As the volume of data grows exponentially, the challenge of maintaining privacy while extracting value becomes increasingly significant, leading to the rise of Privacy Enhancing Technologies (PETs). These technologies enable data scientists to analyze sensitive data without exposing it, ensuring compliance and privacy protection. The primary PETs discussed include homomorphic encryption, multiparty computation, differential privacy, federated learning, secure enclaves, and zero-knowledge proofs, each offering unique methods to process data securely. Additionally, synthetic and anonymized data are sometimes classified under PETs, though they come with the drawback of potential data devaluation due to the alteration of original inputs. These technologies, while varying in application and efficiency, collectively highlight the potential to unlock significant economic value from underutilized data by preserving privacy. As companies navigate these options, combining PETs can provide enhanced benefits, making them a strategic choice for maximizing data value while safeguarding privacy.