Dear Analyst #35: Analyzing what people dream about with the Shape of Dreams data visualization

Have you ever wondered what the underlying meaning of your dreams are? Chances are you may have tried Googling something like “What does it mean to dream about [INSERT DREAM].” In The Shape of Dreams, Federica Fragapane answers this very question of what people around the world dream about by using Google Search queries from 2009 to 2019. Federica uses a mix of data storytelling and data visualizations to show what we collectively dream about based on what we search for in Google. The key takeaway: someone on the opposite side of the world probably has similar dreams as you showing that we are more connected than we think.

Shape of Dreams

Importance of data visualization

Data visualizations are just as important (if not more important) than the number crunching and analysis of the data itself. While Excel and Google Sheets are the standard tools for analyzing data, there are a variety of tools for creating charts and visualizations such as Tableau, Google’s Data Studio, and Microsoft’s own Power BI.

data visualization kevin simler
Source: Melting Asphalt

I’ve posted about the power of data visualizations in the past including New York Times’ data bootcamp (that teaches data visualization), data visualizations to model COVID-19, and my own class on creating a data-driven presentation. Creating meaningful data visualizations requires you to understand the technical aspects of aggregating data and actually creating the visualization itself. It also requires the creative side of telling a story around the visualization. Federica does an amazing job of telling a story about the Google Search queries about what we collectively dream about as a society.

Structure of Shape of Dreams

I really like how Federica gives the reader two options: read the story about the data where she takes you through the visualizations with key takeaways and also gives you the ability to explore the data yourself. In the first chapter, she simply shows the most common types of dreams by keyword across different languages:

Who doesn’t dream about their teeth falling off?

When you explore the data, you can use the arrow keys to see the dreams people search for by language and by year which leads to some interesting results:

Varying the types of visualizations

As you go through chapter 2 and chapter 3, you see Federica utilizing different types of visualizations to better tell the story behind the dream Google Searches. A motif she uses across the visualizations is a flower’s pedals, and you’re able to interact with the pedals in chapter 2. To summarize what I imagine to be a extremely large dataset, we see some general categories of dreams in chapter 2:

Federica discovers that searches in English, Portuguese, and Spanish aggregate up to dreams about animals, family, and relationships.

You’ll see a more traditional time-series chart in chapter 3 showing the popularity of a certain type of dream over time. I’d be curious to see the trend of dreams about “pregnancy” in 2020 given the pandemic:

A network of dreams

My favorite visualization is in chapter 4 where you’ll see a network type of visualization that shows two metrics:

  • Languages that share common searches about dreams
  • The number of dreams in common between languages

We actually use a similar type of visualization at work when we want to see how our customers are related to each other inside an organization (and how they share their Coda docs). What I love about the visualization above is that it shows how connected we are as a society given the same type of dreams we have (and subsequently search for on Google).

Using data to get a edge on human conversations

I also discuss a new podcast I started listening to called Against the Rules by one of my favorite authors, Michael Lewis. The episode is all about how there is research (and companies) helping you optimize your conversations with people to get the most benefit from the conversation. Lewis poses the million-dollar question at the end of the episode which is what are the ethics behind using this data to optimize all of your conversations in life from business to romance?

This question is probably getting addressed already at Harvard Business School Lewis interviews Professor Allison Wood Brooks in the episode who has a class at HBS called How to Talk Gooder in Business and Life. If you don’t have access to these type of classes and resources, will that put you at a disadvantage later on in your career, negotiating a business deal, finding a romantic partner?

Taken to the extreme, this reminds of me this scene from the season finale of Westworld (spoiler alert):

Other Podcasts & Blog Posts

In the 2nd half of the episode, I talk about some episodes and blogs from other people I found interesting: