going digital and And that the officer, that creates a whole new paradigm shift for companies to come almost recreate themselves remained. And so that data becomes the new definition. And that's what we call the thing is you side and fanatical even before the data three dollar word. But data is the center of everything, right? And in basically see the volume of data growth, you know, the utilization of data to make decisions, whether it's, you know, a decision on the shop floor decisions basically related to a cyber security or whatever it is on the keel of your signal is different now. Is the hole e. I assisted data management. I mean the scale ofthe complexity, the scale of growth, you know, multi cloud, multi platform, all the stuff that's in front of us. It's very difficult to run the old way of doing things. So that's where we see the one thing that we see a whole lot is is becoming a lot more mainstream still early days. But it's assisting the whole ability for companies to what I call exploit data to really become a lot more transformative. >> You've been on this for a while again. We get what we had to go back to. The Cube archives were almost pullout clips from two years ago be relevant today. You know the data control understanding. You know that. You know, I understand where the date of governance is ours. So is the foundational thing. But you guys nailed the chat box. You've been doing a Iot of previous announcements. This is putting a lot of pressure on you. The president of products you got. Get this out there. What's new? What's happening inside in from Attica? He's pedaling as fast as you can. What are some of the updates? Give >> us the best example. I was just like the duck, right? You know, you're really selling your Felix comma the top and then you're really finally I think it's great for us. I think I look a tw ee eye ee eye. It's like this so much fun around machine learning. We look at it, it's two different ways. One is how we leverage machine learning Vidin our products to help our customers, making it easy for them to. As I said, so many different data types Think of I ot data instructor data streaming data. How do you bring all that stuff together and married with your existing transaction? It'LL make sense. So we're leveraging a lot of machine learning to make the internal products a lot more easier to consume. A lot more smarter, a lot more. Richard, The second thing is that we what we call his are a clear which we are. Really? If you remember a couple years ago and in America World, how guard then helps our customers make smarter decisions in the in the one of data signs and all these new data workbench is, you know, the old statistical models are only as good as they can never be. So we're leveraging, helping our customers take the value proposition of r B. I clear then what? I make things that, you know, find patterns that, you know, statistical models cannot. So, to me, I look att, both of those really leveraging ml to shape our products, which is married to a lot of innovation and then creating our eclair to that help customers make smarter decisions, easier decisions, complex decisions. Which would I kill the humans or the statistical models? >> Really Well, this is the balance between machines and humans working together. And you guys have nailed this before. And I think this was two years ago. I started to hear the words land adopt, expand from you guys. Write, which is you've got to get adoption, right? And so as you're iterating on this product, focus, you've got to get it working your >> butt looks big, maniacal focus of that. Let's talk about >> what? What you've learned there because that's a hard thing. You guys are doing well at it. We've got to get a doctor. Means you gotta listen to customers going do the course correction. What's the learning is coming out of that. That >> is actually such a good point. We made such. We were always a very customer centric company. But as you said like that, as the world shifted towards a new subscription cloud model, be really focused on helping our customers adopt our products. And you know, in this new world, customers are also struggling with new architectures and everything, so we double down on what we call customer success, making sure we can help our customers adopt the products. And whether it's it's, it's too will benefit. Our customers can value very quickly. And of course, we believe in what we call a customer for life. Our ability to then grow without customers and held them deliver value becomes a lot better, so we're really for So we have globally across the board customers, success managers, we really invest in a customer's. The moment we a customer, buys a product from us, we directly engage with them to help them understand forthis use case. How you >> implement its not just self serving. That's one thing which I appreciate because you know, how hard is it? Build products these days, especially with philosophy, have changed, but it's also we have in the large scale data. You need automation. You've gotta have machine learning. You gotta have these disciplines. Sure this both on your own, but also for the customer. Yes, any updates on the Clare and some customer learnings, and you're seeing that air turning into either use cases or best practices, >> many of them. So take a simple example, right? I mean, we think if we take these things for granted, right? I mean, taking over here to talk about I open these designs on all of these sensors. We were streaming data, right? Or even robots in the shop floor. Sort of. That data has no schema, no structure, nor definition. It's coming like Netflix data has to. And for customers, there's a lot of volume on it. None of it could be junk. Right? So how do you first think that volume of data creates some structure to it for you to do analytics? You You can only do analytics if you put some structure to it. Right. So first thing is that we leverage clear help customers create what are called scheme, and you can create some structure to it. Then what we do allow is basically clear through clear. It can naturally bring what we have. The data quality on top of it. Like how much of it is irrelevant? How much of it is noise? How much would it really make sense? So then what was you said? It signal from the noisy were helping customers get signal from the noise of data. That's where it becomes very handy because It's a very man will cumbersome, time consuming and something very difficult to do. So that's an area of every have leveraged, creating structure, adding data quality on top and finding rules that didn't probably naturally didn't exist, that you and he would be able to see machines are able to do it. And to your point, our belief is this is my one hundred percent believe we believe in the eye assisting the humans. We have given the value ofthe Claire, tow our users that it compliments you. And that's where we're trying to help our users get more productive and deliver more value faster. >> Productivity is multifold. It's like also efficiency. You don't want people wasting time on project that can be automated. You focus that valuable resource somewhere else. Yeah, okay, so let's shift gears on. Taking from Attica World coming up. Let's spend some time on that. What's the focus this year? The show. It's coming up right around the corner. What's going to focus on what's going to be the agenda? What's on the plate >> give you a quick sense of how it's the shape of its going to be our biggest in from Attica well, so it's twentieth year again. Back in Vegas, you know we love Vegas. Of course, we have obviously a couple of days line up over there and you guys will be there too Great sort of speakers. So obviously we'LL have mean stage speakers like so we'LL have some CEO of Google Cloud Thomas Korean is going to be there We'LL have on main stage with Neil We'LL have the CEO of dealer Breaks Ali with me We'LL also have the CMO off a ws ariel there. Then we have a couple of customers lined up Simon from Credit Suisse Daniels CD over Nissan. We also have the head of the eye salmon Guggenheimer from Microsoft, as well as the chief product officer of Tableau Francois on means. So we have a great lineup of speakers, customers and some of our very, very strategic partners with us. Remember last year we also had Scott country. That means too eighty plus session's pretty much a ninety percent led by customers. We have seventy to eighty customers. Presentable sessions, technical business. We have all kinds of tracks. We have hands on labs. We have learnings. Customers really want to come. Lana products. Talked to the experts someone to talk to the product manager. Someone talk to the engineers literally, so many hands on lab. So it's going to be a full blown a couple of days. What's >> the pitch for someone watching that has never been in from Attica world? Why should they come for the show? >> I always tell them three things. Number one is that it's a user conference for our customers to known all things about data management. And then, of course, in that context, they learned a lot about so they learned a lot about the industry. So Dave one we kicked around by market perspective giving Assessor the market is going, how everybody should be stepping back from the data and understanding. Where are these district transformation? E I? Where is the world of detail going? We have some great analysts coming, talking, some customers talking. We'LL be talking about futures over there. Then it is all about hands on learning, right, learning about the product hearing from some of these experts, right from the industry experts as well as our customers teaching what to do, what not to do and networking. It's always great to network writes a great place for people to learn from each other. So it's a great forum for for two of those three things. But the team this year is all around here. I talked about clear. In fact, our tagline Dissidents, clarity unleashed. I really want to, basically has been developing for the last couple of years. It's become becoming a lot who means stream for us in our offerings. And this year we really are taking it being stream. So it's kinda like unleashing it where everybody can genuinely use a truly use it from the data data management. Active >> clarity is a great team. I mean plays on Claire, But this is what we're starting to see. Some visibility into some clear economic benefits, business benefits, technical benefits, kind of all starting to come in. How would you categorize those three years? Because, you know, that's generally the consensus these days is that what was once a couple years ago was like foggy. When you see now you're starting to see that lift. You see economic, business and technical benefits. >> To me, it's all about economic and business. Anniversary technology plays a role in driving value for the business, my gramophone believing that right? And if you think about some of the trans today, right, ah, billion users are coming into play. That he be assisted by data is doubling every year. You know, the volume of data and and amount ofthe amount off. And I obviously business users today. I mean, when I run a business I want, I always say, tomorrow's data yesterday to make a decision. Today it's just in time, and that's where it comes into play. So our goal is to help organizations transformed themselves truly, you know, be more productive, produce operational cost by the government and compliance that's becoming such a mainstream topic. It's not just basically making analytical decisions. How do you make sure that your data is safe and secure? You don't want to get basically hit by any of these cyberattacks. They're all coming after data. So governance and compliance of data that's becoming but in the end got stored on the >> data thing. Yeah, I wanna get your reactions. You mention some shots like some stats here. Date explosion fifteen point three's added bytes per year in traffic, five million business data users and growing twenty billion connected devices. One billion workers will be assisted by learning. So no thanks for putting those stats, but I want to get your reactors. Some of these other points here, eighty percent of enterprises air that we're looking at multi cloud. They're really evaluating their where the data sits in that kind of equation short. And then the other thing is that the responsibility and role of the chief data? Yes, these air new dynamic. I think you guys will be addressing that. And because organizational stuff dynamics, skill, gaps are issues. But also you have multi clouds form. >> And that's a big thing. I mean, look thin. The old World John hatred Unite is always too large in the price is right, and it's going to stay here. In fact, I think it's not just cloud. Think of it this way, one promised. Ilya is not going away. It's producing in school. But then you have this multi cloud world sassafras pass halves infrastructure. If I'm a customer, I want to do all of it. But the biggest problem comes, you said, is that my data is everywhere. How do I make sense of it? And then how do I go on it like my customer data sitting somewhat in this *** up in that platform in this on prime application transaction after running hardware Connect three. And how do I make sense? It doesn't get. I can have a governance and control around it. That's where data management becomes more important but more complex. But that's where it comes into making it easier. One of the things we've seen a lot of you touched upon is the rise of the Sirio. In fact, we have Danielle from the Sanchez, a CD off Mr North America on Main Stage, talking about her rule and how they've leveraged data to transform themselves. That is something we're seeing a lot more because you know, the rule of the city or making sure there is, You know, not only a sense of governance and compliance, a sense of how to even understand the value of dude across an enterprise again. I see one of the things we're gonna talk about this. It's old system thinking around data. We call it system, thinking three daughter data is becoming a platform C. There was always that the hard way earlier, whether it is server or computer. We believe that data is becoming a platform in itself. Whether you think about it in terms of scary, in terms ofthe governance, in terms of e i times a privacy, you have to think of data as a platform. That's the that's the other. But >> I think that is very powerful statement, and I'd like to get your thoughts. You know, we've had many countries. Is on camera off camera around product. Silicon Valley Venture Capital. How come started to create value. One of the old adage is used to be build a platform. That's your competitive strategy. There were a platform company, and >> that was a >> strategic competitive advantage that is unique to the company. And they created enablement. Facebook's a great example. Monetize all the data from users. Look where they are short. If you think about platforms today, Charlie, it seems to be table stakes. Not as a competitive is more of a foundational element of all businesses, not just startups enterprises. This seems to be a common thread. Do you agree with that that platforms were becoming table stakes? Because if we have to think like systems people, whether it's an enterprise show supplier ballistically the platform becomes stable. States that could be on primary cloud. Your reactions >> are gonna agree that I'll say it slightly differently. Yes, I think I think platform is a critical competent for any enterprise when they think of their entire technology strategy because you can't do peace feels otherwise. You become a system integrated over your own right. But it's not easy to be a platform clear itself, right? Because it's a platform player. The responsibility of what you have to offer your customer becomes a lot bigger. So we always t have this intelligent in a platform. Uh, but the other thing is that the rule of the platform is different. It has to be very modeling and FBI driven. Nobody wants to buy a monolithic platform. I don't want as an enterprise it on my own. I'm gonna implement five years a platform you want. It's gonna be like a Lego block. Okay? You It builds by itself, not monolithic, very driven my micro services based And that's our belief that in the new World, yes, black form is very critical for youto accelerate your district transformation journeys or data driven district transformation journeys but the platform better be FBI driven micro services based, very nimble that it's not a precursor to value creation but creates value as you want. It's >> all kind of depends on the customer. Get up a thin, foundational data platform from you guys, for instance. And then what you're saying is composed off >> different continents. For example, you have a data integration platform, then you can do the quality on top. You do. You could do master data management on top. You can provide governance. You can provide privacy. You could do cataloging it all builds its not like Oh my gosh, I have to go do all these things over the course of five years. Then I'LL get value. You gotta create value all along. Today's customers want value like in two months. Three months. You don't wait for a year or >> two years. This is exactly why I think the kind of Operation Storm systems mindset that you're referring to. This is kind of enterprises. They're behaving others the way that you see on premise, thinking around data and cloud multi cloud emerging. It's a systems view of distributed computing with the right block Lego blocks >> that that's what I believe is. That's what we heard from customers. He r I spend most of my time traveling, talking to customers on my way to try to understand what customers want today. And you know some of this late and demand that they have it. They can't sometimes articulate my job. I always end up on the road most of the time just to hearing customers, and that's what they want. They want exactly appoint a platform that Bill's not monolithic, but they don't want the platform. They do want to make it easy for them not to do everything piecemeal. Every project is a data project, whether it's a customer experience project, whether it's the government's project, whether it is nothing else but an analytical. It's a data project, but you don't want to repeat it every time. That's what they want, >> but I know you got a hard stuff, but I want your thoughts on this because I've heard the word workload mentioned so many more times these in the past year. It was a tad cloud of all the cute conversation with a word workload was mentioned to be the biggest fund. Yes, work has been around for a while, but nice seeing more and more workloads coming on. Yeah, that's more important for day that we're close to being tied into the data absolutely, and then sharing data cross multiple workloads. That's a big focus. Perhaps you see that same thing. >> We absolutely see that, Onda. The unique thing that we see also that new work towards getting created and the old workloads are not going away, which is where the hybrid becomes very important. See, these serve large enterprises and their goal is to have an hybrid. So, you know, I'm running a old transaction workload over here. I want to have an experimental workload. I want to start a new book. I want all of them to talk to each other. I don't want them to become silos. And that's when they look to us to say connect the dots for me. You can be in the cloud as an example. Our cloud platform, you know, last time and fanatical will remember we talked about like it wasn't five trillion transactions a month, but it's double that it to pen trillion transaction a month growing like crazy. But our traditional workload is also still there. So we connect the dots for customers. >> I mean, thank you for coming on sharing the insights house. You guys doing well? You got three thousand developers, billions in revenue. Thanks for coming. Appreciate the insight. And looking for Adrian from Attica World. Thank you very much. Meanwhile, here inside the Cuban shot furry with cute conversation in Palo Alto. Thanks for watching.

(pulsating electronic music) >> Welcome everybody, this is theCube, I'm Paul Gillin. Physical security and cybersecurity have traditionally been sort of isolated worlds, they didn't talk to each other. But in the age of the Internet of Things we now have unprecedented opportunities to unite these two traditionally separate areas. Armored Things is a startup out of Boston and is doing some very interesting work in using intelligent devices to make decisions and to intuit patterns in crowd behavior which has applications in cybersecurity, crowd management, traffic management, a lot of different potential uses of this technology. With me are Julie Johnson the co-founder and President of Armored Things, and Chris Lord, the Chief Technology Officer, Welcome. >> Thank you. >> Why don't you describe in a nutshell, let's start out, what you do Julie. >> Great, Armored things is building software to do next generation incident response. We're using the IOT devices and their data to power decisions across large environments used for safety. So for example the data that we're collecting can be used to get better situational awareness within seconds and drive incident response in seconds instead tens of minutes, which is the state of the art today. >> And so it's sounds like, is security the primary target area or are there others? >> That's right, we sit at the intersection of physical and cybersecurity. This information can also be used to drive additional value over time but right now we're really focused on achieving that mission, using these devices, this technology to improve both the physical and cyber realms for Internet of Things. >> Chris why don't you give us an example of how your technology might be applied? >> Sure, so a very common one is, you know active shooter. People are very concerned about active shooter, and so how can you leverage all the data that you have across different devices, different systems that you have out there, in order to understand what happened, and get people the right information at the right time. A more commonplace example might be something like a protest formation. So if you look at a university campus where you might have a controversial group meeting on campus and you need to get early warning when there's a protest forming on the other side. Our technology will allow you to see that before it's gotten to a critical proportion or before it's marching down the street. >> So why don't you take a deeper dive and talk about what, how are you federating these devices? How are you using these multiple devices together? >> Well that's exactly what we are. So we're a data analytics layer across all the silos of data that you already have in your environment. So as you look around you might have motion sensors in your environment, you might have access control systems in your environment, you have wireless infrastructure in your environment, all these things are used for specific purposes now but nothings really trying to correlate and connect the data across all of them. So Armored Things builds a layer across all of them, brings that data together to give you better understanding of what's going on in your environment, people and your physical space. >> Julie talk about how the company came about, what are the origins? >> Sure, so I started working with Charles Curran our CEO about two years ago at Qualcomm. We were really focused on understanding the security portion of the IOT layer and how to manage these things in enterprise. So if you're familiar with IOT in the household there's been a lot of proliferation around turning your lights on, understanding who's at your front door, but in enterprise it's been much slower to adopt. Fundamentally we believe that part of that was because management took a lot of time. Every time you provisioned a device it took a number of minutes and because there was an intrinsic lack of security on each of the devices. So we went around and started talking to different potential customer groups about what it would look like to bring more IOT into their environments. And we really got pulled into universities, and large sporting and entertainment venues, who we're still working with as our primary customers today. Because they saw a desperate need for IOT, not only to save time on managing these devices, and to make sure that they're secure in their environments, but also to use them for physical security. So now that we've spent, you know $15 million in selling IP video cameras, or a few million dollars in selling access control systems, how do we actually elevate their use from what they were initially intended for. That spend has a secondary use when it comes to physical security. That ability to, you know quickly get cameras on the scene of an incident. That ability to harness data coming off of motion sensors or environmental sensors. How do we use all of that information to drive an awareness of our environments day-to-day and then use it in critical emergencies for a better response. >> I understand you're working with some sports teams right now. Can you describe a scenario in which you might be able to help them manage crowds more effectively? >> So there was a great example we heard about two weeks ago from a top team, who's recently hosted some World Series events. They had a unfortunate incident where they were watching, they were hosting a watch party for the World Series in their venue during an away game, and they handed about 40,000 paper tickets out. They got a great turnout, 20,000 people came to the venue. But in the seventh inning of the game the other 20,000 people decided that they also wanted to be in the venue in order to celebrate. That was a pretty unanticipated event. Usually in the fifth or sixth inning you start to consolidate your entrances, you start to consolidate your security personnel and send them to other parts of the venue, and the net result of that was they ended up closing the doors, not allowing additional entrance in, and tweeting that there wouldn't be additional people allowed to enter. There were a lot of security issues with letting 20,000 people in, in the seventh inning, not of the least is you don't know where they're coming from, and you don't really know what their intent is in coming so late to that venue. But there's patterns in the data that we could've seen sooner. So hypothetically, understanding that a normal game day has a couple hundred people entering in the fifth, sixth, seventh innings. Seeing a significant uptick in that number of people coming into your environment should immediately say, what's unique, you know what's different about this situation? Now how do I tie in my resources, my security personnel, my responders, and just maybe notify people who are in charge of making these types of decisions, so that we're not closing the gate and tweeting out to our fans that there's no more entries. >> And getting back to the technical nuances of this situation, how might your technology detect this crowd assembling before it was even visually apparent? >> Good question, so there's many, many different things. So part of what we do is rely on diversity of data from different sources. So that might be mobile devices. That might be from wireless. That might be from cameras that you have there and doing occupancy counts on those cameras. It might be from other, you know motion sensors you have in your environment. All this data gets aggregated so that we can come up with a good understanding of population and flow within your environment. So we would have early indications and bring that awareness to people that have to respond, people who might be sitting in a network operations center, and looking at other cameras but not seeing the information. So we can bring the information right there, notify them that there's a problem forming before it's gotten to critical proportions. >> Fantastic. >> One more thought on that is there's kind of a unique advantage in data to go beyond what humans can perceive. When we're looking at these knocks, you know they have thousands of video cameras potentially united in one central screen. It takes not only having the right camera up but also noticing a degree of difference that might be quite minute, to actually see it as an anomaly in real-time. So you can imagine, you know a university campus where people are walking through the campus at a certain pace every single day. One day everyone's walking just 30% faster, not running just walking, why? You know is there a suspicious package? Is there someone gathered there that you know is attracting people that they don't necessarily want to be associated with, or end up in a vulnerable position? How can we see that in the data faster than someone in the control room might notice it and alert people to respond. >> And with machine learning, of course now we have the means to do that. Chris, talk about the, it strikes me that there must be a lot of complexity involved. You've got a great diversity of devices out there you have to connect to. Every institution would have a different fabric. How are you technically pulling this all together? >> Well the nice thing about a lot of these technologies is there is standardization across many of these different types of devices, and there are, you know there are tiers of players right. And so we do have to be selective about who we integrate with. We are integrated with the top-tier players in all these categories, and we'll prioritize other integrations over time based on our customers and our market so. >> And Julie, what are your plans for deployment? What's your timeframe? >> We're looking to rollout our first generation of product in the next nine to twelve months. That really drives home at that situational awareness piece. So before we even get to building through incident response at scale, the ability to give people very specific cues during a critical emergency. How do we start with getting more information to the people who are there? So getting occupancy, flow, the dynamics of movement around a campus or a large venue. How do we start equipping the police personnel, and security personnel to make better decisions and drive value from there. >> I understand there's no shortage of demand for your solution. >> We do have some top-tier universities, and pro-sporting and entertainment venues who we're working with to build the right solution not just the solution that we think is needed, but the solution that they're telling us, "Hey we would really like to use something like this." >> I also understand you've pulled together a team, kind of a dream team, talk about some of the people that you've brought on board for this operation which few people have even heard of. >> Yeah so I think the first of those you're seeing here, so Chris joined us as co-founder and CTO and has been really an asset to this team given his background in cybersecurity from Carbon Black and before that. And you know if you want to add more to that please feel free to. >> No thanks. >> We've also brought in, I would call it two pillars of our strategy. One one the physical security side and one on the machine learning data analytics side, and those two women are Elizabeth Carter. Who came to us from Apple, where she led crisis management for the Americas. She previously worked at Chertoff Group where she sat at the intersection of physical and cybersecurity, and before that actually worked for the city of New York, where she understood weapons of mass destruction, different types of biological and chemical weapons response planning. So she's kind of the pillar of our physical security response understanding and driving product. The other woman, her name is Clare Bernard and she recently joined us from another Boston startup called Tamr where she was running product and engineering for them. Clare's background is actually in particle physics. She was BU and John's Hopkins, and happened to work with the team that discovered the God particle while she was getting her PhD. So we' think she's as smart as you can find, and is going to help us think about these data challenges, the analytics piece at a scale that, you know we think has the potential to really improve physical security and cybersecurity. I would be remiss if I didn't mention the rest of our team. Our CEO Charles comes from a background in the venture capital community and is just incredibly knowledgeable about the process of building a company from the ground up, and has many skills when it comes to recruiting as well. Really helped drive some of these hires forward and the rest of the team is the next generation of rising stars, people from Oracle, HP Vertica, other Carbon Black individuals. People who just have experience from across the board that's going to help us build the right solution. >> And you know at a time when diversity has been a major issue for tech companies, I understand your team is unusually well represented. >> I think our executive team is about 60% women, which we're very proud of. I think our team in general might actually be, >> About that too, yup. >> About 60% women, which we're also very proud of. And I'd like to say that that's organic. That we've worked with some great advisors and potential customers, and I do think that from my perspective, it's been helpful to have younger women coming in who see a path forward for senior women in executive roles in their company. I think that's something that can't be underestimated. >> Where do you stand in funding right now? >> We just closed our first institutional capital about a week and a half ago. We're still finishing the close of that round but we have a Boston based partner who's very focused on machine learning and analytics, and also has been a well recognized investor in the cyber security realm. So we're very fortunate to have this investor as our partner, and excited to keep working with them. >> Chris, as someone whose background is in cybersecurity how do you see the security landscape changing now with the IOT coming on and the possibility of really transforming the way organizations look at their physical and cybersecurity operations? >> Good question, so over time they're converging, and they're converging I think more rapidly than we expected, so now I'm going to step back a little bit and say that there's a lot of parallels. Cybersecurity I think is probably about five years ahead of physical security in terms of maturity of technology and approaches to problems. And then so what we're seeing right now, and we're part of the force behind that, is taking the learnings from cyber security and applying them to physical security right. So when we talk about situational awareness, when we talk about the data analytics that supports that, and when we talk about incident response and orchestration automation. All of those are core to taking cybersecurity and applying it to physical security. In terms of convergence, we're seeing many cases, and this is going back a number of years, where people are using cyber events to create physical problems right. Stuxnet is a classic example, but you can do the same thing by taking over something and instilling panic in a stadium, and causing you know, all sorts of grief, cyber driving physical. You can also see cases where people who are running cybersecurity operation centers want access to physical knowledge of their environment in order to do their job better. Whether it is a malicious insider that they suspect, whether it's an infection that occurs on a particular machine, being able to pull up the cameras, know who was there at the time, bringing all that information together, is again necessary in order to understand their perception of situational awareness. So two converging towards one, we're going to be building towards that goal from our perspective. >> Now the flip side of federating IOT devices is that the bad guys can do the same thing. So you potentially have a much broader attack surface. That has to be factoring into your thinking. What is the embedded security in your platform? >> So, we're not going to address fully that right now, but so we take advantage of best in breed security principles in our design both for security and for privacy. But in terms of the dependency we have on a lot of IOT devices and IOT systems, part of what helps us is diversity of data across those, and diversity of devices right. And so while you might have compromises in specific cases, the fact that you are dealing with so many, and so many different categories at the same time, allows you to maintain and fulfill your mission, and deliver what you're trying to do regardless of some of those individual compromises. We're also in a unique vantage point where we can actually see the operational integrity of what's going on. So when you look across all those different categories and you look at the data that we're collecting, whether it's malicious or not, we're able to identify a failure, and bring that to the attention of the people who are dependent on those systems. So we could be an early morning to cyber events, malicious or not. >> Julie, entrepreneurs love to dream. I'm sure you are thinking big, beyond the immediate cybersecurity applications. Where could Armored Things eventually go? >> That's a great question. The dream is that we become not only the dominant solution for physical and cyber security for schools and large venues. But we bring our solution into K, 12 where some of this is desperately needed. That's kind of the mission orientation of our team. How do we start to drive value in a way that we can get to every school in the country sooner. In the longer term though, I think there's a lot of opportunities with IOT and we're still kind of at the tip of the iceberg here. We're going to see all sorts of new devices come online over the next two, five, 10 years. The growth of these devices is incredible. And the question is how do we continue this challenge of solving the data at scale in a way that continues to drive value, not just for some of the first use cases, which are often around marketing, and understanding an environment in that sense, but also continuing that physical cybersecurity angle. >> Enormous potential and hope you stay based in Boston. We can use more companies like that. Chris Lord and Julie Johnson, thanks very much for joining us today on theCUbe. >> Thanks Paul. >> Thank you. >> Armored Things, keep your eye on them. You're going to be hearing a lot more about this company in the months to come. I'm Paul Gillin, this is theCube.