Genomic data are widely available in the dairy industry and provide a cost-effective means of predicting genetic merit to inform selection decisions and increase genetic gains. As more dairy farms adapt genomic selection practices, dairy producers will soon have genomic data available on all of the animals within their herds. This is a very rich, but currently underutilized, source of information. Herdmates provide an excellent indication of how a selection candidate’s genetics will perform within a given herd, noting that herdmates often include close relatives that share a similar environment. The study objective was to evaluate the utility of incorporating herdmate data into genomic predictions in a data set comprised of 3303 Holsteins from one herd in Canada and 6 herds throughout the United States. Within-herd prediction accuracy was assessed for milk production and feed efficiency traits determined from genomic best linear unbiased prediction under 4 different scenarios. Scenario one did not include herdmates in the training population. Scenarios 2 through 4 included herdmates in the training population while scenarios 3 and 4 also included modeling of herd-specific marker effects. Leave-one-out cross validation was used to maximize the number of herdmates in the reference population in scenarios 2 through 4, while maintaining constant reference population size with scenario one. Results from the present study reveal the importance of incorporating herdmate data into genomic evaluations. Scenarios 2, 3, and 4 improved mean within-herd prediction accuracy across the 6 milk production and feed efficiency traits by 0.06 ± 0.01, 0.07 ± 0.01, and 0.08 ± 0.01, respectively, in comparison to scenario one which did not include herdmates in the training data. Herds with higher within-herd heritability and low genomic correlation with the remaining herds benefitted most from the inclusion of herdmate data.