Data and the systems that manage it are not neutral but, instead, are part of the process that affects how AI-based technologies work. Not all data and computer scientists, however, design technology to operate in the best interest of end users, from individuals to institutions, and ethical values may mean different things to data producers, consumers, and organizations. Designing and building good systems is a continuous process fundamentally intertwined with ethics data management. However, the ethical frameworks that should guide data gathering and the systems that manage it are spotty, subject to little oversight, few guidelines, and uneven monitoring and enforcement. Moreover, the complexities involved in large data aggregations, transformations, distribution, and reuse, and the limited capacity to validate ethical implications embedded in routine data practices make it difficult to track and prevent ethical breaches. We will investigate how data ethics can be a point of departure to designing and evaluating good systems. We will examine the contradictions and pressure points among various data practices.