Data for Good
The transformative impact of data on development projects, captured in the hashtag #DATARevolution, offers the social and private sectors alike a rallying point to enlist data in the service of high-impact development initiatives.
1) Define the need
At the center of a Data for Good initiative are the individual beneficiaries you are seeking to serve. This is foundation on which the “Good” of Data for Good rests.
Understanding the data and expertise needed to better serve such individuals will bring into focus the areas where your organization can contribute and the partners you might engage. As we’ve covered in past posts, collaboration between agents who bring different layers of expertise to Data for Good projects is a powerful formula for change.
This is central to the Global Partnership on Data for Sustainable Development announced in Addis Ababa this month, with the support of governments from Kenya to Italy and the United States, as well as actors like the ONE organization and MasterCard. “By bringing together organizations from different backgrounds in support of the SDGs, the Partnership will galvanize and sustain political commitments, align strategic priorities and norms, strengthen capacities, foster innovation and build trust in the booming data ecosystems of the 21st century,” says Daniella Ballou-Aares, Senior Advisor for Development to the U.S. Secretary of State.
2) Understand what data can make a difference
Think about what kind of data can tell a story that’s relevant to your mission. Claudia Perlich of Dstillery says: “The question is first and foremost, what decision do I have to make and which data can tell me something about that decision.” This great introduction to what different kinds of data are relevant in different settings can give you concrete examples.
3) Get the right tools for the job
By one estimate, some 90% of business-relevant data are unstructured or semi-structured (think texts, tweets, images, audio) as opposed to structured data like numbers that easily fit into the lines of a spreadsheet. Perlich notes that while it’s more challenging to mine this unstructured data, they can yield especially powerful insights with the right tools—which thankfully aren’t that hard to identify.
“R and Python are amongst the most popular programming languages for statistics. There is often a debate of what language to choose but your mindset is more impactful. The tools are a means to an end,” says Daniel Pedraza, a data scientist at data analytics firm Quid. These languages are helping with the build of natural language recognition programs that enable the mining of insights from unstructured data.
4) Build a case that moves your organization
“While our programs are designed to serve organizations no matter what their capacity, we do find that an organization’s clarity around mission and commitment to using data to drive decision-making are two factors that can make or break a project,” says Jake Porway, founder and executive director of DataKind, a New York-based data science nonprofit that helps organizations develop Data for Good initiatives.
Just how to marshal a clear, mission-driven commitment depends on the nature of your organization. Nonprofits might ask whether an underperforming program could be improved with better insights from data science. Private sector organizations can consider the numerous benefits of a strong mission-driven organization, which a Data for Good initiative can strongly reinforce.
5) Make technology serve people-centric ethics
The two most critical ethical factors to consider are informed consent and privacy—both require engaging the community you wish to serve as individual actors.
Before getting access to specific data sets, be sure to have informed consent from your data sources. “We’re really upfront with our users about the kind of data we collect and what it will be used for,” says Shivani Siroya, CEO of InVenture, a financial services firm that opens access to credit to communities in emerging markets. “We want to know how we can best support them, so being honest is critical.”
Second, be sure to protect the privacy of any sensitive data you may store.
“Employ data-privacy walls, mask the data from the point of collection and encrypt the data you store. Ensure that appropriate technical and organizational safeguards are in place to verify that the data can’t be used to identify individuals or target demographics in a way that could harm them,” recommends Quid’s Pedraza. To understand the technology of data encryption and masking, check out this post.