- Presentation – Bapu Vaitla
Digital data has great potential for helping fill the global gender data gap, but enthusiasm around using big data for public policymaking must be met with realistic understanding of inherent challenges and risks. This session will highlight new findings from five distinct research projects commissioned as part of Data2X’s Big Data for Gender global challenge, a call for proposals that spurred work on using big data to close the global gender data gap. The studies encompass a wide range of thematic and technical expertise related to gender, including urban transport and mobility, gender inequalities in health and education, and gender-based violence, investigated through social media, mobile phone, and geospatial data, often combined with traditional data sources such as official statistics.
This session will feature a moderated, interactive discussion with the audience on three topics: 1) the potential of big data to solve pressing gender policy problems; 2) how traditional data and big data “interact,” their complementarity and/or incompatibility; and 3) the dangers that big data poses to privacy, especially for women and girls, and ideas for how to address these challenges.
Following the panel discussion, representatives from national statistics offices in the countries where these big data pilot projects are taking place will reflect on the role of these projects within their national statistics systems, including the challenges and opportunities of integrating big data with official statistics.
This session will offer a balanced look at the “data revolution,” with a focus on its promise for closing gaps with respect to populations that are traditionally underrepresented in data systems—in this case, women and girls. It will temper the hype with realistic expectations and a frank discussion of big data’s risks; and will also address the anxiety over these risks by discussing a blueprint for managing the explosion of data in ways that benefit women and girls, especially those from marginalized communities.