The modern business world is plagued with a vast amount of information, making it challenging for businesses to remain competitive and relevant. Amid this wealth of data lies a daunting challenge: Raw data is often far from pristine, containing errors, inconsistencies, and missing information that can compromise the accuracy and reliability of analytical results. Data wrangling, an unsung hero of the data-science realm, quietly performs the essential task of cleaning, organizing, and preparing raw data for analysis. By automating repetitive tasks such as data cleaning, formatting, and transforming data, data wrangling increases efficiency, enables better decision making, reduces costs, enhances data integration, accelerates time-to-insight, and improves scalability. Effective data wrangling is critical in extracting value from data by ensuring its quality, accessibility, and relevance for decision-making purposes.