 Appeals to convenience can extend to other aspects of data-intensive analysis. Not all data are equally easy to digitally collect, disseminate and link through existing algorithms, which makes some data types and formats more convenient than others for computational analysis. For example, research databases often display the outputs of well-resourced labs within research traditions which deal with “tractable” data formats (such as “omics”). And indeed, the existing distribution of resources, infrastructure and skills determines high levels of inequality in the production, dissemination and use of big data for research. Big players with large financial and technical resources are leading the development and uptake of data analytics tools, leaving much publicly funded research around the world at the receiving end of innovation in this area. Contrary to popular depictions of the data revolution as harbinger of transparency, democracy and social equality, the digital divide between those who can access and use data technologies, and those who cannot, continues to widen. A result of such divides is the scarcity of data relating to certain subgroups and geographical locations, which again limits the comprehensiveness of available data resources.