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.
Pliable is revolutionizing data management with a universal protocol that allows anyone to curate, share, and consume data. Founded by Jason Raede and Kait Rikkers, Pliable streamlines data cleanup and organization, making critical business data accessible and actionable for non-technical users.
