>> We're here at the Data Cloud Summit 2020, tracking the rise of the data cloud. And we're talking about the ecosystem powering the next generation of innovation in cloud, you know, for decades, the technology industry has been powered by great products. Well, the cloud introduced a new type of platform that transcended point products and the next generation of cloud platforms is unlocking data-centric ecosystems where access to data is at the core of innovation, tapping the resources of many versus the capabilities of one. Casey McGee is here. He's the vice president of global ISV sales at Microsoft, and he's joined by Colleen Kapase, who is the VP of partnerships and global alliances at Snowflake. Folks, welcome to theCUBE. It's great to see you. >> Thanks Dave, good to see you. Thank you. >> Thanks for having us here. >> You're very welcome. So, Casey, let me start with you please. You know, Microsoft's got a long heritage, of course, working with partners, you're renowned in that regard, built a unbelievable ecosystem, the envy of many in the industry. So if you think about as enterprises, they're speeding up their cloud adoption, what are you seeing as the role and the importance of ecosystem, the ISV ecosystem specifically, in helping make customers' outcomes successful? >> Yeah, let me start by saying we have a 45 year history of partnership, so from our very beginning as a company, we invested to build these partnerships. And so let me start by saying from day one, we looked at a diverse ecosystem as one of the most important strategies for us, both to bring innovation to customers and also to drive growth. And so we're looking to build that environment even today. So 45 years later, focused on how do we zero in on the business outcomes that matter most to customers, usually identified by the industry that they're serving. So really building an ecosystem that helps us serve both the customers and the business outcomes they're looking to drive. And so we're building that ecosystem of ISVs on the Microsoft cloud and focused on bringing that innovation as a platform provider through those companies. >> So Casey, let's stay on that for a moment, if we can. I mean, you work with a lot of ISVs and you got a big portfolio of your own solutions. Now, sometimes they overlap with the ISV offerings of your partners. How do you balance the focus on first party solutions and third-party ISV partner solutions? >> Yeah, first and foremost, we're a platform company. So our whole intent is to bring value to that partner ecosystem. Well, sometimes that means we may have offers in market that may compliment one another. Our focus is really on serving the customer. So anytime we see that, we're looking at what is the most desired outcome for our customer, driving innovation into that specific business requirement. So for us, it's always focusing on the customer, and really zeroing in on making sure that we're solving their business problems. Sometimes we do that together with partners like Snowflake. Sometimes that means we do that on our own, but the key for us is really deeply understanding what's important to the customer and then bringing the best of the Microsoft and Snowflake scenarios to bear. >> You know, Casey, I appreciate that. A lot times people say "Dave, don't ask me that question. It's kind of uncomfortable." So Colleen, I want to bring you into the discussion. How does Snowflake view this dynamic, where you're simultaneously partnering and competing sometimes with some of the big cloud companies on the planet? >> Yeah, Dave, I think it's a great question, and really in this era of innovation, so many large companies like Microsoft are so diverse in their product set, it's almost impossible for them to not have some overlap with most of their ecosystem. But I think Casey said it really well, as long as we stay laser focused on the customer, and there are a lot of very happy Snowflake customers and happy Azure customers, we really win together. And I think we're finding ways in which we're working better and better together, from a technology standpoint, and from a field standpoint. And customers want to see us come together and bring best of breed solutions. So I think we're doing a lot better, and I'm looking forward to our future, too. >> So Casey, Snowflake, you know, they're really growing, they've got a pretty large footprint on Azure. You're talking hundreds of customers here that are active on that platform. I wonder if you could talk about the product integration points that you kind of completed initially, and then kind of what's on the horizon that you see as particularly important for your joint customers? >> You have to say, so one of the things that I love about this partnership is that, well, we start with what the customer wants. We bring that back into the engineering-level relationship that we have between the two companies. And so that's produced some pretty incredibly rich functionality together. So let me start by saying, you know, we've got eight Azure regions today with nine coming on soon. And so we have a geographic diversity that is important for many of our customers. We've also got a series of engineering-level integrations that we've already built. So that's functionality for Azure Private Link, as well as integration between Power BI, Azure Data Factory, and Azure Data Lake, all of this back again to serve the business outcomes that are required for our customers. So it's this level of integration that I think really speaks to the power of the partnership. So we are intently focused on the democratization of data. So we know that Snowflake is the premier partner to help us do that. So getting that right is key to enabling high concurrency use cases with large numbers of businesses, users coming together, and getting the performance they expect. >> Yeah, I appreciate that Casey, because a lot of times I'll, you know, I'll look at the press release. Sometimes we laugh, we call them Barney deals. You know, "I love you. You love me." But I listen for the word engineering and integration. Those are sort of important triggers. Colleen, or Casey too, but I want to start with Colleen. I mean, anything you would add to that, are there things that you guys have worked on together that you're particularly proud of, or maybe that have pushed the envelope and enabled new capabilities for customers where they've given you great feedback? Any examples you can share? >> Great question. And we're definitely focusing on making sure stability is a core value for both of us, so that what we offer, that our customers can trust, is going to work well and be dependable, so that's a key focus for us. We're also looking at how can we advance into the future, what can we do around machine learning, it's an area that's really exciting for a lot of the CXO-level leadership at our customers, so we're certainly focused on that. And also looking at Power BI and the visualization of how do we bring these solutions together as well. I'd also say at the same time, we're trying to make the buying experience frictionless for our customers, so we're also leveraging and innovating with Azure's Marketplace, so that our customers can easily acquire Snowflake together with Azure. And even that is being helpful for our customers. Casey, what are your thoughts, too? >> Yeah, let me add to that. I think the work that we've done with Power BI is pretty, pretty powerful. I mean, ultimately, we've got customers out there that are looking to better visualize the data, better inform decisions that they're making. So as much as AI and ML and the inherent power of the data that's being stored within Snowflake is important in and of itself, Power BI really unlocks that and helps drive better decisions, better visualization, and help drive to decision outcomes that are important to the customer. So I love the work that we're doing on Power BI and Snowflake. >> Yeah, and you guys both mentioned, you know, machine learning. I mean, they really are an ecosystem of tools. And the thing to me about Azure, it's all about optionality. You mentioned earlier, Casey, you guys are a platform. So, you know, customer A may want to use Power BI. Another customer might want to use another visualization tool, fine, from a platform perspective, you really don't care, do you? So I wonder Colleen, if we could, and again, maybe Casey can chime in afterwards. You guys, obviously everybody these days, but you in particular, you're focused on customer outcomes. That's the sort of starting point, and Snowflake for sure has built pretty significant experience working with large enterprises and working alongside of Microsoft to get other partners. In your experience, what are customers really looking for out of the two joint companies when they engage with Snowflake and Microsoft, so that one plus one is, you know, much bigger than two. Maybe Colleen, you could start. >> Yeah, I definitely think that what our customers are looking for is both trust and seamlessness. They just want the technology to work. The beauty of Snowflake is our ease of use. So many customers have questions about their business, more so now in this pandemic world than ever before. So the seamlessness, the ease of use, the frictionless, all of these things really matter to our joint customers, and seeing our teams come together, too, in the field, to show here's how Snowflake and Azure are better together, in your local area, and having examples of customers where we've had win-wins, which I'd say Casey, we're getting more and more of those every day, frankly, so it's pretty exciting times. And having our sales teams work as a partnership, even though we compete, we know where we play well together, and I see us doing that over and over again, more and more, around the world, too, which is really important as Snowflake pushes forward, beyond the North America geographies into stronger and stronger in the global regions, where frankly, Microsoft's had a long, storied history at. That's very exciting, especially in Europe and Asia. >> Casey, anything you'd add to that? >> Yeah. Colleen, it's well said. I think ultimately, what customers are looking for is that when our two companies come together, we bring new innovation, new ideas, new ways to solve old problems. And so I think what I love about this partnership is ultimately when we come together, whether it's engineering teams coming together to build new product, whether it's our sales and marketing teams out in front of the customers, across that spectrum, I think customers are looking for us to help bring new ideas. And I love the fact that we've engineered this partnership to do just that. And ultimately we're focused on how do we come together and build something new and different. And I think we can solve some of the most challenging problems with the power of the data and the innovation that we're bringing to the table. >> I mean, you know, Casey, I mean, everybody's really quite in awe and amazed at Microsoft's transformation, and really openness and willingness to really, change and lean into some of the big waves. I wonder if you could talk about your multi-platform strategy and what problems that you're solving in conjunction with Snowflake. >> Yeah, let me start by saying, you know, I think as much as we appreciate that feedback on the progress that we've been striving for, I mean, we're still learning every day, looking for new opportunities to learn from customers, from partners, and so a lot of what you see on the outside is the result of a really focused culture, really focusing on what's important to our customers, focusing on how do we build diversity and inclusion to everything we do, whether that's within Microsoft, with our partners, our customers, and ultimately, how do we show up as one Microsoft, I call one Microsoft kind of the partner's gift. It's ultimately how do our companies show up together? So I think if you look multi-platform, we have the same concept, right? We have the Microsoft cloud that we're offering out in the marketplace. The Microsoft cloud consists of what we're serving up as far as the platform, consists of what we're serving up for data and AI, modern workplace and business applications. And so this multi-cloud strategy for us is really focused on how do we bring innovation across each of the solution areas that matter most to customers. And so I see really the power of the Snowflake partnership playing in there. >> Awesome. Colleen, are there any examples you can share where, maybe this partnership has unlocked the customer opportunity or unique value? >> Yeah, I can't speak about the customer-specific, but what I can do and say is, Casey and I play very corporate roles in terms of we're thinking about the long-term partnership, we're driving the strategy. But hey, look, we'll get called in, we're working a deal right now, it's almost close of the quarter for us, we're literally working on an opportunity right now, how can we win together, how can we be competitive, the customers, the CIO has asked us to come together, to work on that solution. Very large, well-known brand. And we're able to get up to the very senior levels of our companies very quickly to make decisions on what do we need to do to be better and stronger together. And that's really what a partnership is about, you can do the long-term plans and the strategics and you can have great products, but when your executives can pick up the phone and call each other to work on a particular deal, for a particular customer's need, I think that's where the power of the partnership really comes together, and that's where we're at. And that's been a growth opportunity for us this year, is, wasn't necessarily where we were at, and I really have to thank Casey for that. He's done a ton, getting us the right glue between our executives, making sure the relationships are there, and making sure the trust is there, so when our customers need us to come together, that dialogue and that shared diction of putting customers first is there between both companies. So thank you, Casey. >> Oh, thanks, Colleen, the feeling's mutual. >> Well, I think this is key because as I said up front, we've gone from sort of very product-focused to platform-focused. And now we're tapping the power of the ecosystem. That's not always easy to get all the parts moving together, but we live in this API economy. You could say "Hey, I'm a company, everything's going to be homogeneous. Everything is going to be my stack." And maybe that's one way to solve the problem, but really that's not how customers want to solve the problem. Casey, I'll give you the last word. >> Yeah, let me just end by saying, you know, first off the cultures between our two companies couldn't be more well aligned. So I think ultimately when you ask yourself the question, "What do we do to best show up in front of our customers?" It is, focus on their business outcomes, focus on the things that matter most to them. And this partnership will show up well. And I think ultimately our greatest opportunity is to tap into that need, to that interest. And I couldn't be happier about the partnership and the fact that we are so well aligned. So thank you for that. >> Well guys, thanks very much for coming on theCUBE and unpacking some of the really critical aspects of the ecosystem. It was really a pleasure having you. >> Thank you so much for having us. >> Okay, and thank you for watching. Keep it right there. We've got more great content coming your way at the Data Cloud Summit.

>> Live from Orlando, Florida it's theCUBE, covering Microsoft Ignite. Brought to you by Cohesity and theCUBE's eco-system partners. >> Welcome back everyone to theCUBE's live coverage of Microsoft Ignite. I'm your host Rebecca Knight along with my co-host Stu Miniman. We are joined by Mike Flasko. He is the Principal Group Product Manager here at Microsoft. Thanks so much for returning to theCUBE, you are a CUBE alumni. >> I am, yeah thanks for having me back. I appreciate it. >> So you oversee a portfolio of products. Can you let our viewers know what are you workin' on right now? >> Sure, yeah. I work in the area of data integration and governance at Microsoft, so everything around data integration, data acquisition, transformation and then pushing into the governance angles of, you know, once you acquire data and analyze it are you handling it properly as per industry guidelines or enterprise initiatives as you might have? >> You mentioned the magic word, transformation. I would love to have you define. It's become a real buzz word in this industry. How do you define digital transformation? >> Sure, I think it's a great discussion because we're talking about this all the time, but what does that really mean? And for us, the way I see it is starting to make more and more data driven decisions all the time. And so it's not like a light switch, where you weren't and then you were. Typically what happens is as we start working with customers they find new and interesting ways to use more data to help them make a more informed decision. And it starts from a big project or a small project and then just kind of takes off throughout the company. And so really, I think it boils down to using more data and having that guide a lot of the decisions you're making and typically that starts with tapping into a bunch of data that you may already have that just hasn't been part of your kind of traditional data warehousing or BI loop and thinking about how you can do that. >> Mike bring us inside the portfolio a little bit, you know, everybody knows Microsoft. We think about our daily usage of all the Microsoft product that my business data runs through, but when you talk about your products they're specific around the data. Help us walk through that a little bit. >> Sure, yeah. So we have a few kind of flagship products in the space, if you will. The first is something called Azure Data Factory and the purpose of that product is fairly simple. It's really for data professionals. They might be integrators or warehousing professionals et cetera and its to facilitate making it really easy to acquire data from wherever it is. Your business data on-prem from other clouds, SAS applications and allow a really easy experience to kind of bring data into your cloud, into our cloud for analytics and then build data processing pipelines that take that raw data and transform it into something useful, whatever your business domain requires. Whether that's training a machine learning model or populating your warehouse based on more data than you've had before. So first one, data factory all about data integration kind of a modern take on it. Built for the cloud, but fundamentally supports hybrid scenarios. And then other products we've got are things like Azure Data Catalog, which are more in the realm of aiding the discovery and governance of data. So once you start acquiring all this data and using it more productively, you start to have a lot and how do you connect those who want to consume data with the data professionals or data scientists that are producing these rich data sets. So how do you connect your information workers with your data scientists or your data engineers that are producing data sets? Data catalog's kind of the glue between the two. >> Mike wondering if you can help connect the dots to some of the waves we've been seeing. There was a traditonal kind of BI and data warehousing then we went through a kind of big data, the volumes of data and how can I, even if I'm not some multi-national or global company, take advantage of the data? Now there's machine intelligence. Machine learning, AI and all these pieces. What's the same and what's different about the trend and the products today? >> Sure, I think the first thing I've learnt through this process and being in our data space for a while and then working our big data projects is that, for a while we used to talk about them as different things. Like you do data warehousing and now that kind of has an old kind of connotation feeling to it. It's got an old feel to it, right? And then we talk about big data and you have a big data project and I think the realization that we've got is it's really those two things starting to come together and if you think about it, like everybody has been doing some form of analytics and warehousing for a while. And if we start to think about what the Brick Data Technologies has brought is a couple of things, in my opinion that kind of bring these two things together is with big data we started to be able to acquire data of significantly larger size and varying shape, right? But at the end of the day, the task is often acquire that data, shape that data into something useful and then connect it up to our business decision makers that need to leverage that data from a day to day basis. We've been doing that process in warehousing forever. It's really about how easily can we marry big data processing with the traditional data warehousing processes so that our warehouses, our decision making can kind of scale to large data and different shapes of data. And so probably what you'll see actually, at Ignite conference in a lot of our sessions, you'll hear our speakers talking about something called a modern data warehousing and like, it really doesn't matter what the label is associated with it. But it's really about how do you use big data technologies like Spark and Data Bricks naturally alongside warehousing technologies and integration technologies so they really form the modern data warehouse that does naturally handle big data, that does naturally bring in data of all shapes and sizes and provides kind of an experimentation ground as well, for data science. I think that's the last one that kind of comes in is once you've got big data and warehousing kind of working together to expand your analytics beyond kind of traditional approaches the next is opening up some of that data earlier in its life cycle for experimentation by data science. It's kind of the new angle and we think about this notion of kind of modern data warehousing as almost one thing supporting them all going forward. I think the challenge we've had is when we try to separate these into kind of net new deliverables, net new projects where we're starting to kind of bifurcate, if you will, the data platform to some degree. And things were getting a little too complex and so I think what we're seeing is that people are learning what these tools are good at and what they're not good at and now how to bring them together to really get back some of the productivity that we've had in the past. >> I want to ask you about those business decision makers that you referenced. I mean there's an assumption that every organization wants to become more data driven. And I think that most companies would probably say yes, but then there's another set of managers who really want to go by their gut. I mean have you found that being a conflict in terms of how you are positioning the products and services? >> Yeah absolutley. In a number of customer engagements we've had where you start to bring in more data, you start to evolve kind of the analytics practice. There is a lot of resistance at times that, you know, we've done it this way for 20 years, business is pretty good. What are we really fixing here? And so what we've found is the best path through this and in a lot of cases the required path has been show people the art of the possible, run experiments, show them side by side examples and typically with that comes a comfort level in what's possible sometimes it exposes new capabilities and options, sometimes it also shows that there's some other ways to arrive at decisions, but we've certainly seen that and almost like anything, you kind of have to start small, create a proving ground and be able to do it in a kind of side by side manner to show comparison as we go, but it's a conversation that I think is going to carry forward for the next little while especially as some of the work in AI and machine learning is starting to make it's way into business critical settings, right? Pricing your products. Product placement. All of this stuff that directly affects bottom lines you're starting to see these models do a really good job. And I think what we've found is it's all about experimentation. >> Mike when we listen to (mumbles) and to Dell and we talk about, you know, how things are developed inside of Microsoft, usually hear things like open and extensible, you got to have APIs in any of these modern pieces. It was highlighted in the Keynote on Monday, talking about the open data initiative got companies like Adobe and SAP out there, they have a lot of data, so the question is, of course, Microsoft has a lot of data that customers flow through, but there's also this very large eco-system we see at this show. What's the philosophy? Is it just, you know, oh, I've got some APIs and people plug into it? How does all the data get so that the customers can use it? >> Yeah it's a great question. That one I work a lot on and I think there's a couple of angles to it. One is, I think as big data's taken off, a lot of the integration technology that we've used in the past really wasn't made for this era. Where you've got data coming from everywhere. It's different shapes and it's different sizes and so at least within some of our products, we've been investing a lot into how do we make it really easy to acquire all the data you need because, you know, like you hear in all these cases, you can have the best model in the world if you don't have the best data sets it doesn't matter. Digital transformation starts with getting access to more data than you had before and so I think we've been really focused on this, we call it the ingestion of data. Being able to really easily connect and acquire all of the data and that's the starting point. The next thing that we've seen from companies have kind of gone down that journey with us is once you've acquired it all, you quickly have to understand it and you have to be able to kind of search over it and understand it through the lens of potentially business terms if you're a business user trying to understand what is all these data sets? What do they mean? And so I think this is where you're starting to see the rise of data cataloging initiatives not necessarily master data, et cetera, of the past, but this idea of, wow, I'm acquiring all of this data, how do I make sense of it? How do I catalog it? How does all of my workers or my employees easily find what they need and search for the data through the lens that makes sense to them. Data scientists are going to search through a very technical lens. Your business users through business glossary, business domain terms in that way and, so for me it all starts with the acquisition. I think it still far too hard and then becomes kind of a cataloging initiative and then the last step is how do we start to get some form of standards or agreement around the semantics of the data itself? Like this is a customer, this is a place. This is what, you know, a rating and I think with that you're going to start to see a whole eco-system of apps start to develop and one of the things that we're pretty excited about with the open data partnerships is how can we bring in data and to some degree auto-classify it into a set of terms that allow you to just get on with the business logic as opposed to spend all the time in the acquisition phase that most companies do today. >> You mentioned that AI is becoming increasingly important and mission critical or at least, bottom line critical in business models. What are some of the most exciting new uses of AI that you're seeing and that you hope expands into the larger industry? >> Sure. It really does cross a number of domains. We work with a retailer, ASOS. Every time we get to chat with them it's a very interesting use on how they have completely customized the shopping experience from how they layout the page based on your interest and preference through to how the search terms come back based on seasonality of what you're looking at based on what they've learnt about your purchase patterns over time, your sex, et cetera. And so I think this notion of like, intensely customized customer experiences is playing out everywhere. We've seen it on the other side in engine design and preventative maintenance. Where we've got certain customers now that are selling engine hours as opposed to engines themselves. And so if there's an engine hour that they can't provide that's a big deal and so they want to get ahead of any maintenance issue they can and they're using models to predict when a particular maintenance event is going to be required and getting ahead of that through to athletes and injury prevention. We're now seeing all the way down to connected clothing and athletic gear where all the way down, not just at the professional level, but it's starting to come down to the club level on athletes as they're playing, starting to realize that, oh, something's not quite right, I want to get ahead of this before I have a more serious injury. And so we've seen it in a number of domains almost every new customer I'm talking with. I'm excited by what they're doing in this area. >> Well, you bring up an interesting challenges. I've heard Microsoft is really I guess verticalizing around certain industries to put solutions together. One of the challenges we saw, you know, we saw surveys of big data. The number use case came back was always custom and it was like, oh, okay, well how do I templatize and allow hundreds of customers to do this not every single project is a massive engagement. What are you seeing that we're learning from the past and it feels like we're getting over that hump a little bit faster now than we were a few years ago. >> Yeah, so if I heard you correctly, it's a little bit loud so you're saying everything started at custom? And how do we get past that? And I think it actually goes back to what we're talking about earlier with this notion of a common understanding of data because what was happening is everybody felt they had bespoke data or we had data that was speaking about the same domains and terms, but we didn't agree on anything, so we spent a ton of time in the bespoke or custom arena of integrating, cleaning, transforming, before we could even get to model building or before we could get to any kind of innovation on the data itself and so I think one of the things is realizing that a lot of these domains we're trying to solve similar problems, we all have similar data. The more we can get to a common understanding of the data that we have, the more you can see higher level re-usable components being built, saying, "Ah, I know how to work on customer data" "I know how to work on sales data" "I know how to work on, you know, oil and gas data" whatever it might be, you'll probably start to see things come up in industry verticals as well. And I think it's that motion, like we had the same problem years ago when we talked about log files. Before there was logging standards, everything was a custom solution, right? Now we have very rich solutions for understanding IT infrastructure et cetera that usually became because we had a better base line for the understanding of the data we had. >> Great. Mike Thank you so much for coming on theCUBE. It was a pleasure having you. >> Thank you for having me. >> I'm Rebecca Knight for Stu Miniman, we will have more of theCUBE's live coverage of Microsoft Ignite coming up just after this. (techno music)