Sensor data from automation are becoming available on an increasingly large scale. This leads to many challenges, but also new opportunities for assessing and using the genetic potential of dairy cattle. The first challenge is data quality as all uses of sensor data require careful data quality validation, potentially using external references. The second issue is data accessibility. Indeed sensor data generated from automatization often are designed to be available on farm in a given system. However to make these data useful for genetic improvement, the data also must be made available off-farm. By nature, sensor data often are very complex and diverse; therefore, a data consolidation and integration layer is required. Moreover, the traits we want to select have to be defined precisely when generated from these raw data. This approach obviously also is beneficial to limit the challenge of extreme high data volumes generated by sensors. An additional challenge is that sensors always will be deployed in a context of herd management; therefore, any efforts to make them useful should focus both on breeding and management. However, this challenge also leads to opportunities to use genomic predictions based on these novel data for breeding and management. Access to relevant phenotypes is crucial for every genomic evaluation system. The automatic generation of reference data is a major opportunity to get access to novel, precise, continuously updated and relevant data. If challenges of bi-directional data transfer between farms and external databases can be solved, new opportunities for continuous genomic evaluations integrating genotypes and the most current local phenotypes can be expected to appear. Several examples will be given to illustrate opportunities and challenges. In particular, illustrations based on fine milk composition involving on-farm sensors using on-line colorimetry or immune-assays, and also in-line infrared spectrometric solutions will be given.