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.

---

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.

---

The Allure and Danger of Data Stories

Reposted from December 2018

I recently read Has Data Storytelling Reached Its Peak? in which Amanda Makulec suggests that we use the term “data storytelling” cautiously. “Not every piece of data needs to be communicated as a story,” Makulec notes. “Sometimes we need to start with story-finding or just a well structured chart, rather than a full narrative arc.” I’ve had my own concerns about the term “data storytelling” which I’m sharing with you (again) today.

***

“Data” and “storytelling” are an item. You see them together all the time lately. When I first came across the term “data storytelling,” it instantly appealed to me. “Data” suggests credibility, information that has some objective basis. But data, to many of us, is boring. Its meaning is often uncertain or unclear. Or, even worse, it’s both. “Storytelling,” by contrast, suggests clarity, a plot with both excitement and resolution. So, by coupling these two words, we seem to get the best of both worlds. Data lend credibility to stories. Stories lend excitement and clarity to data.

Indeed, that’s the point of data storytelling. As Brent Dykes, a data storytelling evangelist of sorts, noted in a 2016 Forbes article, “Much of the current hiring emphasis has centered on the data preparation and analysis skills—not the ‘last mile’ skills that help convert insights into actions.” That’s where data storytelling comes in, using a combination of narrative, images, and data to make things “clear.”

But let’s step back just a minute. Why are we so drawn to stories? According to Yuval Harari, author of Sapiens: A Brief History of Humankind, the answer is:  survival. Harari maintains that humans require social cooperation to survive and reproduce. And, he suggests that to maintain large social groups (think cities and nations), humans developed stories or “shared myths” such as religions and corporations and legal systems. Shared myths have no basis in objective reality. Reality includes animals, rivers, trees, stuff you can see, hear, and touch. Rather, stories are an imagined reality that governs how we behave. The U.S. Declaration of Independence states: “We hold these truths to be self-evident: that all men are created equal . . . “ Such “truths” may have seemed obvious to the framers, but Harari notes that there is no objective evidence for them in the outside world.  Instead, they are evident based on stories we have told and retold until they have the ring of truth.

So stories (in the past and present) are not about telling the whole truth and nothing but the truth. Instead, they are often about instruction: whom to trust, how to behave, etc. And we should keep this in mind when telling and listening to “data stories.” To serve their purpose, stories leave out a lot of data — particularly data that doesn’t fit the arc of the story. For example, you might not hear about a subgroup whose storyline is quite different from the majority. Or, indeed the story might focus exclusively on a subgroup, ignoring truths about the larger group.

Bottom line: listener beware. A story, whether embellished with data or not, is still just a story. And truth can lie both within and outside of that story.

---

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 + Experience = Insight

Reposted from June 1, 2021

Data can’t take you all the way to a decision. In real life, you will never have enough data. So you’ll need to apply your own experience and your colleagues’ experiences to understand the implications of data for your work. Here’s a great way to apply experience to data. It’s from Dabbling In The Data: A Hands-On Guide to Participatory Data Analysis, a guide from Public Profit.

Dabbling in The Data describes how to do this group activity in person. I’ve adapted it to an online experience using Canva, but you can use any brainstorming app that allows for real-time collaboration. It’s simple yet powerful.

Step 1: Choose a template.

Sign up for a free Canva account. Click on “Create a design” in the upper right corner of the screen, select “whiteboard” from the dropdown list, and then select a template on the sidebar (A). I chose this template from the “Brainwriting” template group.

Step 2: Add a chart and customize.

Next add a chart showing change over time on some key measure you want to better understand. In this example, the key measure is the number of lessons provided to participants over a 7-year period. You can cut and paste an image of the chart from another program like Excel, or you can create the chart in Canva by click on the “Elements” tab in the sidebar (see B above), choosing a chart type, and then entering the data points (see C above.) I’ve added a chart and customized the instructions and notepads below. Note, it’s important to position the chart backward (see D above) and lock the chart in place (see E above) so that others can place notepads on top of the chart.

Step 3: Share the chart with your colleagues and invite them to add milestones.

Your colleagues will need free Canva accounts as well. To share the chart in real time, click on “Share” (see F above) and type in your colleagues' email addresses, making sure to allow them to edit the design. Ask the group to think about the key organizational milestones that occurred during the time represented in the chart and to add those milestones to the chart using the notepads at the bottom of the screen. Encourage them to also add detail to notepads added by others.

Step 4: Discuss.

Discuss how changes in the key metric over time might be related to the organizational milestones. Consider how this understanding of pivotal events can help you better show progress to stakeholders and to plan for the future. For example, if the implementation of a new program was followed by a decrease in participation, what about the new program may have caused the decrease? What else was going on at the time that may have contributed to the decline?

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.

---

The Problem with Large Numbers (And What To Do About it)

Reposted from April 2021

BANs (Big Ass Numbers) — aka “vanity metrics” — are gaining prominent positions in data dashboards, websites, social media, email marketing, and annual reports these days. They are meant to impress. Wow! 359,234 meals served! Cool! $6 million raised!

But there is a problem with big numbers. Our brains can’t fully digest them. As noted in a 2017 Wall Street Journal article, “Big numbers befuddle us, and our lack of comprehension compromises our ability to judge information about government budgets, scientific findings, the economy and other topics that convey meaning with abstract figures, like millions, billions, and trillions.”

When quantifying the breadth of a problem or solution, nonprofits may toss out lots of giant figures, as in the bewildering graphic below. But without context, even numbers in the hundreds or thousands can bewilder.

Source: Rosenberg Advertising

So what can we do to make BANs more meaningful? Researchers at Columbia University and Microsoft found that they could improve numerical comprehension by using “perspectives,” which are simple sentences that relate a large number to something more familiar to us.

They found, for example, that when told that the number of registered firearms in the U.S. is about 300 million, study participants not only had trouble comprehending this number but also recalling it and assessing its likely accuracy. However, when told that there is about one firearm per person in the U.S., significantly more people could comprehend, assess, and recall the quantity. Makes sense to me. We can imagine a group of people, each holding a firearm, but we are hard pressed to imagine a pile of 300 million firearms.

So before you present a large number, consider a perspective that will make it relatable for your audience. Below are some formulas used in the Columbia/Microsoft study for developing perspectives.

Source: Improving Comprehension of Numbers in the News

---

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

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 currents 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.

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.

---

Why Nonprofits Can Ditch Statistics

Today’s tip is to check out Maryfrances Porter and Alison Nagel’s excellent article Why Nonprofits Shouldn’t Use Statistics on the Depict Studio Blog. Here is a 60-second version with my two cents.

I agree with Porter and Nagel that you probably should not be worrying about statistics due to :

1. Small numbers. Most nonprofit organizations are not serving thousands or millions, but rather tens and hundreds. It’s hard to draw scientifically defensible conclusions based on small numbers. Any individual in a small group has an outsized impact on the group as a whole.

2. No reasonable comparison group. To make a scientifically defensible claim about the impact of your program, you usually need to compare your participants to a random group of people who do not participate in the program. And, as Porter and Nagel note, “we’ve never met a nonprofit so flush that they had money to track people they don’t serve.”

So how should nonprofits use all that data that they collect everyday? Porter and Nagel suggests that organizations:

1. Look for pattens, themes and trends. When considering data on participation, feedback from surveys and focus groups, and other data you may collect, look for themes and patterns. Then consider how those themes and patterns change over time and how they differ among subgroups. The best way to see patterns, themes, and trends is in the form of charts, maps, and graphs.

2. Consider possible causes. Based on your experience, what might be the reasons behind the patterns, themes, and trends you see? Consideration of this question with your colleagues can lead to valuable hypotheses. You can use these hypotheses to make program changes and see if the data you subsequently collect suggest that those changes led to more positive outcomes. You are not demonstrating impact in a scientific way, but you are using data to inform your decisions.

I don’t agree with Porter and Nagel that you have to know what graphs you want before creating them in Tableau. Actually, I think Tableau provides a more nimble way of exploring your data in different visual formats than you can in Excel. But use whatever tool you are comfortable with. Or hire someone (like Data Viz for Nonprofits) to visualize your data for you. An interactive dashboard makes it easy to track your progress on a regular basis.

To see past data tips, including lots of tips on ways to visualize nonprofit data, 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.

---

Understand Social Media Impact Using "Pantry Staple" Data

Social media data is another staple in our data pantries. Your organization probably has it. But you might not make good use of it. Why? Well, social media analytics come in many flavors and can bewilder. Posts are like bait. The better the bait, the more nibbles and bites leading to website traffic and eventually to more donations, volunteers, participation, and other types of engagement. But we need a way to easily decipher which types of bait are working best. And drawing this information out of the data can be a challenge.

Need some inspiration? Check out Alice McKnight’s dashboard below. It provides a broad array of social media data in an appealing and accessible format. We can see how effective social media posts are at various times. The charts along the top give us the basic trends over the last several months. It also shows us what topics are drawing attention. Give it a try. Check out the “View By” options which filter the two charts at the bottom. Then consider how you might visualize your own social media data using similar charts.

Source: Alice McKnight on Tableau Public

To see past data tips, including tips on other types of pantry staple data, 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.

---

Understand Your Budget Using "Pantry Staple" Data

Budget data is another staple in our data pantries. We need our staff, board members, funders, clients, and other stakeholders to understand this data. But many of them are not comfortable with financial spreadsheets. Here are some ways to present budget data that allow others to gain quick insight and, perhaps, dig deeper. These examples all come from the public sector but are easily applied to a nonprofit organization’s budget.

2016 U.S. Budget

The Obama administration made the Byzantine federal budget accessible to the world through this simple treemap. We can see where the lion's share of the money goes and which areas receive relatively little funding. Click HERE to see the interactive version which provides more information when you scroll over or click on a rectangle. Treemaps are easy to make in Tableau, Excel, and other apps.

Source: Obama White House Archives

School District of Philadelphia Budget

I find this sunburst chart daunting in static form. But check out the interactive version HERE. If you scroll over the inner ring, you can see that 96 percent of funds went to school budgets. Then move to successive outer rings to see how school and administrative budgets break down into smaller categories. You can build sunburst charts in Excel and other apps.

Source: Code for Philly

Oak Park, Illinois Budget

This interactive area chart showing the Oak Park, IL budget emphasizes change over time in the area chart at the top. But you can click on any year to see how the budget broke down by funds in the bar chart below. If you click on the downward arrow next to a given fund, you get even more detailed information on that fund. You can build this type of interactive viz in Tableau.

Source: BudgetOakPark.com

To see past data tips, including tips on other types of pantry staple data, 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 Gain Insight By Widening Your Perspective

We all live, by necessity, in our own little worlds. We interact with our colleagues and our participants, clients, or audiences way more often that we interact with other folks. This way of life has both its comforts and dangers. One of these dangers may have never occurred to you. Academic types call it “selecting on the dependent variable.” Catchy name, right? It sounds more complex than it is. It simply means paying attention only to cases in which some phenomenon is observed and ignoring cases in which it is not observed.

Here’s an example from my own life. For years I suffered from back pain. I saw all kinds of physicians, and they all agreed that that three ruptured discs in my spine were causing the pain. The ruptured discs were plain enough to see on an MRI, so I accepted the diagnosis. Then, after 10 years of unsuccessful treatments, I learned of many reports in the medical literature of ruptured discs in patients with no history of back pain. The ruptured discs were discovered on CT or MRI studies conducted to investigate other parts of the body. If ruptured discs caused back pain, there should be a clear correlation between the observation of ruptured discs and back pain. We have all heard that correlation does not, on its own, imply causation. But I encountered physicians who were willing to label something as a cause (ruptured discs) without even a strong correlation with the effect (back pain)! It wasn’t until I started looking at other possible causes (stress/anger) that I solved the problem and began to feel better.

My doctors were drawing conclusions based on what they observed in their own worlds: patients with back pain and the MRIs of these patients’ spines. They weren’t looking at the spines of patients without pain. If they had, they might have drawn a different conclusion. If doctors, who know more about science than most of us, can make this sort of mistake, the rest of us should also beware.

Think about the ideas you develop based on just the participants, clients, and audiences your program or organization serves. Then widen the circle. Does your idea hold up when you consider similar participants, clients, and audiences in other programs or organizations? For example, you might think: participants who drop out of this program live far away from the program site. Perhaps. But are there similar programs (within your organization or at other organizations) that participants are willing to travel to? If so, then you might consider what other aspects of the program or characteristics of the participants might lead to drop out.

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 + Experience = Insight

Data can’t take you all the way to a decision. In real life, you will never have enough data. So you’ll need to apply your own experience and your colleagues’ experiences to understand the implications of data for your work. Here’s a great way to apply experience to data. It’s from Dabbling In The Data: A Hands-On Guide to Participatory Data Analysis, a guide from Public Profit.

Dabbling in The Data describes how to do this group activity in person. I’ve adapted it to an online experience using Canva, but you can use any brainstorming app that allows for real-time collaboration. It’s simple yet powerful.

Step 1: Choose a template.

Sign up for a free Canva account. Start a new design; choose a size for your design; and then click on the “Templates” tab on the sidebar. Type in “brainstorm” in the search window to see template options (see A above.) I chose this template from the “Brainwriting” template group.

Step 2: Add a chart and customize.

Next add a chart showing change over time on some key measure you want to better understand. In this example, the key measure is the number of lessons provided to participants over a 7-year period. You can cut and paste an image of the chart from another program like Excel, or you can create the chart in Canva by click on the “Elements” tab in the sidebar (see B above), choosing a chart type, and then entering the data points (see C above.) I’ve added a chart and customized the instructions and notepads below. Note, it’s important to position the chart backward (see D above) and lock the chart in place (see E above) so that others can place notepads on top of the chart.

Step 3: Share the chart with your colleagues and invite them to add milestones.

Your colleagues will need free Canva accounts as well. To share the chart in real time, click on “Share” (see F above) and type in your colleagues' email addresses, making sure to allow them to edit the design. Ask the group to think about the key organizational milestones that occurred during the time represented in the chart and to add those milestones to the chart using the notepads at the bottom of the screen. Encourage them to also add detail to notepads added by others.

Step 4: Discuss.

Discuss how changes in the key metric over time might be related to the organizational milestones. Consider how this understanding of pivotal events can help you better show progress to stakeholders and to plan for the future. For example, if the implementation of a new program was followed by a decrease in participation, what about the new program may have caused the decrease? What else was going on at the time that may have contributed to the decline?

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.

---

Understand Donations Using "Pantry Staple" Data

The vast majority of nonprofits have some type of list of donors and donations. Tell me that you don’t have a database or spreadsheet that looks something like this.

Use Case: Tracking Progress to Goal

Now tell me that this data would not be way more useable in this interactive dashboard. Give it a try. You can see both how you are doing overall in relation to your goal and how different types of donors and donations are contributing to your progress. This dashboard can be created using Tableau Public, the free version of Tableau.

To see past data tips, including tips on other types of pantry staple data, 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.

---

Understand Your Volunteers Using "Pantry Staple" Data

If your organization is like most nonprofits, you rely on volunteers to get the job done. And you probably have at least some basic “pantry staple” data on volunteers.

Pantry Staple Data: Volunteer Data

The volunteer data you already have can be leveraged to:

• Impress funders, donors, and other stakeholders. Show them how you are using this free resource to move the needle.

• Recruit new volunteers. As we have discussed in this blog before, we are all influenced by peers. So show how many volunteers you have to attract even more.

• Manage volunteers more effectively. Seeing clearly what’s going on with your volunteers will help you to retain them, make better use of them, and recruit new ones. This is the subject of today’s tip.

Use Case: Maximizing Volunteer Time and Value

This volunteer data dashboard uses a variety of charts to answer the who, what, where, and when questions that you may have about your volunteers. With this detailed view of volunteers, an organization can start thinking about how to activate inactive volunteers, what types of new volunteers to target, and when during the year to deploy volunteers.

Source: Jin Tat on Tableau Public

This simple map dashboard provides insight into the distribution of volunteers—and volunteer hours—among sites. This understanding can help you decide if and how to redistribute volunteers. Both this dashboard and the one above can be created using Tableau Public, the free version of Tableau.

Source:CCE 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.

---

Understand Program Drop Out Using "Pantry Staple" Data

Data, like ingredients, can be divided into two groups: the data we have on hand (kind of like pantry staples) and the data we’d like to have. We spend a lot of time bemoaning that we don’t have the data we want, the type of data that can really show impact. But while we pursue new and better data, we also can do more with the data already in our databases and spreadsheets.

In the coming weeks and months, your weekly 60-second data tip occasionally will feature a type of pantry staple data and a suggestion on how to visualize it for a particular purpose. This time it’s about using participation data to show drop out rates.

Pantry Staple Data: Participation Data

If you provide any type of human service —education, case work, counseling, job training, whatever — you have participation data. At the least, you probably collect data on participants’ names and the services or programs provided to them. But you also likely have demographic data on them (age, address, employment, etc.), when they participated, and maybe some other data to boot. This type of data can be a gold mine for understanding and showing your reach and comparing how different types of participants fare in your programs.

Use Case: Showing Drop Out

Drop outs are inevitable. Participants leave our programs and services for a wide range of reasons. Ideally, we would survey or interview each participant who drops out to better understand why. But even without this type of data, we can learn a lot about drop outs and apply this knowledge to future decisions.

The funnel chart below shows the decreasing number of participants at each stage of a food service training program. We can see that few of those who attend orientation make it all the way to a job. And we can see at which stages there is the most/least drop off. This funnel chart also can be interactive. A filter can be added to show results for particularly subgroups, such those in different age, race/ethnicity, or family status groups.

It looks cool and makes intuitive sense, but a funnel chart is just a bar chart on its side with a mirror image. Check out these easy instructions for making funnel charts in Tableau and Excel.

The dashboard below also provides a wealth of information on another type of drop out: employee turnover. These charts can be applied to participant/client drop out data as well to show when they leave, what programs they leave from, and why they leave. And even if we don’t have the why data, what we know about the who, what, where, and when of drop outs can help us to discover the why. For example, if more folks are dropping out during the summer, maybe it’s because of childcare issues. If more people in certain neighborhoods are dropping out, maybe the programs in that area need strengthening.

Source: Alexandria Heusinger 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.

---

The Problem with Large Numbers (And What To Do About it)

BANs (Big Ass Numbers) are gaining prominent positions in data dashboards, websites, social media, email marketing, and annual reports these days. They are meant to impress. Wow! 359,234 meals served! Cool! $6 million raised!

But there is a problem with big numbers. Our brains can’t fully digest them. As noted in a 2017 Wall Street Journal article, “Big numbers befuddle us, and our lack of comprehension compromises our ability to judge information about government budgets, scientific findings, the economy and other topics that convey meaning with abstract figures, like millions, billions, and trillions.”

When quantifying the breadth of a problem or solution, nonprofits may toss out lots of giant figures, as in the bewildering graphic below. But without context, even numbers in the hundreds or thousands can bewilder.

Source: Rosenberg Advertising

So what can we do to make BANs more meaningful? Researchers at Columbia University and Microsoft found that they could improve numerical comprehension by using “perspectives,” which are simple sentences that relate a large number to something more familiar to us.

They found, for example, that when told that the number of registered firearms in the U.S. is about 300 million, study participants not only had trouble comprehending this number but also recalling it and assessing its likely accuracy. However, when told that there is about one firearm per person in the U.S., significantly more people could comprehend, assess, and recall the quantity. Makes sense to me. We can imagine a group of people, each holding a firearm, but we are hard pressed to imagine a pile of 300 million firearms.

So before you present a large number, consider a perspective that will make it relatable for your audience. Below are some formulas used in the Columbia/Microsoft study for developing perspectives.

Source: Improving Comprehension of Numbers in the News

---

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.

---

To Improve Anything First Test Your Thinking

Reposted from October 2018

Pop quiz. Take yourself back to your seventh grade science class. Wake up from your drowsy, awkward, tween state and then answer this question: “What is a null hypothesis?”

Stay tuned for the answer. First, why are we talking about null hypotheses? Because if you are going to improve anything, you gotta get your null hypothesis on. As I’ve said before, progress in organizations – and indeed, in all of human history – happens when we admit ignorance. The null hypothesis is all about admitting ignorance.

Your science teacher didn’t tell you to make a guess (or a hypothesis) and then look for evidence to support it. Instead, your teacher said to state the opposite of what you believe (or, more specifically, that no relationship exists between two things) and then try to refute it. That opposite statement is the null hypothesis.

Why go at it backwards? The power of the null hypothesis is that it forces you to look beyond your expectations. For example, your hypothesis might be that girls do best in your life skills program based on what you’ve seen so far. The null hypothesis for such a hypothesis might be: there is no difference in performance in the life skills program based on gender. Looking for evidence to support the null hypothesis opens your eyes to other factors (besides gender) that may be at play. Perhaps kids who can sit for longer periods of time do better in the program, and those patient kids often are girls. If so, then you have some powerful information. Maybe building in some movement time will improve overall performance?

If patience and other factors you explore don’t seem to be related to performance, then maybe gender is the key factor.

I’m not suggesting that you launch highly technical controlled experiments. Instead, I’m asking you to first consider that you might be wrong and then pay attention to data that supports such a conclusion. It can point you to new and powerful strategies.

See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.

Photo by Andrew Shiau on Unsplash

Seeing Red, Blue, and Purple - What We Can Learn From Election Maps

A wide range of election maps are in your immediate future. What you learn from those maps depends, in part, on how they are designed. Indeed, election maps and other types of maps can change our attitudes toward issues. In this 15-second tip, I recommend that you invest an additional couple of minutes watching The New York Times’ excellent slide show on how election maps can fool you. It will help you to be both a better consumer and designer of maps.

To see past data tips, including those about other chart types, click HERE.

Why Our Brains Glitch On COVID Data And Why Nonprofits Should Take Notice

I’ve said it before. It’s not enough to present data clearly and beautifully. If we want the brains of our staff members, board members, policymakers, donors, and clients to absorb data AND use it for decision-making, we have to present it in brain-friendly ways. And brain-friendly means avoiding common brain glitches. I’ve talked about how our brains glitch when dealing with small numbers.

The pandemic has brought to the fore another known issue with our brains. They glitch when confronted with exponential growth. And, as a result, we make the wrong decisions. The good news is that we can learn from this problem. And this learning can benefit our causes and organizations well past the pandemic. Let me walk you through this in the remaining 50 seconds.

We default to LINEAR not EXPONENTIAL growth. Linear growth means that something is growing by the same amount at each time step. Your hair, for example, grows about a half inch each month. Exponential growth is different. It means that something is growing in proportion to its current value, such as doubling at each time step.

Here’s a common example that reveals the glitch: Would you prefer to receive:

1. $1,000 a day for the next 30 days or

2. 1 cent on the first day, 2 cents on the second day, 4 cents on the third day, 8 cents on the fourth day and so on for 30 days?

Given a short time to consider, most folks choose option 1 in which the linear growth results in $30,000. But in option 2, the exponential growth results in over $5 million!

Most of us have heard that COVID grows at an exponential rate. And we probably understand what that means: if one person infects two others and then each of those people infect two others, the number of infections is doubling at each time step. But we fail to appreciate the impact of exponential growth and thus fail to choose the wisest actions when faced with an exponential growth problem or opportunity. For example, there is evidence that those who underestimate the effect of exponential growth on the spread of the virus are less likely to take precautions like social distancing and wearing masks.

We can (sometimes) overcome the glitch with a nudge. There is also evidence that simple nudges can help people to better estimate the impact of exponential growth. Nudges can include showing raw numbers instead of graphs or reminding people that the number of cases doubles at each step rather than grows at a constant rate or asking people to do the math to more clearly see the effect of doubling. It’s important to note, however, that there is also research suggesting that our brains can be pretty resistant corrections.

What does this mean for your organization? Exponential growth crops up in regular, non-pandemic life more often than you’d think. If your organization is dealing with issues as diverse as food spoilage, human population growth, invasive species, forest fires, or cancer, then you need a way to effectively communicate exponential growth. To deal with this glitch you can:

• Turn to the research. Look for studies that tested ways to correct glitches and then apply the effective ones to your work.

• Test your data presentations before they go live. It may sound simple, but it’s a step few of us take. Identify a few people with a similar level of expertise in the subject matter and data as those in your target audience. Then ask them what they think the data in your presentation shows. Perhaps show them a few versions of the same data in different types of charts or tables and see which ones are easiest to process quickly and accurately. But don’t stop there. Ask what actions they might consider based on their interpretations. Then use their responses to revise and test again.

• Ask rather than tell. You can engage your stakeholders while also nudging them toward greater understanding. Ask them how soon they think a particular problem will grow to a particular size if left unchecked. If their answer is off the mark, you’ve got their attention. Now explain how the problem is growing exponentially.

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.

---

Beware of This Brain Glitch When Using Data

Next time your computer spits out an error message, take a moment to thank it. At least your computer knows when a glitch has occurred. Not so with your brain. You can encounter a system error in your brain without ever knowing it.

If you’re interested, there’s plenty to read about brain glitches. A classic article by Tversky and Kahneman in Science back in 1974 does a great job of describing glitches that get us into trouble when making decisions about uncertain events. Since our brains do not send out error messages, the best we can do is to be aware of potential glitches so that we can avoid drawing the wrong conclusion and then acting on it.

Today, I’m just going to mention just one that any smallish nonprofit should know about. I think you’ll appreciate this glitch more if I can reproduce it in your brain right now. Tversky and Kahneman provide a great example that may do the trick:

“A certain town is served by two hospitals. In the larger hospital about 45 babies are born each day, and in the smaller hospital about 15 babies are born each day. As you know, about 50 percent of all babies are boys. However, the exact percentage varies from day to day. Sometimes it may be higher than 50 percent, sometimes lower. For a period of 1 year, each hospital recorded the days on which more than 60 percent of the babies born were boys.” Which hospital do you think recorded more such days: the larger hospital, the smaller hospital, or do you think they recorded about the same number of days? (Let your brain produce an answer and then scroll down.)

.

.

If your brain said: “about the same number of days” then you’ve encountered a common glitch. We know, in the general population, about 50 percent of babies are born with the sex of male. You are more likely to see this 50/50 split in large groups (aka “samples”) than in small groups because larger groups more closely represent the entire population. Thinking about coin tosses often helps to correct glitches. If you toss a coin just 6 times, you are more likely to get a lopsided result (such as 4 heads and 2 tails) than if you toss it 60 times.

Nonprofits often deal with small groups. This can lead to errors. For example, you may go astray by:

• Planning for what’s needed for a large program based on the experience of a small pilot program.

• Projecting what the needs of a certain group may be over a longer period of time based on the needs of a group over a shorter period of time.

• Applying “evidence based practices” based on studies of large groups to your smaller group of clients which may differ in key ways from the large group.

In summary, beware of small numbers when predicting and planning.

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.

---

Beware of Low-CAL Data During Pandemics (And Always)

We are all trying to make sense of how we got here and how we are going to get out — often with the aid of numbers and charts. The most common coronavirus numbers and charts fall into two categories: 1) those with actual data, which is often limited, and 2) those that predict what might happen based on assumptions. Many of the numbers and charts I’ve come across have reminded me of the dangers of Low-CAL data which is any data presentation that is low on clearly articulated: Context, Assumptions, and Limitations. Let’s talk about each one:

Context: Listening to President Trump’s daily briefings, I am reminded of the appeal of BANs (big ass numbers). Trump uses them a lot. For example, on March 23rd, he said that FEMA is distributing 8 million N95 respirator masks and 13.3 million surgical masks across the country. Sounds like a lot, but is it? Numbers, by themselves, have no inherent meaning. You have to put them in context. How does the supply compare to the need?

Assumptions: We make predictions about the future using assumptions which are based on currently available data and data about similar situations in the past or in other places. It’s the best we can do. That’s fine. But the assumptions and the rationale for the assumptions need to be clearly stated. For example, some predictions assume that the spread of the virus will slow down during the summer, but at the time of writing, we do not know how safe that assumption is.

Limitations: The data we have about the coronavirus is limited at the time of writing. We do not fully understand what factors promote or impede the spread of the virus nor do we fully understand how widespread it is. Many sick people have not been tested making it impossible to calculate a reliable death rate (number of deaths caused by the virus divided by the total number of cases.) Most data sets have limitations. To fully appreciate the implications of data, we must know what those limitations are.

Scholarly journals require that authors clarify context, assumptions, and limitations. But websites, tweets, blog posts, newspaper and magazine articles do not. I urge you to be a smart consumer on the lookout for Low-CAL data presentations. And when presenting data yourself, consider adding something akin to the drug facts label you find on medications to your charts, graphs, and maps. Somewhere in or near your data presentation include information on context, assumptions, and limitations so that viewers fully understand what the presentation does and does not show.

To see past data tips, including those about other chart types, 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.

---