(upbeat music) >> Hello everyone, welcome to the special CUBE presentation of the AWS Partner Showcase season one, episode two. I'm John Furrier, your host of theCUBE. We've got two great guest here. Douglas Ko, Director of product marketing at Cohesity and Sabina Joseph General Manager of AWS, Amazon Web Services. Welcome to the show. >> Thank you for having us. >> Great to see you Sabina and Douglas. Great to see you, congratulations at Cohesity. Loved the shirt, got the colors wearing there on Cohesity, Always good I can't miss your booth at the shows, can't wait to get back in person, but thanks for coming in remotely. I got to say it's super excited to chat with you, appreciate it. >> Yeah, pleasure to be here. >> What are the trends you're seeing in the market when it comes to ransomware threats right now. You guys are in the middle of it right now more than ever. I was hearing more and more about security, cloud scale, cloud refactoring. You guys are in the middle of it. What's the latest trends in ransomware? >> Yeah, I have to say John, it's a pleasure to be here but on the other hand, when you asked me about ransomware, right? The data and the statistics are pretty sobering right now. If we look at what just happened in 2020 to 2021, we saw a tenfold increase in a ransomware attacks. We also saw the prediction of a ransomware attack happening every 11 seconds meaning by the time I finished this sentence there's going to be another company falling victim to ransomware. And it's also expected by 2031 that the global impact of ransomware across businesses will be over $260 billion, right? So, that's huge. And even at Cohesisity, right, what we saw, we did our own survey, and this one actually directly to end users and consumers. And what we found was over 70% of them would reconsider doing business with a company that paid a ransom. So all these things are pretty alarming and pretty big problems that we face today in our industry. >> Yeah, there's so many dimensions to it. I mean, you guys at Cohesity have been doing a while. It's being baked in from day one, security in the cloud and backup recovery, all that is kind of all in one thing now. So to protect against ransomware and other threats is huge Sabina, I got to ask you Amazon's view of ransomware is serious. You guys take it very seriously. What's the posture and specifically, what is AWS doing to protect customers from this threat? >> Yeah, so as Doug mentioned, right, there's no industry that's immune to ransomware attacks. And just as so we all level set, right? What it means is somebody taking control over and locking your data as an individual or as a company, and then demanding a ransom for it, right? According to the NIST, the National Institute of Standards and Technology cybersecurity framework, there are basically five main functions which are needed in order to plan and manage these kind of cybersecurity ransomware attacks. They go across identifying what do you need to protect, actually implementing the things that you need in order to protect yourself, detecting things if there is an attack that's going on, then also responding, how do you get out of this attack? And then bringing things, recovery, right? Bringing things back to where they were before the attack. As we all know, AWS takes security very seriously. We want to make sure that our customer's data is always protected. We have a number of native security solutions, but we are also looking to see how we can work with partners. And this is in fact when in the fall of 2019, the Cohesity CEO, Mohit Aron, myself and a couple of us, we met and we brainstorm, what could we do something that is differentiated in the market? When we built this data management as a service native solution on top of AWS, it's a first of a kind solution, John. It doesn't exist anywhere else in the market, even to even today. And we really focused on using the well architected review, the five pillars of security, reliability, operational excellence, performance, and cost optimization. And we built this differentiated solution together, and it was launched in April, 2020. And then of course from a customer viewpoint, they should use a comprehensive set of solutions. And going back to that security, that cyber security framework that I mentioned, the Cohesity data management as a service solution really falls into that recovery, that last area that I mentioned and solution actually provides, granular management of data, protection of data. Customers can spin up things very quickly and really scale their solution across the globe. And ensure that there is compliance, no matter how many times we do data changes, ads and so on across the world. >> Yeah, Sabina, that's a great point about that because a lot of the ransomware actually got bad actors, but also customers can misconfigure things. They don't follow the best practice. So having that native solutions are super important. So that's a great call out. Douglas, I got to go back to you because you're on the Cohesity side and a the partner of AWS. They have all these best practices that for the good actors, got to pay attention to the best practices and the bad actors also trying to get in creates a two, challenge an opportunity. So how do organizations protect their data against these attacks? And also how do they maintain their best practices? Because that's half the battle too, is the best practices to make sure you're following the guidelines on AWS side, as well as protecting the attacks. What's your thoughts? >> Yeah, absolutely. First and foremost, right? As an organization, you need to understand how ransomware operates and how it's evolved over the years. And when you first look at it, Sabina already mentioned it, they started with consumers, small businesses, attacking their data, right? And some of these, consumers or businesses didn't have any backup. So the first step is just to make sure your data is backed up, but then the criminals kind of went up market, right? They understood that big organizations had big pocket and purses. So they went after them and the larger organizations do have backup and recovery solutions in place. So the criminals knew that they had to go deeper, right? And what they did was they went after the backup systems themselves and went to attack, delete, tamper with those backup systems and make it difficult or impossible to recover. And that really highlighted some solutions is out there that had some vulnerabilities with their data immutability and capabilities around WORM. And those are areas we suggest customers look at, that have immutability and WORM. And more recently again, given the way attacks have happened now is really to add another layer of defense and protection. And that includes, traditionally what we used to call, the 3-2-1 rule. And that basically means, three copies of data on two different sets of media with one piece of that data offsite, right? And in today's world and the cloud, right? That's a great opportunity to kind of modernize your environment. I wish that was all that ransomware guys we're doing right now and the criminals were doing, but unfortunately that's not the case. And what we've seen is over the past two years specifically, we've seen a huge increase in what you would call data theft or data exfiltration. And that essentially is them taking that data, a specific sense of the data and they're threatening to expose it to the dark web or selling it to the highest bidder. So in this situation it's honestly very difficult to manage. And the biggest thing you could do is obviously harden your security systems, but also you need a good understanding about your data, right? Where all that sensitive information is, who has access to it and what are the potential risks of that data being exposed. So that takes another step in terms of leveraging a bunch of technologies to help with that problem set. >> What can businesses do from an architectural standpoint and platform standpoint that you guys see there's key guiding principles around how their mindset should be? What's the examples of other approaches- >> Yeah. >> Approach here? >> No, I think they are both us at Cohesity and I'll speak for Sabina, AWS, we believe in a platform approach. And the reason for that is this a very complicated problem and the more tools and more things you have in there, you add risk of complexity, even potential new attack surfaces that the criminals can go after. So we believe the architecture approach should kind of have some key elements. One is around data resiliency, right? And that again comes from things like data encryption, your own data is encrypted by your own keys, that the data is immutable and has that, right, want to read many or WORM capabilities, so the bad guys can't temper with your data, right? That's just step one. Step two is really understanding and having the right access controls within your environment, right? And that means having multi factor authentication, quorum, meaning having two keys for the closet before you can actually have access to it. But it's got to go beyond there as well too. We got to leverage some newer technologies like AI and machine learning. And that can help you with detection and analysis of both where all your sensitive information is, right? As well as understanding potential anomalies that could signify attack or threat in progress. So, those are all key elements. And the last one of course is I think it takes a village, right? To fight the ransomware war. So we know we can't do it alone so, that's why we partner with people like AWS. That's why we also partner with other people in the security space to ensure you really have a full ecosystem support to manage all those things around that framework. >> That's awesome. Before I get to Sabina, I want to get into the relationship real quick, but I want to come back and highlight what you said about the data management as a service. This is a joint collaboration. This is some of the innovation that Cohesity and AWS are bringing to the market to combat ransomware. Can you elaborate more on that piece 'cause this is important. It's a collaboration that we're going to gather. So it's a partner and you guys were going to take us through what that means for the customer and to you guys. I mean, that's a compelling offering. >> So when we start to work with partners, right? we want to make sure that we are solving a customer problem. That's the whole working backwards from a customer. We are adding something more that the customer could not do. That's why when either my team or me, we start to either work on a new partnership or a new solution, it's always focused on, okay, is this solution enabling our customer to do something that they couldn't do before? And this approach has really helped us, John, in enabling majority of the fortune 500 companies and 90% of the fortune 100 companies use partner solutions successfully. But it's not just focused on innovation and technology, it's also focused on the business side. How are we helping partners grow their business? And we've been scaling our field teams, our AWS sales teams globally. But what we realized is through partner feedback, in fact, that we were not doing a great job in helping our partners close those opportunities and also bring net new opportunities. So in our field, we actually introduced a new role called the ISV Success Manager, ISMs that are embedded in our field to help partners either close existing opportunities, but also bring net new opportunities to them. And then at re:Invent 2020, we also launched the ISB accelerate program, which enables our field teams, the AWS field teams to get incentive to work with our partners. Cohesity, of course, participates in all of these programs and has access to all of these resources. And they've done a great job in leveraging and bringing our field teams together, which has resulted in hundreds of wins for this data management as a service solution that was launched. >> So you're bringing customers to Cohesity. >> Absolutely. >> Okay, I got to get the side. So they're helping you, how's this relationship going? Could you talk about the relationship on the customer side? How's that going? Douglas, what's your take on that? >> Yeah, absolutely. I mean, it's going great. That's why we chose to partner with AWS and to be quite honest, as Sabina mentioned, we really only launched data management and service back in 2020, late 2020. And at that time we launched with just one service then, right, when we first launched with backup as a service. Now about 15 months later, right? We're on the brink of launching four services that are running on AWS cloud. So, without the level of support, both from a go to market standpoint that Sabina mentioned as well as the engineering and the available technology services that are on the AWS Cloud, right? There's no way we would've been able to spin up new services in such a short period of time. >> Is that Fort Knox and Data Govern, those are the services you're talking about Or is that- >> Yeah, so let me walk you through it. Yeah, so we have Cohesity DataProtect, which is our backup as a service solution. And that helps customers back their data to the cloud, on-prem, SaaS, cloud data like AWS, all in a single service and allows you to recover from ransomware, right? But a couple months ago we also announced a couple new services that you're alluding to John. And that is around Fort Knox and DataGovern. And basically Fort Knox, it is basically our SaaS solution for data isolation to a vaulted copy in the AWS cloud. And the goal of that is to really make it very simple for customers, not only to provide data immutability, but also that extra layer of protection by moving that data offsite and keeping it secure and vaulted away from cyber criminals and ransomware. And what we're doing is simplifying the whole process that normally is manual, right? You either do it manually with tapes or you'll manually replicate data to another data center or even to the cloud, but we're providing it as a service model, basically providing a modern 3-2-1 approach, right? For the cloud era. So, that's what's cool about Fort Knox, DataGovern, right? That's also a new service that we announced a few months ago and that really provides data governance and user behavior analytics services that leverages a lot that AI machine learning that everybody's so excited about. But really the application of that is to automate the discovery of sensitive data. So that could be your credit card numbers, healthcare records, a personal information of customers. So understanding where all that data is, is very important because that's the data that the criminals are going to go after and hold you host. So that's kind of step one. And then step two is again, leveraging machine learning, actually looking at how users are accessing and managing that data is also super important because that's going to help you identify potential anomalies, such as people sharing that data externally, which could be a threat. It could be in improper vault permissions, or other suspicious behaviors that could potentially signify data exfiltration or ransomware attack in progress. >> That's some great innovation. You got the data resiliency, of course, the control mechanism, but the AI piece machine learning is awesome. So congratulations on that innovation. Sabina, I'm listening to conversation and hear you talk. And it reminds me of our chat at re:Invent. And the whole theme of the conference was about the innovation and rapid innovations and how companies are refactoring with the cloud and this NextGen kind of journey. This is a fundamental pillar of AWS's rapid innovation concept with your partners. And I won't say it's new, but it's highly accelerated. How are you guys helping partners be with this rapid innovation, 'cause you're seeing benefits can come faster now, Agile is here. What are some of the programs that you're doing? How are you helping customers take advantage of the rapid innovation with the secret sauce of AWS? >> Yeah, so we have a number of leadership principles, John, and one of them, of course, is customer obsession. We are very focused on making sure we are developing things that our customers need. And we look for these very same qualities when we work with partners such as Cohesity. We want to make sure that it's a win-win approach for both sides because that's what will make the partnership durable over time. And this John, our leadership team at AWS, right from our CEO down believes that partners are critical to our success and as partners lean in, we lean in further. And that's why we signed the strategic collaboration agreement with Cohesity in April, 2020, where data management as a service solution was launch as part of that agreement. And for us, we've launched this solution now and as Doug said, what are the next things we could be doing, right? And just to go back a little bit when Cohesity was developing this solution with us, they used a number of our programs. Especially on the technical side, they used our SaaS factory program, which really helped them build this differentiated solution, especially focused around security compliance and cost optimizing the solution. Now that we've launched this solution, just like Doug mentioned, we are now focused on leveraging other services like security, AIML, and also our analytic services. And the reason for that is Cohesity, as we all know, protects, manages this data for the customer, but we want to make sure that the customer is extracting value from this data. That is why we continue to look, what can we do to continue to differentiate this solution in this market. >> That's awesome. You guys did a great job. I got to say, as it gets more scale, there's more needs for this rapid, I won't say prototyping, but rapid innovation and the Cohesity side does was you guys have been always on point on the back and recovery and now with security and the new modern application development, you guys are in the front row seats of all the action. So, I'll give you the final worry what's going on at Cohesity, give an update on what you guys are doing. What's it like over there these days? How's life give a quick plug for Cohesity. >> Yeah, Cohesity is doing great, right? We're always adding folks to the team, on our team, we have a few open racks open both on the marketing side, as well as the technology advocacy side. And of course, some of our other departments too, and engineering and sales and also our partner teams as well, working with AWS partners such as that. So, in our mind, the data delusion and growth is not going to slow down, right? So in this case, I think all tides raises all the boats here and we're glad to be innovative leader in this space and really looking to be really, the new wave of NextGen data management providers out there that leverages things like AI that leverages cybersecurity at the core and has an ecosystem of partners that we're working with, like AWS, that we're building out to help customers better manage their data. >> It's all great. Data is in the mid center of the value proposition. Sabina, great to see you again, thanks for sharing. And Douglas, great to see you too. Thanks for sharing this experience here in theCUBE. >> Thanks, John. >> Okay, this is theCUBE's AWS Partner Showcase special presentation, speeding innovation with AWS. I'm John Furrier your host of theCUBE. Thanks for watching. (upbeat music)

>>Hello, everyone, and welcome to track three off beyond. My name is being in Yemen and I am an account executive here at Thought spot based out of our London office. If the accents throwing you off I don't quite sound is British is you're expecting it because the backgrounds Australian so you can look forward to seeing my face. As we go through these next few sessions, I'm gonna be introducing the guests as well as facilitating some of the Q and A. So make sure you come and say hi in the chat with any comments, questions, thoughts that you have eso with that I mean, this whole track, as the title somewhat gives away, is really about everything that you need to know and all the tips and tricks when it comes to adoption and making sure that your thoughts what deployment is really, really successful. We're gonna be taking off everything from user training on boarding new use cases and picking the right use cases, as well as hearing from our customers who have been really successful in during this before. So with that, though, I'm really excited to introduce our first guest, Kathleen Maley. She is a senior analytics executive with over 15 years of experience in the space. And she's going to be talking to us about all her tips and tricks when it comes to making the most out of your center of excellence from obviously an analytics perspective. So with that, I'm going to pass the mic to her. But look forward to continuing the chat with you all in the chat. Come say hi. >>Thank you so much, Bina. And it is really exciting to be here today, thanks to everyone for joining. Um, I'll jump right into it. The topic of evolving your analytics center of excellence is a particular passion of mine on I'm looking forward to sharing some of my best practices with you. I started my career, is a member of an analytic sioe at Bank of America was actually ah, model developer. Um, in my most recent role at a regional bank in the Midwest, I ran an entire analytics center of excellence. Um, but I've also been on the business side running my own P and l. So I think through this combination of experiences, I really developed a unique perspective on how to most effectively establish and work with an analytic CEO. Um, this thing opportunity is really a two sided opportunity creating value from analytics. Uh, and it really requires the analytics group and the line of business Thio come together. Each has a very specific role to play in making that happen. So that's a lot of what I'll talk about today. Um, I started out just like most analysts do formally trained in statistics eso whether your data analyst or a business leader who taps into analytical talent. I want you to leave this talk today, knowing the modern definition of analytics, the purpose of a modern sioe, some best practices for a modern sioe and and then the role that each of you plays in bringing this Kuito life. So with that said, let me start by level, setting on the definition of analytics that aligns with where the discipline is headed. Um, versus where it's been historically, analytics is the discovery, interpretation and communication of meaningful patterns in data, the connective tissue between data and effective decision making within an organization. And this is a definition that I've been working under for the last, you know, 7 to 10 years of my career notice there is nothing in there about getting the data. We're at this amazing intersection of statistics and technology that effectively eliminates getting the data as a competitive advantage on this is just It's true for analysts who are thinking in terms of career progression as it is for business leaders who have to deliver results for clients and shareholders. So the definition is action oriented. It's purposeful. It's not about getting the data. It's about influencing and enabling effective decision making. Now, if you're an analyst, this can be scary because it's likely what you spend a huge amount of your time doing, so much so that it probably feels like getting the data is your job. If that's the case, then the emergence of these new automated tools might feel like your job is at risk of becoming obsolete. If you're a business leader, this should be scary because it means that other companies air shooting out in front of you not because they have better ideas, necessarily, but because they can move so much faster. According to new research from Harvard Business Review, nearly 90% of businesses say the more successful when they equipped those at the front lines with the ability to make decisions in the moment and organizations who are leading their industries and embracing these decision makers are delivering substantial business value nearly 50% reporting increased customer satisfaction, employee engagement, improve product and service quality. So, you know, there there is no doubt that speed matters on it matters more and more. Um, but if you're feeling a little bit nervous, I want you to think of it. I want you think of it a little differently. Um, you think about the movie Hidden figures. The job of the women in hidden figures was to calculate orbital trajectories, uh, to get men into space and then get them home again. And at the start of the movie, they did all the required mathematical calculations by hand. At the end of the movie, when technology eliminated the need to do those calculations by hand, the hidden figures faced essentially the same decision many of you are facing now. Do I become obsolete, or do I develop a new set of, in their case, computer science skills required to keep doing the job of getting them into space and getting them home again. The hidden figures embraced the latter. They stayed relevant on They increase their value because they were able to doom or of what really mattered. So what we're talking about here is how do we embrace the new technology that UN burdens us? And how do we up skill and change our ways of working to create a step function increase in data enabled value and the first step, really In evolving your analytics? Dewey is redefining the role of analytics from getting the data to influencing and enabling effective decision making. So if this is the role of the modern analyst, a strategic thought partner who harnesses the power of data and directs it toward achieving specific business outcomes, then let's talk about how the series in which they operate needs change to support this new purpose. Um, first, historical CEOs have primarily been about fulfilling data requests. In this scenario, C always were often formed primarily as an efficiency measure. This efficiency might have come in the form of consistency funds, ability of resource is breaking down silos, creating and building multipurpose data assets. Um, and under the getting the data scenario that's actually made a lot of sense for modern Sealy's, however, the objective is to create an organization that supports strategic business decision ing for individuals and for the enterprises the whole. So let's talk about how we do that while maintaining the progress made by historical seaweeds. It's about really extending its extending what, what we've already done the progress we've already made. So here I'll cover six primary best practices. None is a silver bullet. Each needs to fit within your own company culture. But these air major areas to consider as you evolve your analytics capabilities first and foremost always agree on the purpose and approach of your Coe. Successfully evolving yourself starts with developing strategic partnerships with the business leaders that your organization will support that the analytics see we will support. Both parties need to explicitly blocked by in to the objective and agree on a set of operating principles on bond. I think the only way to do that is just bringing people to the table, having an open and honest conversation about where you are today, where you wanna be and then agree on how you will move forward together. It's not about your organization or my organization. How do we help the business solve problems that, you know, go beyond what what we've been able to do today? So moving on While there's no single organizational model that works for everyone, I generally favor a hybrid model that includes some level of fully dedicated support. This is where I distinguish between to whom the analyst reports and for whom the analyst works. It's another concept that is important to embrace in spirit because all of the work the analyst does actually comes from the business partner. Not from at least it shouldn't come from the head of the analytic Center of excellence. Andan analysts who are fully dedicated to a line of business, have the time in the practice to develop stronger partnerships to develop domain knowledge and history on those air key ingredients to effectively solving business problems. You, you know, how can you solve a problem when you don't really understand what it is? So is the head of an analytic sioe. I'm responsible for making sure that I hire the right mix of skills that I can effectively manage the quality of my team's work product. I've got a specialized skill set that allows me to do that, Um, that there's career path that matters to analysts on all of the other things that go along with Tele management. But when it comes to doing the work, three analysts who report to me actually work for the business and creating some consistency and stability there will make them much more productive. Um, okay, so getting a bit more, more tactical, um, engagement model answers the question. Who do I go to When? And this is often a question that business partners ask of a centralized analytics function or even the hybrid model. Who do I go to win? Um, my recommendation. Make it easy for them. Create a single primary point of contact whose job is to build relationships with a specific partner set of partners to become deeply embedded in their business and strategies. So they know why the businesses solving the problems they need to solve manage the portfolio of analytical work that's being done on behalf of the partner, Onda Geun. Make it make it easy for the partner to access the entire analytics ecosystem. Think about the growing complexity of of the current analytics ecosystem. We've got automated insights Business Analytics, Predictive modeling machine learning. Um, you Sometimes the AI is emerging. Um, you also then have the functional business questions to contend with. Eso This was a big one for me and my experience in retail banking. Uh, you know, if if I'm if I'm a deposits pricing executive, which was the line of business role that I ran on, I had a question about acquisitions through the digital channel. Do I talk Thio the checking analyst, Or do I talk to the digital analyst? Um, who owns that question? Who do I go to? Eso having dedicated POC s on the flip side also helps the head of the center of excellence actually manage. The team holistically reduces the number of entry points in the complexity coming in so that there is some efficiency. So it really is a It's a win win. It helps on both sides. Significantly. Um, there are several specific operating rhythms. I recommend each acting as a as a different gear in an integrated system, and this is important. It's an integrated decision system. All of these for operating rhythms, serves a specific purpose and work together. So I recommend a business strategy session. First, UM, a portfolio management routine, an internal portfolio review and periodic leadership updates, and I'll say a little bit more about each of those. So the business strategy session is used to set top level priorities on an annual or semiannual basis. I've typically done this by running half day sessions that would include a business led deep dive on their strategy and current priorities. Again, always remembering that if I'm going to try and solve all the business problem, I need to know what the business is trying to achieve. Sometimes new requester added through this process often time, uh, previous requests or de prioritized or dropped from the list entirely. Um, one thing I wanna point out, however, is that it's the partner who decides priorities. The analyst or I can guide and make recommendations, but at the end of the day, it's up to the business leader to decide what his or her short term and long term needs and priorities are. The portfolio management routine Eyes is run by the POC, generally on a biweekly or possibly monthly basis. This is where new requests or prioritize, So it's great if we come together. It's critical if we come together once or twice a year to really think about the big rocks. But then we all go back to work, and every day a new requests are coming up. That pipeline has to be managed in an intelligent way. So this is where the key people, both the analyst and the business partners come together. Thio sort of manage what's coming in, decking it against top priorities, our priorities changing. Um, it's important, uh, Thio recognize that this routine is not a report out. This routine is really for the POC who uses it to clarify questions. Raised risks facilitate decisions, um, from his partners with his or her partner so that the work continues. So, um, it should be exactly as long as it needs to be on. Do you know it's as soon as the POC has the information he or she needs to get back to work? That's what happens. An internal portfolio review Eyes is a little bit different. This this review is internal to the analytics team and has two main functions. First, it's where the analytics team can continue to break down silos for themselves and for their partners by talking to each other about the questions they're getting in the work that they're doing. But it's also the form in which I start to challenge my team to develop a new approach of asking why the request was made. So we're evolving. We're evolving from getting the data thio enabling effective business decision ing. Um, and that's new. That's new for a lot of analysts. So, um, the internal portfolio review is a safe space toe asks toe. Ask the people who work for May who report to May why the partner made this request. What is the partner trying to solve? Okay, senior leadership updates the last of these four routines, um, less important for the day to day, but significantly important for maintaining the overall health of the SIOE. I've usually done this through some combination of email summaries, but also standing agenda items on a leadership routine. Um, for for me, it is always a shared update that my partner and I present together. We both have our names on it. I typically talk about what we learned in the data. Briefly, my partner will talk about what she is going to do with it, and very, very importantly, what it is worth. Okay, a couple more here. Prioritization happens at several levels on Dive. Alluded to this. It happens within a business unit in the Internal Portfolio review. It has to happen at times across business units. It also can and should happen enterprise wide on some frequency. So within business units, that is the easiest. Happens most frequently across business units usually comes up as a need when one leader business leader has a significant opportunity but no available baseline analytical support. For whatever reason. In that case, we might jointly approach another business leader, Havenaar Oi, based discussion about maybe borrowing a resource for some period of time. Again, It's not my decision. I don't in isolation say, Oh, good project is worth more than project. Be so owner of Project Be sorry you lose. I'm taking those. Resource is that's It's not good practice. It's not a good way of building partnerships. Um, you know that that collaboration, what is really best for the business? What is best for the enterprise, um, is an enterprise decision. It's not a me decision. Lastly, enterprise level part ization is the probably the least frequent is aided significantly by the semi annual business strategy sessions. Uh, this is the time to look enterprise wide. It all of the business opportunities that play potential R a y of each and jointly decide where to align. Resource is on a more, uh, permanent basis, if you will, to make sure that the most important, um, initiatives are properly staffed with analytical support. Oxygen funding briefly, Um, I favor a hybrid model, which I don't hear talked about in a lot of other places. So first, I think it's really critical to provide each business unit with some baseline level of analytical support that is centrally funded as part of a shared service center of excellence. And if a business leader needs additional support that can't otherwise be provided, that leader can absolutely choose to fund an incremental resource from her own budget that is fully dedicated to the initiative that is important to her business. Um, there are times when that privatization happens at an enterprise level, and the collective decision is we are not going to staff this potentially worthwhile initiative. Um, even though we know it's worthwhile and a business leader might say, You know what? I get it. I want to do it anyway. And I'm gonna find budget to make that happen, and we create that position, uh, still reporting to the center of excellence for all of the other reasons. The right higher managing the work product. But that resource is, as all resource is, works for the business leader. Um, so, uh, it is very common thinking about again. What's the value of having these resource is reports centrally but work for the business leader. It's very common Thio here. I can't get from a business leader. I can't get what I need from the analytics team. They're too busy. My work falls by the wayside. So I have to hire my own people on. My first response is have we tried putting some of these routines into place on my second is you might be right. So fund a resource that's 100% dedicated to you. But let me use my expertise to help you find the right person and manage that person successfully. Um, so at this point, I I hope you see or starting to see how these routines really work together and how these principles work together to create a higher level of operational partnership. We collectively know the purpose of a centralized Chloe. Everyone knows his or her role in doing the work, managing the work, prioritizing the use of this very valuable analytical talent. And we know where higher ordered trade offs need to be made across the enterprise, and we make sure that those decisions have and those decision makers have the information and connectivity to the work and to each other to make those trade offs. All right, now that we've established the purpose of the modern analyst and the functional framework in which they operate, I want to talk a little bit about the hard part of getting from where many individual analysts and business leaders are today, uh, to where we have the opportunity to grow in order to maintain pain and or regain that competitive advantage. There's no judgment here. It's simply an artifact. How we operate today is simply an artifact of our historical training, the technology constraints we've been under and the overall newness of Applied analytics as a distinct discipline. But now is the time to start breaking away from some of that and and really upping our game. It is hard not because any of these new skills is particularly difficult in and of themselves. But because any time you do something, um, for the first time, it's uncomfortable, and you're probably not gonna be great at it the first time or the second time you try. Keep practicing on again. This is for the analyst and for the business leader to think differently. Um, it gets easier, you know. So as a business leader when you're tempted to say, Hey, so and so I just need this data real quick and you shoot off that email pause. You know it's going to help them, and I'll get the answer quicker if I give him a little context and we have a 10 minute conversation. So if you start practicing these things, I promise you will not look back. It makes a huge difference. Um, for the analyst, become a consultant. This is the new set of skills. Uh, it isn't as simple as using layman's terms. You have to have a different conversation. You have to be willing to meet your business partner as an equal at the table. So when they say, Hey, so and so can you get me this data You're not allowed to say yes. You're definitely not is not to say no. Your reply has to be helped me understand what you're trying to achieve, so I can better meet your needs. Andi, if you don't know what the business is trying to achieve, you will never be able to help them get there. This is a must have developed project management skills. All of a sudden, you're a POC. You're in charge of keeping track of everything that's coming in. You're in charge of understanding why it's happening. You're responsible for making sure that your partner is connected across the rest of the analytics. Um, team and ecosystem that takes some project management skills. Um, be business focused, not data focused. Nobody cares what your algorithm is. I hate to break it to you. We love that stuff on. We love talking about Oh, my gosh. Look, I did this analysis, and I didn't think this is the way I was gonna approach it, and I did. I found this thing. Isn't it amazing? Those are the things you talk about internally with your team because when you're doing that, what you're doing is justifying and sort of proving the the rightness of your answer. It's not valuable to your business partner. They're not going to know what you're talking about anyway. Your job is to tell them what you found. Drawing conclusions. Historically, Analyst spent so much of their time just getting data into a power 0.50 pages of summarized data. Now the job is to study that summarized data and draw a conclusion. Summarized data doesn't explain what's happening. They're just clues to what's happening. And it's your job as the analyst to puzzle out that mystery. If a partner asked you a question stated in words, your answer should be stated in words, not summarized data. That is a new skill for some again takes practice, but it changes your ability to create value. So think about that. Your job is to put the answer on page with supporting evidence. Everything else falls in the cutting room floor, everything. Everything. Everything has to be tied to our oi. Um, you're a cost center and you know, once you become integrated with your business partner, once you're working on business initiatives, all of a sudden, this actually becomes very easy to do because you will know, uh, the business case that was put forth for that business initiative. You're part of that business case. So it becomes actually again with these routines in place with this new way of working with this new way of thinking, it's actually pretty easy to justify and to demonstrate the value that analytic springs to an organization. Andi, I think that's important. Whether or not the organization is is asking for it through formalized reporting routine Now for the business partner, understand that this is a transformation and be prepared to support it. It's ultimately about providing a higher level of support to you, but the analysts can't do it unless you agree to this new way of working. So include your partner as a member of your team. Talk to them about the problems you're trying to sell to solve. Go beyond asking for the data. Be willing and able to tie every request to an overarching business initiative on be poised for action before solution is commissioned. This is about preserving. The precious resource is you have at your disposal and you know often an extra exploratory and let it rip. Often, an exploratory analysis is required to determine the value of a solution, but the solution itself should only be built if there's a plan, staffing and funding in place to implement it. So in closing, transformation is hard. It requires learning new things. It also requires overriding deeply embedded muscle memory. The more you can approach these changes is a team knowing you won't always get it right and that you'll have to hold each other accountable for growth, the better off you'll be and the faster you will make progress together. Thanks. >>Thank you so much, Kathleen, for that great content and thank you all for joining us. Let's take a quick stretch on. Get ready for the next session. Starting in a few minutes, you'll be hearing from thought spots. David Coby, director of Business Value Consulting, and Blake Daniel, customer success manager. As they discuss putting use cases toe work for your business

>> Announcer: Live from Orlando, Florida it's theCUBE covering Pentaho World 2017. Brought to you by Hitachi Vantara. (upbeat techno music) (coughs) >> Welcome to Day Two of theCUBE's live coverage of Pentaho World, brought to you by Hitachi Vantara. I'm your host Rebecca Knight along with my co-host Dave Vellante. We're joined by Robert Walsh. He is the Technical Director Enterprise Business Intelligence at ZeniMax. Thanks so much for coming on the show. >> Thank you, good morning. >> Good to see ya. >> I should say congratulations is in order (laughs) because you're company, ZeniMax, has been awarded the Pentaho Excellence Award for the Big Data category. I want to talk about the award, but first tell us a little bit about ZeniMax. >> Sure, so the company itself, so most people know us by the games versus the company corporate name. We make a lot of games. We're the third biggest company for gaming in America. And we make a lot of games such as Quake, Fallout, Skyrim, Doom. We have game launching this week called Wolfenstein. And so, most people know us by the games versus the corporate entity which is ZeniMax Media. >> Okay, okay. And as you said, you're the third largest gaming company in the country. So, tell us what you do there. >> So, myself and my team, we are primarily responsible for the ingestion and the evaluation of all the data from the organization. That includes really two main buckets. So, very simplistically we have the business world. So, the traditional money, users, then the graphics, people, sales. And on the other side we have the game. That's where a lot of people see the fun in what we do, such as what people are doing in the game, where in the game they're doing it, and why they're doing it. So, get a lot of data on gameplay behavior based on our playerbase. And we try and fuse those two together for the single viewer or customer. >> And that data comes from is it the console? Does it come from the ... What's the data flow? >> Yeah, so we actually support many different platforms. So, we have games on the console. So, Microsoft, Sony, PlayStation, Xbox, as well as the PC platform. Mac's for example, Android, and iOS. We support all platforms. So, the big challenge that we have is trying to unify that ingestion of data across all these different platforms in a unified way to facilitate downstream the reporting that we do as a company. >> Okay, so who ... When it says you're playing the game on a Microsoft console, whose data is that? Is it the user's data? Is it Microsoft's data? Is it ZeniMax's data? >> I see. So, many games that we actually release have a service act component. Most of our games are actually an online world. So, if you disconnect today people are still playing in that world. It never ends. So, in that situation, we have all the servers that people connect to from their desktop, from their console. Not all but most data we generate for the game comes from the servers that people connect to. We own those. >> Dave: Oh, okay. >> Which simplifies greatly getting that data from the people. >> Dave: So, it's your data? >> Exactly. >> What is the data telling you these days? >> Oh, wow, depends on the game. I think people realize what people do in games, what games have become. So, we have one game right now called Elder Scrolls Online, and this year we released the ability to buy in-game homes. And you can buy furniture for your in-game homes. So, you can furnish them. People can come and visit. And you can buy items, and weapons, and pets, and skins. And what's really interesting is part of the reason why we exist is to look at patterns and trends based on people interact with that environment. So for example, we'll see America playerbase buy very different items compared to say the European playerbase, based on social differences. And so, that helps immensely for the people who continuously develop the game to add items and features that people want to see and want to leverage. >> That is fascinating that Americans and Europeans are buying different furniture for their online homes. So, just give us some examples of the difference that you're seeing between these two groups. >> So, it's not just the homes, it applies to everything that they purchase as well. It's quite interesting. So, when it comes to the Americans versus Europeans for example what we find is that Europeans prefer much more cosmetic, passive experiences. Whereas the Americans are much things that stand out, things that are ... I'm trying to avoid stereotypes right now. >> Right exactly. >> It is what it is. >> Americans like ostentatious stuff. >> Robert: Exactly. >> We get it. >> Europeans are a bit more passive in that regard. And so, we do see that. >> Rebecca: Understated maybe. >> Thank you, that's a much better way of putting it. But games often have to be tweaked based on the environment. A different way of looking at it is a lot of companies in career in Asia all of these games in the West and they will have to tweak the game completely before it releases in these environments. Because players will behave differently and expect different things. And these games have become global. We have people playing all over the world all at the same time. So, how do you facilitate it? How do you support these different users with different needs in this one environment? Again, that's why BI has grown substantially in the gaming industry in the past five, ten years. >> Can you talk about the evolution of how you've been able to interact and essentially affect the user behavior or response to that behavior. You mentioned BI. So, you know, go back ten years it was very reactive. Not a lot of real time stuff going on. Are you now in the position to effect the behavior in real time, in a positive way? >> We're very close to that. We're not quite there yet. So yes, that's a very good point. So, five, ten years ago most games were traditional boxes. You makes a game, you get a box, Walmart or Gamestop, and then you're finished. The relationship with the customer ends. Now, we have this concept that's used often is games as a service. We provide an online environment, a service around a game, and people will play those games for weeks, months, if not years. And so, the shift as well as from a BI tech standpoint is one item where we've been able to streamline the ingest process. So, we're not real time but we can be hourly. Which is pretty responsive. But also, the fact that these games have become these online environments has enabled us to get this information. Five years ago, when the game was in a box, on the shelf, there was no connective tissue between us and them to interact and facilitate. With the games now being online, we can leverage BI. We can be more real time. We can respond quicker. But it's also due to the fact that now games themselves have changed to facilitate that interaction. >> Can you, Robert, paint a picture of the data pipeline? We started there with sort of the different devices. And you're bringing those in as sort of a blender. But take us through the data pipeline and how you're ultimately embedding or operationalizing those analytics. >> Sure. So, the game theater, the game and the business information, game theater is most likely 90, 95% of our total data footprint. We generate a lot more game information than we do business information. It's just due to how much we can track. We can do so. And so, a lot of these games will generate various game events, game logs that we can ingest into a single data lake. And we can use Amazon S3 for that. But it's not just a game theater. So, we have databases for financial information, account users, and so we will ingest the game events as well as the databases into one single location. At that point, however, it's still very raw. It's still very basic. We enable the analysts to actually interact with that. And they can go in there and get their feet wet but it's still very raw. The next step is really taking that raw information that is disjointed and separated, and unifying that into a single model that they can use in a much more performant way. In that first step, the analysts have the burden of a lot of the ETL work, to manipulate the data, to transform it, to make it useful. Which they can do. They should be doing the analysis, not the ingesting the data. And so, the progression from there into our warehouse is the next step of that pipeline. And so in there, we create these models and structures. And they're often born out of what the analysts are seeing and using in that initial data lake stage. So, they're repeating analysis, if they're doing this on a regular basis, the company wants something that's automated and auditable and productionized, then that's a great use case for promotion into our warehouse. You've got this initial staging layer. We have a warehouse where it's structured information. And we allow the analysts into both of those environments. So, they can pick their poison in respects. Structured data over here, raw and vast over here based on their use case. >> And what are the roles ... Just one more follow up, >> Yeah. >> if I may? Who are the people that are actually doing this work? Building the models, cleaning the data, and shoring data. You've got data scientists. You've got quality engineers. You got data engineers. You got application developers. Can you describe the collaboration between those roles? >> Sure. Yeah, so we as a BI organization we have two main groups. We have our engineering team. That's the one I drive. Then we have reporting, and that's a team. Now, we are really one single unit. We work as a team but we separate those two functions. And so, in my organization we have two main groups. We have our big data team which is doing that initial ingestion. Now, we ingest billions of troves of data a day. Terabytes a data a day. And so, we have a team just dedicated to ingestion, standardization, and exposing that first stage. Then we have our second team who are the warehouse engineers, who are actually here today somewhere. And they're the ones who are doing the modeling, the structuring. I mean the data modeling, making the data usable and promoting that into the warehouse. On the reporting team, basically we are there to support them. We provide these tool sets to engage and let them do their work. And so, in that team they have a very split of people do a lot of report development, visualization, data science. A lot of the individuals there will do all those three, two of the three, one of the three. But they do also have segmentation across your day to day reporting which has to function as well as the more deep analysis for data science or predictive analysis. >> And that data warehouse is on-prem? Is it in the cloud? >> Good question. Everything that I talked about is all in the cloud. About a year and a half, two years ago, we made the leap into the cloud. We drunk the Kool-Aid. As of Q2 next year at the very latest, we'll be 100% cloud. >> And the database infrastructure is Amazon? >> Correct. We use Amazon for all the BI platforms. >> Redshift or is it... >> Robert: Yes. >> Yeah, okay. >> That's where actually I want to go because you were talking about the architecture. So, I know you've mentioned Amazon Redshift. Cloudera is another one of your solutions provider. And of course, we're here in Pentaho World, Pentaho. You've described Pentaho as the glue. Can you expand on that a little bit? >> Absolutely. So, I've been talking about these two environments, these two worlds data lake to data warehouse. They're both are different in how they're developed, but it's really a single pipeline, as you said. And so, how do we get data from this raw form into this modeled structure? And that's where Pentaho comes into play. That's the glue. If the glue between these two environments, while they're conceptually very different they provide a singular purpose. But we need a way to unify that pipeline. And so, Pentaho we use very heavily to take this raw information, to transform it, ingest it, and model it into Redshift. And we can automate, we can schedule, we can provide error handling. And so it gives us the framework. And it's self-documenting to be able to track and understand from A to B, from raw to structured how we do that. And again, Pentaho is allowing us to make that transition. >> Pentaho 8.0 just came out yesterday. >> Hmm, it did? >> What are you most excited about there? Do you see any changes? We keep hearing a lot about the ability to scale with Pentaho World. >> Exactly. So, there's three things that really appeal to me actually on 8.0. So, things that we're missing that they've actually filled in with this release. So firstly, we on the streaming component from earlier the real time piece we were missing, we're looking at using Kafka and queuing for a lot of our ingestion purposes. And Pentaho in releasing this new version the mechanism to connect to that environment. That was good timing. We need that. Also too, get into more critical detail, the logs that we ingest, the data that we handle we use Avro and Parquet. When we can. We use JSON, Avro, and Parquet. Pentaho can handle JSON today. Avro, Parquet are coming in 8.0. And then lastly, to your point you made as well is where they're going with their system, they want to go into streaming, into all this information. It's very large and it has to go big. And so, they're adding, again, the ability to add worker nodes and scale horizontally their environment. And that's really a requirement before these other things can come into play. So, those are the things we're looking for. Our data lake can scale on demand. Our Redshift environment can scale on demand. Pentaho has not been able to but with this release they should be able to. And that was something that we've been hoping for for quite some time. >> I wonder if I can get your opinion on something. A little futures-oriented. You have a choice as an organization. You could just take roll your own opensource, best of breed opensource tools, and slog through that. And if you're an internet giant or a huge bank, you can do that. >> Robert: Right. >> You can take tooling like Pentaho which is end to end data pipeline, and this dramatically simplifies things. A lot of the cloud guys, Amazon, Microsoft, I guess to a certain extent Google, they're sort of picking off pieces of the value chain. And they're trying to come up with as a service fully-integrated pipeline. Maybe not best of breed but convenient. How do you see that shaking out generally? And then specifically, is that a challenge for Pentaho from your standpoint? >> So, you're right. That why they're trying to fill these gaps in their environment. To what Pentaho does and what they're offering, there's no comparison right now. They're not there yet. They're a long way away. >> Dave: You're saying the cloud guys are not there. >> No way. >> Pentaho is just so much more functional. >> Robert: They're not close. >> Okay. >> So, that's the first step. However, though what I've been finding in the cloud, there's lots of benefits from the ease of deployment, the scaling. You use a lot of dev ops support, DBA support. But the tools that they offer right now feel pretty bare bones. They're very generic. They have a place but they're not designed for singular purpose. Redshift is the only real piece of the pipeline that is a true Amazon product, but that came from a company called Power Excel ten years ago. They licensed that from a separate company. >> Dave: What a deal that was for Amazon! (Rebecca and Dave laugh) >> Exactly. And so, we like it because of the functionality Power Excel put in many year ago. Now, they've developed upon that. And it made it easier to deploy. But that's the core reason behind it. Now, we use for our big data environment, we use Data Breaks. Data Breaks is a cloud solution. They deploy into Amazon. And so, what I've been finding more and more is companies that are specialized in application or function who have their product support cloud deployment, is to me where it's a sweet middle ground. So, Pentaho is also talking about next year looking at Amazon deployment solutioning for their tool set. So, to me it's not really about going all Amazon. Oh, let's use all Amazon products. They're cheap and cheerful. We can make it work. We can hire ten engineers and hack out a solution. I think what's more applicable is people like Pentaho, whatever people in the industry who have the expertise and are specialized in that function who can allow their products to be deployed in that environment and leverage the Amazon advantages, the Elastic Compute, storage model, the deployment methodology. That is where I see the sweet spot. So, if Pentaho can get to that point, for me that's much more appealing than looking at Amazon trying to build out some things to replace Pentaho x years down the line. >> So, their challenge, if I can summarize, they've got to stay functionally ahead. Which they're way ahead now. They got to maintain that lead. They have to curate best of breed like Spark, for example, from Databricks. >> Right. >> Whatever's next and curate that in a way that is easy to integrate. And then look at the cloud's infrastructure. >> Right. Over the years, these companies that have been looking at ways to deploy into a data center easily and efficiently. Now, the cloud is the next option. How do they support and implement into the cloud in a way where we can leverage their tool set but in a way where we can leverage the cloud ecosystem. And that's the gap. And I think that's what we look for in companies today. And Pentaho is moving towards that. >> And so, that's a lot of good advice for Pentaho? >> I think so. I hope so. Yeah. If they do that, we'll be happy. So, we'll definitely take that. >> Is it Pen-ta-ho or Pent-a-ho? >> You've been saying Pent-a-ho with your British accent! But it is Pen-ta-ho. (laughter) Thank you. >> Dave: Cheap and cheerful, I love it. >> Rebecca: I know -- >> Bless your cotton socks! >> Yes. >> I've had it-- >> Dave: Cord and Bennett. >> Rebecca: Man, okay. Well, thank you so much, Robert. It's been a lot of fun talking to you. >> You're very welcome. >> We will have more from Pen-ta-ho World (laughter) brought to you by Hitachi Vantara just after this. (upbeat techno music)