Differential privacy significantly impacts data-driven decision-making by enabling organisations to use sensitive data for analysis while respecting individual privacy. This is particularly important in fields like healthcare, finance, and public policy, where decisions are increasingly driven by data analysis but involve sensitive personal information. By applying differential privacy, organisations can derive insights from their data without risking individual privacy breaches. This not only helps in complying with privacy laws but also builds public trust. However, the added noise for privacy protection means that the results of data analysis might be less precise, which can affect decision-making processes. Organisations must be aware of this trade-off and adjust their strategies accordingly. Despite this, the benefits of being able to use large datasets for analysis without compromising privacy often outweigh the drawbacks of slightly reduced accuracy. Furthermore, differential privacy fosters innovation by encouraging the sharing and use of data that might otherwise be too sensitive to analyse. This can lead to more informed, data-driven decisions across various sectors, ultimately benefiting society as a whole.