Bad color decisions can break an otherwise effective data visualization. In Data Tip #14, I gave you some color dos and don'ts. Today, I offer a simple flow chart to help you choose the right type of color scheme for your viz, based on what you want to show.
See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.
“Standard deviation” sounds like an oxymoron. Any high school student knows that you can’t be both standard and a deviant at the same time. And any high school stats class will clarify, in the first week or so, what standard deviations are really about. And most high school students will hold that knowledge for a semester and then delete it to make space for more valuable knowledge.
But I come to you today to re-enter the standard deviation onto your personal hard drive. Because it is valuable even to those of us who are not statisticians or researchers. It’s a great tool for anyone trying to understand what is going in organizations trying to up their impact.
60-Second Data Tip #13 addressed what averages obscure. The answer was: how spread out your data points are around the average. The standard deviation tells you just how spread out they are. You might think of the average as the “standard” (the person at your high school who was average in every way). And you might think of the rest of the data points as “deviants” with some deviating from average just a bit (perhaps a kid with a nose ring who was otherwise sporty) and others deviating a lot (full-on Goth).
Here’s a more nonprofity example. If the average wage of participants in a job-training program is $19 per hour, this might obscure the fact that a few participants are earning over $30 per hour and the majority are earning below $10 per hour.
A standard deviation close to 0 indicates that the data points tend to be very close to the mean. As standard deviation values climb, data point values are farther away from the mean, on average. So a job-training program aiming for an average wage of $17 per hour among participants might want to see a pretty low standard deviation in wages to feel confident that the large majority has reached the goal.
I won’t scare you with the formula for calculating the standard deviation. You can keep that off your hard drive. Any spreadsheet program will calculate it for you. For example, for data in rows 1-350 in column A on an Excel spreadsheet, just use enter “=STDEV(A1:A350)” to get it.
See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.
This week’s tip is short and extra sweet.
There is data. And then there is real life. Sometimes there seems to be little relationship between the two. Data seems too rigid or too limited or too boring to capture the brilliant vagaries of the real world. We have a different tool for the vagaries. We call it intuition.
I’m not knocking intuition. But our expectations can weaken our intuition. (See last week’s tip for more on this.) The solution is to bring some data to your decision-making game. And how best to bring it? By visualizing it.
This sounds more complex than it is. Imagine you are not at your computer or in a meeting but instead ordering dessert. The dessert menu is long with lots of text. You are a bit drowsy from digesting your dinner. And you are not sure what you want. So you’ve got data (the menu) and you’ve got a decision to make. And you’re tired. Not a bad analogy to a work setting.
If you could visualize the data to aid your dessert decision, what would you do? You might first decide on the salient decision criteria. For example: richness, flavor, price, and gluten content. Then you might place, size, and color the data to allow easy comparisons among your options. See the quadrants chart below. Want a light, fruity, gluten-free dessert at a low price point? Lemon sorbet is clearly your best bet.
Too often our workplaces and restaurants hinder the journey from data to action. But there are better (and sweeter) ways.
See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.
Photos by Jennifer Pallian, Henry Be and Brenda Godinez on Unsplash
There is a certain breed of nonprofit staff who roll their eyes at the mention of “evidence-based practices” or “KPIs” or other data jargon. I myself have experienced mild nausea when listening to someone try to quantify what seems unquantifiable: what a child feels after learning to paint or what a homeless adult feels upon acquiring an apartment.
But I’m here to implore, beseech, even beg you to not write off data. Why? Because of how our brains work.
What we perceive is based not just on what we actually observe but also on what we expect to observe. This is how it works. First, the brain evaluates which of a variety of probable events are actually occurring. Then it uses this information, along with signals from the outside world (aka data), to decide what it is perceiving. And here's the surprising news: there are far more signals coming from within the brain that affect our perception than data signals from the outside.
These inner brain signals or expectations can distort our understanding of a situation. Thus data are quite important to confirming or negating our expectations. But you have to pay attention to data to make that happen. And that’s where things get tricky.
The esteemed philosopher and psychologist, William James, noted in The Principles of Psychology (1890): “Millions of items of the outward order are present to my senses which never properly enter into my experience. Why? Because they have no interest for me. My experience is what I agree to attend to. Only those items which I notice shape my mind .”
So how can we see what we are not attending to, the stuff that eludes us? The answer is to think like a scientist. Rather than operating on assumption or instinct, form hypotheses about how your programs and services work and then gather data to test them. You might be surprised.
(And, if you missed it, check out last week's data tip on how data visualization can help correct misperceptions.)
See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.
Progress in organizations — and in all of human history — starts with the concession that we might be wrong. As Yuval Noah Harari suggests in Sapiens: A Brief History of Humankind, the scientific revolution was the point in history when “humankind admits its ignorance and (as a result) begins to acquire unprecedented power.”
That’s what I said in Data Tip #23, and I’m sticking with it. However, it’s way easier to say than to practice. Particularly in these partisan times.
I recently soaked in a series of well-executed yet oh-so-depressing data vizes in this article from the Pew Research Center. You’ve seen these types of charts before. The liberals are a blue iceberg, the conservatives a red iceberg, and they are drifting apart at an alarming rate. “We” seem to be living in a different reality than “them.”
Shortly after reading the Pew article, I climbed out of my dark hole long enough to happen upon this article from the Washington Post. It describes a series of experiments in which data displayed in charts significantly reduced the misperceptions of subjects, both liberal and conservative, on important poltical issues. And, get this, charts (bar graphs, line graphs) had much more impact than the same information presented in text — perhaps because we process visual information much more efficiently than we process words and numbers.
Okay, it was just a few experiments. But still. Let’s not totally curb our enthusiasm. This is promising. This suggests that there MIGHT be a way to bring all of us back to a somewhat similar reality. And data visualization might help get us there.
In the meantime, when different factions of our boards do not agree or when we are looking for a way to convince a reticent funder to support our work, we should remember the power of a humble chart, map, or graph.
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 Kristina Litvjak on Unsplash
Surveys provide answers to many nonprofit questions. How do participants like this program? What are barriers to enrollment? What types of services do community members lack and need?
It’s easy enough to create a survey on Survey Monkey or the like. It's harder to get an adequate number of responses. And even when you do, the respondents might not fairly represent the larger group you want to know about. But let’s say you get past this hurdle. There’s still a major hurdle ahead of you: extracting meaning from your data.
Surveys include different types of questions. Perhaps the most common one is the Likert scale question. You have seen them a million times. Respondents are asked to indicate how much they agree or disagree with a particular statement using a five to seven point scale.
Let’s say you want to know participants’ feelings about a program. Your Likert scale statements might be; “I feel that I can ask the instructor for help when I’m confused” or “I feel comfortable interacting with the other participants in the program.” Survey Monkey will give you each respondent’s rating of each statement and will also give you the average rating. What meaning can you extract from these numbers?
Many organizations will use just the averages to determine where they are doing well and where they need to worker harder or differently. But there is so much more information in those numbers than averages can tell you, including:
The extremes: Averages can’t tell you what were the lowest or highest ratings on any given statement.
What most respondents said: Averages also can’t tell you if the average is three because most people responded with a “3” or because half responded with a 5 and half responded with a 1.
What subgroups think and feel: Even though the overall average might be high, the average might be low for some subgroups within your group of respondents. Perhaps respondents from a certain neighborhood, for example, had very different opinions than the group overall.
You can extract and show this information using data visualization tools like Tableau that allow you to interact with your data. The viz below shows the range of responses to each survey statement and the proportion of responses for each rating. Moreover, the interactive version allows you to “drill down” into the data an see if whole group results hold for subgroups.
If you are going to go to the trouble of conducting a survey, make sure to squeeze all of the information you can from the data you collect.
See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.
Pretend that you are Goldilocks and your porridge is data. How much data is just right? Just enough to be engage your funders, staff, or board members, but not so much as to be overwhelming?
To answer this question, we might consider Miller’s Law. George A. Miller was a psychology professor at Princeton University and wrote one of the most frequently cited papers in psychology: "The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information."
Miller conducted experiments on human memory and concluded that the number of objects that an average human can remember in the short-term is 7 ± 2. Objects can include symbols like numbers or abstract concepts. We also can hold several groups of related objects.
Armed with this knowledge, we can make visualizations of data (charts, maps, graphs) easier to digest by:
1) Grouping data into categories. Whenever you have a graph or chart with more than 5 to 10 data categories, the individual units start to lose their individuality and are perceived by our eyes as a single whole. In the “before” and “after” examples below, the after chart is easier to process because it combines data on donors into just two groups: those in Chicago and those in other cities.
2) Highlight one or two single categories and gray out the rest. The chart below (called a parallel coordinates chart) includes a line for each of the 50 states showing the prevalence of various diseases in each state. But only one state (Hawaii) is highlighted and the rest are grayed out. Thus there are really only two data categories to compare: 1) Hawaii and 2) the rest of the states.
See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.
We humans might be data averse by nature – especially when presented with data in a spreadsheet (see Data Tip #1). But there is one type of data we can’t get enough of: data about ourselves. So when visualizing data, consider how you can put your viewer/user/reader into the chart, graph, or map.
The Guardian cleverly places their reader squarely in the middle of this simple chart. First, they ask what the reader expects is the correct answer to a question concerning a news topic such as: “Out of every 100 prisoners in the United States, about how many do you think were born in a foreign country?” The reader gives her answer and then can compare her answer to that other others in her country and to the correct answer.
Try it yourself. Would these stats have engaged you nearly as much had The Guardian simply presented them in text form? Are you more likely to remember these stats tomorrow? Did you reflect on how on/off the mark you were? Did you consider why your answer differed from that of others?
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 Ashim D’Silva on Unsplash
Data has no inherent meaning. It only gains significance when put in context. Say the average response on a survey of participants in a program is 5. You need all kinds of context to understand this data point. What were the survey questions? What scale did respondents use in responding to questions: 1-5? 1-10? Do higher numbers represent a positive or negative outcome? How does this result compare to past results? How does this result compare to the organization’s goals?
Indeed we often compare current and past data to our goals. Goals drive how we go about our work. The universe seems to love goals and rarely questions the ability of goals to move us forward. Yet, research suggests that goals are not all they are cracked up to be. There are at least two major problems with goals:
1) When we set big goals (aka “stretch goals”) and don’t meet them, they can reduce our motivation, the opposite of their intended effect.
2) Goals can narrow our focus, making us blind to other important issues and even prone to unethical behavior in pursuit of goals.
The solution? Focus on process. Involve everyone in the organization in determining the most effective and efficient steps to accomplish broader goals. These steps then become what Karl Weick calls “small wins” or manageable interim goals. And what if these interim goals don’t seem to be moving you in the right direction? Be flexible and revisit your plan. As Confucius supposedly said, “When it is obvious that the goals cannot be reached, don't adjust the goals, adjust the action steps.”
For more on the potential downside of goals, see this article in Psychology Today.
See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.
Here’s an easy, quick, and powerful way to visualize your data. It leads to significant insights in seconds.
You have probably seen quadrants graphs. People, programs, or projects are graphed along two measures, one on the Y-axis and one on the X-axis. The graph is divided into four quadrants based on the average or midpoint (or some other meaningful dividing point) of the two measures. That makes it sound more complicated than it really is. Check out the quadrants graph above. Each circle is a participant in a tutoring program. The measures are: grade point average (Y-axes) and attendance in weekly tutoring (X-axis). So participants in the:
Top right quadrant are above average on both their tutoring attendance and their GPA.
Top left quadrant are above average on GPA but below average on tutoring attendance.
Bottom right quadrant are below average on GPA but above average on tutoring attendance.
Bottom left quadrant are below average on both GPA and tutoring attendance.
Well, if the tutoring program is designed to boost GPA, then you’d hope to see most of the participants in the top right quadrant. Or you’d at least want to see participants who are low on attendance also low on GPA. But if there are participants in the other quadrants, we need to figure out why these particular students defy our predictions. For example, what else in going on with participants with high attendance/low GPA that might be undermining their progress?
Other measure pairs of interest to many nonprofits might include:
Value/Action: How do staff members who value a certain program or curriculum actually perform in putting that program or curriculum into action? If not well, why not? (Survey data would be needed here.)
Cultivation/Donation Level: Are the donors you are cultivating the same ones making the largest donations to your organization? If not, why not?
Cost/Funds Raised: Did the highest cost fundraising events result in the most funds raised? If not, why not?
See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.
Sometimes it's best to harness the power of the crowd rather than resist it.
Sometimes we do the right thing not because it’s the right thing but because (wait for it) other people are doing it. And this doesn’t only apply to middle schoolers. It’s all of us. Sociologists call it “social influence,” and it can be a powerful force for good or ill. What does this have to do with data? Well, to follow the lead of others, we must first know what they are doing. And that’s where data comes in.
We all know that teens' friends' drinking habits can affect their own. So a common approach to reducing substance abuse among adolescents is to encourage them to resist the influence of peers. Yet, research evidence suggests that rather than attempting to tamp down the power of social influence, we would do better to harness it. Consider an intervention called “normative education” designed to reduce substance abuse among students. Rather than subjecting young people to long lectures or counseling, this approach is simply about sharing data. Students are shown data about the prevalence of drinking among their peers, which is usually lower than kids expect. This information, in turn, reduces substance abuse among all students in a school, more so than does resistance training. (Check out the research evidence to learn more.)
So if we want to change the behavior of our clients, participants, visitors, or donors, we should consider making data visible about what others, like them, are doing. Take the case of donors. Over a century ago, two YMCA executives developed a potent fundraising strategy that relied on the social influence. As told by Steve MacLaughlin in Data Drive Nonprofits, the strategy included time-bound fundraising campaigns that focused on sharing information with prospective donors about major gifts already made by prominent others. They also published campaign clocks and thermometers to keep the public apprised of their progress and of the urgency to make gifts before the campaign deadline.
This doesn't mean we should give up on convincing clients, participants, visitors, or donors to do something differently, but we also should consider simply sharing data with them.
See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.
When was the last time you pondered the Y-axis? Wait, you might be thinking, what’s the Y-axis again? In a typical chart (think bar chart, line graph), it’s the vertical axis. For example, in a bar chart, the Y-axis indicates the length of the bars and thus how much of something is in a category or at a certain point in time. The categories or time periods are indicated along the horizontal or X-axis. You might recall from your middle school days that the X and Y axes are part of the Cartesian coordinate system that René Descartes (pictured here) invented in the 17th century.
What's to ponder about a Y-axes? Well, at least two questions:
1. What should be the lowest and highest number on the axis?
2. What should be the interval between numbers on the axis?
The lowest point is called the “origin.” It’s where the Y and X axes intersect. Much has been written about the importance of starting the Y-axis at zero because, when you don’t, you can make a small difference look like a big one (see the two bar charts below for a case in point.) However, when all the numbers you are charting are not anywhere near zero, then starting at zero can make differences hard to detect (see the two line graphs below for examples.) And, if your high points are too high, your data will be crammed into the upper part of your chart, leaving a lot of useless empty space below.
Think of the low point and the high point as reference points for your data. Do you want to show progress compared to historic low or high points? Do you want to show progress in relation to goals? The answer to such questions will help you decide where to start and end your Y-axis.
As for what falls in between these two points, you should consider the range of your data points and how much accuracy and ease your viewer will want. If the data ranges from 2 to 12 with slight differences between points, then you might want intervals of .01. However, if the data ranges from 6 to 10,000, then intervals of 10, 100, or even 1,000 might be sufficient to give viewers a general, easy-to-interpret sense of the data.
If you’d like to ponder the Y-axis a bit further, check out this great video from Vox.
See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.
I’m not sure how a term from construction and dentistry became so ubiquitous in the world of data analysis. Perhaps because when you are “drilling down” into data, you are going deeper. Drilling down means viewing data at increasing levels of detail. (By the way, the word for viewing data at decreasing levels of detail is called “rolling-up”, a culinary term?)
Applications such as Tableau and Qlik Sense allow you to create interactive data visualizations, which means users can use filters to drill down into the data. If, for example, you see an overall downward trend in program participation, you might want to see if the trend holds for subgroups of participants such as women, men, or those in certain age groups.
Why is drilling down important? Because it helps you to identify both strengths to build on and problems to address. An overall upward trend hides problems in subgroups. Perhaps participants in a certain age group are not doing as well as others in a substance abuse program. Conversely, overall negative results hide positive findings. For example, although on average the wages of participants in an employment program have gone down, they may have increased for a subgroup who entered the program after a certain date.
If, after using various filters, it appears that results vary significantly across a certain type of category, you might want to create several small visuals and place them side by side to more easily make comparisons among subgroups (for more on this, see Tip #25).
Bottom line: Don’t only look at the forest. Check out groups of trees to get the whole story.
See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.
Charts, graphs, maps, and other types of data visualizations (aka “data viz”) often pull me in, especially if they are visually striking. But until I became versed in the art and science of data visualization, even dazzling charts often would frustrate me. I could not extract their meaning quickly and thus moved on.
There are five steps in quickly consuming a data viz. I know that doesn’t sound quick, but most steps take only seconds to do. In each step, you answer a simple question. The questions are:
1. What’s this about? What question is it answering?
This first question comes from a 1940 classic book called How To Read A Book by Mortimer J. Adler. Adler maintains that you don’t save time on books by learning to speed-read. Instead, you save time by making an informed decision about what to and what not to read. And the best way to make this decision is to do an “inspectional read” which means skimming through titles, headings, tables of context, etc. Similarly, when you encounter a chart, map, or graph in text, skim over it by reading the title and subtitle, and any captions or annotations. Then determine what its about and, more specifically, what question it is trying to answer.
2. What’s my guess about the answer to that question?
This might seem like an unnecessary step, but studies have shown that comprehension increases when a reader forms questions about a text before consuming it. A question primes your brain for an answer. The more our curiosity is piqued, the easier all learning becomes.
3. What’s the quality of the data?
This might be the most important step and the least likely to be taken. At least determine the source of the data and whether the source appears to be reliable and credible. True, individuals will disagree on which sources are reliable and credible. Some of us, however, might be wary of data from institutions with clear political leanings or agendas. If no data source is noted, the viz is not worth your time.
For extra credit, look for information on what is and what is NOT included in the data. Consider, for example, the time period of the data and the demographics of people represented by the data. You are trying to determine if the data are equal to the task of the visualization. Can it really answer its question(s)? Or are there gaps in the data that weaken its ability to answer the questions fully or at all?
4. What more can I learn from the structure of the viz
If you have gotten this far, you are engaged by the viz. Now consider what it all means. A visualization is, by nature, an abstraction of reality. It shows data collected in the real world using position, color, shape, and size to represent the data. Thus it’s important to understand what these visual cues mean in the particular viz you are consuming.
5. What is the answer to the question and what questions am I left with?
Finally, consider what answers you see in the viz and how they compared to your expectations. And to prime yourself to consider future information on the same subject, ask yourself what else you’d like to know about it.
See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.
Infographic and data visualization often are used interchangeably. And, indeed, the distinction is not hard and fast. They both focus on showing rather than telling. They explain something using more visual cues than words or numbers and so take advantage of our visual superpowers. (For more on these superpowers, see Tip #1.) The difference is that an infographic is more of a story, and a data visualization is more of a tool.
An infographic typically uses images to lead the viewer through a story. Some of those images might be visualizations of data. For example, the point of this infographic is to show the viewer the negative impact of homelessness in contrast to the positive impact of a program called Ability Housing. Infographics are usually meant to explain or show something to people who are not all that familiar with the topic.
A data visualization, unlike an infographic, uses visual cues (shape, color, size, etc.) primarily to represent data. Think bar chart, line graph, pie chart, and maps. And though the creator of the data visualization may have a story he/she wants to tell, the viewer can use the visualization to discern any number of stories.
For example, on the quadrants chart below, each circle represents an educational strategy. The strategies are plotted along two measures: how much importance educators place on the strategies and how often they put these strategies into practice. We can use this chart as a tool to decide what to do next. Clearly, most of the educators represented in the data already feel these strategies are important. But they use the tactics less than 50 percent of the time. So we need not waste time explaining the value of the strategies to them. Instead, we should figure out what is getting in the way of their implementing the strategies.
So if you are looking to tell a specific story particularly to an outside audience, consider an infographic. If you are looking for a tool to explore data, consider a data visualization.
See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.
Eyes beat memory according to Tamara Munzner. Her idea (which she shares with others, including myself) is simple. It’s easier to compare two things you can see at the same time than to compare something you can see to something you can only remember.
When several small visualizations are placed side by side (called “small multiples”), you can see the power of eyes over memory. Take a few seconds to check out this great small multiples viz by Doug McCune. You can quickly scan the images to make easy comparisons.
In each chart, the X-axis shows time of day, and the Y-axis shows number of crimes. Daytime crimes are displayed with yellow bars in the top half of the chart. Night-time crimes with blue bars on the bottom.
It’s easy to see that driving under the influence and drunkenness occur more often during the night and trespassing and suicide occur more often during the day. It would be much harder to draw this conclusion flipping through pages or clicking through screens.
See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.
Bells and whistles can be a problem when visualizing data. Edward Tufte, the grandfather of modern data viz, entreated us to remove any non-data ink. The idea is to focus on what matters — the story the data is telling us — without any unnecessary distractions.
Making visualizations look three-dimensional is almost always a distraction and a distortion. To make something look 3D, you have to use a technique called “foreshortening” which means that parts that are supposed to be perceived as closer in space are larger (see red slice of the pie in the image below), and parts that are supposed to be perceived as farther away are smaller (see green and blue slices). The angles represented on the 2D chart on the left, as you can see, are distorted on the 3D chart on the right, making it more difficult to judge the relative size of the slices.
Another way of creating the illusion of three dimensions is to obscure some objects with others to make it appear that one object is in front of another. But, of course, this is a problem for accurate assessment in a data viz. For example, in the 3D bar chart below, the green bars for "C" are barely visible whereas the flat image shows the green bars clearly.
Is it ever a good idea to make data visualizations look 3D? Yes, but rarely. The rule is simple. Only use 3D visualizations for 3D spatial data such as a diagram showing airflow over a spacecraft. Otherwise keep it flat.
See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.
We think we know more than we actually do. In fact, we are wired that way. This illusion helps us to get along in the world. But it also gets us into trouble sometimes. Like when we are planning what our organization should do next.
Overtime, we have relied less on our own abilities to build houses, cure diseases, or fix toilets and more on others’ knowledge in these areas. We each specialize, gaining more in-depth knowledge in one area than any generalist could. Then we trade our knowledge for that of others. Seems like a good idea, but there are downsides.
We so effectively collaborate that, as Sloman and Fernbach argue in The Knowledge Illusion: Why We Never Think Alone, the lines between our understanding and that of others blurs together. We perceive the others’ knowledge as our own, even when that “knowledge” is actually baseless opinion. “This is how a community of knowledge can become dangerous,” according to Sloman and Fernbach.
Every organization has its orthodoxies, but not all of them are true. How can we distinguish the truth from our own and others' deeply-held but false beliefs?
The answer is: data. In other words, we can use the scientific method and put our assumptions to the test. If we cannot find evidence (aka data) that sufficiently refutes our assumptions, we can feel encouraged that we MIGHT be right, as long as new data doesn’t come along and undermine our beliefs.
Progress in organizations — and in all of human history — starts with the concession that we might be wrong. As Yuval Noah Harari suggests in Sapiens: A Brief History of Humankind, the scientific revolution was the point in history when “humankind admits its ignorance and (as a result) begins to acquire unprecedented power.”
On a more modest scale, we can start asking questions like: what would we expect to see in the short and long run if our programs work how we expect them to? And then we can look for data that either supports or refutes our expectations. And if we bristle at spending our time with data when so much else needs to be done, we can make data more digestible by visualizing it. (See Data Tip #1 for more on the power of data visualization.)
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 Tyler Nix on Unsplash
This week's tip is short and sweet: bring your planning, fundraising, communications, and assessments to life by visualizing them. Check out my upcoming workshops below. Three are in the Chicago area and one is online.
Data visualization: Using Your Organization’s Secret Weapons To Boost Fundraising and Impact
Tuesday, April 17, 2018, 8:00 am to 9:30 am at the Evanston Community Foundation, One Rotary Center, 1560 Sherman Ave., Evanston, IL.
Data Visualization for Grantmakers
Thursday, April 19, 2018 9:00am to 10:30am at The Robert R. McCormick Foundation, 205 N. Michigan Avenue, 43rd Floor, Chicago, IL. This event is open to grantmaker members of Forefront. If you are interested and not a member, please contact nonprofitviz@gmail.com.
Using Data Visualization to Boost Your Organization's Fundraising
Friday, May 11, 2018, 9:00am to 12:00pm at the School of Social Service Administration, 969 E 60th Street, Chicago, IL.
Data Visualization Webinar: Using Your Organization’s Secret Weapons To Boost Fundraising and Impact
Thursday, May 24, 2018, 1:00 pm to 2:00 pm online.
Looking into a data mirror can be a powerful experience. In the 60-Second Data Tip series, we have talked quite a bit about nonprofit managers, fundraisers, board members, and funders looking at organization-wide or individual program data to understand what to do next. And last week, we spoke about sharing data charts, graphs, and maps with our clientele to better understand trends. However, data can be a tool not just for planning and evaluation at the organizational level, but for personal change.
You may ask, don’t I already know a lot about myself? Do I really need to consult a data chart for self discovery? Well, research evidence suggests that we often think we know more than we actually do. We are wired to rely on the knowledge of others and sometimes we mistake their knowledge for our own (stay tuned for a tip on this). Also, sometimes we simply are not paying much attention to ourselves.
So a data mirror can be revealing. For example, you may think you spent the whole day on our feet, but the data on your Fitbit may show you otherwise.
As discussed in Data Tip #1, nonprofits tend to have a lot of data that never gets used or used well. Instead, it collects virtual dust on your server. But what if you blew the dust off of some of that data, visualized a single client’s data (e.g. her level of participation in your programs over time) and shared it with her? The data could lead to a conversation about what promoted progress and what stumbling blocks led to downward trends. Regular data feedback can be motivating, as we know from Fitbits and video games and goal-setting apps. Sharing data with clientele could be a secret weapon you didn’t know you had.
See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.