Cities are increasingly understood as systems that can be managed and transformed through scientific methods of data analysis and automated response. According to Batty (2013), cities are ‘sets of actions, interactions, and transactions’ that can be known and mapped with great precision, and it follows that their constituent components can be rearranged to interact and transact in new ways. The most visible manifestation of the ‘science of cities’ is the so-called ‘smart city,’ in which cutting-edge technology ostensibly assimilates diverse streams of data in ‘real-time’ and this informs automated interventions. There has been a proliferation of smart city initiatives in recent years, which scholars have addressed in stark terms. Many scholars caution that smart cities could become urban panopticons that produce docile urban subjects and foster uneven development (Vanolo 2014; Datta 2015), while other scholars have been cautiously sanguine about the potential for progressive alternatives to emerge from within smart cities (Luque-Ayala and Marvin 2015; March 2016; McFarlane and Söderström 2017). With some notable exceptions (see Shelton, Zook, and Wiig 2015) scholarly analysis of smart cities tends to assume that they will function as intended, but there has been less attention paid to the data upon which they rely. The availability of data, its quality and format varies widely from city to city, and this poses a serious challenge to ongoing attempts to internationalize smart city technologies. In response, there is currently an expansive effort to standardize city data, management processes, and interoperability internationally, led by national standards organizations, global corporations and most notably the International Organization for Standardization (ISO).