The Healthcare Industry has large volumes of data which is being generated every day, the healthcare industry has been trying to apply various tools to extract meaningful insights from the given data, but having that it is very much required that the quality of data which is being used should be as clean as possible without any errors. Data quality has an emergent structure. Therefore, to guarantee that sufficient levels of quality are maintained acceptably and transparently, the quality of clinical data should be continuously examined and appraised in an iterative approach. It identifies a prevailing issue wherein despite the vast generation of healthcare data, its utility remains hindered by prevalent errors and inconsistencies. Traditionally, data quality management has been perceived as a one-time endeavor, limited to pre-analysis data cleaning. However, the abstract introduces a novel approach advocating for continuous enhancement of data quality through iterative processes, thereby addressing the concept of “emergent data quality” wherein requirements evolve with newfound data applications. It implies leveraging visualization tools to detect and comprehend data quality issues, ultimately leading to improved analysis and outcomes. By prioritizing continuous improvement and adaptation to evolving needs, the abstract suggests that healthcare can unlock the full potential of its data for enhancing patient care and pioneering research, offering a fresh perspective on data quality management in the healthcare domain.