For donors, NGOs and aid implementers, data collection is critical to demonstrate results and flag failures — and data transparency is essential for sharing lessons learned and having robust public relations.
But trying to balance being transparent while also protecting the privacy of beneficiaries can be a hazardous juggling act.
“Bad data management has gotten people killed,” Ian Schuler, president and CEO of Washington, D.C.-based data mapping firm Development Seed, said this week on the sidelines of a event hosted by the Center for Strategic and International Studies to mark the 20th anniversary of the U.S. Agency for International Development’s Office of Transition Initiatives.
“[OTI] is committed to transparency and real measurable results,” USAID Deputy Administrator Alfonso Lenhardt said in his keynote speech.
The challenge is figuring out how development practitioners can achieve data transparency while respecting the privacy of the individuals, families and communities they serve — a question that according to Schuler it not asked often enough within the industry.
“We as development practitioners need to be more thoughtful about the data of individuals that we’re working with … and we often don’t have this conversation,” he said.
Amy Noreuil, geographic information specialist with OTI, fully acknowledged the importance of privacy while working with data and underscored the importance of “balancing the desire to support open data initiatives as an office against the need to respect grantee privacy.”
One sector that development practitioners could learn from on this “juggling act,” Schuler pointed out, is the medical field, which for years has grappled with privacy issues from U.S. and international laws on patient data.
“Finding ways to get the benefits of doing good evaluation, of tracking that things are happening and what the impacts are, while anonymizing that data and taking out some of that personal identifiable information is just something that is possible to do,” he said.
For instance, one way to ensure data transparency and privacy protection is through “hashing identifiers,” or turning names and personal information into numerical codes that can’t be reversed. This preserves the practitioner’s ability to see trends in the data while still protecting personal privacy.
Another method is “fuzzing locations” — randomizing data in a way that allows an organization to hide the exact location of a home in a particular village, town or city, while still maintaining enough information to be useful for survey purposes. Schuler said organizations conducting health surveys are particularly interested in fuzzing locations.
“There’s a gray area between really really awesome things you can do with data, and really really creepy things you can do with data,” said Schuler, raising the question: “How do we set the right standard so that we know we’re doing as much awesome as possible with as little creepy as possible?”
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