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Show Me: Why Your Data Should Be Seen (and Not Just Read)

June 19, 2017

The following post is part of a year-long series here on PhilanTopic that addresses major themes related to the center’s work: the use of data to understand and address important issues and challenges; the benefits of foundation transparency for donors, nonprofits/NGOs, and the broader public; the emergence of private philanthropy globally; the role of storytelling in conveying the critical work of philanthropy; and what it means, and looks like, to be an effective, high-functioning foundation, nonprofit, or changemaker in the twenty-first century. As always, we welcome your thoughts and feedback.

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"Frothy eloquence neither convinces nor satisfies me. I am from Missouri. You have got to show me."

So proclaimed Willard Duncan, a Missouri congressman, in an 1899 speech. Perhaps because I, too, hail from the Show-Me state, I have taken his advice to heart. Now let me convince you of its wisdom.

First let's talk about data. Nonprofit organizations are lousy with it — participant data, program data, financial data, sales data, fundraising data. Nonprofits are drinking from a fire hose and the water pressure is building. We are scrambling just to find enough bandwidth to store our data. And like secretive hoarders, we are reluctant to admit how little of this data we actually use. We may pay lip service to "evidence-based practices" or "data-driven strategies," or even borrow acronyms like ROI (return on investment) and KPI (key performance indicator) from the for-profit world. But, when pressed, many nonprofit managers admit they are not data people. They care deeply about people and programs, but their eyes glaze over at the sight of a spreadsheet.

It's okay: we're wired that way. (More on our wiring in a minute.) But for now, let's look at some other reasons why nonprofits may not be making good use of their data.

Top Reasons Nonprofits Avoid Data

Nonprofits avoid data for any number of understandable reasons. In my experience, the primary causes include:

Data animus. Many nonprofit staff members possess expertise in environmental issues, the arts, health, or education but not data analysis. Some suffer from data aversion. They admit — or sometimes proudly proclaim — that they are not "numbers people."

Time. Nonprofit staffers do not have time for data analysis. They are struggling to stay afloat, to submit the next proposal, to sustain their programs, to address the huge and varied needs of their clientele, to cultivate donors. As a result, digging through data is almost always a back-burner item.

Fear. Some worry about what their data might reveal. They fear they won't be able to control the narrative, that the data will be taken out of context, or that funders will withdraw their support based on the data.

"Dirty" data. Many nonprofits have entry-level staff or multiple staff entering data into management information systems or spreadsheets. The result can be "dirty" data — data with a troubling level of inaccuracy because it has not been entered correctly and/or consistently. If, for example, Michael Smith is entered twice, once with a middle initial and once without, then tracking his progress through your program will be difficult.

Wrong data. While many nonprofits have data on their financials and clients, they often lack data that demonstrates the positive social impact of their programs. A tutoring program may not track students' school grades or test scores. An employment program may lack data on program graduates' wages over time.

Disconnected data. Rather than maintaining a central management information system, small nonprofits often store their data in separate Excel spreadsheets. Which means Michael Smith's demographic profile might be captured in one spreadsheet while his attendance in various programs is stored in another, making analysis of, say, age-to-program participation next to impossible.

Why Cave Dwellers Drew Pictures, Not Spreadsheets

Our visual system has evolved over millions of years to process images in parallel. We don't "read" the Mona Lisa from top to bottom or from left to right. We take it all in at a glance and understand, almost instantly, that it is a picture of a woman in front of a landscape wearing a dark dress and an inscrutable smile. The cognitive technology of words and numbers, which is only six or seven thousand years old, requires us to scan individual characters arranged in small groupings and piece them together into words or values and then sentences or equations.

Here's an example: Which image do you "get" first?

Kohm_MonaLisa Kohm_Spreadsheet
Source: GoGuiyan.com and SSuite Accel Spreadsheet

 

Because data is encoded in words and numbers, it can be difficult for us to extract the stories that data tells. But if we use visual elements — solid bars, pie slices, sloping lines — to encode the data, the story comes into focus much more quickly. Data visualizations help us understand the significance of data by placing it in a visual context. And if, on top of that, we apply to our data visualizations what we know about how humans process visual cues, they are even easier to digest. Just one example: Humans can discern positions along a common scale more accurately than angles. That's why it is much easier to compare the lengths of several bars on a bar graph than to compare the size of slices in a pie chart.

Florence Nightingale probably wasn't a numbers person, either. She became a nurse to serve others. Yet, she soon realized she could provide care more effectively with the help of data. Working with a statistician named William Farr, Nightingale analyzed mortality rates during the Crimean War. She and Farr discovered that most of the soldiers who died in the conflict died not in combat but as a result of "preventable diseases" caused by bad hygiene.

Nightingale's solution? She invented the polar area chart, a variant of the pie chart meant "to affect thro' the Eyes what we fail to convey to the public through their word-proof ears." Each pie represented a twelve-month period of the war, with each slice showing the number of deaths per month, growing outward if the number increased, and color-coded to show the causes of death (blue: preventable, red: wounds, black: other). Clearly seeing the importance of hygiene, the Queen and Parliament quickly set up a sanitary commission and, as a result, mortality rates fell.

Kohm_NightingaleChart
 
Fig. 1: Florence Nightingale decided to show (rather than tell) her data
Source: Smithsonian.com

 

Getting Started With Data Visualization

Before designing charts, maps, or graphs, you need to know what you want to know. Perhaps your organization or program already has a logic model. If not, it's worth at least one team meeting to draft one. Logic models, like data visualizations, show rather than tell. They show how resources, programs and services, and desired results relate to each other according to your organization's strategic plan. The graphic below comes from the Pell Institute's Evaluation Toolbook, a site that walks you through logic models, other steps in effective program assessment, and the various types of data you can collect.

Kohm_LogicModel
Fig. 2: The components of a logic model

 

If your organization or program doesn't already have clearly articulated goals, benchmarks, or objectives, a logic model is a good first step toward setting them. You can set goals for any stage of the process (what types and amounts of resources you hope to garner, what types and amounts of services you intend to provide, or what types and amounts or degrees of outcomes you expect to see). The trick is determining which data will be most useful in helping you measure progress toward your goals in a meaningful way.

Once you figure out what it is you need or want to know, don't wait until you have data that supports your logic model to visualize it. It's important to bring the data to life for everyone involved, and that means visualizing it sooner rather than hiding it in spreadsheets and databases.

Even a simple line graph showing progress over time toward a goal will make your data perceptible, prompting you and your colleagues to ask important questions. Is our data accurate? What additional data do we need to better understand the trends we see? What is going on in our program or our community/field that might be affecting these trends? Questions like these can strengthen your resolve to gather new and/or better data — or to make changes designed to enhance the efficacy of your program.

Kohm_LineGraph
 
Fig. 3: A simple line graph showing progress over time

 

There are plenty of software programs out there to help you visualize your data. Excel, which you may already have, is perhaps the simplest to use. Other programs such as Tableau and Qlik Sense allow you to create interactive visuals and "drill down" into your 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. Free versions of Tableau and Qlik Sense are available as long as you store your data and visuals on the companies' servers. (Both companies give you the option to hide your data and charts from anyone outside your organization.)

Eventually, you'll identify your most important goals, what data to collect and use to track your progress, and how best to visualize that data. Then you can create a data dashboard that everyone in your organization can use to track progress on key goals and ask ever more sophisticated questions about how better to advance your mission.

But first you need to tell a story with an image or picture. Getting that right is the first step toward greater understanding and success.

Amelia_kohm_for_PhilanTopicAmelia Kohm, PhD, is the founder of DataViz for Nonprofits, where she serves as principal consultant. To contact her and learn more about data visualization best practices, visit nonprofitviz.comFor more posts in the FC Insight series, click here.

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Great piece, Amelia! You talk about nonprofits but the same could be said for most foundations that contribute to this state of affairs by requesting impact data from their grantees, which they often do not have the infrastructure to process or interpret. Hopefully they are storing it, so that when technology, need, competencies and inclination all align knowledge can be produced and shared.

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