The article explores how classical data is encoded into quantum states for quantum machine learning (QML) workflows. It details three primary approaches: fully quantum models using inherently quantum data, quantum data processed with classical algorithms, and the dominant hybrid model combining classical data with quantum circuits. Basis encoding, mapping binary data directly to qubit states, is presented as the simplest method. The hybrid workflow, involving data encoding, quantum processing, and classical optimization, faces the core challenge of efficiently translating classical information into quantum states. Real-world applications are limited by noisy quantum hardware and the scarcity of pure quantum datasets, making hybrid models the practical choice despite theoretical advantages of fully quantum approaches.