Salesforce, Inc. (CRM) Earnings Call Transcript & Summary

Welcome to the Tableau Test Drive session. Before we kick off, you won't need to worry about installing any software. You can conveniently access Tableau Cloud directly through a web browser for our hands-on activities. All the required is for you to follow the provided steps on screen to activate your Tableau Cloud trial. And for those who have already activated the trial in advance, simply navigate to online.tableau.com and log in using the e-mail address you used for registration. This will grant you access to your Tableau Cloud trial site. Let's give a few more minutes for other participants to join, and we will begin shortly. All right. Let's start. And hello, again, everyone. I am Peng Peng, a Solution Engineer from the Tableau Solution Engineering team here in the Southeast Asia region. Delighted to have you join us in a Tableau Test Drive today where we will dive into the exciting [ realms ] of unlocking your data's full potential, harnessing the latest advancements in data, analytics and AI. While I wouldn't label this as a formal training, think of it more as a quick sneak peek and opportunity to personally experience Tableau's capabilities and feel confident using it. We will also try to make this session as interactive as possible, so you are encouraged to ask questions through the Q&A chat box. We have a dedicated moderator to assist in answering them throughout the session. Without further ado, let's embark on this data journey together. I want to first give a quick reminder that Salesforce is a publicly traded company, and customers should base their purchasing decisions on products and services that are currently available. As for our agenda for today, there are 5 items on it: first of all, we will get to know the Tableau analytics platform; we will then learn about the power of visual analytics; and we will be spending most of our time in the hands-on session exploring your data; and then I will demonstrate how easy it is to combine, shape and clean your data with Tableau Prep; lastly, some next steps to elevate your data journey and be part of our passionate community. At Tableau, we have always been singularly focused on our mission to help people, basically everyone and anyone, to see and understand data. It is a simple yet powerful statement, and we believe in times like this, it is more relevant than ever. In short, as the world's leading analytics platform, Tableau provides the broadest and deepest capabilities on the market, empowering individuals and organizations to interpret data effectively. It is flexible and user-friendly. Zero code required with just a few drags and drops, Tableau expedites data exploration and insight discovery while accommodating organizational scalability with data reliability and security. And here is a quick look at our 3 key products in the platform. First thing first, the one at the left side, Tableau Prep Builder, it is to use to prepare data for analysis. It is visual, direct and smart. You can connect to multiple data sources, create a data prep flow to combine, shape and clean your data and then publish to your Tableau Cloud or Tableau Server as a single source of truth. The one in the middle, Tableau Desktop, where you explore your data, create interactive dashboards and stories to answer deeper question, other powerful visual analytics that drive business value and publish the results to Tableau Cloud or Server for collaboration. Lastly, the one on the right side, Tableau Cloud or Tableau Server, it's where you host all the published contents such as your workbooks, your dashboards, data sources, prep flows, et cetera, securely and for altering, further collaboration with multi-tier security and data governance, which include automation like subscriptions and alerts and maintain a single source of truth for everyone. And the published contents are accessible through tablets or mobile as well. And overall, with Tableau Prep Builder, Tableau Desktop, Tableau Cloud or Server, Tableau offers an end-to-end integrated platform for your entire workflow. So now before we begin our hands-on, let's look at why and what is visual analytics, and we will start with that quickly. Here, we are looking at 100 digits in a 10 x 10 box, and I will give you 5 seconds to tell me how many 9s are there. All right. Time's up. I know counting is a bit confusing. It takes time for you to go row by row to find the 9s. So no worries, let me make it easier for you. Now tell me, how many 9s are there? It is much faster, isn't it? Immediately, I can see there's 10 9s. This is basically with visual and counting working together, you can do it in less than 2 seconds. And now let's take a look at a real-life example. So what you are seeing here right now are some of the numbers, right? Pretty common to see data in tables format. These numbers that you're looking at represent profit and for each product category and subcategory along with customer segment. And now I have a question for you, which product subcategory is the most unprofitable? So when you are giving a question like that or when you are asking yourself the question when you look at a data in a table format, so with that question, what you do, my guess is you start scanning the table. So to answer that, you either did, in essence, a table scan from top to bottom and column by column or from left to right and then row by row. And it takes time, of course, to evaluate 60 different data elements, and you need to compare those values with each other repeatedly. But what if I change it to something like this? So for the same question, which product subcategory is the most unprofitable? Adding color here enables the user to reduce the numbers of items that need to be scanned. And in this case, we have gone down from 60 to 16 items. But the drawback is that if the data had a large amount of unprofitable items, the addition of color would not vary much still. And what if we change it to this? Now tell me, which product subcategory is the most unprofitable? Immediately, you spot tables, right, especially in the customer segment of home office or corporate. These are the ones that are making a lot more losses than the rest. Additionally, if you have other questions about the most unprofitable or the most profitable items, now even it's also answerable. I can easily spot office machines under the corporate customer segment. It's making the best profit across all the different products. So why this processing data visually tend to be faster than reading them? It is because our brains use visual perception before knowledge. What we saw enhanced what we understood, and it did it really fast. What we can see almost instantaneously and we detect patterns immediately. If we can put data into a form our visual system can make sense of, we can exploit that power, and this is how visual analytics work. Pre-attentive attributes are generally the best ways to represent data. And these are information we can process visually almost immediately because we can see these patterns without thinking too hard. With this, we have the most commonly used 10 pre-attentive attributes on screen. Now I hope we are all ready and all set to dive into our hands-on together. So by now, I hope you are logged on to your Tableau Cloud trial site on your web browser. If not, you can find a link to activate your Tableau Cloud trial in the Resources pane. And if you have activated it in advance, you can go to online.tableau.com and use your registered e-mail address to sign in to your site. We will start with a quick walk-through on the interface to help you navigate and then we jump straight to exploring the data. Once you've logged on, you should be seeing a similar screen in front of you. You should be at your home page of your Tableau Cloud site. On the left-hand side, you will be seeing a list of menu, basically in your menu bar, and you should be in your home page. All right? In your home page, you will see the middle part. There's a big banner, welcome to your Tableau site, and there is a little drop-down in it. Click on the new drop-down, and these are the items that you can create in your Tableau site. You can create a project. Basically, these are the folders. You can put your contents in. And today, we will be creating a workbook in your Tableau Cloud site. And down the list, you have Tableau Prep flow, data sources, et cetera. And now I would like you to go to your Tableau Cloud site, go to the banner, click on the new drop-down list and select workbook. Once you have done that, you should be able to see a new tab in your web browser, which is a new workbook that you have opened. And there's a pop-up in front of you to connect to your data. There are a few ways you can connect to your different type of data sources. The first thing you will see will be on this site, basically the published data sources in your Tableau Cloud site. Secondly, you can navigate to the second type, which is under the files where you can add your flat files, be it Excel, CSV, JSON, et cetera, you can drag and drop your files here. Third, you will see a connector. These are all the native connectors Tableau have. From the most commonly used on-premise data sources or your data on the cloud, data warehouse, data lake, et cetera, you can connect using these connectors. And as for today, we will be using data that has been published on the Tableau Cloud site. I will need to navigate back to on the site tab. And the first thing on the list that you see is superstore data source. This is the data source that we'll be using for our hands-on today. You can either double click on it to select it or you can select it and click on connect. Once the data has been connected, you will see it in the data pane, superstore data source and below it, there will be the tables that is in your data. So we have orders table, people table, returns table. And the table that we'll be using today will be the orders table. And in it, we will see all the data columns and data fields in that table. And you may notice that Tableau has split the data columns into 2 sections. So the top section is what we call the dimensions, and the bottom section over here is what we call the measures. So what is the measures? Measures are the numerical value in your data. And the dimensions-wise, you can see it's the category, the customer names, the dates, et cetera. And in Tableau, you will always start with measures, and you will use dimensions to slice and dice the measures. All right. So here at the middle, there's the separate pane. There's a little pane here called the Marks card, which we will be using quite a fair bit during our hands-on. This is where the different chart types, it is different colors that you can change, the sizing, the labels, et cetera. And at the right-hand side here is a blank white space, what we call as the canvas. It's where you paint using your data. So without further ado, by looking at this data that we have connected, I have a lot of questions. So for example, the top question I have on my mind is that, what is our top product in terms of sales? In that case, we will start with measures. Double click on sales, and now you see a long blue bar in your canvas. Hover over to it and you'll be able to see your total sales, which is 2.3 million. And with that, I'm going to slice and dice it with product because I want to find out what is the top product in terms of sales. And you can expand in the product field, there's a hierarchy built. And we can just double click on category and now your total number of sales, it's sliced with different categories. And with a quick sort, there's this sort icon on the top, it's sorted by descending order, click on it. And now you know your top category in terms of sales is technology, followed by furniture and office supplies. And of course, when you look at this, you feel that, okay, the bar is a bit small. There's a big space over here, let's utilize this big space. So in this case, there's this little square icon on the top of the columns. Click on it. Instead of standard, let's change it to entire view. Right? And here you go, you are fully utilizing the entire canvas. I'll repeat my steps in case you can't follow. So there's this square box on top of the columns, right? Click on it. Instead of standard, select entire view to maximize the space usage. Okay? So now that we know that technology is our top product in terms of sales and we have created a hierarchy previously, it's very easy to create a hierarchy. So how do you do it? It's just a drag and drop. So for example, if I do have a hierarchy between shipment and segment, I can drag shipment, drop it on top of segment to create a hierarchy. And it's as easy as that. It's just an example to show you how to create a hierarchy. You don't have to do it, right? And now back to our question, we know already that technology is our top product in terms of sales. If I want to drill down to the subcategory, how do I do that? In this case, since hierarchy has already been created, you may notice that there is this little plus sign in the columns over here. And by clicking on it, it now help me expand my category to subcategories. And with this, I know that phones under technologies is our best product subcategory in terms of sales. As for furniture, we have chairs and tables as the top 2 for that subcategory. And if I want to show the numbers on the bar, right, to show the labels on the bar, 2 ways to do it. I can very easily click on this T icons to show the numbers on that or I can drag sales to label in the Marks card. There you go. All right? I'll repeat my steps for you to follow. So it's either you use the T icon to show the labels or you can use the measures and just drag the measures to labels. So basically, you can -- if you want to show profit instead of sales, you can drag profit to label as well. Okay? So sometimes having good sales doesn't mean profit is good. So in this case, let's look at profit in the same chart. I'm going to drag profit to color. With a very quick drag and drop, I can see that the darker the blue is, the better the profit is. That's a legend automatically created on the right-hand side. And the more orange it gets or the more amber it gets, the least profitable the product is. So in this case, I'm surprised to find that tables being the second top product subcategory under furniture, the profit is not so well. And a quick fun fact here, of all colors, why does Tableau pick blue and orange as the default color? Because it is the most colorblind-friendly color, and we have incorporated that into our product as well. But of course, you may have your own color scheme in your organization. So it may require you to change it to green and red, for example. In this case, just go to the Marks card, click on color, any color. And here, we do have a lot of palettes for you to choose from. So you can choose red green diverging, for example. And here you go, you now have the darker the green is, it represents better profit compared to the red ones that are not profitable. And with this, I would like to also look at the average. I'm going to bring in the average line. I want to look at which product subcategory is above average in terms of sales and which product subcategory is below average. So instead of data pane, now we can navigate to analytics pane. Click on it, and there are some of the additional analytics items that you can add on to your chart. In this case, I'm going to bring in the average line. It's fairly easy, just a drag and drop. I will drag it to P because I want to look at the average for each product category. So here, very quickly, I can see that under furniture, chairs and tables sales are above average. And when I hover over to the average line, I can see the exact average value. Right? Good job, everyone. Here, you see our first worksheet or first visual of the day. We have 2 more to go. As for best practices, always give your visual or your worksheet a meaningful name. In this case, I am going to name it sales and profit by products. All right? Just name yours as well. I'll give you a few seconds to do it to make sure that you can catch up before we move on to the next worksheet. All right. So here, we have found out that the tables, surprisingly, being the second top sales product subcategory, it has very bad profit. And now with these insights, I have more questions. Is tables not doing well in a certain state? Or has table been not performing well across different states as well? In this case, let's look at geographical data. I want you to now create a new worksheet, just go to click on the first icon you see at the bottom. For new worksheet, click on it, and you should now have a new worksheet, call it sheet 2, with a brand-new blank canvas. And follow up with our question, how does the sales look like across different states? So with that, sometimes you may have that question, you may have the data, but you may not know what is the best way to regionalize your data. In that case, I'm going to do a multi-select. I want to look at sales, I also want to look at the states. Just hold on your control key to do a multi-select if you are using Windows. If you're using Mac, hold down your command key. Select sales, hold down the control key or command key and select state to do a multi-select. Once you have done that, on the top-right corner, there's something called show me. And in it, we have the most commonly used charts, and you may notice one that is in a red border. And when you hover over, it's what Tableau recommends you to use based on your selection of data. Very quickly, just click on it. And here you go, you have sales across different states. And the bigger the circles is means that the sales is better, whereas the small ones, sales is lesser. And of course, you can adjust the size here in your Marks card very easily and you can even add colors. Let's look at profit. So in this case, drag profit to color, and very quickly, surprised to find out that California and New York is having good sales and very good profit, but not for Texas, even though Texas is one of the top states with good sales amount, profit is bad. Right? And as per best practices, make sure that you are standardizing your color. For profit, we press the -- is using red and green, so make sure that you use the same one as per best practices so that you won't get confusing, it's in different colors for the same metrics. Okay? And now, of course, there are more things you can do. You can adjust opacity, you can add in border if you want to make it more pleasing. Okay? So congratulations. Again, here, we have our second worksheet. Additional insights that we managed to find is that California and New York is doing great in terms of sales and profit, but not much for Texas, even though sales is fairly good over there. So with this, we have more questions. So my follow-up question now is that Texas is not doing well in profit. Has it always been not profitable? Or it is only recently that Texas is facing a profitable issue? Before we move on to a new worksheet to answer our next question, let's name this very quickly by states. And now you know how to create a new worksheet, just maybe click on the first icon at the bottom to create a third worksheet for today and the last one. And based on our question, we want to look at how is the sales and profit performing over months or over the years, basically, we are going to look at trends. So now, again, we are going to start with measures, double click on sales. Again, the same total number of sales, 2.3 million. And now we are going to bring in the dates to look at trends. And you will see all the dates there in the data pane. You can double-click on it or you can drag it to columns. Once you have done that, I want to again maximize the space. What you see is Tableau is aggregating the dates to the year level. So we have 4 years of data over here from the year 2020 to the year 2021 until year 2023, and this is the trend. From year 2020, there's a slight dip to 2021. And from year 2021 onwards, it's been steadily increasing in terms of sales. And if I want to drill down, instead of years, I want to look at it on the quarter, I can click on this little plus sign over here. Because Tableau recognizes dates, it's easy to drill down from year to quarter. Just click on that plus sign, and now you'll be able to see that within that year, how is each quarter performing. So you can see the trend across different quarters within that year as well, right? I'm going to repeat my step so that to make sure that you can follow. So you can go to the year of the order date columns, the blue pill, and you can click on the plus sign to drill down to quarter. And now we can look at different years and the quarters within that specific year. And what if I want to look at different quarters and across years within that quarter? In that case, very easy in Tableau, you can drag quarter to in front of years to see it. And with that, I know that Q4 across all the years, across the 4 years, our Q4 is the best-performing quarter compared to Q1, which is the lowest. Next I would want to look at is the continuous of the trend for my sales. So in that case, whatever we do not need, just drag it away. You can just drag years away. If I don't need years, if I just want to look at quarters, I can do that. And if I want to change this to a continuous month, I want to look at trends on the month level continuously from the first month of year 2020 to the last month of year 2023, for example, I can click on the drill down instead of the split by year, by quarter only, I want to look at it on a continuous trend. In this case, I'm going to look at my [ field of month ] and I'm going to very quickly click on the month, make sure that you are clicking on the month with the year. So with that, we can now see the trend from the first month of the year to the last month of the year 2023. And with this, I can see that February 2020, we have the lowest sales across the years, whereas in the month of November 2023 is our best month by far in terms of sales. And now I know that the sales trend -- how that sales trend is looking at, and I'm going to bring in profit. I can drag profit as well to next to the sales in the rows. And now Tableau has separated them to sales and profit to different measures in different charts. And it's easy to toggle between the chart types. So in case that I want the sales to change it to an area chart instead of a line chart, I can just very easily toggle that in the Marks card, just make sure that you are selecting the right measures. As for the profit, I want to change it to bar chart. So it's just a click away. One thing to emphasize here is that down the road, once you have built the visuals, down the road you have built the dashboard and you have a change of mind, what chart type you want to use, you can always come back here to the editing mode and just, with a quick toggle, change the chart type or make any changes to your charts or to your visuals. So it's very easy to maintain the dashboards in Tableau as well. You do not have to rebuild your visuals or your charts from scratch. You just need to edit it over here. Okay? And with that, we will, again, make sure that we follow the best practices, bring in the colors, and I am going to standardize it with red and green again. Right? And now very quickly, I noticed that across the months, July 2020 and January 2021 is not doing so well in terms of profit. And we have the best month for profit, which is December 2022. And with this, I would also like to find out the profit ratio across the different months. And as of now, I have sales and profit. And I would like to create a new measures using a calculation or a formula to calculate the profit ratio. In this case, it can be done in Tableau as well. There is this little drop-down in the data pane next to the search bar, click on it and you should see the first thing on the list in the drop-down is to create a calculator field. I repeat, there should be a drop-down next to the search bar in the data pane, and here you go. This is it, right? And this is where you can create a calculator field and more. As for today, I'm going to show you the calculator field, just click on it, and now you have a pop-up to create a calculator field. So I'm going to create a calculator field called profit ratio with a percentage mark symbol, and I'm going to write the formula. So basically, profit ratio, going to use sum of profit. So do note that when you start typing, there are suggestions, there's a list of suggestion that's listed out for you to select. And not just that, in terms of the functions, in this case, we are using sum, there are more functions available for you to explore. During your free time, you can expand this little arrow on the right side of the pop-up, and there's this list of functions that you can use in terms of date, in terms of string, et cetera, you can use it. And the best part is that it gives you a short description of how to use it, what's the purpose of this specific function with an example below, so that this helps you to self-learn how to use the functions. And back to our calculation, sum of profit divided by sum of sales. And instead of typing it, what you can do is also drag it from the pill. So I can drag sum of sales and drop it instead of typing it. This is a quick tips and tricks. All right? Once we have done that, I'm happy with it, just make sure that you're following. So I'm giving you a few more seconds to do it, create a new calculator field called profit ratio, which is sum of profit divided by sum of sales. And you may notice that there's this validation at the bottom that helps you validate your formula. So in any case that you are making a mistake, you have missed out a symbol or a slash or something, it would show us an error. And by clicking on it, you can fix it. It tells you where is the problem, so that you can fix it. Okay? So just make sure that your calculation is valid and click okay. And now, very quickly, we have created new measures on profit ratio, and I'm going to use it in my regionals, going to drag and drop it next to profit. And here you go, we have the trend line for our profit ratio. And with that, I would also like to look at trend line because, for example, profit ratio has been fluctuating across different months. And now I would like to look at how the trends look like exactly, a trend line. So go to analytics, there's a very quick -- just drag and drop trend line with fine models that you can use. So the same like average line that we have used earlier, just drag it and there's these 5 models: linear, log, exponential, polynomial, power. So you can try it out, drag and drop it to profit ratio. Let's say, I want to look at how exponential trends look like, just drag and drop, didn't tell me much. I can again drag and drop to polynomial, for example. Okay, this is something I'm looking for. So you can test it out to see which suit best for your use case. Okay? In this case, I'm quite happy with what I have right now in terms of trend, and I'm going to name it trend. Okay. Giving you a few more seconds to do that, drag the trend line in the analytics pane, just drag and drop. You can try it out in different models and then just give it a try. And don't worry to make mistakes in Tableau because there is this undo button there. You can have unlimited undos, you can always undo, redo. So just drag and just drop it to explore the data. Don't worry about making mistakes. Okay? Once you've done that, remember to rename your trend -- sorry, to rename your worksheet, call it trend. And now we have all 3 visuals or 3 worksheets ready to build a dashboard, to build an interactive dashboard. Once you are done, at the bottom, we have -- you know how to create a new worksheet already. As for the second icon, there's this icon that looks like a cookie is where you can create a new dashboard. Click on it and here we go, we have a blank dashboard ready. And you'll notice that on the left-hand side, there is the size that you can adjust. By default, it is a fixed-size desktop browser sizing. You can always change it to different -- to cater to different devices, sizes. You can do that or you can also change it to automatic so that it max out the blank space. And under the size section, there's these sheets that we have built. It's now -- or they are all listed out at the sheet sections. And you can see these are the 3 worksheets that we have built together. And to build a dashboard, fairly easy, just drag and drop, dragging states to the blank space, and now you can see states on the dashboard. And when -- next thing I want to do is drag sales and profit by product. And you will see this gray shadow appearing on your dashboard. When you do that, when you drag it, once you drop it, it is where it's going to place the worksheet. So you can actually adjust that, just drag and drop and here you go, you got that. It is also easy to readjust the worksheets in your dashboard. Just drag this little gray frame, select the worksheet, have this gray frame and drag it around to adjust the space to where do you want to place it in the dashboard. Okay? Great job, everyone. Thanks for follow-through so far. And now that we have a dashboard ready, right, you will see the states on the top, sales and profit by products and the trend at the bottom. Now let's make it interactive. So for example, whatever that we see right now is an overall view. It's an aggregated amount of the data based on the different dimensions. So now that when I want to drill down to California with one click on it, I wanted to be able to drill down to show me the sales and profit of the products and the trend specifically in California. So how do I do that? Very easy, with just one click in Tableau. Select the worksheet that you want to use as the filter. In this case, I'm going to use the states worksheet. And once you click on that, there's this gray frame that appears with a few icons on the top-right corner. That's one looking like a funnel, hover over it, it says use a filter. So just click on it. And now once you have done that, when you select the states, Tableau automatically drill down to that specific state to show us the sales and profit by products and the trend. And if I want to look at New York, for example, I can do the same. It's now interactive with just a click of a button. And of course, we can look at Texas as well, and it's not looking so good in Texas. Besides phones, accessories, copiers, papers, et cetera, being profitable, the rest of the products are not profitable. And when I look at trend, most of the months is not profitable, except for October 2020. And something caught my eye here, in terms of sales, it's been pretty low. It's always been below 10,000, but there's this specific month in September 2020 that we have almost 20,000 sales. So what happened in September 2020? So when you are interacting with your dashboard, when you come across insights like this, you have more and more question that surface, right? In this case, you can either reach out to the person who built this dashboard, tell them or to your data team, have them extract the sales of September 2020 to drill down to the transactions. What happened is there's a specific transaction that is causing the spike in terms of sales. You can do that, that's one way. Another way is to have Tableau helps answer your question. With augmented analytics built in Tableau, when click on the data point, that's something called data guide over here, it's where you can drill down further from the visual. It's to phase out all the insights for you as well, so let me quickly show you that. Click on data guide. And of course, we know that already, right? In September 2020, we have a higher-than-usual sales, and I would like to drill down to it. Click on it, and it would tell me some of the characteristic of this specific data point I've selected, and there is one extreme value. Drilling down to that, it surfaced out all the outliers. So I have indeed one outlier here in terms of the transactions. There's one specific transaction that is causing the spike. And I can drill down to that specific record. And as for our usual sales on an average, it's normally 500 over, there's one at 8,000 that month. Probably this is the reason why there's a sharp spike in sales in September 2020. I can even further drill down from here by going to the worksheet. That's this little icon, open your worksheet and click to that, I can drill down further from there. Let's say, if I want to know which city this specific order is coming from, I can drag city to tool tip. And now when I hover over, this little white pop-up is what we call a tool tip, it tells me which city it's coming from. This specific outlier comes from San Antonio. I can, again, hover to the rest of the transactions or others in the visuals. I can see that this is from Houston, Houston, San Antonio as well, et cetera. So it allows you to very quickly go from dashboard on a high-level view, drill down to a specific state, look at the trends to phase out what are the outliers very quickly within a few minutes. And not just that, there's one more thing I would like to show you as well. Sometimes with an amazing dashboard like this, it's very visual, I can get to my insight very quickly, it may not be apparent what is important from this dashboard. So with the power of storytelling, we have something called data story, which you can just look at my dashboard. You don't have to follow through these steps. I just want to show you that there's this possible data story, automated data story that you can do in your dashboard. So with a quick drag and drop and a very quick configuration of your story, I want to tell a story using the state worksheet, for example, make sure that the dimension and measures are correct as expected. Next, probably a [ discreet ] story to tell. Once done, you have an automated story written for you based on your dashboard, based on your data. And of course, with this, when I look at the summary given to me, if I want to drill down to a specific state, for example, in California, once I click on it, this story dynamically updates to that specific state that I've selected. So California, for example, has a sum of profit of 76,000, for example. If I want to look at the East Coast, for example, I can also do that. And now I have a quick summary of all the sales and profit in the East Coast. And now your next question is, can I configure or can I customize this data story? Yes, you can. So you can go to setting and update, customize, further customize your story. You can reduce the verbosity, less words in your story. You can even change display of the front size to your data font size, for example, very quickly. Okay? And not just that, all these data points, you can also edit them. Just go to the edit, you can hide some of the points that you do not need. You can even add more as you wish, add some additional notes from your point of view as part of the story. All right. A very good job, everyone, for following through so far. So now that we have our dashboard created together, for the past 30, 40 minutes, we have managed to go from data to insights very quickly. You get to see -- you get to find out a lot of insights from your data and even uncover hidden insights with regards to Texas in the month of September, and we have created an automated summary in the dashboard as well. And next thing is to publish it. So save your changes, click on publish this and give it a meaningful name. I'm going to publish it in the default project. I'm going to name this sales performance dashboard. Okay? Once I've done that, publish it so that it gets saved to my Tableau site, to my Tableau Cloud site. And once done, let's now look at the workbook. Go to workbook, and there's more I would like to show you in terms collaboration, some of the cool stuff that helps you collaborate with your team better. So in the dashboard that we have created, here you go, once published, the interactivity remains, right, the selection of a certain area, the West Coast, for example, it helps me drill down to the sales and profit by product and the trend in that specific area. And like we have seen in Texas, there's something that we need to do, definitely. So when it comes to taking action, once you have found the insights, sometimes it may involve another team member. In this example, you may have a salesperson who is in charge of Texas. And once I've gotten this insight, I would like to share this out to that specific person so that we can work together, brainstorm and work on how to improve our sales and profit in Texas. In this case, there's something called comments on the top-right corner, a few icons over there. And once I've selected Texas, I would like to share this insight out to this person, that why it's probably we can start a discussion. For example, Peng Peng is that salesperson looking after Texas. I'm going to call up Peng Peng, let's discuss how we can improve, for example, sales in Texas. And since I have filtered my dashboard to a specific state, which is Texas, I would like Peng Peng to see the same filtered view that I'm seeing right now. And I can add that as a snapshot as part of the comment with just a click. Once I posted, Peng Peng will then receive an e-mail with this alert, with this comment, seeing that I have initiated a discussion how we can improve the sales in Texas. Then with one click, she can come into this specific dashboard, click on this snapshot and look at the exact same view that I'm looking right now, which is a filtered view to Texas. Okay? Once you have done that, of course, there are more of the collaboration functions we have. For example, if this dashboard is a routine or regular dashboard that you would like to share out to a group of users on a weekly basis, instead of manually extracting it and sending it out over e-mail to the specific users, you can use something called subscription, right? Under watch, there's something called subscription. You can subscribe a group of users like Peng Peng or you have -- you can put your users into a group based on departments, based on their roles, you can do that as well and subscribe them to this specific dashboard that you have built. And in terms of format, you can also attach this dashboard in a PDF in that e-mail, you can adjust the size as well, A4, et cetera, put in a message if you want and set a schedule for it to be sent. And I'm going to change it to weekly, maybe 8:00 in the morning or night, every Monday, for example. And once I've subscribed to it, when the time comes, every Monday at 9:00 a.m., a new dashboard -- a new e-mail will be sent with this dashboard as an attachment to the group of users that you have subscribed. Not just that, when it comes to following or keeping an eye on the key metrics that are very important to you, some of the -- for example, if you are a sales director or sales manager, sales numbers, sales figures, very important to you. You would like to be the first to know when certain sales hit their target, for example, or hit their threshold. And in this case, instead of coming into this dashboard, refreshing it every other day, you can create an alert that ties to the sales, for example. So we have something called alert over here. You can create an alert. Let's say, if my sales amount exceed 300,000 threshold, Tableau will shoot me an e-mail, telling me that our sales amount has gone above or equal to 300,000. Likewise, you can change it to below equal to or equal to. And you can again add the recipients who is going to receive the alert when this happens. All right. And of course, there are more to that. You can download your dashboard in different formats, in image and all that, in a PDF, et cetera. And congratulations, we have created our first dashboard together, interactive with story. And I've shown you some of the key collaboration features like comments, subscription, alerts. You can find out more about other collaboration functions we have as well and metrics and all that during your free time or even after this session. And now back to the deck, let me do a quick recap of our hands-on session. Thank you for following through the hands-on. Well done, everyone. Here is a quick recap of what we have experienced earlier. So Tableau makes it quick and easy to connect to all your data. You have seen -- even though we have used the published data source that's readily available in your Tableau Cloud site, there are also ways for you to connect to your different type of data sources, be it on the flat files in a flat-files format, in Excel, CSV, or you can use our native connector, which is out of the box to connect to your databases or data warehouse, be it on the on-prem or be it on the cloud. We have experienced this earlier, answering questions with your data in Tableau at the speed of thought with just drag and drop, there's barely any code involved. And of course, with live visual analytics, it helps fill unlimited data exploration. And I've shown you some of the key collaboration function to help you collaborate better with your team, share your dashboard out. We have taken a closer look at some of the key features that facilitate seamless sharing and collaboration within your team as well, such as subscribe, comment, alert. I hope you had fun exploring your data in Tableau. Next up, we have Tableau Prep. And despite the remarkable ease with which Tableau empowers our customers to visualize their data, a lot of our customers come back to us telling us that data preparation still remain one of their key challenges. In fact, through an article written by Harvard Business Review, we have learned that 80% of an analyst's time is spent on just data cleaning and discovery. And with Tableau Prep Builder, we hope to change that. Instead of 80% of time prepping the data for analysis, we hope with Tableau Prep Builder, you will spend 80% of your time analyzing the data instead of prepping them. So before we jump into the demonstration, here's a brief context on the 3 data sets I will be using. You can imagine your organization, Superstore, has recently acquired a new company called Supply Mix. As your management seeks a comprehensive view of the consolidated business, our focus revolves around combining sales data from both Superstore and Supply Mix. And by joining the product master data, we will be able to uncover insights such as identifying the top-performing product based on the sales performance. With Tableau Prep, we will be combining all these 3 different sources to a single source of truth, removing the duplicates and cleaning them as well. Without further ado, let's start the demonstration. Here, you are looking at Tableau Prep Builder. On the top-left corner, there's this little arrow where you can expand to connect to your multiple different data sources, where the published data sources in your cloud or server, flat files in different format, Excel, CSV, text file, to different databases or data warehouse or the cloud or on-premise, some of the very commonly used ones. And if you do not see your data source on the list, don't worry, we support JDBC and ODBC connection as well. And at the middle section, you will see that there are a few buttons for you to open the flow, you can connect to your data, there are some flows that you have recently built or visited. And on the right-hand side here is something I would like to mention is there this discover pane with some of the short clips to help you get started to use Tableau Prep Builder, some of the introduction, some of the feature and functions, demonstrations, step-by-steps, showing you what can be done in Tableau Prep. So this is a good place to start, to watch some of the short clips, to help you get started easily with Tableau Prep. So without further ado, let's quickly connect to our data. So we will be using for today 2 CSV files, which is the sales data from Superstore and Supply Mix and the product master list in Excel format. So you can mix and match, it depends on where your data sources reside. Some of them might be in databases. You can combine them with Excel or CSV. There's no restriction to what you can or cannot combine. So quickly, let's connect to the CSV file. We will be connecting both of the sales data in Superstore and Supply Mix. And we will -- also, let's bring in the Excel, which is the master product list. Right? Once you have connected the data sources, you will see that all the connections will be listed on the top-left corner. And whatever select -- based on your selection, the data sources that you have connected, they might have multiple tables, which will be listed down below. In this case, we will start with Supply Mix. And one click on this data source icon, you will see that a very quick preview on the data fields that we have in that data source. For example, Supply Mix, we have product ID, shipping cost, some geographical data, customer details, sales figure and et cetera. And now let's create a very quick step. And by creating multiple steps, you will be then building a flow. All right? In the clean step, we are looking at different carts, basically it's different data fields presented in a cart format. So these are your data fields, your data columns, and below it is the data and their distribution. And with this format, you can easily spot anomalies, duplication and dirty data. And this is the second section is what we call the data profiling pane, which is the data fields in a cart format. And down below, we have the data grid, basically your data in a row-by-row format, all the details of data in a row. Okay? Now in the clean step, we will -- let's take a look at the data. First thing first, what caught my eye here is the shipment. There are very obvious duplications in the data. For example, first and first class, second and second class, standard and standard class, these are all the same basically. In this case, I will be grouping them together. You can group values by different ways in manual selection through pronunciation, et cetera, come and correct their spelling. So I'll be using manual selection for now, a very quick one to group the same data together. So first and first class, group them. Next, I'll group being second and second class and standard and standard class. Right? Once that is done, very quickly, we now have cleansed the shipment data, and now shipment looks much cleaner. Another thing that caught my eye here is the shipping cost. Normally, shipping cost, it's either having a value or 0, basically, it's like free shipping. But in this case, I spotted negative values in the shipping cost. And by clicking on it, Tableau will drill down to all the data in rows. It will surface out all the data that is having negative shipping costs. And in this case, in the data grid, I can tell that it is junk data because we do not have such product ID. There's a lot of nulls and all that. Probably it's a test data. And we do not want to have this data as part of our data source to impact our analysis down the road. In this case, I'm going to quickly, one click, to exclude it. Once that is done, data looks much cleaner now, and it seems I'm ready to bring in Superstore. And scrolling up, I'm going to very quickly do a quick drag and drop, dragging Superstore and dropping it on top of the last step of the Supply Mix. I'm going to do a union because the sales data from both Supply Mix and Superstore, they are having similar structure and field names. And once I've done the union, what Tableau did is it combines the data fields with the same names together. You can see their product ID, shipping costs, everything is combined nicely. As for some of the fields, they are mismatched in terms of the field names. For example, units sold and company, you can see that the color is in amber or in orange, which means it comes from Supply Mix as the source. Whereas quantity and region, in blue, it comes from Superstore. So the sources are color-coded, so you can identify where exactly it comes from. In this case, very quickly, I noted that the unit source is coming from Supply Mix. And we have a quantity from Superstore, basically unit source and quantity are the same thing. It has the similar data, how many units has been sold in the order, et cetera, versus the quantity. So because of the few names are different, it's not being matched automatically, but I can manually merge it together. In this case, very easily, I click on units sold, hover over to quantity and click on this plus sign. And now unit source and quantity is now much and combined to become units sold. As for the other 2 mismatched fields, company-wise, I want to keep this field because I feel that down the road during the analysis, I will be able to drill down to the specific company sales data. Hence, I will be renaming the null value to Superstore because it seems like this company field comes from Supply Mix and it's not there in Superstore. So I'm going to rename the null value to Superstore, so now I will be able to drill down to each company sales. And if you notice, there's this table names on the left-hand side showing you the data sources, where does it come from. This is a field that is automatically created by Tableau to include the sources, where does it come from, where does the data source come from. In that case, since I already have my company here, it contains the similar data, I do not need table names anymore. So I'm going to remove the field. Lastly, as for region, seems I may not need the region for my analysis. In that case, I can easily exclude it and remove the column. Once that is done, now it seems my union is all good. One thing to note is that all the changes that we are making will not be changing your underlying original data in Excel or CSV, you will not change those data. What it will change and update is a snapshot that Tableau takes within Tableau. Another good thing is that whatever changes that you have done, when the new data flows in, for example, next month, the sales -- and new sales data transactions are in, you don't have to repeat the cleaning steps anymore because you will be able to rerun the flow, fetch the latest sales data and go through the steps within the flow. Again, you don't have to rebuild every single steps. Another thing is that whatever changes that you have made is recorded. It's basically tracked and there's an audit log for you to refer back whenever required. So you can see, for example, in the clean steps, we have made 2 changes, right? In the changes pane, you can expand it and you see what other change is being made. And this helps when it comes to troubleshooting, when it comes to looking back what changes was done, reviewing the changes, this is a good place to go. And of course, if you change your mind, some of the items, like I want to keep the negative shipping cost to investigate, yes, you can move this step as well. So you can always look back and make changes or troubleshoot. Right? So now at union, so far, we have successfully union-ed both data sources together from Superstore and Supply Mix. And now let's bring in the product master list. Just go down, found it. Again, I'm going to do a drag and drop. And now I'm going to do a join. Once I have attempted to join master product list with the sales data, you can see that what Tableau will do is that it will try to find the common field and use that as a join clause. In this case, it's right. So product ID from the union-ed sales data is now joined with the product ID from the master product list, which is correct. I don't have to correct it. In case that Tableau wasn't able to identify the common fields, maybe because the few names are different, you can always add your join clauses. Sometimes you may have multiple join clauses as well. So you may do that yourself. And if it's not correct, you can also remove the join clause as needed. As for the join type, by default, it will be an inner join. And in this case, I will prefer a left join. Left join makes more sense. It's very visual, you can see. One click on it, I have turned it from inner join to left join. And then moving forward, when I look down at the summary, there is instant join result that is surface of -- that is presented to me. And I noticed that there is not all of the union -- or not all of the sales data is union-ed successfully. It's not joined successfully with my product master list. So now let's look at it if there's any mismatched value. There is indeed a product ID that is mismatched because it has some extra dashes behind it. So let's quickly correct that. I'll remove the extra dashes. And here we go, immediately, we will be able to see the actual join results after we have removed the mismatched values. And now all is matched, all the product IDs in the sales data is matched to the master product list. With that, we will now create a final clean step to doublecheck, make sure that all is good before we output our single source of truth. Looking at that, all looks good, nothing too concerning and it seems like we are good to go. And now the last step of the flow is to have an output. And of course, there are different type of format that we can output our single source of truth to kind of put it to a file format, Hyper, Excel and CSV format. We can also publish our single source of truth as a published data source to our Tableau Cloud site. I will show you in a bit. We can also write this back to a database. For example, you can write it back to the queue or server, et cetera. So for today's example, we will be publishing this as a published data source to our Tableau Cloud site. Select project, give it a name and once you run it, the output will be published as a published data source. Besides that, you can also publish your flow to your Tableau Cloud. And once it's in published, you will see it now in our Tableau Cloud site. This is the exact flow we built together. And here in Tableau Cloud or Tableau Server, you can then schedule this prep flow to run automatically. So you can schedule it based on the schedule that you like. In this case, I'm scheduling it to run every weekday at 2:00 in the morning. And once the task is created, when the time comes, it will automatically fetch the new data and run this flow. And so you will get to start your day with a clean updated data. And that is all for the Tableau Prep demonstration. Of course, there is more to it. What I've shown earlier during the demonstration is just the tip of the iceberg. There are more features, more you can do with Tableau Prep Builder. For example, you can -- there's a smart clean function. You can even add calculator field in your prep flow, pivot, do aggregation. You can even write scripts, integrate with your Python and all that. Feel free to browse around on the web. A lot of resources are available on our website. Everything is just a Google away. Lastly, of course, your data journey with Tableau has just started, and it doesn't stop here. So what's next? After the session, you are highly encouraged to browse or explore Tableau Exchange. In it, we have something called Tableau Accelerators. More than 100 prebuilt dashboards built by the industry experts with best practices, so you can plug in your data, go from data to insights immediately, and it's built for different industry, different departments, different use cases. And not just that, there are extensions that allow you to fit Tableau into your workflow. And they are trusted solutions built by us and our expansive community in the App Store for data. Besides that, I have mentioned this earlier, our official website offers a wealth of resources for learning Tableau according to your preference and pace. You will find a wide range of contents, including free training videos, e-learning, on-demand webinars and events on our web page. So that's a QR code for you to quickly scan. It's basically tableau.com, you can go to it for all these resources. We have something called Tableau Public. It is a free platform to explore, create and publicly share data visualizations, showcasing the art of what is possible. You can think of it as the YouTube of data visualizations. To date, we have millions of visits and orders with an average of about 3 million views per day, and the numbers has only been growing. Besides the visits that are business-related, it also helps foster and develop data culture in the world through exploration and sharing of data stories about everyday topics like sports, music and health, but also social impact topics, making data social, relatable and accessible to anyone. It is a space I often go to, browse to seek for inspirations as well. And in the world of Tableau, we have got a seriously enthusiastic bunch of supporters and users. We are talking over 1 million strong and we affectionately call them the data fan. This community is all about unity, a space where everyone gathers to exchange knowledge, spot inspiration and learn from each other. It's mind-blowing how this community we are in has consistently stepped up to the plate. They have provided a helping hand to individuals, organizations and even nonprofits across the globe, all on their data-driven journey. Time and again, they have been the driving force behind success stories that involve data. As part of the community, there are now more than 500 Tableau user groups in 65 countries. And we have a dedicated Tableau user group for each countries like Singapore, Philippines and Malaysia as well. Feel free to scan the QR code on the screen to join the Singapore Tableau user group. We have quarterly events, get together to share inspiration, to share some tips and tricks, so do join us. So you can put out your mobile, I'll give you a few seconds to scan the QR code on the screen to join. Hope to see you there one day. And thank you so much, everyone. With this, a very warm welcome to the data fan. Thank you for joining us, and we appreciate your time today. If there's any last-minute question, feel free to drop it in the Q&A chat box. Our moderator is still here to answer them. Have a great day ahead, everyone.