This document discusses inconsistencies in big data. It begins by introducing the concept of big data and defining its key characteristics: volume, velocity, variety, variability, veracity, and complexity. It then examines different types of inconsistencies that can occur in big data, including temporal inconsistencies dealing with conflicting time-related information, spatial inconsistencies involving geometric or location-based data, and text inconsistencies found in unstructured language. The document also looks at inconsistencies relating to functional dependencies where certain attribute values should determine other attributes. It concludes by discussing how identifying and resolving inconsistencies can help improve big data analysis through techniques like inconsistency induced learning.