Importance of data visualization to derive actionable insights
By: Sharayu Rane
Tags: Data Visualization

Photo by NASA on Unsplash

Data visualization is not itself about insight, but rather, about communicating insight.

Quantitative insights driven from churning huge amounts of data are often subtle, surprising, and technically complex. Given this it makes it more challenging to communicate these insights to any audience and especially to a business audience who might have interest / time to go into the technical details. Nonetheless there are some people who prefer results being presented in bland black and white numeric tables and equations over more visually stimulating presentations, but the proportion of such people is very less.

Being from a consulting background, we have to be prepared to face all types of clients the ones who prefer tables and equations, and those who want good looking visuals.

Based on my experience, below are some of the thumb rules I have landed on:

1. Clarity

Tell one very important insight per graph and identify elements that tell the story

Everything you state should be linked to or should lead to the overall story

Keep the visualization simple and direct

Simplify but do not oversimplify such that findings or key data features are lost

Let’s look at my favorite example of how important data visualizations are for deriving insights.

The above chart appeared on Bloomberg, concluding that the median income of American male workers has been in drastic decline over the last four decades across all age groups. But is this really true? Let’s look at the below visual representing the same data set.

Now with the above visual you can clearly see that there are a lot of local perturbations that got smoothened out after representing only 2 data points: 1972 and 2012

Also, if you observe that y-axis as it should doesn’t start at zero.

Exact same data and chart visual with just the y-axis being changed to zero.

Please refer to Eric’s original article here for a complete read.

2. Transparency

Explain methodology for deriving insights to appropriate depth

State assumptions underlying the analysis

Without y-axis the above chart has absolutely no meaning. Obviously the scale / unit of the growth of data and growth of data analysts is totally different. You just cannot overlay 2 visuals on one another without taking into account the scale / unit of the individual metric. The most important elements that tell the story should take the X and Y axis; always examine this before coming to any conclusion (lot of ways to misguide viewers).

3. Integrity

Represent uncertainty and limitations accurately and honestly, because it is very common and tempting to exaggerate the implications of the data.

Graphical excellence begins with telling the truth about the data. — Edward Tufte, The Visual Display of Quantitative Information, 2001

This entire blog is inspired by and has references from a data visualization session conducted by Dr. Dakshinamurthy V Kolluru.

Please let us know your thoughts in the comments section.

This content was originally published on my personal blogging website: http://datascienceninja.com/. Click here to view it and subscribe to receive updates on latest blogs instantly.