In public health, data has become the cornerstone of our work. Whether it’s tracking disease outbreaks, measuring health disparities, or evaluating program outcomes, the demand for data has never been greater. Buzzwords like “data-driven decision-making” are often celebrated as the gold standard. But as public health professionals, we know the world isn’t as simple as numbers on a spreadsheet.

An emerging perspective in our field is the importance of being ‘data-informed’ rather than strictly ‘data-driven.’ This shift acknowledges that while data is invaluable, it isn’t infallible—and context, experience, and community voices are equally critical in shaping effective public health strategies.

In this post, we’ll unpack the difference between being data-driven and data-informed and explore why the latter approach may better serve public health goals.

What Does Being ‘Data-Driven’ Mean in Public Health?

A data-driven approach relies heavily on quantitative metrics, assuming that numbers always tell the truth. For instance, public health programs might base funding decisions solely on incidence rates or cost-benefit analyses. Data-driven organizations prioritize hard numbers and predictive models, believing these offer the clearest path to better outcomes.

While this approach has its advantages—especially for resource allocation or epidemiological modeling—it often has significant drawbacks. It can discount critical factors such as social determinants of health, community experiences, and the nuances of human behavior. Moreover, a purely data-driven mindset risks treating all data as inherently reliable, even when it may be incomplete or biased.

What Does Being ‘Data-Informed’ Mean for Public Health?

A data-informed approach, by contrast, sees data as a powerful tool—one piece of the larger puzzle. In this model, data is integrated with community insights, stakeholder feedback, cultural considerations, and professional judgment.

For example, while data might suggest which neighborhoods have the highest rates of chronic disease, being data-informed means working with those communities to understand the underlying causes and co-designing interventions that reflect lived experiences. It’s about blending the science of data with the art of public health practice.

Why Public Health Should Be Data-Informed

1. Context is Essential

Public health operates in the real world, where context is everything. Data can tell us what is happening—rising rates of diabetes in a specific population, for example—but rarely explains the why. Understanding context often requires qualitative insights from community leaders, health workers, and those directly impacted. A data-informed approach values this context and incorporates it into decision-making.

2. Data Can Be Misleading

Not all data is created equal. Collection methods, reporting biases, or gaps in information can skew results. For instance, data on vaccination rates might overlook undocumented populations or those with limited access to healthcare. A data-informed mindset includes space for skepticism and encourages triangulation of data with other sources, reducing the risk of flawed conclusions.

3. Supporting Innovation in Complex Systems

Public health systems are inherently complex. A data-driven approach might favor safe, proven methods, but innovation often requires taking risks and thinking creatively. Data-informed organizations balance evidence with experimentation, using data as a guide rather than a gatekeeper. For instance, piloting a novel community engagement strategy may not initially have robust data support, but it can lead to breakthroughs when guided by informed intuition and trust-building efforts.

4. Centering Communities and Equity

Public health is fundamentally about people. A purely data-driven approach can sometimes dehumanize issues, treating them as abstract problems to solve. Being data-informed ensures that community voices, cultural context, and human emotions remain central. For example, designing an HIV prevention program isn’t just about the prevalence of infection; it’s about understanding stigma, systemic barriers, and community resilience to create interventions that truly resonate.

5. Flexibility in Rapidly Changing Environments

Public health often requires agility—whether responding to emerging threats like COVID-19 or adapting to new policy environments. A rigid reliance on data can hinder timely action, especially when data collection lags behind real-world changes. Being data-informed allows for flexibility, integrating early qualitative signals or anecdotal evidence into responsive strategies that save lives.

Striking the Balance in Public Health

Public health professionals know better than most that there’s no one-size-fits-all approach to solving health challenges. While being data-driven has its place, a data-informed approach provides the flexibility and humanity our field requires. Data can guide us, but it cannot replace critical thinking, lived experience, or the power of collaboration with the communities we serve.

By embracing a data-informed mindset, public health organizations can make smarter, more empathetic decisions that go beyond the numbers to address the root causes of health inequities and improve outcomes for all.

So the next time someone says, “Let’s follow the data,” let’s ask: what else do we need to know to make the best possible decision? After all, the greatest public health solutions don’t just come from data—they come from understanding.

Note: A Previous Version of this Article appeared over at Dawn Chorus

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