Health programs in low- and middle-income countries (LMICs) must make data-driven decisions to optimize implementation and outcomes. However, data for these programs are sparse and the data that do exist are often of limited quality. This talk will review the data sources most commonly available for program implementation and evaluation in LMICs, with a focus on sub-Saharan Africa. The talk will also discuss the opportunities and pitfalls of “borrowing” data from other contexts to fill in the information gaps, an approach commonly used in global health inference. Finally, the talk will conclude with recommendations to statisticians on how to best fill the information and methods void that currently exists in global health.