Data Integration and Analytics in the Dairy Industry: Challenges and Pathways Forward
Víctor E. Cabrera, J.M. Bewley, Mitch Breunig, Tom Breunig, Walt Cooley, Albert De Vries, R.H. Fourdraine, J.O. Giordano, Yijing Gong, Randall Greenfield, Haowen Hu, Andy Lenkaitis, Mutian Niu, Eduardo A. F. Noronha, Michael B. Sullivan
Abstract
The dairy industry faces significant challenges in data integration and analysis, which are critical for informed decision-making, operational optimization, and sustainability. Data integration-combining data from diverse sources, such as herd management systems, sensors, and diagnostics-remains difficult due to the lack of standardization, infrastructure barriers, and proprietary concerns. This commentary explores these issues based on insights from a multidisciplinary group of stakeholders, including industry experts, researchers, and practitioners. Key challenges discussed include the absence of a national animal identification system in the US, high IT resource costs, reluctance to share data due to competitive disadvantages, and differences in global data handling practices. Proposed pathways forward include developing comprehensive data integration guidelines, enhancing farmer awareness through training programs, and fostering collaboration across industry, academia, and technology providers. Additional recommendations involve improving data exchange standards, addressing interoperability issues, and leveraging advanced technologies, such as artificial intelligence and cloud computing. Emphasis is placed on localized data integration solutions for farm-level benefits and broader research applications to advance sustainability, traceability, and profitability within the dairy supply chain. These outcomes provide a foundation for achieving streamlined data systems, enabling actionable insights, and fostering innovation in the dairy industry.