Learning analytics and predictive analytics are being deployed in many post-secondary institutions, both as recruitment resources and as a basis for ongoing student support and guidance. Practices vary, but the underlying challenge of choosing the right data sets and analysis models is universal, as are some of the key issues: algorithmic bias, data privacy and security, and false positives. The Chinese authors are sensitive to these issues, but suggest that improving student completion and success outweigh the concerns.