The Power of Data Storytelling in Libraries

An interview with Dr. Kate McDowell

Posted on January 22, 2025 in Blog Posts

In an era where libraries face increasing pressure to justify their existence with data and metrics, a growing debate has emerged: Should libraries adopt business-like quantitative approaches or emphasize their foundational values, such as intellectual freedom, access to information, and community space? Data storytelling offers a compelling answer by synthesizing data and narrative to foreground an institution’s needs and values. In short, data storytelling is a way of communicating data by contextualizing it within stories and visualizations. Data aren’t value-neutral but represent values and can be harnessed to advance collective interests.

Dr. Kate McDowell, an associate professor at the School of Information Sciences at the University of Illinois Urbana-Champaign, has championed data storytelling as a means of advocating for libraries. Her grant-funded project, the Data Storytelling Toolkit for Libraries (DSTL), is developing a DIY toolkit for librarians to take data they already have and use them to create compelling narratives. In this interview, we discuss not only the toolkit but also broader concerns such as misinformation, fears around data, and the need for stories.

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Efforts over the past couple of decades to run libraries more like businesses and use data, metrics, and benchmarks to justify investment in them have come under scrutiny. Some have argued that libraries should refuse this quantitative approach, which misunderstands the position of libraries in a democratic society, and instead return to appeals to values—freedom of thought, the circulation of good information, the need for public space, etc.—to make the case for investing in libraries. How does data storytelling fit into this debate?

Stories incorporate values. When stories of individuals whose lives are better because of the library connect with data like community demographics and literacy rates, they can be profoundly powerful. An individual example activates emotions; numeric indicators of how many people would benefit in the same way activate resource allocation. It is reasonable to imagine that our values are reflected in our data already, and vice versa. What we count as an institution and as a field indicates what we value. Data can seem not to belong to the institution when they are collected for required reports, but taking active ownership over how those data inform our stories empowers libraries to communicate their impact to their local public (or academic) audiences.

Stories convey values in memorable and meaningful ways, but if we simply collect data without including insights from them in our stories, we miss the opportunity to show how impact aligns with values. I routinely say: we need to put story before storage. It’s not just about putting data into story form; we must use data to build stories that travel on their own. Stories are wonderful, but they are most powerful when they are retold.

Resisting datafication makes sense to me when it comes to large corporate platforms disregarding intellectual property rights. Even then, however, I believe we should use all available tools to support libraries, including leveraging the benefits of large language models to simplify our messages for large audiences—after all, these are language-averaging tools based on large numbers of peoples’ words. If the average way of saying something makes our message stronger, we should use it! However, in specific cases, decisions like this require judgment, discernment, and deep commitment to library ethics and values.

We can wholeheartedly embrace data storytelling and put the data we have to work for expressing our values without losing our fundamental ethics. Indeed, the more effectively we can combine these approaches, the greater reach our values will have.

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What I find intriguing about data storytelling is that it offers a path forward out of this debate in a way that feels generative for libraries. You’ve conducted several surveys and interviews with librarians throughout your scholarship in this area. What sorts of stories have libraries successfully told using their data?

I’ve actually conducted interviews with librarians, fundraisers, IT professionals, and others in fields related to the information sciences. I focused on graduates of information schools, and I began this work back in 2014. At the time, library and information science students were going into a variety of professions, and this is what piqued my interest. There are some fascinating patterns across professions in terms of how people use storytelling in their work.

One of the strongest features of librarian data storytelling is an unusual facility with narrative, perhaps not surprising from a field where so many people joined because of their love of reading. At the same time, the most effective stories are actually things that might be considered mundane from an insider perspective but provide a lot of insight for public stakeholders. For example, libraries routinely survey their patrons to find out what their current and emerging technologies might be. Those surveys produce data that become purchasing plans and budgets, but we rarely tell those stories. If the public understands that the way that we spend money directly depends on how they tell us they need technology to support their lives, then we not only have a storytelling opportunity, but we also have an ongoing opportunity to engage with public audiences and trade the roles of teller and listener between us. This is where it becomes exceptionally powerful. It’s not just that the library tells stories, it’s that all the stories are based in communities.

At the same time, there are those extraordinary moments when the story available from library data helps to combat misinformation. I’ve often shared an anonymized story about a library where the mayor unilaterally decided that the library director had overreached in their plans for library expansion. Rather than tell the board or the city council, the mayor directed the architects to reduce the planned square footage, which is a form of disinformation. And he threatened the library director with termination if the director revealed what had happened, hoping to instigate the spread of misinformation through the altered plans. Fortunately, someone noticed the discrepancy, and both the library board and city council came together to restore the original plans, because that is what the usage data showed as necessary. It’s not just that libraries should always be bigger, better or grander—it’s that they should serve their communities. And that service relies on data about how many people need what, when, and how.

Often, when it comes to presenting data, especially to stakeholders, there’s this assumption that the numbers (or graphs) “speak for themselves.” Certainly, data visualization can be a powerful tool for storytelling, so why do we need stories? You may have alluded to an answer to this question earlier when you said that the goal is to create stories that can “travel on their own.” Can you elaborate on that?

Data visualization is an awesome tool, and in the hands of a good storyteller, it can seem to tell the story. But then again, think about those times when someone shares their raw data with no processing or interpretation. Nobody wants to see a spreadsheet that hasn’t been prepared for public consumption. It’s disorienting, tedious, and overwhelming because, put simply, data do not tell stories. We humans tell stories, in language, supported by images. The insights that might seem to emerge from data, like Athena from the head of Zeus, actually rely on a whole set of epistemological frames that start before the collection of the data and continue through visualization and presentation. In other words, we only count things by determining what counts, and determining what counts is a judgment call. It’s never simple or obvious, even if it is based on a long tradition of doing things that way.

I’ve also seen the concept that data speak for themselves go terribly wrong in library contexts. For example, one librarian shared with me that they had worked up a really elaborate visualization of staffing hours, expertise required, and potential funds available in order to make the case for a new humanities librarian. But when the library director saw the data, they didn’t see the “obvious” story. Instead, they saw an opportunity to cut the existing humanities librarian and replace them with part-time labor. Why risk letting data drive without professional library insights and ethics? The reality is that data don’t drive. They don’t decide. We do that. There are always humans deciding, and humans make sense of things and remember things best through meaningful and memorable stories.

So data storytelling represents both an opportunity and a risk. Data storytelling provides great potential for not only making an immediate change but also for others to retell the story of why that change was important, so that the change sustains for more than just one season. But there’s also tremendous risk in forgoing storytelling and assuming that data speak for themselves. Unless you can read your audience’s mind and know exactly what they will see in the data, it’s much better to craft a story. In story form, at minimum, the data are accompanied by clear and hopefully compelling arguments that may lead to the changes we hope to see.

Your mention of risk reminds me of your discussion of the “fear of data” in your scholarship. Data come with a real and understandable fear, particularly for people with limited data literacy, that they might expose our vulnerabilities, support the wrong stories/values, or simply elude our comprehension. It’s no wonder why some librarians are leery of a data orientation to advocacy. Can you tell us about how the Data Storytelling Toolkit emerged and how it might improve librarians’ relationship to data?

First, three of the four modules focus on aspects of storytelling, because everyone knows something about storytelling. Most people have room to improve their storytelling, and those modules—audience, narrative structure, motivations and goals—are designed to leverage what people already know about libraries and library data to create effective stories. The DSTL is designed to spark real interest in data as a source of impact and advocacy stories. Each of these four components helps librarians and collaborators communicate the value of their work as data in story form. In particular, the motivations and goals module is designed to reflect the “folklore” of library justification (with specific examples from library news stories, press releases, project reports, etc.) while also helping tellers solidify their stance. For example, for library advocates to stay motivated to act, it is important to find strategic and practical ground on which to base a stance against censorship. I talk about this in great detail in my forthcoming book, Critical Data Storytelling for Libraries, which is with ALA Editions now and expected to be published in 2025.

Though we have gotten into broader discussions about libraries, data, and advocacy, what I like about DSTL is that it’s incredibly practical. Within a few clicks in the demo, you can select your motivations, look at story templates, and refer to real-life applications of data-driven storytelling. Can you give us some examples of prospective use cases for this toolkit?

At a time when so many are exploring AI for generating narratives without understanding the criteria that go into constructing them, this could be seen as radical project that re-centers human narration and real people as storytellers. Sadly, rising pro-censorship movements and shrinking budgets for truly public spaces—where you can walk in the door without having to believe or buy anything—threaten to undermine library funding. Inspiring people to tell stories about what libraries actually do is key, especially when so many detractors are reacting to misinformation about libraries rather than understanding their impact. The project is built on a foundation of listening to librarians who have already been successful in advocating, distilling the folklore of library advocacy into practical and highly adaptable guidelines for building library data stories.

Where does the DSTL go from here? What does success look like for this project?

We are in the first year of the new grant project, Implementing the Data Storytelling Toolkit for Libraries, in collaboration with the Public Library Association (PLA). From here, the tool will continue to improve as you see it now, with user-experience design at the heart of our research (over 100 librarians have directly participated, over 1000 have attended webinars!). That will be the Create pathway, supporting the development of stories. With the PLA’s Benchmark team, we will also be designing, implementing, and integrating a new Explore pathway based on topics that are frequently needed to justify investments in libraries, such as digital literacy. The Explore pathway extends the survey research that PLA has conducted for many years, combining it with narrative guidance to help library workers learn more about how to communicate their own library’s impact based on national data and tailored guidance.

With another 10 invited talks scheduled this year, I plan to be very busy with coaching and supporting libraries as they communicate their impact. My core professional mission is to help libraries to advocate for resources so that they persist as institutions that are at the heart of local democracy.

💫 Check out the Data Storytelling Toolkit for Librarians

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