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Four Downsides of Big Data

Downsides of Big Data
It is easy to get excited about all the new information we now have about the world’s development projects. Maps and tables, charts and graphs flood our inboxes with ‘big data.’ Most recently, AidData published a huge dataset on Tracking Chinese Aid to Africa. All the hype has caused some backlash, and rightfully so. Big data is still data and requires the same careful handling as any other dataset. This is not meant to dull enthusiasm or lessen the use of data. This is a precaution against the misuse and overgeneralization of big data. One size does not fit all, and overgeneralizations from large or small datasets can be dangerous. Here are Big Data’s 4 downsides found by practitioners and academics.

1) Big data is not a panacea. One size does not fit all. The dynamic nature of development projects means that many are time-place specific. While sweeping data collection projects can lead to better practices at high-level institutions, implementing policies based on improperly generalized data is bad policy and poor use of data.

2) Difficulties in filtering relevant information. Data from developing countries regarding health systems, political upheaval, natural disasters, etc. are most often reported by vulnerable people experiencing the event first hand. The sourcing of the data is often social media. Aside from possible problems with the validity of the data, the sheer amount of potential data is enormous. Key word searches across selected media yield thousands of data points which have to be carefully reviewed to filter for relevancy. The computer programs are simply not nuanced enough to pull out the differences between hate speech, for example, and slang (as shown in a study on mapping hate speech in twitter recently). Additionally, a parallel problem is availability of reliable and secure statistical processing. Unlike data processing for pharmaceutical companies, aid data processing is not backed by billions of dollars in profit.

3) Data exhaustion on the ground. By the time social scientists are through cleaning, manipulating, and making sense of the data, the situation on the ground has often changed. This is called “data exhaustion.” The big data collectors (UN, World Bank, USAID, AidData) are constantly playing catch up. This means that the people on the ground are not able to use the most up-to-date information. The use of social media has mitigated the delay; however, data extraction and implementation of policies based on data is a top-down approach that may not accord with the culture of the project or practical feasibility. For example, the best way to empower women according to big data analyses might be to get women into the work place allowing them independent incomes. The on-the-ground reality might be that they are already responsible for non-paid work, such as childcare or maintaining subsistence crops, which already takes up their whole day.

4) Validity of data is questionable. As indicated by the debates over the validity of AidData’s Tracking Chinese Aid to Africa, socially sourced data cannot be the only source of data to influence policy. Self-reporting has inherent “barriers, blindspots and biases.” For example, the information collected from the Arab Spring was based on self-reporting of goings-on. The outside world used information from texts, Tweets, Facebook and blog posts to analyze the situation.

These four potential downsides of big data all suggest the need for caution in using data to inform development policy.

– Katherine Zobre

Source: Relief Web
Photo: AidData

AidData and China’s Foreign Aid Policy

AidData and China's Foreign Aid Policy
In the past decade, China has committed at least $75 billion to aid and development in Africa. Since 2000, there has been up to 1,700 projects, and China’s commitment to development in Africa stands as one of the strongest of any donor country. Research in the U.S. has created a large public database of these projects, named AidData, in order to analyze China’s efforts.

While this ongoing data collection could create debate over China’s interests in Africa, it is clear that Chinese engagement in the continent strengthened infrastructure, energy generation, and supply and communications. The ability to measure this aid will allow for transparency in China’s aid processes and strategies. Chinese aid is performed through direct investment “without state involvement and NGO aid” so that there is no middleman and the money can go directly where it is needed. However, this makes it more difficult to track where the money goes, and how it is used.

Ghana, Nigeria, and Sudan are the biggest aid recipients, receiving a quarter of a trillion dollars over the past 10 years. As was earlier mentioned, the biggest priority for Chinese aid is infrastructure. This means that empowering women, providing food aid, and creating education systems rank much lower on the priority list. AidData has suggested that because these are areas that the West tends to focus on the most, China has taken a different route.

In spite of this reasoning, according to AidData, China has backed hundreds of health, transport, and agricultural projects. Doctors and teachers have been sent into Africa as well, while African students have been encouraged to study in China. Some insist that China is only interested in the continent for its natural resources, yet it is clear that China is interested in supporting Africa for the future.

– Sarah Rybak

Sources: The Guardian, ONE
Photo: China Daily