Big Data em Organizações de Médio e Grande Porte do Setor Público Brasileiro: Prontidão e Situação Atual, Replicação do Estudo Holandês de Klievink et al. (2017)
Resumo
Este estudo é a replicação da pesquisa de Klievink et al. (2017) aplicada no governo holandês, utilizando as dimensões de alinhamento para tecnologia da informação, capacidades e maturidade organizacional. A questão de pesquisa é: em que medida as organizações do setor público brasileiro estão prontas e preparadas para utilização de iniciativas de big data? O resultado mostrou que os órgãos públicos brasileiros não estão prontos para a implementação de big data. As conclusões mostram que é necessário fomentar atividades na coleta de dados, compartilhando informações entre organizações e uma mudança de pensamento dos gestores públicos sobre a importância das informações na tomada de decisão.
Palavras-chave:
big data, prontidão, alinhamento, capacidade, maturidade
Referências
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Daniel, E. M. and Wilson, H. N. (2003). The role of dynamic capabilities in e-business transformation. In European Journal of Information Systems, pages 282-296.
Davenport, T. H., Barth, P. and Bean, R. (2012). How big data is different. In MIT Sloan Management Review, pages 22-24.
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Klievink, B., Romijn, B., Cunningham, S. and Bruijn, H. D. (2017). Big data in the public sector: Uncertainties and readiness. In Information Systems Frontiers, pages 267-283.
Kune, R., Konugurthi, P. K., Agarwal, A., Chillarige, R. R. and Buyya, R. (2015). The anatomy of big data computing. In Software: Practice and Experience, pages 79-105.
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Lin, L. M. and Hsia, T. L. (2011). Core capabilities for practitioners in achieving e-business innovation. In Computers in Human Behavior, pages 1884-1891.
Milakovich, M. (2012). “Anticipatory Government: Integrating Big Data for Smaller Government”. In: Internet, Politics, Policy 2012: Big Data, Big Challenges, Oxford.
Munné, R. (2016) “Big data in the public sector”, New Horizons for a Data-Driven Economy: A Roadmap for Usage and Exploitation of Big Data in Europe, J. M. Cavanillas, E. Curry and W. Wahlster, New York, Springer, p. 195-208.
Ngai, E. W., Law, C. C. and Wat, F. K. (2008). Examining the critical success factors in the adoption of enterprise resource planning. In Computers in Industry, pages 584-564.
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Raghupathi, W. and Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. In Health Information Science and Systems, article 3.
Robey, D., Im, G. and Wareham, J. D. (2008). Theoretical foundations of empirical research on interorganizational systems: assessing past contributions and guiding future directions. In Journal of the Association for Information Systems, article 4.
Romijn, J. H. (2014), Using big data in the public sector: Uncertainties and Readiness in the Dutch Public Executive Sector, Delft University of Technology.
Russom, P. (2011), Big data analytics, TDWI Research.
Sagiroglu, S. and Sinanc, D. (2013). Big data: A review. In 2013 International Conference on Collaboration Technologies and Systems (CTS), pages 42-47.
Saha, B. and Srivastava, D. (2014). Data quality: The other face of big data. In 2014 IEEE 30th International Conference on Data Engineering, pages 1294-1297.
Schoenherr, T. and Speier‐Pero, C. (2015). Data science, predictive analytics, and big data in supply chain management: Current state and future potential. In Journal of Business Logistics, pages 120-132.
Simon, P. (2013), Too Big to Ignore: The Business Case for Big Data, John Wiley & Sons Inc.
Valdes G., Solar M., Astudillo H., Iribarren M., Concha G. and Visconti M. (2011). Conception, development and implementation of an e-Government maturity model in public agencies. In Government Information Quarterly, pages 176–187.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., and Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. In Journal of Business Research, pages 356-365.
Weerakkody, V., Janssen, M. and Dwivedi, Y. K. (2011). Transformational change and business process reengineering (BPR): Lessons from the British and Dutch public sector. In Government Information Quarterly, pages 320–328.
Wixom, B. H. and Watson, H. J. (2001). An empirical investigation of the factors affecting data warehousing success. In MIS Quarterly, pages 17-41.
World Bank. (2014) “Central America: Big Data in Action for Development”, https://EconPapers.repec.org/RePEc:wbk:wboper:21325.
World Bank. (2017) Big Data in Action for Government: Big Data Innovation in Public Services, Policy, And Engagement: Solutions Brief”, documents.worldbank.org/curated/en/176511491287380986/Big-data-in-action-for-government-big-data-innovation-in-public-services-policy-and-engagement.
Wu, J. H. and Hisa, T. L. (2008). Developing e-business dynamic capabilities: an analysis of e-commerce innovation from I-, M-, to U-commerce. In Journal of Organizational Computing and Electronic Commerce, pages 95-111.
Yeoh, W. and Koronios, A. (2010). Critical success factors for business intelligence systems. In Journal of Computer Information Systems, pages 23-32.
Zutshi, A. and Sohal, A. (2004). A study of the environmental management system (EMS) adoption process within Australasian organisations-2. In Role of Stakeholders Technovation, pages 371-386.
Chen, M., Mao, S. and Liu, Y. (2014). Big data: A survey. In Mobile Networks and Applications, pages 171-209.
Daniel, E. M. and Wilson, H. N. (2003). The role of dynamic capabilities in e-business transformation. In European Journal of Information Systems, pages 282-296.
Davenport, T. H., Barth, P. and Bean, R. (2012). How big data is different. In MIT Sloan Management Review, pages 22-24.
Ebrahim, Z. and Irani, Z. (2005). E-government adoption: architecture and barriers. In Business Process Management Journal, pages 589-611.
Finney, S. and Corbett, M. (2007). ERP implementation: a compilation and analysis of critical success factors. In Business Process Management Journal, pages 329-347.
Henderson, J. C. and Venkatraman, H. (1993). Strategic alignment: Leveraging information technology for transforming organizations. In IBM Systems Journal, pages 472-484.
Joseph, R. C. and Johnson, N. A. (2013). Big data and transformational government. In IT Professional, pages 43-48.
Kamal, M. M. (2006). IT innovation adoption in the government sector: identifying the critical success factors. In Journal of Enterprise Information Management, pages 192-222.
Kim, G-H., Trimi, S. and Chung, J-H. (2014). Big-data applications in the government sector. In Communications of the ACM, pages 78-85.
Klievink, B. and Janssen, M. (2009). Realizing joined-up government: dynamic capabilities and stage models for transformation. In Government Information Quarterly, pages 275–284.
Klievink, B., Romijn, B., Cunningham, S. and Bruijn, H. D. (2017). Big data in the public sector: Uncertainties and readiness. In Information Systems Frontiers, pages 267-283.
Kune, R., Konugurthi, P. K., Agarwal, A., Chillarige, R. R. and Buyya, R. (2015). The anatomy of big data computing. In Software: Practice and Experience, pages 79-105.
Laney, D. (2001) “3D Data Management: Controlling Data Volume, Velocity, and Variety. Application Delivery Strategies”, blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
Lin, L. M. and Hsia, T. L. (2011). Core capabilities for practitioners in achieving e-business innovation. In Computers in Human Behavior, pages 1884-1891.
Milakovich, M. (2012). “Anticipatory Government: Integrating Big Data for Smaller Government”. In: Internet, Politics, Policy 2012: Big Data, Big Challenges, Oxford.
Munné, R. (2016) “Big data in the public sector”, New Horizons for a Data-Driven Economy: A Roadmap for Usage and Exploitation of Big Data in Europe, J. M. Cavanillas, E. Curry and W. Wahlster, New York, Springer, p. 195-208.
Ngai, E. W., Law, C. C. and Wat, F. K. (2008). Examining the critical success factors in the adoption of enterprise resource planning. In Computers in Industry, pages 584-564.
OpenTracker. (2013) “Definitions of big data”, opentracker.net/article/definitions-big-data, April.
Raghupathi, W. and Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. In Health Information Science and Systems, article 3.
Robey, D., Im, G. and Wareham, J. D. (2008). Theoretical foundations of empirical research on interorganizational systems: assessing past contributions and guiding future directions. In Journal of the Association for Information Systems, article 4.
Romijn, J. H. (2014), Using big data in the public sector: Uncertainties and Readiness in the Dutch Public Executive Sector, Delft University of Technology.
Russom, P. (2011), Big data analytics, TDWI Research.
Sagiroglu, S. and Sinanc, D. (2013). Big data: A review. In 2013 International Conference on Collaboration Technologies and Systems (CTS), pages 42-47.
Saha, B. and Srivastava, D. (2014). Data quality: The other face of big data. In 2014 IEEE 30th International Conference on Data Engineering, pages 1294-1297.
Schoenherr, T. and Speier‐Pero, C. (2015). Data science, predictive analytics, and big data in supply chain management: Current state and future potential. In Journal of Business Logistics, pages 120-132.
Simon, P. (2013), Too Big to Ignore: The Business Case for Big Data, John Wiley & Sons Inc.
Valdes G., Solar M., Astudillo H., Iribarren M., Concha G. and Visconti M. (2011). Conception, development and implementation of an e-Government maturity model in public agencies. In Government Information Quarterly, pages 176–187.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., and Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. In Journal of Business Research, pages 356-365.
Weerakkody, V., Janssen, M. and Dwivedi, Y. K. (2011). Transformational change and business process reengineering (BPR): Lessons from the British and Dutch public sector. In Government Information Quarterly, pages 320–328.
Wixom, B. H. and Watson, H. J. (2001). An empirical investigation of the factors affecting data warehousing success. In MIS Quarterly, pages 17-41.
World Bank. (2014) “Central America: Big Data in Action for Development”, https://EconPapers.repec.org/RePEc:wbk:wboper:21325.
World Bank. (2017) Big Data in Action for Government: Big Data Innovation in Public Services, Policy, And Engagement: Solutions Brief”, documents.worldbank.org/curated/en/176511491287380986/Big-data-in-action-for-government-big-data-innovation-in-public-services-policy-and-engagement.
Wu, J. H. and Hisa, T. L. (2008). Developing e-business dynamic capabilities: an analysis of e-commerce innovation from I-, M-, to U-commerce. In Journal of Organizational Computing and Electronic Commerce, pages 95-111.
Yeoh, W. and Koronios, A. (2010). Critical success factors for business intelligence systems. In Journal of Computer Information Systems, pages 23-32.
Zutshi, A. and Sohal, A. (2004). A study of the environmental management system (EMS) adoption process within Australasian organisations-2. In Role of Stakeholders Technovation, pages 371-386.
Publicado
18/07/2021
Como Citar
SCHAULET, Evandro O.; TREZ, Guilherme.
Big Data em Organizações de Médio e Grande Porte do Setor Público Brasileiro: Prontidão e Situação Atual, Replicação do Estudo Holandês de Klievink et al. (2017). In: WORKSHOP DE COMPUTAÇÃO APLICADA EM GOVERNO ELETRÔNICO (WCGE), 9. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 13-24.
ISSN 2763-8723.
DOI: https://doi.org/10.5753/wcge.2021.15973.
