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Image credit: Organic Valley

7 questions with Kate Campbell, Organic Valley’s new data science chief

January 28, 2021

When Kate Campbell was born in a small town about 20 minutes from the Organic Valley headquarters in La Farge, Wisconsin, she started a life-long journey that would bring her right back to her rural roots.

Campbell studied mathematics in college, and dabbled in software engineering and computer science. But found herself dreaming of the green hills back home in southwestern Wisconsin. She returned to join Organic Valley, beginning a 17-year long career with the organic dairy farming cooperative.

“It was really exciting to be able to start working in an area where I could apply those skills and make a really decent living in rural Wisconsin,” Campbell told AFN

Recently, she was appointed as Organic Valley’s vice president of business insights and data science. Her task is to leverage data to help the co-op make forward-looking decisions and stay ahead of the curve. 

Organic Valley now has women filling 44% of its upper-level leadership positions. Across all employees, women total 50% of its workforce.

AFN caught up with Campbell to learn more about her new role, and the opportunities and challenges that come with it.

AFN: What piqued your interest in data and eventually led you to this new role?

Kate Campbell: Throughout my career, I’ve had this perfect combination of taking whatever information is there and using tools to make it easier to work with, [producing] things like visualizations and statistics reports that make it easier to understand what is happening. I first used those skills at Organic Valley as the payroll coordinator. From there, I moved into benefits and compensation for five years. All of that is very data-driven stuff – trying to predict our expenses and how much we are spending on labor, [and] how much it is going to cost if we switch our benefits package.

After that, I moved into the demand forecasting realm. I was able to apply those math and data skills to start using predictive analytics, to get our heads around what our demand was. My focus has been on the sales and demand side of things for the last 12 years.

What are some of the major trends you’ve seen during your tenure at Organic Valley?

My perspective has been more from the sales side of things with demand forecasting. Over the last 12 years, we’ve had really rapid growth in organics. It seemed like there was just no end to where you could go, and the struggle was largely, “Do we have enough raw supply to meet the demand we are seeing?” Along with that, we saw the private label industry explode. 

What are you most looking forward to in your new role?

The first thing is actually taking the things that I’ve learned on the sales side of the business and applying that to the farm and the supply side of the business where I’ve had less exposure.

For instance, I’m working on enhancing our supply forecasting to really try and move that to the next level. We’ve been using a pretty old statistical modeler for that for quite a long time. We want to look at maybe applying some different methods, maybe trying out some machine learning for some of the predictive modeling, to see if that can bring us additional insights. 

What’s really interesting about milk supply is that milk is a biological process and pretty predictable. Cows put out so much milk every day; it [only] varies a little bit with seasonality. So it seems pretty predictable until you start mixing in other factors. We’re trying to get our heads around what can make that milk supply less predictable than normal. That can be things like the economy, the cull cow market, competition coming at us from other people trying to recruit our farmer members away from us, [and] crazy weather patterns. So there’s a lot of opportunities to say, “We have some anomalies happening in our behavior – how do we explain them?”

Other things that I would love to do on the sales side involve customer segmentation and understanding. [Explaining] why unique groups of consumers behave the way that they do, what they’re looking for, and what they want. I think that you could apply the same kind of framework to segment farms and farmer members to understand how these groups could be uniquely grouped together.

Is it challenging to work with farmers in the realm of technology and data?

I haven’t worked directly with farmers to get them to use new technology. But if there’s anything I understand about our set of farmers, it’s that they’re interested in helping the cooperative [and] using what’s available to understand better what is going on with their farms. Obviously, that includes following organic practices – but if the data can reveal something that they weren’t aware of, I think they’re interested in that, and they’re willing to pursue, change, and grow.

Do you think technology can play a role in boosting the struggling dairy economy?

Part of it has to do with being able to communicate to people about the real benefits of dairy. Dairy fat, especially, is a really important part of the diet and it brings a lot of health benefits.

I think using tech to help people understand those benefits, and to help us understand how to increase the benefits of dairy, are key. [Answering questions like], “How do we enhance the butterfat within our milk? How do we take advantage of the good fats that come along with pasturing our cows? How do we use data to tell that story to really win people over?”

Are there any technologies right now that you’re really excited about?

I’m less connected to the technologies, and more looking for data. Where I think I can play a role is in the creation of new data – [thinking whether] there things happening out there that we could capture, or speed up the capture of. [One example] is automating milk tickets so that when the milk is picked up at the farm the data is recorded electronically, and automatically goes to the plant so we know how much milk is coming.

Smartphones, and the ability to collect information so quickly, is something we can be excited about. We can harness [that] to gather new data that we maybe have not thought about before.

What are your biggest challenges as you enter your new role?

The biggest challenge is maintaining focus and not getting too distracted by all of the different places that we could apply data science to get insights.

I’m running a relatively small team of about seven employees in terms of strictly data science work. You can only focus on so much at one time. Picking out the right projects that deliver the most value to the cooperative will be a big challenge.

In data science, you can have a tendency to get stuck in the weeds and go down an analytical rabbit hole. So being able to recognize when to pull back, or being able to look at the bigger picture to deliver the insights that we need, is important.

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