It might sound counterproductive to get your creative viz juices flowing by following a checklist, but structure can help overcome vizzer’s block. My checklist is divided into two parts, data preparation, and data exploration.
As dull as it sounds, physically looking at your data helps you understand the data set’s possibilities and limitations. Here are some of the things I look at in a data set:
- What fields does the data set contain (and not contain)?
- What kind of data does each field contain?
- How is the data structured and formatted?
- What are the minimum and maximum values in each field?
- Do any fields contain null values?
By going through this list, I could see that my food-survey data set had multiple levels of details in it—some food had up to four sub-categories while others only had two. That meant it would be hard to meaningfully compare two food items unless I knew that they were both at their lowest sub-category.
I think of analyzing a data set as a way of interviewing it. If I’m stuck staring at a blank Tableau canvas, I can fall back on asking traditional interview-style questions of my data:
- Who, what, how, why, when, and where? Go through each field and see how you can apply one of these questions to it.
- Embrace your inner-child and ask, "Why? Why? Why?" of your data.
Here's one line of questions I asked of my data:
- Question: Which category had the sharpest consumption decline when compared to 1974?
Answer: Sugar and preserves.
“But wait, isn’t this the opposite of all the articles I have read saying our sugar consumption is at an all-time high?”
- Question: When did the decline start?
Answer: In 1975.
“This is really different from what I thought. Why is it happening?”
- Question: What food stuffs does this category contain?
Answer: This category includes raw sugar (think: a bag of brown sugar).
“Ah, maybe people are buying fewer bags of sugar from the supermarkets. But are we consuming more sugar in other forms?”
- Question: Are other sugary categories like soft drinks increasing?
Answer: They are. Both soft drinks and confectionery are increasing.
As you can see, interviewing your data gives a structured way to beat the blank-canvas blues.