Book review: THE DATA STORYTELLING WORKBOOK
Genres:
- Information Management
- Data Science
Review posted on:
4.11.2023
The number of pages:
247 pages
Book rating:
4/5
Year the book was published:
First edition published 2020
Who should read this book:
- Data analytics, data scientists, and anyone working with data that is presenting their findings.
Why did I pick up this book and what did I expect to get out of it:
When deciding to pick up “The Data Storytelling workbook” I didn’t have much to go on but the book itself. There are almost no reviews about it online (or at least I didn’t find any). But after reading the back cover and the table of content the book looked very intriguing, especially since I’m interested in both storytelling and data. I’m excited to see how the authors included the part where they combine theory and practice around real-world data storytelling scenarios.
I expect to learn more about how to present different types of data in the role of a story hero. When doing so, what do you need to be aware of, what do you include and what exclude so that the presentation is actually a story and not a presentation of your data, results, or your process? In short, I expect to learn how to make data more human and more relatable so that the listener/reader can easily identify and empathize with the message of the story.
My thoughts about the book:
In “The Data Storytelling Workbook” Anna Feigenbaum and Aria Alamalhodaei made sure that the book is packed with easy-to-read and understand content, practical tips, and exercises that will help you understand how to work with data and use it to create stories that carry your desired message to your target audience. In the book, the authors explain in depth how big of a role narratives in stories have, which I miss in most of the storytelling books I have read so far. They also write in-depth about other important parts of storytelling and not just about data. This is definitely a well-rounded book about data and presenting findings from that data in a story form. The only thing that I missed in this book are more commercial examples/case studies. If you are interested in data and storytelling you should definitely read this book.
If you picked up this book please let me know what you think about it in the comment section.
A short summary of the book:
The book starts with a quick guide on why they wrote the book, how to use it, and why storytelling is one of the main topics of it. In the first chapter “A Narrative Approach to Data Storytelling” the authors point out the importance of understanding your audience and how that affects the language you use, and the data you show when telling your story. The authors share a great exercise in this chapter called “Mind the Gap” where you track the gaps between what your audiences are currently thinking, feeling, and doing versus what you are aiming for them to think, feel, and, do. They also explain in-depth the importance and meaning of the story narrative and types of narratives, story structure, which and how much details to use, how to present data as a character, the role of conflict in the story and to look out for the backstory of the data you are using (this is something many overlook).
In the second chapter “Navigating Data’s Unequal Terrain” the authors cover the growing data divide between small and big companies and what that means for the world. Afterward, they talk about big data now and before and discuss the assumptions many people have that data is objective and in doing so neglecting that many data sets have biases from the beginning when the data was collected or even when people were planning which and what kind of data to collect. This is the part where they discuss more about the “Standpoint theory” which covers the problem of bias in data that people are or aren’t aware of. The authors make a point that data discrimination is uniquely dangerous for two reasons. First, because it is based on automated processes, and second because data tools are embedded within processes and technologies that are highly secretive and subtle and it is hard to uncover the exact mechanisms at work in many algorithms used by companies.
The third chapter “Visual Data Storytelling” covers the importance of understanding the data, where and how it came from, and why it is dangerous to use templates when presenting without questioning yourself “Why does this visualization matter?” An important point the authors make is that when the process of designing a visualization is detached from the process of data collection, preparation, and analysis, it can be hard to create a meaningful data story. In this chapter, you will get to read about “The Four Pillars for Data Storytelling” which will help you navigate the difficult waters of different perspectives and how those perspectives might lead to different interpretations of your visuals. You will also learn about “Chartjunk” – which is a visualization of some data that does not tell the viewer anything, but it may look “cool” to the presenter. You may fall into the “Chartjunk” because you think you have to show some grids of data, or you want to use the latest BI tool. You will also learn when it is a good idea to (and when not to) use Tree Diagrams, Narrative Networks, and Timelines as visualizations. The authors also make a very intriguing case of why and when to use “The Comic book format” for presenting data. This was something I found very insightful and useful and I was surprised that I hadn’t thought of using this format before myself. Did you ever think about it?
In the fourth chapter, the authors delve into Storytelling with Maps, which in my opinion could be part of the third chapter. In this chapter, they go discuss the importance of cartography and the power embedded within maps. They argue that making a map is about making choices as to what will and will not be included. When talking about story mapping we are talking about integrating narratives into maps and also including the right symbols since no symbol you use is neutral. When thinking about symbols you have to think about the culture of your audience, as symbols have different meanings in different cultures.
In the final, the fifth chapter “Future-Proof Principles” the authors discuss the meaning of technology and principles. Technology changes and can become outdated while the principles in the book are long-term. This is more of an “In Conclusion” chapter and only contains a couple of pages of the author’s thoughts.
My notes from the book:
- Storytelling with data involves combining visual and narrative forms of communication. Plotlines are examples of story structure in data storytelling. They act as a device to organize/frame those elements in order to generate different ways of understanding, sharing and communicating the data.
- The more we zoom in on a data point, the more we see its complexity, while the more we zoom out, the more we see its context.
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Before starting any data storytelling project always think first about your audience. Segment them and use appropriate terminology, and amount of data.You also need to find the answer to the following two questions:
1. What do you want your audience to think, feel, or do?
2. Then think backwards from this objective. What key messages does your audience need to receive, understand and believe in order to perform these actions? - It is important to identify gaps between current mindsets and the mindset your work aims to achieve.
- When getting your message across make sure you leave the audience with enough room to explore and reflect for themselves, but also make sure you leave them with the right narrative.
- The most fundamental structure of a data story is its narrative. The narrative is what you construct to deliver your message. It's how you embed what you want your audience to know, how you inspire them to feel, and the way you incite them to act. A narrative is an account of connected events. How these events unfold over time forms the narrative structure of a story.
- When telling "data stories the data analyst (and his processes and methods) must fare into the background. The narrative should represent the emotional journey of the "subject" and not the research journey of studying the "subjects"/data points.
- In order to empathize with a character, the audience needs to connect to that character. Rather than a caricature or symbol, a character should be well-rounded, revealing rather than hiding their flaws. In narratives, characters are given backstories in order to create these connections. Taking this idea and applying it to data we should ask what kind of contextual information is needed to make a data set come alive?
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To begin to investigate a dataset's backstory, select a completed datased and ask the following questions:
- What is the raw data?
- How was the data gathered and refined? By who?
- What analyses were run and why?
- What are the patterns and anomalies so far? -
How do you decide what details to include and what details to leave out? In order to justify its existence, every passage of description should do two or more of the following things:
- Ground the reader in the setting.
- Symbolize or foreshadow something important to the story.
- Add subtext.
- Show something about the viewpoint of the character's personality.
- Add conflict or complication.
- Hint at a backstory. - Whenever we develop a research question, we do so with certain assumptions. Data becomes biased when researchers fail to account for the way that our assumptions can implicitly shape how we gather and interpret data. Standpoint theory is a useful tool to identify and reduce bias in data.
- All of the visual decisions that we make are influenced by existing cultural, social, and economic norms and practices. Are you aware how you are being influenced?
- When the process of designing a visualization is detached from the process of data collection, preparation, and analysis, it can be hard to create a meaningful data story. Understanding the content, backstory, or potential biases of the data you are working with is very important.
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The four pillars of Data Storytelling:
1. Symbols -> The meaning of a symbol must be culturally learned.
2. Colour -> Investigation into cultural perceptions of color is essential whenever designing for a new audience.
3. Caption -> Because our emotions, values, and associations differ according to culture, background, and experience, the meaning of a visual image takes on will be varied. As the same visual image can mean different things to different people, we need to guide viewers to the intended message of our "data story". When creating captions think about the other possible interpretations of your visual images then brainstorm how best to use captions to steer the viewer along the narrative of your data story.
4. Editorial -> Editorial layouts refer to how the different visual elements of your data story are strategically arranged onto your platform. - Gaps in data are often just as revealing as the data available. The task of the data storyteller is not to throw up his/her hands and say "Oh well" to missing data, but to find ways to include it in the narrative he/she tells.
- Although comics are familiar to everyone, they are vastly underexplored for data-driven storytelling. The combination of words and images often make comics more relatable than other graphic forms. Through the use of emotive stories, people can make stronger connections with the data, helping them to make sense of their own personal experiences with the specific topic.
- With the comic format you can for example reveal the unspoken meaning behind the words by splitting the panel and in one half show the spoken words and in the other half the unspoken meaning. This view of someone's inner thoughts illustrates that what we say out loud is not always what we feel inside.
- Storymapping refers to the practice of integrating narrative into cartographic practice. Zooming in story maps offers us a more detailed and subjective accounting of space.
