Consider Outliers

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When you visualize your data (in a bar chart or line graph, for example), your eyes tend to focus on clumps of data. That makes sense. The clumps are where the action is. Groups of data — which appear as the longest bars or data that forms an approximate line on a graph — show us general patterns in the data. For example, they tell us that as one thing increases (like age), another thing also increases (like risk of disease). Or they might tell us that use of counseling services peaks in the months of January, February, and March. These are important stories, so certainly keep your eyes on the action. But also do not ignore small bars or the isolated points or smaller clumps of data points, aka “outliers”. They have important stories to tell too. First, their message might be: “Warning! Human error! The data is wrong and needs to be corrected." Second, if you have confirmed that the data is correct, then these outliers might alert you to distinct subpopulations that, for example, do particularly well or notably poorly in a program. This is an interesting finding, one not to be discounted. They should prompt you to ask: Who are these individuals? What about them sets them apart from the others? Did they have different program instructors? Did they have certain characteristics which would (dis)advantage them in the program? The answers to such questions often prove to be insights that help you to adjust your course and improve curriculum, your recruitment strategy, or other ways in which you do your work.

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

Pies are for eating

There are better ways to show your data.

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Pies are delicious but often inscrutable when applied to data. Humans are pretty good at deciphering some visual cues and pretty bad at others. For example, we do well when comparing lengths along a common scale. So looking at this image, we can confidently proclaim the E bar as the tallest. But we would be hard pressed to pick out which pie slice is largest. That’s because we don’t do so well with angles. So when comparing the quantities of several things, bar charts are almost always better than pie charts. The only exception is when you want to compare a part to a whole. In this case, a pie chart does a good job of showing that girls, for example, represent only a sliver of all the participants in a program or that 30 to 40 year olds are the majority of visitors to an event. But once you get beyond 2 (or maybe 3) slices, skip the pie and dust off the trusty bar chart.

Get the whole picture

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In an ancient Indian parable, a group of blind men encounter an elephant for the first time. Each man feels a different part of the animal and reaches his own conclusions. One feels a tusk and proclaims it a spear. Another feels a leg and decides it’s a tree trunk. The message? Collect more evidence and take a wider view. This is a good message for nonprofits. Notice a 3-month downward trend in participation in one of your programs? Zoom out and see if the trend holds over longer periods of time. If not, is there a cyclical pattern? For example, when looking at the trend over the past 5 years, does participation increase during certain months and decrease in others? Also, zoom in and see if the trend holds for subgroups. Is there a downward trend for boys in your program but an upward trend for girls? Do those in certain age groups have differing trends? Zoom out and zoom in to clearly understand 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.

Photo by jinsu Park on Unsplash

Clean Data Tell Clear Stories

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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.

To make sure data is accurate and thus of any value at all, it should be regularly "cleaned." A few simple procedures for cleaning data include:

  • Spell Check: Use a spell checker to find values that are not used consistently, such as a program name.
  • Remove Duplicate Rows or Entries: Sort data or use conditional formatting in Excel to find duplicates. Filter data for unique values to find near duplicates.
  • Find and Replace: Use find and replace function to correct data entered incorrectly in multiple rows or entries.
  • Use Upper Case and Trim: Change all text to upper case and remove extras spaces before and after values to ensure consistency. The UPPER(text) and TRIM(text) functions in Excel can do this.
  • Make Date Format Consistent: Make sure that dates are entered in a consistent format (such as MM/DD/YYYY). Excel has several functions that can help you convert date formats.

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

Choose Your Data Visualization Weapon

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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 (and you can make your data and charts invisible to anyone without the URL).

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 data tip originally appeared on Philanthropy News Digest’s PHILANTOPIC blog.)

Don't Just Set Goals, Track Them

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Many organizations go to a lot of trouble setting goals, eating up loads of staff and board meeting time, and then neglect to do one or both of the following:

  • Figure out how they will know if they are making progress toward their goals.
  • Track their progress toward their goals.

If your organization or program doesn’t already have clearly articulated goals, a logic model is a good first step toward setting them. Logic models show how resources, programs and services, and desired results relate to each other according to your organization’s strategic plan. (For more on logic models, check out the Pell Institute’s Evaluation Toolbook.)

You can set goals for any stage of the process: what resources you hope to garner, what services you intend to provide, or what outcomes you expect to see. The trick is to make these goals specific and measurable. Don’t say you will work strengthen a program, say that participation in the program will increase to 250 and that evaluation surveys will show average ratings at or above 4 (on a five point scale).

Once you set specific and measurable goals, don’t wait until you have all of the necessary data to visualize it. It’s important to bring the data to life for everyone involved, and that means showing 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 yourselves important questions, such as: 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 or our field that might be affecting these trends? Questions like these can strengthen your resolve to gather new or better data as well as to make changes to enhance the efficacy of your program.

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

Image Source: Pixabay.com

(This data tip originally appeared on Philanthropy News Digest’s PHILANTOPIC blog.)

Go With The Flo (Florence Nightingale)

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Like many nonprofit staff today, Florence Nightingale probably wasn’t a numbers person at the outset. 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 perished 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.

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

Also, see the Smithsonian's excellent article The Surprising History of the Infographic for more on the history of data visualization.

Image Source: Smithsonian

(This data tip originally appeared on Philanthropy News Digest’s PHILANTOPIC blog.)

Admit That You Avoid Data

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Nonprofits avoid data for any number of understandable reasons, including:

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.

Wrong data. While many nonprofits have data on their financials and clients, they often lack data that demonstrates theimpact 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.

See other data tips in this series for how to overcome barriers to data use.

Image Source: smejoinup.com

(This data tip originally appeared on Philanthropy News Digest’s PHILANTOPIC blog.)

 

Make Your Data Visible

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Nonprofits are lousy with data. But, like secretive hoarders, we are reluctant to admit how little data we actually use. We may pay lip service to “evidence-based practices” or “data-driven strategies”. But, if pressed, many of us admit that we care about the people and the programs and glaze over at the site of a spreadsheet.

Indeed, we are not wired well for processing data in spreadsheets.

Our visual system has evolved, over millions of years, to process images essentially in parallel. We don’t read the Mona Lisa from top to bottom and from left to right. We take it all in together and understand, almost instantly, that this is a picture of a woman in front of a landscape, sporting a dark dress and an inscrutable smile. Words and numbers, which only appeared within the last few thousand years, require our visual system to scan individual characters one at a time and piece them together to create meaning.

Data is encoded in words and numbers making it difficult for us to extract the stories they can tell. However, if we use visual elements (like bars, pie slices, and sloping lines) to encode the data, the story can come into focus more quickly. 

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

Image Sources: thinglink.com and perfect-cleaning.info

(This data tip originally appeared on Philanthropy News Digest’s PHILANTOPIC blog.)