Data transformation converts data between formats, structures, or types to enhance usability. It enables data integration, cleansing, normalization, and enrichment, crucial for accurate analysis and informed decision-making across organizations.

Data management organizes, stores, and maintains data throughout its lifecycle. It improves data quality, ensures compliance, and streamlines operations. Key processes include collection, storage, processing, analysis, and visualization.

Data cleaning improves data quality by fixing errors and inconsistencies. It ensures accuracy, consistency, and reliability for better analysis and decision-making. Key techniques include removing duplicates, correcting values, and standardizing formats.

Data standardization establishes consistent formats and structures for data elements, ensuring uniformity across systems. It improves data quality, integration, and analysis, enabling better decision-making and operational efficiency in organizations.

Data modeling is a vital aspect of data management that visually represents data structures and relationships. It includes conceptual, logical, and physical models, ensuring data consistency, efficient database design, and improved communication among stakeholders for better decision-making.