• The data by region is ordered alphabetically, making it easy to find each region.
• The bar chart is sorted by largest to smallest.
• Nice filtering options

• A diverging color palette should only be used when there is a logical midpoint or goal. I don't see those in this viz.
• The squares are hard to understand.
• I don't find the map very useful. It would be more useful if it zoomed in when a region is selected.
• There's no title.
• There's too much text.
• The bar chart seems to go out past the edge, or at least visually it appears that way.

• I created a KPI scorecard so that I could understand the patterns for the overall or an individual country. Are literacy rates improving or regressing?
• Show the distribution of the rates of the countries within each region
• Within each region, which countries are above or below the median for that region?
• How has the literacy rate changed over time?
• Allow simple filtering options.

Enjoy!

What works well?

What doesn’t work well?

I decided to work on my makeover during my flight to Prague, thus imposing a time limit on me. I started by creating a view that simply shows the number of data breaches by year using circles. This basically flattens out the original.

While this shows the distribution nicely, I don’t love it. Next, I converted the circles to squares, hoping the result would be more visually impactful as the squares take up more space.

This is definitely better, however I don’t like how it doesn’t incorporate the records stolen in each data breach well enough for my liking. So I decided to add a dot for every breach in the data set and change the location of each dot to the number of records stolen.

Getting there…iterating is really helpful. This shows some of the outliers really well, but I feel like I’ve lost the distribution a bit. I decided to quickly open the data in Vizable and when I switch the view to records stolen by year, Vizable presented me this interesting view that shows the median and the distribution.

I really liked this so I decided to build upon it in Tableau. My final viz incorporates the view from Vizable, the distribution of each data breach and allows me to focus the story on data breaches that were hacks versus not hacks.

I find this final view much, much easier to look at than the original and also it provides much better context. For me, context is key. Every visualisation you create should include context somehow. Why? Context makes it much easier for your audience to understand the story.

Yesterday at work, I received the following scenario that one of our users was stuck on:

There is a list of products that the user can filter. The monthly sales for each product needs to be compared to median of all products across all of the months, but the median should recalculate if the user filters the region and/or priority. The end result should be a "Relative Sales" calc that is the sales for each subcategory divided by the median of all sales.

The trick here is that the Subcategory filter cannot impact the median calculation. The steps below could also easily be applied to a sum, min or max. I demonstrating a median because that was the question at hand.

First, consider that you have a view like this, which is sales by monthly by subcategory, with quick filters for region, order priority and subcategory.


Next, create the median calculation and add it to the view. Remember, the median should account for the entire view.

I've added Median to the view and changed the Compute using to Table (Across then down).


Duplicate the data source by right-clicking on it and choosing Duplicate.

You should now see a copy of the data source in the data window. Go to your secondary data source and drag Median into the view and change the Compute using to Table (Across then down).


Sweet! We're almost done!

Create a calculated field in your primary data source for the Relative Sales. This is going to use Sales from the primary data source and divide it by the Median Sales from the secondary data source.


Change the default format of this new field to percentage. Choose the right level of precision for your data. Add Relative Sales to the view and again change the Compute using to Table (Across then down).


It might look like we're done now, but we're not.  If I filter by Subcategory at this point, my median changes, which I don't want. To make this work, we have to tell Tableau which fields to use in the blend.

In the Dimensions list, we want Tableau to blend on Order Date, Region and Order Priority, but not Subcategory, so click/un-click the link icons as appropriate.


Wait! What just happened? Our median has changed! Why? Is that right? Yes, that is correct, because we removed Subcategory from the blend, therefore when Tableau calculates the median, it's no longer considering Subcategory in the calculation, which is what we want.


Now I need to make this look a little prettier. I'm going to change it to a line chart and allow the user to pick the Subcategory they want to highlight.


Download the workbook here.