5 Tips for Conquering the Blank-Canvas Blues

Posted by Sophie Sparkeson April 28, 2016

Have you ever found yourself staring at a blank Tableau canvas, unable to start a viz? I have. From vizzer’s block to being paralysed by choice, I know that grey canvas well.

The blank canvas
Hello, blank canvas, my old friend.

This week, I found myself in a familiar situation. I had a great food-survey data set, bursting with stories waiting to be told—but where should I start 1? Thankfully, I have a few tricks to help overcome the blank-canvas blues:

1. Start Drawing

Drawing/sketching/doodling (whatever you want to call it) is one of the best forms of brainstorming I know. The sketches don’t have to be pretty or even legible. Putting pen to paper is my best way to kickstart the creative process.

Talk about not really legible! Here are my sketches for my food-survey viz.
Want to see the finished viz? Read to the bottom, or click here.

And I'm not alone in this camp. From academic papers and news articles to professional visual communicators like Catherine Madden, everyone is drawing.

2. Follow Inspiring Visualisers

One of the best ways to be inspired is to surround yourself with inspiring people and vizzes. For me, this means following data vizzers and data journalists on Twitter, reading data-viz books, and taking notes (or screenshots) of vizzes I like.

This tactic paid off. I came across this visualisation by the Washington Post, and immediately saw how I could make a similar viz to tell my food-survey story.

Percent change from zero
Inspiration from this Washington Post viz

Don’t know who to start following? Check out Andy Cotgreave’s data visualisation Twitter list and follow Viz of The Day on @tableaupublic (or get it delivered to your inbox).

3. Have a Checklist to Clean and Analyse your Data

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.

Data Preparation

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 stuffs 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.

My data
Some items like chips have four levels of descriptions while others like canned potatoes have fewer levels.

Data Analysis

I think of analysing 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.

4. Remember: Building a Viz isn’t a One-Way-Street

Sometimes I find myself unable to start vizzing because I’m worried that the final viz won’t show the deepest insight, or won’t be the best way to tell the story. Remember that there are an infinite number of ways to approach a viz, that there isn’t only one best story to tell with a viz nor a best way to tell it. Lastly, once a viz is finished, that isn’t the end. As projects like Makeover Monday show us, once a viz has been made, you can continue to remake it and tell stories with it in different ways.

The squiggle
Here's my take on Damian Newman’s design process squiggle.

5. Crank Up the Tunes

When I really need to focus, I crank up the tunes. Reducing the sound of outside distractions (phone buzzing, colleagues talking, TV blaring) helps me focus on my work. Whatever your favourite kind of music, get some headphones and let it blast! For reference, Dvorak’s New World Symphony (No.9) got me through writing this blog post.

Here's the viz I finished after beating the blank-canvas blues.

1 In this particular instance, I had a fabulously rich data set from the UK Family Food Statistics. I knew this data set had some great stories to tell from articles such as The ODI’s “How British diets have changed since 1974” and The Guardian’s “Goodbye, fish and chips: National Food Survey data reveals changing trends in British dining”. I also looked at this data set at the London Viz Club. But sometimes too many options is as bad as too few; I was paralysed by choice.

Add new comment