by Jill & Mandi
Information graphics and visualization expert Alberto Cairo caused a bit of a stir last fall when he bluntly tweeted his opinion that data journalism and visualization should stop using the term storytelling.
“Storytelling” is a term that ought to be abandoned in journalism, #dataviz, data, etc. It has no meaning and leads to the wrong mindset https://t.co/5cEv184UsD Alberto Cairo (@albertocairo) September 11, 2017
In the replies, he subsequently clarified that the word need not be abandoned if used properly, but should be considered problematic and approached with caution. However, he later went on to double-down on his initial sentiments earlier this year in another tweet, again eliciting much consternation and gnashing of teeth from his followers.
An argument based on evidence and careful reasoning is not a “story”. More often than not, a #dataviz or infographic isn’t a “story”. “Storytelling” puts you in a frame of mind whereby you feel compelled to fit information into a predetermined mold. Ban “storytelling”. Alberto Cairo (@albertocairo) February 22, 2018
At CRC we’ve become fond of thinking in terms of narrative because our clients are often struggling to tell a clear story about the work they do to different audiences. We’ve been honing our skills in doing “data-driven storytelling” – within brief, digestible reports, visualizations, and communications pieces – ** for some time now. And as evaluators and not journalists, and have a different agenda in applying structures in order to organize and communicate data. But, we share Cairo’s concerns _“about a specific mindset that tries to create stories no matter what even if no real connecting tissue exists between facts presented."**_ (For a counterpoint to Cairo from within the data viz community, see this post from Anna Noble.)
If you find yourself in a similar spot, wanting to create compelling story that’s also accurate and useful for evaluation, what can you do? Putting aside what seems to be us to be a debate over semantics and not “storytelling” itself, we suggest setting your sights on creating a data story (calling it whatever you want), meaning a tool to communicate insights about, and raise new questions about, data that incorporates the data itself (quantitative and/or qualitative), visuals, and a central narrative.
Bearing in mind that the goal of a data story is to enable people better understand data by applying context to it and giving numbers a “voice”, effective data stories are original and reveal trends, correlations, or counter-intuitive surprises. They are based in and driven by your data, so having solid and accurate data is key (and guards against some of Cairo’s concerns). If done well, a data story can help drive change. So, now that you know what a data story is, how do you make one?
First, note these five core principles for telling data stories: 1. Trends: Trend stories focus on how something is increasing or decreasing over time. For example, how a students grades have improved or declined over time.
2. Rank order: Rank order stories focus on how items rank. For example, schools with the highest attendance rates or which areas have the highest infant mortality rates. Your story can focus on why those schools have the highest attendance, or, why those areas have the highest mortality rates.
3. Comparisons: Comparison stories review how one item is performing relative to another. For example, how one school is performing relative to another.
4. Surprising or counterintuitive data: These stories focus on data that challenges something that people to believe true, or data that is surprising.
5. Relationships: These stories focus on the correlation of two sets of data. For example, a students involvement in an afterschool program and their improving course grades.
Second, follow these six steps for writing one: 1. Brainstorm a story concept: Based on the purpose for your story and your understanding of what data is available, form a working idea of what your story will be. You can then look more closely at your data for specific data points that confirm or discredit your ideas. While brainstorming a story concept, it is important to focus on a story that you think will be interesting and relevant to your audience.
2. Go data hunting: First, determine the data you will need to support your story. Once you have done that, it can be gathered and housed in one centralized location (e.g., spreadsheet, tables in word document) that can be easily referenced when crafting your story. Only keep the data that is essential to telling an engaging story and that will help convince your audience of whatever you are trying to convince them of. You should only highlight the key figures that people will remember.
3. Use visuals: Adding visuals to your story can help bolster the story you are trying to tell. Try to keep your visuals limited, honing them down to one key chart or image that you want people to share and remember.
4. Shape your story: Every good story involves characters, a challenge, hurdles to overcome and a clear outcome. Try and outline your data story in a similar manner, with a clear beginning, middle, and end.
5. Make it relatable: Add your own personal touch that makes the story relatable. The best stories focus on things that people care about.
6. Provide insight: All good data stories should provide insight and generate awareness. Whats the point you are trying to make with your story? Would someone have made a different decision if they had your data?
With the above principles and steps in mind, what true stories will you tell with your data?
Select sources: https://buzzsumo.com/blog/how-to-write-data-driven-stories-5-core-narratives/ Tableau’s Best Practices Telling Great Stories https://www.maptive.com/101-guide-telling-compelling-story-data/