>>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

>> From the SiliconANGLE Media office in Boston, Massachesetts, it's theCUBE. Now here's your host, Stu Miniman. >> Hi I'm Stu Miniman and this is a CUBE conversation from our Boston area studios. The ecosystem around data and analytics definitely isn't becoming any simpler today. Joining me for this segment is Drew Clarke who's the chief strategy officer at Qlik. And Drew let's start there, we talk about the wave of big data, a lot of them have wrapped themselves around the cloke of AI today, you've got machine learning in there. So help kinda give us a little bit about where Qlik fits into that ecosystem and differentiates itself from this very diverse ecosystem. >> Yeah, sure and I get that question a lot Stu is, who is Qlik and what makes us unique. And as a strategy, individual and professional, I spend a lot of time talking, working with customers that are looking at companies and I always come back to it like, what is that core kinda part? Every company comes from something and then how does it fit into the landscape, so I use actually our history to explain a little bit about who we are. So we're 25 years ago, or 25 years old and our very first customer was Tetrapak which make cardboard boxes, of all different sizes, so if you think about Amazon when you order something and you get it showed up at your, it shows up at your desktop or your door, it's in a different size box. Well Tetrapak had a problem of their sales people were selling inventory they didn't have. And they needed to be able to sell what they had, but they also wanted to make sure they showed what they did not have. So they signed on and had a project with Qlik. And this is in Sweden, and they developed a product which is really a product configurator tied with a visualization to it. So what they had the answer on a business question was, tell me what products are and are not available and be able to dynamically make selections as sales reps were answering the questions. So that was the genesis of our own kinda product, so we had a choice back then to say, do we stay in a product configurator space, or do we move into the visualization analytics? And so we took that unique kinda package, what we call the associative engine with the visual kinda piece and we went and started on the business intelligence or the analytics journey. And where we've kinda evolved that as a company is we took that, and another great example is another customer a couple of years ago there was the tsunami in Japan, do you remember that Stu? >> Of course. >> When that happened. So one of our customers was in the consumer products and they had a lot of supply or ingredients that came out of Japan. And they also knew that, okay, the tsunami hit, big impact on there supply chain, and they had to actually make an announcement, they had earnings on Wall Street, and they needed to be able to outline to their investors within the week to say, well is this a big impact, is this not a big impact on our forward looking revenues? And they tried answering the question using traditional analytics, you know, show me what products were impacted by the tsunami, and that's a first order question, as you know it's an easy question to ask. Well now you're going down into the ingredients, you're looking at where the data is in the supply chain and you come back with an answer that says these are the ones that are impacted. The next question that the business asks was okay, tell me what products were not affected. And now think about that is not question going through every single row. Oh and tell me what the inventory is, and can we run campaigns and sales where we know we're either A gonna miss our revenue numbers or we're gonna hit 'em. And they used the Qlik, they tried a different kinda traditional way of answering a question, they couldn't answer it 'cause they get stuck at that first. It was Qlik that actually entered and helped them answer the second question, show what products were not affected and do we have inventory, and they would be able to make that decision. And so that's where we start, what we makes us unique is this combination of analytics and visual kinda interface. And that's been kinda our core differentiator in the market from 25 years ago to where we are today. >> Yeah, and boy that history has changed quite a lot. Think about data visualization, we used to do infographics many years ago, just how do I tell a story with that data? There's the creative things you can do with it but as well as us as humans we look at all of those data points out there and most of the times it's not static, I love people when they're sharing, it's like okay, let me give you charts for something over a 100 year period, and you can watch it ebb and flow and change in the like, so there's so many technology. 25 years ago, cloud had many different terms, I can argue I've worked with plenty of people that we had the XSP back in the 90s and the pre-cloud things. But there's some challenges that we've been trying to solve and then some major breakthroughs we've had with some of these journeys and these technology waves, so bring us up to today as to, we talk about things like speed and scale and agility impacting what we're doing, it's got to be, you've got the why and the core, but the how and the what has changed dramatically. >> Stu you really are kind of a technical kinda guy at heart, right, so one of the things you said at the beginning there where you talked about looking at an infographic and the human kinda component of, how do you look at this information, how do you understand it? It's getting bigger and harder to understand. One of the things that we firmly believe in is the human being is an integral part of the decision making process. And so you think about a scatter plot with 30,000 data points, how do you actually make sense of it? And we spend a lot of time about the human brain and how it looks at information on this kinda big data scale and we're a predator as a human, we're binocular and we look for certain things, and so we spend a lot of time around that kinda visual interface. And I think Steven Few writes about this, Edward Tufte and his documentation around kinda how do you present information in a great way. Well, you take that 30,000 data points on a scatterplot, and well bringing it forward in our technology we show density in heat because that's what we look for. And we look for patterns, and we look for outliers as a predator as kind of an individual. And so we present the information in a way that a human is kinda wired to receive it, but underneath, and this is where I think you're second part was going, underneath is like how do you keep that elegance and responding to a kind of now compute and infrastructure and all the sides. >> Yeah and I guess I always worried is we talk about garbage in, garbage out sometimes. How do I make sure I've got good data, how do I make sure the algorithm is learning? There was a tool that was, oh let me train this AI on Twitter and what they got back they had to turn it off really quick because it became a troll and then much worse and the language was awful, so sometimes if you just let the data run wild the algorithm doesn't understand what's going on. How do you balance that and make sure we're getting good decisions and good information? And we say, if you automate a bad process you haven't done a good thing. >> Right, right. Well that comes through a number of layers from automation there's kinda the data, getting it from the raw source, getting it ready for the analytical consumption, and is it a machine, is it a human, is it a human augmented with kinda the intelligence? And as you progress through this data journey of bringing the data into now the common terms are data lakes and data swamps. How do you find the right information and where do you put the right kinda governance? And governance, not being a bad word, but governance being a, I'm confident that information is correct. And so you see the introduction of data catalogs, so much like a card catalog in a library if you're old enough to actually remember that. >> I know the Dewey Decimal System. >> Okay, there you go so I was a page, that was one of my first paying job was to put books back in the library. And you want to be able to find the right information and know that it's been curated, been set up, but it doesn't have to be written all out. You want to have that progressive kind of bringing of that information for the user to be able to do that. And as you kind of fan out from the central that raw data out to kinda where the analytics users are kinda engaging and working with it. That governance allows for that confidence, but then you need to know that you're scaling and the speed. You don't want to wait if you had a request. The decision just like, even what happened to that customer, tsunami happened, I have earnings on a set day in days from an event. I can't wait a month to come up with the answer. I need that speed, I need that faster. >> Alright, so who's the one inside the customer that work's on this, you know, we've all heard that there's skill gaps out there. Years ago it was like, okay we're going to build this giant army of data scientists. It's not like we're saying we don't need data scientists but we don't have enough time to train enough PhD's to fill the jobs. So where are we today, where do the customers fit organizationally, and if you can get into a little bit of where the product touches them? >> Sure, so what you bring up is the. Great interviewer, broad question, so many different ways we can go with this. And I come back to the idea of what a lot of people come and talk about is this citizen data scientist, but it's really about data literacy. And these are individuals who need to be comfortable working with data, and how do you actually have that confidence level of when I'm looking at it do I know is it real? Am I having the right conversation? Just recently I had the opportunity to see a number of presentations by college seniors who were presenting their senior thesis' on how they're working on a particular theme. And I was in this behavioral sciences and leadership department, it was at the United States Military Academy at Westpoint. And when you think about leadership and you think about behavioral sciences and you think about a lot of the softer side of it, but everyone of these cadets had data and you can see them looking at the empirical data, looking at the R coefficients, is this noise, is this signal, what's causation versus correlation. What you see is this language of data literacy in the curriculum and you flash forward and you look at every department in a company and you see people are coming in who understand there's data that can be used to be informing my decision so I don't need to wait for this white lab coat PhD on data science. It's like well, is there causation is there correlation? So marketing, finance, sales, we're seeing this at that data citizen at the edges in a company and it's coming out of the universities. >> Yeah I was at a conference recently and the analysts up on the keynote stage says, you want to teach your team machine learning? Get a summer intern that's taken the courses and have them spend a week training you up on it. So excellent, so sounds like if someone wants to get started with Qlik, relatively low bar. I don't have to go through some six month training class to be able to start getting some business value and rolling this out. >> Yeah, exactly. Stu, you can go right on our website and you can sign up and start to use our product right in the cloud. If you want to put it on a desktop you can do that. And you just drag in your first data files and I encourage you to actually bring in a complicated dataset. Don't go with a simple excel file, a lot of companies can do bars, charts, and graphs. But what you really want to do is bring in two different datasets and bring it into, and remember the associative engine of bringing different data together? And it's the second and third question that you're really looking for those insights. And so you can very quickly assemble the information. You don't need to go back and learn what a left outer joint is because our engine takes care of that for you. You want to understand what's going on? It's transparent. And then you start finding insights within minutes of being able to use that. >> Yeah well if you go back to the Hitchhikers Guide to the Galaxy, sometimes the answer's easy, I have to know the right questions to be able to ask. Alright, Drew I want to give you the final takeaway for this piece. >> Okay, so if you're thinking about dealing with any data and you want to answer not just the question, but it's usually the second and third, and you want to have a speed of use. You can do that with our platform, but think about it really in that concept of data literacy and you want that right information for the individuals to read and write, that's okay and it's easy. It's analyzing and arguing and that's where the competitive advantage so take a look at that. >> Alright, well Drew Clarke really appreciate the updates on Qlik and be sure to check out theCUBE.net. There's a nice little search bar on top, you can search by company, search by person, actually a lot of the key metadata you can search for in there. Thousands of videos in there. Never a registration to be able to get it. So I'm Stu Miniman and thanks as always for watching theCUBE. (upbeat music)

>> Announcer: Live from Nashville, Tennessee, it's theCUBE, covering Commvault GO 2018. Brought to you by Commvault. >> Welcome back to Nashville, Tennessee, the home of hot chicken and Commvault GO this week. I'm Stu Miniman with my co-host, Keith Townsend. Keith wasn't expecting that one. >> I'm looking forward to the hot chicken. >> Absolutely. And happy to welcome to the program first-time guest, Rahul Pawar, who is the head of R&D, research and development, at Commvault. Thank you so much for joining us. >> Thanks for having me on this one. >> Alright, we said, like the hot chicken, I said we need to roll up our sleeves and really get into the sauce-- >> Rahul: Yes, yes. of what we're talking. Alright, enough of the puns on my standpoint. But tell us a little bit about R&D inside, what's your role, what's your team, what's your charter? >> So, we have a team of about 650 very dynamic, young engineers. And what my role, and I'm very excited about that role, is I get to talk with a lot of our customers and partners and understand their pain points. And the majority of my research comes from what the customer is really looking to do and what is hurting them, and trying to solve that and describe. And once I have a problem defined, the team is very, very intelligent at solving them and they come up with various ways to solve it. And then getting that customer satisfaction high is what gives me the high and that's really what's kept me at Commvault for over 17 years now. >> Yeah, 17 years, Rahul. I think back so, 17 years ago, I was working for a storage company. And we talked about data, but it was usually about storing data or protecting data. Now we're talking about how we can get more value out of data. One of the things I was looking at coming into this show is like, okay, you talk about the AI and the ML. Well, how does that fit into this environment? Maybe you can explain why is it different now in 2018? What can you do now that you wouldn't have been able to do 10 years or even five years ago? >> So Stu, you made a good point. Back up, especially, was make a copy, put it on tape, send it to somewhere. Iron mountain, typically. And that has changed now. Everything is available online all the time. And even our thermostat is much smarter than what it was five years back, so we really are expecting, everybody's expecting, a lot more from the retail that is available from all the information that is there and they want to make use of that. So backup can no longer be, hey, I'm backing up these five servers and go figure it out. Backup is now getting tons of VM's, tons of new application swapping in various cloud applications that are coming in. So the IT team is really, really in the middle of this data revolution and getting so much information thrown their way. So that data, and that data is the liquid gold, like Bob and I like to call it, and that has a lot of valuable information. It has information about your patterns, it has information about who is accessing what files, and should they really be accessing it, what data is really, really not needed, and what is the sensitive data that is lurking behind and it could become a problem for you? So that data is a goldmine and the systems and the hard disks are becoming so much cheaper. Storage has become so much cheaper, so having that data accessible all the time, we take it for granted. >> So Rahul, I'd like to say scale breaks things. When I was a young administrator, I literally had a spreadsheet to keep track of my tapes, of where my tapes were, what systems were backed up. So even if I lost my index and my software backup product, I could know where my tapes were at. Now, with organizations with petabytes and petabytes of data, how important is ML, AI to knowing where your data is at and how important is the index to that relationship? >> I really want to say that ML and AI has become what deduplication was five years back, and pretty much everybody is expecting you to have it. Like I said, if my car knows it, if my home knows it, my thermostat knows it, even my phone knows it, like where I'm going, like every week if I travel to a certain place and it knows it, it is something that is expected to be known. And our backup environment has become so dynamic. There's network failures and there's tons of things beyond the control of the backup admin, even the storage admin or the DB-ers or the app developers who are putting in there, that just come in place. And with all of that happening, you need a system that is learning from what is happening and being very smart about doing stuff. So, we learned from yesterday's failures or the failures that were on the backups, we look at the network load that is on right now, the disk load that is on right now, and adapt our backup schedules accordingly. So we know your SLAs. You're trying to get an SLA of a certain number of hours versus minutes, and based on that, we prioritize certain servers over others, or certain VM's that we see brand new over other VM's, and then VM's around certain data stores over others because we want to keep the load on the data storage server or even your network and the proxies minimum, but at the same time we know we are racing against the clock because we want everything to be backed up and even have a secondary copy and all of that. So there we are prioritizing and re-prioritizing our backups and schedules and everything. >> One of the challenges when you talk about automation is there's the technology and then there's the people and in the open to the keynote this morning, the poet was using the GPS analogy >> Yes. and talked about, okay, you have arrived. Well, the admins today, they kind of have their turf that they control versus do I trust that it's doing the job and can automate some of those things and I shouldn't have to worry about it. Does your team get involved in that dynamic? Because I know you listen to the customers how do you help bridge that gap and help? I think of autonomous cars, we said we will soon get to the point, sometime hopefully in the not-too-distant future, where it's not that I don't trust the computers, it's really that I trust them more than I do the people. >> Okay so I'll tell you, trust develops as you use it more. There's a reason why autonomous driving cars still have a steering wheel and a break because, I'm not sure whether I can trust it. But on the other hand, as time passes by, you really see the software in action and you want to see that its really doing the smart thing, and you yield control to it more and more. Like today, I'm like old era, so when I have something important I make an extra copy. Versus my kids, they are on Google files system or cloud files systems. They never even think about making an extra copy. The same thing is going to happen. We do have people who can take control and they can put on their priorities and all of that but we are saying, hey guys, you shouldn't be doing it we are here to help you and we are going to show you and in case you don't like it you can always put your brake on that self driving car or the self driving backup. >> So Rahul would we be remised if we had a researcher on theCUBE and we didn't talk about the art of the possible looking a few years ahead, or even a couple of years ahead. If you've ever been a backup administrator, nothing beats bandwidth. The bandwidth of a station wagon full of tapes. However in this modern digital transformative environment, we have to get data to the cloud as soon as possible. What are some of the unique ways Commvault is tackling getting Big Data from where it's ingested and to the cloud provider so that we can take advantage of stuff like AI, ML, base workloads, and Amazon or Google? >> One thing we have done with the cloud or anything is we have always kept data independent of where it is going. So even if I am taking data from on-print to a cloud provider we will play to their full strength, but we will still keep the data independent where, in case you want to move from one cloud window to another you have that flexibility with Commvault. As for us taking the cloud and its efficiency and using its efficiency what we have done is we always only send re-duplicated encrypted data to the cloud and we have various ways of consuming the cloud. So the cloud is where your storage has become so cheap that you don't have to think about it. In fact, I had a customer who got rid of their whole secondary DR data center, and now they are using the cloud as their DR location and every three months they do the DR test with Commvault, wherein they bring the infrastructure machines up, and its all scripted and orchestrated, they bring the infrastructure machines up, followed by all the VM's and the applications in a certain order. Like database has to come up before AD has to come before exchange anytime it has to come before web server. So all of that happens after their testing is done they have SLA's of four hours and 24 hours on certain servers. After all of that is done they power it off, they get rid of the infrastructure, and then they are back to paying only the storage bill on the cloud. That's just one usage but the cloud has made life so flexible that I don't have to think about my rack space and where does the server go and when do I order it and when does it ship, If I need something I experiment with it, I give it more memory and size and do stuff. Protecting that data and the cloud, and protecting it well, is what we do. We have taken use of all the technologies, like replicating across regions, taking it and replicating it across clouds we have done all of that. >> Keith: Well let's talk about the importance of metadata in all of that. So if I have bits and pieces of data distributed across cloud providers on-prem, how do I keep track of that data? >> That's where our furi index comes in play key because all that is happening is the data is spreading faster than some of the cloud growth because you have data with so many copies and people have made extra copies just to be safe that keeping track of everything, and knowing what is where, and who has access to what, and people change roles, some people leave, who has access after all of that is done? It's very vital and critical for an organization to function So our furi index is keeping track of not just the bare minimum of who has the files and what the files are what we have done is we have worked with several customers where we have allowed them to insert their own custom tags and custom information along with the data. So it's not just the file and file information or the file content awareness. They are able to keep third party extra data along with every piece that is automatically queried from their other databases and inserted in that file. So those are the custom properties that are tagged a lot. >> Stu: Yeah its interesting, you think about metadata I remember five or 10 years ago we were talking about the importance of metadata, but it seems like it's the convergence of the intelligence and the AI paired with that, because it used to be, oh, make sure you tag your files or set up your ontologies or things like that, and now, on our phones, it does a lot of that for us and therefore the enterprise is following a similar methodology. Did we hit a certain kind of tipping-point recently, or is it just some of these technologies coming together? >> I think a lot of that was in the making. We used to have this technology called index cards, where we were keeping track of things, who ever thinks of that, right? Now everything is by search, and that's the new normal. Searching for your thing, thinking that somebody will know what I'm trying to do and telling me ahead of time is where the future is. That's what we are trying to keep up with. >> You're saying my kids don't know the Dewey decimal system because they have Amazon and you know, and now we have a similar thing in business. >> It really to strikes you, for a calculator on a Windows desktop when the kids go and search on the web for a calculator instead of using the calculator app on the desktop, you really know that things have changed and shifted a lot. >> Keith: So thinking about that change and shift before I'm able to add these custom tags to net new data, I'm going to throw you a softball from a use case perspective, but it's a hard technical challenge is, I have 20 years of Commvault data that are data I've backed up with Commvault. Wouldn't it be great if I could teach an ML or AI algorithm to go back and tag that data based on how I tag new data, any requests for that or roadmaps to add that type of capability? >> Alright so if you are a 20 year old Commvault veteran customer, first of all, thank you. (laughing) >> Secondly, the fact that you're index is there and we have built on our existing index and added a lot more attributes to it, we already know a lot about you. If you are starting to beam to our cloud, we know a lot more about how your backups are, and how much you are backing up, and how your licensing is, and what are the typical workloads, and the top error rates, and how the health conditions are, and a lot of that. That is even on your own server dashboard. You don't have to beam it to any public cloud. You could see it on your own dashboard, all those statistics. So we already know all of that information. What we have come and started doing is we are inserting even more and more pieces of intelligence that we are finding because things have changed over the last 20 years. So what used to be just file metadata, user and all of that, now we have a lot more attributes that the file has. >> One of the biggest challenges we see is, I'm a networking person, and when I go to like the Cisco show this year, the network administrator, most of the network that they are responsible for isn't under their purview, and I think we have the same thing in data, a lot of the data that I'm concerned about in my business it's no longer in my four walls and it's spread out in so many different environments. Opportunity? Challenge? Both? >> For us it's very exciting and opportunistic. For our customers and a lot of IT admins if you are dealing with multiple tools to handle that kind of thing its a big challenge. I have met several customers and they wouldn't admit it, but they know that even though their company policy is not to use certain clouds, the people are using it. If their company policy is not to use some doc sharing, people are using it. So, there are two ways you can look at it. You could forget it and then risk. Or you could accept it and analyze everything with Commvault and go ahead. >> So let's talk about Commvault and this ability to know where your data is at with adjacent technology you know data protection is about protecting the data not just from 'oops I lost my data' or even ran somewhere specifically, but security. What is the role of the index or metadata In protecting your data from intruders? >> So as far as 'ran somewhere' is concerned, we have taken a few things. One is, and we are not a 'ran somewhere' production per se, but what we have done is because we are in there and we look at your backup, how often they happen, how much data is changing, adjusted that to seasonality we know per quarter if you have a lot of files changing versus weekends and how things change, adjusted to seasonality if we something that is out of the norm, we are going to alert you. At that point that alert is an actionable alert where you could say, hey, I want to disable data edging on this particular client, or I want to take away access of someone on that. So even data risks like a rogue admin or an accidental admin what we did is we have added almost a two-signature kind of stuff. So if somebody accidentally deletes a client or a storage policy, one admin won't be able to do that. The business workflow says: 'do you also have authentication from Stu?' That 'hey, Keith is trying to delete this'. That's to approve of this and it's and email to which you reply 'yes' or 'no'. The moment it is done, it goes ahead and it deletes it versus it may stop and 'oops' that was an accident Keith didn't really want to do that. So there's that aspect, the second thing is our own media, what we have done is it is completely protected with our drivers, wherein you can't get to it. Only Commvault authenticated processes are able to write to write to our media. When the customer came in this morning and was talking about it, all their infrastructure was affected, but Commvault really hasn't because we had it secured and the ransomer couldn't attack that because they simply were unable to write to it. >> Stu: Alright well Rahul Pawar we really appreciate you giving us an update. Look forward to catching up in the future where we'll see exactly where the research is going. Alright, for Keith Townsend I'm Stu Miniman, we'll be back with lots more coverage here from Commvault GO, in Nashville, Tenessee. Thanks for watching theCUBE. >> Rahul: Thank you Keith, thank you Stu. >> Keith: Thank you.

(upbeat music) >> Announcer: Live from Las Vegas it's The Cube, covering IBM Think 2018. Brought to you by IBM. >> We're back at IBM Think 2018. This is The Cube, the leader in live tech coverage. My name is Dave Vellante. I'm here with my co-host Peter Burris, Moe Abdulla is here. He's the vice president of Cloud Garage and Solution Architecture Hybrid Cloud for IBM and Tim Davis is here, Data Analytics and Cloud Architecture Group and Services Center of Excellence IBM. Gentlemen, welcome to The Cube. >> Glad to be here. >> Thanks for having us. >> Moe, Garage, Cloud Garage, I'm picturing drills and wrenches, what's the story with Garage? Bring that home for us. >> (laughs) I wish it was that type of a garage. My bill would go down for sure. No, the garage is playing on the theme of the start-up, the idea of how do you bring new ideas and innovate on them, but for the enterprises. So what two people can do with pizza and innovate, how do you bring that to a larger concept. That's what The Garage is really about. >> Alright and Tim, talk about your role. >> Yeah, I lead the data and analytics field team and so we're really focused on helping companies do digital transformation and really drive digital and analytics, data, into their businesses to get better business value, accelerate time to value. >> Awesome, so we're going to get into it. You guys both have written books. We're going to get into the Field Guide and we're going to get into the Cloud Adoption Playbook, but Peter I want you to jump in here because I know you got to run, so get your questions in and then I'll take over. >> Sure I think so obvious question number one is, one of the biggest challenges we've had in analytics over the past couple of years is we had to get really good at the infrastructure and really good at the software and really good at this and really good at that and there were a lot of pilot failures because if you succeeded at one you might not have succeeded at the other. The Garage sounds like it's time to value based. Is that the right way to think about this? And what are you guys together doing to drive time to value, facilitate adoption, and get to the changes, the outcomes that the business really wants? >> So Tim you want to start? >> Yeah I can start because Moe leads the overall Garage and within the Garage we have something called the Data First Methodology where we're really driving a direct engagement with the clients where we help them develop a data strategy because most clients when they do digital transformation or really go after data, they're taking kind of a legacy approach. They're building these big monolithic data warehouses, they're doing big master data management programs and what we're really trying to do is change the paradigm and so we connect with the Data First Methodology through the Garage to get to a data strategy that's connected to the business outcome because it's what data and analytics do you need to successfully achieve what you're trying to do as a business. A lot of this is digital transformation which means you're not only changing what you're doing from a data warehouse to a data lake, but you're also accelerating the data because now we have to get into the time domain of a customer, or your customer where they may be consuming things digitally and so they're at a website, they're moving into a bank branch, they go into a social media site, maybe they're being contacted by a fintech. You've got to retain an maintain a digital relationship and that's the key. >> And The Garage itself is really playing on the same core value of it's not the big beating the small anymore, it's the fast beating the slow and so when you think of the fast beating the slow, how do you achieve fast? You really do that by three ways. So The Garage says the first way to achieve fast is to break down the problem into smaller chunks, also known as MVPs or minimum viable product. So you take a very complex problem that people are talking and over-talking and over engineering, and you really bring it down to something that has a client value, user-centered. So bring the discipline from the business side, the operation side, the developers, and we mush them together to center that. That's one way to do fast. The second way-- >> By the way, I did, worked with a client. They started calling it minimum viable outcomes. >> Yes, minimum viable outcomes means what product and there's a lot of types of these minimum viable to achieve, we're talking about four weeks, six weeks, and so on and so forth. The story of American Airlines was taking all of their kiosk systems for example and really changing them both in terms of the types of services they can deliver, so now you can recheck your flights, et cetera, within six week periods and you really, that's fast, and doing it in one terminal and then moving to others. The second way you do fast is by understanding that the change is not just technology. The change is culture, process, and so on. So when you come to The Garage, it's not like the mechanic style garage where you are sitting in the waiting room and the mechanic is fixing your car. Not at all. You really have some sort of mechanical skills and you're in there with me. That's called pair programming. That's called test-driven, these types of techniques and methodologies are proven in the industry. So Tim will sit right next to me and we'll code together. By the time Tim goes back to his company, he's now an expert on how to do it. So fast is achieving the cultural transformation as well as this minimum viable aspect. >> Hands on, and you guys are actually learning from each in that experience, aren't you? >> Absolutely. >> Oh yeah. >> And then sharing, yeah. >> I would also say I would think that there's one more thing for both of you guys and that is increasingly as business acknowledges that data is an asset unlike traditional systems approaches where we built a siloed application, this server, that database manager, this data model, that application and then we do some integration at some point in time, when you start with this garage approach, data-centric approach, figure out how that works, now you have an asset that can be reused in a lot of new and interesting ways. Does that also factor into this from a speed aspect? >> Yeah it does. And this is a key part. We have something called data science experience now and we're really driving pilots through The Garage, through the data first method to get that rapid engagement and the goal is to do sprints, to do 12 to 20 week kind of sprints where we actually produce a business outcome that you show to the business and then you put it into production and we're actually developing algorithms and other things as we go that are part of the analytic result and that's kind of the key and behind that, you know the analytic result is really the, kind of the icing on the cake and the business value where you connect, but there's a whole foundation underneath that of data and that's why we do a data topology and the data topology has kind of replaced the data lake, replaces all that modeling because now we can have a data topology that spans on premise, private cloud, and public cloud and we can drive an integrated strategy with the governance program over that to actually support the data analytics that you're trying to drive and that's how we get at that. >> But that topology's got to tie back to the attributes of the data, right? Not the infrastructure that's associated with it. >> It does and the idea of the topology is you may have an existing warehouse. That becomes a zone in the topology, so we aren't really ripping and replacing, we're augmenting, you know, so we may augment an on premise warehouse that may sit in a relational database technology with a Hadoop environment that we can spin up in the cloud very rapidly and then the data science applications and so we can have a discovery zone as well as the traditional structured reporting and the level of data quality can be mixed. You may do analytic discovery against raw data versus where you have highly processed data where we have extreme data quality for regulatory reporting. >> Compared to a god box where everything goes through some pipe into that box. >> And you put in on later. >> Yes. >> Well and this is the, when Hadoop came out, right, people thought they were going to dump all their data into Hadoop and something beautiful was going to happen right? And what happened is everybody created a lot of data swamps out there. >> Something really ugly happened. >> Right, right, it's just a pile of data. >> Well they ended up with a cheaper data warehouse. >> But it's not because that data warehouse was structured, it has-- >> Dave: Yeah and data quality. >> All the data modeling, but all that stuff took massive amounts of time. When you just dump it into a Hadoop environment you have no structure, you have to discover the structures so we're really doing all the things we used to do with data warehousing only we're doing it in incremental, agile, faster method where you can also get access to the data all the way through it. >> Yeah that makes sense. >> You know it's not like we will serve new wine before its time, you know you can. >> Yeah, yeah, yeah, yeah. >> You know, now you can eat the grapes, you can drink the wine as it's fermenting, and you can-- >> No wrong or right, just throw it in and figure it out. >> There's an image that Tim chose that the idea of a data lake is this organized library with books, but the reality is a library with all the books dumped in the middle and go find the book that you want. >> Peter: And no Dewey Decimal. >> And, exactly. And if you want to pick on the idea that you had earlier, when you look at that type of a solution, the squad structure is changing. To solve that particular problem you no longer just have your data people on one side. You have a data person, you have the business person that's trying to distill it, you have the developer, you have the operator, so the concept of DevOps to try and synchronize between these two players is now really evolved and this is the first time you're hearing it, right at The Cube. It's the Biz Data DevOps. That's the new way we actually start to tell this. >> Dave: Explain that, explain that to us. >> Very simple. It starts with business requirements. So the business reflects the user and the consumer and they come with not just generics, they come with very specific requirements that then automatically and immediately says what are the most valuable data sources I need either from my enterprise or externally? Because the minute I understand those requirements and the persistence of those requirements, I'm now shaping the way the solution has to be implemented. Data first, not data as an afterthought. That's why we call it the data first method. The developers then, when they're building the cloud infrastructure, they really understand the type of resilience, the type of compliance, the type of meshing that you need to do and they're doing it from the outside. And because of the fact that they're dealing with data, the operation people automatically understand that they have to deal with the right to recovery and so on and so forth. So now we're having this. >> Makes sense. You're not throwing it over the wall. >> Exactly. >> That's where the DevOps piece comes in. >> And you're also understanding the velocity of data, through the enterprise as well as the gaps that you have as an enterprise because you're, when you go into a digital world you have to accumulate a lot more data and then you have to be able to match that and you have to be able to do identity resolution to get to a customer to understand all the dimensions of it. >> Well in the digital world, data is the core, so and it's interesting what you were saying Moe about essentially the line of business identifying the data sources because they're the ones who know how data affects monetization. >> Yes. >> Inder Paul Mendari, when he took over as IBM Chief Data Officer, said you must from partnerships with the line of business in order to understand how to monetize, how data contributes to the monetization and your DevOps metaphor is very important because everybody is sort of on the same page is the idea right? >> That's right. >> And there's a transformation here because we're working very close with Inder Paul's team and the emergence of a Chief Data Officer in many enterprises and we actually kind of had a program that we still have going from last year which is kind of the Chief Data Officer success program where you can help get at this because the classic IT structure has kind of started to fail because it's not data oriented, it's technology oriented, so by getting to a data oriented organization and having a elevated Chief Data Officer, you can get aligned with the line of business, really get your hands on the data and we prescribe the data topology, which is actually the back cover of that book, shows an example of one, because that's the new center of the universe. The technologies can change, this data can live on premise or in the cloud, but the topology should only change when your business changes-- (drowned out) >> This is hugely important so I want to pick up on something Ginny Rometti was talking about yesterday was incumbent disruptors. And when I heard that I'm like, come on no way. You know, instant skeptic. >> Tim: And that's what, that's what it is. >> Right and so then I started-- >> Moe: Wait, wait, discover. >> To think about it and you guys, what you're describing is how you take somebody, a company, who's been organized around human expertise and other physical assets for years, decades, maybe hundreds of years and transform them into a data oriented company-- >> Tim: Exactly. >> Where data is the core asset and human expertise is surrounding that data and learn to say look, it's not an, most data's in silos. You're busting down those silos. >> Exactly. >> And giving the prescription to do that. >> Exactly, yeah exactly. >> I think that's what Tim actually said this very, you heard us use the word re-prescriptive. You heard us use the word methodology, data first method or The Garage method and what we're really starting to see is these patterns from enterprises. You know, what works for a startup does not necessarily translate easily for an enterprise. You have to make it work in the context of the existing baggage, the existing processes, the existing culture. >> Customer expectations. >> Expectations, the scale, all of those type dimensions. So this particular notion of a prescription is we're taking the experiences from Hertz, Marriott, American Airlines, RVs, all of these clients that really have made that leap and got the value and essentially started to put it in the simple framework, seven elements to those frameworks, and that's in the adoption, yeah. >> You're talking this, right? >> Yeah. >> So we got two documents here, the Cloud Adoption Playbook, which Moe you authored, co-authored. >> Moe: With Tim's help. >> Tim as well and then this Field Guide, the IBM Data and Analytic Strategy Field Guide that Tim you also contributed to this right? >> Yeah, I wrote some of it yeah. >> Which augments the book, so I'll give you the description of it too. >> Well I love the hybrid cloud data topology in the back. >> That's an example of a topology on the back. >> So that's kind of cool. But go ahead, let's talk about these. >> So if you look at the cover of that book and piece of art, very well drawn. That's right. You will see that there are seven elements. You start to see architecture, you start to see culture and organization, you start to see methodology, you start to see all of these different components. >> Dave: Governance, management, security, emerging tech. >> That's right, that really are important in any type of transformation. And then when you look at the data piece, that's a way of taking that data and applying all of these dimensions, so when a client comes forward and says, "Look, I'm having a data challenge "in the sense of how do I transform access, "how do I share data, how to I monetize?," we start to take them through all of these dimensions and what we've been able to do is to go back to our starting comment, accelerate the transformation, sorry. >> And the real engagement that we're getting pulled into now in many cases and getting pulled right up the executive chains at these companies is data strategy because this is kind of the core, you've got to, so many companies have a business strategy, very good business strategies, but then you ask for their data strategy, they show you some kind of block diagram architecture or they show you a bunch of servers and the data center. You know, that's not a strategy. The data strategy really gets at the sources and consumption, velocity of data, and gaps in the data that you need to achieve your business outcome. And so by developing a data strategy, this opens up the patterns and the things that we talk to. So now we look at data security, we look at data management, we look at governance, we look at all the aspects of it to actually lay this out. And another thought here, the other transformation is in data warehousing, we've been doing this for the past, some of us longer than others, 20 or 30 years, right? And our whole thing then was we're going to align the silos by dumping all the data into this big data warehouse. That is really not the path to go because these things became like giant dinosaurs, big monolithic difficult to change. The data lake concept is you leave the data where it is and you establish a governance and management process over top of it and then you augment it with things like cloud, like Hadoop, like other things where we can rapidly spin up and we're taking advantage of things like object stores and advanced infrastructures and this is really where Moe and I connect with our IBM Club private platforms, with our data capabilities, because we can now put together managed solutions for some of these major enterprises and even show them the road map and that's really that road map. >> It's critical in that transformation. Last word, Moe. >> Yeah, so to me I think the exciting thing about this year, versus when we spoke last year, is the maturity curve. You asked me this last year, you said, "Moe where are we on the maturity curve of adoption?" And I think the fact that we're talking today about data strategies and so on is a reflection of how people have matured. >> Making progress. >> Earlier on, they really start to think about experimenting with ideas. We're now starting to see them access detailed deep information about approaches and methodologies to do it and the key word for us this year was not about experimentation or trial, it's about acceleration. >> Exactly. >> Because they've proven it in that garage fashion in small places, now I want to do it in the American Airlines scale, I want to do it at the global scale. >> Exactly. >> And I want, so acceleration is the key theme of what we're trying to do here. >> What a change from 15, 20 years ago when the deep data warehouse was the single version of the truth. It was like snake swallowing a basketball. >> Tim: Yeah exactly, that's a good analogy. >> And you had a handful of people who actually knew how to get in there and you had this huge asynchronous process to get insights out. Now you guys have a very important, in a year you've made a ton of progress, yea >> It's democratization of data. Everyone should, yeah. >> So guys, really exciting, I love the enthusiasm. Congratulations. A lot more work to do, a lot more companies to affect, so we'll be watching. Thank you. >> Thank you so much. >> Thank you very much. >> And make sure you read our book. (Tim laughs) >> Yeah definitely, read these books. >> They'll be a quiz after. >> Cloud Adoption Playbook and IBM Data and Analytic Strategy Field Guide. Where can you get these? I presume on your website? >> On Amazon, you can get these on Amazon. >> Oh you get them on Amazon, great. Okay, good. >> Thank you very much. >> Thanks guys, appreciate it. >> Alright, thank you. >> Keep it right there everybody, this is The Cube. We're live from IBM Think 2018 and we'll be right back. (upbeat electronic music)