Creating the context for successful analyses

Context is essential.
If you just jump right into analysis of a complex data set and subsequent visualization and story telling without first establishing a proper context, you stand a good chance of misdirecting your focus, time, and energy.

If you are strong analyst, visualizer, and storyteller you can still end up with an interesting discovery, a good story, and exciting graphics to share. But you can have also missed hidden secrets that would have provided even greater understanding and value to your audience.

Forcing your audience to work harder. And worst of all, if you don't supply the context, you force your audience - every reader, viewer, listener, everyone who wants to interact with and learn from your visualization - to work harder with a lot of guess work and uncertainty to put what your findings into a useful perspective.

Templates shut down thinking. It is common to find examples of data analysis and visual reporting online with almost no context at all. For recurring analyses and reporting (such as various government monthly reports) it's also common to see the same basic template and boilerplate re-used verbatim month after month with no sign that any new or fresh thinking about how the context might have changed, or what had been learned in previous months, or how best to present that month's results for maximum clarity and ease of understanding.

Set the stage for discovery. What foundational context is it essential to establish in order to set the stage for the most successful exploratory analysis and discovery of new and surprising and useful domain trends, patterns, and exceptions?

Here are some ingredients that can help create a strong contextual foundation in a given data domain . These are especially important for recurring situations such as analyzing and reporting on employment/unemployment.

What's the central question? One of the best ways to supply context is to list up front the key questions that you hoped to answer as you started your analysis. Then in the storytelling and reporting that you create, make sure you establish a link back to these questions with any answers you have found, any surprises you discovered along the way, and any new questions you are keen to explore during the next round of analysis. In other words, show your thinking and link it back to the context your questions established.

Link to the mission. If the analysis you are doing is in support of an important mission, including a description of that mission and the vision and core values that the mission supports will add power and depth to the context of your work.

Think in advance of full set of key metrics. One other thing to note which we will return to in a future post is that there seems to be a connection between analyses that visualize the fewest domain metrics and the analyses that begin with the weakest contextual foundation.

What principles do you use to establish context for your data analysis/visualization/storytelling?