Data manipulation: What it is, types & 6 best practices

Data manipulation is a crucial process in transforming raw data into actionable insights, essential for understanding customers and making informed business decisions. It involves various techniques to derive insights without altering the original data values, such as data mapping, aggregation, and enrichment, which help merge databases and add context to data points. Unlike data modification, which involves changing the actual data entries to ensure accuracy and cleanliness, data manipulation uses structured data and tools like SQL or visualization software to perform calculations and generate reports. Key manipulation techniques include filtering, sorting, aggregation, and calculations, each serving distinct purposes like refining databases for specific results or organizing data for better functionality. Understanding these processes enables analysts to maintain databases effectively, handle data ethically, and present insights clearly to diverse audiences.