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Data storytelling

for librarians

centering ethics, protection and transparency

Rebekah Silverstein

Elizabeth Szkirpan

06-20-2025

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Hello we're glad you're here!

06-20-2025

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Full name

Book Club Host

Rebekah Silverstein

Elizabeth Szkirpan

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Ethics

Librarian Practices

we collect a lot of data
neutrality is not a thing
we are bound to a code
we collect a lot of data
we are data stewards
new risks blur the lines
consent and privacy
what not to collect

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DEII Protection

ALA Code of Ethics: Upholding access, privacy, and inclusion in data use
OLA Values: Promoting intellectual freedom, protecting against surveillance
FERPA / HIPAA / PPRA: Legal frameworks that guard educational and health data
FAIR / CARE Principles: Community-centered approaches for open, equitable, and respectful data use
Oklahoma Library Law: Title 65 §65-1-105 on confidentiality of user records

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Library Type	What Gets Reported?	What's Missing?	Why It Matters?
Public	Usage, circulation, program attendance, collection size, door counts	accessibility, community, longitudinal outcomes	advocacy opportunities, picture of community needs
School	collection size, circulation stats, instructional sessions	student achievement, (beyond GPA) educator support, demographic maps , OER, ERM	demonstrating value to administrators, advocating for educators & students
Academic	enrollment, staffing, expenditures, titles databases collections, usage statistics	longitudinal student success, diverse usage impact, OER adoption, eResources	ability to align with institutional equity goals, demonstrate CPU, ROI
Private	Similar to Academic, but often more internal reporting	Intellectual freedom & internal policies	Need for robust internal policies to fill legal gaps

Discussion Points for Audience:

"Looking at your own library, what’s a piece of data you currently report that you feel is underutilized or doesn’t tell the full story?"
"Conversely, what data isn't reported that you wish you could collect or share to better advocate for your library and its users?"

Elizabeth Example to Share: Library Services at R1 Libraries Dataset and Mini Presentation

We really don’t want to draw conclusions with data in a way that users do not intend. Think about the Target data pregnancy coupon debacle.
Project Overview: This project investigates the measurable impact of library services on institutional success at Research 1 (R1) universities. It builds a bridge between library-provided services and key university metrics—like enrollment and research output—using a reproducible, data-informed approach. Given libraries’ ongoing need to articulate value in quantitative terms, especially amid declining visibility in strategic campus planning, this work aims to offer an evidence-based framework for advocacy and resource alignment.
Available versus unavailable data and ethical data collection is a major focus of my own research. Open library data are sparse, and are often unreliable or not reproducible. This means that institution-to-institution datapoints don’t carry much weight, though this isn’t to say that our institutional data at our own institutions are worthless but it’s hard to compare meaningfully between institutions. I’ve been working on a multi-year project co evaluate and analyze open data and open metrics beginning with Research 1 (R1) libraries with an end goal of creating benchmark datasets or equations to measure and compare library services between institutions but it’s an uphill battle with a lot of barriers.

Visualizations:

The top left chart highlights the distribution of public vs. private R1 institutions, as well as ARL membership—a reminder that institutional makeup varies widely across the R1 landscape.
The bottom chart reveals the dominant research areas at these institutions—most notably, medicine and engineering—which may influence library needs.
The top right bar chart catalogs the availability of library services across R1s, from interlibrary loan to data services and microcredentialing. Notably, core services like research guides and consultations are nearly ubiquitous, while offerings like microcredentials remain rare.
These visuals emphasize that while institutional goals differ, library services converge—suggesting an opportunity for standardization or benchmarking.

Staffing vs. Enrollment (Page 6)

This scatterplot shows a positive relationship between total enrollment and total library staff. Although the adjusted R² is modest (0.176), it suggests that as universities grow, they tend to modestly increase library staffing. This model helps libraries benchmark staffing needs and advocate for proportional support.

Staffing vs. Research (Page 7)

Here, the data show a much stronger correlation: more librarian FTEs are associated with higher institutional research output (adjusted R² = 0.614). The regression equation offers a practical “plug-and-play” model for estimating impact. This reinforces the message that library investment—particularly in human capital—contributes directly to institutional prestige and productivity.

Resources vs. Research Output (Page 8)

This visualization shows a surprisingly weak correlation between the number of system results (i.e., indexed items) and research output. This might reflect variability in discovery system configurations or limitations in what these broad metrics can capture. It underscores the importance of more nuanced, service-level data—not just collection size.

Infrastructure Services (Page 9)

The bar chart here reveals that nearly all R1 libraries provide extended hours, tech access, and study spaces. These consistent offerings show a shared baseline of physical infrastructure that supports student success, reinforcing that service visibility—not just availability—should be tracked and reported.

What Data Are Missing & Why It Matters

This study relies heavily on public data: IPEDS, Scopus, WorldCat, and library websites. However, these sources have limitations:
They rarely reflect depth or quality of services (e.g., one librarian vs. a dedicated research team).
There is no central or required standard for reporting service availability or usage especially now that IPEDS has planned to eliminate library data from its collection.
Without deeper, more consistent data, libraries risk being left out of strategic planning—despite evidence that they are instrumental to success metrics like research output and degree completion.

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Jurisdiction	Law/Framework	What It Covers	Library Relevance
Federal (U.S.)	FERPA	Student Education Records	Academic/School Library Student Data
	HIPAA	Personal Health Information	Health Services/Partnerships
	Privacy Act of 1974	Federal Agency Records	Gov't/Federally Funded Libraries
	USA PATRIOT Act	Expanded Surveillance Powers	Challenged Library Privacy; Led to Advocacy
	ALA Library Bill of Rights & Privacy Interpretation	Foundational Privacy Ethics	Guides U.S. Library Policy & Practice
State (Oklahoma)	Title 65 §1-105	Prohibits User Record Disclosure	Direct Patron Privacy Protection for ALL OK Libraries
State (Connecticut)	CTDPA & lt;br>(eff. July 2023)	Consumer Data Rights	Vendor Contracts, Digital Services Data Sharing, censorship
International	GDPR (EU)	EU Citizen Personal Data	Vendors/Platforms Serving EU Users
	IFLA Statement on Privacy	Global Library Privacy Principles	International Ethical Guidance

Discussion Points for Audience:

"Considering the differences between the US and UK/EU approaches to data policy, what challenges or opportunities does this fragmented US landscape present for your library in protecting patron privacy?"
"Beyond formal legal compliance, what are some specific ways your library's internal policies empower you to act as a 'trust agent' when it comes to patron data?"
"What's one policy your library has (or wishes it had) that specifically helps you prevent 'bad actors' or inappropriate data use?"
"What steps can librarians take to ensure their library's vendor agreements include strong data privacy protections, even without a comprehensive federal law?“
How can we address gaps in internal policy? Where do legal and regulatory gaps introduce opportunities for policy?

Elizabeth Example to Share: Library Data Privacy Policy

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Policy

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Create

Refine

Advocate

Create foundational policies that reflect librarian professional and core values that should be at the center of library work.

Refine existing policies in response to the changing data landscapes and users needs, including by adjusting for new political pressures.

Advocate by using policies and data to inform stakeholders, shape institutional practices, and guide librarian data practices.

Discussion Points for Audience:

"Considering the differences between the US and UK/EU approaches to data policy, what challenges or opportunities does this fragmented US landscape present for your library in protecting patron privacy?"
"Beyond formal legal compliance, what are some specific ways your library's internal policies empower you to act as a 'trust agent' when it comes to patron data?"
"What's one policy your library has (or wishes it had) that specifically helps you prevent 'bad actors' or inappropriate data use?"
"What steps can librarians take to ensure their library's vendor agreements include strong data privacy protections, even without a comprehensive federal law?"

Elizabeth Example to Share: Library Data Privacy Policy

One ethical approach to ethical data gathering and use is to create and implement a data policy.

Eliz to provide background of policy. Note that the policy was created and implemented before the COVID-19 and library services project that I’ll share soon.
Summary: The uploaded document is a comprehensive Library Patron Privacy Policy developed by a joint public-private academic library. It outlines the institution’s commitment to protecting patron data, emphasizing alignment with legal obligations (e.g., FERPA, GDPR, and Oklahoma state law), as well as professional standards established by the American Library Association. It details what data is collected (both manually and through systems like Sierra, Springshare, ILLiad, and ExLibris), how it is stored and anonymized, and the limitations of third-party protections. Special provisions for Special Collections and vendor relationships are also addressed.

Context: A Rare but Needed Policy

Most libraries—particularly academic ones—do not have a publicly posted, detailed data privacy policy. Among those that do, few offer the level of specificity found here. This policy is not only uncommon in its transparency but also exemplary in addressing:
The use of personally identifiable information (PII),
User consent and opt-in mechanisms,
Third-party vendor data exchange,
GDPR-related rights and opt-outs,
Internal record retention and anonymization procedures.
Its existence fills a critical gap in library privacy infrastructure and sets a high bar for others to follow.

Policy Scaffolding in Practice

This document serves as an excellent example of policy scaffolding, demonstrating how a single institutional document can support long-term library strategy across three critical layers:

1. Creates Foundational Policy

Establishes a broad, values-based framework: privacy as essential to intellectual freedom.
Codifies legal and ethical obligations under ALA, Oklahoma law, FERPA, and GDPR.
Clarifies the library’s internal stance on non-disclosure, use limitations, and retention periods (e.g., Sierra record purges, ILLiad inactivity windows).

2. Refines Practices in a Changing Landscape

Recognizes the evolving nature of data risk by incorporating:
EU privacy standards (even for non-EU users),
Vendor privacy limitations and advocacy efforts,
Use of technologies like EZproxy’s rotating pseudonymous identifiers.
Refines how legacy systems like Springshare and Sierra interface with modern data practices by enforcing anonymization and limiting access to authorized personnel only.

3. Advocates for the Library and Its Users

Transparently communicates rights and protections, thereby reinforcing trust with users.
Provides a basis for advocating stronger contract terms with vendors, emphasizing user control and data minimization.
Encourages patrons to inquire about or request deletion of their data—supporting user agency and positioning the library as a privacy ally.
Important to anticipate versus react in policy: If you’ve ever taken a class with me, you know that I firmly believe in library policies as an advocacy tool. Policy should exist to prevent problems, not be created in response to problems. Good, well-rounded policies tailored for your library address behaviors but also provide a framework to lean on when the library is defending its decisions or practices. Policies allow us to unify our messaging while protecting our staff members.

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Storytelling

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Ethical Framing

Advocacy

Transparency

Equity Method

Storytelling as:

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Storytelling

Converting data to understandable, relatable, and actionable ethical expressions.

Metrics & Metadata

Discussion Points for Audience:

"Can you think of an example where your library used existing, aggregated data to tell a powerful story about impact, without compromising individual privacy?"
"How are you currently using, or how might you use, metadata practices to promote equity and inclusion in your data storytelling?"

Elizabeth Example to Share: University Library and COVID-19

Background: This was created post-library data privacy policy and is an example of what can be done with high-level data, especially when combined with public data.
This presentation tells the story of a private university library's service evolution in the context of the COVID-19 pandemic. Using Power BI, the data visualizations span from 2019 to 2021 and capture:

Changes in library service interactions (e.g., circulation, instruction, digital resource use)
Usage patterns for electronic vs. physical resources
Comparison of library use trends with local COVID-19 case data in Tulsa and Oklahoma
Visual overlays showing an inverse correlation between COVID case surges and library usage

Storytelling with Ethical Data: This presentation exemplifies ethical data storytelling by converting raw institutional and public health data into understandable, relatable, and actionable insights. Here's how:

1. Understandable: Clarifying Complexity

Visualizations are designed to reduce cognitive load: clear labeling, color-coding by service, and segmented time comparisons help non-technical audiences grasp trends.
The data is segmented meaningfully—e.g., instruction vs. database downloads vs. circulation—making the library’s role more visible and easier to parse for stakeholders.

2. Relatable: Connecting Institutional Impact to Human Realities

By contextualizing library usage data within the COVID-19 timeline (see page 4), the presentation anchors usage fluctuations in a shared human experience.
For example, the overlay of average COVID cases with service usage (page 6) invites the viewer to understand how public health realities disrupted students’ access to education and resources—not just abstract trends, but lived effects.

3. Actionable: Guiding Advocacy and Decision-Making

The insights point to concrete action areas:
The surge in resource downloads suggests successful adaptation of digital services.
The steep drop in instruction and in-person services highlights where support or investment may be needed to recover or pivot.
The correlation between case spikes and usage drops can inform future resiliency planning for service continuity under crisis.
Ethical expression is further achieved by transparently showing limits—data is not used to artificially inflate success but to trace real-world impacts.

Why This Matters

By linking library usage to a regional public health crisis, the presentation honors ethical storytelling principles:
It centers user experience over raw metrics.
It resists the urge to smooth over losses, instead offering a truthful account of library adaptability under pressure.
It uses data not to justify status quo, but to provoke informed conversation about equitable access, sustainability, and institutional readiness.

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Misuse & Pitfalls

Overcollection

Gathering more data than necessary or gathering data without clear purpose can weaken the trust users place in libraries.

Even if unused, overcollection also increases the risk of data exposure, misinterpretation, or misuse and can violate privacy laws or policies.

Interpreting data without context, using flawed or incomplete datasets, making assumptions without evidence, failing to apply an equity or bias lens, and neglecting peer review may lead to untruthful, biased, or incorrect interpretations of our data and data analyses.

We may understand what happens in AI at a high level but not what happens in specific AI models. Using AI without proper ethical considerations can lead to perpetuation of bias, erode transparency, and risk user trust, especially if users are uncomfortable with AI.

Unvetted Analysis

Black Box AI

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Misuse & Pitfalls

Telling only positive stories from data that omit complexity, context, or limitations can have consequences outside of eroding user trust, such as accidental erasure, reinforcing bias or narratives, or misrepresentation of libraries or their users.

Conflating operational metrics with impact, identifying correlation as causation, et cetera undermine data storytelling while inaccurately representing quality, equity, and efficacy. Conflation can reinforce some narratives while ignoring others, such as why certain users are missing from our data or why some resources are underutilized.

Be mindful of the data your users have consented you to collect and how they anticipate their data to be utilized. Leveraging data in unexpected ways, even if anonymized, undermines transparency, disempowers users, and contracts core library beliefs.

Storytelling Gaps

Conflation

Neglecting Consent

Elizabeth Examples to Share:

University Library and COVID-19

Quebec Library Documents Published

Example to Share: University Library and COVID-19
While the presentation is a strong example of ethical data storytelling, it also carries potential risks for ethical missteps or pitfalls if not carefully framed or contextualized. Here are several ways it could fall into ethical data misuse:

Overemphasis on Correlation: The presentation shows a clear inverse trend between rising COVID-19 cases and declining library usage (e.g., page 5). However, without further analysis, viewers might mistakenly assume causation, when many variables (e.g., institutional policies, student behavior, tech access) could influence usage. This risks conflating correlation with causation—a common data storytelling pitfall.
Data Without User Consent: Though anonymized, usage data—especially when tied to services like online guides, research consultations, or special collections—may still reflect sensitive user behavior. If the presentation is shared publicly without clarifying what data users consented to share or how it was collected, it could undermine trust, particularly if viewers assume users were unaware their activity was logged. This is why a patron data privacy policy is beneficial in more ways than one!
Potential Erasure of Uneven Impacts: The charts aggregate trends across the library’s entire user base. If not paired with disaggregated data (e.g., by student status, access needs, or discipline), it risks flattening user experiences. For instance, first-gen students or international students may have experienced more disruption than others—but that nuance is lost in cumulative totals.
Framing Loss as Failure: Without careful explanation, steep drops in usage (especially physical services) could be misinterpreted by stakeholders as a failure of the library, rather than a reflection of external constraints or safety priorities. Ethical storytelling requires clear framing to prevent misuse of the data for uninformed critique or budgetary cuts.
Lack of Data Gaps Acknowledgement: The presentation doesn’t overtly discuss data that may be missing—such as informal student use, off-platform resource use, or community impact outside measurable platforms. Failing to acknowledge what is not captured can present an incomplete or overly confident narrative.

Example to Share: Analysis of Documents Acquired in Quebec, 2010–2021

Project Background: Exploration of what can be done/gathered from publicly available library data, especially when combined with other public datasets.
This presentation analyzes over 154,000 documents acquired by the Bibliothèque et Archives nationales du Québec (BAnQ) from 2010 to 2021, focusing on publication trends, language representation, and subject diversity.
After data cleanup, ~105,000 records were visualized using Python and R. Findings show a steady decline in acquisitions after 2018, likely influenced by global publishing slowdowns and COVID-19 disruptions.
French dominates as the publication language, with English as a distant second; 52 languages appear in the data, but most occur fewer than 10 times. Subject analysis reveals concentration in French-language education, literature, and cultural materials, while other languages lack comparable breadth. Additional insights include the overwhelming frequency of non-substantive words in titles and a marked drop in acquisitions paralleling regional COVID case surges. A comparison of Polish-speaking households in Quebec with acquired Polish-language materials further illustrates disparities in representation.
The presentation highlights the importance of robust metadata, equitable collection development, and the potential of library data to support cultural analysis while upholding privacy.

While the presentation demonstrates thoughtful cultural analysis, several potential ethical data misuses or pitfalls could arise if its findings are misinterpreted or insufficiently contextualized:

Overgeneralization from Incomplete Metadata: The dataset contains many missing or inconsistent values, especially in subject fields. Relying on incomplete metadata to draw conclusions about linguistic or cultural representation could lead to misleading claims about publishing equity or collection gaps.
Language Bias in Visualizations: Because French-language publications dominate, visualizations may unintentionally erase minority language presence by compressing or marginalizing other groups. Without disaggregated views or equitable scaling, underrepresented languages may appear nonexistent or irrelevant.
Conflating Absence with Neglect: The absence of Polish or other minority language materials may be due to publisher behavior, cataloging limitations, or other systemic issues. Without nuance, this could be incorrectly interpreted as intentional exclusion by BAnQ, leading to misdirected blame or institutional critique.
Correlation Without Context: Visuals showing a drop in acquisitions alongside rising COVID-19 cases may imply causality without fully accounting for other contributing factors (e.g., budget freezes, digitization priorities, or national policy changes).
Lack of Community Voice: The analysis is conducted externally, without direct input from affected communities (e.g., linguistic minorities). This risks speaking for rather than with marginalized groups, a key concern in ethical data storytelling. As well, while I was able to extrapolate information about the library and its constituency based on my own professional knowledge, ultimately, this is not a library I am intimately familiar with nor is this a community I live in. I risk telling the wrong story or focusing on the wrong metrics by not fully understanding the data and the library it represents.

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Ethical Data Wrangling: This is the process of cleaning, structuring, and enriching raw data into a consistent format. For librarians, this means:

Anonymizing and Aggregating: Your primary tools for protecting privacy. Convert individual records into group statistics: , instead of tracking one student's checkouts, report the total number of checkouts by first-year students in a specific major.

De-identification: Employ techniques like suppression (removing sensitive data), generalization (making data less specific, e.g., age range instead of exact age), or perturbation (adding noise to data) to safeguard privacy while retaining structure.

Data Minimization: Only collect the data you absolutely need for your intentional, ethical purpose.

If you don't need PII, don't collect it.

Protecting PII at Every Step: Librarians are wonder woman unicorn lasso wrangling data stewards.

Librarian Practices: Data Wrangling & PII Protection

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Secure Storage & Access Control: Work with ITto ensure all library data, especially anything sensitive, is stored securely, encrypted, and accessible only to authorized personnel. Many legacy library systems weren't built for modern data security challenges.

Vendor Agreement Scrutiny: Understand what data your vendors collect, how they use it, and what privacy protections are in place. Advocate for strong privacy clauses in contracts.

Clear Opt-in/Opt-out Policies: Provide patrons with transparent choices about what data is collected about their usage and how it's used.

Protecting PII at Every Step: Librarians are wonder woman unicorn lasso wrangling data stewards.

Librarian Practices: Data Wrangling & PII Protection

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If you take anything away today remember that:

Librarian data practice is not about perfection, it’s about intention.
We are here to protect, not exploit.
To represent, not erase.
To advocate, not surveil.
And that’s a story every librarian can tell with kindness.

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Open Discussion Topics:

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What ethical dilemmas related to data have you faced in your own library, and how did you navigate them?
How do you currently advocate for your library's value without violating patron trust or privacy?
What tools, templates, or resources (beyond what we've shared today) would help you better collect, analyze, or share library data ethically?
What are your biggest concerns about AI's role in library data practices moving forward?
What opportunities do you see?

Ask questions about your data using an ethical lens.

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Scan the QR Code to View our Zotero EDII Data Storytelling Group Library:

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https://www.zotero.org/groups/6031043/edii_data_storytelling/library