Understanding social impact of data on local communities

Date15 July 2019
Publication Date15 July 2019
AuthorAyoung Yoon,Andrea Copeland
SubjectLibrary & information science,Information behaviour & retrieval,Information & knowledge management,Information management & governance,Information management
Understanding social impact of
data on local communities
Ayoung Yoon and Andrea Copeland
Department of Library and Information Science,
Indiana UniversityPurdue University Indianapolis, Indianapolis, Indiana, USA
Purpose The purpose of this paper is to understand the social impact of data on communities from cases of
community data utilization.
Design/methodology/approach This study took an interpretive qualitative approach and conducted a
semi-structured phone interview with 45 participants from data intermediaries and local community organizations.
Findings The results demonstrate both direct and indirect impacts of data on local levels, including resolving
local problems from data-driven decisions, realizing unknown problems or correcting misrepresented problems,
changing community data practices, strengthening community identity and enhancing the communitys data skills.
Practical implications The research shows that communitiesdata utilization supported community-led
actions and initiatives from the bottom-up perspective, which demonstrates the need for supporting
communitiesdata work.
Social implications Minimizing inequality in data utilization should be resolved so that all communities
can benefit from the power of data.
Originality/value By demonstrating evidence of data being critical to encouraging communitiesdata
utilization, this study fills the gap in existing research, which lacks a clear explanation for how the potential of
data can be realized at the local level.
Keywords Data reuse, Open data, Social impact, Data curation, Community informatics, Data impact
Paper type Research paper
Many acknowledge the value of data as a source, not just for scientific knowledge, but also
for a communitys economic development, disaster planning and decision making (Heidorn,
2008; Kassen, 2013; Levin and Schneir, 2015). An exponential growth in data volume
supports datas potential as a community source with a recent movement toward open
data making local, regional and national data publicly available (The United Nations
Secretary-Generals Independent Expert Advisory Group on a Data Revolution for
Sustainable Development, 2014; Gurstein, 2011). Community organizations, such as NGOs,
nonprofits and local governments with other public sector organizations, become more
data-driven when making decisions to fulfill their missions of improving their communities
and furthering society. Private sector organizations also utilize data to serve clients and
provide meaningful work for their employees (Howson et al., 2018).
While previous studies have reported growing and urgent data needs in communities
(Yoon et al., 2018), they have also pointed out the lack of clear explanations for how datas
potential can be realized at the local level (Bertot et al., 2014; Kassen, 2013; Yoon et al., 2018;
Keserűand Chan, 2015). Existing literature has also suggested that evidence of the social
and political impact of open data is incredibly scarce, and analysis of data utilization cases
and their impacts is rarely done (Kassen, 2013; Keserűand Chan, 2015). Despite some
studies discussed impact of open data through use cases, mostly in the context of open
government data (e.g. Janssen et al., 2012; Ruijer et al., 2017), other argued that many were
still conceptual (Bertot et al., 2010; McDermott, 2010), and examined a large city at urban
Aslib Journal of Information
Vol. 71 No. 4, 2019
pp. 558-567
© Emerald PublishingLimited
DOI 10.1108/AJIM-12-2018-0310
Received 9 January 2019
Revised 8 May 2019
8 May 2019
Accepted 9 May 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
This project was funded by the Institute of Museum and Library Services (No. LG-96-17-0184-17). The
authors also wish to express their thanks to the nine data intermediary organizations for their help
with data collection and to Paula McNally for her help with initial data analysis.

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