How to Communicate Complex Ocean Data to Non-Specialist Audiences
Ocean data is getting more complex, more abundant, and more important. Advances in ocean observation, such as satellite remote sensing, autonomous underwater vehicles, citizen science networks, and environmental DNA, are enabling us to gather information about the ocean at a scale and resolution that simply wasn't possible a decade ago.
All this data can help us make better decisions for marine life and for people…if we communicate it properly.
Data alone doesn't communicate. Numbers, datasets, and model outputs need context, framing, and relevance before most people can make sense of them, let alone act on them. And the gap between what ocean data shows and what non-specialist audiences can understand and use is wider than many researchers and organisations appreciate.
Here are some tips to help you close that gap.
Start with the story, not the data
This is the most important principle in communicating complex data (and the one most frequently ignored).
Researchers and data scientists are trained to present data first and draw conclusions from it. For specialist audiences, this makes sense. For everyone else, it's the wrong order. A non-specialist audience presented with a dataset, a chart, or a set of figures before they understand why any of it matters will disengage quickly. Not because they're uninterested, but because they have no framework for making sense of what they're seeing.
Start with what the data shows and why it matters. Use the data as evidence for that point, not as the point itself.
Instead of: presenting sea surface temperature anomalies across a thirty-year time series and then explaining what they mean for coral reef bleaching events.
Try: opening with the finding: coral bleaching events are becoming more frequent and more severe. Then, using the temperature data to show why.
The data hasn't changed. The order has. And for a non-specialist audience, the order makes all the difference.
Ask yourself before you write or present anything: what is the one thing I most want my audience to understand or do differently as a result of this? Start there.
Choose the right format for your audience
Not all data communication works the same way for all audiences. The format you choose – a written summary, a chart, an infographic, an interactive visualisation – should be driven by who you're communicating with and what they need to do with the information, not by what's easiest to produce or what looks most impressive.
Written summaries work well when your audience needs to understand nuance, context, or implications. A policymaker who needs to understand the implications of declining fish stocks for a management decision is often better served by a clear, well-written two-page brief than by a dashboard they don't have time to explore.
Charts and graphs work well when the pattern in the data is the point. Showing a trend, a comparison, or a distribution communicates something that words alone cannot - with one caveat. It needs to be clear to someone without specialist knowledge. A chart that requires a legend, three footnotes, and prior familiarity with the dataset to interpret it is not communicating knowledge. It’s displaying data.
Infographics work well for processes, relationships, and summaries, and when you want to give someone an overview they can take in quickly. They work less well for precise data, nuanced findings, or anything that requires careful qualification. An infographic that oversimplifies a contested finding can do more harm than good.
Interactive visualisations can be powerful for engaged audiences who want to explore data themselves, but they require time, technical literacy, and motivation that many audiences don't have. They're rarely the right choice for a first communication with a new audience.
As with all types of science communication, the question to ask is not "what can we make?" but "what does this audience actually need, and what format will help them get there?"
Accuracy without complexity
The tension at the heart of communicating complex ocean data is this: simplifying makes data more accessible, but simplifying too much risks misrepresenting it. This is a genuine tension, and there's no formula for resolving it — but there are some principles that help.
Be specific, not vague. Vague language such as "significant changes," "substantial impacts," or "considerable variation" might feel like it's making data accessible, but actually, it’s obscuring it. Specific language is clearer and more honest. "A 40% decline in seagrass cover over twenty years" is easier to understand and harder to misinterpret than "significant losses of seagrass habitat."
Include the important caveats – but maybe leave out the specialist ones. All data has limitations. Not all of them need to be communicated to every audience. Include the caveats that would meaningfully change how a non-specialist interprets or acts on the finding. Leave out the ones that only matter to other specialists.
Don't let caution drain the meaning. Researchers are trained to qualify, and rightly so. But qualifications that swamp the findings – "while noting the limitations of the dataset and the need for further research, and acknowledging that results may vary under different conditions" – make it harder, not easier, for a non-specialist audience to understand what you found. Be honest about uncertainty - but don’t lose your audience while making the point.
Check your work with a non-specialist. The best test of whether your data communication is working is to ask someone outside the field to read it and tell you what they understood. The gap between the two is where the communication work still needs to happen.
If you need support translating ocean data and research findings for non-specialist audiences, Ocean Oculus can help. From written summaries and briefings to infographics and explainer content, we work with ocean organisations and research projects to make complex data accessible.
