These days, you can throw a rock and reliably hit any number of articles and headlines proclaiming the power of big data, open data, and transparency. The acceptance and adoption of using large, public sets of information to make informed decisions represents a sea change in how the corporate world, inacademia, think tanks and large NGOs are investing in their capacity to crunch more than numbers. No surprise there. But how does the little guy—the small grassroots organization with a small budget and a big mission around social change—fare?
I’ve been thinking a lot about this lately. Last year, I made a job shift. I moved from a very large, well-funded nonprofit to a relatively small healthcare advocacy organization. In my old job, I worked in data visualization and regularly called upon the considerable financial, technological and statistical resources that my employer afforded me. Today is a different story. I work with supremely talented and passionate people, but the data resources that I once took for granted are gone. The “data divide” is now staring me in the face. And that’s the reason for this post—the reality that, for all the promise that big data and technology claims to offer, many of today’s smaller nonprofits and grassroots organizations are not equipped to collect, understand and harness information to move their social mission. We are the “have nots” who look out onto the world of the “haves” with statistical modeling tools, economists or statisticians on hand, coders on staff or on contract.
The data divide—what is it?
The “data divide” is by now a familiar term to many of us. The Guardian wrote about “data apartheid” when it reported on the findings from the recent 2013 Open Data Index last November. Similar findings are in the Open Data Barometer 2013 report released late last year too. And we know how it exacerbates problems faced by developing countries in fostering an open, transparent government and an informed, participatory citizenry. As I wrote last year, a good example of how open data helps citizens overcome these hurdles lies in how La Nacion (Argentina’s national newspaper) teamed up with data journalists to publish data on a variety of indicators to the Argentine public—despite the government’s lack of a Freedom of Information law.
Data divide: Access to data does not translate into results
In a blog post dating back to 2011, Mike Gurnstein describes the data divide in a way that many health care advocates are talking about healthcare today. In discussing the Affordable Care Act, advocates regularly say that access to health care is not enough—it’s the quality of care that matters. And there is an entire movement around health system reform that underscores this. Gurnstein makes a similar point about data: “[A]ccess is not enough,” he writes. “[I]t is whether opportunities and pre-conditions are in place for the effective use of the technology particularly for those at the grassroots.” Go Mike. I haven’t a clue who you are, but you nailed it.
In the same way in which the “digital divide” of the 90s and 00s required education and digital literacy to make real the opportunities that online access offered, bridging the data divide for small organizations relies on more than making data available, but also in affording these groups the ability to use it effectively through knowledge (data literacy—an understanding of how to read data and how to represent/visualize it effectively for a common purpose) and resources (the realization of this understanding into actual tools).
How can data help grassroots organizations and smaller nonprofits?
Here in D.C., Applied Predictive Technology (APT), a tech firm that sells predictive analytics software, volunteered to analyze the data that a local charter school was collecting from the tablet apps that its students were using. APT used this “data dive” to help teachers assess how well the tablet reading apps were working for different kids—allowing teachers to tweak the reading curriculum and apply intervention to different types of students.
One of the best organizations out there is New York-based DataKind. If you really want to understand how socially-conscious data scientists are working to achieve social change through data, take five minutes to check out the variety of projects they work on. Over the past several years, DataKind has been launching “data drives” in cities around the U.S. Similar in nature to “hackathons” or “code-a-thons,” these DataDives team up volunteer data scientists/analysts and social organizations over the course of a few days to build apps or software that solve a well-defined data problem. And then they solve it.
When DataKind held a DataDive for D.C. Action for Children, a small organization that collects data on the indicators that affect the well-being of children to mitigate poverty, good things started to happen. The nonprofit also runs the DC chapter of the Kids Count program and, through Kids Count, it was doing a great job at collecting data (that was their mission). But the work that they were producing was static—PDFs—a situation common to many small organizations. Fortunately, they realized that, to make the data meaningful, easier to analyze, and more effective at highlighting the poverty problems that needed to be solved, they needed to visualize it. This is where DataKind came in. Their volunteers worked for a month to create an interactive data visualization tool (eDatabook) that mapped the well-being indicators and poverty clusters across the District. The best part? It’s replicable. Other DC Count programs across the country can adopt it as well.
Using data and hackathons to help on a local level
The vast global data modeling regularly published by the World Bank is impressive. But municipalities are using data to tackle local problems too. Like D.C. Action for Children, cities are pairing up with volunteer data analysts and coders to sidestep the issues of inhouse capacity and expertise.
To fight ongoing problems with obesity and diabetes, for example, New York City launched its first health data code-a-thon this past December. The result? An app called “Vera.” Based on a user’s risk for diabetes, Vera texts users reminders and tips for physical activity, glucose monitoring and even good food intake.
Leveraging hackathons for broader impact
Voting: On a broader scale, the Voting Information Project, a small group of elections experts who focus on improving the voting experience for the public through cutting-edge technology, held its first hackathon in November, 2013 (disclaimer, I was affiliated with the organization that funds this project). The hackathon yielded fast and effective results, including first-ever voter lookup tools, that were used by Americans everywhere.
Healthcare: On June 2, 2014 Health Code-a-palooza will bring together programmer teams who, over the course of 48 hours, compete to see who can use a Medicare data set to build the best app for doctors to use to improve the quality of care that they deliver to patients. This hackathon is part of the Health Data Consortium’s annual Health Datapalozza, an event that features data and healthcare experts discussing how open data can drive meaningful improvements in the health reform movement. But you have to admit, the coding is pretty cool too. If you’re interested in learning more about how open data is playing out in the field of healthcare, read more about the Health Data Consortium.
Challenges and questions around transforming the data culture in small nonprofits
Lack of data literacy can impede an organization’s ability to articulate its need.
As I mentioned, part of the problem is not just access to data, but being able to frame a goal, understand which data to collect and establish good collection practices—data literacy. For an organization taking nascent steps toward data collection, this can be daunting. It requires a change in the organization’s culture, investment of time (if not technology and staff) and a reprioritization of traditional methods of executing its goals. Much of that work is internal. But some of that can be helped by organizations such as DataKind’s, who actually mentors organizations to help them frame their problem and prepare for the end result.
Sustainability beyond the initial volunteer effort
And what happens after the project concludes? What if something breaks? How do you continue to foster an environment of learning and change in an organization after it takes its first steps toward a data culture? Again, an approach like DataKind’s is promising. They stick around, monitor the project and provide follow-up support to ensure that the work keeps going. That makes sense, because it’s part of DataKind’s mission. In future posts, this is something that I’ll be writing more about, as well as how data volunteers and organizations are finding each other. If you’ve got ideas or stories to share, let me know. You can follow me on Twitter at @uriona.