Design a site like this with
Get started

How I became a Tableau Desktop Certified Associate (and you can too)

Let’s face it, there’s nothing like achieving a goal you’ve set for yourself. With all that’s going on in the world, passing the ‘Tableau Desktop Certified Associate’ exam is definitely one of my personal highlights for 2021 and I’m not done yet … but that’s a different story all together.

In this post I’ll be sharing some of my favourite tips and tricks and give you some insight into how I prepared for the exam and hopefully it’ll give you some new ideas that you can incorporate into your own prep. If you’re interested in getting a taste of the actual exam experience and what to expect, I recommend going through the resource list in one of my previous posts.

Important to note that when I took the exam they were testing version 2020.1, you can view the exam guide here.


For most folks getting ready for the exam all starts with reading up on the different topics in the exam prep guide and watching all the videos on Tableau Help. And while that is certainly a valid approach, I knew I needed something a bit more structured to help me work through all the different topics. Especially because there were a few functionalities that I hadn’t actively used at work, and for that reason were more challenging to relate to because I couldn’t put them into context.

I’m aware that this will be different for everyone (and I’m by no means saying you need to invest in a course to pass the exam), but I find that I learn best by doing so I decided to invest in an online exam prep course. You’ll find that the offering for prep courses and test exams has grown considerably over the years. You just have to check what’s currently available on platforms like Udemy which is where I found  “Tableau 2020 Certified Associate Exam Guide A-Z by Kirill Eremenko”. What I really liked about this course were the videos and exercises with exam tips folded in; and the 2 practice exams with the incredibly detailed solution videos which really helped to solidify my learning because it helped me think through what I was doing.

Next to taking the course, I collected tableau articles & community blogs on all the topics in the exam prep guide and read through them. Remember you can use Google during the exam so while you might not need to know every single piece of theory; it helps when you have read the content in Tableau’s Help section and/or at least know which articles you should not skip on. I’m currently working on consolidating all these materials in an easy to search overview and hope to release something in the coming weeks.

💡 Tip: make sure you check what version of Tableau you’ll be tested on. In some cases you may need to look up an article on a specific feature that is no longer relevant in the current (read: latest) release. This came up in the course a few times as well, so it is useful to know how you can look up specific help articles for older versions.

Current URL >
V2020.1 URL >

When it comes to practice, the exam prep guide contains a set of 15 questions which are representative of the actual exam experience. If you’re looking for more, provides several sets of free practice quizzes which are great for validating your understanding of certain topics; and they also offer a set of 3 practice exams which I believe are $9.99 each ($19.99 for the full set). On the latter I personally found that some of the solutions were a bit dated or not always the most efficient; but it’s still good practice. And for the scope of this particular exam, how you get to a solution is not relevant.

My focus areas

With all the practice exams/tests I took and other blogs I’ve read on the exam experience, this is my shortlist of topics and concepts I paid extra attention to. You will get tested on all if not most of these so make sure are familiar with them.

Boxplots: are you clear on what the different parts of a box & whisker plot are, can you identify them on the plot and do you understand how to interpret the data in the visualization?

Forecasting: do you know how to add/edit, what some of the quality metrics are and when you won’t be able to use this functionality?

Trendlines:  are you familiar with what the different values (P-value/R-squared) represent and how they can change depending on the model you select? My recommendation would be to familiarize yourself with all the different options in the analytics tab (distribution bands & standard deviation etc).

Maps: you’ll get several questions on maps alone (could be theory and/or practice) so make sure you spend extra time on familiarizing yourself with the different map options and layers; and know how (and for what purpose) you can use them. 💡 Tip: it pays to know where you can change units from metric to us.

Unions, blends & joins: another topic on which you’ll be tested on so it pays to understand the differences and under what circumstances one is preferred over the other.

Dashboard actions: knowing the difference between the different actions, how to set them up and how they can impact your visualizations will come in handy during the exam.

Device layouts: be clear on the different options (layout, size etc) and how to add them. Familiarize yourself with the interface, be clear on what goes where and understand the difference between the default and device layouts and how both can be changed.

Sets (& Combined Sets) vs Groups: make sure you’re clear on how they are different and how they behave when placed in the view.

Calculations: do you understand the difference between table calculations and LOD’s and their relationship with the level of granularity in your view? Can you recognize the most common uses cases for each? I found this blog post to be very helpful and I would highly recommend checking out the articles referenced at the end under ‘Learn more about calculations’.

My favourite tips (some borrowed, some newly discovered)

  • When taking the exam, don’t be afraid to skip a question if you find you are not getting anywhere in the first 2 minutes. There are 36 questions to get through and you only have 2 hours so be efficient with how you use your time and flag any question you want to come back to later.
  • This should be obvious but when under pressure it’s easy to miss something. Read your questions! And when the questions are very detailed and lay out specific steps for you to follow; follow them and then double-check.
  • You’ll be taking the exam on a virtual machine and the only applications you can use are Tableau Desktop and Firefox. This means no Excel to quickly check your data or Notepad for taking notes. Learn how to use Tableau for that. 💡 Tip: depending on whether you are using a mac or not, some of your usual shortcuts may not work as expected so know where you can access the options via the browser menu.
  • Keep track of the solutions to your questions by renaming the labels of your worksheet with the question numbers, this will save you time when you want to come back to something later on.
  • You’ll be using the same datasets several times but my recommendation would be to always start with a fresh data source. Some questions will require some data manipulation (joins/unions etc.) so it’s safer to always start from scratch for every question.
  • Use captions to take notes (Worksheet > Show Caption) when needed. For certain questions you might have to run a few scenarios to find the right answer and it helps if you can ‘write down’ down the different answers somewhere making it easier to validate.
  • Always remember the order of operations; especially if you’re solution contains Sets, Top n, Table calculations, LOD’s etc.  
  • When joining data, do a quick check of the data and be on the look-out for duplicate records and how this could impact the calculations you’ll need to use to get to the right solution.
  • Use the ‘Calculation Editor’ to quickly validate the outcome of a calculation and what values could potentially be ignored. It’s a lot more efficient then having to google for the right answer.

Additional resources

For a different viewpoint I highly recommend checking out these posts and Youtube series.

The Tableau Desktop Certified (Qualified) Associate Exam by Sarah Bartlett

Passing the Tableau Desktop Certified exam: My Journey by Anthony Smoak

Preparing for the Tableau Desktop Certified Associate exam by Ella Worsdale

Tableau Certification Exam Tips by SQLBelle

Thanks for reading and good luck with your own certification journey!


How I got started with using Tableau

One of the things I’ve been asked about more and more in recent weeks is how I got started with learning how to use Tableau Desktop and if I could share some of my experiences. So I thought the start of the new year would be a good time to finally write up a few words and share some of my resources. This will be a living document and items will be added as I start scaling this out further; but if 2021 is the year you decided to get serious about picking up a few new data skills I hope that the following will be of use to you as you are about to embark on your own Tableau journey.

Much like everyone else I started using Tableau (Desktop) because it became a requirement for work. So I did what most folks do and that is watch a few free videos and then start learning by trial and error. And while that could be sufficient for what you need to accomplish, when I was preparing to take my first certification exam I realized that I didn’t really understand some of the basic concepts and as a result didn’t really understand what I was doing. I very much believe that if you want to become proficient in using Tableau Desktop, understanding the fundamentals is critical. Next to that, what really made a difference in my learning process is taking part in a social learning project called #MakeoverMonday. Depending on what you’re interested in, there’s several other projects for you to participate in so check them out and see what works for you. I highly recommend you find some space/time to practice weekly over an extended period of time. I committed to 6 months and it really made a difference. And if you don’t have  a Tableau license via work, you can sign up for Tableau Public which is free.

The below is by no means the full overview and I’ll be adding content to this later in the year but I hope at a minimum you’ll found some inspiration to help you get started.

Getting started

The Tableau Student Guide: I really like the Tableau Student Guide as a place to get grounded. It’s has a great summary of short posts that will help you understand what Tableau is and does.

Going Back to Basics: I wrote this post as I was preparing for the Desktop Specialist Exam because I came to realise I was struggling with understanding some of the basics; and doing some homework and writing it down for myself really helped solidify my understanding. Hope you find it valuable.

Getting Started: beyond the free short videos you can find on the different concepts, I recommend you watch this one first. Tableau has also posted a few other recordings from live trainings that are also worth checking out.

Tableau explained in under 10 minutes: this short video created by Tim Ngwena is such a great intro to understanding the entire Tableau ecosystem and what it does, doesn’t do. If you’re new to Tableau, take a few minutes to watch this.


There are a lot of courses out there and Tableau also has a nice eLearning platform that I recommend you check out to see if it works for you. I signed up for the 1yr plan and worked through most of the content and collected a few badges along the way. Definitely make sure you check out ‘Data Literacy for All’, it’s free and a great addition to building out your data skills.

These are 2 paying courses I’ve taken that really worked for me. There is a lot of content out there so my advice would be to check out the different learning platforms and find something that works for you. Udemy has a decent offering and if you wait for a sale you can get most of them pretty cheap. Also most courses will let you sample some of their content so make use of that before making a decision.

Mastering data visualization using Tableau > From basic to advanced: this is the first paying course I took and what really worked for me is how they work through several real life examples and you learn all about key Tableau concepts as you move along and practice.

Tableau 2020 Certified Associate Exam Guide A-Z (w Datasets): i’m currently taking this course as I’m preparing to take my second certification exam and I am really enjoying the detailed content which is pretty extensive. I’ll let you know how if it paid off after I’ve taken my exam 😊

Tableau public

As mentioned Tableau Public is free and a great way to help you learn & practice. Also check out their resource section which contains a nice overview of resources you can leverage (how-to videos, sample data, community resources) as you get started.  The best advice I can give you is to sign up for Tableau Public today, start following the work of others, pin your favourite workbooks to your profile page and publish some of your own visualizations.

Tableau Public is great resource of inspiration as most authors (myself included) who publish their work will make the workbooks available for download. Downloading a workbook and trying to reverse engineer it is another great way to learn. Make sure you sign up for ‘Viz of the day’ and check out the ‘Featured’ gallery.

Social learning projects

If you’re looking for a framework (or datasets that are not ‘work-related’) to help you practice, check out some of the projects listed below. They are not only great for practice but also serve as a resource for inspiration. Check out the community resource section on Tableau Public for the full list or you prefer something a bit more creative check out this visualization created by Samuel Parsons. Take a look at what folks are posting on Twitter, Linkedin etc. using the following hashtags:

#MakeoverMonday: this project posts weekly visualizations including the datasets and challenges everyone to re-create the visualization which you can then submit and receive live feedback on during ‘Viz Review’. Feel free to reach out in case of any questions.

#WorkoutWednesday: this project challenges you to recreate data driven visualizations and will almost always include solutions/techniques you could easily leverage in your work projects.

#SportsvizSunday: if sports is your thing then this is the challenge for you.

#VizForSocialGood: if you want to practice your skills and support a good case at the same time then I can recommend this project.

#SWDChallenge: this project posts monthly challenges for which you submit your work via dedicated platform. Also check out the exercises and other data visualization resources.

#RWFD: this is a great project if you’re looking to practice using ‘Real World’ Fake data. Make sure you check out the summary recaps for inspiration for some of your business dashboards or at a minimum get a taste of what your business dashboards could look like.

#DiversityinData: this project just launched recently and is centered around diversity, equity & awareness. They will be posting monthly datasets.

A few extra’s

The bigger Tableau Community is incredibly generous when it comes to sharing knowledge and resources so to conclude I’m sharing a few of my favourite resources that I continue to go back to for inspiration or learning something new. I kept this list short on purpose as there is so much content out there and rather then throwing everything at you, I thought I would list just a few and hopefully that will encourage you to go out on your own and explore further.

PlayfairData: this is the first blog I ever subscribed to after seeing Ryan Sleeper present his favourite tips and tricks at the Tableau Conference in London in 2018. Even if you’re just getting started, it doesn’t hurt reading up on what’s possible. While you might not feel confident enough to put some of these tips to use, there will always be something for you to learn from.

Lets Talk Data meetup: this group is an initiative from The Information Lab in London and they host webinars/trainings online (often free) so make sure you check them out.

Welcome to the Tableau Portal: this is another great overview created by Sagar Kapoor to help you connect to more resources.

Best of the Tableau Web: these wrap-ups are one of the few blogs I read consistently as they are a great recap of the best content that’s been posted in a given month and will typically have something for everyone.

Tableau Community Hub: I would be remiss if I didn’t mention the recently updated Community Hub which is another great resource to read up on the great content that is being posted by community members. Make sure you don’t miss reading the ‘DataFam Roundups‘ and check the different categories in the blog section.

Tableau Escape Room: if are you looking for something fun to explore what you can do with Tableau; then make sure you check out this ‘Escape Room’ created by Mark Bradbourne.

Good luck with your journey and thanks for reading. I invite you all to leave your comments, questions and additional recommendations in the section below.

Adding ‘context’ to your business dashboards (#LondonTUG)

I’ve just wrapped up my talk @ the London Tableau User Group where I spoke about how adding context to business dashboards supports your users in gaining better insights from their data. The idea is that by adding a few additional ‘elements’ to your design you’re making it easier for your audience to process the information and derive meaning from it. In simple words and as I see it, context is anything that helps users to better understand their data. So it’s not only about how you present the information and the design choices you make, but also the guidance you provide and that’s the focus of my talk. You can see the recording here.


I’ve gotten into the habit of adding ‘info’ buttons to all my work dashboard and my users have really come to appreciate them as they help with building trust and transparency. The ‘info’ will describe how the data is selected, what’s being visualized, what insights users can gain from interacting with the dashboard, how to filter the data and what the different values represent. I will typically also add a few ‘explanatory’ buttons which I’ll place strategically across the dashboard and which contain additional pointers on how users can interact with a particular section/feature to gain additional insights. There’s only so much text you can add without the views becoming cluttered, so I find these buttons are a good workaround and give you a bit more room to provide better ‘guidance’.

Click here to see an example of one of my recent work projects. For obvious reasons I can’t show the full view and had to edit the text a bit but you can see enough to understand how I tend to structure my copy and hopefully you’ll get some new ideas.

Creating the info buttons is actually pretty simple. There’s of course different ways to do this by using custom images or working with shapes; but the technique I’ve started applying doesn’t require any of that and is actually super simple (no kidding). Click here to view my workbook on Tableau Public that will take you through the different steps. If any questions, feel free to reach out or leave a comment in the section below.

Instructive filters

One of my biggest pet peeves when it comes to business dashboards is when filters are added to the view and the designer doesn’t take the time to change the filter title into something that users can actually understand. When changing the title consider using words like ‘select’ or ‘search’; or even turn the title into a short question. Click here to see a few examples

To most these may seem like insignificant details, and while you as the dashboard designer will understand what it all means; consider your users most likely wont.

Guided text & tooltips

I hope that by now you all know you never ever leave your tooltips to their default formatting. It’s sloppy and lazy; and depending on the use case the default text will most likely contain references to calculations and dimensions that without explanation won’t mean anything to your users. So why not take the time and transform your tooltip into a declarative sentence so people can better understand what’s being visualized?

And finally, when you dashboard consists of multiple views; consider making your worksheet titles dynamic by adding in the values of what’s being filtered. It’s a simple technique often overlooked but in my experience by the time users have filtered the dashboard they will have already forgotten what values they selected, so by adding these into the title of the individual sheets you’re reminding your users of what subset of the data they are viewing.

Thanks for reading and I invite you to leave your comments and questions on the section below. What tips do you have for adding context to business dashboards?

How having fun with Tableau became my catalyst for learning (D+W Zurich)

Thoroughly enjoyed sharing my creative journey with Data Plus Women in Zurich yesterday. If you were unable to join I highly recommend you check out the recording at the bottom of this post and listen to the great stories shared by Elissa Fink and Fi Sizeland. Get ready to be inspired!

For those of you that are interested, I’m also sharing the links to some of the resources and sources of inspiration mentioned in my talk, feel free to reach out should you have any questions.

Inspiration for ‘The Colours of TinTin

Colour relations and proportions – by L Jégou

Do we take data visualization to seriously? – by Neil Richards

> Link to Viz on Tableau Public

Inspiration for ‘Lego Art Frida Kahlo’

Lego art in Tableau – by Ken Flerlage (I got the name wrong in my talk, my apologies)

> Link to Viz on Tableau Public

Inspiration for ‘Colours That Defined the Decades’

Systems – a retrospective of 1960s braun design – poster designed by Ross Gunter

> Link to Viz on Tableau Public

Happy viewing!


Spaghetti lines for the win (and how to control the size of your marks in Tableau)

I’ve been meaning to update my original ‘DESI – Digital Economy & Society Index’ visualization with the numbers for 2019 for a couple of months now, and after having an inspiration dry spell I finally got some ideas on how to further polish my initial design.

I knew I wanted to experiment with contrast and see how I could colour certain sections differently to create more visual appeal and impact while at the same time not changing the initial layout/design too much. Looking at it now my first draft didn’t really pack a punch, but I was excited about what I had created after not having published anything on Tableau Public for a while. So before putting it out there it I thought I would ask for some feedback from someone who has a great design aesthetic and Ludovic Tavernier who was generous in sharing some of his wisdom with me. Rather then going through the entire conversation I thought I would share the different iterations so you can see how my design progressed and give you some tips (because I hope that’s why you’re reading this post) on how you can better control the size of the marks in a Tableau visualization.

The iterations

If you take a look at my original design and the different iterations, you’ll notice I didn’t really change all that much.

  • Overall I tried to add more space as looking back at my initial design, it felt like I was trying to pack too much information in a single view and could have probably used a bit more padding.
  • Spaced out the letters in the title a bit more for a slightly softer touch.
  • Added mark labels to the bar charts so at a minimum you would have an indication of which bar represented which country without the text completely taking over. I’m aware for some it’ll be barely visible but I thought it added something extra.
  • Replaced the side by side line chart with 5 individual grids.
  • Changed the colours of the axis’ and tick marks do a darker grey almost matching the colour of the grey lines in the chart.
  • While a blank space is never a bad thing, I felt it threw off the balance of the visualization so I moved the annotation (which was too important to be hidden in an information button) from underneath the bar chart to fill up the space in the grid.
  • And for me the ‘pièce de résistance’ really are the grey ‘spaghetti’ lines in the line charts.

I’m very pleased with how this turned out, and glad I took a few days to work through the feedback which in the end resulted in receiving a second ‘Viz of the Day’ nomination from Tableau Public. While the changes may not seem significant and to others could be considered redundant or even a bit obsessive, remember that the details are never just the details … they make the design. (quote borrowed from Charles Eames).

There’s always a way

One of the things I struggled with (and initially couldn’t get to work) was the size of the grey lines. I had put the ‘Country Colour’ dimension on ‘Color’ and a similar dimension on ‘Size’; and while playing with the sizing of the lines I soon realized I could only push the sizing slider so far to the left without completely washing out the effect of the yellow and blue line which I needed to stand out. At that point I hadn’t really thought of a solution but decided I would come back to it a few days later to see if I could figure something out.

Enter Tableau eLearning

That same evening I was working my way through a few lessons online and learnt about how you can change the range of sizes for your marks. Who knew ?!?

The ‘Size’ slider

Using the ‘Size’ slider when your mark is a line will make the lines thicker or thinner. But as I was using a discrete dimension with 3 members; I had less control over the individual sizes of the lines because Tableau would assign a unique size to each individual member (3,2,1).

And keep in mind that when changing the size of the individual sheets by adding them to a dashboard, the size of the lines can could/would change as well.

The ‘Edit Size’ option

And this is where the magic happens. By clicking on the drop down arrow on the legend, you can access the ‘Edit Size’ option which gives you more control over the individual distribution of the sizes of your marks. By pushing the left slider all the way to the left I was able to resize the grey lines (3) while not changing the sizes of the other lines (2 & 1) significantly and keeping the effect I wanted. I should mention I could only move the left slider by perhaps 1 mm (could have been less, the difference was barely visible) but it achieved the desired result. For more context, I recommend you read this help article from Tableau which goes into more detail.

I’m aware most folks might not even notice the difference but getting those thin lines to work really changed the aesthetic of my visualization.

Click on the image below to view the finished product on Tableau Public.

Thank you for reading! Please leave your questions/thoughts/comments in the section below, I look forward to hearing from you.


Creating colour stacks in Tableau

Since I published by ‘Colours of TinTin’ viz I’ve had a few people ask me how I extracted the colours and created the colour blocks, so thought it was time to finally keep my promise and write up some instructions that will hopefully help you to create your own masterpieces.

Before we get started

When I initially got the idea, I was going through all the tools I had collected; and what I found was that they all extracted the same number of colours and/or didn’t take into consideration the finer differences between colours (and some made extracting the HEX codes a bit too complicated for my liking).

The challenge I had was that I wanted to create a recognizable abstract representation which meant accounting for the fact that all the covers were very different in how they used colour. The tool I ended up using offered more flexibility/control on how those colours would be sampled. If you know of any other tools that could achieve the same (or potentially better); please let me know by leaving a comment at the bottom of this page.

At the end of this post you’ll also find a short overview of some of the tools that I tested as I was working on this project; and I also included a few other favourites (**) that I recently discovered.

For the purpose of this post/tutorial I’m only covering how I prepped the data as that was where I spent most of my time; and where I believe it makes sense to provide a bit more detail. You’ll find that creating the stacks in Tableau is actually pretty straightforward so I invite you to view/download my workbook from Tableau Public (click on image below) to reverse engineer

Creating Colour Stacks in Tableau@2x

A few considerations

Before you start sampling images, please consider the following:

  • Once you have decided on the sampling parameters, make sure you apply the same for all images.
  • The pixel size of your images matters and should be the same for all as the sampling will be impacted by the image quality.

If you’re just looking to create your own colour palettes, then one could argue that the above considerations don’t really matter as it would be a more subjective exercise. But if you are going to make an abstract representation of a series of images, I believe it makes sense to apply the same parameters & pixel ratios for all images allowing for a more objective comparison and analysis.

Two more sidenotes:

  • For obvious reasons, extracting colours from a picture you’ve taken from an object or landscape will yield different results then when you use a cartoon image as your source.
  • The greater the pixel size, the better the sampling

Ok so let’s do this.

Click here for access to the tool I used to extract the colours.

Full disclosure. I used different parameters for my TinTin viz (you can find them in the TXT file of the HEX codes I shared, see link below), but the overall principle is the same.


Step 1: running your image(s) through the tool

For information on the different parameters and what they mean, I refer you to the instructions by the creator of the tool. I should mention they are in French. So if you’re a bit ‘challenged’ in the language department; I recommend using a translator like Deepl to help you out.


To extract the colour information, click on ‘Sv.palette’ underneath the coloured bars and it will extract the details into a CSV file (like the image below).


Step 2: collating the relevant data and creating your data source

Now that you have the data from the tool, it’s time to clean it up a bit in Excel and add in some additional columns. The ‘orange’ columns are what I added in, the other 2 are what’s left over from the CSV file. I’m not going to write instructions on how to clean up the data in Excel, I assume you know you’re way around and understand how you could use ‘Find’ and ‘Replace’ to make that task a bit simpler.



Pixels / Total Pixels per Picture ID

Colour order

It’s very important that when you start building your stacks; that the order of the hex codes in your *.tps file is identical to the order in your data source, otherwise there will be mismatches. So by adding in this unique identifier, it makes it a bit easier to validate. As with anything I’m sure there’s different ways of doing this but this is what worked for me.

Step 3: add HEX codes to your ‘Preferences.tps’ file

This part is always the most tedious as you have to add in some code before you can copy paste to your *.tps file. To make it a bit easier you can create a simple tool in Excel that will make this go a lot faster. You’re welcome to download my version by clicking here or you can simply create your own, it’s not rocket science.


Step 4: connect to your data source and start building your viz

Now that you have completed all the data prep, you simply connect to the data source in Tableau and you’re good to go. You’ll see it’s really very simple. Should you get stuck, feel free to use the comment section below to leave your questions and comments.

Additional resources

Colour extraction tools

TinEye – Color Extraction Lab


Color Extractor

Color Extractor from

Palette Generator

Adobe Capture (**)

Palette Maker (**)

The Colours of TinTin

Viz on Tableau Public


Thanks for reading and feel free to leave your feedback or questions in the comment section below.

How I created the Digital Economy & Society Index (DESI) in Tableau – 4 mini tutorials

Yes – it’s been a while since I blogged about anything; but figured now would be a good time to get back into the groove and release a long overdue post about how I created my ‘DESI – Digital Economy & Society Index’ dataviz which was recently voted as VOTD & VOTW on Tableau Public. Getting recognition for all the time and effort I put in over the last couple of months learning how to use Tableau and apply dataviz best practises was great, so this is my way of giving back.

Side note: if you want to read more about the design process, learn about some of my other fav tableau tips and tricks and what makes a great Liege waffle; make sure you check out my ‘Tableau Community Spotlight’ interview with Michael Sandberg.

In the tutorials below I used a practice version of my DESI viz as the example. Feel free to download the sample workbook I created so you can reverse engineer the techniques using my edited dataset.


Tutorial 1: Parameter ‘Filter’

Let’s start with something simple.

You know when you want to use your parameter as a filter but can’t change the formatting to blend in with the rest of your dashboard? This short tutorial will explain how I worked around that. And I invite you to use the sample workbook in case you want to follow along.

Step 1: create the country parameter

Right click on the ‘Country’ dimension > Create > Create Parameter (exclude the EU average)


Step 2: create the ‘Country Label’ worksheet

Start from a blank sheet, put the ‘Country’ dimension on Columns and Filters and drag the ‘Country Parameter’ you just created onto the sheet.


Next you’re going to add the parameter to the worksheet title via ‘Insert’.

parameter title@2x

Step 3: float the parameter and label worksheet onto your dashboard

And this is where the magic happens. By adjusting the W&H and formatting of the containers, you’re going to make them look as one.


For the ‘Country Parameter’, remove the parameter title & change the width of the container until only the drop-down arrow is visible.


Then float the label worksheet next to the parameter. On the worksheet change the Fit to ‘Entire View’ and adjust W&H of the container to show the country only; and have it line up right next to the dropdown arrow from the parameter.

Took me a while to figure out but it really is as simple as that.

Tutorial 2:  Bar Chart with ‘Dynamic’ Colouring

Next you’re going to create the bar chart and use the ‘Country Parameter’ to highlight a specific country (in yellow) and at the same time show how that country ranked compared to the EU average (in blue).

Step 1: Create a simple bar chart

Put the ‘Country’ on Rows, ‘Weighted Score’ on Columns and ‘Year’ on filters (select 2018). Drag the ‘Country Parameter’ onto the worksheet.


Step 2: Create the calculation for the colouring of the bars/countries

Creating this calculation will:

  • colour the country selected via the parameter colour 1 (yellow)
  • colour the EU average in colour 2 (blue)
  • and colour all the other bars in a different colour (grey)

Step 3: put ‘Country Colours’ on ‘Color’ in the marks card

Now that you’ve added ‘Country Colours’ to the sheet, try toggling the parameter to a different country and you’ll see the bar that’s coloured yellow shifts while the blue one (EU average) remains in the same position.


Step 4: adding the country ranking to the tooltip (first round)

And for some additional detail, you’re going to add the actual ranking number to the tooltips.

First try the the following table calculation.


If you now select Finland via the ‘Country Parameter’; the 3rd bar from the left will colour yellow but if you then validate the tooltip you’ll notice it will rank Finland as #1 which is not correct.

Next you could adjust the table calculation as shown below but that will only solve part of the problem. Finland will be ranked correctly as #3. However with the EU Average being one of the ‘Country’ values, it will be ranked as #15 and Slovenia as #16, while the EU should rank as 0 and Slovenia as #15.


Step 5: adding the country ranking to the tooltip (second round)

Let’s try something else. Why don’t you give the following calculation a try. What it’ll do is:

  • for all the countries ‘before’ the EU Average (so where the total score is greater than the average) … they will be ranked using the calculation from Step 4
  • the EU Average will be ranked as 0
  • and all the countries after the EU average will receive an ‘adjusted’ ranking

Before testing, make sure you adjust the table calculation as shown below.


Pretty cool right?

Tutorial 3: Line Chart with ‘Dynamic’ Colouring & Sizing

The next chart you’ll create will display the scores over time for the different DESI dimensions (the’Indicator’ measure in the dataset).

Step 1: create the line chart

Put ‘Indicator’ and ‘Year’ on Columns, ‘Weighted Score’ on Rows and ‘Country’ on ‘Detail’ on the Marks card.

If you now drag ‘Country Colours’ (the calculation from earlier) to the Marks card and put it on Color, you’ll observe the following … a fine yellow & blue line on top of a bunch grey lines (each line representing a individual country).


Notice how the order of the lines is decided by the order in the colour legend: (1) being yellow on top of (2) being blue on top of (3) being grey and that’s what we want.

colour legend@2x

Now try changing the order by for example dragging the grey box (3) to the top. What would happen with the order of the coloured lines?

Answer: because the position of the colour in the legend dictates which colour will be on top when the marks overlap, the grey lines would be on top of the yellow and blue line which would make them almost invisible; and you want them to stand out. In fact you want the yellow and blue line to be a bit thicker so they are more visible.

Step 2: size the lines

Remember the calculation you created to colour the lines and bars?


If you drag ‘Country Colours’ onto ‘Size’ on the Marks card, this is what you’ll see.


How do you flip the result and have the yellow and blue line be thicker? You simply flip the calculation for ‘Country Colours’ by creating a second version as shown below. Notice the difference with the initial calculation?


Eh voila.


All that’s left to do now is clean up the tooltips and you’re done. The ranking was already added to the dataset for you so there is no need to create calculations;  but if you’re feeling adventurous, give it go and feel free to share the results with me or leave a note in the comments section at the end of the blog.

Tutorial 4: Creating the ‘Chart Header’ with the dimension definitions in the tooltip

And before we can wrap this up, there is one more thing left to do. You’re going to create the ‘Chart Header’ as shown in the image at the top of the page. Instead of showing the header of the ‘Indicator’ dimension (which is what you would normally do); you want to be able to hover over each indicator and show some additional information like the description/definition of each of the indicators (read DESI dimensions).

In order to do this you’ll have to connect the second sheet of the dataset to the workbook and pull in the definition information. For the purposes of this tutorial, I’ve already added it for you (see sample workbook).

Step 1: create the chart header

This is actually very simple.

Start a new worksheet and connect to the ‘DEFINITION DIMENSIONS’ datasource.

Put ‘Indicator’ on both Columns (uncheck ‘Show Header’) and Text on the Marks card and add ‘Definition’ to Tooltip. You’ll have to do some ‘clean-up’ but the end result should look something like the below.


Step 2: align the chart header with the line chart

All that’s left to do now is adding both sheets (the Chart Header & Line Chart) to the dashboard and do some re-arranging so that things line up nicely but I will leave that up to you, pretty sure you can manage 🙂


Thanks for reading and feel free to leave your feedback or questions in the comment section below.

#MakeoverMonday 2018 WK48 – the cost of a night out

I love it when the dataset for Makeover Monday is released early on Sunday; it feels a bit like Christmas coming early 😊. I haven’t had time to actively participate in Viz Review the last couple of weeks, so looking forward to joining the webinar this week and learning from the feedback.

This week’s dataset is looking at the cost of a night out in selected cities around the world; and below you’ll find the visualization referred to in the original article. I highly recommend actually reading the article because it offers additional insight on how the visualization is ‘interpreted’ and there’s lessons to draw from that as well.

Let’s take closer look.


What works well?

  • Clear labels and colour legend.
  • You can easily understand the ranking because the bar charts are sorted from most to least expensive.
  • The Subtitle works well as it gives additional info and explains what the totals represent.

What could be done better?

  • There’s too much clutter, the flags and image don’t serve any real purpose and are fighting for attention with the colourful bar charts.
  • There’s no need for the axis labels as the bars already show the total per city and I found they added little value when trying to figure out the individual cost for each item just from looking at the chart.
  • I can clearly see which city ranks highest based on the total, but I can’t really make out how the cities compare across the different categories.
  • Comparing cities based on the cost of a few items doesn’t seem correct as it doesn’t take into consideration the actual cost of living. While Mexico City may seem like the cheapest option, it could still be an expensive night out for someone who lives there if you take into consideration their earnings and cost of living.
  • Also, it doesn’t specify if this is the total cost for more then one person or not. You could make an assumption based on the cost of the Big Mac but it should be specifically mentioned on the chart so there are no misunderstandings.
  • And finally, I would also question the choice of a ‘Big Mac’ as the late-night snack of choice but that’s a different topic.

My makeover

I went with a slightly different approach because not only did I want to see how cities ranked based on the total but also wanted to add some additional insight on how cities compared on the cost of the individual items.

Click on the image below for the link to the Viz on Tableau Public.


Thanks for reading!

#MakeoverMonday 2018 WK47 – how many hours do Americans need to work to pay their mortgage?

After a few hectic days I finally found some time to dig into this week’s #MakeoverMonday so let’s just get right to it.

The original


What works well?

  • It got my attention, you can NOT not look at it.
  • The title states a clear purpose and the visualization appears to provide the answers.
  • The colours work rather well and they provided a clear legend.

What could be done better?

  • Not all bars on the chart have a label which makes it difficult to make a proper comparison. Also my eye keeps moving back and forth between the bars and the legend while trying to compare individual cities. Because the bars are 3D the proportions appear to be off; and from just looking at them it’s difficult to see the real difference between cities.
  • I don’t believe we need the map because it doesn’t seem to serve a real purpose other then to show we are talking about cities in the US.

My makeover

I’ve been a bit stuck for inspiration this week. I knew I wanted to do Small Multiples because I believe it works well in this case; but was undecided on a chart type (it wasn’t going be bars) until I saw Eva Murray’s viz. I thought the visual of the clock worked really well here so I decided I would try and rebuild it. (also I haven’t done a Pie Chart since I started using Tableau late July this year, and I really really really wanted to but haven’t found an excuse yet; and this seemed like the perfect opportunity to pay homage 😊).

It took a few minutes to figure out the calculations (confession: math’s wasn’t my favourite subject in school) but managed to get there by writing it out. I find that when thinking through a problem, writing it down helps me structure my thinking. The below is a ‘cleaned up’ version of my doodles 😊, I hope they make sense.


Translating this into Tableau was easy, so I recommend you give it a go and consider it good practice.

The rest was just following Andy Kriebel’s tutorial for Small Multiples and boom, I had a screen filled with small pie charts. And if you want to take it one step further and add a Mobile view, I can highly recommend Paul T McHale’s recent post on designing for multiple devices when using Small Multiples.

I went with different colours because I like experimenting with contrasting colour palettes. The idea was that yellow is a happy colour (think sunshine); more yellow means people are happier because less of their time working is going towards paying off their mortgage. Perhaps a bit of a stretch but I quite like looking at it now so decided to leave it.

Click on the image below for the link to the interactive viz on Tableau Public.

How Much of Your Day's Works Goes Towards Paying off your Mortgage_(1)

Thanks for reading and if you haven’t participated in Makeover Monday yet, I can highly recommended it. It’s had such an impact on my learning so click here to find out more. And don’t hesitate to reach here or on twitter should you have any questions about the process, or are just curious and want to know more.

Getting ready for the Tableau Desktop Specialist exam – going back to basics


In my previous post I talked about taking the Tableau Desktop Specialist exam because I felt I was missing a deeper understanding of some of the mechanics of Tableau; and preparing for the exam would be a great way to fill those gaps.

I believe that if you can’t explain a concept to someone else who doesn’t know anything (I always tell folks to explain things to me like I’m 10 because knowledge doesn’t have to be complicated), you probably don’t understand it as well as you think you do. So I decided to do my homework and share my lessons with the broader community. If you’re getting ready for the exam as well, I hope that by going through this post you’ll have gained a better understanding of some of the skills required as listed in the Exam Guide. This post covers the section ‘Understanding Tableau Concepts’ (see screenshot below).

At the bottom of this page you’ll find a list of additional ‘Exam Prep Resources’ that will help you get started and also the links to some of the ‘Source Materials’ I used.


Dimensions & measures

When connecting a data source to Tableau you will have noticed there are 2 main categories on the ‘Data’ pane: Dimensions & Measures. These ‘categories’ or ‘types’ get assigned by Tableau depending on what type of data the columns in your data source contain, and they are considered to be fairly accurate.

So what do they actually mean and what type of data goes where?

Typically, dimensions are used to describe something while the measures will contain information that can be measured and counted.

Let’s take a look at something simple before we get back into Tableau.

A real-life example

I made up this small table representing my immediate family. Can you make out what the ‘Dimension’ and ‘Measure’ is in this case?

table family (measures & dims)
  • ‘# People’ is a measure, it counts the number of family members in each group.
  • ‘Family Relationship’ is a dimension, it describes what the numbers represent and provides the overall context. You could even say that without it the numbers themselves wouldn’t mean much to anyone.

And if I now connect this small data source to Tableau, this is what it would look like.

famil dims and measures in tableau

For most of the content in this post I’ll be working with a dataset provided by I recommend you download it if you’re interested in replicating some of the examples in this blog.  Small side note: I’ll mostly be working with the ‘Flights’ dataset.

Let’s continue

We covered the basics and now understand the 2 different data roles in Tableau so it’s time to connect to ‘Flights’ and explore further. In case you are curious what the different icons in the images below mean, I recommend you check out ‘Visual Cues & Icons in Tableau Desktop’; it’s a great reference guide.

flights data & measures

To be able to better understand and explain the difference between measures & dimensions, I read several blogs/pages/posts available online (they are all listed at the end of this post) and from there I was able to come up this short recap which is when things started to make sense for me (better late than never right?).


  • Measures are data fields that in most cases contain numeric values.
  • They can be counted and aggregated, they are quantitative.
  • Examples from ‘Flights’: Number of Flights, % of Delayed Flights


  • Dimensions are data fields used to slice/group your measures.
  • They provide context and will determine the level of detail (granularity) of your view.
  • Dimensions are qualitative data fields, they describe something and can be numeric or text.
  • Examples from ‘Flights’: Date, City, Airport Name

A simple example in Tableau

I put a Measure & Dimension on the view and it created the image below.

The dimension on Rows is determining the level of detail (read: granularity) in our view by providing context and slicing the measure on Columns across the different airports.

flights bar.png

If I were to take out the dimension, the view would look something like this. I think you’ll agree there’s not much context there unless you’re only interested in the total count.

flights simple bar

So that was it. You now understand what measures and dimensions are, so time to move on to what those green and blue pills represent because you’ll soon find out that green and blue are not synonymous for measures and dimensions.

Discrete (blue) & continuous (green) fields

Without going into the specifics, I hope the following example using different measures helps put the above statement into perspective (‘green and blue are not synonymous for measures and dimensions.’).

A real-life example

You already know I have 2 brothers and in this example we’ll call them Charles & John. While John is 195cm tall, Charles is 185cm (about the same height as myself).

Which is which? And keep in mind we’re looking at different types of measures only.

  • The number of brothers I have is a discrete measure (1,2,3, …) (the chances of me having 1,25 brothers is very unlikely)
2 brothers
  • The height of my brothers is a continuous measure as their height can technically fall anywhere between 0 to 195 cm.

You could say that while continuous data fields form an unbroken chain (read: axis), discrete data fields represent distinct fields/values (read: labels).

Understanding the difference between discrete (blue) and continuous (green) pills is critical as the colour of the pills and where you put them in your view (filters, columns & rows, the marks card) will determine how your data is visualized so it’s important to have a solid understanding of the principles behind it.

discrete vs continous

Now that we’re done talking green vs blue and dimensions vs measures, the below really brought it home for me. Thanks Timothy Manning & Andy Kriebel for putting this out there, I’m totally getting T-shirts made once I’ve figured out a clever design 😊

  • Blue things group your data
  • Green things count your data
  • Dimensions split up the view
  • Measures fill up the view


Now that you understand the overall logic behind those green and blue pills, there is one more concept you need to get your head around. And I would say this is probably the most important one of all, because once you get it you’ll be able to better digest everything else that’s coming your way.

What is it?

In simple words aggregation is the concept of adding up different rows into a single cell of data.

Why would you use it?

When you require less granularity.

Instead of seeing the individual records, you want to go up a few levels to have a better view of what’s going on. The image below will help explain this in a more visual way.


What does aggregation look like in Tableau?

Remember when we connected to our ‘Flights’ dataset? Did you take a look at how the individual records were structured? Because that’s really where it all starts.

For this example let’s say that instead of looking at records for all the number of flights (and their status) per airport on a given day …

flights data.png

… you’re more interested to see the total number of flights per airport and how many of those were on time as that will make it easier to identify trends.

flights category.png

By adding the dimension on to Rows, the measure on to Columns and the dimension ‘Ontime Category’ on the Marks Card on Colours; Tableau will automatically Total (SUM) (read: aggregate) the number of flights per ‘Airport Name’ & ‘On Time Category’.

For more detail on the different types of aggregation I recommend you review the list of predefined Aggregations.

So why are aggregations difficult to relate to sometimes?

For the longest time I heard everyone talk about aggregation but couldn’t get my head around the overall concept, let alone explain it to others. After having done my homework and reading this awesome blog by Mina Ozgen I started to see the light.

What does it all come down to?

Before doing anything else (and I can’t stress this enough), always make sure…

  • You have a proper understanding of how your data(source) is structured before you dive in.
  • You are clear on what questions you are looking to answer. Asking different questions can lead to a different order of operations (do I first aggregate and then apply the calculation or the other way around?) and perhaps even structuring your data differently.

The question Mina was looking to answer was the school wide male to female ratio knowing that each record/row represented a specific class. I took the liberty of making a mock-up in Excel of her example. Do you know which one is correct and can you explain why? I recommend you read her post and find out 😊

Option 1

option 1

Option 2

option 2

In closing

I hope my post will help you get your head around what for me used to be words on a page. Below you’ll find a list of blogs, posts and pages I went back to when trying to figure it all out. I’ve also included a list of ‘Exam Prep’ resources that will be helpful if you’re studying to the take the Desktop Specialist Exam.

If you’re taking the exam, I want to hear from you. And if this post helped you get ready, I would love to know as well so don’t be shy and drop me a note on twitter.

Thank you for reading and remember to always stay curious!

Exam prep resources

Desktop Specialist Exam Guide – from

#CertifiablyTableau Tableau Desktop Specialist – by Alexander Waleczek

Tableau Specialist Practice Exam – from

How to prepare for the Tableau Desktop Specialist Exam – by Michael Sandberg

How do I get certified in Tableau – by Brad Werner

The Tableau Desktop Qualified Associate Exam – by Sarah Bartlett

Becoming a Desktop Specialist – by Christine Rietmann

Source materials

Dimensions & Measures Intro – from

Blue & Green Things – by Tom Brown @ The Information Lab UK

Blue & Green Pills – by Timothy Manning @ Data School UK

What is Data? – from

Dimensions and Measures, Blue and Green –  from Tableau Online

Understanding Pill Types – from Tableau Online

Data Aggregation in Tableau – from Tableau Online

Tableau Foundations: Aggregation, its Powers and Perils – by Mina Ozgen @ The Information Lab UK

Visual Cues & Icons in Tableau – from Tableau Online