Data visualizations are more than pretty pictures, although a nice graph or chart makes the data more appealing. Data visualizations should support decision-making by drawing attention to relationships among data and relationships between your data and influential factors that may not be captured in your data.
Take a look at the graphic above. It contains a huge amount of data regarding employment trends across genders across different job types and industries.
Each block represents a particular job classification and the size of the block indicates the relative number of jobs in that classification. There are supra-blocks indicating the same type of information for various industries. In a larger format of the graph, each block would be labeled for the specific type of job but, for simplicity, we’ve only labeled jobs with major levels of employment.
Colors are used to identify when a particular job class shows increases in male or female employment.
At a glance, you can clearly identify which job classifications are becoming more ‘male’ or more ‘female’. That’s valuable information if you’re working for a group promoting increased participation for women in certain job classifications where they’ve been underrepresented. Or, if you work for the EEO in your company, you can demonstrate that your levels of female participation are comparable to the industry.
And, all that comes without having to pour over huge tables of data hoping to discover something.
Crafting the RIGHT data visualizations
Just putting your data in a visual format isn’t enough. And, putting your data into a poor format is worse as it is misleading and leads to poor decisions.
Different types of data call for different data visualizations.
- Percentage data is often clearest when represented by a pie chart.
- Trend data is often best displayed using a line graph
- Comparing things against other things is often best done with a histogram
- When you want to display trends across a number of things, stacked histograms likely do the job for you
- Complex data is often easiest to evaluate when you have a heat map, like the one above or an association tree like the one below
By the same token, having the wrong data visualizations can make it impossible to derive insights that aid decision-making. Take a look at this hot mess:
There are just too many colors and the slices of pie don’t really help you understand the data. If, instead of using a pie chart, you clean this up with fewer colors and histograms representing the size of the populations, you get a much clearer picture of what the demographics look like relative to each other, which aids decision-making.
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