In this Newsletter
- Introduction to Storytelling with Data
- The Elements of Data Storytelling
- How is Data Storytelling Evolving?
- What is DPM Focusing On?
Introduction to Storytelling with Data
Storytelling with data (aka data storytelling) is the art of blending narrative techniques with data analytics to communicate insights in a clear, engaging and persuasive manner. It goes beyond the traditional data presentation, using the art of character and storytelling to transform complex data sets into compelling narratives that drive decision-making and action.
At the core of data storytelling is the recognition that numbers alone, no matter how compelling, often fail to resonate with or engage an audience. Raw data, expressed through tables, charts, and figures, can make it difficult for audiences to grasp the underlying message or takeaway. Data storytelling addresses this challenge by humanizing the data, making it relatable and understandable through the power of narrative. It’s about connecting the dots between disparate data points to weave a coherent story that elucidates trends, patterns, and insights.
An often-used book for those learning data storytelling is Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Knaflic. According to Knaflic, "there is a story in your data. But your tools don’t know what that story is. That’s where it takes you—the analyst or communicator of the information—to bring that story visually and contextually to life." Knaflic's book provides practical skills in building visualizations that can help you bring your data story to life.
The Spectrum of Data Storytelling Runs Wide
What makes data storytelling a broad practice is that the spectrum is wide for what is defined as a data story. For example, data stories can be as simple as a single, clean visualization that itself tells a story. Alberto Cairo, who is often seen as a luminary in the space of data storytelling, stated the following about his goals with data storytelling in a recent interview:
"I wanted to show journalists or writers (when I say journalists, I mean writers), that in general, creating a graphic is not particularly hard at the basic level. If you really want to understand a dataset or a piece of information it is absolutely mandatory to visualize it. It’s a great tool to use when you’re writing a story, not just to write the story itself but to create some sort of graphics that provide readers with the evidence behind what you are saying."
Conversely, data storytelling can be as complex as a globally-coordinated group of journalists working to expose political corruption, for example, The Panama Papers. According to Mair et al:
The so-called Panama Papers exposed like never before a system that enables crime, corruption and wrongdoing, hidden by secretive offshore companies. It had historic global effects. At least 150 inquiries, audits or investigations were announced in 79 countries around the world due to its revelations. There were resignations from high-ranking officials, including the prime minister of Iceland. The prime minister of Pakistan was removed from office. ICIJ won almost twenty awards, including the Pulitzer Prize and the Data Journalism Award. (p. 33)
Regardless of the project being a simple visualization or a globally-coordinated exposé, both require the ability to translate data into a story. The scale is worlds apart, but the process to get from start to finish is notionally similar.
Data Storytelling is Interdisciplinary
Data storytelling is an interdisciplinary phenomenon that has grown in momentum over the past decade. You can find the origins of data storytelling in a business context (e.g., improving the way in which you tell a story using data in a business context) and journalism (e.g., data-driven journalism, investigative journalism, etc.). We are now seeing data storytelling expand its footprint more deeply into social media content creation – where we're also seeing more growth of independent and progressive journalism.
Data storytelling was fueled by the growth of data analytics and the need to represent good data visualization within content and presentations. However, as data analysis and data science techniques and approaches have become more automated and mainstream, the need for specific skills has deepened.
Technical skills alone, though, are not sufficient. Effective data storytelling also demands strong creative skills. This includes the ability to craft a compelling narrative arc that engages the audience, contextualizes the data, and highlights key messages. Storytellers must be adept at creating a narrative flow that guides the audience through the data, building towards a logical conclusion or call to action.
Visual representation plays a pivotal role in data storytelling. The right visualizations can illuminate insights and make complex data more digestible. This requires a keen eye for design and an understanding of how different types of charts, graphs, and interactive elements can be used to convey information effectively.
The audience is the final but crucial component of data storytelling. Understanding the audience's needs, interests, and level of data literacy is essential for tailoring the story and its presentation. The goal is to make the data story not only informative but also relatable and engaging for the audience, ensuring that the message not only reaches them but also resonates and prompts action.
The Elements of Data Storytelling
Data storytelling brings together various elements to convert raw data into a compelling narrative. According to HBS Online, data stories have three main components:
- Data: Thorough analysis of accurate, complete data serves as the foundation of your data story.
- Narrative: A verbal or written narrative, also called a storyline, is used to communicate insights gleaned from data, the context surrounding it, and actions you recommend and aim to inspire in your audience.
- Visualizations: Visualizations of your data and narrative can be useful for communicating its story clearly and memorably. These can be charts, graphs, diagrams, pictures, or videos.
When data is wrapped with a narrative and visualization, the idea is that the message can reach the audience in a more rational, fact-based way. According to Feigenbaum and Alamalhodaei, "when data and stories are used together, they resonate with audiences on both an intellectual and emotional level” (p4). This can be as simple as a refined visualization or can be more complex and include character plots and arcs.
Let's briefly explore each of these element.
Data: Discovering the Story
The first element of data storytelling is the data itself, which forms the backbone of the narrative. This involves collecting, cleaning, and analyzing data to uncover the significant insights that will drive the story. Quality data is paramount; it must be accurate, relevant, and representative to ensure the story's credibility and reliability. The storyteller must delve deep into the data, employing statistical methods to discern trends, patterns, and anomalies. This rigorous analysis helps in identifying the core message or insight around which the story will revolve.
Narrative: Engaging your Audience
The narrative element of data storytelling is about weaving the data into a coherent and engaging story. This doesn’t mean fabricating a tale but rather constructing a logical and compelling narrative that connects the data points. The narrative should have a clear beginning, middle, and end – introducing the context and problem, presenting the data-driven insights, and concluding with a resolution or call to action.
A good data story is often anchored in a human-centric perspective, making the data relatable to the audience. It involves framing the data within a context that resonates with the audience’s experiences, concerns, or interests. The narrative should guide the audience through the data insights, helping them understand the significance and implications of the data.
Visualization: Enhancing Comprehension
Visualization is the third critical element of data storytelling. It encompasses the selection and creation of visual aids like charts, graphs, maps, and infographics that help to illustrate and clarify the narrative. Effective visualizations can transform abstract numbers into intuitive and accessible information, making it easier for the audience to grasp complex data insights.
The choice of visuals depends on the data and the story’s key messages. Each type of visualization serves a different purpose: line charts can show trends over time, bar charts compare quantities, pie charts depict proportions, and maps reveal geographical patterns. The visual design should enhance the story, highlighting the key data points and supporting the narrative flow.
The Data Story: Creating a Cohesive Whole
Beyond data, narrative and visual design, the essence of data storytelling lies in their integration. It's about balancing the data, narrative, and visuals so that they complement each other and create a cohesive whole. The data provides the evidence, the narrative adds context and emotion, and the visuals offer clarity and impact.
Effective data storytelling requires that the narrative and visuals are aligned with the underlying data insights. The story should be data-driven, not just data-informed, meaning that the narrative and visuals are directly shaped by the data findings. This alignment ensures that the story remains grounded in facts while still being engaging and accessible. However, there must also be a layer of storytelling with that data; to deliver it in an effective way to your audience.
How is Data Storytelling Evolving?
In the last decade, there has been a momentous shift to content and news being served up on devices. For example, nearly 60% of US adults now use their device as the primary medium for engaging with content. This further translates into social media platforms, apps and other content delivery tools/vehicles are adapting to a device-driven delivery system.
For data storytelling, this trend will likely have the following impacts:
- Just focusing on a visualization within the art of data storytelling may not be enough. You need to wrap engaging content (e.g., video, text, etc.) around that analytic to engage an audience – and be multi-channel in the delivery. The story around the visualization and the way in which it is created and deployed is increasingly important. Key, though, is that the visualization or image must be immediately accessible – given the ever-decreasing attention span (and amount of content) of consumers.
- A multi-channel delivery system opens up the opportunity for a more decentralized, independent and progressive journalism/content where anybody can create their own brand of data-driven content and news. This at once means broader opportunity and more skills needed within the individual journalist and content creator.
- A deeper integration of technology within journalism. We're already seeing data engineering and management techniques performed by front-line journalists – e.g. sourcing and cleaning data and doing story discovery through PivotTables. However, this is only the beginning. With the proliferation of open data across the world and NGOs/non-profits providing access to data sources, the need for melding journalism and data technologies will only grow.
What are the Required Skills?
It's challenging for a single person to have all of the skills necessary for large-scale data stories. However, for smaller-scale projects (e.g., articles, TikTok videos, YouTube documentaries, etc.), a journalist or content creator can pick up the necessary skills to be more proficient in data storytelling. We often see writers and content creators with a journalism or broadcast background coming more towards data management and analytics. And as they progress with increasingly more complex projects (e.g., joining multiple datasets to conduct the data analysis, using machine learning, etc.), they naturally grow their skills.
Below is a snapshot of some of the common skills that are required for data storytelling. Each skill is ranked on a score of 1 to 10, where 1 is you won't need to use it very much and 10 is you would use this skill all the time.
Further, with independent journalists increasingly publishing to social media platforms, we've added two new important skills near the bottom of the above list: content production and content analytics.
What is DPM Focusing On?
We commonly see journalists and content creators growing their skill in the direction of data and analytics. Further, new video and content production skills are required for those independent journalists and content creators. So, we are predominantly focusing on those skills that sit outside of writing – e.g., anything to do with data sourcing and transformation, data analytics, machine learning/AI, and content production.
Specifically, we want to bring "the how" to the global community of journalists and content creators. For example:
- Implementing small- or large-scale data engineering, data analytics, AI and machine learning, and predictive modeling within a data storytelling project.
- Integrating multi-channel content into the data storytelling process. This could be video production (e.g., creating a YouTube video), it could be a TikTok post or it could be an online article.
- Reusing an analytic across multiple channels – manifesting in different story archetypes, characters, plots, or narratives.
- Taking a data-driven approach to your content creation, so journalists and content creators understand where their time is most valuable and appreciated by their audience.
These are just a few ways in which we see data storytelling and how we'll bring content, courseware and tutorials to the global community.
References
- Mair, John (2017). Data Journalism: Past, Present and Future. Abramis.
- Feigenbaum, Anna & Alamalhodaei, Aria (2020). The Data Storytelling Workbook. Taylor and Francis.
- Knaflic, Cole (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley.
- Cote C. Data Storytelling: How to Effectively Tell a Story with Data. Harvard Business School Online. https://online.hbs.edu/blog/post/data-storytelling.
- Giorgia Lupi and Emanuela Papone. Shaping Data to Generate Opinions. The Functional Art of Alberto Cairo. https://digicult.it/articles/the-functional-art-of-alberto-cairo-shaping-data-to-generate-opinions/
