How To Activate Board Member Fundraising With Visuals

Reposted from January 2023

I recently came across this excellent article by The Fundraising Authority. As promised in the title, it provides a “Simple, Step-by-Step Process for Getting Your Board to Refer New Prospects to Your Non-Profit.” In a nutshell, here are the four steps:

1. Explain how referrals work and assure your board members that no one they refer will be asked for money until they indicate a desire to get involved.

2. Show board members how many people they actually know through a mind mapping exercise.

3. Ask board members for referrals usually in person.

4. Bring referral success stories back to board meetings on a regular basis.

My tip is to enhance steps 2 and 4 with visuals.

Visuals for Step 2: For the mind map, the point is for board members to brainstorm all the people they know by considering people in different categories of their lives. You can use Canva whiteboards (or a similar tool) to create a mind map that the board member (pictured in the middle) can use to add the names of people in each category on virtual post-it notes.

Visuals for Step 4: The article claims that “this is a key step. Nothing will convince your board members to bring you more referrals than hearing from other board members that have done it successfully.” You can visualize the donors whom various board members brought in using tools like Flourish to show their networks, as in this example. Scroll over the circles to interact with it and learn more. Some board members brought in donors who, in turn, brought in other donors. To make something similar, select one of the network graph templates on Flourish and fill in the data needed. (See snapshots of the data I added for the visual below.)

Links data

(used to show who is connected to whom)

Points data

(used to show groups by color, size points according to amount of donations, and add images for board members)

To see past data tips, click HERE.

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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.

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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.

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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.

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Presenting Data Fast and Slow

Reposted from May 2022

Sometimes we approach the challenge of sharing data with others as if we were trying to con a pet into taking a pill. We think that our audience is too busy, disinterested, or distracted to focus on the data. So we wrap it in something that attracts their attention and feed it to them as quickly as possible. The problem with this approach is that it may get the data into their brains—momentarily—but it won’t stay there long. See where the pill ends up in this video.

If we want others to LEARN from the data — which involves not only retaining it but also drawing knowledge from it and applying that knowledge in the future — then we need a different approach. Daniel Kahneman’s Thinking Fast and Slow can help us.

First a little background on how the brain works, according to the evidence Kahneman presents. For learning to happen, information first must get past System 1 of our brains. This is where fast thinking happens. System 1 is the harried gate keeper, madly processing all of the information that comes in through our senses, pitching most of it, keeping only what is deemed necessary. But making it through the gate is only half the battle. Once in, information confronts System 2. This is the part of the brain that allows for conscious thought or slow thinking. The problem is that System 2 is lazy. Conscious thought is hard, and System 2 is always looking for an excuse to avoid it. However, if System 2 engages with information, the resulting knowledge can find its way to long-term memory and learning happens.

So the challenge when presenting data is to make it past System 1 AND engage System 2. Let’s consider a series of vizes from Harvard Business Review (HBR) that I think meets both parts of this challenge. Yes, it’s an example from the for-profit world, but could easily work with nonprofit data. See snapshot of the first viz in the series below .

Source: Harvard Business Review

How to get data past System 1

Getting data past the System 1 fast-thinking gate keeper is all about grabbing attention. We process images much more quickly than words and numbers, so images are a great foot-in-the-door. The HBR viz does it with bright colors and a cool-looking, somewhat unusual chart. There’s plenty of information out there about how to attract attention, including the use of images with:

• Stand out colors and textures

• Human faces (we are wired to focus on them)

• Novelty (images that are unusual in size, placement, etc.)

Data visualizations can use color as well as images to draw attention. But getting past System 1 is not nearly enough. For learning to happen, the viz also has to engage System 2.

How to engage System 2

System 2 is smart but lazy. So we need to pique its interest. The HBR viz starts with a title that poses a question. When confronted with an interesting question, we may be more likely to stick around for an answer. Then the viz leads you through the answer in a visually engaging way (see interactive version of the viz HERE). These are two great ways to slow down and engage the brain with data. Here’s a list of ways to engage System 2:

• Ask a question in the title as the HBR viz does—questions beg answers.

• Make it personal. We may be more likely to engage with data when we have a personal connection with it. This New York Times viz, for example, allows you to enter in your county to see what the barriers to COVID vaccination are in your area.

• Highlight a surprising finding. Many of us love the counterintuitive and the creative. If you draw attention to something new that the data suggests, you may have a better chance at hooking System 2. For example, this viz from The Economist shows that China emits far less greenhouse gas per person than Western countries at the same stage of economic development. Or check out this viz by Dimiter Toshkov showing that small countries can be big players in development and good governance.

• Hand draw it. There is some evidence that making information harder to consume, for example by presenting it with harder-to-read fonts, makes the brain slow down and engage in effortful and analytic processing. Although the jury is still out on this, I do find myself more likely to engage in hand-drawn vizes like two of the winners of the World Data Visualization Prize in 2019. Perhaps it’s simply the novelty of hand-drawn charts that engages me. Anyway, it’s something you might consider, and all you need is a pen and paper.

• Walk them through it. A great way to slow down your viewers is to set the pace by walking them through the data as HBR does in the example. I love how HBR presents what the data might look like if our assumptions were confirmed followed by what it actually looks like.

Sources: Veritasium, Visual Content Space, MIT News, Springer Link,

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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.

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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.

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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.

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How To Balance Your Information Diet

Here’s a question for you. And don’t go Googling. Just make your best guess.

Have the number of people experiencing homelessness in the U.S. increased or decreased since 2007?

Whatever your answer, you likely drew on your own personal experience as well as images and information from the media when guessing at the answer. Perhaps you drew on some statistics too. But, unless you have expertise in this area, probably not. Stick with me for a minute, and I’ll not only provide an answer to the question but also some insight into how we consume information.

Personal experience, media, and statistics affect how we understand any issue, and there are limits to each of these inputs. So we would do well to understand those limits before acting on our understanding by voting, donating, or making decisions about programs that our organizations operate. Max Roser’s article in Our World in Data (The limits of our personal experience and the value of statistics) walks us through some of those limitations:

Personal Experience

“The world is large, and we can experience only very little of it personally,” Roser notes. “For every person you know, there are ten million people you do not know.” Even the most social and well-traveled among us can have only a limited understanding of the world through personal experience. I, for example, do not know anyone personally who has been unhoused, and most of my interactions with people in this situation occur on the street when someone asks me for money. This experience provides no information about the breadth of the problem or the range of experiences with this issue over time.

Media

“This fact is so obvious that it is easy to miss how important it is: everything you hear about anyone who is more than a few dozen meters away, you know through some form of media,” Roser points out. “The news reports on the unusual things that happen on a particular day, but the things that happen every day never get mentioned. This gives us a biased and incomplete picture of the world; we are inundated with detailed news on terrorism but hardly ever hear of everyday tragedies like the fact that 16,000 children die every single day.” If I recently heard a story about a city clearing homeless encampments, I may assess the problem as larger, and if I haven’t heard about anything on the issue in awhile, I may assess it as smaller.

Statistics

“The collection and production of good statistics is a major challenge,” writes Roser. “Data might be unrepresentative in some ways, it might be mismeasured, and some data might be missing entirely.” But, unlike personal experience and the media, it provides a way of assessing the full range of an issue. So it’s important to add statistics, along with personal experience and the media, to our information diet.

To add some statistics to your understanding of homelessness, the number of people experiencing homelessness in the U.S. decreased from about 650,000 in 2007 to about 580,000 (about 18 of every 10,000 people) in 2022 according to The 2022 Annual Homelessness Assessment Report to Congress.

We should not discount personal experience, the media, or statistics because of their limitations. But we should appreciate their limitations when forming opinions and taking actions based on them. As Roser notes: “Each way of learning about the world has its value. It’s about how we bring them together: the in-depth understanding that only personal interaction can give us, the focus on the powerful and unusual that the news offers, and the statistical view that gives us the opportunity to see everyone.” As described in many tips in this blog, well-designed charts make data/statistics more accessible to everyone and thus allow everyone to see everyone.

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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.

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Free Interactive Viz For You: Giving in the U.S.

As we move into gifting season, I thought I’d toss out a gift to you. It’s a quick interactive viz that you can employ however you see fit. Use it in a website, presentation, or social media post to rightsize folks’ understanding about the state of charitable giving in the U.S. and, perhaps, help to turn the tide. For the link address or embed code, click on the share icon below.

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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.

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Context is King

This “sketchplanation” elegantly demonstrates the importance of context in understanding anything. Since data visualizations are supposed to help people understand something, we should pay close attention to context when creating them, adding as much context as is needed for others to appreciate what’s going on and act on it. Considering the following charts . . .

Low-context chart

This is your garden variety chart. I see them all the time. Sure, it provides some context by comparing clients served in different zip code areas. But it doesn’t give me enough context to understand if these numbers are high, low, or somewhere in between. This chart needs more context.

xx

Moderate-context chart

This chart is better than the one above. By providing the previous year’s numbers, we can see where there has been increases and decreases and how large and small they were as well as compare the number increases (3) to the number of decreases (1).

xx

Moderate-context chart

This chart, too, allows for better assessment of the numbers than the first chart did. By simply adding a reference line for the goal, we have a better understanding of what’s going on and where we might need to take action. A reference line showing the average number of clients per zip code might also be helpful.

xx

High-context chart

This “small multiples chart”* gives us much more context by showing how the current numbers compare to past years. Consider, for example, the trend for 60601. Just knowing the current and past years’ numbers would not give you an appreciation of the overall upward trend.

*Small multiples chart: a series of similar graphs or charts using the same scale and axes, allowing them to be easily compared

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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.

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Why and How To Use Free Data

There’s a treasure trove of free data out there. And I know what you are thinking: “I can barely deal with my own data much less anyone else’s!” But think again. What if you could show the need for your services, your potential audiences, or other factors that affect your work without having to collect any data yourself?

Here’s an example of something you might do. I downloaded free data (in the form of an Excel spreadsheet) from the U.S. Department of Agriculture’s website on food access in 2019. I then combined this data with a list of counties that a (fictional) organization serves in Illinois to create the map below.

You can find any number of websites with lists of free data sources, such as this one. I found the USDA data by following the link from this page to Data.gov which is home to the US government’s open data.* I then clicked on “Data” on the navigation menu and searched for the topic of interest to me.

Fair warning, when searching for free data online, you are likely to find yourself in quite a few rabbit holes. So here are a few tips to make your search more fruitful:

• Before you begin searching, know what you are looking for. Consider what geographies, time periods, or populations you need. Also, think about what data format you need. Perhaps you can only deal with Excel or CSV files. When visiting a free data site, determine if any of the data files available for download meet your needs asap. If it’s not clear, then give up and try another site.

• If you need local data, check out your city government’s website. Many have open data available for download.*

• Look for a data dictionary or some other type of documentation to understand what is included in the data, how the data was collected, and what each data field means. Sometimes this is included on a tab in the downloaded data file. Pay attention to what might be missing from the data and the biases that could be baked into it.

• Link back to data sources (or attribute with text) when showing the data in charts, maps, and graphs.

• Combine free data with your own data using zip codes, city names, census tracts or other data fields to link the two data sources.

  
  * Open data is data that can be freely used, re-used, and redistributed by anyone, subject only, at most, to the requirement to attribute and share-alike.

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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.

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How To Activate Board Member Fundraising With Visuals

I recently came across this excellent article by The Fundraising Authority. As promised in the title, it provides a “Simple, Step-by-Step Process for Getting Your Board to Refer New Prospects to Your Non-Profit.” In a nutshell, here are the four steps:

1. Explain how referrals work and assure your board members that no one they refer will be asked for money until they indicate a desire to get involved.

2. Show board members how many people they actually know through a mind mapping exercise.

3. Ask board members for referrals usually in person.

4. Bring referral success stories back to board meetings on a regular basis.

My tip is to enhance steps 2 and 4 with visuals.

Visuals for Step 2: For the mind map, the point is for board members to brainstorm all the people they know by considering people in different categories of their lives. You can use Canva whiteboards (or a similar tool) to create a mind map that the board member (pictured in the middle) can use to add the names of people in each category on virtual post-it notes.

Visuals for Step 4: The article claims that “this is a key step. Nothing will convince your board members to bring you more referrals than hearing from other board members that have done it successfully.” You can visualize the donors that various board members brought in using tools like Flourish to show their networks, as in this example. Scroll over the circles to interact with it and learn more. Some board members brought in donors who, in turn, brought in other donors. To make something similar, select one of the network graph templates on Flourish and fill in the data needed. (See snapshots of the data I added for the visual below.)

Links data

(used to show who is connected to whom)

Points data

(used to show groups by color, size points according to amount of donations, and add images for board members)

To see past data tips, click HERE.

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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.

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Don't Measure Impact . . . Wait, What?

Ghost of Christmas Yet To Come in The Chrismas Carol by Charles Dickens, Illustration by J. Leech, Source: Flickr

Most organizations should not waste time and money on impact evaluations. Measuring impact is difficult and expensive. It’s difficult because you need a good counterfactual. A counterfactual is what Dickens’s Ghost of Christmas Yet to Come shows Ebenezer Scrooge: what would happen if you did not change anything. The impact of an intervention or program is the difference between what happened and what would have happened without the intervention. Since, in the real world, you can’t observe the same group of beneficiaries with and without the intervention (as we do when we watch The Christmas Carol), you need a good proxy for the would-have-been condition. The best proxy is a group of potential beneficiaries that were randomly selected from a larger group of potential beneficiaries. These folks do not get the intervention. Then you can compare those who did and did not receive the intervention over time to estimate the impact of the intervention. This is called a randomized control trial or RCT.

Of course, withholding an intervention from potential beneficiaries can be a difficult and morally-questionable pursuit. And tracking a large group of beneficiaries and non-beneficiaries over time is expensive. This usually requires a team of skilled data collectors and analysts. Non-randomly-selected comparison groups are not nearly as good because they may differ from the intervention group in known or unknown ways. So it’s difficult to determine if the outcomes observed are due to the intervention itself or to pre-existing biases or characteristics. This costly and challenging process is further complicated by the need to start with a well-established intervention, one that has already worked out the kinks.

Due to the many challenges of measuring impact, most organizations should not waste time and money on impact evaluations. Instead, they should consider interventions that already have a strong research base, ideally because they have been rigorously tested with RCTs. (Check out: Where to Search for Evidence of Effective Programs.)

In a Stanford Social Innovation Review article, Mary Kay Gugerty and Dean Karlan suggest that, before beginning a new program, organizations ask: “What do other evaluations say about it? How applicable is the context under which those studies were done, and how similar is the intervention? Study the literature to see if there is anything that suggests your approach might be effective.”

Rather than assessing impact, your limited resources are better spent assessing implementation. You can do this by collecting data that shows whether what you planned is actually happening. If you can pinpoint where the problems are, you are in a better position to make fixes, alter plans, refine processes.  Many organizations make their plans using a logic model (aka theory of change). A logic model is a flow chart with inputs and outputs. The best logic models draw on past impact evaluations to determine what inputs are most likely to lead to what outputs. And organizations can easily assess progress to date by plugging their logic models into real time data. Interested? Read more about “living logic models” HERE.

To see past data tips, click HERE.

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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.

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How To Motivate Action With Data

Data can either motivate or shut down action, depending on how it’s presented and to whom. Charts, maps, and graphs that convey good news can increase the commitment to an endeavor while those that broadcast bad news can either spur action among the already committed or quash action among the uncommitted.

Think about one of the most common data visualizations in the nonprofit world: the fundraising thermometer. When it shows minimal progress, only those most committed to the cause will likely donate. The thermometer may put off the less committed because it suggests that others do not feel the cause is important or because they feel that their small donation will not make much difference. However, when the thermometer shows considerable progress, the less committed are more likely to jump on the bandwagon. This is why, according to Eli Holder, many organizations don’t officially launch a campaign — and show a thermometer — until they are at least a third of the way to their goal.

Holder also offers up some advice on how to visualize data to maintain motivation in Dashboard Psychology: Effective Feedback in Data Design. Here’s my 60-second version of Holder’s recommendations:

Emphasize Progress Thus Far

You can make change over time appear more dramatic or more gradual depending on the the space between tick marks on your Y (or vertical) axis. Check out these two charts which show the same data, but the one on the right suggests a more dramatic increase and may be more motivating to the less committed. Of course it’s important to use this strategy to emphasize rather than deceive. Thus labeling the tick marks is essential.

Another way to emphasize progress is to give more visual weight to the good news. In this chart, the 7-day moving average trend is generally positive and is emphasized by the thick orange line. The daily changes are played down by using a thinner gray line. Moving averages, in general, iron out outliers and provides a sense of the overall trend.

Emphasize The Need For Progress

Those already committed may find the visualization of the gap between actual data and the goal quite motivating. Similarly, you can show them an expected range for a measure. As long as the data show that the measure is within range, they can feel reassured. However, a chart showing that the measure is above or below the expected range can motivate action. Note that the expected range can change over time with a lower range at the beginning of an endeavor and a higher one later on. Indeed, a changing expected range may reassure the less committed during early stages of an endeavor.

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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.

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Head Versus Gut

Reposted from December 2018

Data is head food. But we often make decisions with our gut. And the gut is fueled by emotions.

It’s hard to defy our gut feelings when deciding. That’s because emotions often drive behavior. Evolutionary psychologists believe that emotions evolved to get us to do what has to be done to survive: to avoid predators, secure nutrients, resist infection, mate . . . you get the idea. So when your head says A, but your gut says B, you might feel an urgent need to do B because your survival button has been pushed.

I’m going to venture to guess that most of your decisions, like mine, are not directly related to survival. And when that’s the case, our emotions can lead us astray. In a 2016 article in the Atlantic, Olga Khazan summarizes research which suggests just how far astray. For example, anger may cause us to be trigger-happy and simplify our thinking. Happiness may lead us to make shallow assessments based on looks and likability. And depression may induce dwelling on particular issues.

It’s important to recognize our emotions and what they are telling us, rather than blindly following them. Sure, we can form hypotheses about gut feelings and see if the data support them. But when faced with a pressing decision in the real world, we often have a strong emotion and little data at hand. In these situations, we can ask ourselves: Is there a way to bring some more data to the problem quickly?

• Perhaps it’s data collected by other people for other purposes. We can look for statistics about organizations similar to ours or about issues that our organization addresses.

• Perhaps it's a quick and dirty anonymous survey of our staff, asking for their knowledge and opinions relevant to the decision.

The idea is to feed the head with some data and, in the process, temper the pleadings of the gut.

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

Presenting Data Fast and Slow

Sometimes we approach the challenge of sharing data with others as if we were trying to con a pet into taking a pill. We think that our audience is too busy, disinterested, or distracted to focus on the data. So we wrap it in something that attracts their attention and feed it to them as quickly as possible. The problem with this approach is that it may get the data into their brains—momentarily—but it won’t stay there long. See where the pill ends up in this video.

If we want others to LEARN from the data — which involves not only retaining it but also drawing knowledge from it and applying that knowledge in the future — then we need a different approach. Daniel Kahneman’s Thinking Fast and Slow can help us.

First a little background on how the brain works, according to the evidence Kahneman presents. For learning to happen, information first must get past System 1 of our brains. This is where fast thinking happens. System 1 is the harried gate keeper, madly processing all of the information that comes in through our senses, pitching most of it, keeping only what is deemed necessary. But getting through the gate is only half the battle. Once in, information confronts System 2. This is the part of the brain that allows for conscious thought or slow thinking. The problem is that System 2 is lazy. Conscious thought is hard, and System 2 is always looking for an excuse to avoid it. But if System 2 engages with information, the resulting knowledge can find its way to long-term memory and learning happens.

So the challenge when presenting data is to get past System 1 AND engage System 2. Let’s consider a viz from Harvard Business Review (HBR) that I think meets both parts of this challenge. Yes, it’s an example from the for-profit world, but could easily work with nonprofit data. Take a look.

Source: Harvard Business Review

How to get data past System 1

Getting data past the System 1 fast-thinking gate keeper is all about grabbing attention. We process images much more quickly than words and numbers, so images are a great foot-in-the-door. The HBR viz does it with bright colors and a cool-looking, somewhat unusual chart. There’s plenty of information out there about how to attract attention, including the use of images with:

• Stand out colors and textures

• Human faces (we are wired to focus on them)

• Novelty (images that are unusual in size, placement, etc.)

Data visualizations can use color as well as images to draw attention. But getting past System 1 is not nearly enough. For learning to happen, the viz also has to engage System 2.

How to engage System 2

System 2 is smart but lazy. So we need to pique its interest. The HBR viz starts with a title that poses a question. When confronted with an interesting question, we may be more likely to stick around for an answer. Then the viz leads you through the answer in a visually engaging way (see interactive version of the viz HERE). These are two great ways to slow down and engage the brain with data. Here’s a list of ways to engage System 2:

• Ask a question in the title as the HBR viz does—questions beg answers.

• Make it personal. We may be more likely to engage with data when we have a personal connection with it. This New York Times viz, for example, allows you to enter in your county to see what the barriers to COVID vaccination are in your area.

• Highlight a surprising finding. Many of us love the counterintuitive and the creative. If you draw attention to something new that the data suggests, you may have a better chance at hooking System 2. For example, this viz from The Economist shows that China emits far less greenhouse gas per person than Western countries at the same stage of economic development. Or check out this viz by Dimiter Toshkov showing that small countries can be big players in development and good governance.

• Hand draw it. There is some evidence that making information harder to consume, for example by presenting it with harder-to-read fonts, makes the brain slow down and engage in effortful and analytic processing. Although the jury is still out on this, I do find myself more likely to engage in hand-drawn vizes like two of the winners of the World Data Visualization Prize in 2019. Perhaps it’s simply the novelty of hand-drawn charts that engages me. Anyway, it’s something you might consider, and all you need is a pen and paper.

• Walk them through it. A great way to slow down your viewers is to set the pace by walking them through the data as HBR does in the example. I love how HBR presents what the data might look like if our assumptions were confirmed followed by what it actually looks like.

Sources: Veritasium, Visual Content Space, MIT News, Springer Link,

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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.

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Let’s Talk!

Thanks for subscribing to 60-Second Data Tips. I hope you are finding the tips useful. I’m writing to offer you some more of my time. Click below to schedule a free consultation about your data. Let’s talk about how you can extract meaning from your data with great charts, maps, and graphs, share them with others, and use them to improve your work.

-Amelia (pictured above drowning in data . . . I feel your pain)

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How To Recognize and Reform Vanity Metrics

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.

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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.

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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.

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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.

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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

How to Show Problems and Solutions in One Chart

Data visualizations are kind of like beards or kale. They used to be decidedly uncool, but are now hip, at least in certain circles. Yet, even with the rising popularity of charts, maps, and graphs, I think many of us have a faint feeling of aversion when encountering them. For one, they may be hard to decipher. But there’s another problem too. They often are the bearers of bad news. They show us how widespread a problem is or how it’s increasing. Worse, they rarely give us any hope of improvement.

Wouldn’t charts, maps, and graphs be more engaging and helpful if they showed both problems AND solutions? Let’s talk about how to get that done.

Show Two Scenarios

Show the difference between how things play out with and without an intervention or program. The now-famous flatten the curve graph (shown below) did this without any real data. The point was just to show how the number of cases would likely differ with and without public health measures to slow the spread of COVID.

Source: C.T. Bergstrom

Here’s a graph that shows two scenarios with real data. The data point labels are particularly helpful in this example. By comparing two different cities, the graph suggests that a delay in the start of social distancing interventions may have a huge effect on the severity of an outbreak.

Source: Washington Post

Show A Change In The Trend

Another way to present a problem along with a solution is to show how a trend alters following an intervention. This graph shows projected data for several types of interventions: the current policy, alternative policies, and the absence of policies. In the absence of policies, global warming is expected to reach 4.1°C – 4.8°C above pre-industrial levels by the end of the century. Current policies are projected to result in about a 3.0° rise over pre-industrial levels. Other pledges and targets that governments have made would limit warming to even lower amounts.

Source: Climate Action Tracker

This one effectively uses bubble size and color to show a trend alteration following the introduction of the measles vaccine.

Source: Sciencemag.org

On the uncool-to-very-cool spectrum, data visualizations that show both problems and solutions are very cool. To see what other things are cool/uncool check out CoolnessGraphed.com.

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

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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.

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Why Your Donors, Volunteers, and Activists Ignore Data

(And What You Can Learn From The Pandemic)

Here are three things we are seeing a lot of these days:

1. Charts, maps, and graphs showing increasing COVID cases in many parts of the country,

2. Appeals to the public to wear masks and social distance when out in public, and

3. People out in public NOT wearing masks and not social distancing.

What gives? I talk a lot about using data viz to shorten the journey from data to action. And, indeed, it can reduce travel times by bringing into focus the message behind the data. But even when our audience clears this first hurdle of understanding what the data show, they confront other hurdles that delay or prevent effective action. These hurdles are likely behind rising COVID cases, and they are likely behind challenges at your organization.

A May 2020 article in Nature Human Behaviour sheds some light on different types of barriers between data and action. A few obstacles that are pertinent to nonprofit organizations are:

• Fear. Those rising COVID slopes can evoke fear. So can data that your organization shares to raise funds, get volunteers, or spur activism (on violence, hunger, health needs, etc. ) Research findings suggest that fear only leads to behavior change when people feel capable of dealing with the threat. Otherwise, it can shut down action. So those rising slopes may be more effective when coupled with information that increases viewers’ sense of efficacy in the face of the problem such as: “Your donation of $100 will provide 50 meals to families in need.”

• Perceived Norms. You’ve heard it before. We are social animals and are keenly affected by the behavior of others. However, research shows we often are wrong about what other folks are doing. So even if we see the danger in charts, we may do the wrong thing to match our perception of the social norm. In this situation, we need some information to correct our perceptions. For example, charts showing that most people are wearing masks or that those whom we admire are wearing masks can help. Similarly, we can show potential donors, volunteers, and activists what others are doing or giving to prompt action.

• Individual Interest. Fighting a pandemic, reducing poverty, addressing climate change, among other aims, require each of us to bear an individual cost for the common good. Charts may suggest how dire the situation is, but it’s hard to relate these big issues to our daily behaviors. We feel the effects of our individual sacrifices, but the impact of those sacrifices on the larger community is harder to perceive — particularly when the impact is invisible because it’s a problem that has been prevented. Research suggests that we can overcome individual interests by providing cues that make the morality of an individual action more clear. Check out this brilliant cartoon which shows COVID cases averted with gray lines. Consider how your organization can show your stakeholders how their (in)actions affect the wider community.

See animated version of the cartoon here.

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

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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.

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Invisible Assumptions Driving Your Organization

People and organizations have ideas about what leads to what. We are aware of some of these ideas. But others are so ingrained that we mistake them for facts of life.

Psychologists call the visible ideas explicit theories and the invisible ones implicit theories. Both explicit and implicit theories affect how we perceive and act in the world. If, for example, we believe—either explicitly or implicitly--that hard work leads to success, we are more likely to perceive evidence that supports our theory (aka confirmation bias) and to work hard ourselves and encourage it in our offspring, clients, and employees.

If this idea is explicit, then we are more likely to examine it, compare it to the ideas of others, and even test it. However, if it’s implicit, then it will probably never occur to us to examine it because we are not fully aware that we believe it or that things could be any other way. (For more on implicit and explicit theories, check out this.)

Implicit theories also affect what data we gather and use. If we have an implicit hard work theory, we might gather data to assess what type of work or effort is most likely to lead to success but, unless we make the implicit theory explicit, we are not likely to collect data to see if clients who put in more training hours actually had more success.

Every organization has implicit theories. Do any of these seem familiar?

• Meetings make people feel included/empowered.

• With more money, we could be more effective.

• Special events help to cultivate new donors.

Some of these assumptions might be true, at least under certain circumstances. But no matter how data-driven we are, we are not going to collect data to test ideas we are not fully aware of.

Staff meetings are great places to listen for implicit theories. Next time your colleagues and you are discussing an issue, see if you can detect a few of them. What assumptions lie behind your assessments, decisions, and actions?

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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.

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Photo by PoL Úbeda Hervàs flickr.com/photos/polubeda/