Data Wrangling involves collecting, cleaning, and transforming raw data into structured formats suitable for analysis. Platforms automate tedious tasks like data normalization, deduplication, and error correction.
Businesses use data wrangling tools to integrate data from multiple sources, ensuring accuracy and consistency before analysis. With AI-driven capabilities, these platforms can detect patterns, suggest transformations, and accelerate preparation for reporting or machine learning models.
As organizations increasingly rely on data-driven decisions, data wrangling solutions are critical for improving efficiency, reducing errors, and enhancing analytical insights.
