First, I must give credit for finding this week’s Makeover Monday to Jade Le Van. She’s a massive supporter of the work everyone has been doing in this project. Thank you Jade for finding this beauty from CNBC.

There are so many problems with this visualisation. However, the most egregious mistake is that the data is completely wrong. The author took the original data set, which has the total money held offshore by a company and allocated that same amount to every country that each country has an affiliate in. For example, General Electric has $199B stored in 18 offshore subsidiaries spread across seven countries. The author allocated $199B to EACH of those seven countries. That’s simply wrong!

What doesn’t work?

• The data is inaccurate.
  
• The pie charts are a ridiculous choice, especially when you show every single company in each country.
  
• The companies aren’t sorted.
  
• The size of the pies isn’t proportionate across the difference columns. In other words, they aren’t all based on the same range.
  
• It’s impossible to find a company you might be interested in investigating.
  

What works well?

• Nothing; the whole visualisation is a complete disaster.
  

I had a few minutes to explore this data set while on the bus home, so I opened up the data in Vizable. Within about 20 seconds I had two views that were far superior to the original.

Quickly and easily, I have a view of just how much money companies are keeping offshore and who they are in a nice, simple ranked list. Great! I already have something much better than the original.

Two more swipes in Vizable and I’m now looking at the companies with the most offshore subsidiaries.

At this point, I felt I basically had all I needed to tell the story in Tableau. I quickly reproduced the two charts from Vizable, then added a Pareto chart for more context. Lastly, I added some headers above each chart to tell a synchronous story.

This week for Makeover Monday we are looking at this Viz of the Day from CNBC as seen on Tableau Public. For me, this is a really poorly done viz that really doesn’t tell us anything. When I look at this and say to myself “so what?”, I can’t answer that question.

Let’s start by looking at what works well:

• The table provides a nice summary of how many countries in each region fall into each tier.
  
• Clicking on a region and/or tier filters the map
• Nice “About” option

Really, that’s about all I can come up with that works well. There are so many things that could be done better:

• The most major problem is that there’s no context. What is good and what is bad? Is the situation getting better or worse? Context is so critical in visualisation and this viz totally lacks context.
  
• The title is misleading; was that intentional? These countries aren’t necessarily trafficking the children. Yes, it’s unbelieably terrible how the children are treated, but the title needs to be more accurate.
  
• The bubbles in the table add no value.
  
• Poor color choices
  
• Tooltips are useless
  
• There are no trends
• There’s no call to action. What am I supposed to do with this information? Again, so what?

Keep in mind that Makeover Monday is NOT about criticizing the author, it’s about critiquing the visualization and trying to create a more compelling story and more interesting visualizations.

What I wanted to try to focus on this week is whether or not childhood slave trade is getting better or worse. The data set that I created contains data back to 2007. Since we have geographical data (i.e., countries), I first wanted to see if viewing the countries by their tier status as a series of small multiple maps would reveal anything.

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Unfortunately seeing trends across multiple maps is extremely difficult. Even if I drill into a specfic region, Africa for example, it’s still really hard to see patterns.

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Ok, so maybe filtering even further by tier status would help. Let’s first look at all tier 3 countries.

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Maybe you now have a case for making sense of the small multiple maps, but it takes too much work to see the rate of change. Enough of the maps, clearly they won’t work for displaying change. Fortunately Tableau lets us fail fast!

An alternative to the small multiple maps would be a heat map. In this view, I placed Country in the Rows and Year in the Columns and added a filter for Region.

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Now we’re getting somewhere. I really like this view because it shows concentrations well. At a glance I can see that there were fewer Tier 2 in 2015 than in 2007 and more contries have moved towards Tier 3. It’s also clear that there are no more Tier 1 countries in Africa.

Contrast that to Europe.

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Nearly every country in Europe is Tier 1 or Tier 2 and have been consistently. The transition in Russia from a Tier 2 Watch List to Tier 3 pops out as well.

So far, the heat map is my favourite. In Tableau though, since it’s so easy to create lots of views, I iterated some more. Next, I created this unit chart which shows each country in each region by year, simply color-coded by their tier status.

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I love the look of this unit chart. Notice that I intentionally made each section 5 circles wide. I did that so they’re easier to count. Two things in particular jump out to me in this view:

1. The number of Tier 2 Watch List and Tier 3 countries in Africa is growing.
   
2. Asia had an increase in Tier 2 Watch List countries early on and is not slowly progressing more countries towards Tier 2 status.

To get an even better look at whether regions are progressing, I can filter the unit chart to only Tier 2 Watch List and Tier 3 countries. Filtering the data makes my two impressions above even more evident.

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Lastly, I wanted to look at a time series view. In this case, I need to aggregate the data up to the Region level and count the number of countries in each Tier. The rationale behind this is to simply see how regions are doing over time. Are they getting better or worse? Is the number of Tier 1 and Tier 2 countries growing or are the number of Tier 2 Watch List and Tier 3 countries increasing?

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It makes complete sense that the time-series view would reveal patterns best. That’s exactly the best use case for a line graph. I like the clean look about this chart. It’s easy to compare within a region and tier or across regions or across tiers or across regions and tiers.

But does this give me the BEST context? Is there a better way to show how the situation has changed since 2007? Yes, by comparing each year to the first year. In this view, I’m now showing the % change in the number of countries in each region in each tier since 2007. I’m using % change because it’s more contextual than simply the change.

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In this post, I’ve shown you many ways to iterate through the data to find a compelling story, but this doesn’t bring it all together quite yet. In order to do so, I need to combine some of these views into a dashboard. I’m going long-form again this week as there’s simply too much information that needs to be shown to be effective in a single screen.

I’ve created both desktop and mobile version with the design a bit differnent in each.