← CHI 2026 map

CHI '26 · Honorable mention · full-paper review · confidence medium-high

Degraded Data in Nonprofit Homebrew Databases

Amy Voida , Ellie Harmon , Temi Olorunsogo , David R Karger

DOI PDF Program page Page JSON

This is a strong qualitative CHI paper because it turns a familiar complaint—bad data—into a more precise account of how nonprofit work actually produces and tolerates degraded records. The contribution is not a new system, but a careful empirical vocabulary and a useful reframing of what counts as a problem under constraint.

Axes Lens

Rare contribution shape, typical evidence profile. The point here is not a score. It is to show what kind of claim the paper makes, and whether the evidence pattern is unusual or baseline in this 268 -review set.

Review Summary

This paper’s value is in how it repositions data quality as an organizationally situated outcome rather than a purely technical failure. The authors do not simply say that nonprofit databases are messy; they show, through interviews with 19 volunteer administrators across 14 U.S. nonprofits, that degraded data takes recurring forms and that those forms are produced by legitimate tradeoffs in time, money, technology, and relationships. That is a meaningful CHI contribution because it gives the field a more grounded way to talk about why “better data” is often not the dominant objective in practice. The five-way characterization— incomplete, out-of-date/out-of-sync, bulk/lack of fidelity, anecdotal/modest, and garbage—helps organize a space that is usually discussed only in vague terms. I also think the paper is careful in a way that strengthens it: it does not overstate harm, and it explicitly asks whether degraded data is even a problem when the alternative is more work to collect better data. That nuance matters. The main limitation is scope: this is a qualitative study of a specific organizational context, so the findings should be read as descriptive and interpretive rather than broadly generalizable. Still, within that scope, the paper is persuasive and likely to be useful for researchers thinking about data practices, nonprofit systems, and design that respects competing priorities instead of assuming data quality is always the top goal.

What Changed

Canon before

Prior CHI work has characterized messy information ecosystems and information work, but this paper shifts attention from system messiness itself to the downstream quality of the data those systems produce and sustain in nonprofit volunteer-management settings.

Departure from common sense

The paper argues that degraded data in nonprofit homebrew databases should not be read mainly as a technical defect to eliminate. Instead, it is presented as a predictable consequence of people pursuing other legitimate priorities—time, money, technology, and relationships—so data quality is often secondary to more urgent work.

Actual novelty

The paper’s novelty is a new empirical characterization of degraded data in nonprofit volunteer-management databases: it identifies five recurring genres— incomplete, out-of-date/out-of-sync, bulk/lack of fidelity, anecdotal/modest, and garbage—and ties them to the practical priorities that shape data work.

Evidence

The paper is grounded in an interview study of 19 volunteer administrators across 14 U.S. nonprofit organizations. It reports five recurring forms of degraded data and argues these arise from legitimate competing priorities rather than simple neglect. The discussion also qualifies the implications, noting that degraded data may not be a problem relative to the costs of collecting better data, while still limiting future uses.

Limits

Method limits

The evidence comes from semi-structured interviews with volunteer administrators in U.S. nonprofits, so the findings are interpretive and context-specific rather than statistically generalizable. The paper’s categories and causal framing are grounded in participants’ accounts, not direct measurement of data quality.

Deployment limits

The design implications are most relevant to nonprofit information ecosystems where volunteer labor, limited resources, and homebrew databases shape data practices. The paper does not establish a universal remedy for degraded data, and any intervention must respect the legitimacy of competing organizational priorities.

Boundary conditions

The findings are bounded by nonprofit volunteer-management contexts and by situations where data collection competes with other work. The paper itself notes that degraded data may have little effect on existing practices, but can foreclose alternate future uses of the data.

Manual check: none

Position in field

This is a CHI qualitative contribution that reframes data quality as an organizational and practical outcome of information work, rather than only a technical database problem. It extends prior work on messy information ecosystems by naming recurring degraded-data forms and connecting them to resource constraints and legitimate priorities.

Abstract

Researchers have characterized contexts for information work that are supported by often-messy ecosystems of information systems. Although important infrastructures for information work, little is known about how these less-than-perfect systems affect the data that is managed. We present results of an interview study of these information ecosystems in the nonprofit context. We find that the quality of data is often systematically degraded in five ways: incomplete data, out-of-date data, “bulk” data, “anecdotal" numbers, and “garbage" data. These forms of degraded data result from informants having other, legitimate priorities in their work—each more important than managing data. We discuss how degraded data often has little effect on organizations’ existing data practices, but forecloses alternate possible uses moving forward. Finally, we reflect on how we might be able to address these challenges while still respecting the legitimacy of choosing other priorities and explore what it would mean to design for a data imagination.