food for thought

Perhaps I have food on the brain. As I write these words, I am sitting in a bar in Mexico awaiting an order of Muchos Nachos after a lovely (and hot!) afternoon touring Monterrey. I'm starving. Though outside of that, I suppose I don't have much to complain about.

That is an unrelated prelude to the topic of today's post: the storytelling with data process, in a 1-page format you can refer to and use as food for thought when you communicate with data. 

I've been asked more than once about creating a checklist. The sentiment is good: something to remind people to put into practice the lessons that we cover in my workshops. But for some reason, I have a negative reaction to the idea of a checklist. Perhaps it's just the word checklist that's getting me, with connotations of being formulaic and rigid or rule-based. I do not teach data visualization in a rule-based way. Rather, the answers to many questions that are posed begin with "it depends...". But it occurred to me today when the question of a checklist came up, that I've actually already created something that would meet this need.

When I teach workshops, we cover what I consider to be the foundational lessons for communicating effectively with data. The core lessons are always the same, but the specific content, examples, and exercises vary quite a bit depending on the given group and the main challenges they face. Often, we practice the lessons piecemeal: for example, storyboard your next presentation or declutter this graph. Increasingly—especially with the longer 1-day public and custom workshops that I've been doing more lately—I've been having people do exercises where they consider the entire storytelling with data process and practice going through it step-by-step for various scenarios. To use for this, I created a 1-pager that outlines the main lessons, with some questions and prompts to remind people of the specifics to consider. This 1-pager doubles as a useful takeaway, perhaps something to be hung by a desk to help people keep the storytelling with data lessons in mind when they are communicating with data.

...kind of like a checklist, I suppose.

You can download the storytelling with data 1-pager here. If you've read the book, the nomenclature should be familiar. If you haven't (you should!), enter any unknown terms (e.g. big idea or "where are your eyes drawn?" test) into the search box that appears below this post to see potential posts of interest.

I hope you'll find this to be a useful tool!

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do you SEE it?

When communicating with data, creating visual contrast is key for directing your audience's attention. Check out the following video for a brief illustration of why contrast is important and an in-depth look at four real-world examples on how to achieve contrast through position, color, and added marks.

For more storytelling with data videos, check out my YouTube channel.

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introducing 1-day workshops

As those of you who read my words here regularly probably know, I spend a good amount of time teaching workshops on how to communicate effectively with data. Mostly, I do this for organizations who have recognized the need to upskill folks in this area (more info here). I also offer public workshops, where individuals wanting to build a foundation and skills in storytelling with data can attend.

I have a ton of fun with my public workshops. The energy and engagement of folks who attend is outstanding. The participants are usually a pretty diverse group, coming from different organizations and industries. Most have the need to communicate and tell compelling stories with data as a core part of their job.  They typically have varying level of skills in this space—some have already been visualizing data for a while, while others are just starting out—and all have the common goal of wanting to do it better.

With my maternity leave wrapping up, I've scheduled public workshops for the remainder of the year, with offerings in San Francisco, Atlanta, and Washington DC in the coming months. I'm revamping and expanding content from what has historically been a short 3-hour overview of the basics to a full day of fun, where we'll cover foundational lessons for storytelling with data and practice applying what we learn through a variety of individual and small group exercises. These sessions are kept super small (between 15-20 participants, depending on venue) and are highly interactive, with plenty of time for questions, discussion, and interaction with me. Participants will leave the day having learned effective strategies they can put to use immediately, with tangible practical takeaways and excitement to further apply what they've learned.

Sound interesting? I hope you can join me. Feel free also to spread the word to others who may be interested. To learn more or register for an upcoming workshop, click here.

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improving upon "good enough"

When perusing my Twitter feed recently, I came across a link to a blog post by CoreLogic on the topic of millennial mortgage credit (here). The short article describes the results of an analysis of a few key mortgage variables (credit score, debt to income ratio, and loan to value ratio) by applicant age. Here's an excerpt:

I like the article, it leads the reader through the analysis and pulls in context to make it all make sense. The graphs, however... They are ok. They get the job done. They are probably "good enough." But they could be so much better. If you've taken the time to do a robust analysis, why not take the time to make your data visualizations reflect that? So much of the analytical process happens behind the scenes—gathering data, cleaning data, and analyzing data—the graphs are what your audience actually sees out of all your hard work. They deserve at least as much time and attention as the other parts of the analytical process.

Let's take a closer look at the graph and portion of the story excerpted above that focuses on Loan to Value (LTV) ratio. When I look at the graph, here are the specific things that I notice and would change: 

  • The y-axis doesn't start at zero. This is a no-no for a bar chart (more on why here). We need the context of the full bars in order to make it an accurate visual comparison. Start the axis at zero.
  • The second series doesn't add much. Unless we have a specific need we can articulate for both data series (single applicant and joint application), I'm inclined to reduce the data shown to a single series. For example, we can graph just the single applicant series and then note via text that the same observations hold true for cases where there are co-borrowers.
  • Color can be used more strategically. With the two data series currently shown, color is used to distinguish one from the other. If we remove one of the data series, we no longer need to use color in this way and can instead use it to draw attention to the focus of the article: Millennials.
  • The category descriptions are far away. If you look at the full article, it begins with a table that defines the birth years and ages of the various generations (Millennial, Gen X, etc.). We can eliminate the need for this table and reduce any back and forth by simply embedding some of that info with the category names directly in the graph.
  • A good portion of the text simply describes the data. By labeling the data directly, we eliminate the need for this and can be more concise with the text, using it to focus on the context and story. 

Here's what it looks like when I make these changes:

Note that in this case, I preserved the y-axis labels to make it clear that the axis starts at zero (but pushed it to the background by making it grey). Given that I've also labeled the data directly in the bars with data labels, I could get rid of this axis altogether. 

Yes, the original visual was perhaps "good enough." But isn't this better?

If interested, you can download the Excel file with the above makeover here.

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the cat in the hat knows a lot about data visualization

I recently had the pleasure of guest lecturing a Stanford MBA class, Strategic Communications. Here, I've recorded a 20-minute segment from that lecture, which covers two basic things that you should do when communicating with data:

  1. Be sparing and intentional in your use of color, and
  2. Put your thoughts into words.

Check out the video below for some quick lessons and examples. Thanks, JD,* for inviting me to share with your class!

Check out my YouTube channel for more videos.

*JD Schramm lectures in the Knight Management Center in the Graduate School of Business at Stanford University; check out his recent presentation, The Secret to Successful Storytelling with Statistics.

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