What can you learn from being adaptable in a data journalism career?

1 Why adaptability matters

Adaptability is the ability to adjust to changing situations and expectations, and to cope with uncertainty and ambiguity. In data journalism, adaptability matters because you will often encounter new sources, formats, tools, and platforms for data collection, analysis, visualization, and storytelling. You will also have to deal with deadlines, feedback, errors, and ethical issues that may require you to revise or rethink your approach. Being adaptable can help you overcome these obstacles and produce high-quality, impactful, and innovative stories.

2 How to develop adaptability

Adaptability is not a trait one has or does not have; it is a skill that can be developed and improved through practice and reflection. To develop adaptability in your data journalism career, seek feedback and learn from mistakes, embrace curiosity and experimentation, and be flexible and open-minded. Ask for constructive criticism from colleagues, editors, mentors, and audience to improve work and skills. Explore new data sources, formats, tools, and platforms to enhance projects. Try different methods and techniques to find the best solution for stories. Learn from other data journalists and their work, share experiences and insights, adapt plans, goals, and assumptions according to the data, context, and feedback received. Don't get attached to one idea or solution but be ready to explore alternative or complementary options. Respectfully respond to different perspectives and opinions while acknowledging the limitations and uncertainties of your data and analysis.

3 Examples of adaptability

To illustrate the importance and benefits of adaptability in data journalism, let's consider some examples of data journalists who have demonstrated this skill in their work. Hannah Fairfield, a graphics editor at The New York Times, created interactive and informative visualizations to help readers understand the spread and impact of COVID-19. She used various data sources, formats, and tools to produce engaging and timely stories. John Burn-Murdoch, a senior data-visualisation journalist at the Financial Times, adapted to the changing needs and preferences of his audience by creating a newsletter that delivered data-driven insights and analysis. He used different platforms and formats to reach and engage his readers. Mona Chalabi, a data editor at The Guardian US, created data visualizations that challenged stereotypes and biases, as well as raised awareness and empathy. She used simple yet creative methods and techniques to convey powerful messages that resonated with her audience. Adaptability is essential for data journalists as it allows them to create content that is both timely and relevant.

4 Challenges of adaptability

Adaptability is a valuable and rewarding skill for data journalists, however, it also brings certain challenges and risks. Balancing speed and accuracy is one of these. Being able to respond quickly and effectively to changing situations is important, but so is being careful and rigorous with data and analysis. Stress and burnout can be another challenge, so it's important to prioritize well-being and seek support when needed. Maintaining ethics and integrity is also key, as being consistent and transparent with data and story is essential. Respect for data, sources, and audience should always be upheld, as well as disclosure of methods, sources, and limitations. These are just some of the challenges of adaptability in data journalism that should be taken into consideration.

5 Tips for adaptability

Adaptability is an important part of data journalism, and there are several tips and best practices that you can follow to help you make the most of it. Start by planning ahead and being prepared with a clear and realistic plan, backup plans, alternative solutions, and a checklist of steps and tasks. Communication and collaboration are also essential, so make sure to communicate regularly with colleagues, editors, mentors, and audience members. Seek advice and feedback from other data journalists and experts who can complement your skillset. Lastly, reflect on your project and evaluate your performance, outcomes, and impacts. Identify your strengths and weaknesses, seek feedback, set new goals, and keep learning.