Narrative Structure Tutorial

Sections:

Three Classic

Narrative Structures

The three classic narrative structures in library data storytelling—transformation, continuity, and discovery—draw from major theorists in narratology, literary criticism, folklore, and semiotics. These frameworks interweave a story's informational content with its emotional impact, creating compelling narratives that resonate with audiences.

These structures describe most stories told by nonprofit organizations and institutions. Each approaches the story differently, evoking specific emotional responses while conveying essential data. Understanding the nuances of these narrative structures allows storytellers to craft more effective advocacy messages tailored to their specific goals and audiences.

Transformation

Transformation narratives follow the classic hero's journey structure, chronicling triumphant change after significant challenges. As Campbell (1949) noted in his analysis of heroic narratives, transformation requires both challenge and assistance. These stories highlight obstacles overcome and celebrate remarkable achievements, creating a sense of awe and inspiration. In fundraising campaigns, transformation stories showcase how donations enabled the library to convert aspirations into tangible improvements. While compelling, transformation narratives should position the library not as the hero but as the essential helper - the guide who provides resources that empower community members to become heroes in their own journeys.The emotional impact stems from witnessing awe-inspiring feats accomplished through perseverance, such as a literacy program that dramatically improved outcomes for struggling readers. Transformation stories work best when they foreground human achievements and demonstrate how library resources catalyzed meaningful change. Libraries are not the hero, but the helper.

Continuity

Continuity narratives celebrate resilience and persistence through cycles of change, highlighting what endures despite disruption. Drawing from Todorov's (1969) equilibrium theory, these stories trace journeys from stability through challenge to renewed equilibrium. Library continuity stories might showcase how services adapted during technological revolutions while maintaining core missions, or how century-old reading programs evolved while preserving fundamental literacy values. These narratives evoke feelings of reassurance and stability, demonstrating institutional dependability across generations. However, continuity structures also effectively illustrate persistent problems—segregated neighborhoods with unequal library access or ongoing funding disparities affecting service delivery. By showing what remains constant (whether positive achievements or troubling inequities), continuity stories provide context for understanding both institutional strengths and areas needing transformation. They connect past and present, helping audiences appreciate the library's enduring relevance while recognizing opportunities for future growth.

Transformation

Continuity

Discovery

Discovery narratives thrive on exploration. They harness curiosity and investigation to reveal unexpected insights about library services and community needs. Rooted in Barthes' (1970) concept of the enigma code, these stories create intellectual suspense as audiences follow an investigative journey toward understanding. Discovery might be the structure when librarians notice disconnects between program attendance and card registrations, prompting surveys that reveal surprising barriers. This kind of story might emerge from collaborative community mapping that exposes transportation challenges preventing neighborhood access. Unlike transformation or continuity stories, discovery narratives don't require neat resolutions—they thrive on questions, exploration, and revelation. The emotional experience resembles a mystery unfolding, with satisfaction coming from newfound understanding rather than triumphant outcomes. Discovery stories particularly excel at engaging stakeholders in collaborative problem-solving, inviting them to share the excitement of unexpected findings—whether that's uncovering unrecognized community needs or identifying untapped resources in the budget. They create momentum for innovation by framing challenges as intriguing puzzles awaiting creative solutions.

Discovery

S-DIKW: A Step-by-Step Approach from Data to Story

DIKW stands for data, information, knowledge, and wisdom. The DIKW pyramid appears regularly in information studies textbooks as one of the most fundamental, widely recognized, and taken-for-granted models for understanding information conceptually (Rowley 2007). T.S. Eliot is often credited with inspiring DIKW with his 1934 poem “The Rock”: “Where is the wisdom that we have lost in knowledge?/Where is the knowledge that we have lost in information?” DIKW has alternately been called the information hierarchy, the knowledge hierarchy, or the wisdom hierarchy. While there have been multiple ways of understanding and visualizing DIKW, the set and sequence of four levels remains: data, information, knowledge, and wisdom.

The S-DIKW Framework (S for storytelling) adapts DIKW for practical applications in data storytelling (McDowell 2021). Each level of the framework relates to human abilities to derive stories from data, to interpret data with context as information stories, to take action based on those information stories, and to enact wisdom based on what they know.

S-Data: Basis of information in story
S-Information: Data interpretation with context as story
S-Knowledge: Actionable information in story
S-Wisdom: Which story to tell when, how, to whom, and more

The S-DIKW framework can be used to transform raw data into a data story.

Click to investigate each level.

S-Data:
Extracting and Representing Relevant Facts

More complex data storytelling needs supporting data visualization. The translation from data to story starts with data, perhaps with a simple visualization like a chart representing an insight extracted from raw data. Then, the visualization (or other representation of data) is enriched with new elements as it transcends into the levels of information, knowledge, and (ideally) wisdom. Before representing data in narrative or visuals, data storytellers should ensure they have permission to represent them. In addition, data should be of high quality, respect data privacy policies, and be as fair and non-discriminatory as possible (Dykes 2020). Data that represent people deserve additional consideration so that they cause no harm to anyone they represent.

Choosing data points depends on the purpose of the story. S-Data is the visual representation of a relevant fact or facts as represented in data. A relevant fact is something worth telling through a story, something that people will want to know. Transforming data into S-Data means building the main scene of the data story, which can be represented through a clear chart.

Various data analysis techniques can be applied to extract relevant facts from data, such as descriptive statistics, inferential statistics, data mining, and machine learning. These techniques enable the identification of patterns, relationships, and trends within the data, making them informative to an audience.

Depending on the analyzed data type, different techniques can effectively represent data visually. For quantitative data, common chart types include bar charts, line charts, scatter plots, and histograms. Qualitative data is often represented using charts like pie charts, stacked bar charts, and word clouds. These visual representations provide a concise and intuitive means of conveying information, facilitating comprehension and interpretation.

For example, imagine that a library has seen slow but steady decreases in donations over the past five years. This trend could be represented as a line chart supplemented by the raw data of the highest donation amount—five years ago—and the lowest dollar amount from the current year. A question from a recent survey might result in a bar chart of responses as to why people do or do not donate to the library. A series of choropleth maps might indicate where donors and, by sharing each map sequentially, could show where people most or least likely to donate have lived over the past five years.

S-Information:
Data interpretation with context as story

S-Information requires turning data into information by tailoring the data and visualization to story form for the intended audience. S-Information has two main considerations: the amount of information and what the audience already knows (or does not know).

The amount of information depends on the type of audience. Audience types can be grouped into three categories with some intersection: the general public, executives, and professionals. The general public may have little to no previous knowledge of the data, so the key tactic is to avoid overwhelming them with too much information. The focus should be on presenting the most relevant facts, visually and engagingly. Executives are typically high-level decision-makers in organizations who rely on data for insights to make challenging choices. They often have limited time and need concise information that is accurate and clear, so they need key findings, trends, and recommendations. Professionals consist of individuals with a specific domain expertise or professional background who have a deeper understanding of data and require more analytical information. Data stories for professionals should explain the method of data analysis, assumptions, and limitations, so that they are well-positioned to communicate their expert insights to executives. The below table provides some recommendations about what general amount and type of information to represent based on the audience type.

To convey data in story form, the data should not overwhelm the audience. It is a classic error to attempt to include too much data in a story. In the above example of decreased library donations over the past five years, the public will likely need just the line chart (amount of decrease over time). Executives may need the line chart and the bar chart (survey responses indicating why people do or do not donate). Professionals, especially advancement and communications or public relations professionals, will benefit from the line chart, the bar chart, and the choropleth map (neighborhoods where donors live) to understand as much as possible about the analysis of the problem and which neighborhoods and local festivals or events they might target with a campaign.

S-Knowledge:
Transforming Data into a Meaningful Story

S-Knowledge is information enriched with context that prepares audiences to understand data as information that prompts them to action. If the data are represented in a clear visual, then the S-Knowledge step means adding to that visualization captions, annotations, or symbols that reinforce the meaning of the data. If the data are narrated, then the S-Knowledge step is the main takeaway or even the moral of the story, in practical terms. In both cases, this transformation involves indicating in clear terms what knowledge is gained. Audience members should understand what should be done and one (or more) examples of how to take action.
Drawing on the above example, data showing a downward trend in year-to-year donations may be prepared as a line chart (S-Data). Then they are transformed for sharing with professionals and executives to indicate the scale of the resulting funding deficits over time. (S-Information). Next, they are transformed into accompanying annotations and narration to support a proposal for what action to take, such as launching a new fundraising campaign (S-Knowledge)

S-Wisdom:
Bigger Picture and Next Steps

S-Wisdom involves guiding the audience toward the bigger picture they should consider when taking action. The story can include a contextual explanation of what is a wise or just actionable outcome. For example, if the five-year donation decrease is the result of a significant regional economic recession, then a fundraising campaign might be an unwise response. Instead, the library might work to see how collaborations with other community organizations that are also suffering from the economic situation can pool resources to increase positive impact for the community.

Sometimes S-Wisdom means supplementing existing data with a speculative visualization of what a continued trend might mean for the future. Most often, this speculation will be added to data visualization as annotation or accompanying narration that explains the bigger picture implications. For example, a library with downward donations trends located in a region with economic stability or growth might benefit from an exploratory visualization of what continued trends in donation decreases would mean in another five years. This visualization could help to emphasize the importance of launching a fundraising campaign as soon as possible, to work toward reversing the trend and increasing library donations for the long term. This could be accompanied by narrating what specific benefits would result from increasing donations, to help motivate the necessary actions.

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Narrative Structure Tutorial

Sections:

​

Three Classic Narrative Structures

S-DIKW: A Step-by-Step Approach from Data to Story

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Three Classic

Narrative Structures

Transformation

Continuity

Discovery

S-DIKW: A Step-by-Step Approach from Data to Story

S-Data: Extracting and Representing Relevant Facts

S-Data:
Extracting and Representing Relevant Facts