One Of The Most Popular Data Viz Technologies May Surprise You

According to the State of The Data Viz Industry Survey, the percent of respondents who used “pen and paper” to create charts, maps, and graphs ranged between 25 and 31 percent from 2019 to 2022 but then shot up to 58 percent in 2023 and 60 percent in 2024. This was the biggest increase of any of the technologies including Power BI, Tableau, and Excel. With so many applications out there to create sleek data visualizations, why are so many drawn to this low-tech, old-school technique?

The short answer is: I don’t know. But I have some ideas . . .

Hand-drawn visualizations are relatable.

We humans seem to be drawn to anything that suggests humanness. Pata Gogova’s What is (not) love? visualization mixes hand-drawn elements with charts created with Tableau to effectively draw our attention to certain aspects of the visualization. “A handmade visualisation can lend a feeling of friendliness to a story,” notes Amelia McNara, “Quite often, computer-generated visualisations feel sterile and can be inaccessible to certain audiences.”

Hand-drawn visualizations suggest uncertainty.

In her Ted Talk called 3 Ways To Spot A Bad Stat, Mona Chalabi emphasizes the importance of showing uncertainty when presenting data. She does that by using hand-drawn charts like the one below. Its unpolished look perhaps prevents viewers from unconsciously accepting what is shown. The result may appear more honest than a sleek presentation with all the requisite disclaimers about the limitations of the data in small type below the chart.

Hand-drawn visualizations aid exploration.

They aren’t limited by an application’s capabilities and thus allow you to think outside the box plot, bar chart, or line graph. And, as Stefani Posavec and Georgia Lupi (who have written several books on visualizing personal data by hand) note, drawing aids memory. Even if you end up visualizing your data digitally, beginning with a pen and paper will help you to explore and absorb your data.

Source: flickr

To see past data tips, click HERE.

Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

How To Present Diversity Data (or What To Steal From This Diversity Scorecard)

Reposted from February 2022

Today’s tip is to take inspiration from Chantilly Jaggernauth’s excellent diversity scoreboard displayed below. It shows diversity among employees in a company but can easily be applied to staff or participants in a nonprofit organization.

I suggest you steal the following ideas from Chantilly:

  • Metric Definitions. In a Tableau Conference session, Chantilly shares the pros and cons of the four metrics in the dashboard. See image of the slide below. None of the metrics are perfect. But together they provide an understanding of where an organization is in its diversity efforts. These definitions are not incorporated in the dashboard itself but could be added through a link or in a tooltip (scroll over) feature.*

  • Views of Diversity. The dashboard provides three views of diversity: overall, gender, and people of color (POC). By providing side-by-side charts with these three views, the dashboard allows users to see variations that overall diversity charts obscure.

  • Color Coding. Each type of diversity has its own color, which makes the comparison among overall, gender, and POC easy, even when you scroll down and can no longer see the column headers. Also the comparison groups (non-diversity, male, and non-POC) are represented by the same colors in lighter shades. This approach makes the dashboard easier to understand. Assigning three additional colors for the comparison groups could be confusing and require a color legend.

  • Simple Charts. These are all charts we all know how to read. So the scorecard is accessible immediately to anyone, even if they are not familiar with the data or the organization.

  • Also, note that the dashboard and the slide use different terms for two of the metrics.

Source: HR Diversity Scorecard on Tableau Public by Lovelytics

Image above from Tableau Conference session called “Next Gen Analytics for Your New Normal” on 11/10/21.


Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

Data Viz Dangers: Hiding Variability

Data visualization has its pitfalls and landmines. Sometimes charts, graphs, and maps can have harmful consequences, intended or not.  So I’m offering up another tip in a series of 60-Second Data Tips that point out pitfalls and landmines to avoid. This time, it’s about hiding variability.

If we see a bar chart like the one below showing median income of Medicare beneficiaries by demographic group, we may make the following mistakes in interpreting it:

  • Assume that all or most of those in the higher income groups are earning more than all or most of those in lower income groups rather than assume that there may be more variation in income within groups than between groups.

  • Assume that the higher median income of a given group suggests that those in that group are more capable, smarter, skilled, etc. than those in lower median income groups rather than assume that those in the lower group faced challenges (e.g. racism, lack of education, etc.) that resulted in the lower median income. (This is called the fundamental attribution error.*)

  • Generalize our error-ridden conclusions about the people represented in the chart to all of those in certain racial, age, gender, or other demographic groups.

Eli Holder and Cindy Xiong conducted studies in which they showed participants charts that emphasized within group variability as well as those that did not like the chart shown above. And they found that participants were less likely to make the mistakes listed above when shown charts that emphasize within group variability such as the one below which shows that there is a lot of variability in scores within schools that one can’t appreciate by only looking at averages (represented by the black horizontal bars.)

Source: Jitter Plots in Tableau, by Ashish Singh

For a great ten-minute summary of the research, check out this video. What to do to avoid this pitfall? The video suggests that we find ways to represent variability within groups as shown below:

To see past data tips, click HERE.

*When visualizing data, we should consider how humans think including heuristics (i.e. mental shortcuts) and biases. Some of them have particular relevance to data visualization such as the fundamental attribution error, which is our tendency to relate behavior to a person’s character and personality rather than to the person’s context. So when someone cuts you off in traffic, if you write them off as a jerk rather than someone who is late for work or distracted, that’s the fundamental attribution error.

Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

How To Make Data Viz Accessible

Data visualization is the translation of data in the form of words and numbers to a visual format, using color, size, shape, and placement to convey trends and patterns in the data which can be much less apparent when looking at tables of numbers or words. Thus data viz communicates best to those with full visual capabilities. To make charts, graphs, and maps accessible to those with visual impairment, we must translate the meaning of a visualization back into words, which can be a challenge. Amy Cesal's article, Writing Alt Text for Data Visualization, can help us address that challenge.

Alt text is a brief description meant to provide the meaning and context of a visual item in a digital setting. And although Cesal notes that, in most cases, it’s impossible to write something short that conveys the whole meaning of a visualization, she maintains that an incomplete description is better than none at all. Here are Cesal’s simple guidelines for alt text for data viz:

Chart type: It’s helpful for people with partial sight to know the chart type. This information provides context for understanding the rest of the visual. Example: Line graph.

Type of data: What data is included in the chart? The x and y axis labels may help you figure this out. Example: number of clients served per day in the last year.

Reason for including the chart: Think about why you’re including this visual. What does it show that’s meaningful? There should be a point to every visual and you should tell people what to look for. Example: more clients are served during the winter months.

Cesal also suggests that you include a link to the raw data somewhere in the surrounding text.

For a deeper dive into this topic, checkout Image Description Guidelines from the Diagram Center.

To see past data tips, click HERE.

Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

Charts That Changed The World

This week’s tip is to check out this video from The Royal Society in which Adam Rutherford shares five data visualizations that have changed the world. Admittedly, this will take you more than 60 seconds to watch (it’s 6 minutes). But it’s worth it. Rutherford shares four classic charts. Two of them clarified a problem so well that they led to solutions. He also shares a chart that drives home the dangers of visualizing lies and thus making them look legitimate. If you’d like to learn more about these charts, I’ve included links below the video. Enjoy!

Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

How To Improve Your Organization’s Time Line

Reposted from March 2022

Here’s a simple data viz idea. Next time you make a time line showing your organization’s milestones, size those milestone markers (usually circles) according to some key measure. Voila! You are not only showing what happened but also your progress along the way.

The data for such a viz is super simple. Something like this:

Screen Shot 2021-08-24 at 9.01.48 AM.png

I connected the data shown above to Tableau Public (the free version of Tableau) to create the time line below. Vertical time lines not only suggest an upward progression but also work better on phone screens.

Dashboard 1 (3) (1).png

Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

How To Make Big Numbers Tangible

Reposted from March 2022

We’ve talked about the problem with big numbers before. Most recently, we considered the difficulty humans have digesting large numbers and how “perspectives” — simple sentences that relate a large number to something more familiar to us — can help us to understand, assess, and recall numbers. (For more on this, check out the data tip.)

I’m returning to the big number problem today and offering up some new tips for dealing with them. The inspiration for these tips came from the data-driven documentaries of Neil Halloran, specifically his first documentary called The Fallen of World War II. If you have a few more minutes to spare after reading this 60-second tip (and are not among the 13 million + who have viewed it already), I highly recommend that you check it out. It’s 18 minutes long, but the techniques listed below all appear in the first 7 minutes.

Halloran uses the following techniques to make large numbers understandable. And you don’t need to be a filmmaker to use them. You can apply them to simple data presentations on websites, reports, and PowerPoints.

  1. Use shapes or icons (rather than bars) to represent one or more people, programs, etc. Halloran uses a human figure shape to represent 1,000 people.

  2. Show an aggregate and then break it down by subgroups and time periods. Halloran shows aggregates, such as the total number of U.S. soldiers who died and then, using animation, redistributes the human figures to show how many soldiers died in the European and Pacific theaters and then how many died over time. The animation is cool but not necessary. You can do the same thing with a series of static images. See example below.

  3. Juxtapose photos and charts. To keep the discussion from becoming too abstract, Halloran reminds the audience what actual soldiers (rather than icons) look like by incorporating photos into his presentation. Again, animation is not necessary. Static photos can be placed alongside charts.

  4. Walk your audience through the data. To give the audience a sense of scale, the video progresses from smaller to larger numbers. Halloran first walks us through casualty stats for the U.S. and European countries. These numbers seem quite high so by the time Russian stats are shown, we are blown away.

Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

Data Viz Pitfalls and Landmines: Polarization

Charts, maps, and graphs can be great tools for illuminating trends and patterns in data. But data visualization has its pitfalls and landmines. Sometimes charts, graphs, and maps can have harmful consequences, intended or not. So I’m starting a series of 60-Second Data Tips that point out pitfalls and landmines to avoid. This time it’s about polarization.

When visualizing data, we should consider how humans think including heuristics (i.e. mental shortcuts) and biases. That’s a tall order given how many shortcuts and biases affect our thinking. See Diagram B below. Some of them have particular relevance to data visualization such as conformity bias, which is the tendency to change one's beliefs or behavior to fit in with others. Can data visualization exacerbate this bias?

You are likely familiar with charts showing U.S. attitudes toward public policies which highlight the gap between Democrats and Republicans. Researchers conducted experiments to explore whether such charts invoke viewers’ social-normative conformity bias, influencing them to match the divided opinions shown in the visualization. In three experiments described in Polarizing Political Polls: How Visualization Design Choices Can Shape Public Opinion and Increase Political Polarization, researchers either aggregated data as non-partisan "All US Adults," or partisan "Democrat" / "Republican." (See Diagram A) They found that the partisan charts tended to increase viewers’ polarization while the non-partisan charts did not, leading the researchers to conclude that visualizing partisan divisions can further divide us.

So when showing differences of opinions across various groups, we should take this finding into consideration. Is our ultimate aim to divide or unite?

Diagram A

Diagram B

Source: https://upload.wikimedia.org/wikipedia/commons/c/ce/Cognitive_Bias_Codex_With_Definitions%2C_an_Extension_of_the_work_of_John_Manoogian_by_Brian_Morrissette.jpg

To see past data tips, click HERE.

Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

How To Visualize Cycles

Reposted from April 2022

Every organization experiences cyclical or seasonal patterns. Understanding how funding, participation, volunteering, and other factors change in predictable ways over time can help us to plan for the future. The problem is that we don’t always see these patterns. We get caught up in current issues and crises, and it’s hard to step back and see what’s coming next. Visualizing your data can reveal cyclical or seasonal patterns in helpful ways. This often involves aggregating data from multiple years by specific time periods such as season, quarter, or day of the week. Here are some examples.

Working with a statistician named William Farr in the 1800s, Florence Nightingale analyzed mortality rates during the Crimean War. She and Farr discovered that most of the soldiers who died in the conflict perished not in combat but as a result of “preventable diseases” caused by bad hygiene. Nightingale invented the polar area chart (shown below), a variant of the pie chart, meant “to affect thro’ the Eyes what we fail to convey to the public through their word-proof ears.” Each pie represented a twelve-month period of the war, with each slice showing the number of deaths per month, growing outward if the number increased, and color-coded to show the causes of death (blue: preventable, red: wounds, black: other). The New York Times showed the seasonal pattern of COVID cases using a somewhat similar chart.

Source: Wikipedia

In the dashboard below, Curtis Harris reveals not only patterns in taxi rides by time of day but also by day of the week. We can see, for example, that few people are using taxis between 2 and 3 am, particularly at the beginning of the week. (Click on this viz to see interactive version.)

Source: Curtis Harris on Tableau Public

This varsity-level viz (below) by Lindsay Betzendahl shows the seasonality of the flu. Each dot represents one week in a particular year. Each “ray” consists of dots for the same week of different years. So the ray at the 12:00 position represents the first week in January for each year between 2007 to 2018. The size of the dots show the number of influenza cases. So we can see that cases surge during the winter weeks, in general, but we also can see outbreaks during other seasons in particular years. Betzendahl explains how to create such a chart in Tableau here.

Source: Lindsay Betzendahl on Tableau Public

Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

Ideas You Should Steal From This Viz (Installment 12)

“All creative work builds on what came before.” —Austin Kleon in Steal Like An Artist.

Today I offer up another steal-worthy interactive viz that I came across in the Tableau Public Gallery.

Here’s what I suggest you steal from this viz:

  • Dot Matrix Chart Type. Each dot in a dot matrix chart is colored to represent a category and sized to represent the magnitude of the category. Thus the chart provides an overview of the distribution and proportions of each category in the data set. By clicking through the ban categories at the top (classrooms, libraries, etc,), we get a quick sense of the magnitude and origin of book ban challenges.

  • Explanation/Directions. The text on the left tells us everything we need to know to extract meaning from the visualization without loading us down with unnecessary details.

  • Details on Demand. For those who want more information, details are available on demand by scrolling over the individual circles and the info button.

Source: Gbolahan Adebayo on Tableau Public

To see past data tips, click HERE.

Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

How To Recognize and Reform Vanity Metrics

Reposted from March 2022

Vanity metrics are like cheap, trendy sunglasses. They may help you to look cool, briefly, but they don’t last long and do little to improve your eyesight. You’ve seen vanity metrics, even if you haven’t used this term to describe them. They are those flashy statistics (sometimes called “big ass numbers”) and charts showing how many services an organization has provided or people they’ve served or some other seemingly impressive stat. The problem is that these metrics don’t help you to better understand your current work and improve it. In this tip, I’ll give you some quick advice on recognizing and reforming vanity metrics.

How To Recognize A Vanity Metric

This Tableau article suggests three questions to ask to identify a vanity metric. I’ve put a nonprofit spin on each:

  • What decision can we make with the metric? If the metric can’t help you to make a decision, it’s probably a vanity metric. For example, does knowing how many meals you delivered help you to decide who, what, where, when, or how to deliver meals in the future? Or do you need a more specific metric such as the gap between need and service provision for various subgroups of clients?

  • What can we do to intentionally reproduce the result? Did some random event produce the big number? For example, did you see a bump in the number of participants last year because another organization, providing a similar service, closed down? If you cannot consistently reproduce the same result next year, this isn’t a helpful metric.

  • Is the data a real reflection of the truth? Let’s face it. There are always ways to misrepresent the truth. You can tell the world that attendance at all of your programs last year totaled 3,237. Sounds good, but that’s probably a “duplicated number” and can be misleading depending on what you want to understand or broadcast to the world. Some people likely attended more than one program. So the total number of individuals who participated in any program could be much lower. The central question to ask yourself when considering a metric is whether or not it will help your organization achieve its goals. If your goal is to reach more folks, this metric is not helpful.

How To Reform A Vanity Metric

  • Provide context. The metrics that are worthy of your attention and your stakeholders’ attention are those that are directly related to your goals. You may have overall goals for all of your participants, clients, audiences, services, programs, etc. But you also might have specific goals for subsets of those groups and for specific time periods. Present your metrics in relation to the goals. And compare metrics for subgroups to each other to see where you are making progress and where you are not.

  • Use more than one statistic. Sometimes what you want to improve cannot be measured with just one metric. For example, if you aim to improve the diversity of your staff, you may want to look at a set of metrics together including number, tenure, and seniority of staff by race/ethnicity, gender identification, age, etc.

Sources: Moving Beyond Vanity Metrics, Stanford Social Innovation Review and Vanity Metrics: Definition, How To Identify Them, And Examples, Tableau.

Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

How To Visualize What Could Have Been or Might Be

In English, there are three verbal moods:

  • The indicative used to express factual statements or positive beliefs;

  • The imperative used to express direct commands, requests, and prohibitions; and

  • The subjunctive used to express hypothetical situations or conditions which are contrary to fact or uncertain.

When we are visualizing data, we often settle into the indicative, the realm of facts. But what if we want to show what might happen if action is not taken or what might have been had action not been taken? In other words, how can we visualize the subjunctive? I’d like to offer up four examples to inspire you. The Flattening The Curve chart shows the impact of COVID mitigation (social distancing, masking, etc.) on the healthcare system and should bring back memories of 2020. The other charts may be new to you.

 
 
 

To see past data tips, click HERE.

Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

Emphasize The Shape of Your Data

“There is a magic in graphs. The profile of a curve reveals in a flash a whole situation — the life history of an epidemic, a panic, or an era of prosperity. The curve informs the mind, awakens the imagination, convinces.” – Henry D. Hubbard in the preface to William C. Brinton’s Graphic Presentation, 1939.

Data visualization translates words and numbers into visual cues such as color, size, position, and shape. Humans can process such visual cues much more easily and quickly than words and numbers. Today, I’m here to talk about shape because I don’t think we turn to shape often enough to engage and educate with data. The three area charts below provide a great example of the power of shape. The charts show estimates of the distribution of annual income among all world citizens over the last two centuries. By considering the change in the shape of the data across the three time periods and in relation to the poverty line, we learn a lot. Most significantly, we learn that, over the 215 year time period, the majority of people went from living below the poverty line to living above it. We learn even more by considering the shape of the distribution for each color-coded region.

So consider taking a page from this playbook. Rather than showing change over time in one chart. Consider using shape to tell the story by showing the distribution of something (e.g. income, course grades, number of clients) at different time periods in adjacent area charts.

Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

10 Essential Data Facts For Non-Data People: The Cheat Sheet

Reposted from January 2022

Here are ten data facts for non-data people who, nevertheless, have to deal with data sometimes (i.e. most of us). This is the cheat sheet. Click on the “Learn More” buttons for additional information served up in comic strip format!

Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

How To Check Your Data Blind Spots

You’ve heard it before. We see what we want to see. It’s called confirmation bias, and we are all susceptible. Confirmation bias is a big problem to those presenting or consuming data (i.e. all of us.) How can we draw our own and others’ attention to the data that does NOT fit our existing beliefs? How, in other words, can we check our blind spots?

The great thing about your blind spot when it comes to driving is that you know it exists. Your rearview mirrors do not show you an area next to and behind your car. So you learn to check that area in a different way. Experienced drivers do it by rote. Wouldn’t it be great if we also could remember that we have data blind spots and learn to check them automatically?

Here are some ideas for making your blind spots visible. All of them involve doing something before you look at data (in the form of a spreadsheet, table, chart, map, or graph) to help you look at the data with fresh eyes.

  1. Make predictions before looking at data. To prevent seeing only the data that confirm our beliefs, we can make predictions before looking at the data. In Staff Making Meaning from Evaluation Data, Lenka Berkowitz and Elena “Noon” Kuo suggest that, before sharing data with program staff, “have them spend 10 minutes writing down predictions about what the data will say. This exercise helps surface beliefs, assumptions, and biases that may otherwise remain unconscious.” This can involve drawing a predicted trend in the data or jotting down guesstimates of key data points. Then look for differences between your predictions and the actual data and consider:

    • What may have contributed to the differences,

    • What more do you need to know to take action, and 

    • What actions might you consider immediately?

  2. Consider your “null hypothesis” before looking at data. This approach is a variation on strategy number one.  Rather than making a prediction, you pose this question to yourself: If what I expect is NOT true, what might I see? This is analogous to how researchers conduct experiments.  Rather than trying to prove that a hypothesis (e.g. A is affecting B) is correct, researchers aim to collect sufficient evidence to overturn the presumption of no effect, otherwise known as the null hypothesis. It’s sort of like innocent until proven guilty. The idea is to take the opposite view to the one that you hold and then look for evidence to support it. If you can’t find that evidence, then your assumption might be correct. This approach makes you think more critically and perhaps more dispassionately when encountering data.

  3. Set decision criteria before looking at data. “Many people only use data to feel better about decisions they’ve already made,” notes Cassie Kozyrkov in Data-Driven? Think again. To avoid this, you can frame your decision-making in a way that prevents you from moving the goalposts after you’ve seen where the ball landed. Before considering the data, determine your cutoffs for action. For example, you and your colleagues might decide that program participation below 150 in any given month requires investigation and possible action. Let’s say that twelve-month data show participation below 150 in six months. The pre-established cutoff can prevent you from only focusing on the worst months when participation was below 75.

Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

How and Why to Visualize Variability

Every dataset includes variability. The people and things we measure differ from one another in many ways. And visualizing data always involves some decisions about how much of that variability to show. There are tradeoffs:

  1. If you show too much variability, you obscure patterns and trends. To understand anything with data, we usually need to reduce its complexity. We can’t extract meaning from a table full of numbers and letters. So we summarize the data through such activities as grouping people, concepts, and time periods; calculating averages; or organizing individuals or groups in a rank order. This process allows us to detect patterns and trends within the data. Patterns and trends become even more apparent when we visualize the data in the form charts, maps, and graphs by assigning visual cues such as color, size, and shape to groups and values. However, too many colors, sizes, and shapes make discerning the patterns and trends more difficult.

  2. If you show too little variability, you obscure reality*. Overly simplified visualizations do not show just how complex and messy the data actually is. And the viewer may mistake the simplified, summarized version of the data as reality.

You can find a great example of problem #2 in Eli Holder’s article Divisive Dataviz: How Political Data Journalism Divides Our Democracy. He describes the danger of red and blue political maps in the U.S. in this way: “there’s no such thing as a “red state” or a “blue state.” Consider Texas, which is often called a “red” state. In the 2020 presidential election, more Texans voted for Joe Biden (5.26 million) than every other “blue” state, except for California. Even New York, a Democratic stronghold, had roughly 20,000 fewer Biden voters than Texas. . . . While popular election maps accurately reflect the ‘winner-take-all’ dynamic of the electoral college, they create the misimpression that state electorates are monolithic blocks of only-Republicans or only-Democrats.”

And the misimpressions such maps engender have real-world consequences. Holder describes an experiment in which people were shown either dichotomous maps or continuous maps (see examples below). Those shown dichotomous maps were more likely than those shown continuous maps to feel that their state was dominated by one party and thus that their votes mattered less because the election outcome was a foregone conclusion.

So when deciding how many shades of gray or circle sizes to show, consider how much summarization is needed to make patterns and trends perceptible without misleading the viewer with an oversimplified view of the data. Take, for example, these three versions of a map. They each show the same CDC chronic illness survey data with a “diverging color palette” in which blue states ranked high on health indexes; orange states ranked low; and gray states were in the middle. The maps differ in the degree of variability shown. Which map allows you to see corridors of good and bad health without oversimplifying the matter?

*More specifically, the full reality of the data. This 60-second data tip doesn’t get into the nature of reality in general!

To see past data tips, click HERE.

Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

One Dataset, 100 visualizations

Today’s tip is to check out the 1 dataset, 100 visualizations project. It shows 100 different ways to visualize this simple dataset:

 

It’s fun to compare the different charts and see how they provide different perspectives on the data. For example, to visualize this data, many of us would create a stacked bar chart like this one and call it a day.

 

But look at how this chart allows you to better understand each country’s relative position in relation to number of world heritage sites between 2004 and 2022. We can more easily see, for example, that Denmark leapfrogged Norway.

 

These 100 charts make a strong case for visualizing your data in a number of different ways before selecting one which provides the perspective needed.


To see past data tips, click HERE.

Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

Not Your Grandmother's Icons

We have all seen icon charts that look like this (even if our grandmother didn’t create them):

Want to make an icon chart that actually looks like real people as in the example below? Well, of course, you can just find some cool silhouette icons and use any graphic design program — such as Canva or Adobe Illustrator —  to layout and color icons as you wish. However, if you want the chart to be interactive, you will need to use a program like Tableau or Power BI. I created the chart below in Tableau. It provides more information when you scroll over the icons. Scroll down for Tableau instructions.

Here’s how I did it:

(Note: These directions assume basic knowledge of Tableau. If you don’t have that knowledge, here’s a good video on Tableau basics.)

  1. I selected 10 head silhouettes to use as icons. You can find great (free) icons on sites like flaticon , The Noun Project, or Canva. Be sure to save each icon as a separate PNG file with a transparent background.

  2. I added the icons to the shapes folder in my Tableau Repository. Here’s a short video on how to do that.

  3. I connected a data file (such as an Excel or CSV file) to Tableau Public (the free version of Tableau). The file included 40 rows, one for each applicant, and columns for:  a unique identifier for applicants (such as an ID number), selection status (whether applicants was or wasn’t selected to be a participant, and 3) the silhouette image assigned to the applicant.

  4. In Tableau, I started a new worksheet, changed the marks type to shape, and dragged:

    1. The ID number to the columns shelf (note that ID number should be a dimension not a measure);

    2. The selection status dimension to color on the marks card; and

    3. The silhouette dimension to shape on the marks card.

  5. I clicked on shape on the marks card, clicked on “Reload Shapes,” selected the shape palette that I added to my Tableau Repository in Step 2,  assigned head shapes to the various values for the silhouette dimension., and then clicked “OK.”

  6. I clicked on color on the marks card and adjusted the colors for the “selected” and “not selected” values.

  7. I dragged ID number to the filters shelf and selected ten ID numbers to show.

  8. I changed the view from standard to entire view and clicked on size on the marks card and adjusted the size of the heads as I preferred.

  9. I hid the header and title, duplicated the worksheet, and selected different IDs using the filter. I then repeated this step to create two more worksheets.

  10. I created a dashboard, added a vertical container, dragged the four worksheets into the container, clicked on the drop down menu for the container (in the upper right corner of the container) and selected to distribute contents evenly. I then adjusted the layout by adding outer padding and added in a text box.

To see past data tips, click HERE.

Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

Data Dashboard Treasure Hunt

Looking for a way to engage your staff, board members, and others in your data dashboard? Want them to understand how to use it and how it can inform their work? Here’s a great idea from MiraCosta College: a dashboard treasure hunt! Each page features one page of the dashboard along with questions that require the user to interact with the dashboard. Correct answers lead the user toward a hidden treasure. Try it for yourself:










Source: Treasure Hunt by MiraCosta College on Tableau Public

To see past data tips, click HERE.

Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.

How to Extract Meaning from Survey Data

You just conducted a survey of your clients. participants, board members, visitors, or community members, and chances are you used some Likert Scales in that survey. In other words, you asked respondents to state their level of agreement or disagreement on a symmetric agree-disagree scale. A typical 5-level Likert scale is:

Strongly Disagree - Disagree - Neither Agree nor Disagree - Agree - Strongly Agree

Here’s a FAQ (which is more like a QYSA: Questions You Should Ask) on visualizing Likert Scale data to extract useful information.

Have you collected data just once or multiple times with this survey?

If this is a one-time deal, then I would suggest that you visualize the data using a stacked bar chart. Exactly which type of stacked bar chart depends on what you are trying to understand and show. Check out this Daydreamming With Numbers blog post: 4 ways to visualize Likert Scales. It walks you through various options. If you have collected the data two or more times, read on.

Should I calculate average scores and compare them?

A common way to look at change over time with Likert Scale data is to assign numerical values to each response (e.g. Strongly Disagree: 1, Disagree: 2, Neither Agree nor Disagree: 3, Agree: 4, Strongly Agree: 5) then calculate the average across respondents at two or more points in time and compare them. Some may even use a statistical test (such as a paired sample t-test) to assess whether the averages are “significantly” different. This may seem like an obvious way to deal with the data, but there are problems with it:

  • The distance between 4 and 5 is always the same as the distance between 2 and 3. However, the distance between Agree and Strongly Agree is not necessarily the same as the distance between Disagree and Neither Agree nor Disagree. So we may be distorting respondents’ opinions and emotions by assigning numbers to these response options.

  • Respondents are often reluctant to express strong opinions and thus gravitate to the middle options. Averaging a bunch of middle options (2, 3, and 4) only amplifies the impression that respondents are on the fence.

  • Averages do not give us a sense of the range of responses. The average of these 4 responses (5,1,1,1) is the same as the average of these 4 responses (2,3,2,1). Also averages result in fractional results which can be hard to interpret. Does an increase from 4.32 to 4.71, even if it’s statistically significant, really mean anything in the real world? At best, we can say that the aggregated results changed from somewhere between Agree and Strongly Agree to another place that is a little closer to Strongly Agree.

What are alternatives to calculating averages?

Visualize the spread of responses. If you don’t have too many questions (or can group questions together) some simple side-by-side stacked bar charts might do the trick. See sketch 1 below.

Use the mode or median rather than average.The mode is the number that occurs most often in a data set and may be a good way to describe the data if one response dominated. The median is the middle value when a data set is ordered from least to greatest. If responses tend toward one end of the scale (i.e. are skewed), it may be more reasonable to use the median rather than the average. If you feel that the assumption of equal spacing between response options is legit, then you might stick with the average.

Visualize average, mode, or median using one of the following chart types (see sketches 2-4) to understand and show change over time.

To see past data tips, click HERE.

Let’s talk about YOUR data!

Got the feeling that you and your colleagues would use your data more effectively if you could see it better? Data Viz for Nonprofits (DVN) can help you get the ball rolling with an interactive data dashboard and beautiful charts, maps, and graphs for your next presentation, report, proposal, or webpage. Through a short-term consultation, we can help you to clarify the questions you want to answer and goals you want to track. DVN then visualizes your data to address those questions and track those goals.