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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hello baby

It has been quiet here for a bit and after this post will likely continue to be for a while. Why?

Two words: baby Eloise.

Like her oldest brother, Avery, Eloise surprised us early, spending several weeks in the Neonatal Intensive Care Unit (NICU) before finally coming home. 

Eloise was born across town at the same hospital as Dorian. Hospitals, as it turns out, are full of data visualizations. We were at the hospital for some routine tests when the adventure began. The machine they hooked me up to drew data on paper. While to me, it just looked like a lot of peaks and valleys, apparently to the doctor, it depicted "active labor." Interesting. But we only looked at that particular graph for a matter of minutes. It was the following ones that we stared at for weeks on end in the NICU, alarms periodically blaring.

In the first image, the bottom graph (right hand side) shows contractions every minute. The second image is a snapshot of ventilator stats. The final image depicts the stats consistently tracked while in the NICU: heart rate (green), oxygen saturation (blue) and respiratory rate (white).

In the first image, the bottom graph (right hand side) shows contractions every minute. The second image is a snapshot of ventilator stats. The final image depicts the stats consistently tracked while in the NICU: heart rate (green), oxygen saturation (blue) and respiratory rate (white).

Spending time in the NICU is a strange experience. The ups and downs are of course scary (when I learned that the treatment for the first several days wasn't working and Eloise would have to be intubated and put on a ventilator was one of the worst moments of my life). There's also a strange tension of emotions. On the one hand, you have professionals taking amazing care of your baby around the clock. But it's difficult every single time you have to leave and aren't able to take your baby with you. Being there is stressful. There's a feeling of guilt, though, whenever you aren't there. Having been through the process once before made it familiar, but not any easier. The day we were finally able to take Eloise home was a glorious one.

And now that Eloise is here with us, the data visualizations don't end. There is the temporal data I'm recording in a list (old-school-style, with my fancy tools of a pen and spiral notebook) on feedings and diapers. There's the Jawbone UP app on my phone, a daily reminder of my interrupted sleep and how little sleep and steps I'm getting in general. There's the automatic graphing that our high-tech scale does of my weight (just what every recently pregnant person wants to see, right?!?). Even one of the bottle packages had a graph on it! 

The first two images are from Jawbone UP, which tracks my sleep and steps; the first image depicts a night of sleep (and wakefulness, shown in orange—feeding times) and the second image shows my total sleep and steps for a given day. The third image is a screenshot of my weight over time collected via our Withings scale (y-axis scale/labels intentionally not shown; if I could annotate the peaks, the first would read "Dorian birth" and the second "Eloise birth," perhaps I'd also draw a "goal" line somewhere near the bottom!). The final image is from a package of Dr. Brown bottles—great use of color and text in graph to highlight the Dr. Brown line.

The first two images are from Jawbone UP, which tracks my sleep and steps; the first image depicts a night of sleep (and wakefulness, shown in orange—feeding times) and the second image shows my total sleep and steps for a given day. The third image is a screenshot of my weight over time collected via our Withings scale (y-axis scale/labels intentionally not shown; if I could annotate the peaks, the first would read "Dorian birth" and the second "Eloise birth," perhaps I'd also draw a "goal" line somewhere near the bottom!). The final image is from a package of Dr. Brown bottles—great use of color and text in graph to highlight the Dr. Brown line.

There are certainly many more stats that I could be tracking and visualizing. But I'm not going to. Rather, I'm going to spend my time staring at this beautiful, tiny creature.

Her loving big brothers and father have been doing the same.

Welcome, Eloise, we are so very happy you are here!

Eloise Noel Knaflic
Born February 19, 2016
5 pounds 3 ounces

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declutter your data visualizations

When I was a little girl, I used to get in trouble for cleaning my room. Check out the following video to find out why and learn why clutter in data visualization is a bad thing and how to avoid it. Specifically, I'll cover five tips and examples from my book, storytelling with data

  1. Leverage how people see
  2. Employ visual order
  3. Create clear contrast
  4. Don't over-complicate
  5. Strip down & build up

This is a slightly modified version of the talk I've been giving on my Bay Area book tour at companies like LinkedIn, Facebook, Pinterest, Dropbox, Tesla, Airbnb, and Evernote. Post any related questions in the comment section and I will respond. I hope you enjoy!

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