>>Welcome back to the doctor khan cube conversation. Dr khan 2021 virtual. I'm john for your host of the cube of mulch, Donny co founder and CTO and see so for accurate hot startup hot company. Uh, thanks for coming on the cube for dr continent and talking cybersecurity and cloud native. Super important. Thanks for coming on, >>appreciate john. Thanks for having me. >>So here dr khan. Obviously the conversations around developer experience, um, making things more productive. Obviously cloud scale cloud native with docker containers with kubernetes all lining up right in line with the trend that's now going mainstream and all commercial enterprises. I mean developer productivity security is a huge times thing if you don't get it right. So, you know, shifting left is that everyone's talking about, but this is a huge challenge. Can you, can you talk about what you guys do at your company and specifically why it relates to this conversation for developers at dr khan. >>Sure. Um, so john as we understand today, there are millions of uh, you know, code comments that are happening in cloud native environments on daily basis. Um, you know, in a recent report, Airbnb reported, they've checked in 125,000 plus times ham charts in an ear. And what that means is that, you know, the guitars revolution is here. Uh, and that also means that, well, you got your kubernetes clusters sinking up with infrastructure as code, such as ham chart customized and yarrow files right almost several times a day now, what that also means is that the opportunity to make sure that your clusters are being deployed securely by these infrastructure as code templates and deployment has called template is available before the deployment happens and not after the deployment. Also, in order to reduce the cost or detecting security challenges. The best option and opportunity is during the development time and during the deployment time, which is the pipeline time and that's what we offer. We shift your cloud, native security posture detection to left. We detect all your security posture related issues while the code is in development in the design phase as well as while it is about to get deployed, that is within the guitars pipelines or your traditional develops pipelines and not only with detect where we sell feel the code as well, specifically infrastructure as code. So we detect the problems and we fix the problem by generating the remediation code which we like to call it as remediation is called. The detection mechanisms like all this policy is called. That's the primary use case that we offer. We help developers reduce the cost of remediation and also meantime to the mediations for security problems >>and actually see them a boatload of hassle to going back and figure out how they wrote the code at that time. And kind of what happened always is a problem. Um, I gotta Okay, so I'm gonna get into this policy is code. You mentioned that also you mentioned Getafe's revolution. Let's get to that in a second. But first I want you to explain to the folks what is cloud native security and what does that mean? And what kind of attacks emerge as that surface area becomes apparent? >>Absolutely. So cloud native security is a very interesting new paradigm. Uh it's not just related with one single control pain like take, for example, Cuban haters, it's not just that, it's also the supply chain elements that go into the deployment of your cloud native clusters. Like see if kubernetes cluster you need to secure not just the application code which is running inside your container images, but also the container image itself, then the pod, then the name space, then the cluster. And also you need to do all the other cyber hygienic, high generated things that we were doing previously. So it's so much of complexity because availability of different control planes, you need to be able to make sure that you are doing security, not just right, but at a very, very cost effective in a very, very cost effective manner. And the kind of attacks that we are predicting we're going to see in cloud native world are going to be very different from what we have seen so far. Especially there's a new attack type that I am have coined. I call that as cloud native waterhole attack. What it means is that imagine that most of the cloud native infrastructures are developed out of a lot of different open source components and pieces. So imagine you're pulling up a container image from a open source container agency and that continued which contains a man there container image can directly land into your cluster and not only can enter into your so called secure cluster environment. Usually the cluster control planes are not exposed to internet but deployment of one supply chain element like a Mallory's container image and exposed to an entire cluster. And that's what is waterhole attack when it comes to chlorinated water hole attacks to supply chains. So these are some very innovative and noble attacks that you know, we Uh you know, predict are going to come to our weigh in next 12-18 months. >>So you say it's a waterhole attack. That's the that's the coin term that you've made. So basically what you're saying is the container could be infected with all the properties that is containing into a secure cluster. It's almost been penetrated like malware would or spear phishing attack, it targets the cluster and then infects it. >>So not only that because your continuing images that you're pulling in um from your registries registries can be located anywhere right? If you do not do proper sanitization and checking off your supply chain components such as a continuing image, it can land insecure zones like this. So not only in a cluster, it can become part of a system named space very soon and and that's where the risks are that, you know, you had a parameter, you know, at least of some sort when it was non cloud native environments. And now you have a kind of false sense of security that I have equivalent is cluster, which sort of air gap in one way like there's no exposure to internet of the control plane control being a P. I. Is not supposed to Internet, that doesn't mean anything. A container enters into your cluster can take over the entire cluster. >>All right, so that's cool. So I love that attacks kind of attack. So back to cloud native security definition. So you're defining cloud native security as cloud native clusters. Is it specific around kubernetes or what specifically the cloud native security? What's the category? If the if water holds the attack vector, what's cloud native security means? >>So what it means is that you need to worry about multiple different control planes in a cloud native environment. It's not just a single control pain that you have to worry about. You have to worry about your uh as I said, kubernetes control plane, you have service measures on top of it, You could have server less layers on top of it and when you have to worry about so many different control pains, but it also means is that the security needs to become part of and has to get baked into the entire process of building cloud native environment, not afterthought or it shouldn't happen after the fact. >>See the containers for containers that watch the containers security for the security to watch the security. So you get so let's get we'll get to that. I want to get back to the solution, but one more thing. Um this one piece. So your c so um there you have a lot of shops in there from your background, I know that. Um So if if people out there, other Csos are looking at expanding, You know, day one day 2 ongoing, you know, ai ops get upstate to operate what everyone call it cloud native environments. How do they consider figuring out how to deploy and understand cloud need to secure? What do they have to do if you're a c So knowing what, you know, what steps are you taking? >>Yeah, it's funny that, you know, there's a big silo today between the sea, so organizations and the devops and get ops teams. Uh so the number one priority, in my opinion, that the sea so s uh you know, have to really follow is having visibility into the uh developers. So developers who are developing not just code but also infrastructure as code. So there is a slight difference between writing python code versus writing uh say ham charts or customized templates. Right? So you need as a see saw, you know, see so our needs to have full visibility into Okay, out of 100 developers, how many do I have who are writing deployment as code? And then how many of them are continuously checking in code and introducing security issues? Those issues have to be visualized while the issues are written in code and as they are getting checked into the repositories, so catch the security issues while the code is getting checked into the repository. And the next best stages catch the issues while the pipelines are picking up the code from the repository. So sisters needs to have visibility into this. I call it as shift left visibility for CSOS. So sisters need to know, okay, what are my top 10 developers who are writing infrastructure as code? How many of those developers are committing wonderful code. How many of these pull requests which have been raised have got security violations? How many of them have been fixed and how many have not been fixed? That's what is the visibility that can uh you know, provide opportunities to seize organizations to >>react and more things to put KPI S around two to understand where the gaps are and where the potential blind spots are. Okay, shift left visibility to see. So if you've got the get ups revolution, you got the waterhole attacks. You have multiple control planes obviously complex. The benefits of cloud native though are significant and people doing modern applications are seeing that. So clearly this is direction that everyone's going. The consensus is clear. So how do you solve this? You mentioned policy as code. I'm kind of connecting the dots here. If I'm going to understand what's going on in real time as the code is in flight as it's checking in. For instance, this is kind of in the pipeline as you say. So this has to be solved. What is the answer to this? Because it's clearly the way people want it. No one wants to come back and say we got hacked or development being pulled off task to figure out what they fixed or didn't do what's the policy is code angle? >>So um you know, of course, you know, there could be more than one ways to solve this problem. The way we are solving this problem is that first thing we are bringing all top type of infrastructure as code and the control planes into a single uniform format, which we like to call it as cloud, as code. The reason why we do that so that we can normalize the representation of these different data sets in one single normalized format. And then we apply open policy agent which is a C N C F uh graduated project, which is kind of the de facto standard to do any kind of policy is called use cases in the cloud native world today. So we apply open policy agent to this middleware that we create, which basically brings all these different control plane data, all the different infrastructures code into anomalous format. We apply O P A and we use policies to apply uh Opie on this data this way. What happens is that we write, for example, we want to write a policy, you don't want certain parts to be exposed to Internet in a given name space. You can write such a policy. This policy, you can run on life cluster as well as on the hand charts, which is your development side of the artifact. Right. Because we're bringing both these datasets into middleware. So in short, one of the solutions that we are proposing is that different control planes, different infrastructures, code has to be brought into a normalized format. And then you apply frameworks like Opie a open policy agent to achieve your policy is called use cases. >>What is the attraction for this direction? O. P. A. In particular obviously controlled planes. I get that. I can see the benefit of having this abstraction away with the normalization. I think that would enable a lot of innovation on top of it. Um Makes a lot of sense, totally cool. What's the attraction? What's the vibe? Are people reacting to this? Uh Some people might say whoa hold on, you're taking on too much uh your eyes are bigger than your stomach. You're taking on too much territory. Whoa, slow down. I can I I want to own that control plane. There's a lot of people trying to own the control plane. So again it's a little bit of politics here. What's your what's your thoughts on the momentum? What's the support, what's it look like? >>Yeah, I think you are getting it right, the political side of things. So, um, you know, one responses that, look, we have launched our open source project contour a scan uh last year and uh you know, we're doing pretty well. It's a full opium based uh in a project which allows you to do policies code on not only new cloud control planes, like, you know, kubernetes and others, but also the traditional control planes provided by CSP s like cloud security, cloud service providers. So parents can can be used not just for hand charts and customized, but also for terra form. What we are uh promoting is open culture. With scan. We want community to contribute, become part of it. Um yes, we are promoting a middleware here uh but we want to do it with the help of the community and our reaction what we're getting is very very good. We are in our commercial offering also we use opa we have good adoption going on right now. We believe will be able to uh you know with the developer community, you have this thing going for us. >>I love cloud as code. It's so much more broader than infrastructure as code and I'll see the control plane benefits. You know when I talk to customers, I want to get your reaction to this because I really appreciate your experience and and leadership here. I talked to customers all the time and I wont say name, I won't name names but they're big, big and fintech and you'll big and life sciences in other areas. They all say we want to bring best to breed together but it's too hard to make it all work. We can get it done, but it's a lot of energy. So obviously building code and getting into production that is just brute force. Anyway, they got to get that done and they're working on their pipe lining. But getting other best of breed stuff together and making it work is really hard. Does this solve that? Do you, are you helping solve that problem? Is this an integration opportunity? >>Yes, that and that is true and we have realized it, you know, uh long back. So that's why we do not introduce any new tooling into the existing developer workflows, no new tool whatsoever. We integrate with all existing developer workflows. So if you are a, you know, modern uh, you know, get off shop and you're using flux or Argo, we integrate terrace can seamlessly integrated flux in Argo, you don't even get to know that you already have what policy is called enabled if you're using flux Argo or any equivalent, you know, getups, toolkit. Likewise, if you are using any kind of uh, you know, say existing developer pipeline or workflows such as, you know, the pipelines available on guitar, get lab, you know, get bucket and other pipelines. We seamlessly integrate our motor is very, very simple. We don't want to introduce one more two for developers, we want to introduce one more per security. We want to get good old days, >>no one wants another tool in the tool shed. I mean it's like, it's like really like the tool shit, they get all these tools laying around. But everyone again, this is back to the platform wars in the old days when I was younger. Breaking into the early days of the web platforms were everything you have to build your own proprietary platform Wasn't some open source being used, but mostly it was full stack. Now platforms are inter operating with hybrid and now Edge. So I want to get your thoughts on and I'm just really a little bit off topic. But it's kind of related. How should companies think about platform engineering? Because you now have the cloud scale, which in a way is half a stack. You don't really if you're gonna have horizontal scalability and you're gonna have these kind of unified control planes and infrastructure as code. Then in a way you don't really need that full stack developer. I mean I could program the network. I don't need to get into the weeds on that. I got now open policy agent on with terrorists. Can I really can focus on developing this is kind of like an OS concept. So how should companies think about platforms and hiring platform engineers and and something that will scale and have automation and all the benefits and goodness of the cloud scale. >>Yeah, I mean you actually nailed it when you began uh we've been experienced since we've been experiencing now since last at least 18 months that and if I were specifically also, I'll touch based on the security side of things as well. But platform engineering and platforms, especially now everything is about interoperability and uh, what we have started experiencing is that it has to be open. The credibility any platform can gain is only through openness interoperability and also neutrality. If these three elements are missing, it's very hard to push and capture the mind share of the users to adopt the platform. And why do you want to build a platform to actually attract partners who can build integrations and also to build apps on top of it or plug ins on top of it? And that can only be encouraged if there is, you know, totally openness, key components have to be open source, especially in security. I can give you several examples. The future of security is absolutely open source, the credibility cannot be gained without that. A quick example of that is cystic. I mean, who thought they were gonna be pulling such a huge, you know, funding round, of course that all is on the background of Falco, Right? So what I'm trying to play and sing and same for psyllium, Right? So what I'm clearly able to see is the science are that especially in cybersecurity community, you are delivering open source based platforms, you will have the credibility because that's where you will get the mindshare developers will come and you know, and work with you of course, you know, I have no shame naming fellow vendors right, who are doing this right and this is the right way to do it. >>Yeah. And I think it's it's totally true and you see the validation on that just to verify your point out that we have a little love fest here on open source, it's pretty obvious the the end user communities are controlled not the hard core and users like the hyper scholars, you know, classic enterprises are are starting not only contribute participate but add value more than they've ever have. The question I want to ask you is okay. I totally agree on open as data becomes super important because remember data is only as good as what you have and the more data the better the machine learning the better the data scale, um, sharing is important. So open sharing kind of ties into open source. What's your thoughts on data? Data policy, is this going to extend out into data control planes? What's your thoughts there? I'd love to get your input. >>We are a little little bit early in that thought. I think it's gonna take a little while uh for you know, the uh for the industry bosses to come to terms to that uh data lakes and uh you know, data control planes eventually will open up. But you know, I I see there is resistance in that space today uh but eventually it's gonna come around. You know, that has because that would be the next level of openness, you know, once the platforms uh in a mature as an example right today. Um you want to write uh you know, any kind of say policies for your same products, right. Uh you have the option available to write policies and customized, you know, languages. But then many platforms are coming up which are supporting policy is developed in in languages which are open and that's data which is going to open up, you know very soon. So you will not be measured in terms of how many policies you have as a product, but you will be measured. Can you consume? Open policies are not so i that it is going to go there, it's going to take a little while, but I think he is going to move that. >>It makes sense. Get the apparatus built on the infrastructure side. Once you have some open policy capability that's going to build an abstraction on top of it, then you can program data to be more policy driven or dynamic based upon contextual behavioural dynamics. So it makes a lot of sense. Oh, great insight here, love the conversation, Congratulations on your success. Love the vision. Love the openness. I'll see. We think uh data as code is big too. Obviously media's data where CUBA is open. We have we have the same philosophy. So thanks for sharing. Love the vision. Take a minute to plug the company. What are you guys looking to do? Uh you guys hiring, take a minute to put the plug out for the for the company? >>Absolutely. We are absolutely hiring great ingenious, you know, a great startup mind folks who want to come and work for a very, very innovative environment. Uh we are very research and development, you know driven and have brought various positions available today. Um we are trying to do something which has not been attempted before. Our focus is 100% on reducing the cost of security. And uh you know, in order to do that, you really have to do things that previously were not in development environments. And that's where we're going. We're open source uh, you know, open source initiatives, big open source lovers and we welcome people come in and apply our positions, >>reduce the cost of security, do the heavy lifting for the customer with code and have great performance, that's the ultimate goal. Great stuff. Cloud need security, threat modeling, deV stickups, shifting left in real time. You guys got a lot of hard problems you're attacking? >>Um well, you know, some of the good things uh that we're doing is also because of the team that we have right. Most of our co team comes from very heavy threat modeling, threat analysis and third intelligence background. So we have we're blending a very unique perspective of allowing developers to tackle the threats, which they're not supposed to even understand how they work. We do the heavy lifting from threat intelligence point of view, we just let the developers work on the code that we generate for them to fix those threats. So we're shipping threat intelligence and threat modeling also to left. Uh we're one of the first companies to create threat models just out of infrastructure is called, we read your infrastructure as code and we create a digital twin of your cloud late at one time, even before it has been actually built. So we do some of those things which we like to call it just advanced bridge card prediction where we can predict whether you have reach parts a lot in your runtime environment that would have been committed. >>And then the Holy Grail obviously the automation and self healing um is really kind of where you've got to get to. Right, that's the whole that's the whole ballgame, right? They're making that productive. Oh, thank you for coming on a cube here. Dr khan 2021 sharing your insights, co founder and CTO and see so. Oh much Danny. Thank you for coming on. I appreciate it, >>monsieur john thank you for having >>Okay Cube coverage of Dr Khan 2021. Um your host, John Fury? The Cube. Thanks for watching. Yeah.

>>Yeah, yeah. Welcome back to the third session in our building, A vibrant data ecosystem track. This session is unleash the power of your cloud data warehouse. So what comes after you've moved your data to the cloud in this session will explore White Enterprise Analytics is finally ready for the cloud, and we'll discuss how you can consume Enterprise Analytics in the very same way he would cloud services. We'll also explore where analytics meets cloud and see firsthand how thought spot is open for everyone. Let's get going. I'm happy to say we'll be hearing from two folks from thought spot today, Michael said Cassie, VP of strategic partnerships, and Vika Valentina, senior product marketing manager. And I'm very excited to welcome from our partner at AWS Gal Bar MIA, product engineering manager with Red Shift. We'll also be sharing a live demo of thought spot for BTC Marketing Analytics directly on Red Shift data. Gal, please kick us off. >>Thank you, Military. And thanks. The talks about team and everyone attending today for joining us. When we talk about data driven organizations, we hear that 85% of businesses want to be data driven. However, on Lee. 37% have been successful in We ask ourselves, Why is that and believe it or not, Ah, lot of customers tell us that they struggled with live in defining what being data driven it even means, and in particular aligning that definition between the business and the technology stakeholders. Let's talk a little bit. Let's look at our own definition. A data driven organization is an organization that harnesses data is an asset. The drive sustained innovation and create actionable insights. The super charge, the experience of their customers so they demand more. Let's focus on a few things here. One is data is an asset. Data is very much like a product needs to evolve sustained innovation. It's not just innovation innovation, it's sustained. We need to continuously innovate when it comes to data actionable insights. It's not just interesting insights these air actionable that the business can take and act upon, and obviously the actual experience we. Whether whether the customers are internal or external, we want them to request Mawr insights and as such, drive mawr innovation, and we call this the for the flywheel. We use the flywheel metaphor here where we created that data set. Okay, Our first product. Any focused on a specific use case? We build an initial NDP around that we provided with that with our customers, internal or external. They provide feedback, the request, more features. They want mawr insights that enables us to learn bringing more data and reach that actual data. And again we create MAWR insights. And as the flywheel spins faster, we improve on operational efficiencies, supporting greater data richness, and we reduce the cost of experimentation and legacy environments were never built for this kind of agility. In many cases, customers have struggled to keep momentum in their fleet, flywheel in particular around operational efficiency and experimentation. This is where Richie fits in and helps customer make the transition to a true data driven organization. Red Shift is the most widely used data warehouse with tens of thousands of customers. It allows you to analyze all your data. It is the only cloud data warehouse that sits, allows you to analyze data that sits in your data lake on Amazon, a street with no loading duplication or CTL required. It is also allows you to scale with the business with its hybrid architectures it also accelerates performance. It's a shared storage that provides the ability to scale toe unlimited concurrency. While the UN instant storage provides low late and say access to data it also provides three. Key asks that customers consistently tell us that matter the most when it comes to cost. One is usage based pricing Instead of license based pricing. Great value as you scale your data warehouse using, for example, reserved instances they can save up to 75% compared to on the mind demand prices. And as your data grows, infrequently accessed data can be stored. Cost effectively in S three encouraged through Amazon spectrum, and the third aspect is predictable. Month to month spend with no hitting charges and surprises. Unlike and unlike other cloud data warehouses, where you need premium versions for additional enterprise capabilities. Wretched spicing include building security compression and data transfer. >>Great Thanks. Scout um, eso. As you can see, everybody wins with the cloud data warehouses. Um, there's this evolution of movement of users and data and organizations to get value with these cloud data warehouses. And the key is the data has to be accessible by the users, and this data and the ability to make business decisions on the data. It ranges from users on the front line all the way up to the boardroom. So while we've seen this evolution to the Cloud Data Warehouse, as you can see from the statistic from Forrester, we're still struggling with how much of that data actually gets used for analytics. And so what is holding us back? One of the main reasons is old technology really trying to work with today's modern cloud data warehouses? They weren't built for it. So you run into issues of trying to do data replication, getting the data out of the cloud data warehouse. You can do analysis and then maintaining these middle layers of data so that you can access it quickly and get the answers you need. Another issue that's holding us back is this idea that you have to have your data in perfect shape with the perfect pipeline based on the exact dashboard unique. Um, this isn't true. Now, with Cloud data warehouse and the speed of important business data getting into those cloud data warehouses, you need a solution that allows you to access it right away without having everything to be perfect from the start, and I think this is a great opportunity for GAL and I have a little further discussion on what we're seeing in the marketplace. Um, one of the primary ones is like, What are the limiting factors, your Siegel of legacy technologies in the market when it comes to this cloud transformation we're talking about >>here? It's a great question, Michael and the variety of aspect when it comes to legacy, the other warehouses that are slowing down innovation for companies and businesses. I'll focus on 21 is performance right? We want faster insights. Companies want the ability to analyze MAWR data faster. And when it comes to on prem or legacy data warehouses, that's hard to achieve because the second aspect comes into display, which is the lack of flexibility, right. If you want to increase your capacity of your warehouse, you need to ensure request someone needs to go and bring an actual machine and install it and expand your data warehouse. When it comes to the cloud, it's literally a click of a button, which allows you to increase the capacity of your data warehouse and enable your internal and external users to perform analytics at scale and much faster. >>It falls right into the explanation you provided there, right as the speed of the data warehouses and the data gets faster and faster as it scales, older solutions aren't built toe leverage that, um, you know, they're either they're having to make technical, you know, technical cuts there, either looking at smaller amounts of data so that they can get to the data quicker. Um, or it's taking longer to get to the data when the data warehouse is ready, when it could just be live career to get the answers you need. And that's definitely an issue that we're seeing in the marketplace. I think the other one that you're looking at is things like governance, lineage, regulatory requirements. How is the cloud you know, making it easier? >>That's That's again an area where I think the cloud shines. Because AWS AWS scale allows significantly more investment in securing security policies and compliance, it allows customers. So, for example, Amazon redshift comes by default with suck 1 to 3 p. C. I. Aiso fared rampant HIPPA compliance, all of them out of the box and at our scale. We have the capacity to implement those by default for all of our customers and allow them to focus. Their very expensive, valuable ICTY resource is on actual applications that differentiate their business and transform the customer experience. >>That's a great point, gal. So we've talked about the, you know, limiting factors. Technology wise, we've mentioned things like governance. But what about the cultural aspect? Right? So what do you see? What do you see in team struggling in meeting? You know, their cloud data warehouse strategy today. >>And and that's true. One of the biggest challenges for large large organizations when they moved to the cloud is not about the technology. It's about people, process and culture, and we see differences between organizations that talk about moving to the cloud and ones that actually do it. And first of all, you wanna have senior leadership, drive and be aligned and committed to making the move to the cloud. But it's not just that you want. We see organizations sometimes Carol get paralyzed. If they can't figure out how to move each and every last work clothes, there's no need to boil the ocean, so we often work with organizations to find that iterative motion that relative process off identifying the use cases are date identifying workloads in migrating them one at a time and and through that allowed organization to grow its knowledge from a cloud perspective as well as adopt its tooling and learn about the new capabilities. >>And from an analytics perspective, we see the same right. You don't need a pixel perfect dashboard every single time to get value from your data. You don't need to wait until the data warehouse is perfect or the pipeline to the data warehouse is perfect. With today's technology, you should be able to look at the data in your cloud data warehouse immediately and get value from it. And that's the you know, that's that change that we're pushing and starting to see today. Thanks. God, that was That was really interesting. Um, you know, as we look through that, you know, this transformation we're seeing in analytics, um, isn't really that old? 20 years ago, data warehouses were primarily on Prem and the applications the B I tools used for analytics around them were on premise well, and so you saw things like applications like Salesforce. That live in the cloud. You start having to pull data from the cloud on Prem in order to do analytics with it. Um, you know, then we saw the shift about 10 years ago in the explosion of Cloud Data Warehouse Because of their scale, cost reduced, reduce shin reduction and speed. You know, we're seeing cloud data. Warehouses like Amazon Red Shift really take place, take hold of the marketplace and are the predominant ways of storing data moving forward. What we haven't seen is the B I tools catch up. And so when you have this new cloud data warehouse technology, you really need tools that were custom built for it to take advantage of it, to be able to query the cloud data warehouse directly and get results very quickly without having to worry about creating, you know, a middle layer of data or pipelines in order to manage it. And, you know, one company captures that really Well, um, chick fil A. I'm sure everybody has heard of is one of the largest food chains in America. And, you know, they made a huge investment in red shift and one of the purposes of that investment is they wanted to get access to the data mawr quickly, and they really wanted to give their business users, um, the ability to do some ad hoc analysis on the data that they were capturing. They found that with their older tools, the problems that they were finding was that all the data when they're trying to do this analysis was staying at the analyst level. So somebody needed to create a dashboard in order to share that data with a user. And if the user's requirements changed, the analysts were starting to become burdened with requests for changes and the time it took to reflect those changes. So they wanted to move to fought spot with embrace to connect to Red Shift so they could start giving business users that capability. Query the database right away. And with this, um, they were able to find, you know, very common things in in the supply chain analysis around the ability to figure out what store should get, what product that was selling better. The other part was they didn't have to wait for the data to get settled into some sort of repository or second level database. They were able to query it quickly. And then with that, they're able to make changes right in the red shift database that were then reflected to customers and the business users right away. So what they found from this is by adopting thought spot, they were actually able to arm business users with the ability to make decisions very quickly. And they cleared up the backlog that they were having and the delay with their analysts. And they're also putting their analysts toe work on different projects where they could get better value from. So when you look at the way we work with a cloud data warehouse, um, you have to think of thoughts about embrace as the tool that access that layer. The perfect analytic partner for the Cloud Data Warehouse. We will do the live query for the business user. You don't need to know how to script and sequel, um Thio access, you know, red shift. You can type the question that you want the answer to and thought spot will take care of that query. We will do the indexing so that the results come back faster for you and we will also do the analysis on. This is one of the things I wanted to cover, which is our spot i. Q. This is new for our ability to use this with embrace and our partners at Red Shift is now. We can give you the ability to do auto analysis to look at things like leading indicators, trends and anomalies. So to put this in perspective amount imagine somebody was doing forecasting for you know Q three in the western region. And they looked at how their stores were doing. And they saw that, you know, one store was performing well, Spot like, you might be able to look at that analysis and see if there's a leading product that is underperforming based on perhaps the last few quarters of data. And bring that up to the business user for analysis right away. They don't need to have to figure that out. And, um, you know, slice and dice to find that issue on their own. And then finally, all the work you do in data management and governance in your cloud data warehouse gets reflected in the results in embrace right away. So I've done a lot of talking about embrace, and I could do more, but I think it would be far better toe. Have Vika actually show you how the product works, Vika. >>Thanks, Michael. We learned a lot today about the power of leveraging your red shift data and thought spot. But now let me show you how it works. The coronavirus pandemic has presented extraordinary challenges for many businesses, and some industries have fared better than others. One industry that seems to weather the storm pretty well actually is streaming media. So companies like Netflix and who Lou. And in this demo, we're going to be looking at data from B to C marketing efforts. First streaming media company in 2020 lately, we've been running campaigns for comedy, drama, kids and family and reality content. Each of our campaigns last four weeks, and they're staggered on a weekly basis. Therefore, we always have four campaigns running, and we can focus on one campaign launch per >>week, >>and today we'll be digging into how our campaigns are performing. We'll be looking at things like impressions, conversions and users demographic data. So let's go ahead and look at that data. We'll see what we can learn from what's happened this year so far, and how we can apply those learnings to future decision making. As you can already see on the thoughts about homepage, I've created a few pin boards that I use for reporting purposes. The homepage also includes what others on my team and I have been looking at most recently. Now, before we dive into a search, will first take a look at how to make a direct connection to the customer database and red shift to save time. I've already pre built the connection Red Shift, but I'll show you how easy it is to make that connection in just three steps. So first we give the connection name and we select our connection type and was on red Shift. Then we enter our red shift credentials, and finally, we select the tables that we want to use Great now ready to start searching. So let's start in this data to get a better idea of how our marketing efforts have been affected either positively or negatively by this really challenging situation. When we think of ad based online marketing campaigns, we think of impressions, clicks and conversions. Let's >>look at those >>on a daily basis for our purposes. So all this data is available to us in Thought spot, and we can easily you search to create a nice line chart like this that shows US trends over the last few months and based on experience. We understand that we're going to have more clicks than impressions and more impressions and conversions. If we started the chart for a minute, we could see that while impressions appear to be pretty steady over the course of the year, clicks and especially conversions both get a nice boost in mid to late March, right around the time that pandemic related policies were being implemented. So right off the bat, we found something interesting, and we can come back to this now. There are few metrics that we're gonna focus on as we analyze our marketing data. Our overall goal is obviously to drive conversions, meaning that we bring new users into our streaming service. And in order to get a visitor to sign up in the first place, we need them to get into our sign up page. A compelling campaign is going to generate clicks, so if someone is interested in our ad, they're more likely to click on it, so we'll search for Click through Rape 5% and we'll look this up by campaign name. Now even compare all the campaigns that we've launched this year to see which have been most effective and bring visitors star site. And I mentioned earlier that we have four different types of campaign content, each one aligned with one of our most popular genres. So by adding campaign content, yeah, >>and I >>just want to see the top 10. I could limit my church. Just these top 10 campaigns automatically sorted by click through rate and assigned a color for each category so we could see right away that comedy and drama each of three of the top 10 campaigns by click through rate reality is, too, including the top spot and kids and family makes one appearance as well. Without spot. We know that any non technical user can ask a question and get an answer. They can explore the answer and ask another question. When you get an answer that you want to share, keep an eye on moving forward, you pin the answer to pin board. So the BBC Marketing Campaign Statistics PIN board gives us a solid overview of our campaign related activities and metrics throughout 2020. The visuals here keep us up to date on click through rate and cost per click, but also another really important metrics that conversions or cost proposition. Now it's important to our business that we evaluate the effectiveness of our spending. Let's do another search. We're going to look at how many new customers were getting so conversions and the price cost per acquisition that we're spending to get each of these by the campaign contact category. So >>this is a >>really telling chart. We can basically see how much each new users costing us, based on the content that they see prior to signing up to the service. Drama and reality users are actually relatively expensive compared to those who joined based on comedy and kids and family content that they saw. And if all the genres kids and family is actually giving us the best bang for our marketing >>buck. >>And that's good news because the genres providing the best value are also providing the most customers. We mentioned earlier that we actually saw a sizable uptick in conversions as stay at home policies were implemented across much of the country. So we're gonna remove cost per acquisition, and we're gonna take a daily look how our campaign content has trended over the years so far. Eso By doing this now, we can see a comparison of the different genres daily. Some campaigns have been more successful than others. Obviously, for example, kids and family contact has always fared pretty well Azaz comedy. But as we moved into the stay at home area of the line chart, we really saw these two genres begin to separate from the rest. And even here in June, as some states started to reopen, we're seeing that they're still trending up, and we're also seeing reality start to catch up around that time. And while the first pin board that we looked at included all sorts of campaign metrics, this is another PIN board that we've created so solely to focus on conversions. So not only can we see which campaigns drug significant conversions, we could also dig into the demographics of new users, like which campaigns and what content brought users from different parts of the country or from different age groups. And all this is just a quick search away without spot search directly on a red shift. Data Mhm. All right, Thank you. And back to you, Michael. >>Great. Thanks, Vika. That was excellent. Um, so as you can see, you can very quickly go from zero to search with thought Spot, um, connected to any cloud data warehouse. And I think it's important to understand that we mentioned it before. Not everything has to be perfect. In your doubt, in your cloud data warehouse, um, you can use thought spot as your initial for your initial tool. It's for investigatory purposes, A Z you can see here with star, Gento, imax and anthem. And a lot of these cases we were looking at billions of rows of data within minutes. And as you as your data warehouse maturity grows, you can start to add more and more thoughts about users to leverage the data and get better analysis from it. So we hope that you've enjoyed what you see today and take the step to either do one of two things. We have a free trial of thoughts about cloud. If you go to the website that you see below and register, we can get you access the thought spots so you can start searching today. Another option, by contacting our team, is to do a zero to search workshop where 90 minutes will work with you to connect your data source and start to build some insights and exactly what you're trying to find for your business. Um thanks, everybody. I would especially like to thank golf from AWS for joining us on this today. We appreciate your participation, and I hope everybody enjoyed what they saw. I think we have a few questions now. >>Thank you, Vika, Gal and Michael. It's always exciting to see a live demo. I know that I'm one of those comedy numbers. We have just a few minutes left, but I would love to ask a couple of last questions Before we go. Michael will give you the first question. Do I need to have all of my data cleaned and ready in my cloud data warehouse before I begin with thought spot? >>That's a great question, Mallory. No, you don't. You can really start using thought spot for search right away and start getting analysis and start understanding the data through the automatic search analysis and the way that we query the data and we've seen customers do that. Chick fil a example that we talked about earlier is where they were able to use thoughts bought to notice an anomaly in the Cloud Data Warehouse linking between product and store. They were able to fix that very quickly. Then that gets reflected across all of the users because our product queries the Cloud Data Warehouse directly so you can get started right away without it having to be perfect. And >>that's awesome. And gal will leave a fun one for you. What can we look forward to from Amazon Red Shift next year? >>That's a great question. And you know, the team has been innovating extremely fast. We released more than 200 features in the last year and a half, and we continue innovating. Um, one thing that stands out is aqua, which is a innovative new technology. Um, in fact, lovely stands for Advanced Square Accelerator, and it allows customers to achieve performance that up to 10 times faster, uh, than what they've seen really outstanding and and the way we've achieved that is through a shift in paradigm in the actual technological implementation section. Uh, aqua is a new distributed and hardware accelerated processing layer, which effectively allows us to push down operations analytics operations like compression, encryption, filtering and aggregations to the storage there layer and allow the aqua nodes that are built with custom. AWS designed analytics processors to perform these operations faster than traditional soup use. And we no longer need to bring, you know, scan the data and bring it all the way to the computational notes were able to apply these these predicates filtering and encourage encryption and compression and aggregations at the storage level. And likewise is going to be available for every are a three, um, customer out of the box with no changes to come. So I apologize for being getting out a little bit, but this is really exciting. >>No, that's why we invited you. Call. Thank you on. Thank you. Also to Michael and Vika. That was excellent. We really appreciate it. For all of you tuning in at home. The final session of this track is coming up shortly. You aren't gonna want to miss it. We're gonna end strong, come back and hear directly from our customer a T mobile on how T Mobile is building a data driven organization with thought spot in which >>pro, It's >>up next, see you then.

>>welcome back. And thanks for joining us for our second session. External data, your new leading indicators. We'll be hearing from industry leaders as they share best practices and challenges in leveraging external data. This panel will be a true conversation on the part of the possible. All right, let's get to >>it >>today. We're excited to be joined by thought spots. Chief Data Strategy Officer Cindy Housing Deloitte's chief data officer Manteo, the founder and CEO of Eagle Alfa. And it Kilduff and Snowflakes, VP of data marketplace and customer product strategy. Matt Glickman. Cindy. Without further ado, the floor is yours. >>Thank you, Mallory. And I am thrilled to have this brilliant team joining us from around the world. And they really bring each a very unique perspective. So I'm going to start from further away. Emmett, Welcome. Where you joining us from? >>Thanks for having us, Cindy. I'm joining from Dublin, Ireland, >>great. And and tell us a little bit about Eagle Alfa. What do you dio >>from a company's perspective? Think of Eagle Alfa as an aggregator off all the external data sets on a word I'll use a few times. Today is a big advantage we could bring companies is we have a data concierge service. There's so much data we can help identify the right data sets depending on the specific needs of the company. >>Yeah. And so, Emma, you know, people think I was a little I kind of shocked the industry. Going from gardener to a tech startup. Um, you have had a brave journey as well, Going from financial services to starting this company, really pioneering it with I think the most data sets of any of thes is that right? >>Yes, it was. It was a big jump to go from Morgan Stanley. Uh, leave the comforts of that environment Thio, PowerPoint deck and myself raising funding eight years ago s So it was a big jump on. We were very early in our market. It's in the last few years where there's been real momentum and adoption by various types of verticals. The hedge funds were first, maybe then private equity, but corporate sar are following quite quickly from behind. That will be the biggest users, in our view, by by a significant distance. >>Yeah, great. Thank um, it So we're going to go a little farther a field now, but back to the U. S. So, Juan, where you joining us from? >>Hey, Cindy. Thanks for having me. I'm joining you from Houston, Texas. >>Great. Used to be my home. Yeah, probably see Rice University back there. And you have a distinct perspective serving both Deloitte customers externally, but also internally. Can you tell us about that? >>Yeah, absolutely. So I serve as the Lord consultants, chief data officer, and as a professional service firm, I have the responsibility for overseeing our overall data agenda, which includes both the way we use data and insights to run and operate our own business, but also in how we develop data and insights services that we then take to market and how we serve our dealers and clients. >>Great. Thank you, Juan. And last but not least, Matt Glickman. Kind of in my own backyard in New York. Right, Matt? >>Correct. Joining I haven't been into the city and many months, but yes, um, based in New York. >>Okay. Great. And so, Matt, you and Emmett also, you know, brave pioneers in this space, and I'm remembering a conversation you and I shared when you were still a J. P. Morgan, I believe. And you're Goldman Sachs. Sorry. Sorry. Goldman. Can you Can you share that with us? >>Sure. I made the move back in 2015. Um, when everyone thought, you know, my wife, my wife included that I was crazy. I don't know if I would call it Comfortable was emitted, but particularly had been there for a long time on git suffered in some ways. A lot of the pains we're talking about today, given the number of data, says that the amount of of new data sets that are always demand for having run analytics teams at Goldman, seeing the pain and realizing that this pain was not unique to Goldman Sachs, it was being replicated everywhere across the industry, um, in a mind boggling way and and the fortuitous, um, luck to have one of snowflakes. Founders come to pitch snowflake to Goldman a little bit early. Um, they became a customer later, but a little bit early in 2014. And, you know, I realized that this was clearly, you know, the answer from first principles on bond. If I ever was going to leave, this was a problem. I was acutely aware of. And I also was aware of how much the man that was in financial services for a better solution and how the cloud could really solve this problem in particular the ability to not have to move data in and out of these organizations. And this was something that I saw the future of. Thank you, Andi, that this was, you know, sort of the pain that people just expected to pay. Um, this price if you need a data, there was method you had thio. You had to use you either ftp data in and out. You had data that was being, you know, dropped off and, you know, maybe in in in a new ways and cloud buckets or a P i s You have to suck all this data down and reconstruct it. And God forbid the formats change. It was, you know, a nightmare. And then having issues with data, you had a what you were seeing internally. You look nothing like what the data vendors were seeing because they want a completely different system, maybe model completely differently. Um, but this was just the way things were. Everyone had firewalls. Everyone had their own data centers. There was no other way on git was super costly. And you know this. I won't even share the the details of you know, the errors that would occur in the pain that would come from that, Um what I realized it was confirmed. What I saw it snowflake at the time was once everyone moves to run their actual workloads in this in the cloud right where you're now beyond your firewall, you'll have all this scale. But on top of that, you'll be able to point at data from these vendors were not there the traditional data vendors. Or, you know, this new wave of alternative data vendors, for example, like the ones that eagle out for brings together And bring these all these data sets together with your own internal data without moving it. Yeah, this was a fundamental shift of what you know, it's in some ways, it was a side effect of everyone moving to the cloud for costs and scale and elasticity. But as a side effect of that is what we talked about, You know it snowflake summit, you know, yesterday was this notion of a data cloud that would connect data between regions between cloud vendors between customers in a way where you could now reference data. Just like your reference websites today, I don't download CNN dot com. I point at it, and it points me to something else. I'm always seeing the latest version, obviously, and we can, you know, all collaborate on what I'm seeing on that website. That's the same thing that now can happen with data. So And I saw this as what was possible, and I distinctly asked the question, you know, the CEO of the time Is this possible? And not only was it possible it was a fundamental construct that was built into the way that snowflake was delivered. And then, lastly, this is what we learned. And I think this is what you know. M It also has been touting is that it's all great if data is out there and even if you lower that bar of access where data doesn't have to move, how do I know? Right? If I'm back to sitting at Goldman Sachs, how do I know what data is available to me now in this this you know, connected data network eso we released our data marketplace, which was a very different kind of marketplace than these of the past. Where for us, it was really like a global catalog that would elect a consumer data consumer. Noah data was available, but also level the playing field. Now we're now, you know, Eagle, Alfa, or even, you know, a new alternative data vendor build something in their in their basement can now publish that data set so that the world could see and consume and be aligned to, you know, snowflakes, core business, and not where we wouldn't have to be competing or having to take, um, any kind of custody of that data. So adding that catalog to this now ubiquitous access, um really changed the game and, you know, and then now I seem like a genius for making this move. But back then, like I said, we've seen I seem like instant. I was insane. >>Well, given, given that snowflake was the hottest aipo like ever, you were a genius. Uh, doing this, you know, six years in advance. E think we all agree on that, But, you know, a lot of this is still visionary. Um, you know, some of the most leading companies are already doing this. But one What? What is your take our Are you best in class customers still moving the data? Or is this like they're at least thinking about data monetization? What are you seeing from your perspective? >>Yeah, I mean, I did you know, the overall appreciation and understanding of you know, one. I got to get my house in order around my data, um, has something that has been, you know, understood and acted upon. Andi, I do agree that there is a shift now that says, you know, data silos alone aren't necessarily gonna bring me, you know, new and unique insights on dso enriching that with external third party data is absolutely, you know, sort of the the ship that we're seeing our customers undergo. Um, what I find extremely interesting in this space and what some of the most mature clients are doing is, you know, really taking advantage of these data marketplaces. But building data partnerships right there from what mutually exclusive, where there is a win win scenario for for you know, that organization and that could be, you know, retail customers or life science customers like with pandemic, right the way we saw companies that weren't naturally sharing information are now building these data partnership right that are going are going into mutually benefit, you know, all organizations that are sort of part of that value to Andi. I think that's the sort of really important criteria. And how we're seeing our clients that are extremely successful at this is that partnership has benefits on both sides of that equation, right? Both the data provider and then the consumer of that. And there has to be, you know, some way to ensure that both parties are are are learning right, gaining you insights to support, you know, whatever their business organization going on. >>Yeah, great one. So those data partnerships getting across the full value chain of sharing data and analytics Emmett, you work on both sides of the equation here, helping companies. Let's say let's say data providers maybe, like, you know, cast with human mobility monetize that. But then also people that are new to it. Where you seeing the top use cases? Well, >>interestingly, I agree with one of the supply side. One of the interesting trends is we're seeing a lot more data coming from large Corporates. Whether they're listed are private equity backed, as opposed to maybe data startups that are earning money just through data monetization. I think that's a great trend. I think that means a lot of the best. Data said it data is yet to come, um, in terms off the tough economy and how that's changed. I think the category that's had the most momentum and your references is Geo location data. It's that was the category at our conference in December 2000 and 12 that was pipped as the category to watch in 2019. On it didn't become that at all. Um, there were some regulatory concerns for certain types of geo data, but with with covert 19, it's Bean absolutely critical for governments, ministries of finance, central banks, municipalities, Thio crunch that data to understand what's happening in a real time basis. But from a company perspective, it's obviously critical as well. In terms of planning when customers might be back in the High Street on DSO, fourth traditionally consumer transaction data of all the 26 categories in our taxonomy has been the most popular. But Geo is definitely catching up your slide. Talked about being a tough economy. Just one point to contradict that for certain pockets of our clients, e commerce companies are having a field day, obviously, on they are very data driven and tech literate on day are they are really good client base for us because they're incredibly hungry, firm or data to help drive various, uh, decision making. >>Yeah, So fair enough. Some sectors of the economy e commerce, electron, ICS, healthcare are doing great. Others travel, hospitality, Um, super challenging. So I like your quote. The best is yet to come, >>but >>that's data sets is yet to come. And I do think the cloud is enabling that because we could get rid of some of the messy manual data flows that Matt you talked about, but nonetheless, Still, one of the hardest things is the data map. Things combining internal and external >>when >>you might not even have good master data. Common keys on your internal data. So any advice for this? Anyone who wants to take that? >>Sure I can. I can I can start. That's okay. I do think you know, one of the first problems is just a cataloging of the information that's out there. Um, you know, at least within our organization. When I took on this role, we were, you know, a large buyer of third party data. But our organization as a whole didn't necessarily have full visibility into what was being bought and for what purpose. And so having a catalog that helps us internally navigate what data we have and how we're gonna use it was sort of step number one. Um, so I think that's absolutely important. Um, I would say if we could go from having that catalog, you know, created manually to more automated to me, that's sort of the next step in our evolution, because everyone is saying right, the ongoing, uh, you know, creation of new external data sets. It's only going to get richer on DSO. We wanna be able to take advantage of that, you know, at the at the pacing speed, that data is being created. So going from Emanuel catalog to anonymous >>data >>catalog, I think, is a key capability for us. But then you know, to your second point, Cindy is how doe I then connect that to our own internal data to drive greater greater insights and how we run our business or how we serve our customers. Andi, that one you know really is a It's a tricky is a tricky, uh, question because I think it just depends on what data we're looking toe leverage. You know, we have this concept just around. Not not all data is created equal. And when you think about governance and you think about the management of your master data, your internal nomenclature on how you define and run your business, you know that that entire ecosystem begins to get extremely massive and it gets very broad and very deep on DSO for us. You know, government and master data management is absolutely important. But we took a very sort of prioritized approach on which domains do we really need to get right that drive the greatest results for our organization on dso mapping those domains like client data or employee data to these external third party data sources across this catalog was really the the unlocked for us versus trying to create this, you know, massive connection between all the external data that we're, uh, leveraging as well as all of our own internal data eso for us. I think it was very. It was a very tailored, prioritized approach to connecting internal data to external data based on the domains that matter most to our business. >>So if the domains so customer important domain and maybe that's looking at things, um, you know, whether it's social media data or customer transactions, you prioritized first by that, Is that right? >>That's correct. That's correct. >>And so, then, Matt, I'm going to throw it back to you because snowflake is in a unique position. You actually get to see what are the most popular data sets is is that playing out what one described are you seeing that play out? >>I I'd say Watch this space. Like like you said. I mean this. We've you know, I think we start with the data club. We solve that that movement problem, which I think was really the barrier that you tended to not even have a chance to focus on this mapping problem. Um, this notion of concordance, I think this is where I see the big next momentum in this space is going to be a flurry of traditional and new startups who deliver this concordance or knowledge graph as a service where this is no longer a problem that I have to solve internal to my organization. The notion of mastering which is again when everyone has to do in every organization like they used to have to do with moving data into the organization goes away. And this becomes like, I find the best of breed for the different scopes of data that I have. And it's delivered to me as a, you know, as a cloud service that just takes my data. My internal data maps it to these 2nd and 3rd party data sets. Um, all delivered to me, you know, a service. >>Yeah, well, that would be brilliant concordance as a service or or clean clean master data as a service. Um, using augmented data prep would be brilliant. So let's hope we get there. Um, you know, so 2020 has been a wild ride for everyone. If I could ask each of you imagine what is the art of the possible or looking ahead to the next to your and that you are you already mentioned the best is yet to come. Can you want to drill down on that. What what part of the best is yet to come or what is your already two possible? >>Just just a brief comment on mapping. Just this week we published a white paper on mapping, which is available for for anyone on eagle alfa dot com. It's It's a massive challenge. It's very difficult to solve. Just with technology Onda people have tried to solve it and get a certain level of accuracy, but can't get to 100% which which, which, which makes it difficult to solve it. If if if there is a new service coming out against 100% I'm all ears and that there will be a massive step forward for the entire data industry, even if it comes in a few years time, let alone next year, I think going back to the comment on data Cindy. Yes, I think boards of companies are Mawr and Mawr. Viewing data as an asset as opposed to an expense are a cost center on bond. They are looking therefore to get their internal house in order, as one was saying, but also monetize the data they are sitting on lots of companies. They're sitting on potentially valuable data. It's not all valuable on a lot of cases. They think it's worth a lot more than it is being frank. But in some cases there is valuable data on bond. If monetized, it can drop to the bottom line on. So I think that bodes well right across the world. A lot of the best date is yet to come on. I think a lot of firms like Deloitte are very well positioned to help drive that adoption because they are the trusted advisor to a lot of these Corporates. Um, so that's one thing. I think, from a company perspective. It's still we're still at the first base. It's quite frustrating how slow a lot of companies are to move and adopt, and some of them are haven't hired CDO. Some of them don't have their internal house in order. I think that has to change next year. I think if we have this conference at this time next year, I would expect that would hopefully be close to the tipping point for Corporates to use external data. And the Malcolm Gladwell tipping point on the final point I make is I think, that will hopefully start to see multi department use as opposed to silos again. Parliaments and silos, hopefully will be more coordinated on the company's side. Data could be used by marketing by sales by r and D by strategy by finance holds external data. So it really, hopefully will be coordinated by this time next year. >>Yeah, Thank you. So, to your point, there recently was an article to about one of the airlines that their data actually has more value than the company itself now. So I know, I know. We're counting on, you know, integrators trusted advisers like Deloitte to help us get there. Uh, one what? What do you think? And if I can also drill down, you know, financial services was early toe all of this because they needed the early signals. And and we talk about, you know, is is external data now more valuable than internal? Because we need those early signals in just such a different economy. >>Yeah, I think you know, for me, it's it's the seamless integration of all these external data sources and and the signals that organizations need and how to bring those into, you know, the day to day operations of your organization, right? So how do you bring those into, You know, you're planning process. How do you bring that into your sales process on DSO? I think for me success or or where I see the that the use and adoption of this is it's got to get down to that level off of operations for organizations. For this to continue to move at the pace and deliver the value that you know, we're all describing. I think we're going to get there. But I think until organizations truly get down to that level of operations and how they're using this data, it'll sort of seem like a Bolton, right? So for me, I think it's all about Mawr, the seamless integration. And I think to what Matt mentioned just around services that could help connect external data with internal data. I'll take that one step beyond and say, How can we have the data connect itself? Eso I had references Thio, you know, automation and machine learning. Um, there's significant advances in terms of how we're seeing, you know, mapping to occur in a auto generated fashion. I think this specific space and again the connection between external and internal data is a prime example of where we need to disrupt that, you know, sort of traditional data pipeline on. Try to automate that as much as possible. And let's have the data, you know, connect itself because it then sort of supports. You know, the first concept which waas How do we make it more seamless and integrated into, you know, the business processes of the organization's >>Yeah, great ones. So you two are thinking those automated, more intelligent data pipelines will get us there faster. Matt, you already gave us one. Great, Uh, look ahead, Any more to add to >>it, I'll give you I'll give you two more. One is a bit controversial, but I'll throw that you anyway, um, going back to the point that one made about data partnerships What you were saying Cindy about, you know, the value. These companies, you know, tends to be somehow sometimes more about the data they have than the actual service they provide. I predict you're going to see a wave of mergers and acquisitions. Um, that it's solely about locking down access to data as opposed to having data open up. Um to the broader, you know, economy, if I can, whether that be a retailer or, you know, insurance company was thes prime data assets. Um, you know, they could try to monetize that themselves, But if someone could acquire them and get exclusive access that data, I think that's going to be a wave of, um, in a that is gonna be like, Well, we bought this for this amount of money because of their data assets s. So I think that's gonna be a big wave. And it'll be maybe under the guise of data partnerships. But it really be about, you know, get locking down exclusive access to valuable data as opposed to trying toe monetize it itself number one. And then lastly, you know. Now, did you have this kind of ubiquity of data in this interconnected data network? Well, we're starting to see, and I think going to see a big wave of is hyper personalization of applications where instead of having the application have the data itself Have me Matt at Snowflake. Bring my data graph to applications. Right? This decoupling of we always talk about how you get data out of these applications. It's sort of the reverse was saying Now I want to bring all of my data access that I have 1st, 2nd and 3rd party into my application. Instead of having to think about getting all the data out of these applications, I think about it how when you you know, using a workout app in the consumer space, right? I can connect my Spotify or connect my apple music into that app to personalize the experience and bring my music list to that. Imagine if I could do that, you know, in a in a CRM. Imagine I could do that in a risk management. Imagine I could do that in a marketing app where I can bring my entire data graph with me and personalize that experience for, you know, for given what I have. And I think again, you know, partners like thoughts. But I think in a unique position to help enable that capability, you know, for this next wave of of applications that really take advantage of this decoupling of data. But having data flow into the app tied to me as opposed to having the APP have to know about my data ahead of time, >>Yeah, yeah, So that is very forward thinking. So I'll end with a prediction and a best practice. I am predicting that the organizations that really leverage external data, new data sources, not just whether or what have you and modernize those data flows will outperform the organizations that don't. And as a best practice to getting there, I the CDOs that own this have at least visibility into everything they're purchasing can save millions of dollars in duplicate spend. So, Thio, get their three key takeaways. Identify the leading indicators and market signals The data you need Thio. Better identify that. Consolidate those purchases and please explore the data sets the range of data sets data providers that we have on the thought spot. Atlas Marketplace Mallory over to you. >>Wow. Thank you. That was incredible. Thank you. To all of our Panelists for being here and sharing that wisdom. We really appreciate it. For those of you at home, stay close by. Our third session is coming right up and we'll be joined by our partner AWS and get to see how you can leverage the full power of your data cloud complete with the demo. Make sure to tune in to see you >>then

>>Yeah, >>yeah. Hello and welcome to Day two at Beyond. I am so excited that you've chosen to join the building a vibrant data ecosystem track. I might be just a little bit biased, but I think it's going to be the best track of the day. My name is Mallory Lassen and I run partner Marketing here, a thought spot, and that might give you a little bit of a clue as to why I'm so excited about the four sessions we're about to hear from. We'll start off hearing from two thought spotters on how the power of embrace can allow you to directly query on the cloud data warehouse of your choice Next up. And I shouldn't choose favorites, but I'm very excited to watch Cindy housing moderate a panel off true industry experts. We'll hear from Deloitte Snowflake and Eagle Alfa as they describe how you can enrich your organization's data and better understand and benchmark by using third party data. They may even close off with a prediction or two about the future that could prove to be pretty thought provoking. So I'd stick around for that. Next we'll hear from the cloud juggernaut themselves AWS. We'll even get to see a live demo using TV show data, which I'm pretty sure is near and dear to our hearts. At this point in time and then last, I'm very excited to welcome our customer from T Mobile. They're going to describe how they partnered with whip pro and developed a full solution, really modernizing their analytics and giving self service to so many employees. We'll see what that's done for them. But first, let's go over to James Bell Z and Ana Son on the zero to search session. James, take us away. >>Thanks, Mallory. I'm James Bell C and I look after the solutions engineering and customer success teams have thought spot here in Asia Pacific and Japan today I'm joined by my colleague Anderson to give you a look at just how simple and quick it is to connect thought spot to your cloud data warehouse and extract value from the data within in the demonstration, and I will show you just how we can connect to data, make it simple for the business to search and then search the data itself or within this short session. And I want to point out that everything you're going to see in the demo is Run Live against the Cloud Data Warehouse. In this case, we're using snowflake, and there's no cashing of data or summary tables in terms of what you're going to see. But >>before we >>jump into the demo itself, I just like to provide a very brief overview of the value proposition for thought spot. If you're already familiar with thought spot, this will come as no surprise. But for those new to the platform, it's all about empowering the business to answer their own questions about data in the most simple way possible Through search, the personalized user experience provides a familiar search based way for anyone to get answers to their questions about data, not just the analysts. The search, indexing and ranking makes it easy to find the data you're looking for using business terms that you understand. While the smart ranking constantly adjust the index to ensure the most relevant information is provided to you. The query engine removes the complexity of SQL and complex joint paths while ensuring that users will always get thio the correct answers their questions. This is all backed up by an architecture that's designed to be consumed entirely through a browser with flexibility on deployment methods. You can run thought spot through our thoughts about cloud offering in your own cloud or on premise. The choice is yours, so I'm sure you're thinking that all sounds great. But how difficult is it to get this working? Well, I'm happy to tell you it's super easy. There's just forced steps to unlock the value of your data stored in snowflake, Red Shift, Google, Big Query or any of the other cloud data warehouses that we support. It's a simple is connecting to the Cloud Data Warehouse, choosing what data you want to make available in thought spot, making it user friendly. That column that's called cussed underscore name in the database is great for data management, but when users they're searching for it, they'll probably want to use customer or customer name or account or even client. Also, the business shouldn't need to know that they need to get data from multiple tables or the joint parts needed to get the correct results in thought spot. The worksheet allows you to make all of this simple for the users so they can simply concentrate on getting answers to their questions on Once the worksheet is ready, you can start asking those questions by now. I'm sure you're itching to see this in action. So without further ado, I'm gonna hand over to Anna to show you exactly how this works over to you. Anna, >>In this demo, I'm going to go to cover three areas. First, we'll start with how simple it is to get answers to your questions in class spot. Then we'll have a look at how to create a new connection to Cloud Data Warehouse. And lastly, how to create a use of friendly data layer. Let's get started to get started. I'm going to show you the ease off search with thoughts Spot. As you can see thought spot is or were based. I'm simply lobbying. Divide a browser. This means you don't need to install an application. Additionally, possible does not require you to move any data. So all your data stays in your cloud data warehouse and doesn't need to be moved around. Those sports called differentiator is used experience, and that is primarily search. As soon as we come into the search bar here, that's what suggestion is guiding uses through to the answers? Let's let's say that I would wanna have a look at spending across the different product categories, and we want Thio. Look at that for the last 12 months, and we also want to focus on a trending on monthly. And just like that, we get our answer straightaway without alive from Snowflake. Now let's say we want to focus on 11 product category here. We want to have a look at the performance for finished goods. As I started partially typing my search them here, Thoughts was already suggesting the data value that's available for me to use as a filter. The indexing behind the scene actually index everything about the data which allowed me to get to my data easily and quickly as an end user. Now I've got my next to my data answer here. I can also go to the next level of detail in here. In third spot to navigate on the next level of detail is simply one click away. There's no concept off drill path, pre defined drill path in here. That means we've ordered data that's available to me from Snowflake. I'm able to navigate to the level of detail. Allow me to answer those questions. As you can see as a business user, I don't need to do any coding. There's no dragon drop to get to the answer that I need right here. And she can see other calculations are done on the fly. There is no summary tables, no cubes building are simply able to ask the questions. Follow my train or thoughts, and this provides a better use experience for users as anybody can search in here, the more we interact with the spot, the more it learns about my search patterns and make those suggestions based on the ranking in here and that a returns on the fly from Snowflake. Now you've seen example of a search. Let's go ahead and have a look at How do we create a connection? Brand new one toe a cloud at a warehouse. Here we are here, let me add a new connection to the data were healthy by just clicking at new connection. Today we're going to connect Thio retail apparel data step. So let's start with the name. As you can see, we can easily connect to all the popular data warehouse easily. By just one single click here today, we're going to click to Snowflake. I'm gonna ask some detail he'd let me connect to my account here. Then we quickly enter those details here, and this would determine what data is available to me. I can go ahead and specify database to connect to as well, but I want to connect to all the tables and view. So let's go ahead and create a connection. Now the two systems are talking to each other. I can see all the data that's available available for me to connect to. Let's go ahead and connect to the starter apparel data source here and expanding that I can see all the data tables as available to me. I could go ahead and click on any table here, so there's affect herbal containing all the cells information. I also have the store and product information here I can make. I can choose any Data column that I want to include in my search. Available in soft spot, what can go ahead and select entire table, including all the data columns. I will. I would like to point out that this is important because if any given table that you have contains hundreds of columns it it may not be necessary for you to bring across all of those data columns, so thoughts would allow you to select what's relevant for your analysis. Now that's selected all the tables. Let's go ahead and create a connection. Now force what confirms the data columns that we have selected and start to read the medic metadata from Snowflake and automatically building that search index behind the scene. Now, if your daughter does contain information such as personal, identifiable information, then you can choose to turn those investing off. So none of that would be, um, on a hot spots platform. Now that my tables are ready here, I can actually go ahead and search straight away. Let's go ahead and have a look at the table here. I'm going to click on the fact table heat on the left hand side. It shows all the data column that we've brought across from Snowflake as well as the metadata that also brought over here as well. A preview off the data shows me off the data that's available on my snowflake platform. Let's take a look at the joints tap here. The joint step shows may relationship that has already been defined the foreign and primary care redefining snowflake, and we simply inherited he in fourth spot. However, you don't have toe define all of this relationship in snowflake to add a joint. He is also simple and easy. If I click on at a joint here, I simply select the table that I wanted to create a connection for. So select the fact table on the left, then select the product table onto the right here and then simply selected Data column would wish to join those two tables on Let's select Product ID and clicking next, and that's always required to create a joint between those two tables. But since we already have those strong relationship brought over from Snow Flag, I won't go ahead and do that Now. Now you have seen how the tables have brought over Let's go and have a look at how easy is to search coming to search here. Let's start with selecting the data table would brought over expanding the tables. You can see all the data column that we have previously seen from snowflake that. Let's say I wanna have a look at sales in last year. Let's start to type. And even before I start to type anything in the search bar passport already showing me all those suggestions, guiding me to the answers that's relevant to my need. Let's start with having a look at sales for 2019. And I want to see this across monthly for my trend and out off all of these product line he. I also want to focus on a product line called Jackets as I started partially typing the product line jacket for sport, already proactively recommending me all the matches that it has. So all the data values available for me to search as a filter here, let's go ahead and select jacket. And just like that, I get my answer straight away from Snowflake. Now that's relatively simple. Let's try something a little bit more complex. Let's say I wanna have a look at sales comparing across different regions, um, in us. So I want compare West compared to Southwest, and then I want to combat it against Midwest as well as against based on still and also want to see these trending monthly as well. Let's have look at monthly. If you can see that I can use terms such as monthly Key would like that to look at different times. Buckets. Now all of these is out of the box. As she can see, I didn't have to do any indexing. I didn't have to do any formulas in here. As long as there is a date column in the data set, crossbows able to dynamically calculate those time bucket so she can see. Just by doing that search, I was able to create dynamic groupings segment of different sales across the United States on the sales data here. Now that we've done doing search, you can see that across different tables here might not be the most user friendly layer we don't want uses having to individually select tables. And then, um, you know, selecting different columns with cryptic names in here. We want to make this easy for users, and that's when a work ship comes in. But those were were sheet encapsulate all of the data you want to make available for search as well as formulas, as well as business terminologies that the users are familiar with for a specific business area. Let's start with adding the daughter columns we need for this work shape. Want to slack all of the tables that we just brought across from Snowflake? Expanding each of those tables from the facts type of want sales from the fax table. We want sales as well as the date. Then on the store's table. We want store name as well as the stay eating, then expanding to the product we want name and finally product type. Now that we've got our work shit ready, let's go ahead and save it Now, in order to provide best experience for users to search, would want to optimize the work sheet here. So coming to the worksheet here, you can see the data column that we have selected. Let's start with changing this name to be more user friendly, so let's call it fails record. They will want to call it just simply date, store name, call it store, and then we also want state to be in lower case product name. Simply call it product and finally, product type can also further optimize this worksheet by adding, uh, other areas such as synonyms, so allow users to use terms of familiar with to do that search. So in sales, let's call this revenue and we all cannot also further configure the geo configuration. So want to identify state in here as state for us. And finally, we want Thio. Also add more friendly on a display on a currency. So let's change the currency type. I want to show it in U. S. Dollars. That's all we need. So let's try to change and let's get started on our search now coming back to the search here, Let's go ahead. Now select out worksheet that we have just created. If I don't select any specific tables or worksheets, force what Simply a search across everything that's available to you. Expanding the worksheet. We can see all of the data columns in heat that's we've made available and clicking on search bar for spot already. Reckon, making those recommendations in here to start off? Let's have a look at I wanna have a look at the revenue across different states for here today, so let's use the synonym that we have defined across the different states and we want to see this for here today. Um yesterday as well. I know that I also want to focus on the product line jacket that we have seen before, so let's go ahead and select jacket. Yeah, and just like that, I was able to get the answer straight away in third spot. Let's also share some data label here so we can see exactly the Mount as well to state that police performance across us in here. Now I've got information about the sales of jackets on the state. I want to ask next level question. I want to draw down to the store that has been selling these jackets right Click e. I want to drill down. As you can see out of the box. I didn't have to pre define any drill paths on a target. Reports simply allow me to navigate to the next level of detail to answer my own questions. One Click away. Now I see the same those for the jackets by store from year to date, and this is directly from snowflake data life Not gonna start relatively simple question. Let's go ahead and ask a question that's a little bit more complex. Imagine one. Have a look at Silas this year, and I want to see that by month, month over month or so. I want to see a month. Yeah, and I also want to see that our focus on a sale on the last week off the month. So that's where we see most. Sales comes in the last week off the month, so I want to focus on that as well. Let's focus on last week off each month. And on top of that, I also want to only focus on the top performing stores from last year. So I want to focus on the top five stores from last year, so only store in top five in sales store and for last year. And with that, we also want to focus just on the populist product types as well. So product type. Now, this could be very reasonable question that a business user would like to ask. But behind the scenes, this could be quite complex. But First part takes cares, or the complexity off the data allow the user to focus on the answer they want to get to. If we quickly have a look at the query here, this shows how forceful translate the search that were put in there into queries into that, we can pass on the snowflake. As you can see, the search uses all three tables as well shooting, utilizing the joints and the metadata layer that we have created. Switching over to the sequel here, this sequel actually generate on the fly pass on the snowflake in order for the snowflake to bring back to result and presented in the first spot. I also want to mention that in the latest release Off Hot Spot, we also bringing Embraced um, in the latest version, Off tosspot 6.3 story Q is also coming to embrace. That means one click or two analysis. Those who are in power users to monitor key metrics on kind of anomalies, identify leading indicators and isolate trends, as you can see in a matter of minutes. Using thought spot, we were able to connect to most popular on premise or on cloud data warehouses. We were able to get blazing fast answers to our searches, allow us to transform raw data to incite in the speed off thoughts. Ah, pass it back to you, James. >>Thanks, Anna. Wow, that was awesome. It's incredible to see how much committee achieved in such a short amount of time. I want to close this session by referring to a customer example of who, For those of you in the US, I'm sure you're familiar with who, Lou. But for our international audience, who Lou our immediate streaming service similar to a Netflix or Disney Plus, As you can imagine, the amount of data created by a service like this is massive, with over 32 million subscribers and who were asking questions of over 16 terabytes of data in snow folk. Using regular B I tools on top of this size of data would usually mean using summary or aggregate level data, but with thoughts. What? Who are able to get granular insights into the data, allowing them to understand what they're subscribes of, watching how their campaigns of performing and how their programming is being received, and take advantage of that data to reduce churn and increase revenue. So thank you for your time today. Through the session, you've seen just how simple it is to get thought spot up and running on your cloud data warehouse toe. Unlock the value of your data and minutes. If you're interested in trying this on your own data, you can sign up for a free 14 day trial of thoughts. What cloud? Right now? Thanks again, toe Anna for such awards and demo. And if you have any questions, please feel free to let us know. >>Awesome. Thank you, James and Anna. That was incredible. To see it in action and how it all came together on James. We do actually have a couple of questions in our last few minutes here, Anna. >>The first one will be >>for you. Please. This will be a two part question. One. What Cloud Data Warehouses does embrace support today. And to can we use embrace to connect to multiple data warehouses. Thank you, Mallory. Today embrace supports. Snowflake Google, Big query. Um, Red shift as you assign that Teradata advantage and essay Bahana with more sources to come in the future. And, yes, you can connect on live query from notable data warehouses. Most of our enterprise customers have gotta spread across several data warehouses like just transactional data and red Shift and South will start. It's not like, excellent on James will have the final question go to you, You please. Are there any size restrictions for how much data thought spot can handle? And does one need to optimize their database for performance, for example? Aggregations. >>Yeah, that's a great question. So, you know, as we've just heard from our customer, who there's, there's really no limits in terms of the amount of data that you can bring into thoughts Ponant connect to. We have many customers that have, in excess of 10 terabytes of data that they're connecting to in those cloud data warehouses. And, yeah, there's there's no need to pre aggregate or anything. Thought Spot works best with that transactional level data being able to get right down into the details behind it and surface those answers to the business uses. >>Excellent. Well, thank you both so much. And for everyone at home watching thank you for joining us for that session. You have a few minutes toe. Get up, get some water, get a bite of food. What? You won't want to miss this next panel in it. We have our chief data strategy off Officer Cindy, Housing speaking toe experts in the field from Deloitte Snowflake and Eagle Alfa. All on best practices for leveraging external data sources. See you there