Holly Foster Media


Most people would agree that the word data is not a warm and fuzzy word. It conjures up images of zeros and ones, machine language, and code. But if you take the time to ask the right questions and cozy up to the data, what you learn may surprise you.

Good data visualizations tell a story.

Let me show you what I mean.


In a data experiment between two information designers that has now become famous, Giorgia Lupi and Stefanie Posavec got to know each other by creating beautiful visuals of weekly data they collected over the period of a year. In their beautiful book Dear Data, they describe their experience as “A friendship in 52 weeks of postcards” (Lupi & Posavec, 2016).

Over the course of three different weeks I too committed to gather daily data that might unveil some hidden meaning. Now to be honest, I really hated the data collection itself. But I must admit that I did enjoy creating the visuals even though I am clearly NOT an artist.

Here’s my handiwork. Visual on top with corresponding legend and explanation on the bottom. I love the first one; the other two not so much.

But the point of it all is to get to know ourselves and others a bit better … so here’s what I learned.

  • In the first week, Walking in Sunshine, I learned that wearing sandals is a better way to go than sneakers. It was a game changer for me (thanks for suggesting it Ryan).
  • Since I had collected so much data on the first go around I decided to create a second visual, A Week of Walking. This visual showcases the number of people I encountered and their response to my greeting. I’m sure I already knew this, but it reinforced the fact that people generally respond positively to even the smallest display of kindness or greeting. It makes the world a kinder place.
  • And finally … How do I feel about tasks? Can we find joy in the mundane? There’s a decent amount of green in the visual (which represented happiness), so the answer is definitely YES. We can choose our attitude when we put our mind to it.

We’ve always conceived Dear Data as a ‘personal documentary’ rather than a quantified -self project which is a subtle — but important — distinction. Instead of using data just to become more efficient, we argue we can use data to become more humane and to connect with ourselves and others at a deeper level.

– Giorgia Lupi and Stefanie Posavec


Moving from the personal to the public, I had previously created the Wedding Outfit Survey as a gateway to finding guests for my podcast Choosing Your Reflection. The survey takes about five minutes, and it contains questions that seek to address the mystique that revolves around choosing a wedding day outfit.

The total data set contains 171 responses, which boiled down to 165 records after the data was “cleaned” (duplicates removed and data standardized). The visualization below summarizes the total responses to the four pivotal survey questions, which are:

  • Does the perfect wedding day outfit exist?
  • How often do brides and grooms find it?
  • How important is it to find the perfect outfit?
  • Do you feel you found it?

Here are the results, boiled down into one simple graphic.


There are a lot of believers out there (67%), over two thirds (68%) think brides and grooms find the perfect outfit OFTEN, finding it is important to pretty much EVERYONE (96%), and 3 out of 4 people feel like they successfully found their own perfect outfit.

As a former unbeliever, I must admit I was surprised.

If that wasn’t enough to blow me away, seeing all that data boiled down to something that could tell a story in less than a minute did.

That’s the prize.

That’s what data visualization, when it’s done well, is all about.

… visualizing information … is a form of knowledge compression. It’s a way of squeezing an enormous amount of information and understanding into a small space.”

– David McCandless


There are fantastic open source tools out there that make creating a data visualization easy, such as RAWgraphs and Datawrapper. Like Datawrapper’s home page states “No code or design skills required” (Datawrapper, 2019). But just because it’s easy to create doesn’t mean it’s useful or accurate. You need to think it through.

“To improve visual communication, fight the impulse to go right from getting data to choosing a chart type … First spend time creating context and thinking through the idea you want to convey” (Berinato, 2016, p. 105).

Is the data appropriate for your message? Is it accurate? Have you chosen the best chart type to send your message in a clear fashion?

Important questions.

And speaking of questions, what overarching question are you trying to answer and does it align with the story you are creating?

Like a well written narrative, good dataviz takes time, a lot of patience, and a good dose of investigative work. Like a great piece of art, you’ll know it when you see it.

” … an excellent visualization … tells a story through the graphical depiction of statistical information” (Stikeleather, 2013).

until nxt time …


Berinato, S. (2016). Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations. Harvard Business Review Press.

Datawrapper. (2019, January 30). Create Charts and Maps with Datawrapper. https://www.datawrapper.de/index.html

Lupi, G., & Posavec, S. (2016). Dear data (First edition). Princeton Architectural Press.

McCandless, D. (2010). The beauty of data visualization. https://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization

Stikeleather, J. (2013, April 24). How to Tell a Story with Data. Harvard Business Review. https://hbr.org/2013/04/how-to-tell-a-story-with-data