For many years, higher education institutions have been contemplating solutions to unsatisfactory student success”. The issues include attrition rates, retention and throughput, content knowledge, fit for purpose, learning outcomes and time taken to graduate. Dealing with these issues is compounded by the increasing demands for accountability and efficiency, in particular, whether university expenditure is being used effectively to help students succeed. It is with this in mind that the field of data analytics has emerged quite recently with rapid growth and uptake in higher education institutions worldwide. Universities have realised the potential of the vast quantities of data produced through their information systems in addressing these strategic challenges in the current volatile educational landscape. While these data have been used retroactively to assess student success and progress, they also offer new possibilities which include better placement of students in optimal qualifications and early warning systems to identify and assist ‘At Risk’ students. Further, through analytics which go beyond descriptive statistics, institutions can find metrics to measure success and participation in higher education as well as new approaches to making decisions. The combination of embedded administrative and academic technologies, big data, powerful analytical tools, and sophisticated data-mining techniques is revolutionising education delivery and is making vast in-roads into measuring the efficacy of the education and is, at the same time driving and improving access and success. This theme calls upon papers which explore the application of data analytics to the current challenges facing higher education. Topics include: enrolment patterns, retention and graduation, tuition and fees, course taking patterns, and workloads.