Movie previews, elevator pitches and…Tableau visualizations. They all give you a small window to capture audience attention. And, you can as easily lose your audience by overloading them with info as by underwhelming them. Tableau makes it incredibly easy to create exploratory dashboards, but not all dashboards are created equal. Why do some really engage users while others fall flat and get no traction?
In this webinar recording, we look at the specific characteristics of well-designed dashboards. Calling upon our years of experience in Tableau development, we share best practices and offer concrete, real-life dashboard design examples. You will be able to apply the concepts learned in this webinar to your own visualizations, understanding what design elements to consider and what to avoid.
Tableau Solutions Architect
Kyle is a Tableau Solutions Architect at Senturus and has over 20 years of experience in data analytics, with 11 of those years working with Tableau. Kyle is focused on helping clients use Tableau in novel and efficient ways by helping them see and interact with their data in ways they hadn’t before.Read more
Greetings and welcome to today’s installment of the Senturus is Knowledge Series. Today’s topic is Tableau Dashboards that captivate and communicate. Well, discuss the characteristics of well-designed interactive dashboards. A few housekeeping items before we get going. Please feel free to use the GoToWebinar control panel to make the session more interactive. We have everybody muted by default. But you can ask questions and we encourage you to do so via the Questions pane that you can see. You can minimize or restore the control panel using the orange arrow that you’ll also find on the Control Panel. If we do try to answer the questions live during the webinar.
But time, if time prevents us or for some reason we’re not able to do it, we will fill out the Q and A and post that to the senturus’s.com website where you will also be able to find the presentation. Slide deck and a recording of today’s webinar, so at Senturus…
senturus.com/ resources, you can go out there and browse our full library of webinars we’ve presented historically, as well as two days. And you can search for things by title, popularity, date, et cetera, et cetera. We encourage you to go out there and take advantage of that.
Today’s agenda, we will do some brief introductions, and then I’ll hand it over to our featured speaker to cover a definition of interactive dashboards, the use of context color, understanding your audience, use of parameters.
And then, for those of you who aren’t familiar with us, we’ll go through a Brief Senturus’s overview, provide some great additional resources for upcoming events and other things that we offer, and make sure you stick around for the end where we will cover Q and A live with our presenter.
So, our presenters today, I’m pleased to be joined by Kyle Bailey, who is a Tableau Solution Architect here with Senturus. Kyle has over 20 years of experience in data analytics with 11 of those years working with Tableau, Kyle’s focused on helping clients use Tableau and novel and efficient ways by helping them see and interact with their data in ways they hadn’t before.
My name is Mike Coyne, our Practice Area Director here and Solutions, Architect, and Product Manager as well here at…. And I’m pleased to be your emcee today.
So, we like to get a feel for where our audience is, what our audience is doing with Tableau, And, I’m going to post a poll here asking you from where are you sourcing your data? And the three options. Are, are you using extracts or Excel spreadsheets? Are you pulling directly from the data source from the source, the database, or are using some kind of centralized model, like SQL Server Analysis services, to control your metadata.
So we’ll give you a few seconds to respond to that we’re at about 50% there, get your answers in, then I’ll share the results with you all.
OK, so are up around 80% here. I’m going to close it off and share the results with you all. So, you can see about half to two thirds are going directly against the data source. But a full 40% are using data extracts or Excel, Excel, spreadsheets. That’s a, that’s a pretty big number. I’d be curious what the split out is between extracts and Excel. I’m going to bet that a good chunk of that extracts and then 6% of you are using some form of centralized model, so thanks for sharing your insights there. Hopefully, you find this stuff interesting. I, this go back to the presentation, and now I’m going to hand it over to our featured speaker, Kyle, who is going to dive into the characteristics of well design, interactive dashboards. Kyle, I’m going to make you the presenter.
And the floor is yours.
Hey, everyone. Good to be with you this morning.
Expect a friend of mine a couple of weeks ago, and I told him I was putting this presentation together, and I said, I was doing a presentation on that, creating well designed interactive dashboards, characteristics of those dashboards. He said, isn’t that just sort of, like, obvious, what makes up a well-designed dashboard?
And basically, it should efficiently communicate data, but it is kind of obvious, and I think that a lot of what I’ll be talking about today is, is something that many of you might think is obvious, or it’s stuff you knew or didn’t know, you know, new, and it’s, you know, it’s just good to kind of go through it and review it, And, and, uh, and keep this stuff top of mind. So, just a quick overview of kind of what we’ll be covering.
I’ll go into a definition of what I think an interactive dashboard is.
What the specific characteristics of those are?
Then talk a little bit about why we, we visualize information or visualize data, and this will be, you know, what, we’ll get into.
Some thoughts on chart selection can’t get too deep on that, but these are some, some ideas around chart selection, and then, specifically, visualization: best practices.
Like, what do we remember when we use color and dashboards, when we create contexts, where what are the things we’re trying to accomplish, and what should we be thinking about, or we’re pulling our dashboards together?
The last half or last third of the presentation will really be around kind of pulling this, pulling these elements together into an interactive design, so I can show you some tips and tricks and things to consider when you’re when you’re building your dashboards.
So, what is an interactive dashboard? It’s, it should be fairly, you know, fairly straightforward, just from the, the name. But it’s, it’s different from a static dashboard. Right, a static dashboard is sort of point in time you handsome and it can be printed on a piece of paper.
And it just shows you, kind of, state of your company, your business, on a given day or given month or year, Interactive dashboard really allows you to take that same, you know, kind of go to the next, you know, drill down on that, now, that information, and really explore and interrogate the data.
This is really that, I should say this, this, Mike mentioned, and I think it’s in the title that this is a presentation has done. This whole presentation will be in Tableau. And it’s, it’s, I’m using Tableau to do these visualizations, but I think most of what I’ll be discussing is applicable and extensible to any BI dashboard tool. Just good things to keep in mind. And really, what we want to be thinking about with interactive dashboards is this idea of getting to Y, Right?
You want to be offering your audience, the ability to drill in and, and answer questions from the data explorer that the dataset in, in ways that allow them to draw out insights.
And, and do whatever you can to really kind of create like an immersive, analytical flow for your users to get to that. Why?
So, these are the characteristics that I’ve come up with, right. This is not an exhaustive list.
It’s not anything pulled out of a book, but this is just for my, as Mike mentioned, I’ve been working with Tableau for about 12 years now, and I’ve built hundreds of dashboards, and these are the things that I think are the characteristics that that good dashboards tend to contain. I mean, the first one is the obvious, right? It’s, they’re visual, they should be, and you should be working with pictures instead of numbers.
That’s how we power. We make comparisons.
I’ll talk a little bit about that later, but, it’s really that’s, you want to have the numbers, be secondary to the visuals.
They should be intuitive, you should just kind of get it.
When you, when you, when you pull one up, you should just sort of, like, understand what it is communicating to you, there’s, there’s certainly a lot of, you know, incredible visualizations and chart types out there that you could be using I mean, they’re not always not every chart type makes sense to everybody.
So, when you’re creating a dashboard, think about your audience! What, what’s going to make sense to them? I, I worked on a project a number of years ago, where I
built a dashboard.
That was pretty complex. And, and I took it to the client, and I said, let me, let me show you how this works, and, and she stops me. She’s like, no. I don’t want you to explain it to me. I just want to be able to get it. Right. And that’s a really good sort of litmus test, because when you do, when you develop dashboards and publish them throughout your organization, you’re not always going to be there to be able to explain it to folks.
This is really about making sure you have all the information in your dataset to get to and answer the question that your audience is expecting, right?
You want to make sure that you’ve got it, it’s everything they need. Is there. And that really is, it’s easy to say, but it’s harder to do, right. Kind of thinking through what you’re going to need. This is about going back to you? To the, the users of the dashboard and find out what they’re going to need and then going and getting that data, and putting it into a format that’s going to be usable in the dashboard.
But it’s that sort of like the upfront piece of that, dashboard creation, clean and efficient. This is just the idea of kind of eliminating clutter. You want this. You want your dashboards to be engaging. You want to look at them and kind of go, ah, you know, this is nice, you don’t want to go, ah, it shouldn’t be a jarring thing. You should be want you should be inviting folks into your into this experience. And as part of that, like, you know, we want to eliminate stuff that doesn’t need to be there.
Idea of consistency can be both within the dashboard itself. So if your dashboard has multiple charts or multiple tabs, making sure your, your color legends or your color and coatings are consistent across, making sure that your, you know, the filters are always on.
The left is kind of like, you know, what the user expects is expecting is there. This can also be applied to organizationally if you have. If your organization. For example has four regions and you know you always use.
Blue to color code the west to make sure that you’re doing that in your dashboard. Keeping that in mind that this is this is how the organization thinks about data and I’m consistent with how the organization is using data.
Idea of immersive nus is really is really important right.
You want to allow you’re your end users to get into that flow to dive into the data and not be distracted. So anything that’s pulling them out of that immersive experience is something to be avoided.
An inexhaustible is not necessarily something every dashboard needs to have, but it’s certainly something that you can strive for, right? You can always get to answer another question.
And, and there’s a walk through some features in Tableau that allow you to kind of create infinite number of views from the data, from very simple dataset. This, the dataset I’m going to be working with today is the superstar dataset that that that ships with Tableau.
So, it’s not a super **** dataset, but it’s one that I think a lot of you, if you’re working with Tableau, are familiar with. And I’ll show, you know.
We’ll talk about ways that we can, you know, make it inexhaustible.
Now the tradeoff of that is you also want to make sure that you’re not comp over complicating your dashboard with too many choices. Right? This is, this is kind of a danger zone that we can get into. We keep putting more stuff in. And we might want to be limiting that.
So just need to be working towards that balance.
So the last thing here that I’ll mention is.
Idea of you always want to be comparing. This is, this is key to data visualization. Specifically interactive and exploratory visualization. You always want to be comparing, like, how big is the difference and do I care, right?
So, just keep keeping that in mind. And am I providing that sort of context.
Why should we visualize data? You know, and you look at these two images like which one jumps out at you, right? Is it that the Leopard? Or is it the number 27? You know, our brains are just wired for pictures. And that’s what we’re tapping into when we’re doing data visualization.
So it, visuals that visualization and really allows us to easily make comparisons, identify trends and spot outliers, which, which gets to, that sort of, like, how are we getting to quickly getting to the findings that we’re trying to drive towards with our, with our analysis.
So a lot of us come to Tableau from Excel or some similar type of environment. Maybe we’re SQL people, right? And we’re used to looking at numbers and spreadsheets and pivot tables.
So everyone’s first dashboard looks a little something like this, right? We put every number that we can think of on the page, and we make it available to our users and say, there you go. It’s like, you wanted to know, like, what was what sales were in the central furniture. It’s like, it’s on my dashboard. It’s right there, right? I can you can, you can filter. If you only want to see furniture, we got that. So this is interactive.
And it is comprehensive, but it’s not terribly visual. Right.
And it’s also, its obscuring information. This is something that you’ll notice in a lot of Tableau dashboards. In fact, it’s, it’s hard to it’s hard to alleviate this problem. The idea of this scroll bars, right.
Anytime you see a scroll bar money, your views, it means that there’s something I can’t see on the screen, that might, that might seem like an obvious statement. Again, this is sort of, you know, a lot of the things we’re going to be discussing. Two efforts should, It seems sort of obvious, but, but this is also saying, I don’t know what I’m not seeing if, I don’t know if it’s not. If it’s important or not, right. So when you’re building dashboards, if you see scroll bars, make sure you know that, like, whatever’s underneath is something that shouldn’t be presented right out of the gate to your users.
So, let’s just do a couple tests around, like, this idea of, like, why do we visualize?
So, if I asked you to go through this grid and say, and count up the nine’s, you would have to go and find, you know, look through the whole thing and find every nine shape, go row by row or column by column.
But if I give it to you this way, it’s easy to see that there are four nines, and it’s because we’re leveraging this pre attentive ability that our visual processing power around Q so we can group those together now and count them more easily. So these pre attentive attributes are what we’re constantly tapping into when we’re doing visualization.
There’s attentive and there’s pre attentive.
So attentive is like looking at this spreadsheet, right? This is all attentive.
Finding numbers, pre attentive, allows us to actually leverage.
This capability. Our brain to quickly find, likenesses are differences. So the ones. So it’s specifically in dashboards. We want we’re doing a lot of quantitative analysis. So those pre attentive attributes that are most valuable for making quantitative distinctions are length.
This one and two D position to the position is like for scatter plots and for line charts.
So those are the most powerful.
Pre attentive attributes for making quantitative comparisons. You can also use intensity. You can use width. You can use size. Those are all, those are all interpretable quantitatively as well, but not it’s not as easy to be as precise. So this is why we see so many.
No, bar charts are just everywhere because they did this the most precise measure and people get them.
So here’s another quick test for the category binders, which is the third column, which state had the highest sales in the period and how many states had a negative profit. So, you’ve got sales and profit, right. Here are the states. So, again, you can kind of count up, look, and scroll through the list. But, again, using your attentive processing power that I give it to you like this.
And tell you that the bar length is sales, and the color is profit, and if it’s orange, it’s negative. Now we look at binders, and we say, oh, Georgia had the highest sales and there were three states with a negative profit.
This might seem very obvious, but it no, it’s just, it bears repeating why? We want to make sure that we’re always kind of tapping into these pre attentive abilities, because this is what we want to do quickly. We want to understand and isolate those, those differences as part of, you know, good, interactive design.
So, now, this is, you know, pie charts have gotten a bad rap over time, and I’m sure most of the people on the call have heard you know ad nauseum about like why we know why they’re not good but it just again bears repeating.
If I asked you this question, in which region to technology products, which is the Mavs Section represent the highest percentage of sales, right, you have to you are comparing the size of the slices and it’s hard to tell. Right, it’s just you can kind of get a relative sense that these two are about the biggest, but I’m not sure which one is biggest, but if I show it to you like this, right, now we can see it was the east. We can do it.
Again, it’s just it’s, it’s the, it’s why these are so, you know, bar charts are so common in interactive visualizations.
There’s just the best way to quickly get at precise comparison.
So a colleague of mine a number of years ago, led a workshop with some data visualization students, and he posed the question, how many ways could the students come up with to visualize the smallest possible dataset. They could think of two numbers.
And in the course of like I think an hour, they managed to come up with 45 different quick visualizations that they sketched out. And this is just a handful of the ones that they came up with. And the reason I bring this up is because this is this idea of chart selection, what should I be?
What chart should I be using?
And, you know, there’s, there are answers that can guide us.
There are certain charts that you can only use in certain situations, but there’s certainly lots of options too.
So, that same, uh, dataset like wondering which, which that that same question, which region, did technology products represent, the highest percentage of sales?
It’s all here. These are these are five different views of that data. And which one, you know works best. Every one of these has its own sort of pluses and minuses, right, bubble plots and doughnut charts are kind of ****. And it’s they, they provide that sort of experience because you’re not used to seeing them.
So it’s kind of a nice, new, blending a visualization but they’re not as precise, right? I don’t really know which of these from really comparing the size of the circles. I really can’t tell stacked bar charts are great. They can offer a level of precision and everyone gets them and they are quite common, but what we, we really can only is equally or easily compare. The bottom segment, right. It’s on the on the on the.
Once you get above that, when everything is once it’s stacked, you can’t compare these segments as easily because there’s they’re not starting in the same place.
Tree maps are great, but again, what’s, you know, that comparing the different shaped boxes is, is not particularly efficient.
It can give you a general sense, and they can they’re really nice, compact, way to visualize, to do parts of a hole, and sub segmented parts of a whole, which can be great.
But, again, it’s, it’s, you know, it’s, it’s, and there are tradeoffs.
Now of all of these, the one that actually is getting to that question, answering the question, the easiest to the stack circles. Now, this is leveraging the two-dimensional pre attentive attributes, right? It’s to the two-dimensional position but with this one, we have an issue of occlusion. Right? There’s this kind of laying on top of each other. So, again, there’s tradeoffs. It’s precise but it’s not, it’s not perfect. And you know that there is no right answer to this it’s like if your if your client just loves doughnut charts then you’re kind of stuck with doing doughnut charts, but, you know, I will say that even though these, you know, some of these sexier new chart types offer some eye candy, that eye candy quickly can become distracting. Like, after the first time you’ve looked at it.
And if you had that sort of neat surprise, the 15th time you go to the dashboard, It might just become, you know, it’s, it’s not as useful.
Context is key. This is something we need always be kind of asking ourselves.
So, I saw a presentation, remember, years ago, where, that, the presenter pose this question. How big is the Moon? It’s like, I know, I have no idea in mind, that the number of miles, right? We might, some, might have a sense of how big it is. But, But, I certainly didn’t, I just look at it every night and thinking, Maybe it’s about half the size of the world of the Earth, I’m not sure. But, you know, he offered it this way. He provided context related on top of Australia, which was his home country, and now you kind of get it, right?
So, it’s that, this idea of always be comparing, and always show me what’s, how big is it, and do I care, is, is important.
Same idea if, you know, of contexts we can lie with, with our numbers, right? This, in this example, it looks like if I’m checking then the sales from August to September, it looks like there’s a huge spike.
But you notice right away that the axis is shrunken. So, there is no zero in on my axis here.
It’s only spread of $10000 in sales if I put it in context where that’s the July to August or September jump and looking at it year over year and across the same year, it looks like it’s actually quite flat, right? So this is where, you know, context is really important.
Now, I’m going to talk about color, and my colleagues, its interests will tell you that I can go on go on ad nauseum about color.
But it’s something that’s that again, it’s really important for us to be mindful of when we’re building dashboards, especially in Tableau.
Tableau makes it really easy to drop a color dimension on just about any chart. And we want to just be judicious in how we apply color. So if you don’t need color, don’t use color, right? If this is I’m trying to communicate, is this simple stacked bar, than a label works just as well as color? The color on this side, actually, just, it’s adding complexity uncluttered where I don’t need any, it’s telling me that, you know, this purple really mean to, do, I need to know that, this is purple now. I just need to know that, that’s a table.
The other example of clutter in this view is, is I’ve got a label on every bar, right. Is that something that’s unnecessary? Probably not. You can, you know, in this side, I just giving you the top and the bottom, which is something I do quite a lot, just for reference. I just want to see relative size. How big is this?
That tells me, know, you can actually leverage this to get rid of you. Sometimes you can get rid of your axis, right? By just putting in the top and bottom marks, you get a sense and then you do. Then you leverage tool tips. So I can always see if I need the number. The numbers available with a click away.
Now, again, a difference between an interactive and a static dashboard, right. If I need that number and then stag dashboard, I got to provide it. But with interactive, I can go get it. So don’t clutter up your views if it’s not if it’s absolutely necessary.
Another thing about color is you less can be more, right?
I’ve got that same color encoding here for the subcategories and our brain really can’t process more than can’t keep more than five colors straight.
So it’s really hard to, to use these colors efficiently.
Now, what we have here is like, this is a very typical sort of representation of data. We’ve got the chart. We’ve got the color legend, right? And the legend is creates what’s called a figure when a problem.
Where it requires that I pull myself out of the data and go look up what this is. So this is one of those sort of disconnects. Or one of those where you’re, you know, you’re taking someone out of the flow.
In this example, I’ve opted instead of putting the dollar amount on the, on the view, I’ve labeled the lines with there, segment, right, so now I’m staying right in the flow, and I don’t have to go out and look anything up. I’ve grouped these rolled these up into three, you know, to category to subcategory, and it works nicely in. The colors are nice and distinguished.
And I also have tool tips that can help me with additional whatever additional context I want to provide. I mean, I’ll, and I’ll keep repeating that during this presentation.
Tool tips are just tool tips your friend, I mean, leverage the leverage of the heck out of them.
This is one of my biggest pet peeves with dashboards, LSC, especially folks that are kind of new to it, is creating.
You’ll have a dashboard that has two different color dimensions, two different categorical values with color on them, and brothers or dimensions with different categorical values.
And one color can mean is appearing in both legends. So, in this dashboard, that light purple means south, and it also means office supplies, Right?
So, you create this sort of confusion where you don’t need to have any confusion. So, this is something to be really careful about. Because even though you’ve selected in Tableau, you could select distinct color palettes. There’s still overlap in the color and the color palettes.
Just, again, around, like, what’s the minimum amount of color, I need to communicate my, when I’m trying to communicate this, is another way to be mindful of the dynamic.
Color range, that can That can happen, especially with debt, with datasets where you’re using something, like percent of profit or percent of growth without, you know, growth, growth percentage. Any percentages, right? That can change when you filter.
So, you’ll notice that these two maps are basically, I’m color coding the regions on profit and in this, on the, on the right side, I’ve basically fixed my legend to be negative deposited blue as positive and oranges negative.
On this side, I still use that same orange to blue color palette, but it’s mapping the darkest orange to the, to the darkest blue based on the range that’s available to me in the view right now. So right now, the lowest profit is 21%, which is the central region, and the highest is the west, where you can see it looks like the central it got a problem. That’s really not the case of a look over here. You can see they’re pretty much the same, right.
And when I click around, no, I’ll see that, you know, that you can get wildly different results.
From the two different, two different color ranges. So just something to be mindful of when you’re doing this, just as soon as you are doing a positive to negative axis, just fix it and set it and forget it.
The last thing is the last thing I’m going to say about color.
Is this idea of you want to make sure you’re avoid, using, kind of bright colors, overusing, and bright colors? Bright colors serve a great purpose. If you want to call attention to something, especially red. Red is the color. That like has carries a lot of baggage, right? It’s the one we go to first.
It also can tend to mean bad.
If it’s if there’s a green to red palette, it’s usually, you know, red is bad.
So, red has a embedded meaning to it. That not heartland of the color does.
But the biggest thing to take out of this view is that if everything is important, which is what this is saying, it’s screaming hammy and everything is important, then nothing’s important, right?
So, just know, when you’re working with colors, select palettes that are little less jarring that are more subtle. You just don’t you need to create difference, but you don’t need to create distraction.
So the information seeking mantra, which is overview, first zoom and filter and details on demand. This is developed by a guy named Ben Schneider man, his.
Professor in data visualization. And this is basically Tableau’s DNA. I mean it’s the interactive analytics DNA, where you want to provide some sort of overview first of like, what’s my total population look like? And then you want to provide your users with the ability to zoom and filter.
And then you want to provide links at details on demand, which is in this example, we’re going all the way down. We start with a region, we go to Product.
I’ve got one color, Color Legend in here, which is like negative to positive profit. And then, I can go to Details on demand.
Just show me the, or the order specific to that, product.
I’ve put these, you know, these two click actions result in something that I think my user wants.
This is really, kind of, this is part of the, developing these dashboards effectively, and efficiently is really kind of knowing what that next question is going to be, and understanding what the next question you, your user is going to ask, and, and anticipating that with your click action, and then similarly, making sure whatever this click action is.
It’s, that the result that comes up, makes sense.
Seems like an obvious point, but, uh, know, it’s, it’s something that Bears stating explicitly. So we saw the first dashboard, which is pretty much, you know, a big spreadsheet, and the second dashboard looks something like this right now. It’s like aha, I’ve gotten, I’ve unleashed the show me attribute in Tableau and, and I came up with all these really different cool chart types and I want to put them all on my dashboard.
And, what you wind up with is just a mishmash that that’s, that’s probably adding more confusion. Creating more confusion than it’s creating clarity, right?
And, and to be honest, Tableau is partly to blame for this.
Tableau makes it really easy to create just a number of different views of your data and just drag them into a dashboard and then just say, and use this filter, and I can click and drill and everything, you know, I can always filter the dashboard in any element that I want and, and everything is there.
But you got to be asking yourself in my is that, you know, everything here really kind of meeting the needs of the end user are These charts are contributing to insight.
Is this what was asked of me?
So you need that sort of touchstone to kind of like, you know, saying, is it is it really need to be in there?
There it’s clutter.
And in the way that there are a lot of charts, just there’s a lot of sort of things screaming for your attention here. But there’s other number of problems with this dashboard.
Clutter is also example, exemplified by the use of labels that we don’t, we don’t really need these labels, right. We can have those again in the tool tip. The use of these really bright borders for every chart. It’s nice to create separation, but you don’t want to do it in such a way that it’s distracting. This is a very typical looking table, something we’d see in Excel all the time with hard borders around the cells. Again, all these are just, this is ink. That is not really in service of the data itself. And we have this issue again with the scroll bar.
A lot of problems with, with this dashboard.
It works, all right, it’s, it’s, it’s telling a story. Just not sure what the story is.
So, that the trick, then, is to kind of get at what is the story, and what does my audience want to know? And this is on you as an analyst to go and find out, Right? So, you make your life a lot easier if you asked the questions upfront of, you know, what? What does someone want to know? All right. What do you want to know next? What are the characteristics of that? That you’d like me to be able to describe the dashboard. So, if someone came to you with the problem.
The problem is that I just want to know what the trends of sales by category, Right were.
These three charts, I’ll answer that question.
But which one’s right? I mean, they’re not, they’re all they’re all correct.
But what is your user want to know? Maybe wants to know all of them, maybe you need to include all of it.
This one’s telling me, like, total sales over time, which is great, but I really can’t see how my Sales By category or doing relative to themselves are relative to the other, relative to the other categories. That’s what this does. The line chart. But now I don’t have the total sales.
The, on the right, we have the, you know, year over year view, which might be, like the most important thing to anyone. I really want to know how I’m performing relative to last year, but you don’t know that. That’s what they want to know until you ask the question.
So, it’s really important to, you know, get at, you know, and ask those questions upfront.
And understand what you’re audience is trying to.
Trying to distill from the dataset.
Alright, so let’s just continue to expand on this now. So now we’ve got, you know, they asked us what were the sales buy?
So it was the turn of my sales by Office category, and we decided we wanted both the line chart and the area chart, but we also want to know Total sales by category. Oh, OK.
Well, that’s really easily made, met by adding a bar chart.
And now we get to this point of, like, I’ve got two charts on my dashboard, and now we can start to do some visualization. I mean, sorry, some interactive visualization, right? It’s not just a static snapshot. And I apologize, we’re like 30 minutes into my talk, and we’re now finally going to start to use some interactivity.
This is pretty typical. You know, putting the filters, I think Tableau defaults with filters on the right, I’d like to put my filters are on the left, almost all my dashboards have filters on the left, part of the consistency of my dashboards, I usually gray the dashboard filter, filter cards usually are great backgrounds, but this is, you know, this is infinite, This is intuitive. I know that I’m assuming that when I click this, that something’s going to change my dashboard.
Would, I don’t know, is what, you know, what’s going to happen when I do a click action, but we do want to click on outliers, right?
We want to know what’s underneath this, this spike, So this is what your users are going to want to do. So, you want to ask, you know, what the next thing they want to know is. So, the question was, who are the top customers in each category?
I click on this, and it’ll pop up a little top customer list over here. I don’t need to expose that. I don’t need to have that always available. I just need to have it on demand. That’s what my user asked for, so, that’s what I’m going to give them. And similarly, you know, this click action will filter I get the trend for that specific Alphas category in the East top customers, right?
So, this allows that, you know, we’re immersive the click actions are keeping us in the data. I don’t necessarily need to jump out and filter on the east. You know, if I had an east filter here, I can just do it. I can do it in my dashboard.
All right, so, context, again, back to this idea of like, how big is it and do eye care? One of the, the handiest chart types for context, is the bullet chart.
And this is, this is a chart type that’s available in, in, right out of the box and Tableau, and allows us to compare multiple metrics. So, in the, you know, the previous one, we’re just looking at the bars relative to each other.
But this one’s not allow me to look to look at those same bars at the same sales relative to A point in time, which is last year, which is my hash?
And then this gray is my targets if I have targets that I want to hit I can put those into my dashboard. Now, I’ve got, it’s, it’s, you know, it’s like, I’m better or worse than last year, I’m better than last year, but I haven’t hit my target. That would be this one.
Already know, that’s so that’s so behind this behind both, I should say.
Anyhow, you can, you know, you can leverage these.
These, providing this context is a super powerful way to just, like, keep the people in, in the view.
And the other thing that, another thing that I expose in this, this dashboard is the inclusion of hierarchies. So.
This is the all up view total sales by region.
And I’ve put in a hierarchy that goes the branches down to subcategory and this allows us to imbed another level of detail, like, again, more details on demand and staying in, in the view itself, not jumping out to another chart. I’m staying in the view, itself.
I’ve also noticed I’m not putting the quantitative labels on every bar. I’m just putting on that the highest and lowest in my view. The other thing you’ll see here when I mouse over these guys is the question that they want to answer next is, what’s the top product in each category?
We could do it, you know, similar to what we did here, which is, like, I could click and pull up the products in the category, similar to what we did here, right, same idea, or same on this is pulling up the products and the category.
But, Tableau, now, as with the, one of the recent releases has a feature called charting tool tip, So, I can basically just show those products. As, you know, as a mouse over, I don’t need to have it take up additional space on my dashboard.
I can just pull them up on demand and go really, really nice feature.
And, again, staying, staying immersive. Right. It’s that sort of, it’s, it’s allowing, you’re answering the next question, your state, you’re allowing the user to stay immersed in the, in the dashboard itself.
So, this is basically doing the same thing and I’ve just added that we continue to layer on additional complexity. So the next question might be, you know, we’ve got that, now we have the trend over time. We have our year over year or year two or year to target, or you know our sales to Target rather, we have our hierarchy, all these are clickable.
We show the top products.
And maybe they ultimately I want to know what the orders were associated with that.
So this is the last, sort of them, you know, really getting down to the finest grain of the data we can get to, this is the true like details on demand, show me the orders that make up that that point.
It’s, uh, in this example, I didn’t, because there’s so many, I didn’t put it in the dashboard itself and I’m branching out to another dashboard. And I’m also, making it, there’s a, there’s a there’s two different types of click actions that you can put into your, into your dashboard.
So you can either just have the, the, the action run right when you click, or you can have a menu up here, there’s a menu options. So it’s like, I only get it. I’m only going to go there when I, you know, when I asked that second question, you sure, you want to show the orders here, the orders.
So, this is.
Again, so now we’re getting to this, you know, we’re starting to build out, you know, that the components of what make up a really good interactive dashboard, we have multiple charts providing details on demand. We’re consistent. We have our filters in the same place, there are always the same place.
So now, let’s jump out and look at a way to add even more complexity to your dashboards. And this is what I was talking about in the beginning about making it.
So this is a parameter used dashboard, and there’s, when I’m creating parameters, I’m use my, my parameter cards look a little different than my filter cards. So these are all light blue.
And what parameters allow us to do is really layer in infinite number of ways to visualize our data. So right now we’ve got this. We’re getting kind of sick of looking at office category, right? We’ve looked at it through the whole presentation, but I’m also hammering home that consistency. It’s always the same thing, I always know technology is MOV. Right? But say I want to see this by Region instead.
So what this allows me do, the parameters, swapping out the dimension on that, that underlies the bar chart.
And say, I want to see it by Region and by Office category. So I can create a column dimension.
You can use Office Category to create a separate column, and say, I want to. That’s great, but I really want to understand the how those different Office know. There’s different regions are performing by category over time, relative to each other, So I can swap this out as a line chart.
And say it’s like, oh, that’s too many months. Maybe I just want to see it by quarter.
You can continue.
You can add all these, excuse me, infinitely configurable views.
Say, I want to see the data. I want to see it, you know, the table or the data, not just line charts. It’s like, show me the table and what’s that point? It’s like there’s that, you know, I got to click action. I’ll take you to that exact that that item.
The reason that I’m no showed you that like I can’t, couldn’t possibly, you know, show you every possible permutation of this of this dashboard.
But, what this allows us to do is to efficiently use the space that, you know, the restricted space on the dashboard, to create multiple views, right?
This dashboard, let’s go back to a bar chart.
I also, let’s take this, turn the table off.
Can do this as a map.
And here, I’m actually going to show you some.
It’s not good as Region. Let’s do it as Office Category. Let’s take the column dimension off.
It’s a, it’s a circle for every in every region. It should be. This should be consistent, but if I tend to change this to office category, I’ll get pie charts.
Here, pie charts can be effective. I just kind of get it want to get a relative sense of size and you know, less than five colors. It’s easy to, to consume.
But if I go back to bar and I’m slick, I’m going to look at Sales by Month.
If I look at, I’ve also got my, my parameter as my measures here, right? So I can look at this by profit.
I can look at it by, you know, number of customers, average order size.
Now, the thing that’s challenging about these is our profit percentage.
Here, it’s stacking percentages, which makes no sense, right? This is how you would want to look at profit percentage. So, I’ve not baked that into this view. I’m allowing the user to make those choices, like, I get, it’s infinitely configurable, but this is also delivering results that make no sense. So, that’s something else to keep in mind. You also want to be sort of guiding the user to view. That makes sense. So, tradeoff right, that, there’s, you know, infinitely configurable, but you also got to make sure that what they’re coming up with is, is accurate and an intuitive and sensible. Right. And the other thing I’ll say about parameters, I think they’re really powerful, and I use them quite a bit in my, in my dashboards, but there’s also a switching cost.
So, you know, there’s, there’s something to be said for having, you know, the line chart and the bar chart.
Both available. Like, we had earlier, right, that stacked area chart, and the line chart. You want to be able to see both. You want it, you don’t want to have to settle for one or the other. But in this example, you have to kind of pick and choose.
So, when we’re pulling it all together, you know, I like to think of, you know, the dashboards is something that our idea of, you know, that engaging and you want to, you know, it’s, it’s all very esthetically pleasing.
Everything should be in its place. You shouldn’t have any sort of overlap or repetition, if you can help it.
And, you know, everything should be contributing to the parts of the whole. So that’s another sort of touchstone, is this when I pull something to my dashboard, is that adding insight or value? Or is it just another way to say the same thing?
So, it’s just something to be mindful of, right. That everything’s got to a place.
So, this is the last visual that I’m going to walk through, and it’s kind of taking into account the things that we’ve already discussed and shown.
One difference is, is I’m not as, I’m not being completely restrictive on color on this. I am trying to limit my color palette but you can see that there are two here, so now this is a pretty typical dashboard, right? We start with our KPIs. I really want to know, for the population I’m looking at, which is 2016.
Am I better or worse than last year?
This is a pretty standard thing, and when I say, I just wanted to, I only want to look at it for central, central, better or worse than last year, Right? I can filter it for just the region that I care about and those, these arrows are going to change. There’s no, there’s no waiting to them.
It’s just positive or negative, red to black, that red to black performance, though, are that, that scale is shared down here. This is just showing, this is better or worse than last year. This is also better or worse than last year. The color is always going to be consistent. I’m not saying this is percent profit or anything.
It’s always just better or worse now when I change my measure.
All right, this values are going to change, but this color encoding is still team doing the same thing. Am I better or worse than last year by state? You’ll notice something when I when I selected customers.
From sales, my chart changed, right, so now I am like I am guiding or restricting the user.
Based on what I think they need to see. When you select this, when you select customer, customers are not a summative metric across Office categories, so you want to see how many unique customers you had in each of these categories and the gray areas.
Give me the total, right. Similar, similar to that, if I selected Profit Percentage, same idea, right?
I want to understand what the profit percentage by office category was, and what my overall profit was. That’s the gray. So you start to introduce these visual cues that folks understand.
You can, one thing I’ll do is use titles or use. This is just a little dashboard with a description on there with the calculated field, where I’m telling you what we’re showing in in the view and what the shaded area means.
Again, I’m, you know, doing that here, too, I’ve got, this is better or worse, better is black and worse is, is read, I mean, you’ve got the arrows to. It’s sort of redundant view but I don’t need a color legend here.
I can just communicate that to you with that, with the title.
Similar to this, I’ve got now, I’ve, I don’t have targets in this view, but I do have year over year totals, right?
So, if I look at, this is telling me that I’m looking at sales by office category, and the hash, the vertical line is my prior, your reference.
I have those same, uh, hierarchical values available to me here, similar to the view we’re looking at before.
In addition, I’ve actually also baked in, regional hierarchy, so if I read, do My View by region, and now, and the hierarchy is going to go from region to State.
Same views are available. Everything is, you know, I’m still looking at that.
Same sort of better or worse than last year. The Q’s are all the same. And the last, you know, click through is like, I want to see products.
So I can show my products and here, the color encoding is consistent with the map where black is better or worse than last year. Right? And I can kind of see that because I’m still doing a line chart. And if it’s below the line, it should be red. And if it’s through the line, it should be block.
So, the, and, you know, and again, that the format is consistent. These are all parameters. They’re colored one way. The filters are all colored one way, they’re always on the left.
That, there’s also these different elements you can add to your dashboards. Like, what’s the as of data, my data, with how current is it. Something to be helpful, people don’t know, like when was the last Refreshed.
And, you know, and then I typically also add, sort of get info popup. Again, this is just a little sheet with a, with a tooltip underneath it.
Tell people about the dashboard, gives them some context, or like, what they’re doing, or how the, you know, what the click actions mean. What, you describe, how the dashboard works.
So, again, sort of everything in its place, not doing too. Everything’s got to offering that, that, you know, a different look at the view, We’ve got trend, we’ve got total, we’ve got geography view, We’ve got our overall KPIs. We can drill down to products, if we want.
It’s, it’s all kind of there. But it’s also, you know, based on some, some input that I got from an end user that says, this is what I want. This is what I’m expecting to see next, these are things that are important to me, that’s really driving the design here.
So, just some things to remember before I turn it back over to Mike.
You want to, you know, make it intuitive leverage chart types that your audience understand. There’s a lot of blaney stuff out there, but, know that there’s nothing quite as useful as a good old-fashioned bar chart.
The interaction should be intuitive, you should be designing your interactions, to anticipate the next question and answer them with a click. And if you don’t know what that is, ask, you want to minimize your colors.
One thing I want to mention about this, you notice I am not popping up a color legend here because this chart in the middle is my color legend, so when I switch out my dimension to be customer segment, for example.
Now, I have those labels here, so I’m basically doubling up on, you know, using the chart as the legend itself, and other kind of easy, little trick. You can just save yourself some space, make that make the legend data.
Parameters views really can enhance your analytical capability, but they can also, you know, you got to use them judiciously and make sure you’re providing options that make sense to the users.
Just leverage tool tips as much as you can. And then you know, the visual should always be comparing to something. You should always be showing people. How big is this? Do I care? Why do I care? So I’ll always be comparing.
So that’s it. I’m going to attempt to turn this back over to Mike now.
I can grab it back.
OK, great, thank you, Kyle, that’s amazing. I’m always super impressed with how clean your dashboards are. So please stick around everyone. I’m going to do a quick presentation on, on, on Senturus is just a few slides here, but we have Q and A at the end, Get your questions into the question logs and we will address those.
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You can run SQL and MDX or whatever and create some charts. They have a deep and rich understanding of customer experience and user interface design, which is really kind of that, that unicorn of talents that you need to bring together to really make tools like Tableau come to life.
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So one of the questions we had was about one of the new features, Kyle. And that’s around parameter action. So choosing the chart view. Can you manage? That was something like Parameter Actions. That’s a brand-new feature.
I’m not sure if you’re prepared to speak about that.
But do you have anything to say?
I have not worked with parameter actions yet. So I can’t. I can’t speak to that.
I know there’s some, there’s actually some examples on Tableau Public, where you can see how that, how they’re using … interactions.
And I know, when you go to the new features, in 2019, they talk about all the key new features. And I’m sure they have some, some examples there, but now parameter actions are a great new feature, in 20 19.2, that was just released.
So, look for more on that later. There’s another question about thoughts on colors for colorblind consumers, the available colors are very limited. Can you speak to that Kyle? Yeah, I mean, Tableau does a pretty good job of creating colorblind specific palettes. I mean the orange to blue palette is a pretty common one. That’s when I use all the time. I try to avoid the red to green palette, which is the most common form of color blindness.
Yeah, there’s definitely ways to, to, if you’re aware, of any other folks, and organizations, that are dealing with that, too, to know, steer clear of stuff, that will create a conflict for … club colorblind folks.
Yeah, yeah, exactly. And I think, you know, your color palettes um, that’s probably the number one thing I’m most impressed with this the cleanliness of your dashboards there. But the color palettes are amazing. I always get a little dizzy when I’m looking at all the various color palettes, but you find these great combinations.
And I think that’s again, that’s sort data artisan thing. A quick clarification. Someone asked about how many courses to do Tableau prep certification. The slide where it says prep for Tableau certification is not specifically about Tableau prep. So we probably need to reword that slide.
It is prepare for Tableau certification, so specifically, Tableau, Desktop Qualified Associates, so our courses, or even the sort of the next level up. So we offer everything fundamentals through to intermediate advanced, and an expert class, and those tally, up to about five days of, of, of coursework. And those should leave you in really good shape to get your Tableau certification. So definitely reach out to us if you’re if you’re interested in that. I had a question for you to hear Kyle about how do you, you talk about what’s the sort of the ideal setup for these dashboards? And you talk about there’s all these whiz bang things that you go to Tableau Public.
And its mind bending and really cool the stuff they do on Tableau Public and they always have Sankey diagrams and all these really advanced things.
But then you come back and you tend to land on much more simplistic in traditional bar chart types. And some of that is your personal preference, but how do you, what’s the process you sort of go through? And how does Table Tableau enable that for you to interact with your clients to land on what’s best for them?
Yeah, it’s It has a lot of trial and error. I mean, I definitely do try to spice stuff up with the visualizations just because I mean the visualization styles because I get bored.
But, you know, I, kind of, yeah, I do go back to the sort of tried and true, more often than not.
And you’re right, there are, there are tons of amazing visualizations in Tableau and outside Tableau, Right, D three as an example of a visualization tool kit that just, this creates mind bogglingly, beautiful data visualizations. But, I might litmus test if it takes me more than two minutes to figure out what it’s telling me, it’s like, it failed. And, and that’s why, I think that, even though a lot of what, you know, I showed today is Amy, I ever made this point is kind of boring. People get it and, you know, there are, you know, there are obviously some chart types that.
Lend themselves to specific use cases that, you know, that you would want to leverage. Like, for example, like the Gantt view, when you’re looking at, you know, orders over time or, you know, project tracking, those are ones that be specific to the use case.
But, you know, if you’re doing a trend, a, really, it’s hard to beat a line chart. You know, you’re doing a distribution, it’s hard to beat a bar chart.
So, sorry. I can’t be more.
Right now, that’s fine. And there’s a lot of resources out there that talk about, what’s the best chart type for a given type of visualization? So there’s, that’s well documented, and I was just curious about that. And in terms of the last question, I know are at the top of the hour, so we want to be respectful of everybody’s time.
A lot of times, we find ourselves at in tourist, and I know you do, personally, you’re going into an organization where visual analytics is a really is a new thing, and you showed that first dashboard, which is 100% true. So many of our clients, we go in and they say, well, can you just reproduce this spreadsheet first?
Can you talk about how you use Tableau and these practices to then, move these, move the culture, right? You’re trying to get the consumers of this, and the authors of this, to think, like analysts to ask questions, think about the next question, How do you, how do you go about doing that? Yeah, I mean, so one approach that I’ve taken in the past is to actually show them, you know, I can do, you know, I will replicate what they have currently maybe in their current system, And say, Here’s what the spreadsheet looks like, right? And then move them sort of down the path, it’s like here are those same metrics shown visually, right? I’m sure on the exact same data in two places and here, see how you can actually get to the outliers much quicker than scanning. Down a column of numbers.
You know, it’s a challenge because you don’t want to just push people into the deep end, right? You want to kind of leave them there.
But you got to show them why this is better and why, you know, what, what Tableau really empowers is that ability to continue to, you know, kind of drill down and get to the details.
I mean, one thing is, I don’t know that I said it in there, in the presentation, but one thing I want, one thing I always talk about is, if you want to, you don’t want to start with the numbers you want to end with them. You know, like that spreadsheet.
It should be the last view you see, it shouldn’t be the first one you see.
So it’s kind of trying to get people to flip that on their head, and, and get that, that like, Oh, I can get to that number eventually, but it’s not where I should start.
Yeah, I agree. And I think he get people that they’re so used to their precious spreadsheets that you have to kind of, you know, pride from their hands a little bit and thus pre attentive attributes are really, you know, they, they’re the reason.
You’re sitting at the top of the food chain to get. Exactly. Exactly right. The extent to which you can leverage, as you can. And Tableau makes it so easy to get to the numbers with tool tips.
And you can always, I always like to tell people to, you can always right click a visual, and you can export it to a spreadsheet, if you want, right, if you’ve enabled that functionality, so you can always get to that, those numbers. So, I think that’s funny. I mean, that’s another thing I’ll say. Like, when a client says, what can you just show me how I can get this into Excel? Which is which is so common.
It’s like, if you have to put this in Excel, then I failed. That’s kind of like my mindset, I mean, I get it that you, that’s where people are comfortable, but, like, what are you trying to do? What do you need to do in Excel?
And let’s figure out a way to do it in Tableau first. Alright. It’s, so part of part of that, sort of?
Creating a culture change is getting people out of their comfort zone, kind of forcing them, you know, into the, into the visualization well, I mean, I had a client recently where I showed them, like, here’s this dashboard.
They created based on that this, the charts that he showed me he’d done in tablet and in Excel, right? And I said, here’s, here’s new way to look at this system be refreshed every day.
Like, darn because what am I going to do with that? Our I have every day, you know, they’ve been allowing allocating every day to do these charts, you know, it’s like getting people thinking more about like, how could I be better using my time than managing data in Excel, right? And how does tap right? A little bit easier to go home and see your family, and yeah.
Or, you know, that, we all know that the, the best performing companies are the ones that successfully use analytics. So if you free up an hour or more, you can use that to figure out how to sell more or reduce costs or, you know, turn some of those ***** and levers that really make your organization perform better. Exactly.
Great. Well, we’ll wrap their then. So thank you so much, Kyle, for your presentation today. Thank you all for your time and attention. Again, please reach out to us at our website or at info @.com or the AAA number there, if you have any questions or any analytics needs. Also encourage you to join us on our future events and connect with us on the various social media platforms like LinkedIn and Slide share, YouTube, Twitter, and Facebook, where we have a presence on all of those. And thank you again for your time today. And we look forward to seeing you on the next installment of the Senturus Knowledge series. Thanks and have a great rest of your day.