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Using data to make better business decisions

Jennifer Lanier for Progressive Dairy Published on 30 April 2021
Data being used to make decisions

Data is everywhere in today’s modern dairy industry – from milk meters to robotic milking systems, automatic calf feeders to activity monitors, all giving us more data than we know how to handle.

And while having data at your fingertips gives you the ability to better manage your farm and improve profitability, it can also feel like you’re on data overload.

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Do you want more data, or do you find yourself wondering, “How can I make better decisions with data I have?” Here are some tips.

Ask the right questions

When a decision needs to be made – regardless of what it is – I instinctively go straight to data. Yet, data can be misleading if you don’t know your end goal. First, you need to ask the right questions before even looking at data. What’s the question behind the question? What are you really wanting to know? What’s driving that question?

In many cases, situations may seem black and white when we are really looking at gray areas and unknowns we need to evaluate. For example, a farm has facility capacity to milk 100 first-lactation heifers annually, but this year they have 120 bred heifers to calve. The black-and-white approach to this example is to add the extra 20% to the herd and face overcrowding. Great questions to get started are: Why do we have 20% more heifers, how much is this costing the business, and how is this affecting overall herd production?

Work with your team

With data available today, it’s easier to find alternative, targeted solutions not considered profitable in the past. Solutions are not one-size-fits-all, but you can look upstream of the problem to find them. You can refine decisions and manage uniquely based on data you have. In this example, the farm could breed cows with lower genetic potential to beef semen if there is a beef market. But if they are already set up to raise high-quality replacement dairy animals, they may have a better opportunity to sell calves or bred heifers as dairy replacements. Further, they may find culling unthrifty heifers at weaning is the easiest solution to manage first and add additional steps later as they are needed. In any change, management teams must update protocols on farm to manage the downstream impact.

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Manage your variation

When we look at data, we often look at averages. What’s my average milk production per cow? What’s my average treatment cost per month? To make the best decisions that will deliver the most impact, we should manage variation, not averages.

For example, if your goal is to reach 43 kilograms of milk per cow, rather than looking at your average milk production numbers, look at where outliers are and decide how to bring them closer to 43 kilograms. Smart decisions help you reduce variation and more consistently meet your goals.

Knowing your cost of production and how much you’re spending on preventive measures compared to reactive measures can help you find areas to focus your dollars to reduce variation. Investing more in preventive measures to help maintain animal health will help reduce variation in achieving your goals.

Evaluate benchmarks for new data sources

Any new technology – be it a parlour, robotic milking system or activity monitoring system – has established standard start-up protocols and benchmarks. While they are a good starting point, you must find the right settings for your specific facilities and management style.

It takes time to get to know the equipment and what data are telling you. Around six months after the initial install, re-evaluate what you’re measuring and benchmarks you’re evaluating your data against. At that time, you can see what the true performance is compared to what variations may be attributed to onboarding the new system.

Then, continue re-evaluating what you are analyzing every year. What advancements have been made? Have you reached your initial goals? If so, what are the next set of benchmarks to monitor? Even if you’re looking at the same datasets every year, you may be interpreting those numbers differently and making different decisions as you refine your process and management system.

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Make the most of what you have

Data accuracy is critical to inform decision-making. Yet, many times, we get paralyzed by wanting data to be 100% perfect. If it’s not perfect, we decide the entire set of information is worthless. However, there’s a difference between bad data and imperfect data we can still use to make decisions.

For example, you have individual milk weights, but the weights are not tracked to all cows or cows are not milked with the right pen. While this is frustrating, if the weights are accurate, identify where milk production is down and make a plan to address it.

Ask yourself if data are usable and what you can do to make data even more useful for the future. To ensure data are accurate, set protocols for when and how data are recorded. Data are often inaccurate because of variation around how employees across the farm and between farms record data, especially when it comes to subjective information like animal health. Before comparing your data to other farms, standardize your protocols and recording processes to be more accurate and provide better insights to make decisions that impact your profitability.

Data are useful to help you and your team make informed decisions that benefit your bottom line. Work with your team to ensure data are recorded accurately. Then leverage outside partners to develop questions that help you focus upstream to manage variation and optimize the use of technology to make better business decisions. end mark

PHOTO 1: More data isn’t always the answer – but we can better use our available data to make informed business decisions.

PHOTO 2: Work with your team to ensure data is captured and interpreted consistently across the entire farm. Photos courtesy of Purina Animal Nutrition.

Jennifer Lanier
  • Jennifer Lanier

  • Technical Support Consultant
  • Purina Animal Nutrition
  • Email Jennifer Lanier

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