Nuclear data uncertainties have proven to be crucial in many areas of nuclear science such as the reactor or isotopic calculations. Nevertheless, when it comes to fuel cycle simulations (which are nowadays including uncertainty analyses), their relevance is discussed. Although it has been reported that they have an impact on the inventories, it is not clear if their effect can be negligible when compared with uncertainties produced by other fuel cycle parameters and consequently, if nuclear data uncertainties have to be considered in the fuel cycle studies. In this work, this comparison has been addressed for an open fuel cycle. First, the nuclear data uncertainties contained in the covariance matrices have been propagated with a Monte Carlo methodology, which has been verified with an integral experiment. After that, the results have been compared with uncertainties typically taking part in fuel cycle simulations from two different perspectives: an unknown future fuel cycle with relatively large uncertainties and a past known fuel cycle with small uncertainties. Results show that even for the most conservative cases based on open fuel cycles using PWR technologies, the uncertainties due to nuclear data are larger or at least comparable to the fuel cycle uncertainties due to parameters. Henceforth, with the adoption of advanced nuclear fuel cycles using newer technologies, it is expected that nuclear data uncertainties will play even more relevance in fuel cycle analyses. With these results, it has been concluded that nuclear data cannot be disregarded in uncertainty assessments in fuel cycle studies.