How important is data in modern dairy production, and how do you turn it into real outcomes on the factory floor?In this episode, we talk with Erik Vedfald, Chief Architect for Production IT at Arla. Erik explains why quality beats quantity in data and what it takes to move from proofs of concept to trusted tools operators actually use. We discuss sensors, governance, UX and the long game of preparing today’s datasets for tomorrow’s analytics and AI.If you work in operations, engineering or production IT, you will get practical guidance on where to start, how to involve your teams, and of course, how to avoid the common pitfalls.In this episode, you’ll discover:1. Why data quality matters more than having lots of data2. How to pick high-impact use cases that pay back3. Ways to structure “data zones” and connect them with a digital thread4. How to earn operator trust and avoid AI “first-try” failures5. Practical steps medium-sized dairies can take to get startedEpisode content01:18 Why “keeping up to speed” in production is an illusion03:55 Are dairies slow or simply paced by ROI and business cases06:12 Why you can’t just drop AI on raw production data07:36 Sensors and “dumb vs. deep” data that actually matter09:21 The skeleton and digital thread analogy for connecting data10:33 Building high-quality “data zones,” starting at milk intake11:47 Adoption challenges: UX, change management, and operator workflows14:50 Data governance and the three-year horizon for training models16:58 Trust is fragile: the high cost of early bad AI answers26:37 Bottom-up innovation and scaling local wins across sitesProductionThis podcast is brought to you by Au2mate.This podcast is produced by Montanus.

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