Why Dairy, Why Analytics?

People ask me all the time how I got into analytics.

Honestly? For a long time, I never really knew how to answer that question.

The easy answer is that I’ve always liked numbers. I loved math growing up, especially algebra. I liked patterns, logic, and solving problems. But the older I get, the more I realize analytics was never really about numbers for me.

It’s about curiosity.

It’s about understanding why things happen.

And maybe more importantly — trying to figure out what happens next.

What’s funny is that if you had asked me as a kid where I’d end up, I definitely would not have said dairy analytics.

Competitive dance was my entire life growing up. I spent more time in the dance studio than I did in school from the ages of six to eighteen. But even then, I gravitated toward structure. While many dancers loved the creativity and freedom of other styles, I always loved ballet most. I liked the discipline of it — the positions, the technique, the precision, the fact that everything had a purpose.

Looking back now, that probably should have been my first clue.

I actually fell into agriculture almost by accident. In high school, I attended an agriculture charter school largely because I needed a smaller, more flexible environment that would work with my dance schedule. At the time, I didn’t know much about the food and agriculture industry outside of the stereotypes most people grow up with.

But the more exposure I got, the more I realized agriculture was so much bigger than “just farming.”

I started seeing the complexity behind the scenes — the business operations, supply chains, manufacturing systems, commodity markets, risk management, forecasting, and strategy that all work together to help feed people every single day. I loved that the industry had both purpose and complexity. It was grounded, tangible work that mattered.

So I kept pursuing it in college, and somewhere along the way I discovered economics and financial modeling.

That’s where things really clicked for me.

I realized I loved scenario analysis, forecasting, and connecting historical trends to future outcomes. I loved being able to look at data, understand the drivers underneath it, identify risks, and build a story around what might happen next. More importantly, I realized I was naturally good at it.

That combination — passion for the industry and a genuine love for analytical thinking — ultimately shaped my career.

I started my career through Land O’Lakes’ rotational supply chain program, which began with a year working at a butter plant. At the time, I knew I eventually wanted to move back toward corporate strategy and analytics, but looking back, that year on the plant floor was one of the most valuable experiences I could have had.

It taught me how the industry actually works at the ground level.

I visited farms. I learned how milk is separated and manufactured into countless dairy products. I learned the realities of plant operations and supply chain challenges. But most importantly, it taught me about people.

It taught me there is value at every level of an organization — whether it’s a plant floor operator, a planner, an analyst, or a CEO.

That perspective has stayed with me throughout my career.

During my second rotation, I joined the dairy analytics team and worked on a solids loss reduction project tied to the same plant I had just come from. That was the first time I really got to connect operational understanding with analytics and build what felt like my first “big” model.

At first, I thought I wanted to spend my career in commodity markets and risk management. And while I still love the strategy and analytics behind those areas, I realized I didn’t necessarily have the personality for trading itself. What I truly loved was using analytics to support decision-making — calculating exposure, evaluating risk, forecasting supply and demand, and helping leaders understand the bigger picture.

Over time, my career expanded across many different parts of the dairy industry — operations, commodity markets, supply and demand planning, logistics optimization, member relations, network balancing, and strategic planning.

Today, what excites me most is being able to connect all of those experiences together.

One thing I’ve become increasingly passionate about is helping people understand the why behind the numbers.

Because to me, analytics is not about building the flashiest dashboard or the most complicated model. In fact, one of my biggest frustrations is when analytics becomes more about showing off complexity than helping people make better decisions.

A dashboard that doesn’t help someone understand or act is just decoration.

My goal has always been the opposite: simplify complexity to a point where anyone can follow the story, regardless of how sophisticated the analysis behind it may have been.

That’s where storytelling matters.

One example of this in today’s dairy market is how we think about milk balancing decisions. Right now, the nonfat dry milk market is extremely strong, which means using NFDM to standardize cheese vats no longer makes economic sense in many situations.

A year ago, that wasn’t the case at all.

To a planner or plant team, a directive like that can sometimes feel arbitrary or disconnected from operations. But when you explain the why — the market dynamics, the economics, the tradeoffs, and the financial impact — people begin to understand the bigger picture and make stronger decisions themselves.

That’s what I care about most as a leader.

Not just telling people what to do, but helping develop the intuition and confidence to understand why decisions change under different market conditions.

Because early in your career, it’s easy to focus only on the numbers themselves. But great analytics requires context. It requires understanding the business, listening to subject matter experts, and developing the instinct to know whether the answer the data gives you actually makes sense.

Lead with insights, not numbers.

At the end of the day, I think the best analysts are the ones who can bridge complexity and clarity. The modeling and analytics are only half the battle. The other half is communicating it in a way that resonates across an organization.

If a producer, a plant operator, and a board member can all nod at the same explanation, the strategy probably isn’t done yet.

And honestly, that’s what still excites me most about this work.

Not just analyzing data — but using it to help people understand systems better, make smarter decisions, and build a stronger future for the dairy industry.