The success of artificial intelligence (AI) models depends heavily on the quality of the data used to train them. Poor data quality can lead to degraded model performance and loss of customer trust in applications built using these models. Anomaly detection is a critical component of AI, as it enables the identification of unusual behaviors that do not align with expected outcomes. There are two types of anomalies: intentional and unintentional. Intentional anomalies occur due to planned actions or specific events, while unintentional anomalies arise from noise and errors in data. Time series data can exhibit point-based, collective, or contextual anomalies. Anomaly detection has a wide range of applications across industries, including finance, manufacturing, healthcare, and cybersecurity. Various techniques exist for anomaly detection, such as statistical methods, machine learning approaches, and deep learning methods. Building an effective anomaly detection pipeline requires identifying objectives, defining expectations, collecting data, preprocessing data, selecting models, training models, evaluating performance, refining models, and addressing challenges like data quality, training size, imbalanced distributions, and false positives. Encord is a specialized tool that can help address these challenges through its features for data management, labeling, and evaluation.