Have you ever had a meeting when everyone in the room looked at the data and understood exactly what do next, all at the same time? Years ago, I was a children’s librarian working in a building that needed renovation. Engineers explained that an old cement ceiling could not be removed. We were gathered at a table, looking at the ceiling height problem and reading through data about lighting challenges, from lack of windows to wiring limitations. Suddenly, a colleague said: “Why don't we dig the floor down deeper?” It was the solution! Because we dug deeper, the children's department in the basement is as bright and airy as we dreamed.

Great data stories thrive at the intersection of information and emotion, and a handful of approaches can help library staff interpret data in memorable ways for advocacy using data storytelling. Data storytelling for libraries is in demand. The IMLS-funded Data Storytelling Toolkit for Librarians (DSTL) planning grant project guides users through advocacy arguments, data as evidence, audience attitudes, and narrative strategies to produce a tailored guide for crafting an effective data story. The first DSTL workshop in fall 2022 had just over 680 registrants, and the publicly available webinar recording has had almost 200 views since then. Last year’s Public Library Association webinar on “Data Storytelling: Advocating for the Library and Community” had almost 400 attendees. We are already looking ahead to a second implementation grant, and seeking to ensure project longevity and reach through collaborative development with several national library organizations.

Data storytelling is the art of communicating data as information in story form. It makes information memorable by taking inspiration from narratives in fields as diverse as journalism, business, epidemiology, education, and more. Like any stories, data stories can be classified in cultural patterns. Yet each specific account emerges between a teller and audience. Audience reactions inform how any story is told, so that they are more than receivers of information; they are our collaborators. Audience responses polish stories much in the way that editors polish essays, with responses or comments that refine the next version. Audiences also keep the story alive through retelling.

However, most conversations about data storytelling focus mostly on data and typically miss the importance of the story. While much has been made of the promises of data analytics—with special excitement about predictive potential—most libraries and librarians need to start with the basics of communicating data as information in story form. Through eight years of workshops and 15 years of teaching, with input from audiences through discussion, workshops, and interviews, the Data Storytelling instruction team has developed and refined many guides for great data storytelling. The team started out in 2017 with just two people: a storyteller with a background in library data and advocacy and an astronomer whose open-source tool building led him to the information sciences. Initially, the course was an eight-week improvisation on the theme of data storytelling; six years later, it is a well-structured learning experience that moves students through critique of data as misinformation, critique of journalism and data interpretation, and a three-step process (proposal, draft, presentation) of building students’ own data stories and storytelling skills based on their passions. These are a few of our pedagogical approaches and narrative structures for library data storytelling.

WHERE TO START?

Librarians are often awash in data and need someplace to begin. A few simple questions can help to prepare for data storytelling. The first is: What information problem are you trying to solve? After that, consider any related data sources available, always working first from what has already been collected, and what that might mean. Next, consider that all stories need action and usually involve some kind of conflict. Finally, choose context wisely, leaving only what is necessary for your specific target audience to understand your story.

Imagine that the information problem you are trying to solve is why the community should invest in updating a public computer lab. The data sources will be usage statistics, certainly, but the story will need some action. The problem might be how often users struggle with outdated technology or what kinds of basic information needs have to be met, from completing a job application to doing homework. Finally, the audience may need context such as how long the computer lab has been in existence, an account of where else (if anywhere) basic computing needs can be met, and how many people have benefited from this resource over time. If the numbers have dropped and that correlates with the outdated state of the available computers, that could be the start of a compelling story advocating for increased funding.

KINDS OF STORIES

More familiar kinds of stories are easier to remember. Providing they are accurate to the data, narrative structures can help solidify a data story and make sure it will be memorable. Based on a broad overview of narratology and related narrative theories, three narrative structures and their possible combinations cover most library data storytelling needs.

The continuity narrative structure is about cycles, journeys from stability to disruption and on to a new stability. The emotional impact is resilience or stability despite challenge. It is most useful when libraries reorganize to sustain great services. For example, when data indicate changes in community demographics such as language diversity, collections and the ways services are promoted must change. The continuity narrative structure, from literary critic Tzvetan Todorov’s ideas, might start with the long history of services in multiple languages, invoke democratic ideals of access, detail the gaps that have emerged with recent demographic changes, and propose a way forward that meets the new needs while continuing the library’s longstanding mission.

In the transformation structure, based on Joseph Campbell’s hero’s journey, a protagonist encounters obstacles and is transformed by the process of overcoming them. This structure conveys a feeling of awe at the hero’s triumph, but it is not a universal structure and should be used with caution. Library workers are heroic, certainly, but self-aggrandizing stories can backfire and risk losing the audience’s interest or trust. When we tell transformation stories, it is good practice to tell them about someone other than ourselves. In this structure, the library is the helper that assists with key resources or treasures along the hero’s journey. A better strategy is to tell a data story about how the library helped a hero—a specific individual or an anonymized amalgamation example of community heroes—with data about how library services made an impact.

The discovery narrative structure comes from the hermeneutic or enigma code, which is one of five codes defined by Roland Barthes that define ways that meaning drives a narrative. This is a recurring cycle of suspense and discovery, intrigue and information, curiosity and satisfaction. Emotionally, it is like a mystery story, where the audience follows the investigation of a detective-as-narrator in coming to understand what happened, why, and how things could be different. When a children’s librarian asks the audience, “What do you think will happen next?” or a presenter opens with a question such as, “Have you ever found the book drop too full to return another book?” this is the discovery structure in action. Rounds of suspense and satisfaction may repeat within a discovery narrative. For example, a discovery-based story about children in a community lacking library card access would ask and then answer questions about how many children don’t have cards, what they could find in the library, what they are missing compared to their peers, and how that gap matters for homework, learning, and other measures. As library staff tell this story, they bring the audience along with to understand what’s happening and toward a concluding argument about what should be done.

“If you want a healthy community that seeks out knowledge, and seeks informed conversation, then advocate for it beyond your walls,” writes library scholar R. David Lankes. While the story of one hero might seem simplistic, it can be important to tell. First, if the story is accurate to the available data and gives it context, then that story can be shared explicitly as an example of what the data do or could mean to one individual. Second, talking about large numbers of people tends to numb audiences, and showcasing one person can counteract that. The emotional impact of a transformation story is the joy of seeing the impossible made possible in a person’s life. Audiences need to see themselves as potential heroes of stories in which libraries are the helpers.

Libraries are full of continuity, discovery, and transformation stories. Data are not merely indicators to be measured and collected—they are the source of our best stories. Librarians are excellent collectors, but our tendency to amass data without a clear communication plan is limiting. We need to put story before storage. Metaphorically, we need to dig down deeper into the data we have already collected. These narrative strategies are blueprints, and they should be adjusted for audiences and with the knowledge that we are all storytellers already. Turning our deeply human storytelling habits to library data can help us to advocate for our institutions, making that data meaningful and memorable.

Dr. Kate McDowell is an associate professor at the University of Illinois at Urbana Champaign who focuses on storytelling as information research, social justice storytelling, and how the history of library storytelling can enhance contemporary data storytelling. Her projects engage contexts such as public libraries, non-profit fundraising, and health misinformation in online discourse.