Graphs are an essential part for any presenter trying to back up their message with cold, hard data, but when was the last time a graph excited you?
Let’s face it: although you know the underlying meaning of a data set, tweaking a graph so it’s both accurate and easy to understand is an art, even if you are a math wiz.
As a result, graphs usually don’t live up to the same standard as the rest of the slide deck. And worse, if they aren’t presented in an easy, accessible way, your analysis can be misinterpreted. Or even worse: ignored.
However, as long as you nail a few fundamental principles, not only will your charts and graphs will go from average to amazing, but also…
...you’ll gain some of the most desirable skills in the industry today– demand for data visualization skills have increased 2500% over the last five years.
In our free ebook, we cover both the basics pie charts, advanced treemaps and show how to use highlighting to ensure your takeaways are imprinted on the audience's internal hard drive.
But first thing’s first: why does data visualization even matter?
Your aim, with any presentation, should be to save the audience’s time and help them make the right decisions. You want to reinforce the right data, draw their eye to the right section of the page and do every bit of the analysis for them, without creating a bias.
Look: the quality of your graphs and charts can be measured by how fast the audience reaches the conclusion you intended, and how well they understood the information you presented.
The Basic: Pie Charts
What: Pie charts are best used when you need to compare how much different fractions make up of a whole. For example, you might use it to compare the distribution of product sales, such as soft drinks making up 40% of total sales.
Advanced: Pie Bars
Now that you nail the Pie Chart you can become more granular by adding a bar chart component. A good time to use the bar component is when you need to show the split of different sales categories.
How to do it? Figure out which variables are most important and extract them from the pie.
Highlighting provides simplicity and gives the audience an easy way to understand the main point quickly and clearly.
Let’s use highlighting in a scatter plot as an example.
In a scatter plot, it becomes more and more difficult to derive meaningful insights as the number of data points in the plot increases.
The key is to use a balanced combination of shapes, lines, numbers, and call-outs.
In this case, we highlight the take-aways on the relationship between income and education across Danish municipalities. Observe how certain numbers and shapes can be combined to highlight specific patterns and extremes.
The average income is over 3x higher for the richest municipalities compared to the poorest.
In 80% of all municipalities, less than 10% of the population has at least a bachelor’s degree – a strong correlation between income and education exists within this segment.
The correlation between income and education decreases as the education level exceeds 20% of the population.
Some of the most important decisions are made from conclusions drawn from computationally-derived data sets and span innumerable industries, from corporate finance to manufacturing. But with questionable charts and graphs, doubt may be casted on your findings.
So it’s paramount that your graphs and visuals are doing your analysis justice. Head over no-more’s checklist to make sure you’ve covered the most important points, provided in our free ebook. No-more subpar graphs, no-more faulty analyses, no-more unanswered questions.
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