Demystify Data So Employees Can Understand It
More technology generally means more data, which means more time needed for data analysis. Unfortunately extra time is not a luxury offered to most dairy producers.
Even if there is enough time, all of that data can be overwhelming.
“For some, the idea of using data to inform decision-making can feel intimidating,” says Charles Wheelan, noted author and professor at Dartmouth College. “Maybe they don’t consider themselves to have strong analytical skills. Maybe they like to “go with their gut”, or simply dread the idea of wading through a ton of data.”
The strategy for managing large amounts of data is the same strategy as eating an elephant: take it one bite at a time.
That’s how Samantha Craun approaches managing the data produced by the activity monitoring system used at the 850-cow Davis Brothers Dairy near Philadelphia, Tenn. They’ve used the SCR activity monitoring system for the past four years, tracking cow activity and rumination.
The best way to manage all of the data coming out of the system is to break it off into smaller chunks, Craun says.
“It can seem overwhelming at times,” Craun admits. “My biggest tip for managing it is to set myself up for the week with different tasks. For example, on Monday I know I’ll be doing set up shots for repro, and on Tuesday I’ll be drying cows off. So I’m looking at those reports in different areas.”
Breaking it down helps her pull what she needs to manage daily activities.
Focusing on a few key areas also helps Craun prioritize work schedules for her and the other 18 employees that work with her on the dairy. Here are the data reports she goes through each morning as she starts her day:
- Heat Detection Report. First-time breedings are done using timed AI, but subsequent breedings are done based on activity monitoring. “I look at [cows in heat] right away in the morning, and then I look at it right after lunch because we try to breed twice a day based of what the data shows,” Craun says.
Keep in mind what the data represents, however.
“Big data tends to produce patterns, but it is not deterministic,” Wheelan says. “Plus, all data [is] inherently backward-looking. By definition, they come from the past. Because of this, data analytics will miss inflection points.”
Looking beyond the data is something Craun does with the reports she generates.
“A cow might be on the list but is she ready to be bred? Is she really in heat or did she get moved yesterday and her activity spiked?” she says. “You will have cows on there that are flagged [as in heat] that you’re not going to necessarily do anything with, so you have to cipher through that.”
- The Health Report. This report tracks cow rumination and flags cows with a lower rumination score than the previous day. These are cows that Craun needs to check on.
“A lot of these cows will already be in the hospital pen so we already know about them,” Craun says. “But we can see if are improving or not improving based on what they did yesterday.”
- Feeding Rates. In addition to rumination scores on individual cows, she also watches rumination scores on each cow pen to adjust feeding rates. Rations are bumped up or down according to how well cows are ruminating.
“Organization is the biggest key,” Craun says. “Keeping up with all of that data is important, especially getting everything into a format so you can actually analyze it.”
For a daily view of what's happening on Davis Brothers Dairy, follow Samantha Craun on Instagram at @Missfarmersam.