My ‘Makeoversary’

Intro

Makeover Monday is a weekly social data project led by Andy Kriebel and Eva Murray. It was officially started in 2016 by Andy Kriebel, who was joined on the project by Andy Cotgreave (I believe prior to that it was a personal project of Andy Kriebel’s). The project has grown considerably over the last two years. At the Tableau Conference in Vegas this year a group of about 500 people attended a live session, so it’s fair to say that it’s rather popular. The commitment and effort it must take to lead this project from week to week is unreal, so thank you so much to Andy, Andy and Eva for their amazing dedication.

I discovered the project in Week 41 of 2016 so my ‘Makeoversary’ (I have taken this term from Colin Wojtowycz, thanks!) has passed quite recently. I thought it would be worthwhile reflecting on my participation over the year and the project in general (you can find the interactive version of the above visualisation here). If I recall correctly, I first saw the project mentioned when I stumbled upon Andy Kriebel’s blog last year and investigated further. Every week, a new data-set and visualisation are published and people are challenged to do a makeover of the original visualisation. Contributors then share their work on Twitter with the hashtag #MakeoverMonday. You can read more about it on the website. It sounded interesting so I decided to give it a go.

Contributing to Makoever Monday was my first real exposure to, and engagement with, the wonderful Tableau Community. While I had a Twitter account, I rarely posted tweets, so I was quite the novice. It’s fair to say that the first time I tweeted to share a visualisation was something of an ordeal. What I liked about the project was that it offered me a framework within which to engage with the community and share my work. I would otherwise have felt less comfortable sharing work publicly. I remember being welcomed by Andy Kriebel and Andy Cotgreave, which really made me feel like I was taking part in a live, social project. Over the course of the year I shared 30 visualisations (I have three that are still in draft and in all likelihood will never again see the light of day). Prior to creating my first viz for Makeover Monday I had a grand total of six visualisations on my Tableau Public profile. Makeover Monday is responsible for about two thirds of the vizzes in my profile and you can see them below:

MM1617

Below I’ve listed the nine key reasons I’ve found this a worthwhile project to be involved in. People’s motivation will vary (there have been several examples of hiring managers using it as a way of spotting Tableau talent). If at least some of the points mentioned below resonate with you, then I’d recommend you check it out, if you haven’t already done so. It’s worth noting that although most people seem to use Tableau for Makeover Monday, it’s not a requirement.

Benefits

1. Masters to learn from

There are so many brilliant people contributing to Makeover Monday, so participating in the project is a great way of learning and getting inspired by the work of others. It’s all the more relevant and valuable as you are all working on the same data sets and you can look at how other people have analysed the data. So when I say masters, I’m not just referring to Tableau Zen Masters here, but let’s look at a couple of them first. Andy Kriebel is the first one that comes to mind, given that he co-leads the project. I’ve been hugely inspired by the work Andy creates on Tableau Public and his videos and blogs. You get some idea of what it takes to become a Zen Master given the quantity and quality of content and learning material Andy produces. Then we have Pooja who is one of those people in the Tableau Community who is so well known that a first name might suffice (you certainly can’t say that about Andy given the high frequency of Andys), but in case you’re not familiar with her work, it’s Pooja Gandhi. She has been a regular contributor and the quality of her vizzes are to such a high standard that an adjective has been coined in her honour – ‘poojatastic’ (not sure who to give credit to here). Pooja is a Zen Master and one half of the amazingly creative The Data Duo. Over the course of my time participating in the project I’ve downloaded lots of her workbooks and picked up many great tips. She has a really clean and informative style, a phenomenal command of Tableau functionality and an incredible eye for design. Thanks to Pooja for all the amazing work that she shares. Adam Crahen is the other half of the duo and also produces stunning masterpieces on Tableau Public. He has an unbelievable amount of Tableau knowledge which he shares freely and helps people through his blogs and engagement on Twitter. Check out their latest project #DuoDare. I wasn’t sure whether to name some of the other people that have inspired me personally at the risk of forgetting someone, but decided I would. If you’re looking for some people to follow on Tableau Public, you should check them out. In no particular order they are Ann Jackson, Ravi Mistri, Brittany FongRyan Sleeper, Brit CavaPablo Saenz de Tejada, Sarah Bartlett, Mike Cisneros, David Krupp, Michael Mixon, Lindsey Poulter, Rody Zakovich, Rodrigo Calloni, Chantilly Jaggernauth, Jeffrey Schafer, Neil Richards, Steve Wexler, Charlie Hutcheson, Andy Cotgreave, Chris Love, Amanda Patist, Sebastian Soto Vera, Emily Kund, Matt Francis, Chloe TsengPablo Gomez, Fi Gordon and Eva Murray. I’ve been able to learn from them either as a direct result of the project or indirectly through exposure to the wider Tableau Community.  Thanks to everyone, but especially to Andy and Eva, whose huge efforts create opportunities for us all. It would be remiss not to thank the fantastic Tableau Public team who support the platform that facilitates it.

2. Maximise Learning

People often learn Tableau in different ways. It’s common to attend classroom-style training or structured virtual sessions and avail of all the great tutorials and content online. Ultimately, to consolidate all that learning, you need to apply it, be exposed to different scenarios and get experience. This project provides you with a fresh data set each week and allows you to come up with questions of the data, find stories through exploration and apply your knowledge. The software is continually evolving, so it allows you to try new functionality, deepen your competencies with more complex analysis, or simply cement your knowledge of the fundamentals if you’re new to Tableau.

3. Meaningful feedback

Once you have shared your visualisation, there are opportunities to get feedback on your work. Sometimes the feedback is volunteered, other times people might respond to requests for feedback. When I shared my first visualisation I recall receiving a comment from Adam Crahen. He pointed out one thing he liked about my viz and offered a suggestion on how to improve one view, which I then incorporated. I found this very helpful. I think it’s fair to say that in general, feedback is either complementary or contains constructive criticism and intentions behind comments are good. I find the feedback meaningful because you know that the others are familiar with the data set and have worked on it too. It can lead to good discussions about how to effectively communicate the data. However, there are obvious limitations to feedback given in the form of a tweet. Andy and Eva also write comprehensive recap blogs with feedback every week and hold webinars in which they review and give feedback on visualisations shared with the hashtag #MMVizReview.

4. Motivation

The project has proven to be a great motivator in developing my Tableau and data visualisation skills. There is a social aspect to participating, a feeling that others are working on the same challenge. While there is no deadline as such, it’s fun to engage when lots of others are sharing their work. In weeks where I haven’t had time to participate I often still searched for the hashtag to see what was being created. In weeks where I find a data set particularly interesting, the analysis is likely going to be more compelling than, for example, analysing how Superstore sales compared in 2016 versus 2015 (not that I have anything but positive feelings towards the infamous Tableau dummy data set, which can be leveraged in so many ways , in this example from Ryan Sleeper).

5. Make connections

You get to engage with like-minded people who have a passion for data visualisation. While you may only ever interact with some of the people on Twitter, if you ever attend Tableau conferences, User Groups or events, there may be people you will recognise or have made a connection with already. I found it great to meet a few of the people participating when I went to the Tableau Conference on Tour this year in London, several of whom are featured on the Makeover Monday community blog. It was also great to attend the live Makeover Monday session hosted by Andy and Eva in Dublin which I wrote about here.

6. Myriad approaches

You get to see such a wide range of approaches each week, with people sharing some outstanding work. Some weeks you will be hard pushed to find two submissions alike, other weeks you will see several people who have very similar ideas. From slick, corporate style dashboards or great examples of data journalism, to visualisations that can truly be described as works of art, you get to see the huge breadth of applications of Tableau. There are a few examples from a selection of authors below. The visualisations are from different weeks, so the data sets analysed were not the same, but I think they give a flavour of the variety of work that’s shared from week to week.

Dashboards

7. Means of experimenting

One thing I like about the project is that I get to experiment and try things that I’m not necessarily getting an opportunity to do in my day job. In work you are often designing to someone else’s specifications, whether it’s an internal stakeholder or a client. When I download the data for Makeover Monday it feels like I am essentially creating something for myself. Granted, it is still beneficial to think about the audience and what might work well, but you are not restricted in any way. Each week you start off with a blank canvas and it’s a great way to try things out and it’s interesting to see what others make of it. It’s valuable to get this opportunity to have something validated, or not, as the case may be. For me it’s like a proof of concept, whether that’s from a functional or a design perspective and it’s a great learning experience.

8. Many topics and types of data sets

As I already mentioned, the data sets themselves are often interesting to analyse. There is a list of them on the website and you can see the diverse range topics. One week we looked at world internet usage, another at youth employment rates in Latin America and another at German car production figures. You can end up learning about a domain you mightn’t ordinarily have come across.

You also get exposure to a wide range of data sets and data types. The smallest data set I recall contained two rows in an Excel sheet, and in contrast there were weeks where a connection to an Exasol server was provided, in one case to a data base containing 800M rows of UK prescription data (you could easily have thought you were connected to a two row Excel file as it was exceptionally fast). You get an opportunity to think about how you might best tackle different types of data e.g. survey data, polling results, migration and tourism statistics, indexes, dates, averages and financial data. It allows you then to transfer an approach to data you come across in a professional or personal context.

9. Minimal data preparation

There is minimal effort required from a data preparation perspective. This might contrast to scenarios you encounter in a professional setting, where data will most likely need to be shaped, cleaned and transformed in different ways before it can be analysed in a meaningful way. You might decide you want to bring in additional data sets or decide to shape the data in a different way, maybe trying to develop skill-sets in tools such as Alteryx. If that’s what you want to focus on, there are opportunities to do that too. While this part of the data analysis process can be rewarding, I like being able to skip to the ‘fun’ part, which for me is analysing the data, exploring and visualising it in different ways and thinking about how to present it. Getting the data served up each week allows you to do just that.

Final thoughts

It has been announced that there will be some changes and improvements to Makeover Monday in 2018 and if you want to hear more there will be a webinar on 21 December. I’m planning to continue to participate where my schedule allows and especially when the data set piques my interest. There are lots of other worthwhile projects to check out in the Data Viz Community. Viz for Social Good is one that is led by Chloe Tseng and supports non-profit organisations, through engaging volunteers in the data visualisation community. Makeover Monday and Viz for Social Good collaborated twice this year. Data for a Cause is led by Olga Tsubiks and is another project that you should check out if you’re keen to put your data visualisation skills and passion to good use.

That’s it from me for now. Thanks for reading!

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