We live in a world where activities and interactions are recorded as data: food consumption, workout activities, buying and selling products, sharing information and experiences, borrowing and lending money, and exchanging excess resources. Scientists use the rich data of these activities to understand human social behavior, generate accurate predictions, and make policy recommendations. Data science and machine learning traditionally take such data as given, often treating them as independent samples from some unknown statistical distribution. However, such data are possessed or generated by potentially strategic people in the context of specific interaction rules. Hence, what data become available depends on the interaction rules. For example, people with sensitive medical conditions may not reveal their medical data in a survey but could be willing to share them when compensated; crowd workers may not put in a good-faith effort in completing a task if they know that the requester cannot verify the quality of their contributions. In this talk, I argue that a holistic view that jointly considers data acquisition and learning is important. I will discuss a few projects where the joint consideration of incentives for eliciting data from strategic people and objectives of learning algorithms leads to better outcomes for both elicitation and learning.