>>Yeah, yeah. >>Hello and welcome back. You're just in time for our third session spot. I Q amplify your insights with AI in this session will explore how AI gets you to the why of your data capturing changes and trends in the moment they happen. >>You'll >>start to understand how you can transform your data culture by making it easier for analysts to enable business users to consume insights in real time. >>You >>might think this all sounds too good to be true. Well, since seeing is believing, we're joined by thought spots. Vika Scrotum, senior product manager. Anak Shaped Mirror, principal product manager to walk you through all of this on MAWR. Over to you actually, >>Thank you. Wanna Hello, everyone. Welcome to the session. I am Action Hera, together with my colleague because today we will talk to you about how spot I Q uses a. I to generate meaningful insights for the users Before we dwell into that. Let's see why this is becoming so important. Your business and your data is growing and moving faster than ever. Data is considered the new oil Howard. Only those will benefit who can extract value of it. The data used in most of your organization's is just the tip of the iceberg beneath the tip of the iceberg. What you don't see or what you don't know to ask. That makes the difference in this data driven world. Let's learn how one can extract maximum value of the data to make smarter business decisions. We believe that analytics should require less input while producing more output with higher quality in a traditional approach. To be honest, users generally depend on somebody else to create data models, complex data queries to get answers to their pre anticipated questions. But solution like hot spot business users already have a Google like experience where they can just go and get answers to their questions. Now, if you look at other consumer applications, there are multiple of recommendation engines which are out there, which keep recommending. Which article should I read next? Which product should I buy? Which movie should I watch in a way, helping me optimized? Where should I focus my time on in a Similarly in analytics, as your data is growing, solutions must help users uncovered insights to questions which they may not ask, we believe, and a I automated insights will help users unleash the full potential off their data Across the spectrum, we see a potential in a smart, AI driven solution toe autonomously. Monitor your data and feed in relevant insights when you need them, much like a self driving car navigates our users safely to their desired destination. With this, yeah, I'm happy to introduce you to spot like you are a driven insights engine at scale, which will help you get full potential off your data like you automatically discovers, personalize and drive insights hidden in your data. So whenever you search to create answers, spot that you continues to ask a lot more questions on your behalf as it keeps drilling and related date dimensions and measures employed insights which may be of interest to you. Now you as a user can continue to ask your questions or can dig deeper into the inside, provided by spotted you Spartak. You also provides a comprehensive set of insights, which helps user get answers to their advance business questions. In a few clicks, so spotted it. You can help you detect any outlier, for example, spot that you can not only tell you which seller has the highest returns than others, but also which product that sellers selling has higher returns than other products. Or, like you can quickly detect any trends in your data and help us answer questions like how my account sign ups are trending after my targeted campaign is over. I can quickly use for, like, toe get unanswered how my open pipeline is related to my bookings amount and what's the like there. What it means is that how much time a lead will take to convert into a deal I can use partake. You, too, create multiple clusters off my all my customer base and then get answers to questions that which customer segment is buying which particular brand and what are the attributes last and the most used feature Key drivers of change spotted you helps you get answer to a question. What factors lead to the change in sales off a store in 2020 as compared to 2019? We can do all this and simple fix. That's barbecue. What is so unique about Spartak? You how it works hand in hand with our search experience, the more you search, the smarter. The spot that you get as it keeps learning from your usage behavior on generates relevant insights for you for your users. Spartak. You ensures that users can trust every insights. A generator. It broadly does this and broadly, two ways. It keeps their insights relevant by learning the underlying data model on. By incorporating the users feedback that is, users can provide feedback to the spot I Q similar to any social media back from, they can like watching sites they find useful on dislike. What insights Do not find it useful based on users. Feedback Spot like you can downgrade any insight if the users have not find it useful. In addition to that, users can dig deep into any Spartak you insight on all calculations behind it are available for a user to look and understand. The transparency in these calculations not only increases the analytical trust among the users, but also help them learn how they can use the search bar to do much more. I'm super excited to announce Partake you is now available on embrace so our automated A insights engine can run queries life and in database on these datasets so you do not need to bring your data to thoughts about as you connect your data sources. Touch Part performs full indexing value to the data you have selected, not just the headers in the material and as you run sport in Q, it optimizes and run efficient queries on your data warehouse on. I am super pleased to introduce you. This new spot like you monitor the spot that you monitor will enable all your users to keep track of their key metrics. Spartak, you monitor will not only provide them regular updates off their key metrics, but we also analyze all the underlying data on related dimensions to help them explain. What is leading to the change of a particular metric monitor will also be available on your mobile app so that you can keep track of your metrics whenever and wherever you go, because will talk for further detail about this during the demo. So now let's see Spartak in action. But before we go there, let's meet any. Amy is an analyst at a global retail about form. Amy is preparing for her quarterly sales review meeting with the management, so Amy has to report how the sales has meat performing how, what, what factors lead to the change in the sales? And if there are any other impressing insights, which everyone should off tell to the management? So but this Let's see how immigrant use part like you to prepare for the meeting. So Amy goes to that spot, chooses the sales data set for her company. But before we see how many users what I Q to prepare for the meeting. I just wanted to highlight that all this data which we're going to talk about is residing in Snowflake. >>So >>Touch Part is going to do a life query on the snowflake database on even spot. A Q analysis will run on the Snowflake databases, so we'll go back and see how you can use it. So Amy is preparing for the sales meeting for 2019. We just ended. So images right Sales 2019 on here. She has the graph of the Continent tickets, >>so >>what she does is immediately pence it >>for >>the report. She's creating Andi now. This graph is available >>there now. >>Any Monnet observed >>that >>the Q four sales is significantly higher than Q >>three, so >>you she wants to deep dive into this. So she just select these two data points and does the right click and runs particularities. So now, as we talked earlier, Spartak, you recommends which columns Spartak Things Will best explains this change >>on. >>Not only that, you can look that Spartacus automatically understood that Amy is trying toe identify what led to this change. So the change analysis we selected So now with this, >>Amy >>has a bit more business context when he realizes that she doesn't want to add these columns. So she's been using because she thinks this is too granular for the management right now. >>If >>she wants, she can add even more columns. All columns are available for her, and she can reduce columns. So now she runs 42 analysis. So while this product Unisys is running, what the system will do with the background, this part I Q will drill across all the dimensions, which any is selected and try to explain the difference, which is approximately $10 million in sales. So let's see if Amy's report is ready. Yeah, so with this, what's product you has done is protect you has drilled across all dimensions. Amy has selected and presented how the different values in these dimensions have changed. So it's product. You will not only tell you which values in these dimensions have changed the most, but also does an attribution that how much of this change has led to the overall change scenes. So here in the first inside sport accuse telling that 10 products have the largest change out of the 3 45 values and the account for 39% increase. Overall, there has been look by the prototype category. It's saying that five product types of the largest change out of the 15 values, and they account for 98.6% of total increase. And they're not saying the sailors increased their also demonstrating that in some categories the sales has actually decreased to ensure the sales has decreased. Amy finds this inside should be super useful so immediately pins this on the same pain, but she was preparing for and she's getting ready with that. Amy also wants to dig deeper into this inside. My name goes here. She sees that spot. I Q has not only calculated the change across these product types, but has also calculated person did change. So Amy immediately sorts this by wasn't did change. And then she notices that even though Sweater as a category as a prototype, was not appearing in the change analysis but has the most significant change in terms of percentage in comparison to Q two vs Q four. So she also wants to do this so she can just quickly change the title. And she can pin this insight as well under spin board for the management to look at with this done. Now, Amy, just want to go back to this sales and see if she can find anything else interesting. So now Amy has already figured out the possible causes. What led to the increase in sales? So now, for the whole of 2019, as this is also your closing, Amy looks, uh, the monthly figures for 2019, and she gets this craft now. If Amy has to understand, if there is an interesting insight, she can dig into different dimensions and figure out on her own or immigrant, just click on this product analysis. That's product immediately suggest all the dimensions and measures immigrant analyze sales by Andi many. We will run this What will happen is this barbecue system will try to identify outliers. The different trend analysis Onda cross correlation across different measures. So Amy again realizes that this is a bit too much for her. So she reduces some of these insights, which she thinks are not required for the management right now from the business context and the business meeting. And then she just immediately runs this analysis. So now, with this, Amy is hoping to get some interesting insights from Spartak, which immigrant present to her management meeting. Let's see what sport gets for her. So now the Alice is run within 10 seconds, so spot taken started analyzing. So these are the six anomaly sport like you found across different products, where their total sales are higher than the rest. He also founded Spot. I just found eight insights off different product types which has tired total sales and look across these enemy sees that oh jackets have against the highest sales across all the categories in December as well. Amy wants toe been this to the PIN board on M. It moves further now. Amy's is that it has also shown Total Country purchased their product a me thinks this is not a useful insights. Amy can get this feedback. The system and system asked, Why are you saying you don't find this useful so the system can remember? So you can also say that anomalies are obvious right now and give this feedback and the system will remember. In addition, Amy finds that the system has automatically correlated the total sales in total contrary purchase. Amy Pence this as well to the pin board. Andi. She loves this inside where she she is that not only the total sales have increased, but total quantity purchases have increased a lot more on their training, opposed as well. So she also opens this now anything. She is ready for her meeting with the management. So she just goes and shares the PIN board, which she just created with the management. And you know what happens immediately? The jacket sales category Manager Mr Tom replies back to Amy and says in the request, Any d really like this? So now we will see how Spartak you can help any educators as request doesn't mean really need to create these kind of reports every month to cater toe Tom's request. So with this, I will handle it because to take us walk us through How spot that you can cater this request. Hi, >>everyone. So analysts like Amy are always flooded with such requests from the business users and with Spot and you monitor. Amy can set up everyone who needs updates on a on a metric in just a few simple steps and enable them to drag these metrics whenever and wherever they want. And north of the metrics, they also get the corresponding change analysis on the device off their choice with hot Spot. What I give money being available on both Web and the mobile labs. So let's get started with the demo will be set up a meet and go to the search tab and creator times we start for the metrics you want to monitor, right? And please know if the charges already created is already created. All is available is, um, usually a section in a PIN board. Also dancer. Then there's no need to create a new child. She can simply then uh, right click on the chart and select moisture from the menu, which then shows, which then shows the breakdown off the metric he's going to monitor, including the measure. What it's been grouped by on what it is filtered on. Okay, and also as this is a weekly metric, all the subscribers are going to get a weekly notification for this metric had been a monthly metric. Then the notifications would have been delivered on a monthly cadence. Next she can click on, continue and go to the configure dimensions called on Page. Here A is recommending what all dimensions could best being the change in this metric, she can go ahead with default recommendation, or she can change the columns as she seems very she can click, she conflict, continue and go to the next page, which is the subscriber stage. It is added by default to the subscriber, but she can search everyone who needs update on this metric and add them on this metric by clicking confirmed, she'll see a toast message on the bottom of the page, taking on which will take a me to this page, which is a metric detail page On the top of this page, we can see the movement of the metric and how it is changing over time, 92 you can see that the Mets jacket, since number has increased by 2.5% in the week off 23rd of December has compared toa the week off 16th of December and just below e a has invaded the man is generated in sites which are readily available for consumption. Okay to discharge. Right here says that pain products have the largest change out of all the 28 values and contributes to the 88% of the total increase in the same. And this one right here is that Midwest is the larger Midwest has the largest change and accounts for 55.66% off the total increase. Now, all this goodness is also available on the mobile lab. Right? So let me just show you how business users are going to get notified on the based. On this metric, all the business users who are subscribed to this metric are going to get a regular email as well as push notifications on the mobile lab. And when the click on this, they line on a metric detail page which has all the starts, which I just showed you on the on the bed version, okay. And one cyclic on back burden. They land on this page, which is a monitor tab, and it summarizes all the metrics Which opportunity monitoring and gives them a whole gave you to stay all I want to stay on top of their businesses. Okay. Eso that folks was monitor. Now I'll search back to slaves and cover. Summarize the key takeaways. From what? That she and I just don't know. So it's part of you wanted, uh, Summit Spartak you. It automatically discovers insights and helps you unless the full potential of your data and that's what I do is comprehensive set off analysis. You can answer your advanced business question in just a few simple steps and the end speed of your time. Bring state. And with a new support for embrace, you can run sport like you on your data in your data warehouse and with spotted you monitor, you can monitor all the business metrics and not just died. We can also understand that teaching teaching drivers on those metrics on the platform of your choice. So with that, I'll hand over toe, you know. >>Thank you so much. Both of you That was fantastic. Um, I just love spot like, because it makes me look like much more of a rock star with data than I really am. So thank you guys for that fantastic presentation. Um, so we've got a couple of minutes for a couple of questions for you. The first one is for action. Um, once spot I Q generates a number of insights. Can you run spot I Q again on one of those insights? >>Yeah, As a philosophy off Spiric, you sport like you never takes the user to the dead end Spartak. You also transparently shares the calculation. So user can not only the keeper that on edit Understand how this product you inside has been calculated, but user can also run us for like you analysts is honest for data analysis as well. Which music? And continue to do not on the first level. Second level in the third level as well. >>That's cool. Thank you. Actually on then The next one is for because for spot ik monitor is it possible to edit the dimensions used for explaining the factors to change that was detected? >>Yes. It's an owner of the metric you can change the dimensions whenever you want and save them for everyone else. >>Okay, well, I think that's about all we've got time for in this session. So all that remains is for me to say a huge thank you to Because an Akshay Andi, we've got the last session of this track coming up in a few minutes. So grab a snack. Come right back and listen to an amazing customer story with Snowflake on Western Union, they're up next.

>>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.

>>live from Las Vegas. It's the Q covering AWS executive. Something brought to you by extension. >>Welcome back, everyone to the cubes. Live coverage of the ex Censure Executive Summit here in AWS. Reinvent I'm your host, Rebecca Knight. We have three guests for this segment. We have Fang Deng. She is the big data and an Advanced Analytics program. Lead analytic Seo hee at Novartis. Thank you so much for coming on the show. Thank you. We have low eq zero. He is Novartis head of Analytic Seo Hee. Thanks so much. Look, and Vika sinned. Wan Hee hee is applied intelligence delivery lead at Accenture. Thank you so much. Thank you. So I want to start with you. Look, no. Novartis, of course, is a household name. It's one of the largest pharmaceutical companies in the world. But that left you to just walk our viewers a little bit through your business and sort of the pain points you were looking to solve with this journey Thio to the cloud >>you think you ever care? So I think if I if we look at the company, I think Wayne realized that it is more and more difficult to bring new trucks to market, so it takes about 12 years and on $1.2 billion to find a new trick. So at the same time, we see that there's more and more patient that need access to medicines. So in the last two years, I think we tried toe clear the new strategy where we're trying to re imagine medicine for user's data and technology. So in 2018 we've recruited a new studio that's came and I tried to build a digital ambition which is around fabulous, which is the innovation, the operation and the engagement on the innovation. What we're trying to do is to find new compound, will application off existing compounds into our business, make sure that I think patients can get access to drugs much faster and earlier on in the operation. We are trying to optimize the backbone off day to day processes, beat in the manufacturing or in the supply chain, or in the commercialization to ensure that the patient also get access to that much faster in the engagement. We're trying to healthy a cheapie and the players and then the and the patients to better understand the tracks reproduce as well as on the medication they need to have to receive treatment. So if you look at these three pillars, the cloud strategy is an essential portion of it. Because in all of its processes we have a lot of data and full cloud. I think we can make use off his data to help to innovate, open, right and engage. >>So as you as you said, it's really about reimagining medicine. I mean, from the drug discovery process to how it's helping patients live, live longer, healthier lives. Thanks. So talk about the vision for the Formula One platform. >>Yeah, aside, like a mission before we trying to re imagine our products for the patient. And we're trying to use more the more data history data and also the public data try to support our products. And the Formula One is our future enterprise data and the next perform for our new artists. So our objective is trying to love you all the new technology and also trying to consolidate over data in our Macleod and build up this platform for the whole notice Users support our business, do better products full patient. >>So when it comes to these these new new platforms, new technologies that are being introduced. We know that oftentimes the technology is the easy part. Or at least the more straightforward part I should say. But it's it's sort of getting people on board the change management. What are some of the challenges that you that you know of artists faced in terms of of the culture and the skills for your for your workforce? >>So if you look at that, the are in disgrace, very traditionally nature. And when we embarking the details confirmation, I think the first thing we had to change the culture of the company. So when you when you listen to our CEO, I think you tried to promote this invoice culture where all of us are Syrian leaders. And then we walk, you know, as a thing as an organization where we try to help each other and more and more collaborate when it comes to digital transformation. When we started this having this period, we've realised actually that workforce was not trained, so the first few things that we did disease is a tight wire new workforce, but also try to actually identify the advocate ambassadors. I could go and then go into residual confirmation early on to be able to help and to guide the office to get for that. So it's actually it's totally immaterial, Johnny. And then we are now in the second year and we've seen already a tremendous four guys, right? >>Can you describe some of the changes that you've seen him? I mean, I'm really interested in what you talk about. The ambassador's, the people who are going to spread the good word. What are what are some of the changes that you've seen in your workforce? Yeah, we can mention >>that. It's like you mentioned before. Um, like, talking about regarding overall catch a bus back to tried to leverage a new attack. Knowledge like the delivery perspective. We trying to do more automation, and the May 1 side is trying to get more efficiency and also another side. Try to ensure the intern responsibility for one product to be produced and also at the same time, let me through more automation to think about this secret inside the compound inside. Help us a lot of in pulling that part also, because >>maybe I can compliment that so I think if you look at it when the initial studying part of our journey, I think that a lot of people were reluctant to go and then tie to work on a cloud and to work with digital technology. So we found few projects where we felt there's a good ready for money. And as we can deliver fast in fact, Andi to things like, I don't get reviewed t piece every. Make sure that when we went, our field falls, go then and talk to the hippies. They know what to talk about an orphan, and then which format. We also look at that we can reduce costs internally and for the food, different projects and then on product that we've established, we build credibility within the organization that helped to disseminate the cultural transformation. >>So once others air seeing, seeing the benefits that that captured, they're more likely to to feel good about the cloud work. >>Yeah, that's that's the true and also notes of the news. Things like our teams, they are interesting about that. You see more and more people talking about our driveway and also talk about the UAV's and how can we improve the did he re efficiency and the same time is come back to say that teams think about how to make themselves to be a product owner and the product the way of the great. Let's the glistening for the whole team >>because I want to bring you in here a little bit. So talk to me about how ex Center is helping Novartis, particularly in in this eight of us. Caught initiative. >>Six incher is a leader in business and technical i t transformation programmes. So what we're bringing on the table is in the expertise with not only the technology and the AWS elements, but also the business and technical transformation expertise that have we have over the years in the firm. On additionally, I think you know, it's not only about technology change. As you mentioned, it's all a lot of change and operating model and and also kind of working with a very blended team. Across that expertise and experience is what you bring to the table >>a blended team, culturally, regionally, actually, all of it >>one of that belief. I mean, just to give an example. We are working across steams in roughly about six geography ese from various cultures. Where's countries? And it's it's, ah, various time zones, which makes it quite challenging to make it all work together. So you started the journey. I hope you succeed in it. And, uh, you know, it's working well, so far, >>so Cloud is is really a megatrend right now. What are the differences that you're seeing across Regions, countries, industries? >>So I think it's this many answers many parts of the answer to the question. So I think if I talk about, um, industries So you know, initially when clouds started, we had seen a major up take off the cloud technology and the company that manufactured the clown technology and telecommunications, and you know where the older infrastructure and technology aspects were, Whereas companies like health care and media and metals and mining, We're kind of behind the curve in adoption rates because off their respective, you know, concerns around compliance and security of data. But I think that trends is slowly shifting. US. Companies are becoming more open. I think I've seen how the public cloud has matured. The security models, you know, are speaking for themselves. People can understand the benefits from moving to the cloud in terms off, you know, cost rationalization from producing maintenance costs, focusing their proteins on things that they were not able to divert their attention on. >>The fact we had, I think I will say for me and then where I've seen a Novartis if it is access to innovation. So I think loud offering brings a lot off innovation at happy face. That's one hand and also access to extend our collaboration. So when you're in, you know, inside focus I think the relatives from over there wants to walk and collaborate with you. But when you work on the cloud, everybody goes on the cloud. So that's really a stream manifested ate a collaboration with Nextel Partners. >>So how is that changing the culture of Novartis itself? In terms of there, there are more opportunities to collaborate. And it also is maybe changing the kinds of workers you attract because it is is people who want to be doing that in their day to day. >>Well, if you look at it, um, in the past, I think we used to have our own workforce, and then we tried to do a lot of things with our own workers, but I think he's in the on Monte. Workers are full of us, so we have more and more partnerships being announced, and this publishing, I mean used actually to help the company to in revenge himself. So that's actually on one hand on the other side. As you said, I think that to attract with talents I think you need. You also need to have a different future. But you need also to be able to give them the flexibility to work and do the things they like, and we're in a context and a framework. >>One of the things that we hear about so much at the's technology conference is this buzzword of digital transformation and of artisans obviously embarking on its own digital transformation as well as his journey to the cloud. There happen. They're powering each other, they're accelerating each other. How would you describe what is happening to the industry and to know Vargas with it within this, the pharmaceutical industry? >>Yeah, I think, based on our knowledge, to send the why this may be the first. The company can't be trying to build this kind of enterprise level data and also an Alex platform, and based on that, we will be able to counseling date off the history potato intended date on public date, huh? And the Human Industry Day. Then they tried to help us to produce the better products for the patient the same time it gave also the team a chance as you mentioned before, and the look at former more opportunities and the China to leverage in your technology particles of Kayla. >>It's also changed the way that we work every day. So if you look at it now, um, we won't be virtual assistant. We I think we use machine learning elements politics to be able to talkto you are a cheap piece. We actually monitor clickers, Kyle real time having using common centers. So every single day, I think the use off, digital at work and atom in the physical man thinks. And I think we have seen that the adoptions has increased since we have I ever to launch successful products. And I think >>one of the things which, which I really like about working in the bodies, is also I think there's there's an ambition to drive business value quickly. So you know you take a very agile use case, best approach on things rather than having to wait for very long years of time. Plus, the company kind of encourages a culture which is based on mutual cooperation and sharing knowledge, which is great >>because Novartis is really on the vanguard of companies in terms of how much it's embraced, the cloud and how much it's using it. What do you think? Other companies, pharmaceutical companies, but maybe even in other industries as well could learn from the nerve artists example. >>I think one thing people really shy about is, you know, when they moved to the cloud is the security aspect. I think what people probably had failed to realize in the past that there's been so much developments on security in the public cloud, which has bean key focus areas, something nobody's has taken the challenge and has understood that very well. And I think companies can learn from all the different aspects of security that you know were built into our entire transformation work, starting from ingesting data, the user management to access and all of that thing, so that's kind of one thing. Similarly, compliance related aspects as well, you know, So we've g x p compliance is at the core off how we're building our solution. So I think on dhe, if you understand how we built the rules around compliance. But in architecture, I think couples can learn from that a swell and build that is integral part off your not only technology solution, but the process that goes along with it. >>We started our conversation talking about Novartis and its quest to reimagine medicine. How How do you think that your industry is gonna look 5 10 years from now? I mean, the drug discovery process is slow on purpose. I mean, we need to think of patient health and safety for most. But how do you think it really could change the course of how we treat people? >>If if you look at it is more and more treatment required that actually I used and required data as a service or are being actually process for data. So when I am, when we look at the things the way that the industry is changing, I think the times to develop drugs, yes, takes longer. But I think for your use off the data that you have. I think you can try to reduce I cycle. So one of the objective is to reduce the cycle by one firm. Between that, we could bring the day. Is a new director market in eight years, rescues 12 years Today. The other thing is that way for user's data. You can monitor them patient, and you can recommend it the treatment of 80% off foundation. They don't go in and finish her treatment. So I think if we can show the audience to treatment, then there's a lower risk off the admissions to the season and sickness that they have. >>So it's not even not not just Novartis seeing the value of the date. It's the patients themselves, efficiency >>and the d. A r C as well, right? Because I think if you're if the situation is not six and I think the insurance doesn't have to pay. So I think all the value chances is being comes from >>well, sang Loic, because thank you so much for coming on the Cube. It was a really fascinating segment. Thank you. I'm Rebecca night. Stay tuned for more of the cubes. Live coverage of the Ex Center Executive Summit coming up in just a little bit