In the early months of the COVID-19 pandemic, our team at ABC 10News was covering the surge in San Diego County the way most newsrooms were — tracking case counts, reporting on health orders, updating audiences as numbers climbed. The stories were accurate. The data was current.
But something wasn’t landing.
If cases were still spreading, the messaging was failing. Audiences weren’t connecting with the urgency the numbers represented. Charts and tables told people how many. They didn’t tell people where — or what it meant for them specifically.
Maps are my go-to storytelling tool. They help audiences understand where something is happening and what it means in relation to their own lives. So when I started thinking about what was missing, a heat map came to mind almost immediately.
Inspired by the Johns Hopkins global tracker, I built San Diego County’s first COVID-19 data dashboard — an interactive heat map that visualized cases by demographics, geography and community hotspots. Our team sourced county health data daily, built a backend spreadsheet to fuel automation and maintained updates as the situation evolved.
What the map revealed changed the conversation.
Certain zip codes were deeply red. And that raised a question that no case count chart had surfaced: why? The answer led directly to investigative reporting on pandemic inequities affecting underserved communities — people who were still going to work, sending children to childcare, visiting grocery stores, doing the essential activities that kept their families going. The visualization didn’t just inform. It generated story ideas that mattered.
The dashboard reached more than a million people locally and globally. It also trained the team — in data gathering, interpretation and visualization, including Tableau — leaving lasting new capabilities inside the newsroom long after the pandemic receded.
That project crystallized something I’d been learning across different environments for years: data doesn’t create mission. It clarifies whether you’re serving people effectively.
Data Reveals Patterns, Not Priorities
The decision to build the dashboard didn’t come from analytics. It came from editorial judgment about what the community needed in a moment of uncertainty. Data played an essential role once it existed — showing how people were using it, which features were most valuable and where clarity could be improved. But the strategy was grounded in audience need, not traffic forecasts.
That pattern has repeated itself throughout my career. Strong content strategies start with purpose. Data validates execution. It doesn’t create mission.
What Data-Informed Strategy Looks Like in Practice
This approach held up across very different environments.
At KRON4, making performance data visible inside the newsroom helped teams adjust coverage in real time without waiting for directives. At ABC 10News, data showed which stories were reaching large audiences and which ones were building deeper engagement — a distinction that informed how we balanced breaking news with enterprise reporting and where we invested additional time or promotion.
At SDSU, speed mattered less than trust and comprehension. Data helped identify which stories helped audiences navigate the institution — and which ones created confusion or disengagement. That informed not just what we published, but how we structured, timed and followed up on coverage.
Different contexts. Same principle: data clarified effectiveness without dictating purpose.
Measuring Engagement, Not Just Reach
Early on, I made the same mistake many teams make: treating all traffic as equal.
High page views felt like success. Growth curves felt reassuring. Over time, it became clear that volume alone doesn’t indicate value. Platforms change. Algorithms shift. Audience habits evolve.
Instead, I focus on signals that suggest usefulness and trust — how long people stay with content, whether they complete videos or audio, whether they return over time, whether they share intentionally rather than impulsively. These metrics don’t always produce the biggest numbers, but they more accurately reflect whether content is doing its job.
Some of the most meaningful work I’ve been part of — investigative databases, public service resources, long-term explanatory projects — never produced viral traffic. What they produced was sustained attention from audiences who needed them. That’s a tradeoff I’m comfortable making.
Using Data to Identify Gaps
Data becomes most useful when it highlights what isn’t being addressed well.
By looking at performance by topic, format and audience segment, patterns emerge. Certain stories attract attention but don’t retain it. Others draw smaller audiences who engage deeply and return repeatedly. In institutional settings, this kind of analysis often reveals gaps between what organizations emphasize and what audiences actually value.
The most effective strategies I’ve worked on used those insights to adjust emphasis, not abandon purpose. When storytelling aligned more closely with audience interest and lived experience, engagement followed.
What Data Can’t Tell You
Data can’t tell you whether a story is worth pursuing. It can’t define mission. It can’t replace editorial responsibility.
I’ve seen teams struggle when short-term performance metrics begin to outweigh long-term public value. Important work can appear expendable if success is defined too narrowly. That isn’t a failure of data — it’s a failure of interpretation.
Used well, metrics clarify whether execution aligns with intent. Used poorly, they become justification for decisions already made.
The strongest strategies I’ve worked on began with clear purpose and audience need. Data helped refine execution, surface gaps and measure impact — without replacing judgment.
If your metrics are pushing you away from your mission, you’re asking the wrong questions. If they’re helping you serve people more clearly and consistently, they’re doing their job.
