When Data Becomes the Story

In a recent planning meeting, my VP said something that stuck with me: “I get using data to tell a story, but at the end of the day, what matters is our customers, and these numbers are only a small part of that.” It wasn’t a dramatic moment. But it named something I had seen throughout my career and, if I’m being honest, something I had learned to accept. In most organizations, the metric becomes the end goal. Not the customer. Not the worker. Not the community affected by the decision. The number.

Numbers Don’t Lie

Let me be precise about what I mean. Telling stories with data is real, and it matters. Numbers carry weight. The number of lives saved by a vaccine, the percentage of children lifted out of poverty, and the change in wages over time help make abstract experiences visible. Used well, data is one of the most powerful tools we have for understanding the world.

But that is not what “data storytelling” has become. In many corporate and analytical settings, data is no longer supporting the story; it is the story. The numbers are not evidence for a claim about human experience; they are treated as the claim itself. And in that shift, the people those numbers represent begin to disappear.

The Economics

Economics has long warned us about this mistake. Goodhart’s Law tells us that when a measure becomes a target, it ceases to be a good measure. Campbell’s Law takes it further: the more we rely on quantitative indicators for decision-making, the more those indicators distort the very systems they are meant to monitor.

What’s changed is not the problem, but how we talk about it. We’ve wrapped this mistake in a more appealing label. “Data storytelling” sounds human. It sounds like progress. But too often, it simply means we’ve become better at presenting metrics while thinking less critically about what those metrics actually capture.

You’ve probably seen this play out. When schools are judged primarily by test scores, they begin teaching to the test. Rather than focus on developing better teachers and students. When hospitals are evaluated based on wait times, they focus on moving patients faster rather than necessarily treating them better. And in retail, where I’ve spent much of my career, inventory decisions can look perfectly efficient on a dashboard while consistently failing certain communities. If a neighborhood’s purchasing patterns don’t fit the model, it gets underserved. The data told a clean, defensible story. It just wasn’t the right one.

When organizations optimize for what is measurable rather than what actually matters, resources are misallocated. Decisions that look rational in a model create outcomes that don’t show up in the data. And because those outcomes are harder to measure, they’re easier to ignore.

The answer is not to reject data. It’s to understand its limits. Data is a tool. It helps us answer specific, well-defined questions. But it cannot tell us which questions are worth asking. It cannot capture every human experience. And it cannot replace judgment. That part still belongs to us.

The real risk isn’t bad data, it’s forgetting what the data represents. Behind every number is a person you will never see in the dashboard. When we lose sight of that—when we start treating the map as the territory—we don’t become more informed. We just become more confident in incomplete answers.

Data storytelling is a great tool to communicate data to your audience, but remember that every data point is a human story.

The Bottom Line

Data should inform your decisions, not define them. Whether you’re running a business, shaping policy, or managing your own finances, the goal is not to optimize a metric; it’s to improve outcomes for real people. The moment you forget that, the numbers may look better, but the decisions will not be