The 7 types of Data Story

When analyzing and presenting data it is useful to present it in the form of a story to help the audience follow along with a narrative and present different angles.

Much like Christopher Booker’s 7 basic plots. A data story can be stripped down to 7 basic concepts:

Change Over Time:

This can be useful to explain how data points collected can change over a temporal axis. A basic example of this is visualizing the rate change of something, for example, sea level change at different times of the day. Conclusions such as when the maximum and minimum point of a particular factor occurred can be drawn.

Drill Down:

With the drill down data story, we can look at the overall big picture of something and hone into a particular factor that is causing this within the data. For example, you can drill down into data from a particular region of the world by focusing on a particular country within that region and how much of an effect this particular country has on the data of the whole region.

Zoom Out:

If we do the reverse of the drill down story method, we have the next data story type: zoom out. This works by starting with a micro view and narrating the data in a way that you take a step back and take a more macro view.

Contrast:

With multiple data sets, you can compare them to push a particular narrative. This can help the audience to not only understand how two different data sets can be different but also similar. This method helps the brain to remember key points of the story you are presenting.

Intersections:

When 2 lines of data diverge and in some cases overlap, this can tell an interesting a story of the data and in many cases can represent one variable overtaking another.

Factors:

A lot of times multiple factors can be used to represent one big story as these factors tend to have a relationship when added or multiplied together. When telling the story of economic growth, there are multiple factors that all combine to represent this. For example, consumer spending, business investment, government spending and net exports are all factors which can be used to represent the larger story that is economic growth.

Outliers:

Outliers are plot points which fall outside the overall trend and can allow speculation as to why this particular value falls outside. This is important because this can skew the overall value of the data set. When an outlier is highlighted, this can be focused on as what is affecting the data set and possible steps can be taken to reduce the effect of this outlier.

Author:
Habeeb Gayle
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