Tableau – DG Analysis

Introduction

From reading a lot of data viz experts and tutors comments on platforms such as LinkedIn, one recommendation that keeps popping up is that any aspiring or practising “visualizer” should maintain a portfolio of work to showcase the skills and breadth of work that they may have.

I have taken that advice, and put together this portfolio style blog. Most of the visualizations that I create in a professional field are not something that I can necessarily share in the public domain, therefore I am opting to showcase the fun challenges and trials I have taken part in over the last 6-8 months.

These mostly include the Maven Analytics challenges, and are mostly performed in Power BI, although there are a few Tableau examples in there too. There are other examples from Onyx and Dataworld.

I have decided to post the visualizations in reverse chronological order for each section, to (hopefully) show some kind of progress in technique and presentation.

I will keep this “live” as a repository for my public displays.

Maven Challenges

The details for all these challenges can be found here.

2022

Maven Space Challenge – Current Entry

This is the current challenge in August and September 2022, and relates to analysing space mission data from the 1950s to present, and coming up with a visual that captures the awe of space travel. I felt that the data required some supporting context to bring alive the “awe” of travel, and focused on missions which either visited other planets within our solar system, or journeyed beyond our realm.

As such, I experimented in some visuals to try and bring out that story. I took a little inspiration from the Beastie Boys, with the title of the visual, and the fonts taken from their album (Hello Nasty). Below is my entry, and my LinkedIn submission post.

Maven Telecoms Churn Challenge – Winner

This challenge was in finalised in July 2022, and related to a set of customer profile data for a fictional telecoms service provider in California. The challenge brief was to identify high value clients, examine churn risk, and look at what steps can be taken to retain clients.

This analysis involved creating a set of normalised metrics to look at both value and risk, based on the profile characteristics of the clients. I have done a more detailed write up on my approach here.

This entry was the winner out of over 300 entries, with the Linkedin notification here, and the judging panel video here.

Maven Airlines Challenge – Finalist

This challenge ran from May through to mid June 2022, and was in relation to a fictional airline which had crossed the line of having more than 50% of passengers feeling unsatisfied by their experience in travelling with them.

The challenge was to analyze around 130,000 survey responses which included Likert type data, and investigate key areas of improvement which could help get the airline back on track with their passengers. Here is a link to my LinkedIn post, and a snapshot below of my work performed in Power Query and Power BI.

Maven Unicorn Challenge – Joint Winner

This challenge was between April and May 2022, and used a summary dataset looking at the valuation, funding, location and investors involved in global unicorns – the $1bn+ privately owned companies, and not the other fantasy figures!

The brief was to illustrate the global landscape of unicorns, and below is my proposed report, which was posted on LinkedIn as usual.

Maven Remote Work Challenge – Finalist (2nd)

This challenge took place during March 2022. It involved analysing the results of two comprehensive surveys which posed over 100 questions to in excess of 1,500 people on the subject of remote working both during and after the recent COVID pandemic lockdowns in NSW, Australia.

I had to provide advice on a proposed remote working policy in a post-COVID world, and translate the qualitative data from the survey to provide quantitative insights into what effects remote work had on productivity and morale. Below is my final report, which was also posted on LinkedIn. I note that I also used this visual in the March SWD challenge.

Maven SuperBowl Challenge – Finalist

The challenge ran from January to February 2022, and involved analysing historical SuperBowl advertising data in order to propose an upcoming advertising strategy for an up and coming car company. My original posting is on LinkedIn.

2021

Maven Magic (Harry Potter) Challenge – Finalist

This challenge ran from December 2021 to January 2022. It involved reviewing the film scripts for the Harry Potter movie series and coming up with a way to visualize the “magic” of the movies.

My submission scored as a Finalist on this challenge (my third in a row), with the critique for my submission recorded here by the team at Maven Analytics. I also created a few of my own videos showing some of the techniques I used in my data prep and analysis.

Maven Taxi Challenge – Finalist

This challenge ran from November to December 2021, and involved following a set criteria on what needed to be presented. This was a real challenge from a data prep point of view, as it encompassed in the region of 27 million line items of journey data.

Because of the complexity of the data load and set of data cleaning steps, I created a video showing my strategy and steps, which has proved to be relatively popular. I made the finals for the second time, which I was very pleased with, and took into account the comments provided by the Maven team.

Maven Restaurant Investments Challenge – Finalist

This challenge ran from October to November 2021. It involved assessing customer and restaurant related data in several cities in Mexico to assist investors in selecting a location and type of restaurant that would prove popular.

This was around the time I was experimenting in Tableau, and decided to use it for this challenge. A good advantage of Tableau, is the public platform allows everyone to visit and fully interact with each visualization or dashboard.

I was pretty chuffed and surprised to make the finals for the first time, and I learned that the use of key questions and summary take-aways were powerful techniques in laying out a presentation.

Maven Olympics Challenge – Entrant

This challenge ran around the time of the Tokyo Olympics, and was my first foray into the Maven Challenges, after having taken their Excel courses and a few Power BI courses. Instead of using all the data from both the summer and winter games, I decided to focus on only female participation during the summer games only.

Looking back on it now, although I tried to provide some structure and flow, it was quite busy and maybe included too much information. I was quite interested to see the difference in participation between communist and non-communist countries during the cold war.

Looking back, I should have honed in and explored that in more detail. But it was a good introduction, and spurred me on to learn more.

Onyx

I have followed Onyx challenges for several months, but finally decided to join in in June 2022, starting with the Forbes Billionaires challenge.

Forbes Billionaires

This base requirements of this challenge was reviewing the raw Forbes data and providing a summary of the greatest philanthropists, as well as the industries which had the most successful billionaires. Here, I focused on those base requirements only, and aimed for the feel of a Forbes type article, giving a high level synopsis rather than a deep dive. The work was done in Power BI, and the LinkedIn post is here.

forbes-billionaires Download

storytelling with data

Being an avid listener of the storytelling with data podcast and the book by Cole Nussbaumer Knaflic, I joined their online community to take part in their monthly challenges. These are more “sprint” like challenges when compared to Maven, and are good to hone in on particular aspects of improving visual communication.

February 2002 – Focus and Declutter

This challenge can be found here. The goal here was to take busy and potentially confusing data visual, and apply techniques that are taught by Cole in order to declutter the noise surrounding the visual, and attempt to focus in on the story behind the graph. Removing unnecessary components, and appropriate use of colour to focus, I wanted to be able to take the following away within 5 seconds of looking at the graph.

Philadelphia is the only city to have a higher crime rate than 1990
Other cities generally have seen reductions in crime rates
New York is the stand out in terms of overall reduction.

Dataworld

I had started to look to take part in the Makeover Monday data viz series, but as soon as I started, it ceased to exist! I had thought I would have time to go back and try out the old challenges, but just haven’t had any spare time lately, so there is only one example below.

Workout Wednesday has however taken on the mantle, and I intend to work on some of the Power BI related challenges, which are sometimes a challenge to recreate a Tableau visual.

The datasets are usually quite small when compared to the Maven challenges, so in theory they should be used for “quick” practise.

The Dark Web

This challenge involved looking at the price index for various illegally obtained goods on the dark web.

I used this challenge to practise the use of parameters in Tableau, which I thought was put to relatively good use when I try it out in Tableau Public, as well as trying a kind of “dark mode” background with light coloured text.

Other Miscellaneous Work

I created some template Power BI dashboards to present the interactions of a fictional company on LinkedIn, as well as Twitter. I used this mostly as practise for Time Intelligence functions. I created an accompanying video and blog, which allowed people to recreate it, and it has been relatively popular.

Weather Data

This was quite a simple dashboard put together in Power BI to look at data collected by a weather balloon travelling over Germany. I used this primarily to practise working with geospatial data as well as grouping scatter charts.

Sankey Charts

This was in some way related to my work life, as I used it as a pilot to then present at work.

This is a visual rather than a full dashboard, but I had thought that Sankey Charts would be a good way of visualising the change in risk status before and after mitigating steps were applied.

I also learned how to use particular DAX functions to create the necessary data table structure to utilise the Sankey Chart visual in Power BI. Again, I created an accompanying video and blog, which I still see people interacting with most days.

Where it all began!

This is where it began about 9 months ago in mid 2021. As I was taking the basic Tableau course with Maven Analytics, I began to experiment with some data I picked up. In this instance it was the great debate of Ronaldo vs Messi.

Looking back now, I can see plenty of little touches I could make to improve or other ways I could display this, but it is also a good reminder of the journey taken, mostly with Maven, in the last 9 months!

So, if anyone is looking for inspiration to create their own portfolio, or see what improvements you can make to your visuals inside a few months, hopefully you can find it here.

Good luck, and keep vizzing!

Everyone loves a BAN (Big Aggregate Number). They are your all important key numbers in your dataset and should be jumping off the screen and and ingraining themselves in the back of your retinas!

But the standard way of creating them in Tableau can be a bit dull and monochrome.

Below I’ll step through the usual way of creating a set of BAN, followed by 5 quick tips to take them up a level in a recent dashboard I created as part of a Maven challenge

Standard BAN Creation

Normally, you will have a relatively small set of categories that you wish to show an aggregate value for (example being “Age Group” below.

We traditionally create this by dragging the category (Age Group) into the columns, then pulling the calculation into Text marks area.

Voila! We have a BAN. Not very pretty, but a BAN all the same.

You can adjust the header and value for font type, size and colour. Normally, that is about as far as most people go.

What’s the alternative?

But hey – what if you want your BAN in a single horizontal or vertical line, or you want to colour code based on the category or value?

What can we do to customise our BAN and make it that little more memorable?

5 Simple Tips to go from “Boring BAN” to “Badass BAN”

1. Orientation

It is easy to change from a single horizontal to a vertical line, by simply dragging the category from column to rows. This allows you to maximise your data real estate depending on how how you are structuring your overall visuals.

2. Headers

We can drop a duplicate category “Age Group” onto the Text marks card. Then right click on the category header and select remove. This will result in the second image below – still not too pretty, but we are on our way.

You can keep the category above or below the BAN by shifting it up or down on the marks card.

However, my preference is to keep it below, as it keeps the focus on your Big Number!

3. Font

Many people may have different views, but my preference is to keep a single font on a dashboard. Having multiple fonts can become an unwanted distraction and give a clunky look.

4. Size

For the numbers, bigger is better!! Make the size of the numbers much larger than any adjacent text to emphasize the contrast.

To do this, select the Text icon in the marks card, and click on the three little dots on the side.

This will bring up the “edit label” input box. Here you can adjust the size and font attributes (bold, underline, italic).
In my example, I chose 36 for the Aggregate Number and 16 for the underlying category.

5. Colour

Adding some colour can help place emphasis on numbers or categories, and help improve the aesthetics and feel of your visual.

Ctrl dropping the category onto the colour mark would allow you to assign distinct colours based on each category, whereas Ctrl dropping the calculations “CNT(Consumers)” onto the colour mark will allow you to assign colour.

You can also maybe apply a quick table calculation. Here I opted to show a % of total, rather than the straight numbers. This gives a good overall perspective. As shown in the below picture, you can right click on the aggregate number and select the quick table calculation.

Lastly, in my example, I opted for adjusting the colour based on the BAN value itself. As I wanted to draw the eye to the highest value, I used a diverging scale from a green (#00aa00) to a white, which was offset at -20%. This enabled my lowest value to still be almost visible, while keeping the focus squarely on the largest number.

Overall

I was pretty happy with the outcome, and was able to apply the same effect to two sets of BAN. This helped maintain the overall important consistency and look when they were brought into the main dashboard.

What do you think?

As always, if there are any questions or comments, please reach out. I am happy to help where I can, and always open to feedback on alternative methods and learning new tricks from the data fam.

Happy Analysing – DG