No matter where he goes or what he does, Neil Hoyne can’t shake the data nerd label. When you’ve generated $6 billion in revenue and helped search platform advertisers improve conversion rates by more than 400% as the chief measurement strategist at Google, it kinda clings to you like plastic wrap around a plate of cookies.

Hoyne has done nothing in his career to counteract this perception. He holds a bachelor’s degree in management science from Purdue University – a mecca for STEM-focused business education – and an MBA from the UCLA Anderson School of Management. Before beginning his run at Google, he was the director of analytics at SourceForge. And he currently serves on the board of trustees for Purdue University Global.

Yet you won’t find a pocket protector within 500 miles of Hoyne. He’s a geek who gets the real world.

After all, you don’t rack up more than 9,000 hours advising CEOs and boards on the intersection of technology, customer analytics and marketing strategy unless you’re a gifted storyteller. And that’s why administrators who have read his book, Converted: The Data-Driven Way to Win Customers’ Heart, published in 2022, leave glowing testimonials: “It’s worth every penny for those of us that think data is complex.”

We sat down with Hoyne, who was a keynote speaker at the 2025 CASBO Annual Conference & California School Business Expo in April, to dig into his stories of how school leaders can use data to their advantage, too.

What’s something you thought you knew that you found out you were wrong about?

People face a predictable learning curve when engaging with data. At first, they’re overwhelmed by the sheer volume and often come away thinking, “This is way too much. I’m not going to get involved.”

But, as they start exploring and discover what’s available, their confidence grows dramatically. Now, they begin thinking, “With all this data, I can tackle almost anything!”

Then reality hits. When applying what they’ve learned, they encounter the historical challenges, the data scarcity challenges, the unknowns – where data bends and breaks. It’s not surprising that many realize that the more they learn, the more they see what’s missing. They circle back to their original state, understanding why they lacked confidence initially and why data work is genuinely difficult.

Some assume that us data people have all the answers. I’ve discovered that what we really have are better tools for translating other people’s ideas into something more quantifiable.

What’s the best advice you’ve ever received? From whom?

I don’t think I can pick one best piece of advice, but what comes to mind is something from my time in UCLA’s MBA program. Before graduating, I asked my professors, “Now that grades are in, what didn’t you teach me that I need to know?”

And, a marketing professor told me, “Neil, avoid the whole first page of Google.” I’ll admit that I was a bit confused. While I had no ambitions to work for the company at that point, it was the gold standard for research.

Her advice was actually the opposite of any sort of distrust. She explained that people search for data like “How big will this market be next year?” take the first answer and plug it into their spreadsheet without question. While Google gives excellent results, with historical data and future predictions, there’s never just one answer. There are always many possibilities.

The problem is simply that people close their minds too early. When you explore the second or third page of search results, you discover ideas your peers aren’t considering. If page one says “50% market growth” but page two suggests “20%,” you start wondering why the difference exists and what factors each considers – or misses.

The deeper lesson here: We save our cognitive energy by telling ourselves we’ve found the answer. With data, it seems simple: What number goes in the spreadsheet cell? But reality is more complex. Curiosity should lead us to ask not, “What’s the answer?” but, “What are the possibilities?”

And, when you understand these possibilities, if things unfold differently than predicted, you’ve already considered variables and options, and perhaps even planned for contingencies.

What are some of the challenges or common gaps you see within organizations that hold them back from being able to effectively apply data?

I’ll give you a few. One is that we’re all human. A 2023 KPMG survey found nearly 65% of CEOs would ignore data that contradicted their intuition. We all do this. When we feel that data doesn’t tell the whole story, we default to our lived experiences over machines. Yet, we’ll still hold the idea of data-driven decision-making in high regard.

The second human aspect comes in recognizing that organizational changes have consequences. Any type of transformation involves shifting resources between projects. Maybe it’s declaring one initiative more effective than another for student outcomes. If people face budget cuts or see programs they value lose funding because they’re no longer deemed a priority, it leads to the very human response: “The data doesn’t show everything, so shouldn’t we consider these other factors?” The result? You find yourself in organizational gridlock.

I know people really aspire to “make mechanical decisions based on data.” But the reality will always include human biases and consequences to these actions. Organizations need to figure out how to manage these processes.

Many believe the answer is “more systems, more software, more data scientists.” While these certainly can help improve data-driven decisions, they miss the bigger issue: How do humans make data-driven decisions in complex organizations with competing incentives?

I mention this not to complicate the conversation but to be more inclusive in them. If we view data as exclusively belonging to data scientists or AI systems, we don’t invite others to the conversation to address crucial questions like: “What are your incentives for this change? What would make you act on this data?” We then risk that these questions might never get asked.

What are the biggest challenges school districts face in using data to drive change, and how can these be overcome?

One challenge everyone faces – whether in education, retail, telecommunications, insurance or something else – is understanding what truly drives the desired outcomes.

When we invest in a program, what do we get back? What actually drives student outcomes? It’s a balancing act. We want comprehensive data to understand everything, but if we wait for perfect information before acting, we can’t effectively serve those who need us today.

This creates a tension. We want smart, data-driven decisions, but we also need to move quickly enough to provide cutting-edge education.

Take AI in the classroom. Do we embrace this developing technology before fully understanding its full impact on learning? Or do we hold back, potentially depriving students of valuable tools while we study the effects? It’s a difficult choice every organization must navigate effectively.

How can school districts balance the need for innovation with the practical realities of limited budgets and resources?

Consider that only 5% to 6% of organizational decisions today use data. While we might aspire to 90% to 100%, that’s also pretty unrealistic.

While many think “more systems and data will improve outcomes,” I focus on questions like: How do I help to empower the people? How do I help them become more effective with existing tools? That’s the real opportunity – investing more in the people before the software. Now, I’m not asking people to work longer hours or take on more jobs, but simply to join the conversation.

In every organization, there are people who, if asked how to improve something, they likely have the suggestions and some data ready, they just don’t know how to move their organization to act. I want to empower these voices and, in my position, identify the barriers.

Where is friction occurring because we lack proper decision-making processes? Where are incentives misaligned? Where do people feel unheard? These are the areas where meaningful change can happen.

What advice would you give to school leaders who are just starting their data journey?

My first pro tip for anyone interested in data is to recognize that you bring fresh viewpoints simply by being part of the conversation. And those perspectives matter far more than you might think.

Second, remember that people gravitate toward those with the most technical expertise. That’s fine. But don’t forget to look for people with diverse backgrounds (like someone who moved from sales to engineering) or those with excellent communication and storytelling abilities.

These people will be much more additive in your learning journey than someone who simply has technical skills alone.

Julie Phillips Randles is a freelance writer based in California.