NEW: Data Storytelling Toolkit for Libraries: Explore Stories

Data Storytelling Toolkit for Libraries: Explore Tutorial

The Data Storytelling Toolkit for Libraries (DSTL) is a do-it-yourself guide to turning your library data into compelling stories. It has been developed through two rounds of funding from the Institute of Museum and Library Services, led by Professor Kate McDowell at the University of Illinois Urbana-Champaign, with the Public Library Association as a partner in the current implementation grant (RE-256709-OLS-24). The DSTL has been designed with and for librarians, building on a long tradition of storytelling in libraries.

DSTL has two interrelated pathways: Create and Explore.

The Create pathway walks you through all the components of a data story with diagnostic questions and guidance on understanding your audience, narrative structure, motivations, and goals. There is no data stored – it’s up to you to bring your own and assemble the story based on guidance and examples. Learn more about the Create pathway from the University of Illinois.
The Explore pathway also walks you through the storytelling process with guidance on selecting evidence, motivations, and a narrative approach. The difference is this pathway is based on pre-loaded data for your library. Use this guide to navigate the Explore pathway: available from the Stories tab in the account header for Benchmark subscribers.

The DSTL pathways are in development, and the final versions will be published by July 2027.

Step 1: Topic

The first step in the path is choosing a topic for your story to focus on. When the pathway is complete, there will be four topics: digital equity, literacy, staffing, and facilities. Currently, only Digital Equity is available.

Hover over a topic to see a tooltip with a brief definition. Anywhere you see an info icon like the one next to the Explore logo, you can also hover over that for additional information.

Click on a topic to move to the next step of reviewing Evidence.

Step 2: Evidence

What are you trying to tell a story about? What information do you need?

At the top of this page, you’ll see some instructions and questions to consider as you review the data for your library.

Scrolling down, you will see a dashboard where you can explore evidence and peer comparisons for this topic. This dashboard draws on multiple sources of data related to your library and community, including the Census Bureau’s American Community Survey, the IMLS Public Libraries Survey, and the PLA annual topical surveys.

Use the toggle to filter the data by a peer group or by key library characteristics. If your library has created custom peer groups in Benchmark, you will find them in the Peer Group dropdown list. Learn more about effective peer comparisons in the Creating a Custom Peer Group tutorial.

If you choose to instead use key library characteristics, use the filters to select peers. The filter options are locale (city, suburban, town/rural), legal basis (local government structure), service area population groups, state or region, and administrative structure (single or multiple outlets).

Next, choose whether to compare library outputs based on annual totals or per capita measures. Select total to compare your library’s total annual reported output measures to the median for peers. Select per capita (per person) to compare rates per person or per 1,000 people in the library’s legal service area. Per capita measures are useful for comparing libraries of different sizes.

Once you’ve chosen your peer group and total or per capita measures for comparison, you can then begin reviewing the evidence.

The text blocks throughout are dynamic and summarize the results.
Radio buttons in tables indicate where you can choose your area of focus that will carry forward to the final story.
In all tables and charts, you see your library in purple compared to selected peers in dark gray.

In the example below, Public Computers were chosen as the output measure to feature in the chart. The text below the chart summarizes the trend shown for your library and selected peers.

Image showing the middle section of the Evidence page: Peer Group and Characteristics filters at the top; Community Needs and Library Outputs tables below the filters. There is a red diagonal line from Public Computers in the outputs table to the visualization of the Public Computers trend chart farther down the page.

Scroll further to continue reviewing evidence from the PLA annual surveys.

The data in the first table are based on multiple-choice survey questions.
The first column shows the number of options available to select, and the Your Library column shows the count of your selections. For selected peers, the counts reflect a weighted average.
The second table shows your library responses for each of the options available to select.

Since this is the Digital Equity topic, the PLA annual survey data comes from the Technology Survey. You can now select an area of focus for your digital equity work – in this case off-site technology was selected in the first table, and you see more detailed information about those services in the second table.

Image showing the near bottom of the Evidence page: table showing the PLA topical survey question selection categories and option counts at the top. There is a red rectangle around the list of selections, with Off-site technology chosen, with a red line indicating Off-site technology populating the table at the bottom. There is a black rectangle around the Your library count column in the table at the top, with a black line indicating where the count is populated in the table at the bottom.

If the library is looking for opportunities to improve their digital equity work, one thing that stands out here is off-site early learning devices. This library does not offer early learning devices for checkout (there’s a red X in that row), but 63% of their peer libraries do. That evidence could help the library make the case for funding, for example.

If your library did not complete the most recent PLA Technology Survey, you will see open circles rather than checkmarks or X’s. You will still see results for selected peers.

Step 3: Motivation

What are you trying to achieve by telling this story?

As you move through these steps, you can see where you are in the breadcrumbs at the top of the page. You can also go back as needed at any time.

Image of the top of the Motivation page: the breadcrumb navigation is highlighted by a red rectangle; the Summary and Recommendation descriptions are also highlighted with a red rectangle.

At the bottom of the evidence step, and the top of the next step – choosing your storytelling motivation – you will see a summary of the evidence based on your selections. You also get a recommendation for the motivation to choose based on that evidence. You don’t have to abide by the recommendation! You can make any choice you want at any step – that’s part of the training and skill-building component of this tool. You will need to make sure that the final story you get makes sense.

Step 4: Narrative Approach

There are three classic narrative frameworks in data storytelling: continuity, discovery, and transformation. There’s no data in this step, just some text to help you pick a narrative approach that works for your needs.

Step 5: Data Story

And now you are at the final step – a data story! The first page you see here has dynamic text and charts based on your previous selections. The data is based on the evidence selected in step 2, while the structure and wording are determined by the motivation and narrative approach you chose in steps 3 and 4. You can export this to PDF or a Word document.

There is also a second page that has more contextual information. It includes details of the storytelling selections and peer characteristics, as well as a description of the topic and ideas for adding to the narrative and telling your library’s story.

Most importantly, this is meant to give you a starting point that you can add to and adapt for your library’s storytelling needs.

Updated: 10/23/2026