This article emphasizes the significance of data quality in data-driven strategies and highlights the consequences of implementing data warehousing scenarios that need more attention to data quality. It introduces the concept of data quality management and discusses the ISO/IEC 25012 standard’s fifteen data quality characteristics. The article explains the different stages of the data lifecycle and the varying quality requirements for each stage. It explores data quality assessment techniques, including objective measurements such as data profiling and integration and subjective techniques like crowdsourcing and surveys. The article also mentions data quality improvement techniques such as data cleansing, data enrichment, and machine learning-guided cleaning.