An Introduction to Data Journalism: Part 1

Banner image: Data on sulfur dioxide levels in China and India visualized on a map / Credit: NASA Earth Observatory via Wikimedia Commons.

An Introduction to Data Journalism: Part 1

We get it: journalists and numbers aren't always the best of friends. If the thought of wrestling with percentages, pivot tables and pie charts gets you a bit panicky, don't worry: you're not alone.  

Here's the good news: doing great journalism using data as a primary source (that’s essentially what 'data journalism' really is) isn't just for journalists who also majored in mathematics. As with many things in life, once you jump in and give it a go, you’ll find it’s more doable than you may have initially thought. With this introduction, we’re kicking off a new series of EJN resources designed for journalists who want to make use of data in their stories but aren’t sure where to start.  

Data journalism involves identifying changes, trends and patterns, or analyzing raw data to gather evidence. This makes for powerful storytelling; as an example, take a look at EJN’s Mekong in Deep Water special report, which relies heavily on data to investigate the complex dynamics relating to dams, droughts and diplomacy in the Mekong River Basin.   

Data-driven journalism can be powerful and useful for a number of reasons, including:  

  • Influencing policy 

  • Informing public debate 

  • Exposing wrongdoing 

  • Creating awareness and understanding of complex issues 

  • Exploring options for solving problems using data 

So, where do you start? Once you’ve identified a story idea, you need to find relevant and reliable data to inform the story. All journalists use data in some form or the other to substantiate their stories; in data-driven journalism, the story emerges from the data.  

The data journalism process / Credit: Mirkolorenz via Wikimedia Commons.
The data journalism process / Credit: Mirkolorenz via Wikimedia Commons.


Where to find data  

  • The Earth Journalism Network maintains an extensive Earth Journalism Data Compilation, which is a useful starting point for finding data relating to environmental topics. Datasets and other data-related resources are available for a wide variety of environmental topics, including: Agriculture & Food Security, Biodiversity, Cities, Climate Change, Energy, Environmental Health, Forests, Mining, Natural Disasters, Oceans, Policy, Pollution, Water and Wildlife Trafficking. 

Remember, data and datasets on their own are often not very compelling or interesting and can be hard to digest. Data only becomes interesting and meaningful when turned into information (contextualized) and communicated in a story. Going from raw data to completed story typically involves the following process:  

Cleaning the data and ‘interviewing’ it 

Just as you would interview a human source, you also need to ‘interview’ the data you’re working with. What is it showing and telling you? Is there anything interesting or unusual? Is there something missing? Are there any red flags or anomalies? These are all potential story hooks. 

A good piece of journalism becomes a great piece of journalism when the journalist skillfully selects the most credible and relevant sources to include in their story and balances their inputs to weave together a clear picture of reality for their audiences. Those sources are often human but can also be data. But, like people, spreadsheets can’t always be trusted: 

“Data can be the source of data journalism, or it can be the tool with which the story is told — or it can be both. Like any source, it should be treated with skepticism; and like any tool, we should be conscious of how it can shape and restrict the stories that are created with it.” 

— Paul Bradshaw, Birmingham City University 

A good habit to get into when commencing with any data-driven story is to ask yourself (and your dataset) some basic questions:  

  • Who — Who published the dataset? Who collected the data? 

  • What — What is the data telling you that’s unusual, unexpected or concerning?  

  • When — How old is the dataset? Is it current or has it been overtaken by newer events?  

  • Where — Where was the dataset published or made available?  

  • Why — Why could this data be important or useful to your audience?  

  • How — What methods were used to collect the data? 

Next, you need to visualize key data in impactful ways. 

How to visualize data 

To get started with creating visualization with your data, we recommend using Flourish. This powerful tool requires no technical knowledge or previous data visualization experience, and allows you to create attractive charts, maps and interactive elements to embed within published stories. There are many examples and tutorials available for inspiration and a helping hand.  

Below is an example of a Flourish visualization made using the  “Time map” template using data from hundreds of thousands of weather stations across the world. Use the arrows to navigate through the various views: 

Over the next few months, we will be publishing several short guides as part of this data journalism series, to take a closer look at how to make sense of and ‘interview’ your data and visualize it in effective ways. Stay tuned!  

If you have specific questions about data journalism you would like EJN’s data journalists to break down in this series, email us at [email protected]


Banner image: Data on sulfur dioxide levels in China and India visualized on a map / Credit: NASA Earth Observatory via Wikimedia Commons.

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