learning through questions

Ask Cole FINAL.png

If you are a parent or spend time with young children I’m sure you can relate when I say, "Wow, kids ask a ton of questions, like, a TON of questions!" The remarkable thing is that they do so, all the time, everywhere, throughout the day and for some reason especially at bedtime (though I’m starting to become wise to their crafty delay tactics). Between my two boys, they often take a tag team approach—one asks the initial question, then the other chimes in with a follow-on query. Take for example, a recent dialogue during lunch:

AVERY: Why did the squirrels eat all the apples on our tree?
ME: Well, squirrels have to eat, just like you. They find their food outside, in places like our apple tree.
AVERY: But why doesn't the squirrel's mommy make them peanut butter and jelly sandwiches so we can have our apples?
DORIAN: I like peanut butter and jelly sandwiches. Do you like them too, Mommy?
ME: Yes, Dorian. Avery—squirrels can't really make sandwiches, that's why they look for nuts and fruit. Since our apple tree is there, they found the apples. Maybe a mommy squirrel was finding food for her baby squirrel.
DORIAN: Where do baby squirrels come from?
ME: Where's Daddy, boys?

If I step back, I can see that the seemingly never ending series of “Why? But why? How come?” is actually a very important part of kids' learning, development and retention. One can practically hear the gears turning in their heads as they process things from multiple angles.

Shifting to my work with storytelling with data—I notice that you have a lot of questions as well. Your queries come to me through many different channels—during workshops, after speaking engagements, via email, TwitterLinkedIn, Facebook & Instagram, in comments on my blog and YouTube channel. I enjoy engaging on these questions because I know this helps with the learning process and ultimately helps you be more effective and confident telling your stories with data. I also know that if someone takes the time to ask a question, there’s a good chance someone else was pondering the same thing.

My limited bandwidth makes it challenging to answer every single inquiry (and I'm sure I've missed some over time), so I’m excited to launch a new forum for answering a number of your questions each month. I'll be doing so through a novel medium for me—a podcast. I love podcasts because you can listen (and learn!) almost anywhere—on your morning run, during your daily commute, or while lounging at home. The SWD team will scour the various channels I mentioned for posted inquiries, but you can jump ahead of those lines by simply emailing your question to us at: askcole@storytellingwithdata.com. We’re recording our first ask cole podcast now to be aired soon, so submit the questions that are top of mind and will help you learn and make progress with your work.

...and for now, if we could just hold off on the squirrel chatter, that’d be great!

Looking forward to hearing from you!


SEARCH STORYTELLING WITH DATA: © 2010-2017 Cole Nussbaumer Knaflic. All rights reserved. STORYTELLING WITH DATA and the STORYTELLING WITH DATA logo are trademarks of Cole Nussbaumer Knaflic.

novel vs. the boring old bar chart

Often, to kick off a workshop we’ll do a quick round of introductions, where I ask participants to tell me something they are hoping to learn over the course of the day. It is not uncommon for someone to respond with something like, “I want to learn some new exciting ways to show data so I’m not just using boring old bar charts all the time.” I jot down a note, silently challenging myself to convince them otherwise over the course of the following hours.

This happened just last week, where a participant voiced a wish to learn novel ways to show data. I can understand this desire. But it’s not the graph that makes the data interesting. Rather, it's the story you build around it—the way you make it something your audience cares about, something that resonates with them—that’s what makes data interesting.

We circled back to this novel-ways-of-showing-data idea later in the workshop when looking at some of the team’s specific examples. I want to share with you the makeovers and discussion; it was another important reminder to me that simple often beats sexy. A “boring old bar chart” can get the job done—and even end up being people's preference.

The team was looking at some market research data, wanting to compare their company to their main competitor across a few dimensions. They originally visualized this with a connected scatterplot. It didn’t work well. People found they were having conversations about how to correctly read the graph for too long before they were getting to the point where they could really look at the data and see what they might say with it. I won’t go through the work of fully recreating it here, but to give you a sense, I’ll do a quick sketch (all data has been changed and scenario generalized to preserve confidentiality):

Connected scatter 1.png

There were two main points to make with this data:

1.    The company was doing better than the competitor across all areas except ATTRIBUTE 1.

2.    The company was beating their target across all areas except ATTRIBUTE 5.

This seems pretty straightforward, right? You can get to these takeaways through the above visual, but there are improvements we can make to it and other potential views that could also work. I originally thought we should look at two alternatives: (1) a dot plot and (2) a slopegraph. It was actually Elizabeth on my team who added a third option—the “boring old bar chart”—into the mix (I'm glad she did!). Let’s take a look at each of these.

First, the dot plot. This was mainly an attempt to improve their original visual with a similar view and some slight modifications. I don't use these super often, but have found there are some good use cases. I thought this would be an appropriate scenario for it. The base visual looked like this:

Sexy vs boring 1.png

Rather than connecting the dots downward, as in the original connected scatterplot, which makes the primary comparison how OUR COMPANY is doing across the various attributes, the horizontal lines here draw the eyes from left to right. This makes the primary comparison OUR COMPANY vs. the COMPETITOR, which seemed to be the main point here. 

Next, let's apply the brand colors:

Sexy vs boring 2.png

Now that I've incorporated color, I can vary intensity to emphasize certain points. For example, we could first draw attention to ATTRIBUTE 1, where OUR COMPANY scores lower than the COMPETITOR:

Sexy vs boring 3.png

I could also add in a marker and text designating the target and use the same strategy to draw attention to ATTRIBUTE 5, where we score below TARGET:

Sexy vs boring 4.png

I actually thought the dot plot worked well. But I wanted to show some alternatives. Slopegraphs can sometimes be a good way to visualize group comparisons, like we have here. By putting the COMPETITOR at the left and OUR COMPANY at the right, the relative slopes of the lines give a sense of how we're doing across the various attributes compared to the competition. Where the line slopes upwards, OUR COMPANY is outperforming the COMPETITOR and vice versa.

Sexy vs boring 5.png

I could emphasize just the ATTRIBUTE 1 line to draw attention to the one area where we score lower than the COMPETITOR. Note that with the slopegraph, since I already have the clear spatial separation between COMPETITOR at the left and OUR COMPANY at the right, I don't need to introduce color as a means of telling the groups apart (vs. in the dot plot, where OUR COMPANY was left of COMPETITOR for ATTRIBUTE 1, but right for all the others, so we need some other way to distinguish one from the other). Here, I can instead use color for emphasis, or simply keep everything grey and use intensity to draw attention to where I want my audience to look.

Sexy vs boring 6.png

Similar to the dot plot, I can also add a symbol and text showing where the TARGET is and drawing attention to ATTRIBUTE 5, where we fall below it.

Sexy vs boring 7.png

Dot plots and slopegraphs aren't anything crazy. But they aren't as popular or well-known as traditional lines bars, and pies, which means they sometimes carry that novel appeal that those wanting something fresh desire. 

Next, let's look at the data in a bar chart:

Sexy vs boring 8.png

As with the slopegraph, position distinguishes for us OUR COMPANY vs. the COMPETITION (the former is always first, the latter second) so we don't necessarily have to introduce color here. Instead, we could use intensity—pushing some elements back by lowering intensity and drawing others forward via higher intensity—to focus our audience's attention. We might focus first on ATTRIBUTE 1:

Sexy vs boring 9.png

As in the other views, we can incorporate the TARGET into the visual and draw attention to ATTRIBUTE 5, where we missed it. Let's try a different view of the TARGET this time, using light background shading to illustrate the region where we are above target and emphasizing ATTRIBUTE 5, where we fall short:

Sexy vs boring 10.png

There's no "right" answer in terms of how one should display this data. Any of these visuals could work. The different views let us more or less easily see different things. Let's look at the base versions (without any emphasis) side by side:

Sexy vs boring all.png

I showed a similar side-by-side after we discussed each of the options during the workshop and asked people to vote which they liked best. Remember, this was the audience who said they were seeking novel approaches. Which did they choose? I'm sure you've guessed it—the "boring old bar chart."

To take the next step and put words around it so my audience knows why they are looking at this data and why they should care, we could do something like the following:

Sexy vs boring 11.png

Meta-lesson: novelty may not be the best goal. Bars don't have to be boring when you've used them to help make the data accessible and made it clear to your audience why they should care.

What do you think? Which view of the data do you like best? Why? Leave a comment with your thoughts!

If interested, you can also download the Excel file with the above graphs.

visualizing change via slopegraph

Elizabeth Ricks recently joined the storytelling with data team after spending the past decade in various analytical roles in the healthcare, manufacturing, retail and payments processing industries. Most recently, she was Assistant Vice President of Analytics for Bank of America Merchant Services, where she strengthened her data storytelling skills by using the key lessons covered in the storytelling with data workshop. Elizabeth has a passion for helping her audience understand the "so-what?" when communicating with data. Join me in welcoming Elizabeth and her first blog post here! You can connect with her on LinkedIn or Twitter.  

Communicating the “so what?” is fundamental to telling a story with data and I can’t overemphasize the importance of choosing an intuitive visual. Often our story is lost, simply because because we’ve chosen a graph that forces the audience to do more work than necessary. Today’s post illustrates this transformation with a real-world (de-identified!) example.

Imagine you’re a marketing analyst tasked with evaluating your product’s market share and communicating the growth opportunity to your senior marketing leadership team. You’ve gathered the data on the 14 states in which you operate and visualized your market share over the past decade in this bar chart:


This graph is functionally adequate. It’s thoughtfully designed using pre-attentive attributes. The color blue cues us where to look first (that’s our market share now!), which allows our second series (our market share then) to fade to the back.  

Additionally, horizontal bar charts have many visual advantages as outlined in storytelling with data:

  • Familiar and easy to read

  • Useful for long category names

  • Align well to how we typically process information: starting from top left and zig-zagging across the page so that we process the category names before interpreting the data

We see this final point demonstrated here, as a quick vertical scan makes it relatively easy to see that our product’s market share is down in every state, except Michigan and Oregon.

That’s fantastic if that’s the end of the story. However our task is twofold: we also need to communicate how our market share has changed over time and our recommendation for the opportunity. With the current design, how easily can you see which state(s) had the greatest decline in market share? Between Michigan and Oregon, which had the greatest improvement?  


As the designer of this information, we are asking our time-crunched marketing executives to do a lot of work to scan the graph and make 14 different comparisons. Never make the audience do more work than necessary to understand a graph! Perhaps a different visual would make the task easier.

Enter the slopegraph.  

The slopegraph is a visually intuitive way to see what’s changing in your data. For a deeper analysis of the beauty of slopegraphs, check out this post.

Let’s instead connect the data points with a line. Notice where your eyes go first now.    


A few interesting things emerge. We can immediately see that some states have higher rates of change than others, both positive and negative. That’s the "so-what" what we want our audience to understand!

We can further improve by using color to focus our audience’s attention on specific takeaways. For example, we might use blue to highlight the positive story: we’ve improved in 2 states!


Or we could focus attention on Texas, the state with the greatest market share decline.  


Finally, we’d add a call to action emphasizing how the audience should use this information. Remember, we always want our audience to do something!


In conclusion, if your “so-what?” is what’s changed over time, then the slopegraph can be an extremely effective visual. If interested, you can download the Excel file with the above graphs.  

From a formatting standpoint, slopegraphs can take some time to set up. However, that’s time well invested if it means your audience clearly understands the story. Here’s a handy Excel template to get you started.   

how would you show this data?

While riding the subway from Manhattan to Brooklyn this morning (which I mention simply because that's not a sentence I get to say every day), I came across the following graph from the Economist, headlined, "Hurricanes in America have become less frequent."


This graph gave me pause for a number of reasons. But rather than recount them here, I thought it would be interesting to turn this graph (and the data) over to you. If you were reporting on this data, how would you show it? What would your headline be?

Send your makeover and headline to makeover@storytellingwithdata.com (if you'd like, include a social media profile you'd like me to link to) by next Friday, 9/22, and I'll follow up with a blog post featuring the remakes I receive. I look forward to seeing what you come up with!

Update: big thanks to all who participated! Here is the follow-up post with all of the makeovers I received.