Data Integration and Analytics in the Dairy Industry: Challenges and Pathways Forward (2025)

3.1.1. Importance of Data Integration for Actionable Insights

The integration of diverse data sources is essential for deriving actionable insights and making informed decisions on dairy farms (Figure 1). Effective data integration combines data from herd management software, sensors, genetic evaluations, milk analysis and diagnostics laboratories, and feeding systems, enabling farmers to implement prescriptive tools that enhance productivity, health, animal welfare, profitability, and environmental sustainability. For example, farmers can use heat sensor data to identify which cows to breed or leverage somatic cell count (SCC) data from laboratories to detect mastitis and prioritize treatment. These practical applications demonstrate how integrated data supports daily herd management decision-making processes.

The data integration process involves several critical steps to ensure usability and consistency (Figure 1; [9]). Data decoding translates raw data into structured formats that are compatible with analytical tools. Data cleaning eliminates errors, duplicates, and inconsistencies, ensuring the integrity of datasets. Once cleaned, data homogenization standardizes units, formats, and terminologies across diverse sources, enabling seamless integration. This is followed by data standardization, which aligns metadata, coding systems, and frameworks to ensure compatibility across platforms. Finally, data aggregation consolidates multiple datasets, such as feed intake, milk production, and health records, into a unified repository, providing a comprehensive foundation for advanced analysis and decision-making. These steps collectively enhance the reliability, accessibility, and utility of data in dairy operations [9].

This holistic approach helps in identifying patterns and trends, leading to more efficient farm management practices. While challenges such as non-uniform data formats exist, systematic integration steps, like cleaning and standardization, can provide actionable insights, driving informed decision-making and operational efficiencies. Further complicating the issue, these data sources are often stored on different computers or systems and are not readily available online, making integration efforts even more difficult.

International cooperation is crucial in achieving seamless data integration. Global standardization efforts, exemplified by the International Committee for Animal Recording (ICAR; https://icar.org accessed on 15 December 2024) and AgGateway (https://aggateway.org accessed on 15 December 2024) provide foundational tools to enhance interoperability and improve data-sharing capabilities across systems [10]. These standards facilitate interoperability between different systems, allowing for the aggregation of data from various sources.

To deepen our understanding of global data standardization efforts and explore how these frameworks could enhance data integration in the dairy industry, we invited representatives from two leading organizations: ICAR and AgGateway. Both organizations presented their ongoing work in creating standards for data exchange, discussed their approaches to addressing interoperability challenges, and engaged in a Q&A session to clarify the applicability of their initiatives within the dairy sector. Their insights provided valuable context for our exploration of data integration solutions and highlighted potential pathways for collaboration.

The presentations highlighted the role of international organizations like ICAR and AgGateway in fostering standardized data exchange across agricultural sectors. ICAR’s work in developing independent guidelines and certification services, including animal identification and milk analysis, supports data standardization, which is essential for interoperability. AgGateway complements these efforts by facilitating data standards specifically for digital agriculture and providing resources like the Agricultural Data Application Programming Toolkit (ADAPT) for field operations and supply chain messaging. Together, these organizations help bridge gaps between proprietary systems, fostering collaboration and setting the foundation for effective data integration.

One major challenge is the differing business rules regarding the interpretation of data across regions, particularly between Europe and the US. These differences affect the applicability of data integration solutions and require careful consideration when developing global standards. For instance, European data handling practices often differ from those in the US, necessitating adaptations to ensure compatibility. For example, differences in date formats, such as mm/dd/yyyy used in the US versus dd/mm/yyyy commonly used in Europe, can lead to errors or misinterpretations when integrating data from these regions.

Another key aspect of data integration is its role in optimizing various operational aspects, such as feed management, milk production, and animal health monitoring. By integrating data from these diverse sources, farmers can gain comprehensive insights into their operations, enabling them to make more informed decisions. This integration supports the development of prescriptive tools that not only predict outcomes but also provide actionable recommendations, thereby improving overall farm efficiency. Furthermore, data integration can significantly enhance animal welfare, reproduction performance, and health. By combining health and production data, farmers can better monitor and manage the well-being of their herds. This integration allows for early detection of health issues, timely interventions, and continuous monitoring, ultimately leading to improved animal health and productivity.

The discussion also emphasized the importance of addressing technical challenges associated with data integration. These include ensuring data quality, managing large volumes of data, and maintaining data security and privacy. Internet connectivity was identified as a critical challenge, particularly in the US, where inconsistent or limited internet access can cause disruptions in data exchange. These breakdowns hinder real-time communication between systems, leading to inefficiencies and delays in managing day-to-day farm operations. The group highlighted the need for robust data management systems that can handle these challenges while providing reliable and accurate data for decision-making, even in low-connectivity environments.

Moreover, the potential for leveraging advanced technologies, such as artificial intelligence (AI) and machine learning (ML), through data integration was discussed. These technologies have the potential to enhance data analysis capabilities, providing deeper insights and predictive analytics that can further optimize farm operations. AI and ML can help in processing large datasets, identifying complex patterns, and generating actionable insights that are beyond the reach of traditional analytical methods.

In summary, the integration of diverse data sources is a critical factor in driving actionable insights and informed decision-making in dairy farming. While significant challenges exist, international cooperation, standardization efforts, and the adoption of advanced technologies can help overcome these barriers. By addressing these challenges, the dairy industry can harness the full potential of data integration to enhance productivity, profitability, animal welfare, and environmental sustainability. Figure 2 depicts the challenges, proposed solutions, and potential outcomes from the group discussions.

3.1.2. Challenges in Data Integration

• Challenges in Data Exchange and Standardization

Discussions highlighted the difficulties in resolving technical and business-related issues in data exchange. The importance of standardization and data definition to improve data transfer was stressed. One of the primary challenges is the lack of standardized data formats and protocols across different systems. The group emphasized the need for global data exchange standards, referencing ongoing efforts by ICAR and AgGateway. Differences in data handling and interpretation across countries, particularly between Europe and the US, present unique challenges that must be addressed in data integration solutions.

This lack of standardization makes it difficult to integrate data from various sources, such as herd management software, milking parlor, sensors, and feeding systems. Different data systems often use proprietary formats, making interoperability a significant hurdle. The group emphasized the need for data exchange standards within the dairy industry, particularly noting the absence of a national animal identification system in the US. This lack of standardization makes it difficult to connect data across different systems.

In contrast, European countries and Canada benefit from having unique animal identifiers, which facilitate better data integration. These systems are mandatory, ensuring that animals are identified either at birth or prior to leaving the farm. Additionally, they include ID validation capabilities that help maintain data accuracy and traceability—features currently lacking in the US. Implementing similar systems in the US could significantly improve data connectivity, reduce errors, and streamline integration across platforms.

Additionally, there is an ongoing international effort through organizations like ICAR, involving manufacturers, maintenance, repair, and operations (MROs), and data record processing centers (DRPCs), to develop data exchange standards that enhance global interoperability. Differences in data handling and interpretation across countries present unique challenges. For example, variations in breeding practices and the (lack of) recording of dry dates between the US, Europe, and Canada highlight the need to incorporate these nuances in data integration solutions. Addressing these differences is crucial for developing robust and effective data integration frameworks that can be applied globally.

One major challenge discussed was the inconsistency in data standards and the lack of harmonization between regions [11]. For example, ICAR’s initiatives to standardize milk analysis and animal identification are crucial steps, but adoption varies by region. ICAR is widely recognized as a global leader in setting standards for data integration, particularly in the dairy industry (e.g., Animal Data Exchange Working Group; https://www.icar.org/index.php/technical-bodies/working-groups/animal-data-exchange-wg/ accessed on 15 December 2024), while AgGateway focuses more on data interoperability frameworks primarily in North America. However, there is currently no clear global authority or certification system that ensures compliance with these standards, adding to the complexity. Third-party verification mechanisms for data exchange could play a vital role in ensuring that companies follow established protocols and improve trust among stakeholders.

AgGateway’s data standards (AgGateway ADAPT Standard; https://adaptstandard.org/ accessed on 16 December 2024) also illustrate the difficulties in achieving industry-wide compliance, especially given that some standards apply primarily to North America. Ease of implementation remains a concern, as companies may face challenges in adopting these frameworks due to costs, technical expertise, or conflicting business priorities. This lack of harmonization across global dairy sectors limits data interoperability and complicates integration efforts. The inclusion of clear certification systems and improved resources to support adoption would help address these gaps, encouraging broader compliance.

• Data Granularity and Usability

The level of data granularity required varies depending on the application. For research purposes, highly granular data is critical as it allows for detailed analysis of specific variables and conditions, such as individual cow behaviors or minute-by-minute environmental changes. This granularity helps develop insights, refine models, and validate hypotheses with high accuracy. In contrast, farm management often benefits more from aggregated data that highlights actionable insights, such as average milk yield, overall herd health, or the identification of individuals or groups of cows in need of interventions, to make quick and effective decisions. Aggregated data is more manageable and directly applicable to optimizing daily operations.

Balancing the need for detailed data in research and simplified data in farm management is essential. Systems should be flexible enough to capture and store detailed data while also providing tools to aggregate and simplify them as needed. This ensures both researchers and farm managers can access data in the most useful form. Additionally, the cost and resource implications of data granularity must be considered, as collecting and managing highly granular data can be resource-intensive. Therefore, the benefits of detailed data must be weighed against the costs and feasibility of managing them.

• Data Ownership and Privacy

Concerns about data ownership, privacy, and monetization are critical challenges in facilitating data sharing. While companies may appear willing to share data, they often perceive limited value or face competitive disadvantages due to intellectual property concerns. This reluctance is especially evident with granular data, which include highly specific insights like individual cow performance metrics or sensor readings. Developing standardized contracts and agreements can address these issues by protecting stakeholders’ interests and clarifying data ownership, usage rights, and associated costs. Furthermore, ensuring that farmers retain control over their data and are adequately compensated for their use is essential to bridge the gap between the needs of academic research and commercial priorities.

• Infrastructure Barriers

Infrastructure barriers, including varying levels of IT infrastructure and connectivity, pose significant challenges. Computing devices, such as edge computing systems and central servers, are essential but require significant investment and expertise. In the end, each integration between two data sources inherently entails programming time which has a real cost at multiple levels. Also, whereas these devices generate valuable data, integrating these data into a cohesive system requires addressing compatibility and standardization issues. Network infrastructure, including reliable routers and switches, is crucial for seamless data transfer but can be difficult to establish and maintain, especially in rural areas. Additionally, firewalls are increasingly being implemented to secure systems, but they often create unintended barriers to data transfer. Larger dairy operations, in particular, may rely on local IT providers unfamiliar with the specific requirements of interconnected farm systems. This lack of expertise can result in breakdowns in data exchange, further complicating integration efforts. Backup power supply systems are also needed to prevent data loss and transfer during power outages. While cloud-based storage and computing offer benefits for data management and utilization, reliance on cloud solutions can introduce risks, such as data accessibility issues during internet outages, and cyber-attacks. Cybersecurity challenges further compound these infrastructure issues, as farm systems often lack robust defenses against cyberattacks, putting sensitive data and operational continuity at risk.

• Interoperability Issues

Different data systems often use proprietary formats, making interoperability a significant hurdle. The need for data exchange standards within the dairy industry was emphasized. This lack of standardization makes it difficult to connect data across different systems. Developing APIs (Application Programming Interfaces) and adopting common data exchange standards can mitigate this issue. Value propositions and incentives for data sharing throughout the value chain are required. The use of middleware solutions to bridge gaps between disparate systems was also suggested. Additionally, differences in how data are applied present another challenge. Business rules regarding data use can vary significantly between regions. For example, common practices in the US may not exist in Europe, where most major manufacturers are based. Interoperability issues arise when European manufacturers of milking robots, sensors, and monitoring systems, such as those for cow morphological features, body condition scoring, or rumination behavior, use proprietary data formats or communication protocols that are incompatible with US herd management software. These manufacturers often apply business rules tailored to European dairy management, causing compatibility issues in the US.

Furthermore, these challenges extend beyond the US and Europe, as data handling practices vary widely in regions such as Asia, Oceania, and Latin America. Differences in regulatory frameworks, infrastructure development, and the adoption of technological standards create additional barriers to achieving global data integration. Addressing these disparities will require international collaboration to ensure that standards and tools are flexible and adaptable to regional contexts.

One key issue identified by AgGateway is the difficulty of achieving consistency in data terminology and format, which often varies by region and even by manufacturer. For example, AgGateway pointed out that different software platforms may use unique terms or measurement units for the same data points, creating confusion and hindering effective data sharing. The organization’s efforts in developing a common data framework, such as through its ADAPT and AGIIS (ag Industry Identification System) tools, are steps towards resolving these issues by offering a standardized “language” for data that can be used across systems.

• Cost and Resource Allocation

Developing an in-house system to integrate data often requires substantial IT resources, which can be a significant barrier for many stakeholders, including researchers, smaller companies, and farms, particularly small to mid-sized operations. The high costs associated with hardware, software, and skilled personnel can be prohibitive, limiting the ability to leverage integrated data systems for improved decision-making and operational efficiency. While larger farms and companies may have the capacity to invest in these systems, smaller entities often lack the financial and technical resources to develop and maintain such solutions independently.

Even large companies may be reluctant to invest in development due to unclear or delayed return on investment. Despite the potential long-term benefits, the uncertainty surrounding immediate financial gains makes them hesitant to allocate resources to such development. This reluctance is often compounded by the need for ongoing maintenance and upgrades, further increasing the total cost of ownership. As a result, the adoption of integrated data systems remains challenging across the industry, necessitating alternative solutions such as cost-sharing models, government subsidies, or collaborative industry efforts to make these technologies more accessible.

3.2.1. Enhancing Collaboration and Developing Comprehensive Guidelines

The primary recommendation is to develop comprehensive guidelines for data integration and analysis. These guidelines should address:

Standardized protocols can reduce the need for customized interfaces and facilitate smoother data exchange across different systems. Implementing a national animal identification system can significantly enhance data integration. A unique identifier for each animal can enable seamless data tracking across various systems, improving data accuracy and connectivity.

The discussion emphasized the importance of developing global compliance and standards for data integration and analysis in the dairy industry. The adoption of standardized data exchange frameworks, like those developed by ICAR and AgGateway, exemplifies how cross-industry collaboration can address key integration challenges. However, the existence of multiple standards can create confusion and hinder progress. The group noted the need for one clear governing body, such as ICAR, to lead these efforts, with organizations like AgGateway potentially aligning their frameworks with ICAR’s standards. Harmonizing data protection laws and regulations across regions can ensure consistent data management practices and facilitate international collaboration. Addressing legal and ethical considerations is critical for successful data integration, as these frameworks help safeguard stakeholder interests and foster trust among industry participants.

Effective data integration requires collaboration among various stakeholders. The discussion included several approaches to foster collaboration. Forming industry consortia and working groups can bring together stakeholders to develop and implement data integration standards. These groups can facilitate knowledge sharing, coordinate efforts, and advocate for common interests. Leveraging the expertise and resources of different stakeholders can accelerate progress. Public–private partnerships can provide funding, technical expertise, and policy support for data integration initiatives. Government agencies, industry associations, and academic institutions can collaborate on projects that benefit the entire dairy sector. Ensuring that these partnerships are transparent and inclusive is essential for their success.

There is a need for clear guidelines and agreements on data ownership, usage rights, data exchange costs, and privacy to facilitate data sharing. The discussion underscored the necessity of addressing data monetization and ensuring farmers are aware of the costs associated with data exchange. Developing standardized contracts and agreements that outline these aspects can facilitate data sharing while protecting stakeholders’ interests. The group proposed leveraging ICAR and AgGateway standards to enhance consistency and encourage broader adoption. Ensuring that farmers retain control over their data and are adequately compensated for their use is crucial. Additionally, the monetization of data exchanges needs to be addressed. While farmers should control who can access their data, they often have no control over the costs associated with data exchange, ultimately bearing these fees.

To address these challenges, both ICAR and AgGateway emphasized the need for collaboration across manufacturers, technology providers, and data platforms. ICAR is working on creating unique identifiers for devices, which will allow data from different manufacturers to be traced back to their source, helping researchers and farmers alike to standardize analyses. AgGateway also supports collaborative initiatives through projects like AGUS (AgGateway Unique Identifier System), a repository for unique identifiers, which promotes consistency across the supply chain. Such initiatives aim to make data integration more manageable and enable interoperability across systems.

Despite the competitive nature of the dairy industry, international cooperation with regard to data integration is both necessary and feasible. Organizations such as ICAR and AgGateway have already demonstrated success in creating standardized frameworks that enhance interoperability without compromising proprietary information. These standards allow companies and nations to innovate while ensuring a level playing field, particularly in areas of global concern such as sustainability, food safety, and climate change mitigation. Collaborative efforts on data integration can drive mutual benefits, including improved traceability, efficiency, and market access, while maintaining competitive boundaries.

3.2.2. Enhancing Farmer Awareness and Training

Empowering farmers with knowledge about data integration and its benefits is crucial. Partnerships with universities were suggested to promote data analytics workshops for dairy farm owners and managers. Clear language should accompany equipment purchases, detailing data sharing policies and potential costs. Training programs and awareness campaigns can help farmers understand the value of integrated data systems and how to leverage them for improved decision-making. Partnerships with universities to promote data analytics workshops for dairy farm owners, managers, and consultants can further enhance their understanding and skills in this area. Additionally, dairy farmers need clear language accompanying the equipment they purchase, outlining how data can be shared, with whom, pass-through sharing of data, associated costs, and other relevant details. Many farmers are unaware of these aspects and often find out only after making their purchase.

Data security has emerged as a critical concern in the dairy industry, particularly as advanced technologies become more integrated into farm operations. Real-world incidents, such as the cyberattack of a milking robot in Switzerland, demonstrate the vulnerabilities faced by both large and small-scale operations. To address these risks, farmers must be equipped with practical cybersecurity knowledge and tools. Suggested strategies include adopting secure data transfer protocols, implementing firewalls, using reliable authentication methods, and ensuring regular software updates. Additionally, collaborative efforts between technology providers, industry associations, and academic institutions can help create affordable and accessible cybersecurity solutions tailored to the needs of dairy farmers.

The cooperation between farmers and dairy processors or cooperatives plays a pivotal role in effective data management. Farmers often have the greatest trust in their dairies, especially in cooperative structures where they are co-owners. Dairy processors or cooperatives can leverage this trust to offer targeted support in adopting and managing technical solutions. This includes providing advice on purchasing equipment and software, organizing training programs on their operation, and guaranteeing ongoing technical assistance.

3.2.3. Encouraging Collaborative Efforts, Funding Alternatives, and User-Friendliness

Collaboration among stakeholders—farmers, industry players, academics, and policymakers—is vital. Creating platforms (and open access platforms) for ongoing dialogue and cooperation can help address emerging issues and refine data management practices.

Exploring cost-sharing models and funding opportunities to support data integration projects could alleviate this burden. Additionally, developing user-friendly tools that do not require extensive IT expertise can make data integration more accessible. The group emphasized the importance of collaborative efforts in addressing data integration challenges. International efforts, such as those by ICAR and AgGateway, were highlighted as models for establishing effective data exchange standards. However, the lack of a clear authority in this field remains a significant barrier, as it creates uncertainty and inefficiencies in standardization efforts. The group explored the idea of collaborating with ICAR to minimize duplication and maximize expertise. They discussed the distinction between technical and global issues addressed by ICAR and considered aligning efforts while maintaining valuable contributions.

3.2.4. Localized Data Integration

Data integration could be separated into two levels: local integration for individual farms and global integration for researchers. The complexity of data integration in the dairy industry stems from several interconnected factors. Local integration, such as the Dairy Brain example, could focus on specific management strategies, whereas global integration could address broader research goals. Focusing on localized integration at the farm level can provide immediate benefits. By integrating data from various on-farm systems, farmers can gain valuable insights into operations, such as feed optimization, milk production, and animal health. This localized approach can also serve as a model for broader industry-wide integration efforts.

3.2.5. Leveraging Advanced Technologies

Advanced technologies, such as the Internet of Things (IoT) devices, artificial intelligence (AI), and cloud computing, can facilitate data integration. However, it is crucial to balance the benefits of these technologies with considerations of cost, data accessibility, and security. Hybrid systems that combine local and cloud-based storage may offer a practical solution. IoT devices, such as sensors and smart tags, are increasingly used in dairy farming to monitor various parameters, including animal health, milk production, and environmental conditions.

AI and machine learning (ML) algorithms can enhance data analysis, providing deeper insights and predictive capabilities. Developing AI/ML models tailored to dairy farming can optimize operations and improve decision-making. Ensuring these models are transparent and interpretable is crucial for gaining farmer trust and adoption. The rapid advancement of technology presents both opportunities and challenges for data integration in the dairy industry.

The effectiveness of traditional statistical methods versus AI in data analysis was debated. The importance of understanding the problem before choosing a method was highlighted, along with the challenges of working with massive amounts of data and the potential need for machine learning.

3.2.6. Role of Standardization Initiatives

Standardization initiatives, such as those by ICAR and AgGateway, play a pivotal role in addressing data integration challenges in the dairy industry. ICAR has developed independent guidelines and certification services, including animal identification and milk analysis, to foster interoperability and consistency across platforms. Similarly, AgGateway promotes data standards tailored to digital agriculture and provides tools like the ADAPT toolkit for streamlining field operations and supply chain messaging. These initiatives serve as critical examples of how collaborative, cross-industry efforts can enable seamless data exchange while respecting proprietary concerns. Emphasizing their approach highlights the importance of fostering global cooperation and adopting scalable frameworks to address the growing complexities of data integration.