I’ve been thinking a lot lately about why I do what I do with technology. Not just the technical aspects—the macros, the data analysis, the automation—but the deeper why behind it all. Do I create these tools simply because I can, or is there something more meaningful happening beneath the surface?
When I read through Dave Vellante’s Breaking Analysis collection, I’m struck by how he connects technological developments to broader impacts. His observation that GTC24 was “the most important event in the history of the technology industry” isn’t just hyperbole—it represents a recognition that technology fundamentally changes how we interact with our world.
Tools – The Tool-Builder’s Paradox
I build data science tools for a living—macros, algorithms, automated reports. Sometimes in the midst of coding, I lose sight of why it matters. The immediate gratification of solving a technical problem becomes the focus, and the larger purpose fades into the background.
But here’s what I’ve realized: the tools we build are extensions of human curiosity. When I create a macro that analyzes patterns in customer behavior, I’m not just saving time—I’m creating a lens that helps us see the world more clearly. The technology itself isn’t the end goal; it’s what the technology enables us to understand.
Tools – Finding Purpose in Practical Applications
The practical applications of my favorite technologies reveal their deeper purpose:
1. Automating the Mundane to Enable the Meaningful – Tools
When I automate repetitive tasks through macros, the immediate benefit is efficiency. But the real value comes from what happens next—the freed mental space to ask better questions. My most meaningful work comes after the automation, not during it.
“I don’t automate reports just to save time,” I told a colleague recently. “I do it to create time for the questions we haven’t thought to ask yet.”
2. Pattern Recognition Beyond Human Capacity – Tools
The data analysis tools I build can process information at scales that would overwhelm any human mind. This isn’t about replacing human judgment—it’s about extending it.
I remember building a clustering algorithm that identified customer segments we’d never recognized before. The marketing team was skeptical until they saw the results—revealing behavior patterns that had been invisible to us. The technology didn’t replace their expertise; it enhanced it by revealing what we couldn’t see on our own.
3. Connecting Insights Across Siloed Data
Perhaps what gives me the most satisfaction is creating tools that bridge disconnected information systems. In large organizations, critical data often sits in separate departments, never talking to each other.
My favorite project last year involved creating a dashboard that pulled customer service interactions, marketing campaign data, and sales information into a unified view. The technology itself wasn’t particularly groundbreaking, but what it revealed was transformative—cause-and-effect relationships that had been hidden by organizational structure.
“This isn’t just about convenience,” I explained to the leadership team. “It’s about seeing the whole truth instead of fragments.”
The Human Element: Where Technology Ends and Meaning Begins
I’ve had to confront an uncomfortable reality in my career: the most sophisticated tools still require human wisdom to create meaningful change. The data can tell us what is happening, but rarely why it matters.
This tension became clear during a recent project analyzing employee retention patterns. The algorithms identified which employees were most likely to leave, but they couldn’t tell us why those patterns existed or what to do about them. That required human empathy, ethical judgment, and organizational knowledge—qualities no algorithm possesses.
I’ve come to see this not as a limitation of technology but as a beautiful complementarity. The tools I build are most powerful when they enhance uniquely human capacities rather than try to replace them.
Reconciling Technical Excellence with Deeper Purpose
Sometimes I worry that I’m getting lost in the technical details—optimizing a function here, tweaking an algorithm there—without connecting to the bigger picture. This is the occupational hazard of anyone who loves the craft of building tools.
But I’ve found a few practices that help me maintain perspective:
1. Start with the Question, Not the Tool
Before writing a single line of code, I try to clarify the fundamental question we’re trying to answer. What human need or curiosity drives this analysis? How will people use this insight to make better decisions?
2. Include End Users in the Development Process
Some of my most meaningful work has happened when I brought non-technical stakeholders into the development process early. Their questions often reveal the true purpose behind the technical requirements.
“I don’t understand what this variable means,” a marketing manager once told me during a demo. That simple statement led to a complete redesign of an interface—one that prioritized meaning over technical completeness.
3. Measure Impact, Not Just Performance
Technical metrics—processing speed, accuracy rates, code efficiency—matter for tool builders. But they don’t capture the true value of what we create. I’ve started asking for feedback on how my tools change decision-making processes, not just how they perform technically.
The Ethical Dimension of Technology Creation
As I’ve grown in my career, I’ve become increasingly aware that the tools I build embed my values—whether I intend them to or not. The variables I choose to include in an analysis, the defaults I set in a dashboard, the metrics I highlight—all reflect judgments about what matters.
This realization has made me more thoughtful about the design choices I make. When building a customer segmentation tool recently, I deliberately included factors beyond revenue potential—like customer loyalty and support needs—to avoid creating a system that would inadvertently prioritize short-term gains over long-term relationships.
Learning from Technology Leaders
Reading about companies like Nvidia and Broadcom in Vellante’s analysis, I’m struck by how differently they approach similar markets. Their contrasting strategies remind me that there’s no single “right way” to develop technology—the key is alignment between purpose, capability, and market need.
I find myself wondering about my own approach. Am I more like Nvidia—pushing the boundaries of what’s possible with new innovations? Or more like Broadcom—focusing on established markets with durable solutions? The answer probably lies somewhere in between, and varies depending on the problem I’m trying to solve.
The Journey Forward
I’m still figuring out how to consistently connect my technical work to deeper meaning. Some days I get caught in the minutiae of debugging code or optimizing algorithms. But increasingly, I find myself stepping back to ask: “What human capacity am I enhancing with this tool? What new understanding will it enable?”
The most satisfying moment in my work isn’t when the code finally runs without errors—though that’s certainly a good feeling. It’s when someone looks at the results of an analysis I’ve built and says, “I never saw that before. This changes how I think about our business.”
In those moments, I remember that the technology itself isn’t the point. It’s the new perspectives, the better decisions, the deeper understanding that the technology enables. That’s where the true meaning of my work as a tool-builder lies—not in what I create, but in what it helps others see.
