The Real Product Isn’t the Device. It’s the Data.

I just opened up my Dexcom box to tee up my first attempt at continuous glucose monitoring. I’m excited. Not in a “biohacker influencer” way—but in a very practical, self-aware way. I eat more garbage than I’d like to admit, and I’m curious what the data will actually say. Maybe it nudges me toward less sugar, more protein. Maybe it just confirms what I already know.

Either way—data.

Then I hit the signup screen.

Two checkboxes:

1. Okay to use my anonymized health data to advance scientific research.

   Sure. Happy to help humanity.

2. Okay to provide my data to Dexcom’s marketing team to customize messaging.

   …ugh.

I paused.

The moment made me think. As someone who has built a career on data—helping companies turn messy information into decisions—I’m not particularly privacy-paranoid. If anything, I skew the other way. I’m an optimist. I believe in what data can do. Plus, I clock in as an "adventurer" on the enneagram side.

But something about that second checkbox felt different.

Not because I don’t want better recommendations on protein vs. sugar. But because it revealed what this really is.

Dexcom Isn’t Just a Device Company

Dexcom operates in a $13.6B industry and holds ~74% market share in continuous glucose monitors. But that stat undersells what’s actually happening. This isn’t just market share of devices. It’s market share of continuous, longitudinal metabolic data.

And increasingly, it’s a company building an AI-powered system on top of that data—turning raw glucose signals into insights, recommendations, and eventually behavior change.

The hardware? That’s just the intake mechanism.

In my world—working with CFOs and CEOs on data strategy—we talk a lot about this distinction:

Some companies use data. The most valuable companies are built on it.

Dexcom is quietly becoming the latter.

From Data → Insight → Behavior

Dexcom has been very intentional about this shift. This isn’t just about collecting more data—it’s about expanding who they collect it from and what they can do with it.

Historically focused on Type 1 diabetics, they’re now moving aggressively into Type 2, prediabetic, and even general wellness populations. Products like Stelo—available without a prescription—signal a deliberate move beyond clinical use into everyday consumer health. That’s not just a go-to-market change. It’s a data strategy.

More users → more data → better models → better insights → more value → more users.

A classic data flywheel.

Layer in AI, and the system becomes even more powerful: translating complex physiological signals into simple, real-time recommendations. The end goal isn’t just insight—it’s behavior change.

They describe it as “nudge, not judge.”

That framing matters. Because it highlights a broader shift we’re seeing across data-driven companies:

The goal is no longer just understanding behavior—it’s influencing it.

At the surface, a metabolic data set unlocks what you'd expect:

• What you eat

• How your body responds

• When your glucose spikes or crashes

  
  

But underneath that? It’s a continuous signal of human behavior. With enough data, you can start to understand:

• When someone is likely to feel low energy

• How they respond to specific foods

• Early indicators of metabolic risk

• How lifestyle choices impact long-term health

  
  

And when you layer in AI, that data becomes:

• Personalized recommendations

• Predictive insights

• Real-time nudges

  
  

This is where the checkbox moment comes back.

Because the question isn’t:

“Can we personalize your marketing?”

It’s:

“Can we use your biology to understand—and eventually shape—your behavior?”

And to be fair—this isn’t inherently negative.

In fact, much of the value depends on this capability:

• Nudging healthier behavior

• Preventing disease earlier

• Improving outcomes at scale

But it does raise a more nuanced question:

At what point does insight become influence?

As a Data Strategist, This Is the Real Trend

In most companies I work with, the challenge is still:

• Getting access to the right data

• Building reliable reporting

• Turning data into decisions

But we’re moving into a different phase. The most valuable datasets are becoming:

• Continuous

• Behavioral

• Personal

• And increasingly, actionable in real time

Metabolic data checks every box. And companies that control these datasets won’t just have better analytics. They’ll have better leverage over outcomes.

I know what you’re thinking. After all that—did I check the box? I did. Health and science won. Marketing… we’ll see. Hopefully there’s an unsubscribe button.