Do you know that IBM’s bad data cost around $3.1 trillion dollars every year? Such a big loss it is! It’s all because of data inaccuracies, which clarify how precious high-quality data is. Therefore, it’s a must to identify, segment, and fix typos, and duplicates, and fill in missing details so that data analysts can draw feasible strategies or business ideas. Let’s talk about the most common data quality issues that are no less than big challenges. Most Common Data Quality Issues • Segmenting Semi-Structured and Unstructured Data Fortunately, we have technologies and data management tools that make it easier to create a centralized database. But, this fortunate value for nothing when data warehouses or servers prove inefficient in effectively dealing with relational datasets. It’s because of different data qualities, which can be good and bad, structured and unstructured big data. So, data managers should emphasize the structuring of unstructure