Go! Tableau

Alright. Okay. Fantastic. Welcome, everybody. As Vicky just mentioned, today's workshop is gonna be go Tableau. And we're gonna start pretty promptly today because we do have a lot of content to cover. You'll be pleased to hear. This is an hour and a half session, where we kind of try to uncover and and get over that initial hurdle. So like other BI tools, there are concepts we have to learn within the Tableau environment. I think it's quite a straightforward tool. There's not that many different concepts that we have to get, to grips with. But there are some that operate a little bit differently from the other tools. We're gonna be digging into those to get us over that first hurdle. And by the end of today's session, we should be able to be connecting up to data, doing a little bit of data preparation and understanding how that concept works within the Tableau environment, as well as then more importantly being able to create visual charts with our data and be able to draw with those numbers, to be able to create interfaces for the rest of our colleagues to be able to use in order to analyze, the data that we have prepared for them. On the right hand side, there is a link, a QR code there also for our world famous blog. Please feel free to take that into Google and have a look for Tableau content as well as lots of other content from the world of analytics. My name is Max, and I work within the solutions team, of EMEA. So we will, join lots of calls with lovely clients like yourselves, and understand a bit more about what you're trying to achieve with data and how our different partner products will, fit or can fit into, solving the problems that you may have, as well as, rolling out different consultancy, services, at the same time to help you. Interworks, if you haven't heard of us before, go on the vlog, have a little look. What we have been known for over the past fifteen years or so is our Tableau expertise, and we've been offered and and given lots of different awards, for those, those those expertise. We are global, so we operate out of US primarily as well as massive amounts of talented consultants in EMEA and also APAC as well. And like I said, we do have that. So today, we are going to be covering few different sections of the Tableau ecosystem. Tableau is an ecosystem. We'll come back to this idea, shortly, where there's lots of different tools and platforms that are at play. When we say Tableau, it means different things to different people depending on where your expertise and priorities lie. So we're gonna see how some of those interplay together. The main focus of today is going to be in the Tableau Desktop product. And that Tableau Desktop product is what allows us to create analytics and really design and develop those, high class and high quality interfaces so that people can explore their data more fully. And that's what we're gonna get into within the Tableau essential session where, as I said before, we're gonna be digging into concepts like data preparation, creating calculations, and visualizations, as well as finally building an interactive dashboard and then sharing our work with our colleagues by publishing that dashboard to a Tableau server or Tableau cloud environment. And that's gonna allow people to be able to get that information at the browser level, be able to log in to that platform in their browser, using their credentials and be able to see the things that are most pertinent to them. Throughout this session, we're gonna cover several exercises. These exercises are gonna be specific to these different sections that we've got on screen in this tab of essentials section at the bottom there. We're gonna do one exercise, for instance, for preparing data. We're gonna do another exercise for building out a dashboard. And to help us to do that, we have a packaged workbook, which is a GoTableau starter workbook. TWBX is their file format for Tableau. And we also have an Excel workbook as well, which is going to allow us to populate those data libraries that are gonna allow us to then draw with the numbers that are in the Excel file. If you haven't done so, it's worth downloading the materials, that would have been shared on the link in the invite. And you will once downloaded, you will see a zip folder that will look something like this. If I go back a little bit, actually, you will download this. Go Tableau materials dot zip in order to play along in today's session. And by extracting that, you will see a folder like this that will then have, have these, these these files within that. So you've got your starters, TWBX, which will be used to document the insights, and we also have that global Superstore XLSX. Oh, I see somebody's got their hand up. I think Vicky mentioned that we are muting lines for today's session, but, please feel free, Karen, if you want to, drop a message in the chat, and I can I can answer you as we go? So while that message is getting dropped in the in the chat, again, the, the materials are available for us. So is Tableau Public okay for this is a question from the, from the chat there. So Tableau Public is a fantastic, resource that we can we can kinda touch upon. Let me show you Tableau Public. So Tableau Public is basically a really large version of Tableau Server or Tableau Cloud. This is this will allow you to sign in, create your own account, and you can then publish any documentation or any, any workbooks rather onto your account for Tableau Public. So the short answer to that, Lewis, is yes. Absolutely. Once you've created your dashboard, you're more than welcome to publish that to Tableau Public. It is publicly available data. So you're, you're you're more than welcome to to publish this, and I can discuss that when we get to that point if I answered that question. Still waiting for admin rights to download the app. No problem. The link is being posted in the channel. Thank you very much, Paul. That's fantastic. So, generally, this is a hands on session. So over the next hour and a half, we will be working through those practice questions together. As we mentioned, though, this is a sort of webinar format. So, unfortunately, it's not gonna be I'm not gonna be checking if all, how many people do we have? Hundred and fifty one people on the call. I won't be checking if everybody is up to the same stage same same stage within each of those exercises as we go. There is a solution document within that folder structure. So go Tableau version twenty twenty one dot one forward slash solution or or dash solution. We'll allow you to come back to this at a later day and have a look and see if you can then solve the problem if you do get stuck as we go. So if in doubt, don't worry too much if you miss a step or you've got, you've you've kind of hit a bit of a stumbling block. And likewise, if you're waiting to download Tableau Desktop, I'll hopefully try and keep the pace to the point and I'll have to keep the pace to the point today where, where you should you should be fine just to consume the information that we're presenting today and come back to that at a later date with your newfound knowledge in the Tableau ecosystem. So I'm gonna start off today with discussing a little bit about the landscape of Tableau. So where did Tableau come from, and what does it aim to do as a data visualization BI platform? So in two thousand and five, Pat Hanrahan, who, was a doctorate, at Stanford, I believe, was asked by the US Department of Defense, to prepare a or create a product that would allow the defense analysts to, analyze the data that they currently have, which completely outweighs the amount of resource that they had at the time to create what was pretty ubiquitous at the time in terms of SQL reporting or crystal reports. So these reports that required a lot of SQL knowledge in order to populate tables. I remember, at the time or not at the time, maybe a little bit later, I was kinda asked to do some SQL reporting with, with the with the product that I was working with in order to have, the software, analysis kind of pushed in front of the the customers of that software. And I was quite junior, I suppose, at the time and not really that familiar with SQL, and not really that familiar with SQL reporting. And it was giving loads of errors, and it was a very long life cycle to even create a simple table of data or a bar chart that could be shared with our clients, natively in our application. So, the real focus of Tableau at the time was really quick analysis, just trying to shorten that hurdle, shorten that timeline to analytics, be able to slice and dice things visually and quickly, using, a a more interactive product. And this is where, Pat Hamren and Chris Stolte, created the database querying language of VizQL, which is so used to this day in Tableau. It really defines Tableau compared to the other products in the market. This same lab also, hatched Yahoo and Google as well as, coming from the, the the the rendering graphic work, at Pixar. So two companies were born out of this groundbreaking technology of this QL, And those two companies were, Pixar and Tableau. So the same thing that powers, Tableau Desktop that we'll be using today is the same thing that, powers our jolly little friends there at, at Toy Story, which I think is quite a nice origin story. As I mentioned, this whole concept is kind of predicated or predicates the the, the the kind of, foundational concept of democratization of data. Now this is quite ubiquitous now in the BI world that we see. Everyone's trying to get to this point or have somewhat, in some cases, achieved this point of democratization of data, which is really the concept of pulling, data expertise and data manipulation expertise and visual analytics out of the hands of the IT team where it used to be. It kind of almost sounds strange now when we say this. Used to be very much an IT coding function. Nowadays, this is much more accessible to the business user and the people who are actually trying to get the analysis, trying to understand and use the data. These are, the people that are now becoming more and more empowered to be able to learn platforms that are simplifying the process like Tableau and its competitors. So it's really, making sure that we work in a sort of agile manner where people who are gonna be using the data are the people that are creating the visualizations. Maybe there's an analytics team in between that host the the main expertise on these products. But, generally, we want people to be able to look at their own data, to be able to manipulate their own data, and take it out of the kind of long life cycles and waterfall life cycles that we had at the time where IT were responsible for most of that work. That shortens time to value in terms of the the dashboards and visualizations that you're creating and allows people to be more flexible. So by the time we would get these sort of charts and visualizations in the hands of the people that actually need it, the requirements may have moved on. The scope might have changed. There might be things that you're more interested in at the time, and you don't want to start that entire process again. So Tableau, we can see today that we have a very quick time to value and when we start to learn and use the tool, with with more, with more experience, that time is much, much quicker. I use Tableau probably on a daily basis even though nowadays, my job doesn't require me to. I use it because I want to explore and I want to find out more about the the different data sources that we have at Interworks to see, for instance, how my team are performing or, if there's any, similarities or trends up present down within the different services that we're offering. So this allows me to understand more about the the the environment of the landscape of my data, without, without making any sort of assumptions or, or or or having any guesswork in there. So Tableau is a data visualization platform. I promised at the start of the call that we talk about what Tableau is, what it means to different people. And it does lots of different things. And again, Tableau is an umbrella term for lots of different products. We've already talked about Tableau Desktop where we are gonna be jumping in today to analyze a lot of the data. This is the primary analysis tool I would consider. And for me, this is what Tableau means. It means Tableau Desktop. So other people that are more gonna be consuming that data and they want to see their charts and their visualizations, maybe they don't come from a data development background such as me, then they'll be more useful, or more used to thinking of Tableau as being a Tableau server or a Tableau cloud. A Tableau server is a self hosted version of, the online platform that's accessible through the browser that I mentioned previously, where you would log in to that platform. You see the data that's been you've been permissioned to see as well governed data so that you can only see things that are relevant to you, and and not more usually. And also Tableau Cloud is the same platform, but that is a SaaS version. That's something that's hosted by Tableau themselves and by Salesforce in this case. We mentioned previously Tableau public. I shared that on screen. The Tableau public is, again, very similar. It's accessed through the browser, and it's a great source of inspiration where you can go on to that platform. You can go on to the discover page of that platform, and you can see the amazing stuff the people in the community, are creating in order to inspire and show off and, and maybe even bolster their CVs with their Tableau expertise. I would say that the community element of Tableau is one of the main reasons that Tableau, I think, in my opinion, has, the success that it has had over the years. It's got a vast community and much outweighs any of the other communities of similar tools in the area from my experience. And I don't know whether it's chicken or the egg there. Could be that Tableau is a fun tool to use, which has inspired a community, or the community is inspired to go with the Tableau. I think it's probably a bit of both. The other ones on this slide, I mean, the Tableau reader and embedded analytics, we won't go into too much detail today. But feel free to to to ask or drop me a line, and, and and I can tell you more about the Tableau Reader and embedded analytics opportunities that, that are available. Preparing our data. So I did skip that past that one. We're gonna look at this in our first exercise this morning. So Tableau has lots of different methods of preparing data. The first and primary one I would suggest is usually within the Tableau Desktop product where you can combine and merge different, tables together. You can create joins or relationships. You can also create unions and kind of append fields and kinda consider multiple files as one file if you've got several that are split by time, for instance. You've also got Tableau Prep Builder. The Tableau Prep Builder is more of a workflow based tool where you might want to do a lot of different transformations to your data sequentially. It's a lovely graphical graphical interface where you can click and drag different types of tool, and you can build out your workflow by ingesting data on the left hand side and have that pipeline created so that you're outputting the final data, all clean, all transformed, all combined together, and you can output that to your Tableau cloud or Tableau server interface for your colleagues to be able to use. And you know at that point that that data is clean or was clean at the time of development. And you can even schedule that to run maybe every day if you've got more complex logic, using Tableau Prep Conductor available with the data management add on. I would usually say that Tableau Prep Builder is ninety five percent of the time not required, and you can do most if not all of that transformational key all of those capabilities are available in Tableau Desktop. It's only when you're having multiple different, sort of chronological based sort of transformations together in that kind of workflow that pipelines require. That's when Tableau prep can get you out of some sticky situations. So we're gonna focus today, as I mentioned, on Tableau Desktop because that's where most of the goodies kinda lie, in terms of functionality. There's also the ability to govern your platform. Again, we can talk about that later where if we get time, but the Tableau server, Tableau cloud environment are really the the, the places where you would lean on governance so that the right people are seeing the right data and they can do the right things with that data. Whether they can download the underlying data, that's a decision that you can make as somebody who's managing a project, for instance, within Tableau cloud. So Tableau essentials. Let's get into, the different exercises and the different concepts that we're gonna be talking about today. The first concept we're gonna be talking about is data connections. And within data connections, we're gonna explore a little bit about what you can connect to in terms of the, the the the files and servers and types of supported connectors that come out the box with Tableau. Then we're gonna move into data preparation. And what do we mean by data preparation? So having nice clean organized data is very important, and we'll share some tips and tricks, to keep in mind as, as you prep data for Tableau. We're also gonna build some charts. Thankfully, we're gonna build a series of different charts in today's session. And those charts are gonna lead us to building a dashboard within that second to last session, spoiler alert. And we're gonna build, timelines. We're gonna build bar charts. We're gonna build some maps. We're gonna have a look at across the different sort of plethora of charts that are available. And we're gonna think about the different concepts, in terms of the underlying mechanics of what we're asking Tableau to do and how it's interpreting those results. We're also gonna hopefully look at calculated fields. Now calculated fields similar to Excel in this case are ways for us to enrich our data with extra information that might not be in the underlying data source. We can logically get some more information by potentially performing little transformations to the data, difference in aggregations, and we can materialize those into our data sources so that maybe our colleagues can use those, as well. Then we're gonna build a dashboard. We're gonna think about interfaces and how we can combine multiple visualizations together in order to create a powerful sort of application where people can explore their data a little bit more. Very, very flexible plat platform here that we've, we've got with Tableau. So we're gonna be exploring exactly how we can merge all of those different concepts together before publishing them to our, to our Tableau server, Tableau cloud, infrastructure in the final section. Also, Tableau Public, as mentioned in the chat, is available to publish that too as well. See a couple of people have their hands up. Again, due to the volumes that we've got on today's session, please feel free to drop a message in the chat, and I'll try to get to them as we go. But we are in quite a tight timeline today as you can appreciate it. So first off, data connections. Let's go back to the beginning and connect to some data. Before we analyze the data, we need to tell Tableau what it is we need to analyze. There's a lot of different areas and environments that we can be pulling data from, and Tableau has a huge amount of supported native connectors that we'll dive into. So what does it involve to connect the data? Well, that really depends on where you're pulling data from. We could be connecting to data in our CRM environment, for instance, in Salesforce. We might be looking at a data source that somebody's already prepared for us, within our Tableau server or Tableau cloud environment in this one here, which would be a Tableau data source. Maybe we've got Snowflake or maybe we're focused on analyzing data from files. So we're gonna use file based connection today because we've shared those files with you in that zip file. And we're gonna jump into our Tableau desktop product. I mentioned here we do have about ninety different native connectors. I think this has increased to over a hundred, from, at the time of recording at the moment. And every new version of Tableau will have new connectors available for it because of the ever changing nature of the data landscape out there. There are new tools that are available. So Tableau creates those connectors so people can get access to the data within, those environments. So let's jump into our first exercise. And to do this, I'm gonna hop into that folder structure that we showed before, and I'm going to jump into my TWBX starter. And I'm gonna do that by double clicking on my Tableau starter TWBX. And this is gonna open up Tableau in the same way that if I click on an Excel, it's x file. Excel is gonna open, funnily enough, on the on the Windows machine. And when I open up my Tableau desktop product here, I can see that we've got that kind of prebuilt information and exercises that's, that's currently on the tab that's indicated at the bottom here, which is my sales dashboard. We're not there yet, so we've kinda jumped the gun a little bit in the the presaved version of this, of this starter workbook. What I'm gonna do, first of all, is just gonna show you, this little Tableau button at the top left. So if you have just opened Tableau Desktop by maybe doing Windows queue and jumping into Tableau Desktop or clicking something on your desktop to order open up, for instance, just your little Tableau symbol there, then you'll probably see something that looks a little bit more like the screen in front of you. You don't have to do this at the time. This is just demonstration only. So in this screen, we can see a kind of splash screen of Tableau desktop. And similarly to Excel, you're invited to potentially start a new folder or start a new file rather. You might want to jump back into some of the work that you've been working on recently, or maybe a dashboard that you, are are kinda got finished at five PM yesterday and closed down. So there is the ability to kinda hop back into work that you have recently started as well as some sample workbooks and accelerators down here to get you going on some of the more standard, data sources that you may be looking at. These ones down here are excellent if you are learning Tableau. Just to pull these apart, these do ship with Tableau, so everybody should have access to the sample workbooks down there, which will open in a new version of Tableau desktop. On the left hand side, we have a connection window. Now this is where we want to start a brand new workbook, which we'll be doing today. So in this case, we're wanting to add a new data source to Tableau to create a query or a table of data that we want to consume data from. So we're gonna be doing that very, very shortly. And on the right hand side, Tableau are gonna give you access to some of that fantastic community that we mentioned before. So they may, advise that there's a new version of Tableau available. They may give you, some suggested links to their blog articles or, you know, information about getting Tableau prep, for instance, as we discussed. So if you have some spare time in your hands, this is a great place to start in terms of looking at some new, material and, and reading from that community. So I'm gonna hop back into my workbook, and just clicking on that Tableau icon will show or hide that start page, that kind of initial splash screen. And I'm gonna go back to my tab at the bottom. Now Tableau, again, it kinda feels a little bit like Excel, from from this perspective where you have tabs across the bottom. In this case, the tabs one to seven are showing us different visualizations. These are placeholders for our exercises that we're gonna be running through today. So I'm gonna click on tab one, which is my connect to data. And here we've got the exercise on the right hand side. Again, if you don't get finished on this one, then you can come back to this later. And the solutions exercise will have prepopulated version of somebody who has, someone who's graciously completed all the exercises for us. This is the canvas. So this is where, if we have a look within that blue square, this is everything that's specific to this tab in the bottom here. This is a sort of atomic visualization that we're creating, and we can see at the moment that we don't have anything populated in this view as of yet. And what we're gonna be doing is we're gonna be taking information from the table of data that we want to have in here, and we're gonna be dragging these into our various different sections of our canvas so that we can start drawing with those numbers. But, unfortunately, at the moment, we have nothing on this left hand side. So exercise one over here is really asking us to connect the data to populate that sort of library of data on the left hand side. So to do that, I'm gonna click on connect to data. In Tableau, there's multiple ways of doing most different things that we're gonna be asking for, which is helpful if you use Tableau a lot. It can be a little bit confusing if you're gonna use a Tableau. So initially here, I can either click on that little data source symbol symbolized by that little cylinder. I can click on that that has a little plus next to it, or I can click on connect the data. These will do the exact same thing. If I go down here and I and navigate to my Microsoft Excel, this is gonna prepare Tableau for an Excel file. Sorry. This Excel file selection is gonna then filter down the browser window, depending on your operating system. In this case, I'm on Windows machine. So I'm gonna see, this new section filtered down to Excel workbooks, and I'm gonna direct Tableau to my go Tableau folder where I have my materials that we discussed earlier in the session. So you'll need to have unzipped that file first in order to get access to this Excel file. Otherwise, you'll see an error when you click on open. And what we're doing here is we're opening that Excel file within the context of our Tableau desktop environment. And this will then go and connect to that Excel file. We can see we have a connection at the top left here where Tableau has now gone and grabbed that data from the Excel file. And because we've only got one tab here in the Excel file, it's actually pulled all of that in automatically. If we had two or three different tabs in here, we would need to drag in our, our orders table into our view here, and and create that initial table within the connection. But since we've only got one, Tableau has given us that, profile of data, selected the top one hundred rows in here. We can see all those tables. If that's too all those headers of the tables rather, if that's too much information, you can collapse that one down. And we can see here that we've got that profile of data, and that should match pretty nicely with the information that we have in our, our Excel file if we were to open that up separately. So we can see here we've got, different transactions from our global superstore, which is the data source that people familiar with Tableau will have seen in the community. Again, the ships with Tableau or a version of this ships with Tableau for you to play around with some of the functionality with publicly available demonstration data that we're gonna focus on today. Each of these rows is one transaction. So we can see that there was a date where Annie Thurman or Thurman from Budapest has bought something. And in this case, it is a product called the ten x box single width. We can see the region. We can see the row ID. We can see the consumer segment, and we can see when it was shipped and some other information that we are gonna use. Interestingly, we have these, oops, we have these, numerical pieces of information at the end here such as the sales value, the amount of revenue we've got from the sale, the profit that we've made on top of that sales value, as well as how much it's cost us to ship, for instance, and the quantity of products that, Annie in this case has purchased. So hopping back into Tableau, let me just minimize that for now. I'm actually gonna click on extract for now, and I'm gonna pull that data out of the Excel file and store that locally in my workbook, which will make things just that little bit quicker for me while I'm, while while I'm demonstrating here so that it's not constantly chatting back and forth with that Excel file. I could also go in here and I could add a filter. I'm not going to save any of these filters, but just for your information, I could go in here and I could say, well, I'm only interested in a particular country, and that country is Austria, for instance. And that would then be the equivalent of filtering down that connection so that no data from that file is gonna arrive in my analytics that's not within the Austrian region. I'm gonna cancel that. So I'm not gonna I want the global information to be available here. But, if you did have a sort of requirement to only show data from a particular place, then the data source is the first place that that is then gonna take effect. We do have the availability within each of the visualizations to define more filters as we go, and I'll show you that very shortly. So now that we've defined this, I can give my data, my news of, query here, my new model a name. I'm gonna call this orders, go Tableau, twenty of November twenty twenty four. And this is gonna allow me to if I have multiple different data sources in my workbook, I can organize these so that I know exactly what data is coming from each one and make that nice and intuitive for anyone else that's coming to my analysis. So now I've clicked on my connected data, one of the tabs down there, and Tableau is now refreshing that extract. So it's pulling the data from Excel, and now I have my library on the left hand side populated. So at the top there, I can see the name of my data source. I can see that it's an extract because I have two little cylinders with an arrow pointing from one to the other. And I can see my data table on the left hand side with several blue elements at the top and green elements at the bottom. And I think if you remember one thing from today's session, it's gonna be, hopefully, the difference between blue and green in Tableau. There's some other very fundamental stuff that will come onto very, very shortly, but the difference between blue and green is very apparent when we start playing with these data points. So first of all, I'm gonna go back to my exercise, and I can see that I've been asked to drag sales to columns. And I've also been asked to drag segments to rows. And this is gonna create a very simple, very simple bar chart in tablet. We're combining a green object and a blue object. So the blue object so anything from this side of the table there's some special cases in here. But anything from this side of the table is going to give us a grouping. This is gonna be displayed if I drag them into rows and columns as labels. So if, for instance, we drag our country onto our row shelf, we are going to see each of those distinct countries listed up and down the page, and I'll come back to that shortly. And this is gonna essentially allow us to group our numerical data for each of the objects which is in that column. So in that case, it would be our different countries. We can say also sum of sales by country, by, you know, the kingdom versus USA versus Turkey and so on. Down here, we have the opposite. We have those green objects, and these green objects are our measures. So just for completeness, the blue objects are called our dimensions. So measures, conversely, dimensions are going to be aggregated. Now aggregation is one of these concepts that I've kinda come back to as, my my, sort of data career has progressed, I suppose. Initially, it's a very simple concept. We've taken, for instance, a hundred different roles because we have a hundred transactions within the United Kingdom, for instance. And we've taken, let's say, the sales figures for all of those United Kingdom transactions. We've got a hundred different numbers in there. One of our sales was twenty pounds. One of them was forty pounds. One was sixty. One was fifteen, so on, all the way up to a hundred different roles. Now aggregation is the concept of resulting in one number out of those lots of or those a hundred different rows. How do we get one number out of those hundred rows? Well, there's lots of different ways that as analysts, we can get to one number off the back of those hundred rows. The most common I would probably suggest is just adding them all up. So that would be our sum aggregation. So that's giving us a total of all of those numbers within that column. We could also divide that by the number that of of rows. So summing them all up to get into something like ten thousand pounds and dividing it by the hundred rows, is gonna give us the result of a hundred in this case. So that would be our average calculation. We could also take the smallest one, which would be the min, and we could take the maximum one as well. And there's a plethora of other different aggregations like standard deviations and that kind of thing that we can add in here. These are probably the most common. That's kind of the start of our ten here. What Tableau is gonna do is when we aggregate data in this way, and we drop this into the raw shell, for instance, this is gonna show rather than showing as a label, this is gonna show as a scale axis, and I'll show you exactly what that means shortly. So by combining both of these concepts so if I take, let's switch back to maybe a neutral color like orange. If we have our segment in rows, then we're gonna have labels of each of the segments going down the page. So I'm gonna grab my segment section here. I'm gonna drag that onto rows very shortly. This is gonna give us three different bars in the Tableau world, and these different bars could be sorted. We can see that these are my three different segments, and the green object here is then our sales figure, which is gonna be added from here onto our column shelf. So if that's not messy enough for you, let's, let's get into just demonstrating what that means. So the order that I do this in in Tableau doesn't necessarily matter too much. I can do my sales first, but let's start with segment. And I'm gonna click on this blue segment option here. If I double click on that, then I'm gonna be asked to rename it. We can rename and re alias things so that they are, maybe different in terms of the naming conventions that they are in Excel, for instance. But Tableau has pulled in the defaults from those Excel headers. I'm gonna drag segment onto rows to give those three different consumer, corporate, and home office segment values coming from my underlying data. And I can see that I have these placeholders of a, b, c in here because we're not told Tableau what else to do. We've pushed something onto our row shelf, which is by definition in terms of rows and columns. The row shelf will distribute things up and down the page, whereas the column shelf will distribute things, to the right and left of our page. So if I bring in my sales figures and I drag them onto the column shelves, we're gonna see then that our our our bar chart is complete in this case. So we have our scale axis because we've got something green on our column shelf, and I've got my labels because I've got something blue on that row shelf. We're summing up all of our sales column, and then we're grouping for each one of our segments. Now I could swap these around, and I could have my I didn't swap them around. There we go. I swapped them around now. And I can now have my segment going across the page and my row shelf with my sum of sales. So in this case, we are basically having our measure going up and down the page and the dimension going across the page, which will result in this vertical bar chart rather than horizontal bar chart. If you get it wrong and you drag the thing onto the wrong, the wrong panel, then you can just use that quick button there at the top within this toolbar. And, likewise, you can use it to sort your bar chart up here as well. So this is really giving us the where each of our marks are gonna appear within our visualization. If you can see in this summary pane at the bottom, we've got three rows by one column because we've got three unique values within our segment field and one bar in our aggregated sales fields at the top here. If I was to replace this with, say, for instance, let's go with subcategory. If I pick that up and replace my segment, I can now see that we have, seventeen different subcategories here, and I can sort that descending. So we can see that very quickly, we can start analyzing our data, slicing and dicing based on maybe adding profit up here to give us two bars. We can see the tables are doing very poorly in terms of profit, and this is giving us exactly where our marks are sitting. So in this case, we've got seventeen marks, each of them sitting at a particular sort order, based on the fact that we've got this sort order disappeared and we've added onto our subcategory. And these seventeen marks are positioned on our axis based on the logic that we've provided Tableau. Now the second part of the puzzle here, the first part being where those different marks are gonna be positioned and even the concept of what a mark is might not be obvious to begin with. That's the number of aggregated values that we have in our view. If we go over to our marks card on the left hand side here, we can see that automatically we are drawing a bar here. We're creating a bar chart. If I was to change that to be a circle, for instance, then we can see that the position of these marks, the the seventeen different objects that Tableau has drawn has not changed. Nothing about where they've they're being drawn has changed, but the style of the thing that has been drawn there has. So we have the ability to potentially make these squares or, or, again, make these bars or even do something weird like make them align, which doesn't actually make sense for the analysis that we're creating. But we're allowed to do that, and that's where the kinda creativity and flexibility of Tableau kinda comes into its own here. We're using a couple of, of interesting sort of concepts and fundamental concepts within developing within Tableau, like the rose column shelves, and then we're combining that with the properties of the mark, like making it an orange color, for instance, or maybe making these based on our different ship modes by dragging ship mode onto color. We can see how many out of the sales for each subcategory are first class or same day or second class, for instance. So we can combine all of these elements together to create the analysis nice and quickly that we're after for our organization. So I've moved past what we were supposed to be doing in exercise one. I got excited. So let's go back into our PowerPoint and discuss data preparation. Now as many analysts will know, if you have bad data, then you're probably gonna get bad analytics. We talked a little bit about prep builder, previously. And if you've got bad data, in this case, bad data is probably a bit of a loaded phrase. If you've got data that has lots of cleanup requirements to it, you've got lots of preparation that you need to be performing on that data before it's available or it's ready for analysis, then Tableau Prep Builder is a great option for that. We're not getting into Tableau Prep Builder much today, but it's worth remembering that that is a product that's available for you with your creative license. Most data, if you have it fairly well prepared, within the database that you're querying or the Excel file that you're querying, can be cleaned up further within Tableau Desktop. We can apply aliases to columns as we'll see. We can, we can transform that data to maybe strip out certain values, which we'll see very shortly, and create new columns and add calculated fields and that kind of thing to tidy things up. But major structural changes might need something like a Tableau prep to, to to to jump into. So if your data is really horrible, then it might be you need to go back to source. Tableau is not that magic wand essentially to kind of fix data that's completely broken essentially. You will probably need to figure out how you can fix, those fundamental problems. But if you just need to do some tidying up, then Tableau Desktop's a great place to do that. This is the same slide again. Let's just go through that. There we go. So data preparation. What do we mean by data preparation? In an Excel world, you might have been, you might have been, showing some data that looks like this Or, yeah. You you might have some data that is going across the page. So this data is some sort of figure, some measure. In this case, it might be a number of hours that have been spent on particular projects. In this case, within quarter one twenty twenty, quarter two, and so on. And the person in Excel is using this structure because it's easier for us to read this as humans in Excel. So going across the page, we're more likely to see trends than we would do if, for instance, we had a date column and a value column. So this is great for Excel, but in Tableau, this isn't so good. So usually wider columns aren't as good as if you can narrow those out and make them thinner and longer data sources. And this is called pivoting in the the data, preparation world. So we can take this wide data, and we can make an equivalent data source where we pivot that round so that for the first row, for project name alpha and project lead Jane, we've actually got four rows instead. So we've got long thin data rather than wider, shorter data that tends to be, green tick if you've got one same concept like amount. In this case, it could be number of hours or sales figures. It's not particularly clear, so we might want to improve that. But if you've got one measure type of value, one of those green objects that you think is being split into multiple based on the structure of the data, that is a good concept or a good use case to potentially look at pivoting that data out. And you can do this in Tableau Desktop as well. Another concept of data preparation is where your Excel file might have lots of different, nested fields in there. So it might be that you've got that merge in center, in the Excel file. There's been lots of formatting that's maybe confusing Tableau in the way that, that it's gonna now interpret the data. Is it gonna use biology as one of the field headers? Is it gonna know that Jan students and Feb students are the headers that we actually want in this case? We might need to do some further preparation in order to get to the required output. And in this case, we might be pivoting as well as potentially changing the names of those headers, and adding some calculations as we go here. So let's have a look at exactly what that means in Tableau and have a look at another example of where we can strip out some of the product names from our product name column. So we're gonna jump into exercise two. I'm gonna open again my Tableau where we were before. And in the second section here, we've got data preparation. If I just hop back into my data source and, again, there's a few ways I can do this. I can right click on this library, the query, the model of data up here, and I can click on edit data source. Or because there's only one, I can just pop back into this data source menu down here. And that's gonna take me to that familiar page that we were at before. And I kinda think of this as being before the analysis. I'm popping back in that sort of iterative process of performing that analysis, changing the data as required, and I'm going back into that connection menu. Now in this case, everything has worked pretty nicely because of that fundamental structure of the data. I don't have any sort of starter header rows up here. My data starts on row one. I don't have any merge and center formatting in here. But if I did, I could use this data interpreter button up here to clean that out. So what that would do is it would take and look for so named range or look for a table of data, and it would try and ignore some of the extra stuff that would be around it. Now this is a kind of one and done. It's only gonna allow us to perform this once, and it's a true false essentially. It's a very binary column. Have a go at cleaning this up Tableau, and we'll see what the result is, unfortunately. If it doesn't then give you the perfect output or what you were expecting, then you may have to go and duplicate that tab in Excel, for instance, and kind of do a little bit of manual cleanup before bringing it back into Tableau. But it has been very useful for me in the past, that data interpreter. Interpretations that it's made as you go. Now if we did have, for instance, a, we had q one sales, q two sales, q three sales, I could select multiple of those, and I could go in and I could select pivot as we talked about before, which is gonna give us those, those, the the the longer data rather than the wider data. And, again, if you're interested in pivoting your data in Tableau, the community is fantastic with lots of, different concepts on the community to help you out with the pivoting that you, may require, which is only available in file, file based sources. If you're connecting to the SQL database, for instance, then you can perform those pivots in the SQL table. This is much more, it's much more likely that your data is in the structure that you need when you're looking at, database tables than if, for instance, you're looking at Excel. So that's why they're available for those Excel and and file based, you know, CSVs and JSON, that kind of thing. So what we're gonna do is we're gonna take this product name, and we can see in this product name, if I hop back into my second tab here, I can see if I drag product name onto my row shelf, it's gonna warn me because there's six thousand different members, I. E. Six thousand unique values within this table. We can see they go on for quite a while. And we can see here that if we look at sorting these maybe alphabetically, I can click on that little alphabetical sort here. I can go down to these rows here, and we can see a bit of the symptom of what we're trying to fix in this exercise. So in this exercise, we've got five different rows here. We've got five different rows here of unique products. One of them is the AKKO three hole punch. Another one is the clear version, the durable version, the economy version, and the recycled version. And, really, we're not interested in all of those versions. We just want to know how many of the three whole punch have we sold. So we can see that the data is actually more granular than we need in this example, and I want to roll all of those into one figure. So what I can do here is I can go to my product name on the left hand side, and I can right click it, and I can go into transform. And transforming this is gonna allow me to either split the data. So Tableau, in this case, similar to the data interpreters, is gonna guess what thing I want to split this on. It might take the first space. It might take the first comma. It might take the first letter or number. And sometimes if it's consistent across all of the different values in the columns, it will get this right. If it's very obvious thing that you're trying to split, I tend to go with the custom split approach because then you get this little dialogue box, that is gonna allow you to be more specific. And in this case, I want to use the separator of a comma because I want to take everything up to the comma, and then I want to stop. And I want to ignore everything else, and I want to create that as a new field in my data source. Now like we talked about previously, Tableau is very focused on that democratization of data. So in other products, you might have to write a large piece of text to strip that information out to define the logic of how we want to strip that out. We're gonna look at calculated fields later, and that's where you need to go a little bit further and define the logic a little bit more explicitly. A lot of the things that you'll have to do regularly with data, Tableau has a very quick and convenient way to do that within the graphical user interface itself. In this case, we can see that there's a lovely little pop up window where we can say, give me the first value before the the the comma. I can say give me, you know, two or three resulting columns or just give me as many as you find by selecting all or take the last value rather than the first one. In this case, I just want the first, and I want to split off based on that color, comma, and then Tableau. When I click okay, it's gonna give me this little equal sign in front, which means that that has not come from the Excel file. This is a new field that I have to find as the analyst. And I can drag that into my row shelf, and I can see exactly what has been prepared here. So for my aqua three hole punch, I can see that that has accurately given me everything before the comma. And if I reorder, sample is then gonna roll those up into one value and show me that as a sort of hierarchy from one to the next, which is very, very convenient. Now I can remove my product name. If I was to push sales onto my column shelf, I want to maybe remove the difference between these four or five different rows here by removing that aggregation, I can now see that the echo three hole punch is one different one one mark here rather than five. And I can even click on the little data symbol here, go into full data, and I can see the product names are all different within that one bar that is now, shown to me. So, hopefully, that makes some sense. I've not seen any questions in the chat so far, so I think people hopefully are still with me. And we've managed to do a little bit of data preparation inside our data source, before, we we we perform some analysis. And now I can sort this to see that, that my Cisco smartphone is now the highest selling product. We could change the name here to be maybe just product, and then product name could be product model because the recycled version, for instance, is is, we don't care. We just want the one, that is the higher level in that hierarchy. And if I was to drag that over here, the product model, we could see that it wouldn't necessarily be the Cisco smartphone. We can see that the Apple smartphone wins if we want the overall product model that sold the highest. So it will be quite pertinent to your analysis to understand the level of aggregation of your data and how you can roll up and roll down, using some of the key concepts in Tableau. So that's a little bit about data preparation in Tableau. Now we're gonna move into building more and more charts. So I've, I've already told you a bit about how charts work in Tableau. You'll be thankful to hear. And really, hopefully, already, we've demonstrated that this is quite a quick process. Once you've got these core concepts, you can drag and drop, and you can change your mind, and you can hit back, and you can do lots of really good stuff in the Tableau interface. It allows us to explore the data through visualizations. And, again, I use Tableau pretty much daily to explore data sources and check on things, in in into words. We will be able to identify some insights to answer questions, and, again, that ties back to the speed. If I want to just explore and profile that data, I can do that very, very quickly so that I don't you know, I'm pretty set of brains. So I don't want to forget what I'm doing as I'm doing it. I can't get into the flow state in Tableau to look for interesting analysis. And then I can tell the story using visual analytics, which is what we're gonna come to now. We want to tell a particular story or narrative with our data and maybe even give an exploratory dashboard to somebody to be able to define their own narrative. So in this case, we're gonna define several different charts. We're gonna do this again pretty quick fire, and we're gonna develop these charts in Tableau Desktop. And before we do that, we're just gonna square away a couple of different housekeeping concepts, many of which we've already discussed. So the first is metadata. Metadata is data about data as the term meta is, is thrown around quite a lot nowadays. This could include things at the column level or the field level within your data. So when we connected to that superstore data set, we had things like sales or city or order date. And these are all very different types of metadata. And there are different well, different data types as Tableau calls them. So one of them, the order date is a date. The sales or revenue field is a number that's a decimalized number, for instance. So there's information that is stored about the underlying data. So it's data about data that is gonna be very useful when we define our charts. So if we've got a date field, then Tableau by default is gonna draw a line chart with those dates. If we've got a geographical role, which we can assign based on city or country, for instance, or state name, then Tableau's already gonna understand that these are cities, countries, and states, and it's gonna allow us to map our latitudes and longitudes automatically. The default aggregation for profit, for instance, might be a sum, but for a discount, we might not want to sum up all of our five percent and ten percent discounts that we've given to our clients. We might want to take the average discount. So we can store that information on the column itself so that, again, our colleagues won't make the mistake of summing up discounts, which is an obvious one, but sometimes they're less obvious. It's a very useful thing to store in your data source. We've also changed a few field names already. So making sure that those field names are intuitive and are not gonna be misinterpreted by people can be a very useful way of maybe avoiding some of the more techy engineering terms that might be coming from the database and cleaning that up before it goes in front of people who might be then using it. And then there's all the descriptive information like comments that we might want to be using, in our metadata as well. We've talked about blue fields versus green, but in case I forgot something, let's just go through that very, very quickly and disambiguate the two. So blue is about qualitative. So it discusses the quality or the the the the what of the data. So this could be a city name or a customer name or even potentially a date. It discusses something that is not quantitative. It's not a number that we're trying to aggregate. It also provides us buckets of data. So there may be lots of transactions that have happened in Edinburgh as a city we're in for, for instance. So we might want to sum up all of the values that are in Edinburgh for each city, for instance. So they give us buckets. They're either or. The sale either took place in Edinburgh or Glasgow or London or Sheffield. So it doesn't exist in a continuum in this case like we would for a number. There's not a sort of logical, sort of pattern there. Okay. Geography is maybe the the the wrong example to use there. But if it's a table that's being sold or a, a pencil that's being sold, then they are very distinct objects. And when we drag them into the view, then we have headers, labels, and text, that show on our rows and columns depending on where we've dropped them into, rather than the scale axis that is gonna be shown if we are using continuous measure, for instance. So in this case, we have quantitative information like profit or or or revenue or sales. We want to aggregate them like we discussed before. Minimum, maximum, sum is most, probably the most common and average. And there is a range between these, which is why the bar chart is so powerful because we can see in one dimension the outliers there. We can see the trends. We can see how big a jump there is from the second ranked product to the third ranked product. So there is a range between these, and that allows us to kinda construct visual analysis. When we drag these into the view, we get a scale, we get an access, or we get a timeline. So this is a kind of cartoon version of our, of our rows and columns analysis that we did before. So we've got our continuous access on the left hand side and our discrete headers on the bottom, giving us those sort of Cartesian coordinates for anyone in the kind of maths world. That gives us the where, but the what that is being developed or shown in in front of the users is gonna be developed using the marks card that we showed before. This is where we change the bar chart to be a circle chart, for instance. And the map here is gonna give us something slightly different, which we're gonna come up to very shortly. This allows us to plot rules for the color that's being used for the bar. It could be based on the, ship mode that we've shown before. It could be based on the sum of profit. So we want the higher profit values to be a deeper blue and the negative profit to be an orange. We'll come back to that later. You can select different values in here. You can define different rules, and we'll see these as we go today. We can even define the level of detail which we'll be showing when we drill into our states and cities within our map very shortly. And we can even add things to that hover over tool tip as we go, so that more information is available when people kinda click and hover. There's some fundamental concepts within designing good visual analysis that I think is probably worth touching on. The first is defining a good chart type. If we think of data over time, then hopefully everybody's thinking of a timeline. There's kind of a joke that all visual analytics should be bar charts because they're very, very effective. And there's hundreds of different types of bar charts that we can create within Tableau by kind of choosing and defining our style and where we put the labels and all those sorts of things. So defining an intuitive chart type for the visual analytics is, a central key component. So making sure we're not maybe reinventing the wheel every time. We've got something that is gonna be familiar to the end user and shows and highlights the information that we think is most pertinent to the view. Level of detail. If we want to go down to the product's name level or the product model level, the more granular one, then fantastic. But maybe we want to roll that up so that we only see the product level. Maybe it's at the subcategory level and we want to show the more high level numbers Or maybe it's even at the category level, which is gonna roll up to three maybe different categories within the organization. We can compare those two against each other. So understanding the granularity of the data and what's most appropriate for the users leads to better and more intuitive analytics, as I've said. Data to ink ratio is kind of a even few concept if anyone's interested in that where we don't want to clutter everything up with lots of lots of scroll bars potentially or lots of, grid lines or complicated access with lots of kind of dots and lines everywhere. I tend to strip everything off so that everything looks as kinda clean as possible, and then maybe adding in some column banding or row banding as appropriate so that it helps people, leverage the data by controlling people's eye line and that kind of thing. So making sure that we're not adding too much clutter. Tableau does quite a good job of this, but sometimes it does add extra stuff so we can go into the formatting menu and we can clear that up. Color, American spelling here, is, something that we want to be cognizant as well. We might want to use our own organization's branding colors. We may want to avoid things like green and red due to color blindness, and Tableau layers in a lot of that accessibility by default. You can select green and red, but it doesn't give you that by default. It does suggest other color palettes. And then layout and user interface. So layout, we want to prioritize that top left space, which again we'll come back to shortly, so that people looking at this dashboard can understand what the dashboard's about by by leveraging that important real estate on screen to do, to show the most informative information and the most sort of analysis defining information up there before having maybe those more subtle use cases surrounding that. And then user interface, if you click on something, does the user know that that's then gonna filter it down? We're gonna show that as we, go throughout the next, few, exercises. So the next exercise we're gonna be, showing whether or not our superstores are profitable, and we're gonna show that globally. So let's hop back into our demonstrative workbook here and go into number three, which is our map. So in this section, we want to be using geographical information over here, and we can see that there's this little circle, with lines through it like this, which is Tableau's symbol for a globe. And we've got this or Tableau's got this automatically from the fact that this is called country. So this, is a country, so we can go in and see that this has been defined geographically as a country or region, which allows us to double click on this. And then Tableau is going to plot a map by default. And because Tableau has an underlying repository of different country names and where they exist on the globe, it can generate a latitude and longitude and distribute those up and down the page for latitude, intuitively again, and longitude going across or around the globe as it spins on its axis. So, our longitude and latitude allows us to essentially define the scatter plot, and we're disaggregating that by the country field that we have in here. The show me panel up here will give you some sort of cheats here if you do have, the requirement to kinda pull in some, default or or preset visualizations. And because we've got our country field, which is a geographically map field, we can choose whether we want this as a filled map or as a circle plot, and Tableau will then alter that marks card, if you can see on the left hand side there, depending on which one we select. We can do this manually by going in and selecting map instead, in which case, we just get a nice blue filled map because we haven't told Tableau how to color this in. So I'm gonna change this back to be circles. The problem with filled maps are a slight issue is that we're gonna be prioritizing the real estate on the screen based on the size of the country. It's gonna be very difficult to ignore. Your Canadas and Russia's, even Australia's. Those bigger countries are gonna get a lot of priority, to the eye line. And in this case, they obviously, the the centroid of those countries will be further away, so they're still gonna get a bit, but it's gonna mitigate that slightly. I might want to then define the rest of the properties of these marks. So first of all, I might want to say I want a big circle for a country that has sold a lot of, revenue essentially. It made a lot of revenue from those products. So I can drag sales, and I can drop that onto the circle's shelf. So here, I can see we've got large circles for countries that have sold a lot, almost a million in Australia, and maybe in Democratic Republic of Congo, we've got about a tenth of that. So I can maybe choose to increase the overall size of those circles like this, so we can see those bigger ones and even those smaller ones. And then I might want to enrich that even further by adding in my profit onto the color card there, and I can see that a dark blue is gonna be higher profit, and a large dark blue is gonna be high profit, high sales. That's the golden, the golden ratio. That's what we want there. And we can see that Turkey is a large circle. We're selling a lot, but we are making a big loss because maybe that's a new market for us. We don't have have the supplier set up or maybe we're spending a bit more on marketing or something like that to penetrate into that region. So now we might want to do what I said before. I'm going to clean this up. There's lots going on in this map. We've got these state borders, the country borders, state over here we can see. So if I go into, background layers over on this map, then right clicking, I can go into this menu on the left hand side, and I can say, I don't necessarily need those state and province borders, so I can remove that. Maybe I want to remove the terrain. So I'm trying to get rid of some of the mountainous regions over there. Because they're not important for my analysis. The land cover, I can probably get rid of, so everything's kinda gray scale. Maybe even the coastline, I can get rid of. And now we have a much sort of cleaner map, which I can choose to maybe color in the sea with a different map style. Maybe I want this to be a dark map. Probably not, although I do like a dark color palette. Maybe we can add in a more cartoony version of that or even a satellite image of that map depending on what we want. I'm just gonna go with a light map at the moment. Now we can see there's a little plus button here, and this indicates that we've got a hierarchy in this data. We can see here Tableau has created that geographical hierarchy where I've got my country, my state, and then finally a city in there as well. So I can click on that little plus, and I can drill into maybe the state level, or maybe I can go even further and drill into the city level. Now some of these aren't known, and I'm gonna ignore that for now, but there are several ways of maybe adding in the latitude and longitude, if you wanted to drill into those eighty one unknown cities where Tableau hasn't managed to find the latitude and longitude. I can increase the size even more. And we can see there's a little bit of a problem in the city level where we might have those larger circles, which are they do tend to be at the bottom, which is useful. But we can see that these are kind of obscured by some of the other ones. So I tend to go in and just change that color palette a little bit to maybe add in some, transparency to those circles, maybe even adding a little sort of dark border around them as well so that you can see uniquely, I mean, where each one of those circles exists for each one of those cities. Now I might want to roll up to the state level. That seems kind of more useful probably. And now I might want to add in a market filter as instructed in the question here. So my market filter is gonna go on that filter shelf. And what this is gonna do is it's gonna allow me to apply a sort of filter in between the data source, so that Excel file. And just on this sheet, potentially, I might want to filter this down to be just the EMEA market. So if I drag that into the view, I can say I just want the EMEA market, and that's then gonna filter my map down to just show those values. I could change that to be the Latin American market, and now that's gonna zoom into that appropriate area on the map. I could even include all and click on okay. I still have a filter definition here, but I can maybe right click, show that filter, and now my end user can play around with that themselves to zoom into individual markets as we go. I can tweak this by clicking on the little transformation drop down arrow, and I can say maybe I want this to be a single value drop down. So I want either all or APAC or EMEA. I don't want people to combine two of these together. If I did, I could use the multiple value drop down, and they can then combine them together. So I can choose again that user interface, how I want this to display for my users. And then I can even make this available to filter across all of the different date, visualizations using this data source. So anything that's connected to my Excel file, I want this filter to cascade across all of them. So APAC and EMEA, for instance. So I'm gonna click on all for now, and we're gonna come back to that later. Now line charts. So line charts here are gonna allow us to define, a timeline of our data. Tableau does this slightly differently for from some other ways that people might be used to integrating or or, analyzing data using dates. So I'm gonna drag order date onto my column shelf, which is gonna distribute those dates of the order across the screen. Table's quite lazy here. It's just gonna roll that up to the yearly level. But I could right click, and I can see there's two different types of dates that I can disaggregate the data based on. The first one is a bucket. So here, we've got maybe the day number, so that'd be one to thirty one. We've got the month, May, for instance. And this is gonna roll up all of the data points that occurred in, say, for instance, January or February. It's gonna roll them all up into one mark. So we're only ever gonna see twelve of them in there, and it doesn't care what year that they've been put into. If I go down to the other month, the month down here, you can see in the on the the right hand side, there's a really convenient and useful little, helper function here that will show you an example like May twenty fifteen. Now this is a date value rather than a date part. So this is gonna show us May twenty fifteen, and all of the days within that are gonna be rolled up into that one month. So if I want that timeline, that's a better option than rolling up all of the maze together. But you could see how maybe that would be useful to apply if you want only the first day of the month, for instance. You could roll that up where day equals one, for instance. And you can get very nice seasonal analysis by showing maybe the different years or rows versus the months within those years, and and show, how you've progressed in in sort of visual, in a visual fashion across all of those different years in terms of sales. So now I've got my little placeholders. I can see here I've got, forty eight, which is I've got four complete years in my underlying data. So four times twelve is forty eight. And I might want to bring my sales onto my row shelf, and that's gonna then allow me to distribute that up and down the page in my scale axis. And I can see how my data has sort of improved over time. And I can even add something like a label by holding control, dragging that onto text. Maybe that's a little bit too busy for me. So I can go into label, and I can say maybe you only want to show the line ends. Maybe I only want to show the end line there instead. So this is now a nice, a nice label that we've got in there, and I might want to format that, to be a currency. Maybe I want to make that in US dollars and roll that up to the thousands. So we can tweak the definition from Tableau, and I can even go in and format that even more to maybe hide those grid lines that I said before. Again, feel free to play around with this format pane. So this is a very convenient, nice, easy way of drawing, drawing a line chart in Tableau. I can also go into this analytics menu and maybe even add a trend line or a forecast here. So I'm just gonna drag a trend line in there so that when I do use this filter, I might want to compare, how those different trends, compare with each other. So we can see that most of them are going in the right direction, which is nice. Good. Now we're gonna go back to the simple bar chart, and we're gonna use this in our analysis shortly. And we're gonna touch back to the concepts that we talked about previously. In this case, I'm gonna drag in my subcategory onto my row shelf because I want this to be a vertical bar chart. I'm gonna drag sales onto columns again. I'm gonna sort that descending. I might also want to add category above this. So each one of these subcategories exist in one category. So by dragging category and nesting that, I'm now gonna have a way of grouping up those so I can see individually what these look like. And now I also want to add profit onto my color shelf so that I can see again similarly to the visualization that we have on the map. I might want to look at my, might want to look at profit as a color and, and my dark blue as a, high profit and orange as low profit and the size length of those bars are, are going across the page so I can see again where we've made loads of sales. Sorry. I just realized that my chat window hadn't scrolled down. So, yeah, thanks, Vicky, for jumping in on several of those questions. Company templates. Yes. These are available. Absolutely. If you have a company template, it should be available. If it if you've got your preferences file set up, then you should see that company template down here. If you've got a discrete one, like for instance, if I want segment on here instead, then I can go and I can see one that I think I created very recently. My colors. Yep. So that's one that I added myself. And then, that is available. Lots of information available about that, online using the TPS file. I can, demonstrate if we have time at the end. How do you ensure data connections are updated regularly? If I have data that needs to be imported daily or weekly, how do I set that up? Also export reports, for page searches, how do I export PDF view of a dashboard monthly with refreshed data, custom date ranges? Great. Great question. Okay. So, when we go to publish this at the end today, I'll show you how so I've created this as an extract. If I didn't want this as an extract, maybe this is a Snowflake or a or a SQL connection, for instance. Then if I wasn't using an extract, we would see that this is just shown as a little cylinder, and that cylinder basically means a live connection. Whenever I do anything, I'm gonna be chatting to that data source, whether it's Snowflake or SQL, for instance. If I choose this to be an extract, then, I'm gonna create that as an extract now. There we go. When I go to publish this, I can see how often I want that extract to refresh. So it could be I want that to refresh daily or monthly, or maybe at the start of every week on a Monday morning. That's a good way of controlling how often the queries are getting sent to the database, but Tableau does offer live connections there. So if I didn't have this as an extract that I published, every time somebody accesses that dashboard, that query is running on the database. So it is live refreshing, depending on caching and that kind of thing. So hopefully that answers the first question, Britney. The second question, how do I export reports, for page refresh page searches? How do you export PDF view of a dashboard monthly with, refreshed data and custom date ranges? So, again, when we hop into the server, you can, choose to subscribe to views, and that will send an email with, with that image of the data at the time they refresh. So you can say, on Monday morning, email my boss with this dashboard, and you can just put your boss's email address or your own email address in there, and they will get that, visualization with the refresh data. They can click on it. They can go into, they can go into the dashboard, and see the the more granular information and use filters and that kind of thing. In Tableau Desktop, you can print a PDF, which is gonna give you lots of information or lots of different things that you can tweak here as well as exporting to PowerPoint is available here as well. So you can play around with that functionality as well as, you know, PNGs, but I tend to just use screen grabs to be honest with you. If you've got something more complicated that you want to do, and you want to maybe export and send to multiple people or create something a bit more kind of bespoke, then there's lots of API endpoints of Tableau that you can use a pack token, and you can write a little script that says pull this into this folder, maybe pull the data as well, send that as an attachment altogether, and maybe do something a bit custom. But let us know if you want to start playing around with that, and we can we're more than happy to help you out. Great. Sorry for anyone else's questions to me. I I've missed. I will go back, and I will, I will, I'll have a look through the the chat questions, later to make sure everything has been answered. Can you use conditional formatting with a data table for for certain columns? For example, showing a, data bar for product revenue, the higher the revenue, the larger the bar. Yeah. So, I mean, that's kind of what we're doing here. The larger revenue, the, the the larger the bar. So for a data table, then correct me, Lars, if I'm misunderstanding. For data table, we may want to do something like a bit more tabular. Like, like, maybe we want to look at this by year, and we set up our structure like this, and maybe I want that sales information. If I change that number format, it's gonna annoy me. Let's do that in thousands and give a dollar. Let's drag that onto our pane, and we can then see very quickly and easily fitting that to the entire view. I can then format that to look much more like table, and I can then tweak that information. If I want to then conditionally format this information, then I want to then define what that format is. If I drag and control drag sales of the color, then I can see that the the labels are now colored by sales, but I can't really see that one very easily. So maybe I want to change that to be a square, which is gonna control that background color, instead. And then I can kind of bring that out and maybe even add a border around all of them so that they look a little bit neater. I can change that to be a black border. That would annoy me. But you can do that, and you can even change that to be maybe profit instead. So you're highlighting different pieces of information, as you go. Is there any limit of broadcasting members? Not sure, Garmil, what broadcasting member is. That's more information on that one. And, do you mean who can access the dashboard? That's controlled by the number of licenses that you procure from tablet. So every viewer comes at a certain cost, and every creator license that has, desktop also comes at a certain cost. So I'm gonna add some more information about that. How many people can receive the dashboard? Okay. Yeah. So from that subscription conversation we had previously, receiving the dashboards, you need to be a member of the Tableau server in order to have your email address attached in there. But to be honest, you could just send it to a central mailbox and then forward it on to there from there. So generally, people that need to view the dashboard, you would be a viewer on the Tableau server or Tableau cloud. Hopefully, that answers the question, but I'll put my email in the chat actually, if there's any more, inter works dot co dot u k. Please feel free to reach out if you have any more questions. Appreciate we're just getting into the final fifteen. So I want to make sure we do cover all the topics today, and appreciate the questions. So calculated fields. We're gonna just touch on calculated fields, so I think they're very important, in this case. So if we go back actually, let's use this worksheet that we, that we prepared there for the conditional formatting. We've got sales and we've got profit. We're not showing them both at the same time, so it's a little bit confusing, what is being shown where. I may want to look at something that shows me a percentages a percentage of my sales versus profit. In this case, maybe if I've made, a hundred and forty seven k, in sales of tables, and I've got a negative profit, so it's gonna give me a negative percentage. But if I have made, eleven thousand profit, negative eleven thousand, then I might want to divide my profit by my sales in order to give me that percentage. If I've made a hundred pounds profit and I've, and I've received a thousand pounds of sales, that's a ten percent profit margin. So that data is not in my underlying repository, my Excel file. So by right clicking and creating a calculated field, I can define that logic based on some very simple syntax. So I will create a calculated field like that, and I'm gonna call this my profit margin. And I can even add, some comments in here to say percentage of profits divided by sales. And then I can start typing my syntax. So in this case, I want to sum up all of my profits. So I can just start typing sum, and then I'm gonna add in my profit in there. And that's gonna then reference my profit figure down here. And then I'm gonna close that bracket, and I'm gonna divide that by maybe my sum of sales. So I can just click and drag that in from here. So I've got my sum of profit divided by my sum of sales. I'm gonna click on okay. That's gonna then add a new column into my underlying data. I can change this again to update the, percentage number format so I'm not getting a decimal. And then I can just drag that onto my text, and I can see where I have large profit, based on the overall profit and color, but I can then move that over to be using that profit margin as well. So I can now see that percentage, which wasn't, again, it wasn't available in my underlying, my underlying data source. So logically, I can combine fields together very, very easily in this case. So in the, for time's sake, I'm gonna skip the sales KPI. I would recommend people go through this one. I'm basically just making very big numbers, but, in fact, I'll do it very, very quickly, while talking, which might be a bit of a stretch. Let's just hide that one. So I've got placeholders for all of these. I'm gonna put my sales figures in there. I'm gonna put my profit margin figures in there. I'm also gonna have my segment name in there as well. And then I'm just gonna update my, the size of these. Let's make this nice and big. And let's bring this to the bottom. Sort of rich text editor on the, the the the text card here. And I'm gonna just make sure that that's obvious. Sales figures and my margin over here. Yep. So we can tweak this as much as we want. It's not gonna be particularly beautiful, but we spend a bit more time here and make that nice if we had a bit more time. Again, today's session is a very rapid one. Doesn't want to take that. Let's unload those. Doesn't want to do is that gonna work? Close enough to rock and roll. Okay. So now we've got these kinda high level headlines that we can add more information about and people can use these to click in. I might want to turn my tooltips off for this one so that when we hover over, we don't see anything because we just want people to click on these and drill into those different consumer elements. Maybe move that down to be twenty four instead. Okay. So now let's get into creating a dashboard. And I'm actually not going to use this visualization, this this prebuilt, version here. I'm gonna start a brand new dashboard here. I'm gonna call this my Tableau sales overview. So this is where all of those different charts, the the kind of rubber the rubber hits the road on those charts. You see there's a couple more questions in there. I'll stay around at the end and answer some of those questions. I appreciate people probably have to hop off in ten minutes. So in this case, I want to bring in those visualizations that are seen on the top left. We can notice that we don't have a data source on the, on the left hand side anymore. We've got those constituent charts instead, like our map and our line chart. So I'm gonna start bringing them into my visualization to construct an overall dashboard and add some visualization to that as well. So I'm gonna start off by showing the dashboard title. Then that gives me an object here surrounded by a gray border when I click on it, and I can double click on that one, and I can format that to push it into the middle, maybe make it bold and give it a bit more space. So now I might want to think about what size does this dashboard need to be? What size of the screens in my organization that people are gonna look at this on? And I can maybe make this a little bit wider or a bit shorter, people are more widescreen. And I can define the sort of boundaries of my visualization there. It seems about right. And now I want to bring in what's the most important sort of concept of this visualization. I would argue this, maybe the map. This is kind of our global sales dashboard. Can even change this to be global sales. Let's just call it global sales overview. Name check or Tableau on this. Global sales overview. And in this case, maybe I want to bring in that map first. So I can click and drag, and I can bring that into the view. I'm a big fan of using vertical and horizontal containers that kind of act like dev blocks in a an HTML world where where I might want to bring in a vertical container here so that I can then position things a little bit better under the hood. We've got this layout pane here where I can define backgrounds and borders and paddings and all that sort of good stuff, and I use that quite regularly, to be honest. So I'm gonna bring in my map. I'm gonna drop that into the view. I'm gonna right click, and I'm gonna hide that map title because it just says map, which is useless. Size of the circles and the profit. I'm actually gonna cheat, and I'm going to remove those even though that doesn't really help my visualization. If I bring back that title, I can then say, this is my global overview or something similar. And I can then say, orange, orange is low profit. Blue is high profit. Size indicates sales revenue. So I can add a bit more information here. I can move that into the middle. I can make this nice and small. And this is where the kind of styling of your dashboard can be very sort of easily tweaked, and you can kinda guide the user to see more, out of the information that you've got in front of them. So that looks okay. We might change some of the titles because that's a bit of a duplication, but let's move past that for now. So now I might want to put in here maybe my line chart. And because I have that vertical container, I can drop my line chart underneath. Sales overtime is pretty obvious. We can see the order date there, so I don't need to add anything there. I might want to change this so that my line's a bit thinner. So I can't do that from here. So I need to go back into the visualization by clicking that go to and then just reduce the size here so that I then have a nice thin line. And do I need month of order date? Probably should have it in there, but I'm gonna reduce it because, again, I want that to be, nice and kinda subtle. So that looks a little bit better. Okay. And then maybe I want to add in some, maybe I want to add in my bar chart. So I could drag this bar chart in here. Don't like how that goes to the top. So let's add in a horizontal container, down here. Drag in that bar chart. Drag in that global summary, and then maybe give this a little bit more more space. So that's looking a bit better. I would spend a bit more time tweak that structure if I had it. Maybe hiding that title so that goes to the top. Okay. Again, we've got that legend here, so that works. And I can then make this floating instead and remove that underlying container so I get a bit more space to kinda play around on this. And we might want to even kinda tweak the banding or the padding up here so that they start at the same place. Maybe if I add fifty or maybe fifty five, I can kinda start them at the same sort of place. I could pull this out as text fields or maybe even just remove that and add that in there. So that's a bit better. We're getting there. So I can kinda tweak the visualization as I go. And now the final thing I wanted to kinda talk about here, is the ability to add in a bit of user activity. So if I want to kinda look at how tables are doing, they're not doing particularly well, but I want to see when and where the tables have not done particularly well. I want to kind of do a bit more diagnostic analysis here. So I can click on this use as filter button. And then this is now gonna mean that every time I click on something in this chart, it's gonna filter all of the other charts based on the information in the mark that I have clicked. So only the data from this one mark is gonna get into all of the other visualizations. So clicking on that, we can now see very quickly and easily the hot spots around the globe of where things have not sold well in tables, where they might actually be doing okay. So maybe we copy the, the English model in, in Indonesia, for instance, and see get those two sales leads talking to each other or something like that. Maybe we want to do the same on the map. So if I go in here and I select particular region, how is that region doing compared to APAC, for instance? Remember, we do still have this filter. So I can zoom into APAC and then say, Queensland and New South Wales seems to be doing well. What's the biggest impacting products in New South Wales? How's New Zealand doing? Not too bad at all. So this allows us to really dynamically analyze our data in a very simple interface, and we've barely spent any time on actually tweaking and making this look, exactly as we want it to. Again, I would probably go in, and I would edit some of these, grid lines and make sure that everything looks exactly as I want it to look. Maybe remove some of those and add in, again, a little bit of banding so that we can see, that b b is a little bit more subtle now. Yeah. Not too bad. And that trend line in our in our line chart is really helping there to show that actually, even though tables are kinda lacking in sales, we are actually seeing that sales figure go up. Be interesting. We could probably change that to be profit instead or have an alternative view on that to show the profit if we had a bit more time to play around. So now that we're somewhat happy with the dashboard, bit of pedantic changing here on some of this stuff. Maybe I want to add in a little background, color to that. Yeah. That's maybe a little bit better. Move that down slightly. Okay. I could spend hours on this, but I won't. I'll save you save you the pain. Now that I'm happy with, everything that we've got here, I can now I can now publish this to my Tableau cloud or Tableau server environment. In this case, my Tableau server, or my Tableau cloud environment is actually posted in the US, from our US team. But now that I've logged in, so I could, sign in to another server, which Tableau will then open up. You know, what URL do you want to sign into, what's your credentials, do you have access to be able to publish here, all that sort of good stuff. And now because I am signed into that, I can now publish this workbook. So I can click on publish. And like the question asked before, this is where I would see if this was connected up to something other than a file. I would see the ability to add a refresh extract, schedule. So it might be every Monday morning. It might be every morning at five AM before everybody gets into work. So I can add that in there. I can also, add this to one of the several projects that we've got in here. I'm gonna add this to Max g. That's me. I'm gonna add that to Max g demo as a project. So I know all the people who have got access to that project, and I know that this is the right place for it logically in that, Tableau cloud infrastructure. And now I want to call this global sales overview or something like that. Maybe I wanted to match this, but that's fine. And I would probably only want to publish, the dashboards. I don't need to publish these underlying worksheets because they will exist in different URLs. That would be confusing. And I don't need to publish that sale dashboard. I just want this global sales, which Tableau's conveniently shown me on the left hand side. So that's good. I can include the external file if I want. Yeah. Let's let's let's not include that for now. It will be packaged as part of the extract, so that's fine. And I can edit the permissions if I have access to on that project as my project. So I can say, maybe Vicky gets access to this. So now when I click on publish, that's now uploading my definition of the dashboard with the data onto that cloud environment. And, hopefully, that will pop open in the browser. We can see here, and we can see a little bit of Tableau Cloud in action before we wrap for today. So this is our Tableau environment. Again, the URL there is the Tableau URL for Tableau Cloud. You've got Tableau Server, then there's maybe some DNS, information that you can add there to make it a little bit like analytics dot acme dot com. So in this case, we've got our global sales overview. It's taken me right to that. But before I get into that, what you will see when you first log in to your Tableau cloud or Tableau server, environment is probably this explore page or this home page where you can see some of the analysis that people have created, over, over the lifespan of this, of of this platform. So I'm gonna get Vango into explore, and this gives you all of the different projects which I'm allowed to see as a a I think I'm an admin on this account. But you might see a much more reduced, sort of maybe department specific view of your dashboards. And I can go into my demo project. I can see lots of different information here that I've published. Not lost, but some. And then I can click on that global sales, and I can send this link out to people that might be, using the analysis that I've created for them. And I can see here that we've got that dashboard, and all of that functionality that I baked into the dashboard is available for me to combine different filters together and explore that underlying data. Because I have the, the permissions to do that, I could maybe go into the underlying data, and I can maybe export that if I wanted to. I can download that to CSV, but you don't have to allow people to do that. There's also the ability to go into the watch and add in subscriptions as we heard about before. So if I want to add in a subscription, I could just put, again, here, and she's not. I'm gonna put Alastair Young. I can say subscribe now to this view and maybe, maybe the yeah. Just this view. Maybe send this every four or five minutes. No. I'm not gonna do that too. But I can select a schedule here where I send this refreshed data again every Monday morning. For instance, you'll get an email, that will send this data out with an option of enjoy that data that I've subscribed you to. I'm not gonna add that just for his sake, but if I was, I can also add in a comment here that says maybe, Alastair, I've subscribed to you to review and me later. And I can then just add that, and you'll get a notification that, that says he's been subscribed or have a look at this particular interest in data point and so on and so forth. You can also download from here, and you can even see information about the underlying data with the data guide to see what calculated fields have been used and a bit of profile about the data itself. Now I appreciate that we are absolutely out of time now. I saved you the pain of going through all of these PowerPoint slides. There's lots more, stuff that we could have talked about today, but, again, it's a ninety minute session. And, hopefully, that gives you a useful overview of the Tableau Desktop product. Please feel free to reach out to me, see that there are, there are some extra questions in there. So I will, I'll go and, respond to to several of these via emails. So expect an email from me if you did have a question in the chat. Thanks very much for your time and attention today. Very much appreciate that. And, hopefully, that gets you kicked off with Tableau Desktop.