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Makeover Monday – Week 8, 2017: The European Potato Industry

Time for a European dataset once again. And if you’ve ever been around me eat breakfast, lunch or dinner time, you’ll know that I simply LOVE to eat. Potatoes feature right at the top of my list of all-time favourite vegetables. But in recent times people have shunned the humble potato for fear it will make fat (what???). So to ensure potatoes get a bit of love (it was Valentine’s Day only last week after all), this week is all about potatoes…

The original ‘viz’ comes from a Eurostat report, which contains a bunch of tables, pie charts and line charts. Our focus in particular is on these two beauties:

What I like about them:

  • they’re simple and everyone would know how to read them, looking at the intersection of the rows and columns to understand each value in its appropriate context
  • countries are listed in alphabetical order, which makes it easy to find specific ones the reader may be looking for
  • equally, years (in table 1) are in chronological order
  • having years on columns (table 1) makes sense for reading tables, rather than stacking them in a single column
  • The report clearly states all the data sources and provides links to them

What I don’t like about them:

  • no interactivity, tables don’t appeal to me visually, they don’t draw my attention and don’t make me want to look at this further
  • by shading each cell according to the value, this could have been easily turned into a highlight table which would be better than the uninspiring black and white data sheet
  • it’s very hard to compare countries over time and across metrics as I have to keep values in memory (i.e. in my brain) while searching for those values I want to compare them to, then calculate stuff and make sense of the results. A simple bar chart or line chart would help me a lot to better understand this dataset.

What I did:

  • first I needed some inspiration… I wanted a fresh colour palette to use for this week’s challenge so I wasted hours searched on Pinterest and stumbled across this beautiful photo by Chris Court Photography. Perfect!

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  • so as a first step, I created my ‘French Fries and Ketchup’ palette for Tableau. It was fun 🙂

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  • then I went back to pen and paper to draft some ideas for my dashboard and created my first version in Tableau. It was a pretty busy viz, mostly because I love the colour palette and wanted to use it to the max :-). I sent Andy a screenshot of the viz and got some rather frank feedback on my viz. And I’ll admit, it was a bad viz, because I just threw everything at it without thinking about what questions I wanted to answer.
  • so back I went to pen and paper once more and came up with a much simpler dashboard that provides an overview of the European Potato Industry in 2014…

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  • having gone through the data for my first version certainly helped clarify what I wanted to show in my final dashboard so this paper version took about 5 minutes to create. After that I took a break to do some real life stuff in the outside world with other humans, it’s the weekend after all.
  • returning to my desk a couple of hours later I managed to create my final viz in about 50 min, formatting and all. Very happy with that and it confirms once more that a good draft gets you half-way there. At least!
  • So without further ado, here is my final viz… (click for the interactive version)

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Makeover Monday- Week 7, 2017: How Much Do Americans Spend on Valentine’s Day?

Just as I started preparing my blog and viz for this week’s Makeover Monday, there was a change in the planned dataset to something a bit more, well, romantic…
It all started with this tweet earlier today:

Andy being ever responsive went out to chase down a poor visualisation and some data and I got to start from scratch ;-). But hey, it’s all in the name of love, so who am I trying to be all pragmatic and cynical? Love it is.

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What I like about it:

  • the colour choice helps me to easily understand that we’re talking about something love related. Pink and purple with nice contrasts
  • the heading is short and clear, it tells me immediately what I am going to learn more about
  • I like the context that is provided in the first paragraph
  • the images suit the different categories pretty well
  • I don’t even mind the donut chart, I think it is okay for showing me that a little over half the population is planning on celebrating Valentine’s Day
  • the data source is stated

What I don’t like:

  • It’s a bit too much pink for me. A white background with pink and purple as colours for the data would have worked just fine
  • the people category labels seem a bit awkward, they’re too long and descriptive
  • the labels for the $$$ values could be larger, I’d prefer to focus on those rather than having so much space ‘wasted’ on images
  • what year are we looking at? The source quoted at the bottom suggests it is 2015, but I don’t know for sure

What I did:

  • I started on paper and thought it would be fun to show you guys what my first draft looked like

  • I then moved on to pulling it together in Tableau. My colour scheme is based on a set of writing paper I have (and never use, let’s be honest), because I anticipate plenty of red and pink vizzes this week, so I needed something to break it up 🙂
  • I decided to focus on a couple of key statistics from 2016 and to look at how people (and pets) rank in terms of the money that is spent on them
  • apart from that I wanted to keep it simple and create something quick

(Click on the image below for the interactive version)

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Makeover Monday – Week 6, 2017: Inside Chicago’s Taxi Data

For week 6 of 2017 Andy and I decided to go large at Makeover Monday HQ (i.e. the WhatsApp chat window…). If you’re curious, you can read a bit more about it on the Makeover Monday website.

Taxi Taxi

We chose Chicago Taxi data that has been published by the City of Chicago. The dataset is pretty large with 105 million records and we chose it because it gives people an opportunity to work with data that contains geographical fields, dates and payments data and has plenty of scope for various types of visualisations.

Yes, there are A LOT of rows in this dataset, but the fields are pretty simple and the measures are limited to distance, duration, fare components and number of trips. Nothing complicated and hopefully something everyone can relate to.

The original viz comes from an article on transportation in Chicago and looks like this:

What I like about it:

  • simple line chart with a single colour showing taxi trips and taxi fares over time (by month)
  • the y-axis has been truncated to highlight the movement in trips taken over time. This helps to see monthly changes more clearly
  • while I’m usually not a fan of gridlines, I think they actually work quite well here to divide up the chart area and guide the viewer to the values
  • the chart titles are clear and concise and tell the viewer immediately what they’re looking at
  • the data source is clearly stated at the bottom of each chart

What I don’t like:

  • the data is monthly but it is really hard to identify the individual months in the chart because there are no labels. The heading of chart 1 and my crude counting attempt makes me assume that the vertical gridlines show the end of each year rather than the middle or start. But I would like to know for sure, so some form of labeling or minor tick marks would help
  • does the second chart (taxi fares) start in Jan 2013 as well? I guess so but I can’t be certain.
  • I would also appreciate some labels along the line for the peaks and troughs in the data, especially those data points that are referred to in the article
  • the axis labels could be shortened with an ‘M’ suffix to save some space
  • the article states the data release following the Freedom of Information Act request included data from January 1, 2013, to June 30, 2016. Does that mean all of that data is included in these charts? I can’t figure out the range just by looking at them

What I did:

  • this time I wanted to go mobile again but with a landscape layout rather than a long form dashboard
  • My design inspiration actually came from the appstore and an app called 303 Taxi, which is a provider of Taxi services in Chicago
  • I started my design process on paper because I was actually really pressed for time yesterday and needed to have a really solid draft version before starting to work in Tableau to bring it all together

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  • given that I had my paper version, creating the actual viz wasn’t that time consuming (formatting is another matter though…). I simply followed my draft and adjusted the content based on what I found interesting in the data. Along the way I updated the colours and formatting to what I wanted to final viz to look like.
  • given that I worked with the 105 million record data set, I couldn’t just publish my viz to Tableau Public, because workbooks there are limited to 15 million records of data. So I pointed my finished dashboard to the extract from the Makeover Monday website and changed a couple of charts to ensure there was still an interesting story (e.g. the highlight map on the right of the original viz is limited to a single row with the extract because we only included a single community area for pick-ups in that extract).
  • following my adjustments, I published the new workbook to Tableau Server, while the original viz can be seen below:
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The original dashboard designed for mobile, using 105 million records live in EXASOL
  • If you want to check out the interactive viz on Tableau Public, please click on the image below.
    Please note: Unfortunately Tableau Public on mobile browser doesn’t allow for scrolling sideways, so this landscape format has been a complete fail from that perspective. I still like it. I might create a vertical version once I stop being annoyed about it…

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London Partner Summit

This past week I spent in London for work. Besides client and partner meetings I was there with my colleague, Carsten, to attend the Tableau Partner Leadership Summit for Tableau partners in EMEA.

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The summit is for Tableau’s partners which includes Resellers, Consulting and Technology partners across the many countries of Europe, the Middle East and Africa. It’s an opportunity for partners to learn, hear about updates, discuss strategy, sales, etc. and meet other partners.

My first Partner Summit

I hadn’t been to a partner summit before, but a couple of Tableau Conferences (data15 and data16) as well as two Tableau Conferences on Tour (Melbourne 2015 and Sydney 2016). While I didn’t know what to expect, I know ‘my Tableau people’ and figured it would be pretty fun, especially because I deal with many Tableau partners on a regular basis as part of my job. A lot of them happen to be my friends too.

It was really good to spend time with the great bunch of people working with and for Tableau here in Europe. A bit like a family reunion, but in suits instead of geeky t-shirts.

Presenting on the EXASOL & Tableau story

As EXASOL, the awesome company I work for, was one of the sponsors this year, we had a speaker slot for the breakout sessions. That meant Carsten and I had to prepare a presentation for Thursday morning, which wasn’t without its challenges in the past couple of weeks as I struggled to find time. But it was all finished and submitted with a few hours to spare and we spoke to an interested audience on Thursday morning about the EXASOL story and how we became a Technology Partner for Tableau.

While it’s always daunting to present, especially when speaking on behalf of your company, I really enjoyed it. I love talking about Tableau and the projects we have planned for 2017 (as far as I can talk about them at this point, need to keep a few surprises, right?), so that makes presenting fun. And of course, it’s good to practice public speaking as much as possible, because I find that as I progress in my career those speaking gigs feature more and more in my diary.

Networking marathon

Aside from our own presentation I didn’t actually get a chance to attend any sessions because I was literally talking to people non-stop from Wednesday evening until Friday afternoon.

I usually bumped into a few familiar faces during break times between sessions and we had a number of scheduled and ad hoc meetings with people interested in our software.

The discussions were engaging and really valuable, plus I learned a whole lot about various partners around the region and how they help their customers and clients with their data analytics and reporting challenges by introducing Tableau to the BI landscape.

The European buzz at PLS2017

Wednesday evening was the start of the summit with a welcome reception which meant a lot of ‘Hellos’ and hugs and sharing stories about what happened between data16 and now.

I enjoyed the mix of languages all around me, including a lot of German being spoken. That was super cool, because at the global conference English is pretty much the exclusive language you hear. Over the past few days in London there were French, German, Italian, Spanish, Portuguese, Russian, Polish, Arabic, Swedish, Finnish, Dutch and many other languages and it made my heart sing. Europe is my happy place and I just love having people from so many different cultures in one place. It’s a little bit like looking through your holiday photo album while listening to your favourite playlist.

Thursday started with an inspiring keynote by James Eiloart, VP, EMEA Sales, Tableau

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while Andy Cotgreave, Technical Evangelist at Tableau, was the MC for the event and brought plenty of enthusiasm and laughs to the stage.

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On Thursday evening the Partner Awards Dinner was held. I’ll never pass on an opportunity to get dressed up and to use multiple sets of knives and forks over the course of the evening. I might be a low-maintenance, practical person in daily life, but special occasions are fun!

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The after-party in the sky bar of the hotel was a nice way to finish the evening and a chance to chat with everyone in a more relaxed atmosphere. And the view from above was pretty cool, too!

The summit finished on Friday after a few more sessions, meetings and another great keynote, this time by Dave Story, VP, Product Development, Tableau.

Cool down

Following the summit Carsten and I didn’t have much time to relax as the next appointments on Friday afternoon were already in the diary, but thankfully we had Saturday to unwind from a pretty exhausting week.

After a delicious and indulgent breakfast at Borough Markets we split up to play tourists and pick up some presents for our families.

I spent a few hours wandering the streets of Southwark, Waterloo and the City of London districts, looking at shops, eating more food and visiting churches, taking photos and crossing bridges.

With every visit I fall in love with London a little bit more. Even the rain can’t put me off. It’s a fun and energetic city with so many sights, monuments, churches and historical buildings on every corner. You can hardly catch your breath and I often find myself standing in amazement, staring up at some ancient church or a plaque indicating a significant place.

There are so many alley-ways, traditional pubs, glitzy stores, posh restaurants and quirky shops, you simply can’t get bored…

I soaked it all up for a few hours before heading to the airport for the journey home. On the train I started getting ready for the next Makeover Monday challenge with a few conversations on Twitter that have me super excited about the data for week 6. I cannot wait to see what people will create!

And that brings me to the end of this week. With the Partner Summit over I hope to have a bit more breathing space  to return to regular blogging and work on my Alteryx workflow projects, so you should find more frequent posts on this page in the weeks to come.

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Makeover Monday – Week 5, 2017: Employment Growth in G-7 Countries

For this week, Andy once again delights us with a very small dataset: 1 dimension, 2 measures, 7 rows of data. I can already sense the frustration that some people will feel because how big and elaborate a dataviz can one build with only 14 values? I am, on the other hand, delighted. A great dataset to keep things simple. As simple as possible.

So let’s look at this week’s challenge. It’s an article by Elena Holodny in Business Insider UK discussing the 2010 (Q1) to 2016 (Q3) growth of employment in G-7 countries. These include Canada, France, Germany, Italy, Japan, the United Kingdom and the United States.

The data visualisation in the article shows the following pie charts, which depict employment growth as Share of Total Employment and Share of Net Growth in Employment with each country being a wedge of the pie.

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What I like about it:

  • the colours for each wedge are very distinct, so it is easy to differentiate the countries based on colour
  • the labels are large enough to be legible
  • the title is pretty clear, identifying what (employment growth) was measured, in which context (G-7 countries) and for which timeframe (2010:Q1-2016:Q3)
  • the headings for each of the two pie charts also specifies which measure is being shown and that the numbers are percentages, which by including in the heading saves space on the chart
  • I’m glad they included the note on rounding to ensure people understand the ‘limitations’
  • the data source is stated

What I don’t like:

  • the formatting of the title and individual chart headings looks cluttered. Keeping the font regular instead of bold, or using a lighter font would have helped, as well as leaving more white space – it’s all a bit too dense for me
  • it’s a pie chart, with 7 wedges. It’s not the easiest for viewers to visually comprehend the proportions attributed to each G-7 country
  • the labels for the small wedges sit off the side of the chart, which looks inconsistent
  • having the pie charts next to each other almost feels like we should be comparing them like a ‘before and after’, but they need to be looked at separately, because they visualise completely different measures that don’t have a direct relation
  • the branding/logos take up valuable space. Are they really necessary? Would a small icon in the corner suffice?

What I did:

  • after playing with the data and reading a bunch of articles to get a better understanding of it, I decided to settle on a simple bar chart to just show the differences in percentages between the countries
  • I wanted to finally design a mobile viz, so I went fully mobile rather than adding different device layouts
  • I’ve also grown quite fond of large labels and dual labels on a single mark, so I spend a bit of time trying out different approaches until I settled on something I liked
  • and because I created a new colour palette this week, I chose to use a different colour for each country. In part that’s also because I divided the charts across 2 mobile screens (when you scroll down), so I wanted to make it easy for the viewer to recognise the countries based on colour.

(Click for the interactive version)

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Work Life Balance

Work-Life Balance, in my opinion, is finding enough time for work and personal life, but what ‘enough’ looks like changes constantly. It’s also about attitude. For those who hate their jobs, even just working 10 hours a week won’t let them find that perfect equilibrium where they experience a state of bliss and a sense of satisfaction with their life as a whole.

Luck has nothing to do with it

Admittedly, it’s easy for me to make these statements, because job-wise I have hit the jackpot and life-wise it’s been smooth sailing since day 1. But what a lot of people may attribute to ‘luck’ and ‘good fortune’ is simply the result of deliberate work that started all the way back in my high-school days.

Achieving a balance between work stuff and life stuff is something I definitely see as important, but for me there are no clear rules for it that apply universally and there certainly is no way of saying that a 20/30/40 hour work week will achieve the desired balance.

Some weeks I work 40 hours as per my contract, have plenty of time to ride my bike, go on weekend adventures with my husband and binge-watch whatever TV series we happen to enjoy at that time. Other weeks, like the last few, I work significantly more. But it’s hard to measure how much exactly, because the lines become blurred and it doesn’t stop with me stepping out the office at the end of the day.

You see, my job involves A LOT of networking with people from the Tableau community and beyond. That happens face to face, via email, WhatsApp, Twitter and LinkedIn, via webex and Skype and at various events. Of course some of that is between 9am and 5pm, but more often than not, communicating with ‘Tableau people’ across the globe happens at all hours of the day. It starts when I wake up at 5am (Hello, Twitter notifications!) and finishes when I switch off the light at 9.30pm. And you know what? I like it that way. It’s up to me to put my phone down and close my laptop but I also have the freedom to do what I love whenever I want to.

My job isn’t unique with regard to the type of things I do, and lots of people operate the same way. It doesn’t feel like work to me though, because I would talk to many of the people in my network anyway. Now I just have many more things to talk to them about, because my role gives me the opportunity to get more involved, try out new ideas and drive forward initiatives that contribute to the community.

And my private life? Leisure time? When does that happen?

Well the way I see it is that leisure time is what I need to ‘recover’ and to recharge my batteries. Yes, they need recharging but when I do something I love and enjoy and find fulfilling, there isn’t really much recovery needed. Yes, I need sleep and my brain needs a break, but it’s not like I come home from work on a Friday evening wishing for a 4 day weekend. In fact, after a couple of hours of watching brainless TV shows and eating whatever is left in the fridge, I’m usually already thinking about something work-related and I haven’t had a single weekend when I didn’t look forward to heading back to the office on Monday.

And I’m not writing this just because my colleagues or my boss might read it. I’m German, we don’t mince our words, and we’re certainly stingy when it comes to compliments. So I truly mean it, I do love Mondays because it means there’s a week of fun and challenges and hard work and rewarding achievements ahead.

The right job and a good plan

Having a job that I love makes it easy for me to achieve work-life balance, because neither work nor personal life tips the scales into an imbalance that I need to somehow compensate for.

The only challenge that a demanding (but fulfilling) job brings, is the need for excellent time management and organisation skills and some good support at home.

I’ve always been pretty good at getting stuff done and planning my schedule to fit everything in. To achieve my weekly training load I need to be diligent to get enough sleep and food at the right time, but it’s just a habit now, much like brushing my teeth. It doesn’t require natural talent or some serious willpower, it’s just doing the things that work over and over again until they become so ingrained that you can’t mess them up.

Plus a bunch of supportive people

I also have the benefit of a supportive husband who takes on many of the tasks at home so I can focus on the stuff I do best. It’s all well and good to bring home the money for food and rent, but without someone to actually go to the supermarket (which is often closed by the time I get home), the fridge would be pretty empty. Having Paul take on the ‘household logistics’, manage our cleaner, help my parents run their business and study German, takes an immense load off my shoulders. It means I can tightly pack my schedule during the week. And on the weekend I fill my day however I please.

My parents are also pretty legendary in helping me. Whenever I visit them, mum has something ready for me to eat. Whether I’m hungry or not. They let me use one of their cars when I need it and get groceries from the local market gardener down the road. They pick up my bikes from the mechanic (open 10am to 6pm on weekdays – how convenient… not…), water our plants when we go away for a few days and are always ready to lend a hand when I dream up a new DIY project to make it look nicer around here. They’re also happen to listen anytime and are my biggest cheerleaders even when they don’t understand what on earth I am talking about.

You’ll know when you’ve found ‘The One’

If you were hoping for the magic work-life balance formula, I’m sorry to have disappointed you here. I don’t think there is one. But I strongly believe that many jobs, especially the ones in the new economy that offer flexibility, interesting challenges and have a scope that can be redefined as required, have the chance to enable anyone to find the sweet spot where things are humming along. Those jobs let you finish each day with a smile and the satisfying exhaustion that mean your last thought of the day is not ‘thank God it’s over’, but rather ‘I can’t wait to do it all again tomorrow!’

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Makeover Monday – Week 4, 2017: International Tourism Spend in New Zealand

This week’s data takes us all the way to a beautiful little country at the end of the earth. That’s what it feels like to live there and it certainly is far from most people’s minds and their maps as they were designed with Europe at the centre of everything.

But New Zealand, this humble nation downunder, thousands of kilometers even from its neighbouring big brother, Australia, is such a cool place that I thought more people should learn about it. That’s the reason why I chose to find an NZ data story for this week’s makeover challenge.

Aside from that, I’ve been looking for more localised content that goes beyond UK and US or global data and am pleased to have found some data on NZ tourism spending as well as an optional data set with geospatial data for all those contributors who want to do some mapping.

Let’s start with the original viz…

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In the accompanying notes, Figure.nz provide a lot of information around the data, where it was sourced from, how it was collected and what limitations there are, as well as what’s included and excluded. The notes proved to be very useful for understanding the data and I hope that all those who participate in this week’s challenge read the notes as well…

What I like about it:

  • Despite the multiple colours, it is actually a fairly simple bar chart with the height of the bar indicating the regional tourism index by month across three different years
  • The colours are quite distinct from each other, so are easy enough to differentiate but still work fairly well together as a palette; the legends are prominent but not ‘in the way’
  • The baseline index of 100 (from 2008) is stated in the subheading, which helps to put the results into perspective
  • The data source is listed (this is always a favourite of mine!)
  • The heading is succinct
  • The gridlines are kept to a minimum so they guide the viewer without cluttering up the viz

What I don’t like:

  • It would help if the main heading specified that the tourism spend is shown as an index rather than a dollar figure, which is what people would likely assume when they first read it
  • With the calendar year shown in the usual order, the story to me has a negative connotation because in both charts we can see a dip as the year progresses before spend picks up again.
    Given that New Zealand is in the Southern Hemisphere, the seasons are reversed from those in the Northern Hemisphere, so the middle of the year is winter and therefore low season.
    I think it would be more reflective of the overall trend of increased tourism spend to align the months accordingly and start the x-axis with either June (the beginning of winter, the lowest month usually) or September (the beginning of spring and when tourism tends to pick up)
  • As much as I love bar charts, I think they’re not perfect for visualizing this kind of comparison of different years across months. With the above visualisation I find myself ‘starting from scratch’ every month and rethinking what I’m looking at. I’d like to be able to see the overall trend much more easily and at first glance.
  • A bit of commentary or some annotations would be nice to explain some of the pattern or point out certain events that may have impacted the data.

What I did:

(Click for the interactive version)

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  • I wanted to created a bit of a story rather than ‘fixing’ what I didn’t like in the original chart, so I opted for a long-format dashboard with three sections
  • I opted for a black colour scheme which I decided on right away. New Zealand has a strong association with the colour black, thanks to its sporting teams, so it is involved in branding all over the place, especially with Tourism NZ.
  • I had a grand design in mind, but I lack the skills for it so after some initial experimenting I dropped the lofty ideas and settled for simplicity instead
  • As a German who lived in NZ for 8 years and also has an NZ passport, I found the focus on international visitors more interesting so I stuck to that
  • As part of my iterative data exploration I created a simple line chart for international visitors and flicked through each region to see where the interesting trends were. I had thought that The Hobbit or Lord of the Rings would provide an interesting angle and the data confirmed this and the story started to come together
  • I decided to focus on the region of Matamata-Piako where the Hobbiton film set is located. This region has seen a huge growth in tourism spend over the last years and that’s what I wanted to show
  • Colour wise I needed to find something that goes well with the black background. White has the strongest contrast so that was the main colour for the line charts and labels etc. As a secondary colour and to highlight Matamata as the focus of my story, I picked a golden hue of deep yellow which aims to remind people of the ring from the movies.
  • To provide a bit of context of where Matamata actually is, I wanted to include a map as well. New Zealand is so far away, most people don’t really know much about its geography. So here’s a chance to learn more and I included a small map highlighting the region in focus.
  • Lastly I added some text boxes listing data sources and my details and applied the final formatting.
  • This viz took longer than 1 hour. I probably wasted 1 hour trying to be artsy before going back to the start. The core content and main formatting took around 2 hours, then some fine tuning and layout changes before I was finished.

Andy promised me that it wouldn’t hurt if I tried to go for the long-format layout and he’s right, it wasn’t that bad. I tend to prefer to see ‘everything on a page’, but it’s nice to have more room. Containers can still be a headache but for the most part it was easy to use this format and allowed me to think in ‘sections’ or chapters. Check out how Andy approached the challenge this week here

I genuinely hope that everyone who participates this week enjoys this dataset and learning a little about Aotearoa, the land of the long white cloud, and a stunning place on this planet that you ought to visit at some point.

If you want to read something non-data related on the topic, feel free to check out an article I wrote last year during our visit to Wellington before moving to Germany…

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All or nothing

Last year I participated in Makeover Monday around 20 times. That’s not even 50% of the vizzes. This year I committed to not only submitting a viz every week, but to also write a blog post for each viz, find articles and data for 26 of the weekly challenges, write 26 weekly recap posts, and engage in the social media fun that comes with it: tweets, retweets, responses, messages, likes, sharing elsewhere and spreading the love.

Absolutely no regrets! Ever since week 1 it’s been great fun and I’m really enjoying it, because I get to be so much more involved in the community.

When I do something I find it easier to commit 100% or not at all. So doing every makeover for me is an easier commitment than saying ‘I’ll do the makeovers that look fun’ or just focusing on the topic that is easiest to visualise etc.

Joining Andy in running the project meant that I have made it part of my daily and weekly routine. Yes, there is a lot of ad hoc work, especially around Twitter, but certain tasks have a reasonably set schedule: Sunday is time to post the data. Doing it every 2 weeks means I can spend the ‘off’ weeks searching for good MM articles instead of preparing data. Sunday afternoon I usually spend doing my Makeovers because I don’t have time to do so at work on Mondays. Unless there is an opportunity to do a ‘Live Makeover Monday event’ like we did in London last week.
While I build my viz, I also draft the blog article that goes with it, including my critique of the original article and chart. Then it all gets published.

Once the data is out there, the real fun starts. Don’t get me wrong, I love building the vizzes and I also enjoy writing about the process, but the really fun aspect of this whole project is talking to you guys, the community, via Twitter about the submissions. It’s hard not to miss a submission as they come in very quickly on Sunday evening and then on Monday it’s a real deluge :-). But I love that. I wake up on Monday morning and my Twitter feed is full of vizzes, tweets and retweets from the Makeover Monday crew.

After getting through the first wave of that and eating some breakfast before I start work, everything starts humming at a more consistent pace and there are usually a number of discussions along the way. And of course as the week progresses we get to choose our favourites from all the submissions for our Friday recap blog post. In between Andy and I chat about Makeover Monday related stuff pretty much every day: new ideas for MM challenges, changes to the website, data we have found, interesting initiatives that would align with MM, potential live MM events, etc.

And then just like that it’s Friday, the second ‘hump’ of the week when we publish the recap post before settling into the weekend and gearing up for the next challenge to be released.

It’s a great project and I love being part of it. Compared to last year when I submitted vizzes every now and then (with a big hiatus over summer), this year my commitment has been much easier to maintain. Doing it every week is easier. It’s all or nothing. And that’s how I like it 🙂

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Alteryx – let’s start right at the beginning…

As promised, I’ve decided to kick off a series of blog posts focusing on Alteryx as I go about learning how to use the tool most effectively for my data adventures in 2017.

Before I get into the technical details, I should back up a little and talk about ‘me and tech’ to help you understand the tone and content of these articles better. (if you don’t like detours to the 90’s, feel free to skip to the instructions further down)

Tentative first steps

Growing up, we’ve had a computer for as long as I can remember and I was lucky enough to start using the internet in 1995 at the tender age of 10 to research information on gerbils (by necessity: I ended up with a pair rather than brothers and needed to figure out a way to control the reproduction efforts they went to before the house was overrun by cute little brown mice).

I’ve always enjoyed the world that was opened up to me through the internet and using technology from an early age.

But no one ever taught me the really techie bits. I had many friends who worked as DBAs, network or system engineers and some even founded their own tech companies that are still going strong today. But I was more the ‘mascot’ of the club than a contributing member.

I tried to learn how to code but never went beyond ‘Hello World’. It just didn’t really appeal to me and reading heavy books with no clear idea of what I could do with the knowledge and skills afterwards.

This was around the year 2000 and the internet certainly wasn’t the same as the one we know and use today.

What this meant for me was that I never considered a career in IT or a degree in a tech subject. A bit of a regret to be honest, not at least giving it a try, but from where I stood it didn’t look like my kind of thing.

The downside is that for me using a new tool always causes a bit of anticipation.

Intimidation

For one, I don’t want to break anything. But more importantly, I’m worried that I’ll look stupid when I ask someone a question and don’t understand the answer. Sounds mad? Maybe, but to me a lot of the tech talk is like a foreign language and people assume I have certain knowledge which I don’t. So they use terms which make me just smile and nod while scratching my head and heading straight into a google search after our conversation.

Or someone tells me “just do A, then B, then E and you’ll get to the solution”. Hold on there! How do I do A? Why do I have to do B? And can you please tell me about steps C and D so I can actually get to E?

I have found a way to solve this problem: Find a friendly person who explains a process to me once from start to finish with all the detail before I go off and do it on my own. You see, I’m not dumb or slow. I just need a decent explanation once and then I can be left to my own devices. Usually. In high school I had really shoddy grades in maths. But not because I’m stupid, because once I got a private tutor who explained the material in a way that I understood, I managed to finish my secondary education career with a very decent 12 out of 15 possible points. Not bad after many years of suffering.

How this applies to Alteryx

The way I approached Alteryx was a bit similar. Minus the suffering.

At first when I tried to build a workflow, I looked in the online help and played around in the tool but couldn’t get anything to work. Yes, I hear you, it’s just ‘drag and drop’. But which of the gazillion tools am I meant to use? And what’s the tool configuration all about?

People say it’s very intuitive, but to me it certainly wasn’t. I’ll have to be honest there. I guess I was used to the simplicity and instant visual feedback I enjoyed in Tableau.

Alteryx was an enigma for me, so I closed it and rather disappointingly gave up for a few days.

Thankfully I had to use it for work, so there was no way around figuring it out. So I sat down with my colleague who’d been using it a while and got him to walk me through building my first workflow.

He pointed out all the essential configuration details, the tools I’d need for my specific task and let me play with it. And voila, I was able to successfully blend and transform some data and publish it to different types of output files. That first success was all I needed to continue with some more enthusiasm.

In one of my work projects I had to create a lot of Alteryx workflows to pick up data from many different sources and turn it into a consistent output for consumption by Tableau. That was fun and a good opportunity to get more practice.

Re-acquainting myself after a bit of a break

After a lengthy break (moving countries and changing jobs) during which I didn’t use Alteryx at all for several months, I am now back in the game.

In my day job I obviously deal with data a lot as well, so there’s a good chance that I’m using Alteryx regularly to blend, enhance and enrich datasets for analysis. But mostly my Alteryx practice comes from Makeover Monday. As I obtain datasets for Makeover Monday challenges, I transform them into .tde files at a minimum, but sometimes I need to do a bit of tidying up along the way, so I get a fair bit of practice doing that.

The second week of Makeover Monday was the first week in which I chose the dataset and it was a nice and simple one. I obtained the original data as an .xlsx file from Statista (add hyperlink) and then used Alteryx to turn it into a .tde as we usually publish both formats.

Yes, I could just open the .xls in Tableau and save it as a .tde but why not just get into the habit of using Alteryx for the transformation?

Using Alteryx to speed up my Makeover Monday data prep

In the next couple of paragraphs I will explain what I did in Alteryx and what the results were for each step.

To many this will seem super-basic, but there might be a couple of people just starting out with the tool and having similar reservations to those I held when it was all new to me.

Not to worry, I’ve got you covered ;-). I’ll promise to break everything down as far as possible and give you the ‘why’ as well to help you understand the reasons behind choosing certain tools and changing configurations.

I will assume no prior knowledge because, well, you know why… 😉

Using Alteryx to convert an Excel spreadsheet into a .tde file

First you’ll need an Excel spreadsheet you’d like to transform. Pick something nice and simple: a few columns and rows with no fancy formatting or so (remember we’re just getting started here…). Start with a new workflow in Alteryx. Your screen should look like this:

starting-screen

At the top of the screen you have a menu with all the tools you can use. For this workflow we will only use three of them.

On the left-hand side you can see the configuration menu where we will adjust the setting for each tool as required. The white space in the middle is your canvas where you’ll drag your tools. And finally there is the results window at the bottom of the screen. This is where you can see whether what you did worked out J.

Tools required for this workflow

In the workflow for my Makeover Monday data I needed only three tools:

  • Input Data: this is where I choose the Excel file that contains my data
  • Select: this is where I remove unnecessary fields from the dataset and change the data type of the fields I want as needed
  • Output Data: this is where I tell Alteryx to create a .tde file of the data in a specific location

Let’s build it!

Step 1: Drag the Input Tool onto your canvas

Step 2: configure the Input Data Tool

Step 3: add the Select Tool

Step 4: configure the Select Tool

Step 5: add the Output Data Tool

Step 6: configure the Output Data Tool

Step 1: Drag the Input Tool onto your canvas

step-1

 

Step 2: Configure the Input Data Tool

We now have to configure the tool, which in this case simply means telling Alteryx where to pick up the Excel file from.

step-2

a) Click the downward button and select ‘File Browse’

step-2a

b) Choose your Excel file from its location

step-2b

c) Pick the sheet (if applicable) and click OK

step-2c

d) Alteryx will give you a preview of the data in the configuration window on the left-hand side

step-2d

e) Press the ‘Run Workflow’ button to see the data in your results window

step-2e

f) You’ll be able to see the result below your workflow

step-2f

Step 3: Drag the Select Tool onto your canvas and connect it to the Input Data tool

step-3

Step 4: Configure the Select Tool

We now have to configure the tool, which allows us to pick and choose which fields stay in the dataset as well as what data type those fields should be.

Go from this:

step-4a

To this:

step-4b

First I changed the year field to an Int16 to change it to a whole number rather than a decimal number.  Then I unselected the fields F4 and F5 because they are emtpy and not required.

That’s it. Run the workflow again and see the results in the results window…

results-step-4

Stay with me, we’re almost done here.

Step 5: Add the Output Data Tool

The data has to go somewhere and that somewhere is going to be a .tde file. First we add the Output Data tool to our workflow:

step-5

Step 6: Configure the Output Data Tool

a) Let’s tell Alteryx where to save the .tde and all that stuff. This is similar to the input steps…

step-6a

b) select a location for your file and give it a name…

step-6b

c) choose your Output option: when you run the workflow, do you want to overwrite the data, append new data or create a new file?

step 6c.png

d) Run your workflow and you’re done!

step-6d

 

And that’s all there is to it. Not that tricky, right?

I obviously went into a LOT of detail here but if you’ve never used Alteryx before you may find this useful and next time we can skip over half the detail and just focus on the punchy things.

I am excited about my next Alteryx post because I have something really cool coming up that Chris Love helped me with, so I can’t wait to get cracking with those next workflows…