Official statisticians around the world are faced with the herculean task of populating the Sustainable Development Goals global indicator framework. As traditional data sources appear to be insufficient, statisticians are naturally considering whether big data can contribute anything useful. While the statistical possibilities appear to be theoretically endless, in practice big data also present some enormous challenges and potential pitfalls: legal; ethical; technical; and reputational. This paper examines the opportunities and challenges presented by big data for compiling indicators to support Agenda 2030.

The papers in this special issue provide accounts of the politics and knowledge that shaped the Sustainable Development Goals (SDGs). The open and transparent processes in the Open Working Group (OWG) and Post?2015 agenda consultations challenged the MDG paradigm and set more transformative and ambitious goals. But across many goals, there was slippage in ambition when targets and indicators were selected. In some cases, this is due to genuine difficulty in defining a suitable indicator. In other cases, there is clearly a contestation about the agenda, and indicators are used to reorient or pervert the meaning of the goal. The accounts of the negotiations– concerning inequality, sustainable agriculture, access to justice, education, environment – show how the selection of an indicator is purportedly a technical matter but is highly political, though obscured behind the veil of an objective and technical choice. The papers also highlight how the increasing role of big data and other non?traditional sources of data is altering data production, dissemination and use, and fundamentally altering the epistemology of information and knowledge. This raises questions about ‘data for whom and for what’ – fundamental issues concerning the power of data to shape knowledge, the democratic governance of SDG indicators and of knowledge for development overall.