Stop Designing for Exploration. Start Designing for Explanation.

Many data products are built for the wrong user.

They’re designed for someone who arrives with a clear question, the patience to navigate filters and dropdowns, and the analytical confidence to draw their own conclusions from a chart. That user exists, but they’re a small fraction of the audiences government agencies are trying to reach. The rest need something different: not a tool to explore, but an experience that explains.

This is the shift at the heart of closing the interpretation gap: moving from data products that make information available to ones that guide audiences toward understanding and action.

A Framework for Getting There

Think about data communication in four levels (sometimes called the DIKW hierarchy) and ask honestly where your agency’s data products land:

• Data is the raw numbers: accurate and available, but requiring interpretation.
• Information is data with context: comparisons, trends, benchmarks that help audiences understand what they’re looking at.
• Knowledge is information with meaning: What does this tell us about what’s happening and why?
• Wisdom is knowledge with direction: What should we do about it?

Most government data products stop at the first or second level. They make data available and provide some contextual information. Very few reach the third and fourth levels, where behavior actually changes.

The goal is not to tell people what to think, but to guide them far enough through the interpretation process that they can reach their own conclusions and know what action is available to them.

Three Practical Shifts

An exploratory tool says: Here is the data, find what you need. An explanatory experience says: Here is what the data shows, here is why it matters, here is what you can do next.

The difference is not just visual design. It is a fundamental shift in how you think about your audience. Instead of asking “how do we display this data?” ask “what does our audience need to understand, and what do we want them to do after they understand it?”

Practically, this means:

• Lead with the finding, not the data. Instead of presenting a chart and waiting for the audience to draw a conclusion, state the conclusion first. “Childhood asthma rates in this county are 40% higher than the state average.” Then show the data that supports it.
• Build in the “so what.” Every data point your agency publishes should be accompanied by an explicit statement of why it matters and, where appropriate, what action is available in response.
• Reduce choices, increase guidance. Every filter or dropdown you add to a dashboard increases the cognitive load on your audience. For general public audiences, fewer choices with clearer pathways almost always outperform more flexible tools.

The Trusted Messenger 

One of the most consistently underestimated factors in whether data drives action is who delivers it.

Research on public health communication during COVID-19 made this visible at scale. The same data, delivered by different messengers, produced dramatically different behavioral responses. Trusted community figures, local officials, and peers were often far more effective at moving behavior than technically authoritative federal sources.

For government agencies, this has a practical implication: Think carefully about who is amplifying your data, not just how you’re presenting it. A finding that lands flat on an agency website may be highly effective when communicated through a community organization, a state or local partner, or a stakeholder who has credibility with the specific audience you’re trying to reach.

This is especially important for agencies working on issues where public trust in government institutions is complicated. The data you publish is only as effective as the channel it travels through. Choosing those channels intentionally is as important as the data product itself. 

Rethinking how you present data and who amplifies it will close much of the interpretation gap. But some AI tools are making it possible to go further and faster. 

(This is the second article in a three-part series. Look for “From Dashboards to Decisions: Using AI to Make Data Work Harder,” coming soon. Read Part One — “Your Agency Published the Data. Why Isn’t Anyone Acting on It?” — by clicking here.)

Elisabeth Bradley, CEO of Forum One, brings 20 years of experience helping government and nonprofit organizations translate complex digital challenges into real-world impact. She is a regular writer on digital strategy, digital transformation, and organizational change and brings practical, practitioner-level insights on how government and public-sector organizations can make smarter digital investments, build more user-centered experiences, and achieve lasting transformation. 

Previously, Elisabeth worked inside nonprofits, at the Nature Conservancy, the League of Conservation Voters, the Environmental Defense Fund, and the United Nations. Elisabeth holds a Bachelor’s Degree in Civil and Environmental Engineering from Columbia University, and a Master’s Degree in Global Policy Studies, International Energy, Environment, and Technology Policy from the LBJ School of Public Affairs from the University of Texas at Austin.