Three Classic
Narrative Structures
The three classic narrative structures to use in library data storytelling are transformation,
continuity, and discovery. The three ways of describing narratives draw from major theorists in
narratology, literary criticism, folklore, and semiotics. These descriptors interweave a story’s
informational content of a story with its emotional feeling.
These three structures can describe a vast array of stories, but for our purposes they capture the majority of stories told to advocate for many nonprofit organizations or institutions. Each is worth exploring in more detail to delve into the nuances of how these narrative structures work as advocacy stories and how audiences experience each kind of narrative differently.
Transformation
Transformation stories are usually triumphant, told after the fact of a major transition and in celebration of what was accomplished. A common example is that of a successful fundraising campaign. There are many data stories embedded in any fundraising campaign, including how many people donated overall, how many people donated at different levels of contribution, and how a few major gifts enabled the library to move from speculative dreams into real changes in infrastructure. The information in this story structure emphasizes trouble and obstacles. The action is usually struggle to overcome challenges. The ending is the success with a feeling of awe.
The transformation structure is the hero’s journey. A hero encounters obstacles and is transformed by the process of overcoming them (Campbell, 1949). The emotional impact of a hero’s journey story is the joy of watching someone do the impossible, which might include sustaining a seemingly impossible level of dedication to service over many years. In superhero stories, the joy is seeing the impossible made possible through their special powers. This narrative structure is appropriate in any data story that involves awe at transformation, wonder at scale, or marvelous accomplishments.
Libraries themselves, however, do not make great heroes to outside audiences. Although they transform, and those who transform them see their work as heroic, the library itself doesn’t translate well as a hero to others. Why? Because the way this story structure works typically involves a person who transforms by triumphing over obstacles. The library is more like a helper character, the mysterious figure who appears on the path just in time, provides the magical key to unlock barriers ahead, and disappears again into the background. The library is the helper in stories of success. The people are the heroes.
Continuity Story
Some of the most important heroes in the histories of institutions are quietly legendary people who persevere despite incredible changes in society, technology, and world history. The typical continuity story structure is a cycle. These stories are journeys first from stability to disruption and then forward to a new stability or equilibrium. A continuity story emphasizes what persists despite disruption.
The equilibrium concept comes from narrative theory about the structure of the fantastic (Todorov, 2004). The concept of continuity in this book is informed by many retellings of a story called “The Stonecutter,” in which a person makes a series of wishes to have more power and becomes many things—a prince, the sun, a cloud, a mountain—only to ultimately return full circle to work as a stonecutter (Sturm, 2008). The emotional impact of the equilibrium structure is resilience or stability despite challenge. This structure is often used when narrating institutional history—from founding through struggle to present day—or family history, or even species survival despite seasons of famine, plague, and disaster.
The typical feeling of this narrative structure is the assurance of long continuity, but this structure works for both feel-good stability stories celebrating longstanding libraries and troubling stories about the persistence of injustice. For example, in many places, the overlap of poverty and geography continues, generation after generation, and extreme economic inequities mean that some people do not own anything at all. People who need the public library and everything it provides the most can’t always access it. Children from every neighborhood should easily be able to get to the library and back home again safely, so this kind of story would combine the two structures. Acontinuity story would lead to a follow-up story about transformation and what should be done.
Discovery
The discovery story can occur when a library looks outward into the community. Imagine that library staff are working on a proposal to collaborate with a neighborhood community center. The first meetings go very well until the issue of transportation arises. Discovery begins as the library staff and center volunteers look at maps together. They see that the library and the center are about five miles apart, but to make that journey, children and families would have to take streets with no sidewalks or a trip on public transportation that involves transfers from bus to train to bus. They begin to understand why children in this neighborhood are less likely to have library cards than those in other parts of the city. Discovery stories often result from combining two kinds of data, like information about geography or neighborhood and library users/cardholders.
Discovery stories also involve the library looking inward. Imagine a library that has been doing a series of very well-attended programs. However, staff notice that program attendance is not translating into more library cards or higher circulation. People seem to be coming in for the programs and leaving without connecting with the array of opportunities that the library offers. Why? To discover more, they decide to survey people as they come in for the next program to learn more about what they like—and don’t like—about the library.
Discovery stories don’t necessarily have all of the answers. They center around questions of why or how something about the library is working or not working. The discovery structure comes from Roland Barthes’ comprehensive semiotic analysis of a novella, an attempt to understand the ways that meaning drives a narrative. Barthes’ enigma or hermeneutic code is a narrative structure organized around a sense of intrigue or curiosity (Barthes, 1970). A discovery narrative structure centers on suspense and discovery, intrigue and information, curiosity, and satisfaction. It feels like a mystery story, where the audience follows the investigation of a detective as they come to understand “whodunnit” or what happened. The emotional experience is that of suspense and intrigue followed by the satisfaction of coming to understanding. This narrative structure appears in library storytelling when a librarian asks a young audience: “What do you think will happen next?” This enigma structure of suspense and surprise or satisfaction may be an overarching story theme or a micro-structure repeated many times within a narrative that combines various structures.
Just like mysteries, discovery stories can be very exciting. For example, discovering there is more money in a fund than was previously thought is often a great way to instigate and easily justify a special project. Grant funds and library staffing turnover sometimes unexpectedly accumulate budget surplus. The suspense for the audience is: “What should we do with the money?” And the narrative progresses with an aim to convey excitement, potential, and possibility about what happens next.
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.
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S-Data: Basis of information in story
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S-Information: Data interpretation with context as story
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S-Knowledge: Actionable information in story
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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.
