Data as a critical asset for modern dairy farming
As dairy farming faces increasing pressure to improve efficiency, profitability and sustainability, data is fast becoming one of the sector's most valuable assets. That was the clear message from a recent Farmers Guardian podcast episode hosted in partnership with Dairy Data Warehouse (DDW), and chaired by Alex Black, Farmers Guardian deputy editor, which explored how data consistency, artificial intelligence (AI) and trusted data-sharing are transforming decision-making on farm.
Unlocking value from existing farm data
Speaking during the session was Fernando Mazeris, managing director of Dairy Data Warehouse and a veterinarian by training, with more than 30 years' experience working at the intersection of cow data, sensors and software. He explained that the original idea behind DDW was simple: to help the dairy industry unlock value from the vast amounts of data they already collect every day to improve efficiency and sustainability.
"Farmers invest a lot of time and effort into collecting data through herd management systems, milking equipment and sensors," Mr Mazeris said. "Our goal was to allow them to safely share that data with trusted advisers – nutritionists, vets, breeding and reproduction specialists – so those advisers can help improve efficiency, profitability and sustainability."
Farmer control and the challenge of data inconsistency
A key principle underpinning DDW's work is that farmers remain the owners of their data. They decide who can access it and for what purpose.
However, one of the major challenges advisers face is that different farms use different software platforms, each applying its own logic when calculating key performance indicators (KPIs).
"That means the same KPI can mean different things depending on the software being used," Mr Mazeris explained. "The inclusion and exclusion criteria for cows can vary significantly, so the results are not directly comparable."
Creating one standard dataset for meaningful comparisons
To address this, DDW focuses on extracting raw ‘cow-level' data from multiple systems and processing it using a single, consistent logic. This creates one standardised dataset, allowing advisers to compare performance across multiple farms on a like-for-like basis.
"For a consultant working with 20 or 30 farms, consistency and comparability are essential," he said. "If the baseline data isn't comparable, you can't properly assess whether a management change or intervention has actually made a difference."
Breaking down data silos on farm
The issue of data silos was also highlighted during the discussion. Many farms operate several systems simultaneously – for example, herd management software, milking equipment data and feeding or TMR wagon data – which do not always ‘talk' to each other. Bringing those datasets together into one format allows for far deeper analysis.
"This makes it possible to cross-reference information from different sources within the same herd," Mr Mazeris said. "That's where you really start to see the impact of changes such as ration modifications or management decisions."
From reactive to predictive decision-making with AI
Beyond consistency, the episode explored how AI is enabling a shift from reactive to predictive management. Traditionally, farm data has been used to analyse past performance, make changes and then monitor results. AI models, however, can analyse far greater numbers of variables simultaneously and identify patterns that would be difficult for humans to detect.
One example shared was the use of predictive models at dry-off to assess the risk of individual cows developing metabolic diseases in the next lactation. By analysing data from previous lactations, AI tools can forecast health risks before they occur and support preventative management.
"These models don't just predict outcomes; they can also help prescribe actions," said Mr Mazeris. "By combining predictions with economic and management variables, we can suggest interventions that prevent losses, improve animal welfare and optimise herd performance."
The importance of richer, integrated datasets
The value of richer datasets was a recurring theme. Integrating information from genetics, feed, sensors and production data improves both human decision-making and the accuracy of AI-driven models.
"As long as the data is accurate, more variables mean better outputs," Mr Mazeris said. "That helps consultants fine-tune operations, driving gains in efficiency while also improving environmental performance."
Looking ahead, Mr Mazeris believes the dairy industry is still at the very beginning of its AI journey. He expects AI tools to support both tactical, day-to-day decisions – such as health monitoring – and longer-term strategic planning, including decisions that affect milk production several years into the future.
"When you inseminate a cow today, you're influencing milk production years down the line," he explained. "AI can help farmers understand the long-term consequences of today's actions, balancing productivity, costs and sustainability."
Measuring and improving environmental sustainability
Sustainability was another major focus of the discussion. Dairy Data Warehouse is running the the development of the Milk Sustainability Centre (MSC), an initiative founded by John Deere and DeLaval. The platform allows dairy farmers to connect data from both land and barn, – regardless of brand or equipment – and calculate environmental sustainability KPIs.
"Farmers can see their carbon footprint, nutrient use efficiency and how effectively inputs like nitrogen are converted into milk and meat," said Mr Mazeris. "It gives them a clear picture of where they are today."
Data-driven foundations for the future of dairy
Importantly, the platform is free for farmers to use and is designed as a starting point. Future developments, expected from 2026 onwards, aim to highlight specific areas where improvements can be made, helping farms progress on their sustainability journey.
"This is about supporting farmers, not judging them," he added. "The industry has a responsibility to provide tools that genuinely help farmers respond to environmental expectations while remaining productive and profitable."
The discussion underlined the growing role of trusted, high-quality data in shaping the future of dairy farming. As AI tools mature and datasets expand, the potential to improve animal welfare, reduce environmental impact and strengthen farm businesses is significant – provided farmers remain in control of their data and its use.
If you want to find out more about DDW and MSC, you can visit their websites: www.dairydatawarehouse.com and www.milksustainabilitycenter.com
