Big Data e Data for Good na Ciência da Informação: aproximação teórica

Patricia Siqueira  Santos; Nathalia Berger Werlang

doi:10.5433/1981-8920.2026v31n2p322

Authors

Patricia Siqueira Santos Federal University of Santa Catarina https://orcid.org/0000-0003-3622-3372
Nathalia Berger Werlang Federal University of Santa Catarina https://orcid.org/0000-0003-0172-6025

DOI:

https://doi.org/10.5433/1981-8920.2026v31n2p322

Keywords:

Big Data, Data for Good, Information Science, Literature Review

Abstract

Introduction: based on the scope of Big Data and its relationship with Information Science, this work investigates the concepts and applications of Data for Good, an approach to using data for the common good.
Objective: To understand how the themes already studied in Information Science are embedded in discussions about Data for Good, as well as to understand which paths, based on the Enancib Working Groups, best engage with the identified publications.
Methodology: A qualitative research with an exploratory and documentary character is developed through a literature review. Thematic analysis is used to identify the relationships found, and four main themes are defined: Cities; Education, Health, and Community. Data from the researched articles are also cross-referenced with information from the abstracts of the working groups of the National Meeting of Research in Information Science (Enancib).
Results: The retrieved works present research with a diversity of data collection and analysis methods. These studies, even without directly referencing Information Science, explore themes that are transversal and clearly relevant to the area. The four working groups that stand out in the relationship made are WG 05 – Information Policy and Economics; WG 11 – Information & Health; GT 08 – Data, Information and Technology and GT 06 – Information, Education and Work, finding a relationship with the proposed themes. Conclusions: it is possible to draw a parallel in their attempts to use Big Data to support the solution of socio-environmental problems, with the identification of risks, benefits and indication of good practices for informed decision-making.

Downloads

Download data is not yet available.

Author Biographies

Patricia Siqueira Santos, Federal University of Santa Catarina

PhD candidate in Information Science at the Graduate Program in Information Science, Universidade Federal de Santa Catarina (UFSC). Florianópolis, Brasil.

Nathalia Berger Werlang, Federal University of Santa Catarina

PhD in Administration from the Universidade Federal de Santa Catarina (UFSC). Faculty member in the Department of Information Science and the Graduate Program in Information Science at the Universidade Federal de Santa Catarina (UFSC). Florianópolis, Brasil.

References

ASSOCIAÇÃO NACIONAL DE PESQUISA E PÓS-GRADUAÇÃO EM CIÊNCIA DA INFORMAÇÃO (ANCIB). Coordenações e Ementas de GT. 2026. Disponível em: https://ancib.org/coordenacoes-e-ementas-de-gt/. Acesso em: 7 maio 2026.

BORKO, H. Information Science: what is it? American Documentation, New Jersey, v. 19, n. 1, p. 3-5, 1968. Disponível em: https://doi.org/10.1002/asi.5090190103. Acesso em: 21 ago. 2025.

BRAUN, V.; CLARKE, V. Using thematic analysis in psychology. Qualitative Research in Psychology, [S.l.], v. 3, n. 2, p. 77-101, 2006. Disponível em: https://www.tandfonline.com/doi/abs/10.1191/1478088706qp063oa. Acesso em: 13 mar. 2025.

CALZADA, I. Smart Citizens: From Data Providers to Decision-Makers? The Case Study of Barcelona. MDPI Sustainability, 10(9), 3252, p. 1-25, 2018. Disponível em: https://doi.org/10.3390/su10093252. Acesso em: 09 ago. 2026.

CHAI, X.; GUO, X.; XIAO, J.; JIANG, J. Analysis of spatiotemporal mobility of shared-bike usage during COVID-19 pandemic in Beijing. Transactions in GIS, [S.l.], v. 00, p. 1-22, 2021. Disponível em: https://doi.org/10.1111/tgis.12784. Acesso em: 22 fev. 2025.

CHANDY, R.; HASSAN, M.; MUKHERJI, P. Big Data for Good: Insights from Emerging Markets. Journal of Product Innovation Management, [S.l.], v. 34, n. 5, p. 703-713. Disponível em: https://onlinelibrary.wiley.com/doi/abs/10.1111/jpim.12406. Acesso em: 13 mar. 2025.

D4GX INDIA. Data for Good Exchange 2024 India. 2024. Disponível em: https://www.d4gxindia.com/. Acesso em: 13 mar. 2025.

DATA KIND. Our Story. 2025. Disponível em: https://www.datakind.org/about-us/our-story/. Aceso em: 13 mar. 2025.

DATA SCIENCE ACADEMY. Big Data Fundamentos 3.0. 2023. Disponível em: https://www.datascienceacademy.com.br. Acesso em: 10 ago. 2024.

DAVENPORT, T. H. Big data no trabalho: derrubando mitos e descobrindo oportunidades. Rio de Janeiro: Alta Books, 2017.

DAVENPORT, T. H. How strategists use “big data” to support internal business decisions, discovery and production. Strategic Leadership, Leeds, v. 42, n. 4, p. 45-50, 2014. Disponível em: https://doi.org/10.1108/SL-05-2014-0034. Acesso em: 22 ago. 2025.

DRIVENDATA. Data science competitions to build a better world. 2024. Disponível em: https://www.drivendata.org/. Acesso em: 13 mar. 2025.

GUTIERREZ, M.; BRYANT, J. The Fading Gloss of Data Science: Towards an Agenda that Faces the Challenges of Big Data for Development and Humanitarian Action. Development, [S.l.], v. 65, p. 80-93, 2022. Disponível em: https://doi.org/10.1057/s41301-022-00327-2. Acesso em: 22 fev. 2025.

HÖPER, L.; SCHULTE, C. The data awareness framework as part of data literacies in K-12 education. Information and Learning Sciences, [S.l.], v. 125, n. 7/8, p. 491-512, 2023. Disponível em: https://doi.org/10.1108/ILS-06-2023-0075. Acesso em: 22 fev. 2025.

INSTITUTO DE CIDADANIA EMPRESARIAL - ICE. Tecnologias e uso de dados para transformação social: Movimento Data for Good começa a se estruturar no Brasil. 2018. Disponível em: https://ice.org.br/2018/10/17/tecnologias-e-uso-de-dados-para-transformacao-social-movimento-data-for-good-comeca-a-se-estruturar-no-brasil/. Acesso em: 13 mar. 2025.

JIA, S.; KIM, S. H.; NGHIEM, S. V.; DOHERTY, P.; KAFATOS, M. C. Patterns of population displacement during mega-fires in California detected using Facebook Disaster Maps. Environmental Research Letters, [S.l.], v. 15, n. 7. 2020. Disponível: https://doi.org/10.1088/1748-9326/ab8847. Acesso em: 07 maio 2026.

KONTOKOSTA, C. E.; MALIK, A. The resilience to emergencies and disasters index: applying big data to benchmark and validate neighborhood resilience capacity. Sustainable Cities and Society, v. 36, p. 272–285. Disponível em: http://dx.doi.org/10.1016/j.scs.2017.10.025. Acesso em: 09 ago. 2026.

LANCY, D. 3D Data Management: Controlling Data Volume, Velocity, and Variety. Application Delivery Strategies. Stamford: Meta Group. 2001. Disponível em: https://studylib.net/doc/8647594/3d-data-management--controlling-data-volume--velocity--a. Acesso em: 13 mar. 2025.

LUCCHINI, L.; CENTELLEGHER, S.; PAPPALARDO, L.; GALLOTTI, R.; PRIVETERA, F.; LEPRI, B.; NADAI, M. Living in a pandemic: changes in mobility routines, social activity and adherence to COVID-19 protective measures. Scientific Reports, [S.l.], v. 11, n. 24452, p. 1-14, 2021. Disponível em: https://doi.org/10.1038/s41598-021-04139-1. Acesso em: 22 fev. 2025.

MANN, G.; SAHUGUET, A. With Great Data Comes Great Responsibility: How Data Science Can Be Applied to Solve Public Interest Problems Without Losing Its Soul. Journal of Technology in Human Services, [S.l.], v. 36, 2018. Disponível em: https://doi.org/10.1080/15228835.2017.1419157. Acesso em: 13 mar. 2025.

MICHAŁOWICZ, A. Data Sharing for the Common Good – Remarks on the Data Altruism Framework Established under the Data Governance Act. Yearbook of Antitrust and Regulatory Studies, [S.l.], v. 17, n. 29, p. 209-234, 2024. Disponível em: https://doi.org/10.7172/1689-9024.YARS.2024.17.29.7. Acesso em: 22 fev. 2025.

MOHAN, P. S.; RAMSAWAK, R. Using Crowdsourced Data to Assess the Impact of Shelter-in-Place Orders (SIPO) during COVID-19: Lessons from a Small Island Developing State. Journal of Health Management, [S.l.], v. 26, n. 3, p. 555–570, 2024. Disponível em: https://doi.org/10.1177/09720634241244422. Acesso em: 22 fev. 2025.

MONTEIRO, M.J., MAIA, P. A Framework for Building pro-bono Data for Good Projects. In: KAMP, M., KOPRINSKA, I.; BIBAL, A.; BOUADI, T.; FRÉNAY, B.; GALÁRRAGA, L.; ORAMAS, J.; ADILOVA, L.; KRISHNAMURTHY, Y.; KANG, B.; VIARD, T.; WELKE, P.; RUPCCP, M.; AUNE, E.; GALLICCHIO, C.; SCHIELE, G.; PERNKOPF, F.; BLOTT, M.; FRÖNING, H.; SCHINDLER, G.; GUIDOTTI, R.; MONREALE, A.; RINZIVILLO, S.; BIECEK, P.; NTOUTSI, E.; PECHENIZKIY, M.; ROSENHAHN, B.; BUCKLEY, C.; CIALFI, D.; LANILLOS, P.; RAMSTEAD, M.; VERBELEN, T. FERREIRA, P. M.; ANDRESINI, G.; MALERBA, D.; MEDEIROS, I.; FOURNIER-VIGER, P.; NAWAZ, M. S.; VENTURA, S.; SUN, M.; ZHOU, M.; BITETTA, V.; BORDINO, I.; FERRETTI, A.; GULLO, F.; PONTI, G.; SEVERINI, L.; RIBEIRO, R.; GAMA, J.; GAVALDÀ, R.; COOPER, L.; GHAZALEH, N.; RICHIARDI, J.; ROQUEIRO, D.; MIRANDA, D. S.; SECHIDIS, K.; GRAÇA, G. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Springer: [S.l.], 2021. v. 1525. Disponível em: https://doi.org/10.1007/978-3-030-93733-1_19. Acesso em: 22 fev. 2025.

NGUYEN, H.; NGUYEN, T.; NGUYEN, D. T. A Graph-Based Approach for Population Health Analysis Using Geo-Tagged Tweets. Multimedia Tools and Applications, v. 80, p. 7187-7204, 2021. Disponível em: https://doi.org/10.1007/s11042-020-10034-0. Acesso em: 22 fev. 2025.

PATIL, D. J; MASON, H. Data Driven: Creating a Data Culture. California: O'Reilly Media, 2015.

SANDER, I. Critical datafication literacy – a framework for educating about datafication. Information and Learning Sciences, [S.l.], v. 125, n. 3/4, p. 270-292, 2024. Disponível em: https://doi.org/10.1108/ILS-06-2023-0064. Acesso em: 22 fev. 2025.

SANTOS, A. P.; HEIDER, K.; GRESSE JUNIOR, S.; RODRIGUEZ LOPEZ, J. M. The uneven burden of COVID-19 in the metropolitan region of São Paulo, Brazil – Risk analysis from a bottom-up perspective. Applied Geography, [S.l.], v. 162, 2024. Disponível em: https://doi.org/10.1016/j.apgeog.2023.103146. Acesso em: 22 fev. 2025.

SHERBININ, A.; BOWSER, A.; CHUANG, T. R.; COOPER, C.; DANIELSEN, F.; EDMUNDS, R.; ELIAS, P.; FAUSTMAN, E.; HULTQUIST, C.; MONDARDINI, R.; POPESCU, I.; SHONOWO, A.; AIVAKUMAR, K. The Critical Importance of Citizen Science Data. Frontiers in Climate, [S.l.], v. 3, p. 1-12, 2021. DOI: 10.3389/fclim.2021.650760. Disponível em: https://www.frontiersin.org/articles/10.3389/fclim.2021.650760/full. Acesso em: 22 fev. 2025.

SILVEIRA, M.; MARCOLIN, C. B.; FREITAS, H. Uso Corporativo do Big Data: Uma Revisão de Literatura. Revista de Gestão e Projetos, [S.l.], v. 6, n. 3. set/dez. 2015. Disponível em: https://periodicos.uninove.br/gep/article/view/9627/4372. Acesso em: 13 mar. 2025.

SOCIAL GOOD BRASIL – SGB. Quem somos. 2025. Disponível em: https://socialgoodbrasil.org.br/quem-somos/. Acesso em: 13 mar. 2025.

WANG, G.; ZHAO, J.; VAN-KLEEK, M. G.; SHADBOLT, N. R. ‘Don’t make assumptions about me!’: Understanding children’s perception of datafication online. In: ACM CONFERENCE ON COMPUTER-SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING, 22, 2022, Nova Iorque. Anais eletrônicos […] Nova Iorque: Association for Computing Machinery, 2022. p. 1-24. Disponível em: https://dl.acm.org/doi/10.1145/3555144. Acesso em: 22 fev. 2025.

WITAYANGKURN, A.; ARAI, A.; SHIBASAKI, R. Development of Big Data-Analysis Pipeline for Mobile Phone Data with Mobipack and Spatial Enhancement. ISPRS International Journal of Geo-Information, [S.l.], v. 11, n. 196, p. 1-21, 2022. Disponível em: https://doi.org/10.3390/ijgi11030196. Acesso em: 22 fev. 2025.

XIA, J.; WANG, J.; LAI, Y. Development Strategy Based on Combination Typologies of Building Carbon Emissions and Urban Vibrancy—A Multi-Sourced Data-Driven Approach in Beijing, China. Land, [S.l.], v. 13, n. 1062, p. 1-19, 2024. Disponível em: https://doi.org/10.3390/land13071062. Acesso em: 22 fev. 2025.

YOON, A.; COPELAND, A. Understanding social impact of data on local communities. Aslib Journal of Information Management, [S.l.], v. 71, n. 4, p. 558-567, 2019. Disponível em: https://doi.org/10.1108/AJIM-12-2018-0310. Acesso em: 22 fev. 2025.