The Metrics That Matter: A TPM's Tale of Data Storytelling

In the world of AI and TPM, understanding metrics is crucial. Join me as I reflect on the complexities of leading vs. lagging indicators, the art of crafting KPI trees, and the dangers of vanity metrics—all while sharing lessons learned from my journey.

Metrics: The Story Behind The Numbers

It was a rainy Tuesday afternoon when I found myself staring at a dashboard filled with numbers that felt more like a foreign language than the key to our project’s success. As a Technical Program Manager, I had spent years wading through data, yet there I was, wrestling with the very metrics I needed to guide my team through a critical AI initiative. It was a moment of clarity that I wish I could bottle and hand to every junior PM I mentor: metrics are not just numbers; they are a narrative waiting to unfold.

In my early days, I often mistook sheer volume for value. I thought if I could just collect enough data, I would finally understand our project’s trajectory. However, I quickly learned that not all data is created equal. This is where the distinction between leading and lagging indicators became my lifeline.

Leading indicators are like the canaries in the coal mine. They signal potential issues before they fully materialize. For instance, in an AI project, tracking the number of model iterations in development can give you insight into how quickly your team is innovating. If you notice that the iterations are stagnating while the deadline looms, it’s time to dig deeper. On the other hand, lagging indicators—like the final performance metrics of the AI model post-launch—are crucial for understanding how well you've done, but they often come too late to change the course of a project.

When I first started using KPI trees to visualize our goals, it was as if a fog lifted. A KPI tree breaks down objectives into measurable components, helping teams see the connections between their efforts and the overall goals. Imagine a tree where the trunk represents your main objective, and the branches are the KPIs that feed into it. For example, if your trunk is “Improve AI model accuracy,” your branches could include “Data quality score,” “Model training time,” and “Feature engineering effectiveness.” This structure not only clarifies what matters most but also highlights the trade-offs you might need to make.

But here’s where the rubber meets the road: making trade-offs visible is essential, especially in the complex world of AI. I remember a project where we were torn between improving data quality and reducing model training time. Each team member had valid arguments, but the KPI tree allowed us to visualize the potential impacts of our choices. We could see that while a focus on data quality might delay immediate results, it would ultimately lead to a more robust model—an invaluable insight that might have been lost in a sea of numbers.

As I engaged with my team during this discussion, I emphasized the importance of storytelling with data. It’s not enough to present metrics; you need to weave them into a narrative that resonates. For instance, instead of simply stating, “Our model accuracy improved by 10%,” I’d share the journey: “After a rigorous testing phase, where our team faced challenges with data quality, we implemented a new

Boosting Accuracy While Avoiding Vanity Metrics

validation process that resulted in a 10% increase in accuracy. This improvement not only enhances user experience but also strengthens our market position.” This approach not only informs but also inspires.

However, as we bask in the glow of our metrics, we must be wary of vanity metrics. These are the numbers that look good on paper but don’t truly reflect the health of your project. I once led a team that proudly reported a 50% increase in user engagement, only to discover that the increase was primarily due to a promotional event. When the dust settled, engagement fell back to baseline levels. What we needed were metrics that provided actionable insights, not just applause.

In the realm of artificial intelligence, where the stakes can be high and the landscape ever-changing, it’s critical to foster a culture that values meaningful metrics. I encourage junior TPMs to ask questions: What does this metric really tell us? How can we use it to inform our next steps? And most importantly, how does it align with our overarching goals?

As I reflect on my journey, I realize that the most effective TPMs are those who embrace metrics as storytelling tools. They understand that each number represents a decision, a trade-off, and ultimately, a path forward. In a world where AI is reshaping industries, the ability to interpret and communicate metrics with clarity and purpose is not just a skill; it’s an art.

So, to my fellow TPMs, as you navigate the intricate dance of data, remember to keep the narrative alive. Let your metrics guide you, but don’t let them define you. The real power lies in the story you tell with those numbers, and that’s what will drive your team—and your projects—toward success.