The social sector aims to empower communities with tools and knowledge to effect change for themselves, because community-driven change is more likely to drive sustained impact than attempts to force change from the outside. This commitment should include data, which is increasingly essential for generating social impact. Today the effective implementation and continuous improvement of social programs all but requires the collection and analysis of data.
But all too often, social sector practitioners, including researchers, extract data from individuals, communities, and countries for their own purposes, and do not even make it available to them, let alone enable them to draw their own conclusions from it. With data flows the power to make informed decisions.
Recognizing Data Colonialism
In their 2019 book The Costs of Connection, Nick Couldry and Ulises Mejias apply the term “data colonialism” to the practice of extracting data in ways that repeat or mimic historic colonialist practices of extracting natural resources and reinforce the colonial paradigm of exerting decision-making authority over native peoples.
Some of the earliest references to data colonialism we have found come from Indigenous communities, who have long been fighting the profitable extraction and exploitation of local knowledge by others. Today, the Urban Indian Health Institute, a Seattle-based health-research center that serves American Indian and Native Alaskan communities, is one of the organizations that are working to “decolonize data, for indigenous people, by indigenous people.”
From our experience, data colonialism has at least three harmful consequences. First, it generates low-quality decisions. Because analysis happens without community involvement, data colonialism leads to conclusions that are often divorced from the reality on the ground and the rightful data owners’ perspectives and interests.
Second, it disempowers. Data colonialism removes decision-making power from the country or communities directly impacted.
Third, it misappropriates resources. Data analyses are often used to determine how and where money flows. Consequently, data colonialism cuts out the rightful data owners when funders and decision makers allocate capital and other resources.
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