This dissertation consists of two essays that examine issues related to data - how data is generated, used and monetized. In Chapter 1, I study how intermediaries such as Amazon and Google recommend products and services to consumers for which they receive compensation from the recommended sellers. Consumers will find these recommendations usefulonly if they are informative about the quality of the match between the sellers’ offerings and the consumer’s needs. The intermediary would like the consumer to purchase the product from the recommended seller, but is constrained because consumers need not follow the recommendation. I frame the intermediary’s problem as a mechanism design problem in which the mechanism designer cannot directly choose the outcome, but must encourage the consumer to choose the desired outcome. I show that in the optimal mechanism, the recommended seller has the largest non-negative virtual willingness to pay adjusted for the cost of persuasion. The optimal mechanism can be implemented via a handicap auction. I use this model to provide insights for current policy debates. In Chapter 2, in the joint work with Mallesh Pai and Rakesh Vohra, we propose a statistical test for identifying whether a policy or an algorithm is designed by a principal with discriminatory tastes. The test can be used for identifying, for example, whether predictive policing algorithms are discriminatory against minority neighborhoods. We also argue that the marginal outcome test (Becker (1993)), the most popular test of taste-based discrimination, fails for policies. We consider a canonical setup where the principal designs a policy (algorithm) that maps signals (data) to decisions for each group, such as whether to patrol or not for each area. The principal commits to the policy, which in turn affects agents’ incentives to take action, such as whether to commit a crime. In this environment, the marginal outcome test fails because the principal not only cares about the marginal benefitof catching a criminal but how patrolling changes agents’ incentive to commit a crime. We propose a new statistical test that deviates from the marginal outcome test precisely as much as the incentive effect.