In his MERL Tech London Lightning Talk, George Flatters from the Open Data Institute told us that M&E is extractive. “It takes data from poor communities, it refines it, and it sells it to to rich communities.” he noted, and this process is unsustainable. The ease of deploying a survey means that there are more and more surveys being administered. This leads to survey fatigue, and when people stop wanting to take surveys, the data quality suffers, leading to an M&E apocalypse.
George outlined 4 ways to mitigate against doomsday:
1) Understand the problem–who is doing what, where?
At the moment, no one can be totally sure about which NGOs are doing what data collection and where. What is needed is the Development equivalent of the Humanitarian Data Exchange–a way to centralize and share all collected Development data. Besides the International Household Survey Catalog and NGO Aid Map (which serve a similar function, but to a limited degree), no such central location exists. With it, the industry could avoid duplication and maximize the use of its survey-administering resources.
2) Share more and use existing data
Additionally, with access to a large and comprehensive database such as this, the industry could greatly expand the scope of analysis done with the same set of data. This, of course, should be paired with the appropriate privacy considerations. For example, the data should be anonymized. Generally, a balance must be struck between accessibility and ethics. The Open Data Institute has a useful framework for thinking about how different data should be governed and shared.
3) Focus on the right users
One set of users is the data-collectors at the head office of an NGO. There are M&E solutions that will make their lives easier. However, attention must also be given to the people in communities providing the data. We need to think about how to make their lives easier as well.
4) Think like a multinational tech corporation (and/or get their data)
These corporations do not sit there and think about how to extract the maximum amount of data, they consider how they can provide quality services that will attract customers. Most of their data is obtained through the provision of services. Similarly, the question here should be, “what M&E services can we provide and receive data as a byproduct?” Examples include: cash-transfers, health visits, app download & usage, and remote watch sensing.
These principles can help minimize the amount of effort spent on extracting data, alleviating the strain placed on those who provide the data, and staving of the end of days for a little longer.
Watch George’s Lightning Talk for some additional tips!