The amount of data collected on dairy farms will expand exponentially with computerized milking systems and sensors, including cameras and scene recognition algorithms to capture animal behavior. In addition, the volume of data generated by various organizations such as milk recording (DHI) and genetic evaluation organisations, veterinary clinics and feed suppliers will keep increasing. For example, mid-infrared analysis of milk samples produces data points for more than 1000 wavelengths, allowing for new information such as fatty acid profiles. Building data pipelines between these silos and connecting them to on-farm sensing devices data has a potential to improve herd performance and sustainability. Although redundant to a certain extent, all sources can contribute on their own. For example, spectral signatures of bulk tank milk samples provide precise herd information every other day, while milk recording leads to detailed data on each cow about 10 times per year. On-farm devices produce data in a less controlled way but at a much higher frequency, increasing considerably the volume of phenotypic information. Once integrated, all this data has the potential to improve relevant traits (e.g., feed efficiency, milk quality, and animal health and welfare) while improving labor efficiency, decreasing environmental footprint, and increasing ROI on digital investments. Data integration can also lead to new benchmarks, research, and innovation, large-scale monitoring such as health issues, and transparency for consumers. However, with so much data, producers will need assistance and guidance toward the most critical aspects of their business to focus on. This can be achieved through advanced analytics. First, farm specific, highly dimensional predictive algorithms based on machine learning will detect and diagnose anomalies, weaknesses, or opportunities for process improvement. The models and their predictions will need to be explainable. Second, and even more challenging, prescriptive algorithms using artificial intelligence techniques will recommend ways to remediate problems or improve upon a situation. Additional challenges for the dairy industry will include the adoption of advanced computational platform to process massive data sets, and protocols for cybersecurity and ethical data handling.