“how do I incorporate visual design into our monthly deck?”

After reading storytelling with data or participating in a workshop, people often ask how they can incorporate the lessons into a recurring (i.e. monthly, quarterly) report. These reports often materialize as a PowerPoint deck, which started sparsely, but over time has taken on a life of its own and now resembles the “slideument”: part presentation, part document but not exactly either at its best.

Consider the slide below, which is based on an actual slide from a recent client workshop. (I’ve anonymized the client’s data to preserve confidentiality.) Today’s post demonstrates how to apply data storytelling lessons to a visual from a monthly deck, illustrating the thought process to improve it.


This slide shows a monthly trend of customer service complaints: in total (top chart) and broken down by category (bottom chart). The commentary section tells us (the audience) what the important points of reference are: what happened this month compared to last month (complaints are up 14%), where it changed (Employees) and their proposed next steps. However, notice how much work takes to read through all this text and then find evidence of this in the graphs.

Imagine if you were given this slide to determine an action plan. If you were in a live meeting, would you be able to read all of this text and listen to the presenter at the same time? If you weren’t in the meeting and were reading through the deck, how much time would you realistically spend trying to digest the information presented? We can improve on this visual in both scenarios with a few design changes.

In both cases, I used the commentary as a guidepost for the important takeaways and re-designed the visuals accordingly.

First, let’s a closer look at the top chart. The commentary tells us that complaints were up 14% vs the prior month.


Where did your eyes go first in this graph? Mine went to the red Average line, which I visually estimated to be about 410 per month.  In looking for evidence of the 14% increase in December, I had to do a lot of mental math (add the Solicited + Unsolicited for November and compare it to Solicited + Unsolicited for December) which took more time than someone would likely spend doing this.

If that 14% increase is what the audience should know, check out the difference between the original visual and this:


When applying the “where are your eyes drawn?” test, my eye went straight to the data markers & labels at the end of the total line, where I could see both the absolute numbers and annotations telling me it’s a 14% increase. Since we’re visualizing time, I changed the graph type from a bar chart to a line chart, unstacked the data series, and added a series for the total. This was intentional based on the commentary, which only referenced the total trend. I chose to de-emphasize the subcomponent pieces (Unsolicited and Solicited) by using grey.

Side note: what about the Average line? If the monthly deviation from average was really important, one option would be to keep it in the graph for reference with the tradeoff that adding a fourth data series could create clutter. Another option is an entirely different choice of visual, depicting the monthly change (from average), with a visual cue to indicate that December’s data is acceptable. Both are choices the information designer would make knowing the audience and what context is relevant. In this case, I didn’t feel that this additional point added anything to the overarching story, so I chose to eliminate it altogether.

Let’s take another look at the second visual now. The commentary tells us that complaints were up in a specific category: Employees. Not only did they increase, but they increased from 87 to 117. Apply the “where are your eyes drawn?” test again with the original visual.


If I took an informal poll of readers here, some might have gone to the black line, others might have noticed the blue list first and others (like me) went to the red line. Regardless of which line you focused on first, I’d likely bet that you didn’t focus first on the November to December increase in the Operations line (red).  In fact, it’s difficult to discern the absolute numbers (87 and 117) here because of the general clutter: overlapping data series, gridlines, color, heavy chart border and legend at the bottom requiring some visual work to figure out which line goes with which complaint category.

When setting out to improve a visual, there’s not necessarily a right or wrong answer in choosing a visual type: it often takes looking at the same data several different ways to find which view is going to create that magical “lightbulb” moment. Let's look at a few different variations of this visual.  

First let’s keep the existing line chart, remove some of the clutter and focus attention on the November to December change in Employees.


This view gives the audience the full context of the 12 month trend, while focusing attention strategically on a specific point. However, if the emphasis is really about the November to December change, we could also visualize only those two data points. Let’s look at a few different ways of displaying this.

First, this horizontal bar chart compares this month (December) to last month (November). Horizontal bar charts are useful when your category names are long and therefore can be displayed horizontally from left to right on the y-axis without having to rotate or shorten them.


Another option is a vertical bar chart, if you’re more inclined to preserve the left-to-right construct of displaying time.


As a third option, we could use a slopegraph. Slopegraphs can work well in making change visually apparent across categories. Check out how clear it is that some of these categories changed more drastically than others. In fact, looking at the data this way, we see that there was also a marked increase in service-related complaints, something that didn't stand out as much in the other views of the data. You can read more about slopegraphs, including design considerations, in this previous post.


Any of these three visuals could work for depicting this data, I chose the slopegraph for the final version to keep the emphasis on the change in the two data points.

Here's what it could look like if all of this needed to be on a single slide:


In the remade version, I’ve moved the text to be closer to the data it describes and used color strategically to create a visual link between the text and where to look in the graphs for evidence. I’ve also made the call to action more visible—remember when communicating with data for explanatory purposes, we should always want our audience to do something with the data we’re showing them!

Check out the difference between the original and the remade version:


This single view works well as a remake of the original, but not as well in a live presentation. There’s still too much text to read and process, while listening to a presenter at the same time. For a live setting we can still use the same visuals, but build piece by piece (using animation), which forces the audience to listen to the presenter describing the data. For example, consider the Complaints over time visual again:


Now imagine if each of these images were its own slide. Sparse slides lead to better presentations because a person is there to narrate what’s happening.


One final note on the choice of red as the emphasis color. Some readers may be surprised to see something different from our usual blue & orange as emphasis colors (and readers who are Michigan fans are probably having heart palpitations!). In this case, red was the client’s brand color so we chose to stay consistent with the rest of their visuals. If that weren’t the case, we might avoid red because it could a negative connotation, even though this is a somewhat positive story (complaints declining over time).  

In conclusion, we can indeed incorporate visual cues such as strategic use of color and words into a monthly recurring presentation so that our audience clearly knows 1) what’s important and 2) what action to take.  You can download the Excel file with accompanying visuals here

Elizabeth Ricks is a Data Visualization Designer on the Storytelling with Data team. She has a passion for helping her audience understand the "so-what?" Connect with Elizabeth on LinkedIn or Twitter .  


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#SWDchallenge: basic bars

If I could only use a single graph forever forward, it would be a bar chart. It's sort of like that exercise: if you were stranded on a deserted island and could only bring a single book to read or only have one type of food to eat from then on out—what would it be? (Easy: The Great Gatsby and peanut butter toast). Is it a realistic scenario? No (I'd have my Kindle with me and likely not an endless supply of PB and bread...or electricity...or a toaster). Would I get sick of my choices? Probably. Would it work all of the time? No. But I really do have an affinity for stories about the Jazz Age and peanut butter toast.

I also really like bar charts.

Bars are my go-to graph for a number of reasons. They are common. While this might be cause for some to avoid, this is one of my top reasons for embracing: your audience already knows how to read a bar chart, so you don't face a learning curve for getting your information across. They are not intimidating—you aren't likely to scare anyone with a bar chart. Bar charts are also easy for us to read. When we look at a bar chart, our eyes compare the ends of the bars relative to each other and relative to the axis. Because of the alignment to a consistent baseline, it's easy to see which category is the largest, which is the smallest, and also the incremental difference between categories. Note that for the visual comparison to be accurate, bar charts must have a zero baseline (read more). Bar charts can be vertical (also known as a column chart) or horizontal (great use case: if category names are long—allows you to orient text in a legible fashion, avoiding slow-to-read diagonal text that would be needed if you stick with vertical orientation). Below is an example of each from storytelling with data:

Basic Bars - Vertical.png
Basic Bars - Horizontal.png

The #SWDchallenge this month is to create a basic bar chart. Nothing fancy. No need to stack it or do anything else crazy. Have you made a bar chart before? Probably. But here is an opportunity to focus on making your best bar chart yet. Find some data and teach us all something new. While not a strict requirement, I do encourage you to look to the prior challenges when it comes to annotating and thoughtful use of color and words (including the takeaway title!)—these bar charts are meant to inform, so those important lessons will apply here as well. DEADLINE: Wednesday, 3/7 by midnight PST. Specific submission details follow.


  • Make it. Identify your data and create your visual with the tool of your choice. If you need help finding data, check out this list of publicly available data sources. You're also welcome to use a real work example if you'd like, just please don't share anything confidential.
  • Share it. Email your entry to SWDchallenge@storytellingwithdata.com by the deadline. Attach your image as a .PNG. Put any commentary you’d like included in my follow up post in the body of the email (e.g. what tool you used, any notes on your methods or thought process you’d like to share); if there’s a social media profile or blog/site you’d like mentioned, please embed the links directly in your commentary (e.g. Blog | Twitter). If you’re going to write more than a paragraph or so, I encourage you to post it externally and provide a link or summary for inclusion here. Feel free to also share on social media at any point using #SWDchallenge.
  • The fine print. I reserve the right to post and potentially reuse examples shared.

I look forward to seeing your beautiful bars! Stay tuned for the recap post in the second half of March.


SEARCH STORYTELLING WITH DATA: © 2010-2018 Cole Nussbaumer Knaflic. All rights reserved. STORYTELLING WITH DATA and the STORYTELLING WITH DATA logo are trademarks of Cole Nussbaumer Knaflic.

connecting... the dots

Today's post is a guest post written by Daniel Zvinca. When Dan first reached out to me via email about a blog post I'd written, I thought to myself, That name sounds familiar... Why do I know that name? Some time later, it struck me—it was because I'd recently read one of Stephen Few's quarterly updates that introduced the Zvinca plot named after—you guessed it—Daniel Zvinca.

Dan has a mechanical engineering background, spent much of his career developing business related data applications in the ERP area and beyond, and today enjoys, among other things, considering and practicing data sense-making and data visualization. He lives in Romania (Romanian is his first language), but most of his projects were implemented in Belgium, where he runs a small IT company together with his business partner, Wilfried Van den Bosch.

Dan and I have had some good conversations in blog comments as well as behind the scenes (I'll use this as a opportunity to mention how impressed I am at the technical conversations we have with him speaking in a non-native language). At one point, we were discussing lines and all the ways they can be used. I've run into the scenario several times recently in workshops where it's become clear that many people are under the false impression that lines can only be used for continuous data. That's actually not true. The guideline is that with graphs that use lines, you have to make sure that those lines make sense. In some cases, that can be true for non-continuous data as well. At any rate, Dan and I were discussing this at one point and I invited him to pen a guest post outlining the many uses of lines in data visualization. The following is his post. I hope you'll find it eye opening how many different ways we can use lines in data visualization.

A good communication requires reasonable knowledge of a common language in order to succeed

I had the experience of learning English by starting with a minimum vocabulary and only a few grammar rules. At one point my job required me to travel in another country where English was commonly accepted for communication, even though none of my coworkers were born in a natively English-speaking country. After a while I thought I had a reasonable command of English, but all my confidence collapsed during a 2-hour meeting in London. I could not understand half of what the participants were saying and I probably misunderstood the other half. I am still not sure if they used the cockney dialect or another language but, I am sure it felt awkward to me. Obviously, we can’t properly communicate if we don’t speak the same language, in some cases even the same dialect. The same rule applies so well to data visualization as an extension of our communication language.

The line, a powerful encoding graphical element

One of the basic entities we see in different forms of display is the line. Actually, we use the line term to describe what in geometry is called a line segment. Sometimes we use it to describe curved graphs as well. Just to make sure we are on the same page, in data visualization a line has a finite length, being delimited by two points in a two-dimensional space (paper, screen). Besides its ends coordinates, a line also features a geometric property called slope. This is defined by the rise over the run, the change of “Y” over the change in “X.” The most common data visualization form using lines to encode data is, obviously, the line chart, but there are many other graphs that use lines to encode information. It might be useful to see a few of them and the roles that lines have to encode information. Basically, in data visualization we use lines to:

  1. Encode end points position.
  2. Show connection between two points.
  3. Show orientation, direction or sense.
  4. Encode variation. A slope shows the change in vertical direction over the change in horizontal direction.
  5. Show pattern of change. A group of connected lines show pattern of change, possibly indicating trends.
  6. Define separation. A line can indicate the separation between two regions.

Although important, I deliberately skipped the use of lines for reference constructions like axis, ticks or gridlines. Before I dive into the subject, I would like to remind a couple of things. First, in data visualization we encode two types of variables: quantitative and categorical. Examples of quantitative variables: Cost, Price, Volume of Sales. Examples of categories: Country, Customer, Months of the year, Days of the week. I should also mention that some of the quantitative variables can be considered categories in certain contexts where they can be used for grouping or aggregation purposes. Another thing I want to remind is the nature of information assigned to variables useful to describe the axis of a graph. Psychologist Stanley Smith Stevens developed Scale of Measurement, a classification that is widely accepted in the Data Visualization world. According with this, there are four levels or scales of measurement:

  1. Nominal (items have no particular order and no quantitative meaning),
  2. Ordinal (items have an intrinsic order, but not necessarily a quantitative meaning),
  3. Interval (items have an intrinsic order and the same difference between consecutive values), and
  4. Ratio (items have an intrinsic order, same difference between consecutive values and have a zero as reference).

With these two pieces of information we may consider that most of the graphs are forms of display that encode one or more variables (quantitative and/or categorical) with the help of one or two scales of measurement (nominal, ordinal, interval, ratio). Let’s have a look at the different roles the lines can play across several forms of display.

Line graphs


The most common form of display that uses lines to encode values, is the line chart. While in most of the graphical tools a line chart is considered just an alternative to a bar chart, they are important differences between these two graphs beyond the variable types they share. A bar chart encodes the values of a quantitative variable across a categorical variable for comparison purposes. A line graph displays the variation of a quantitative variable across the items of a categorical variable. Its main purpose is to show the pattern of change across all the items of a categorical variable. Each end of a line encodes the value of the quantitative variable (Y) associated with an item of the categorical variable (X). 

In line graphs, the slopes encode the variation of the quantitative variable between two successive categorical items. For this to work the change in X direction has to make sense. In Data Visualization it is widely accepted that a line chart works fine with interval (and implicit ratio) scales, for which the difference between consecutive items of categorical scale has a quantitative sense.

For instance, a time series (fits into the definition of interval scale) works well with line charts. A chart showing the sales over the months of a year, the sequence March ($50M), April ($60M), May ($40M) can be interpreted as: Sales increased (in one month) from March to April by $10M, but then significantly decreased in May. Trying a similar exercise and use a line chart to encode the sales across the product categories ordered alphabetically, we might have Computers ($40M), Mobile Phones($70M), TV’s ($20M). The interpretation of the slope would be something like: Sales increased from Computers to Mobile Phones, but then significantly decreased for TV’s. It doesn’t make sense. We can repeat the same exercise and order the product categories in descending order of sales adding as prefix their rank: 1. Mobile Phones($70M), 2. Computers ($40M), 3. Televisions ($20M). This time we may read the graph as: If we look at figures from sales rank perspective, the variation from 1st category (Mobiles) to 2nd category (Computers) is as large as $30M, almost as much as the value of 2nd category... This time it works because each category is displayed in its rank position, so actually our categorical variable is the rank (1, 2, 3, …), an integer variable for which we can obviously have clear metrics defined.

In case we don’t remember the classification of S.S. Stevens mentioned above, we can consider that line graphs can be used only with those categorical variables that:

  1. Have an intrinsic order,
  2. The change (difference) between consecutive items makes sense, and
  3. All the changes between consecutive items have a similar meaning.

Please notice that I avoided the strict definition of interval scale that requires the same difference between consecutive items, in the favor of more general, similar meaning to make possible the inclusion of logarithmic and fractional scales. 

I made a short list of examples of categories that can or cannot be used with a line chart.

  • January, February, March (it works, intrinsic order, difference between any two consecutive items is 1 month)
  • January, February, September (it doesn’t work, intrinsic order is there, yet the difference between February and January is 1 month, but between September and February the difference is 7 months)
  • Monday, Tuesday, Wednesday (it works, intrinsic order, difference between any two consecutive items is 1 day)
  • 1, 2, 3 (it works)
  • 2, 1, 3 (it doesn’t work, not ordered)
  • 1, 2, 4 (it does not work, order exists, but the difference between consecutive elements is different)
  • 3, 2, 1 (it still works, descending order, the difference between consecutive elements is -1)
  • Apple, Oranges, Pineapples (doesn’t work, no intrinsic order)
  • 1st Oranges, 2nd Apples, 3rd Pineapples (it works, the rank is actually the categorical variable)
  • 1, 10, 100, 1000 (it works, logarithmic scale, but does not fit into S.S. Stevens' classification)
  • 1, 1/2, 1/3, 1/4 (it also works, fractional scale, but does not fit into S.S. Stevens' classification)



A slopegraph is a form of display that shows the variation of one quantitative variable over two categorical variables. The quantitative variable value is encoded by Y, first categorical variable is identified by the lines (S-Category, S from Slope), and second categorical variable is encoded by X (X-Category). Each end of a line encodes the quantitative variable value (Y) and X-Category while the line itself identifies the S-Category.

On his site, Edward Tufte writes “Slopegraphs compare changes usually over time for a list of nouns located on an ordinal or interval scale.” I agree, but I think that slopegraphs usage can be extended just fine to show the comparison between two groups of any categorical type, therefore they can belong to a nominal scale as well. Cole wrote this post about a slopegraph showing the comparison between groups.

When there are more than two elements of the X-Category, slopes comparison has to make sense across the entire graph, therefore the above mentioned line graph rules apply (intrinsic order, differences between any two consecutive items have sense and similar meaning). Or, if you prefer, follow Edward Tufte guidance, but you might consider also the cases that do not fit S.S. Stevens’ classification (for example, logarithmic, fractional, and cyclic scale).

Frequency Polygon


A frequency polygon is similar to a histogram. It displays the distribution of a quantitative variable over bins defined for the same quantitative variable. Instead of using bars to encode values it uses lines to connect the encoded values. A frequency polygon is the preferred form of display when we need to look for the distribution shape. I haven't figured out why it is called a polygon (this is the name used in geometry for closed two-dimensional figures), but I assume that polyline was not good enough. Each end of a line uses Y to encode the frequency value (the counter of the values that belongs to certain bin) and X to encode the bin position. The slope can be interpreted as the frequency variation between two consecutive bins.

Parallel Coordinates


Parallel Coordinates is a form of display that shows relations between multiple variables. Used in multivariate analyses, Parallel Coordinates usually works better with quantitative variables. Categorical variables also work, but in their case the lexicographic order is used to define the scale. Each line end uses Y to encode the value of one quantitative variable and X to identify the variable. Parallel Coordinates are used to discover relationships between variables. For this form of display the lines have just two roles: to encode the values and to connect correspondent values of two adjacent variables. Unlike Line graphs and Slopegraphs, the line angle has no meaning for this form of display. To remind the definition of a slope as the change of Y over the change in X, we cannot give any quantitative sense to X, other than a conventional position for variables axis.

Pareto Chart


Pareto charts are one of the few cases where it’s acceptable to use a secondary axis. This chart shows the values of a quantitative variable (encoded by bars) and their cumulative values (encoded by lines) calculated in the descending order of values. A correct design should have the two scales synchronized to make sense of dual data encoding/decoding in variable unit of measure and in percentages. The line uses Y to encode the quantitative value and X to encode the category. The slope of one line can be interpreted as the change of the cumulative value between two consecutive ranking positions.   

Connected Scatterplots


Connected scatterplots are scatter plots that have connection lines between the encoded X and Y positions given by a third ordered variable (very often time). The only role of the line is to show the order of the pairs. Sometimes the lines can be decorated with arrows to indicate the parsing order. There were many discussions over the years about the utility of connected plots. I participated in one of them on Stephen Few’s forum. I need to admit that since then I found a very useful particular type of connected scatterplot, that is often assimilated with a line graph, but is not. This is a design I made a few years ago in a forum as a respond to one participant question. Is the below line graph correct or not (the elections are not equally spaced in time)? The graph was designed to show the decline of the interest in politics by measuring % of participants from total possible electors for all elections organized between 1949 and 2009.  


My makeover is a correct and useful connected scatterplot, but it is not a line chart. The slopes of the lines connecting consecutive events indicate the participation change from one event to another and the distance in time between events can vary. This particular connected scatterplot has the third variable (connection order) the same with X variable (time).

Contour Plots


Contour plots are forms of display that encode 3 quantitative variables with a continuous variation, two of them encoded accurately by X and Y position and the third one (commonly Z, elevation) encoded by the variation of a color intensity. A sequential palette usually works best. The lines which shape the contours have the role of delimiting the bins of the third variable (Z-levels).

Tukey Bagplot


Tukey Bagplot is the two-dimensional generalization of a boxplot. Investigating the distributions of both variables with independent boxplots does not reveal anything about the simultaneous behavior of paired values. The dark gray area is called “the bag” (containing 50% of the points), the light gray area is called “the loop” (the other 50% of the points minus the outliers) and the outer polygon of the light gray area is called “the fence.” Without going into details, a Tukey Bagplot reveals similar metrics as a boxplot: location (the depth median, white cross), spread (bag size), correlation (bag orientation), skewness (the shape of the bag and the loop), and tails (the points near the boundary of the loop and the outliers in red). Even if the drawn polygons go through different points, they are just conventionally computed convex hulls, used to enclose sets of values. In this case, the lines have just a separator role.

This was by no means an exhaustive list, but it gives a good indication of the many roles lines can play in different forms of display. There are many other graphical forms that feature lines: regression lines, dendrograms, hierarchical trees, and the list goes on. Do you know of any other roles for lines in visual displays? What are your thoughts on this subject? Leave a comment!

Note from Cole: Dan, HUGE thanks for writing this post and teaching us all about the many different roles of lines in data visualization!


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#SWDchallenge: education, color, and words

Throughout my life, I’ve known February to be Black History month. Interestingly, though, that’s simply on account of my age, as this year marks only the 43rd year we celebrate and recognize African American achievements in the US and Canada—achievements that took place against a backdrop of inequalities and often injustices politically, economically, and socially. For me, I believe that one of the most important pillars to ensuring access and opportunity for all (as well as ending ignorance and racism) is education.

To raise awareness and celebrate Black History Month, storytelling with data is collaborating with data.world, Tableau Public, #MakeoverMonday, Viz for Social Good, and Data for Democracy to ignite the imaginations and talents of our respective community members around the datasets and data stories connected to Black History. Each week’s focus is on a different sub-topic. I’ve decided to make this month’s #SWDchallenge to be centered on education, specifically the access, benefits, opportunities, and ignorance-curbing power. Create a visual with this in mind and let’s use data to recognize the importance—today perhaps more than ever before—of education in our society.

Your work doesn’t stop there. Last month, the challenge was to create an annotated line graph (nearly 90 people shared their creations!). I felt that singling out a graph type here would be too limiting, however (we’ll come back to that in future challenges). Rather than dictate a type of visual, this month we will put into practice a specific tip I find myself giving often when it comes to creating effective visual stories: be thoughtful in your use of color and words.

This may sound like simple advice. It is, I suppose, but there are nuances and the impact of these two straightforward elements executed well can be huge—and can even help overcome other design issues. Let’s talk a bit more about each of these.

Color, used sparingly, is one of your most strategic tools when it comes to the visual design of you data stories. Consider not using color to make a graph colorful, but rather as a visual cue to help direct your audience’s attention, signaling what is most important and indicating where to look. Note that for this to be effective, the use of color must be sparing. If we use too many colors, we lose the ability to create sufficient contrast to direct attention.

Words used well will both ensure your visual is accessible as well as indicate to your audience what you want them to understand in the data. There are some words that must be there: every graph needs a title and every axis needs a title (exceptions will be rare!). Don’t make your audience work or make assumptions to try to decipher what they are looking at. Beyond that, think about how you can use words to make the “so what?” of your visual clear. I advocate use of a “takeaway title”—meaning, if there is something important that you want your audience to know (there should be), put it in the title so they don’t miss it. Also, when your audience reads the takeaway in the title, they are primed to know what to look for in the data. When I’m putting a graph on a slide, I’ll use the slide title for the takeaway (and put a descriptive title on the graph). When the graph is on its own, I’ll often title with both—typically “descriptive title: takeaway.”

As illustration, below is an example. Here, I’ve shown the progression (no need to do this for your challenge, you can simply share the final product) from base graph, then added color, and finally words. Notice how we immediately know what to look for and where to look in the final graph.

Education color words.png

To recap the #SWDchallenge: find some data of interest related to education (you have free range within this: academia, higher education, black scholars, access, how education has helped ensure progress and opportunity, etc.). Data.world has curated a short list of datasets, or you can find even more in this list of publicly available data. Analyze the data to determine the specific story you’d like to tell. Harness the power of color and words to create your visual story. DEADLINE: Wednesday, 2/14 by noon PST. Specific submission details follow.


  • Make it. Identify your data and create your visual with the tool of your choice. If you need help finding data, check out this list of publicly available data sources.
  • Share it. Email your entry to SWDchallenge@storytellingwithdata.com by the deadline. Attach your image as a .PNG. Put any commentary you’d like included in my follow up post in the body of the email (e.g. what tool you used, any notes on your methods or thought process you’d like to share); if there’s a social media profile or blog/site you’d like mentioned, please embed the links directly in your commentary (e.g. Blog | Twitter). If you’re going to write more than a paragraph or so, I encourage you to post it externally and provide a link or summary for inclusion here. Feel free to also share on social media using #SWDchallenge and #VisualizeDiversity and/or upload to the data.world page.
  • The fine print. I reserve the right to post and potentially reuse examples shared.

I look forward to seeing what you come up with. Thank you for helping to celebrate Black History Month and the importance of education in our society. Stay tuned for the recap post!


SEARCH STORYTELLING WITH DATA: © 2010-2018 Cole Nussbaumer Knaflic. All rights reserved. STORYTELLING WITH DATA and the STORYTELLING WITH DATA logo are trademarks of Cole Nussbaumer Knaflic.