After the corporate sector and journalism, non-profit organisations are slowly starting to put their data to use and visualise it—for example, using numbers, maps, and charts in their communication campaigns or creating structured internal KPI (key performance indicator) monitoring processes.
I used to work in digital communication in an NGO, and now I’m a data visualisation consultant: naturally, I’m interested to see how mission-driven organisations will seize the opportunities hidden in their data and what impacts may result. My impression is that datavis is still gaining traction in the non-profit sector, and my hope is that it will be used more widely soon.
In this piece, I will present the benefits and challenges of using data visualisation in non-profits, according to two experts in the field: Stina Bäcker and Neil Richards. Hopefully, their answers will help demystify data visualisation for those working in non-profits who may feel tempted to try but don’t dare to take the plunge yet.
First things first, let’s be precise: what will we be discussing in this double interview? “Data visualisation” can refer to different types of products, with different goals, for different audiences.
Data can be used for internal monitoring and management, in which case data visualisation will often take the form of interactive dashboards created for internal staff and volunteers, to help them track key indicators and make decisions affecting their work.
Data visualisation can also be used for external communication, alongside more traditional communication tools such as photos, illustrations, texts or videos, in order to inform, raise awareness, and spark action.
Finally, somewhere between the previous two, elements of data visualisation can be found in digital and print publications created for accountability purposes, such as evaluation reports and annual compilations of the organisation’s activities.
In this last case, as in external communication, the target audience is the general public, sometimes with a focus on a specific segment such as donors or project stakeholders. However, unlike external communication, the publications often do not take a particular angle in an intent to convey objectivity and neutrality. In such cases, a descriptive approach is used, similar to that of internal dashboards.
This piece focuses on non-profit data visualisations targeting the general public that aim to spread knowledge, raise awareness, and encourage action.
Datavis: What For?
The first question that someone working in a non-profit could ask is: Why? What are the benefits to a non-profit of including data visualisation in its communication strategies?
Neil and Stina’s answers can be summarized as: having more impact, attracting attention, facilitating understanding, conveying a message.
Stina: “Non-profits all over the world are collecting both quantitative and qualitative data on a regular basis, but most of the time this data is stuck in spreadsheets, in long research papers, or in monitoring and evaluation reports that very few people are able to access or understand.
Communicating this data in a more engaging way — through data visualisations or through other means such as data journalism, data sonification, data art, etc. — provides opportunities for non-profits to engage with broader audiences and have more impact.”
Neil: “Data visualisation, if used well, is a great additional aid for understanding data and hence understanding issues that a non-profit is facing. We are used to the explanation of data visualisation being a tool to help people see and understand their data. It’s likely that part of a non-profit’s communication strategies will be in getting across certain messages which are underpinned by data.”
“Data visualisation, as well as aiding in the understanding of data, can also be a powerful method of attracting attention and drawing in those who are potentially interested.” — Neil
Datavis: How Do You Do That?
At this point, you’re probably thinking: “Use data visualisation to have more impact — that’d be nice, but it’s easier said than done!”
Indeed, for a non-profit willing to leverage the communication opportunities offered by data visualisation, the challenge often starts at the first step: collecting data.
Stina: “A lot of Data4Change’s partners are grassroots and emerging non-profits working to support at-risk communities in complex environments. They’re operating in countries that are experiencing varying degrees of government corruption and authoritarianism, political unrest, armed conflicts, natural disasters, and accessibility constraints such as intermittent electricity and internet connectivity. Collecting data in these contexts presents a logistical challenge and is potentially dangerous.
At-risk groups, such as refugees, LGBTQI+ communities, sex workers, migrant workers, undocumented people, disabled people, and so on, are often excluded from government and open data sources in the countries our partners work in. It is also hard for large organisations like the U.N. or World Bank to gain the trust of these at-risk groups to collect data about their lived experience.”
“The fact that official datasets are not disaggregated on such a level that speaks to the experience of these groups makes them effectively invisible. This causes lots of problems, especially when trying to advocate for their basic human rights.” — Stina
Thinking about the steps to take to collect data that can support a non-profit’s cause can be daunting. But it is worth the effort, because once a data collection system is established and a sufficiently rich dataset is available, the data can be exploited in several ways, sometimes both for internal KPI dashboards and for external communication (provided that the necessary work is done to adapt the data to the specific project and target audience). Multiple stories worth telling can be found in one dataset, allowing it to be used in more than one project.
From one organisation to another, data availability can differ considerably, as Neil explains:
Neil: “No two projects are the same, just as no two non-profits are the same — much of the variety and satisfaction of being involved with Viz for Social Good comes from the fact that we can be working with sparse data from a tiny local charity one week, and multiple dense, detailed sets from international non-profit agencies the next.”
For some data visualisation projects, open data sources or data from partners may be a suitable and time-saving option, allowing non-profits to try data visualisation without a demanding data collection process.
That being said, wherever your data comes from, you still need basic skills to turn it into effective visuals (and sometimes to clean it before visualisation). After collecting data, the second challenge facing non-profits is accessing data skills.
“Often the most frequent challenge is in the completeness and cleanliness of data. The chances are that the non-profit you are working with does not have a dedicated data scientist, much less likely a team of people to devote attention to data.” — Neil
Neil: “Even if the data person at the non-profit is highly data literate and conscientious, they may not necessarily have the tools to ensure clean and complete data at the source, nor might that be a priority in the person’s job role.”
Stina: “It remains prohibitively expensive to hire data analysts and data visualisation experts, which means that working with data is often a luxury the underfunded nonprofits we work with can’t afford.”
In some cases, data skills can be accessed for specific projects through initiatives like Viz for Social Good. For more complex or long-term projects though, solutions at an affordable cost are harder to find.
“Data visualisation is also, in many ways, exclusionary: it requires access (to the internet or the printed or physical product), data literacy, literacy, and vision, and many people in the world lack at least one of those attributes.” — Stina
Datavis = Oversimplification?
Non-profits often work on complex social issues that can be explained from multiple angles and are deeply rooted in a region’s history and context. How can numbers describe such complexities? Can data visualisation lead to oversimplification, and if so, how can that be avoided?
Neil: “The best way to avoid oversimplification is [for data visualisation professionals] to work closely with the NGO. In the same way that we may not be dealing with experts in data, they are most certainly not dealing with experts in their complex issues when they are dealing with us.
It’s true that sometimes the best data visualisation will result in simplification, so that our audiences may more easily understand the issues at hand. But when it comes to oversimplification that’s something we can potentially run the risk of.
“We make sure to research and understand the issues as best as possible, so that we don’t rush into a solution that oversimplifies.” — Neil
Stina: “Any method of communication runs the risk of oversimplification, and data visualisation is no exception. The non-profits we work with have to communicate their data to diverse audiences, some with high levels of data literacy and some with none.”
“To avoid oversimplifying or even overcomplicating things you have to tailor your output to your target audience.” — Stina
Stina: “For instance, an exploratory interactive data visualisation platform about refugees and internally displaced people might be great for academic researchers and journalists to find the stories and information they are after on this topic, but it is not going to be useful to the refugees or internally displaced people themselves who have limited internet access and are often using mobile phones.
For this audience group, it would be more effective to use other data communication techniques such data storytelling with simple static charts, or infographics, GIFs, short videos with voice-overs, etc. that can be distributed easily and at low cost.”
“The issues our nonprofit partners work on are incredibly complex, but the methods they use to communicate them shouldn’t be.” — Stina
Data vs. Humans?
A data-based communication approach can show trends and general overviews well, but with big numbers, we risk of losing sight of individuals. For this reason, datavis may seem too cold and distant to some, as non-profits often work on issues that affect individual lives in a very concrete way.
How can this gap be filled? How can data visualisation be humanised?
Stina: “Humanising data is essentially our modus operandi! Although all of the projects we work on are informed by data, data is not always at the forefront of the final output. Here are two examples of data-led projects we’ve worked on that fill this gap.
A Bride With A Doll: Arab Women Organization of Jordan works to prevent girls in Jordan from entering early marriage. In Jordan, girls who are older than 18 when they get married are ten times more likely to get a job than those who marry before they’re 18. But these numbers are not going to convince a family to decide against early marriage for their child. For this project, the creative team transformed the numbers into a compelling narrative.
The outcome is a printed storybook about a 14-year-old girl who is forced to marry an older man. Her story describes the physical, emotional, and economic consequences of child marriage. The story is presented as part of a package that includes a series of posters, social media assets and interactive exercises that Arab Women Organization can run during their workshops with mothers and daughters. This project was created in 2018, and it’s still going strong!
I Am Binadam: In Tanzania, LGBTQ people face discrimination and persecution. The country’s president, John Magufuli, who came to power in 2015, has been leading a crackdown on LGBTQ rights in the country, and homosexuality is now a crime punishable by up to 30 years in prison. The organisation we worked with on this project collected 896 survey responses from LGBTQ Tanzanians. The goal was to create a campaign targeting members of the LGBTQ community who had not yet come out and their friends and family.
The team created two fictional characters called Manka and Mashaka. They are composites created from the data and represent two young adults grappling with their sexual identities. Manka and Mashaka humanise the story, making it approachable in a way that charts and graphs could not.”
In Stina’s examples above, data is used as working material in the design process, but the final output uses storytelling and a personal focus to create empathy.
Another method often used in data journalism to create empathy while leveraging data is combining a chart showing the global extent of an issue with a written testimony of one specific person affected by the situation. Alternatively, you can let readers interact with the data. Readers enter information about their own situation, so they can see where they sit compared to the rest of the population on a specific issue and how their life could be different if one or two parameters changed.
This method is applied in this interface called “Are you a gentrificator?” created by the French datavis agency Wedodata. Readers fill in information on where they live, how much they earn, and their family composition. Based on that data, the interface tells them if they are contributing to gentrification or not.
These and other approaches can be used to help readers empathize with individual stories and sometimes to even find their own place in the story, revealing a deeper, more human meaning to otherwise cold numbers.
In a blog post summarizing her talk at the 2016 Chaos Communication Congress, data visualisation expert Lisa Charlotte Rost explains that there are two different modes of thinking that data visualisation can speak to:
- The “slow system,” which is based on abstract rationality and is responsive to numbers
- The “fast system,” which is based on emotional experience and is responsive to narrative
Lisa writes: “We still need to address both modi in fields like news or advocacy: The fast, emotional system to motivate people THAT they should do something (e.g. donating), and the slow, analytical system to help them decide HOW they should do it. […] Datavis can do both, since it’s a tool like language, that can speak to both modi. […] The right use of tools depends on the goals.”
Neil elaborates:“There are always several different approaches to visualising data, and that’s a great advantage to an initiative such as Viz for Social Good.”
“Sometimes it may be more appropriate to view and visualise the issue at an aggregated level, and sometimes there is a greater personal focus to be had by taking a more humanised approach to visualisation — the first step is to have the minimum level of disaggregation necessary in the data in the first place.” — Neil
Neil: “Of course we would always take steps to make sure that individuals weren’t personally identifiable, but apart from that there is often the opportunity to take a very humanised approach to data visualisation.
The important thing is that all visualisations we submit are done as a partnership with non-profits, so they are able to discuss and choose those that best represent the message they were trying to convey. We never request or suggest different approaches to our volunteers so the likelihood is that in any given project, if the data is disaggregated enough, we will find data stories both at a granular human level and at an aggregated level.”
After these insights from Neil and Stina, I’d like to leave you with two examples of data visualisations that I think manage to beautifully communicate a story at both the granular, individual level and the global one.
The first is a piece by The Guardian on gender pay gap entitled “When does your company stop paying women in 2018?” In this piece, the pay gap is calculated as unpaid days of work for women. While scrolling through a calendar view, readers see points representing the number of companies that “stopped paying” women by that date. A counter shows the total number, as individual points visually pile up.
The second example is a well-known visualisation created by the agency Periscopic on the number of years of life stolen as a result of gun killings in the U.S. in 2018. Each line represents the life of a person killed by gun, with years lived in orange and years lost in grey. Lines pile up, and two counters show the total number of people killed and years lost.
As in the previous example, individual stories and aggregated numbers are brought together in a single visual, but here the result is even stronger as the single data point represents a person’s life. If there is a gap between data and humans, in this visualisation it is definitely filled.
Of course, data visualisation isn’t the perfect solution for all topics a non-profit needs to communicate about, but in some situations datavis can add depth to a claim and provide effective communication angles to engage the audience. In data visualisation, individual stories are able to reveal themselves not as simple anecdotes but as part of a larger issue impacting society.
Side by side with other communication methods, data visualisation can be a valuable tool in a non-profit’s communication toolkit. Starting to use data visualisation requires time, data, and skills. However, options like open data sources and volunteer datavis initiatives can help non-profits get a foot on the ladder and try data visualisation gradually, one project at a time.