Data analysis advice from a dairy consultant of nearly 30 years
Dairies today are more data-driven than ever before. From milk production to health events, inventory and even feed management, there’s no shortage of data points to analyze. However, more data doesn’t always mean more clarity, so for many producers and consultants, the real question becomes where to start.
With nearly 30 years of experience as a dairy veterinarian turned nutrition and management consultant, Dr. Enrique Schcolnik of Progressive Dairy Solutions, Inc., says effective data analysis starts with seeing the bigger picture and knowing when and where to drill down.
Seeing the big picture
When Dr. Schcolnik works with clients, he begins by tracking a core group of key performance indicators in DairyComp to help establish what “normal” looks like for each herd. While his primary focus is the milking animals, this approach can be applied across nearly every area of dairy management.
“I have a list of about 20 different key performance indicators that are kind of like the big picture,” he explained. “I track them month to month, and then I run a 12-month average on that as well. Then I look at any deviations month to month.”
This high-level view allows him to quickly identify when something moves away from expectations – without getting lost in the day-to-day variation that naturally occurs in herd data.
“As soon as I see any deviations, then we’ll dig in,” Dr. Schcolnik said. He notes that having the right tools is essential for this, and that’s why he’s used DairyComp for the span of his entire career.
“The number one thing I like with DairyComp is the flexibility. Once you learn to speak the language, you can really build almost any report you want. You’re not given something and told, ‘This is all you can do.’” Dr. Schcolnik added, “I can get on DairyComp and build almost anything I need. It’s like you’re given the power to develop the program to your clients’ needs.”
Drilling down
Once a deviation appears, Dr. Schcolnik digs deeper, using DairyComp to narrow the focus by timeframe, group of animals or remarks, which are notes in the system that provide extra details about an animal. “If there’s any deviation from normal, DairyComp really allows me to start digging in deeper and deeper and deeper until I can come up with a good differential of why we were outside of our goals,” he explained.
One area he recommends producers pay especially close attention to is cows in the first 60 days of their lactation, since what happens in those first few weeks can set the tone for the rest of her lactation. “Early milk is such a good indicator,” Dr. Schcolnik said. “Any issue you may have on the dairy – whether it’s health, hoof health or metabolic issues at freshening – will translate into milk production. I keep a really close eye on those week four or week eight milks – some people look at week six. Everyone looks at it a little differently, but that early window tells you a lot.
Another report he looks at frequently is inventory, which helps him forecast replacements and plan for cows entering the milking herd. “I like that report because it lets us look six or seven months ahead and have those conversations early,” he shared. “If we see a lot of fresh cows coming in a certain month, it creates awareness that we’re going to need everyone on board to manage that workload. For example, if April is going to be a big month, that’s probably not the time for our main maternity or fresh cow employees to take vacation. Having that visibility ahead of time helps us plan better.”
Knowing inventory projections can also help inform breeding decisions. Dr. Schcolnik compares the number of fresh cows to available heifers to identify gaps and advise producers on making proactive adjustments to their breeding strategy. “If we have a hole coming in 16 months, we can lower our black calf or beef-on-dairy breedings and breed more to sexed semen.”
Validate data trends, then act
While data is the first step in identifying an issue, Dr. Schcolnik recognizes that numbers alone may not tell the whole story. Once a red flag comes up in herd data, he recommends validating that the data matches with what’s actually happening on the farm. This can mean walking pens, observing cows’ behavior or having an exploratory meeting with the dairy team to understand what could be causing the off numbers.
For example, one of his herds showed a higher-than-usual death rate in the lactating herd. He immediately flagged the issue in the data, but it wasn’t until Dr. Schcolnik walked the pens and worked with the farm team that they uncovered the root cause.
When the team combined what the data was telling them with what they saw walking the pens, the pieces started to align. They pinpointed problem spots in the barn, made intentional management changes and ultimately cut the death rate in half – bringing it back into the range Dr. Schcolnik typically sees on the dairies he works with.
That ‘validate, then act’ philosophy guides how Dr. Schcolnik works with producers and is the advice he would offer to other fellow dairy consultants and producers. “Data is a great starting point, but even if the numbers are good, go behind the cows and make sure they match what you’re seeing with your eyes. Never stop at just looking at the numbers. Whether they’re good or bad, always make sure they make sense.”
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