Success of many machine learning and offline measurement efforts is highly dependent on the quality of labeled data that they use. Development of supervised machine learning models and quantitative research rely on the assumption of annotation obtained through human reviewers being “ground truth”. Annotation quality issues result in violation of this assumption and corrupt quality of all the downstream work and analysis. Through a series of analyses we have identified a highly pressing need for development of a quality framework that will allow creation of a robust system of label quality monitoring and improvement. In this paper we will present an overview of the Accuracy, Credibility, and Consistency (ACC) framework, which consists of three elements: (1) understanding of what annotation quality is and what metrics are required to be tracked (2) implementation of the concepts and measurements and (3) intervention protocols for identified annotation quality issues.