Qualitative Data is Data too! : Exploring assumptions about what counts as credible and “scientific” data

By: Bangirana Albert Billy, University of KwaZulu-Natal

Today I’d like to contribute to a conversation opened up earlier this week in published CIHA Blog post by Halimatou Hima on the pervasive assumption that “there is no data in Africa.” I extend Hima’s point–that (of course) there is data in Africa– to also ask researchers to reflect on what type of data is considered “data.” I have encountered similar narratives as raised by Hima and this post builds on these experiences to point out a few of the underlying assumptions about what is credible, scientific data.

First, the lack of “data” in Africa narrative is not limited to all data but often to specifically large datasets drawn from extensive “scientific” studies. The drive for big data points to the desire of big “representative” datasets for policy development, medical purposes or other governance agendas as most, if not all large data funding is controlled and targeted. This is echoed by recent social studies of science which have investigated how data is co-produced and performed (Tichenor 2017; Biruk 2018). For example, Tichenor (2017) argues that the processes of data collection and synthesis of malaria data in Senegal maintains the model that funding agencies construct and reifies both the definitions of health problems and the power relations embedded within global health flows of capital, technology, and knowledge.

The funding incentives and privileging of particular kinds of data has affected the quality and volume of data generated with results and statistics sometimes being manipulated for selfish reasons. Some governments and research institutions have not been genuine on this issue and this has rendered some of the available data-sets from the continent questionable.

Second, we must also talk about the bias against qualitative data. The “scientific” world largely favours quantitative data to qualitative data, even when both are mutually important. This bias can disadvantage African research since the continent has a long history of oral tradition with many of the indigenous knowledge resources in oral or hieroglyphic codes. These forms in and of themselves are not deemed sufficient by western standards and therefore often privilege a Western researcher who can render these “indigenous” forms of knowledge understandable to global (Western) audiences (thereby receiving accolades from the scientific academy for “discovering” new knowledge). It is important to acknowledge that indeed there IS (and has always been) an incredible wealth of data in/on/from Africa if we broaden our definition of what counts as “data.” But if we do this (broaden our definition of data), we must also be wary of who can and should benefit from this data, putting in place locally contextualized measures to manage, own and store this information and data. Extending the questions suggested for researchers by Hima, communities should also have the right to refuse to be turned into data or refuse for their data to be collected and made public by researchers, carefully reviewing for whom and what agendas the “data” will be in the service of.

Finally, I am of the view that Africans should spearhead these studies as custodians of their own scientific history and progressive reality in order to systematically and coherently manage, learn from and contribute back to this data for the future of the continent. More locally based research done by communities could help to correct for many of the uninformed prejudices that emerge from Western-based scholarship as well as opening up spaces for robust research from African contexts that provide leadership for research work within and beyond the continent.

Featured image source: Mail and Guardian (Simon Allison). Image and blog post edited by Angela Okune.