Big Data Analytics and local government: considerations about a process model focused on management
DOI: https://doi.org/10.32586/rcda.v22i1.882
Keywords:
big data analytics, local government, process model, focus on managementAbstract
The paper analyzes the aspects that should be considered when proposing a model of Big Data Analytics (BDA) processes in local government with a focus on management. We sought to: carry out a systematic review of the literature to identify the main works aligned with the theme; analyze the trend of BDA use in local governments through the synthesis of publications; and propose, through the key points identified in the analyzed publications, a model of big data analytics processes in local government with a focus on management. The methodological trajectory is defined by a qualitative approach with an exploratory and descriptive objective. To answer the question that guides the conceptual fragment of the study, we chose to carry out a systematic review of the literature. Among the standardized procedures, the Prisma`s model was chosen. The sources of information were some of the main electronic databases in the fields of applied social sciences, focused on administration and public administration, including Web of Science, Scopus, Ebsco and Emerald. A promising trend was noticed in the use of BDA in local governments, with several application cases and a very clear emphasis on its still unexplored potential to offer or transform the relationship between local governments and their citizens. It is concluded that democratic processes can be substantially improved with data-oriented policies, but several factors need to be carefully analyzed and discussed so that the use of opportunities derived from BDA is effectively greater than their possible investment costs and implementation risks.
Metrics
References
ABITEBOUL, S.; STOYANOVICH, J. Transparency, Fairness, Data Protection, Neutrality. Journal of Data and Information Quality, v. 11, n. 3, p. 1-9, 2019.
AHMAD, S. et al. Optimal Policy-Making for Municipal Waste Management Based on Predictive Model Optimization. IEEE ACCESS, v. 8, p. 218458-218469, 2020.
AL NUAIMI, E. et al. Applications of big data to smart cities. Journal of Internet Services and Applications, College of Information Technology, UAE University, P.O. Box 15551, Al Ain, United Arab Emirates, v. 6, n. 1, p. 25, 2015.
ALVARENGA, A. et al. Digital Transformation and Knowledge Management in the Public Sector. Sustainability, v. 12, n. 14, p. 5824, 2020.
BABAR, M.; ARIF, F. Smart urban planning using Big Data analytics to contend with the interoperability in Internet of Things. Future Generation Computer Systems, v. 77, p. 65-76, 2017.
BOEING, G. Spatial information and the legibility of urban form: Big data in urban morphology. International Journal of Information Management, v. 56, 2019.
CHIAPPINI, L. The urban digital platform: Instances from Milan and Amsterdam. Urban Planning, v. 5, n. 4, p. 277-288, 2020.
CONFORTO, E.; AMARAL, D. C.; SILVA, S. L. D. A. Roteiro para revisão bibliográfica sistemática: aplicação no desenvolvimento de produtos e gerenciamento de projetos. In: 8º Congresso Brasileiro de Gestão de Desenvolvimento de Produto. Anais do 8º CBGDP. 2011.
CURRIE, M. Data as performance - Showcasing cities through open data maps. Big Data & Society, v. 7, n. 1, 2020.
DENCIK, L. et al. The ‘golden view’: data-driven governance in the scoring society. Internet Policy Review, Cardiff School of Journalism, Media and Cultural Studies, Cardiff University, United Kingdom, v. 8, n. 2, 2019.
DU, R. Urban growth: Changes, management, and problems in large cities of Southeast China. Frontiers of Architectural Research, v. 5, n. 3, p. 290-300, 2016.
ERCOLE, F. F.; MELO, L. S.; ALCOFORADO, C. L. G. C. Revisão integrativa versus revisão sistemática. Revista Mineira de Enfermagem, v. 18, n. 1, p. 9-12, 2014
FERENHOF, H. A.; FERNANDES, R. F. Desmistificando a revisão de literatura como base para redação científica: método SSF. Revista ACB: biblioteconomia em Santa Catarina, v. 21, n. 3, p. 550-563, ago./nov. 2016
GAO, Z.; WANG, S.; GU, J. Public Participation in Smart-City Governance: a qualitative content analysis of public comments in urban China. Sustainability, v. 12, n. 20, p. 8605, 2020.
GIEST, S. Big data analytics for mitigating carbon emissions in smart cities: opportunities and challenges. European Planning Studies, v. 25, n. 6, p. 941-957, 2017.
HENRICH, B. A. et al. The Use of Energy Models in Local Heating Transition Decision Making: Insights from ten municipalities in the Netherlands. Energies, v. 14, n. 2, p. 423, 2021.
HOLZER, M.; YANG, K. Performance Measurement and Improvement: an Assessment of the State of the Art. International Review of Administrative Sciences, v. 70, n. 1, p. 15-31, 2004.
KANDT, J.; BATTY, M. Smart cities, big data and urban policy: Towards urban analytics for the long run. Cities, v. 109, 2021.
KASSEN, M. Understanding motivations of citizens to reuse open data: open government data as a philanthropic movement. Innovation: organization and management, Graduate School of Public Policy, Nazarbayev University, Nur-Sultan, Kazakhstan, v. 23, n. 1, p. 44-70, 2021.
KELLER, S. et al. Harnessing the power of data to support community‐based research. WIREs Computational Statistics, [S.l.], v. 10, n. 3, 2018.
KELLER, S.; LANCASTER, V.; SHIPP, S. Building Capacity for Data-Driven Governance: Creating a New Foundation for Democracy. Statistics and Public Policy, [S.l.], v. 4, n. 1, p. 1-11, 2017.
KIM, B. et al. A value of civic voices for smart city: a big data analysis of civic queries posed by Seoul citizens. Cities, [S.l.], v. 108, 2021.
KIM, E. S. S.; CHOI, Y.; BYUN, J. Big data analytics in government: Improving decision making for R&D investment in Korean SMEs. Sustainability (Switzerland), Data Analysis Division, Korea Institute of Science and Technology Information, 66 Hoegi-ro, Dongdaemungu, Seoul, 02456, South Korea, v. 12, n. 1, 2020.
LEE, J. W. Big data strategies for government, society and policy-making. Journal of Asian Finance, Economics and Business, [S.l.], v. 7, n. 7, p. 475-487, 2020.
LEVY, Y.; ELLIS, T. J. A systems approach to conduct an effective literature review in support of information systems research. Informing Science, v. 9, n. 1, p. 181-212, 2006.
LI, F. et al. How smart cities transform operations models: a new research agenda for operations management in the digital economy. Production Planning & Control, [S.l.], v. 27, n. 6, p. 514-528, 2016.
LUCIANO, E. M. Information management hits and misses in the COVID19 emergency in Brazil. International Journal of Information Management, [S.l.], v. 55, p. 102194, 2020.
LUTHFI, A.; JANSSEN, M. Open data for evidence-based decision-making: Data-driven government resulting in uncertainty and polarization. International Journal on Advanced Science, Engineering and Information Technology, v. 9, n. 3, p. 1071-1078, 2019.
MATHEUS, R.; JANSSEN, M.; MAHESHWARI, D. Data science empowering the public: Data-driven dashboards for transparent and accountable decision-making in smart cities. Government Information Quarterly, v. 37, n. 3, p. 101284, 2020.
MEES, H. Local governments in the driving seat? A comparative analysis of public and private responsibilities for adaptation to climate change in European and North-American cities. Journal of Environmental Policy & Planning, v. 19, n. 4, p. 374-390, 2017.
MENDES-DA-SILVA, W. Contribuições e limitações de revisões narrativas e revisões sistemáticas na área de negócios. Revista de Administração Contemporânea, v. 23, n. 2, p. 1-11, 2019
MICHENER, G.; RITTER, O. Comparing resistance to open data performance measurement: public education in Brazil and the UK. Public Administration, v. 95, n. 1, p. 4-21, 2017.
MOHER, D.; LIBERATI, A.; TETZLAFF, J.; ALTMAN, D. G; The PRISMA Group. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS MED, v. 6, n. 7, 2009.
MOHER, D.; SHAMSEER, L.; CLARKE, M.; GHERSI, D. et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement, Systematic Reviews, v. 4, n. 1, 2015.
OSMAN, A. M. S. A novel big data analytics framework for smart cities. Future Generation Computer Systems, v. 91, p. 620-633, 2018.
PÉREZ-CHACÓN, R. et al. Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities. Energies, v. 11, n. 3, p. 683, 2018.
PIRES, F. M.; MENDES, L. de S.; QUIÑONEZ, L. L. Integrated system architecture for decision-making and urban planning in smart cities. International Journal of Distributed Sensor Networks, v. 15, n. 8, p. 155014771986782, 2019.
RICHARDS, G. Big Data and Analytics Applications in Government: Current Practices and Future Opportunities. Auerbach Publications, 2017.
SANDERS, C.; SHEPTYCKI, J. Policing, crime and “big data”; towards a critique of the moral economy of stochastic governance. Crime, Law & Social Change, v. 68, n. 1-2, p. 1-15, 2017.
SANGER, M. B. From measurement to management: breaking through the barriers to state and local performance, 2008.
SEREY, J. et al. Methodological proposals for the development of services in a smart city: a literature review. Sustainability, v. 12, n. 24, 2020.
SIMON, P. Too Big to ignore: The business case for Big Data. Wiley Inc, 2013.
ŠPALKOVÁ, D.; ŠPAČEK, D.; NEMEC, J. Performance Management and Performance Appraisal: Czech Self-Governments. NISPAcee Journal of Public Administration and Policy, v. 8, n. 2, p. 69-88, 2015.
SOOMRO, K. et al. Smart city big data analytics: An advanced review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, v. 9, n. 5, 2019.
QUAGLIO, G. Q. S. P. et al. Overview of future EU health systems. An insight into governance, primary care, data collection and citizens’ participation. Journal of Public Health, v. 40, n. 4, p. 891-898, 2018.
RIZK, A.; STÅHLBRÖST, A.; ELRAGAL, A. Data-driven innovation processes within federated networks. European Journal of Innovation Management, 2020.
RICKER, B.; CINNAMON, J.; DIERWECHTER, Y. When open data and data activism meet: An analysis of civic participation in Cape Town, South Africa. The Canadian Geographer/Le Géographe canadien, Copernicus Institute of Sustainable Development, Utrecht University, Netherlands, v. 64, n. 3, p. 359-373, 2020.
SUSHA, I. Establishing and implementing data collaborations for public good: A critical factor analysis to scale up the practice. Information Polity: The International Journal of Government & Democracy in the Information Age, v. 25, n. 1, p. 3-24, 2020.
VAN ROESSEL, I.; REUMANN, M.; BRAND, A. Potentials and challenges of the health data cooperative model. Public Health Genomics, v. 20, n. 6, p. 321-331, 2018.
VALDEZ, A.-M.; COOK, M.; POTTER, S. Roadmaps to utopia: Tales of the smart city. Urban Studies (Sage Publications, Ltd.), v. 55, n. 15, p. 3385-3403, 2018.
VOGL, T. M. et al. Smart Technology and the Emergence of Algorithmic Bureaucracy: Artificial Intelligence UK Local Authorities. Public Administration Review, v. 80, n. 6, p. 946–961, 2020.
WEERAKKODY, V. et al. Open data and its usability: an empirical view from the Citizen’s perspective. Information Systems Frontiers, v. 19, n. 2, p. 285–300, 2016. WHITEHORN, M. Big Data Bites Back: How to Handle Those Unwieldy Digits When You Can’t Just Cram It into Tables. Disponível em Acessado em: Maio 2021.
References
ABITEBOUL, S.; STOYANOVICH, J. Transparency, Fairness, Data Protection, Neutrality. Journal of Data and Information Quality, v. 11, n. 3, p. 1-9, 2019.
AHMAD, S. et al. Optimal Policy-Making for Municipal Waste Management Based on Predictive Model Optimization. IEEE ACCESS, v. 8, p. 218458-218469, 2020.
AL NUAIMI, E. et al. Applications of big data to smart cities. Journal of Internet Services and Applications, College of Information Technology, UAE University, P.O. Box 15551, Al Ain, United Arab Emirates, v. 6, n. 1, p. 25, 2015.
ALVARENGA, A. et al. Digital Transformation and Knowledge Management in the Public Sector. Sustainability, v. 12, n. 14, p. 5824, 2020.
BABAR, M.; ARIF, F. Smart urban planning using Big Data analytics to contend with the interoperability in Internet of Things. Future Generation Computer Systems, v. 77, p. 65-76, 2017.
BOEING, G. Spatial information and the legibility of urban form: Big data in urban morphology. International Journal of Information Management, v. 56, 2019.
CHIAPPINI, L. The urban digital platform: Instances from Milan and Amsterdam. Urban Planning, v. 5, n. 4, p. 277-288, 2020.
CONFORTO, E.; AMARAL, D. C.; SILVA, S. L. D. A. Roteiro para revisão bibliográfica sistemática: aplicação no desenvolvimento de produtos e gerenciamento de projetos. In: 8º Congresso Brasileiro de Gestão de Desenvolvimento de Produto. Anais do 8º CBGDP. 2011.
CURRIE, M. Data as performance - Showcasing cities through open data maps. Big Data & Society, v. 7, n. 1, 2020.
DENCIK, L. et al. The ‘golden view’: data-driven governance in the scoring society. Internet Policy Review, Cardiff School of Journalism, Media and Cultural Studies, Cardiff University, United Kingdom, v. 8, n. 2, 2019.
DU, R. Urban growth: Changes, management, and problems in large cities of Southeast China. Frontiers of Architectural Research, v. 5, n. 3, p. 290-300, 2016.
ERCOLE, F. F.; MELO, L. S.; ALCOFORADO, C. L. G. C. Revisão integrativa versus revisão sistemática. Revista Mineira de Enfermagem, v. 18, n. 1, p. 9-12, 2014
FERENHOF, H. A.; FERNANDES, R. F. Desmistificando a revisão de literatura como base para redação científica: método SSF. Revista ACB: biblioteconomia em Santa Catarina, v. 21, n. 3, p. 550-563, ago./nov. 2016
GAO, Z.; WANG, S.; GU, J. Public Participation in Smart-City Governance: a qualitative content analysis of public comments in urban China. Sustainability, v. 12, n. 20, p. 8605, 2020.
GIEST, S. Big data analytics for mitigating carbon emissions in smart cities: opportunities and challenges. European Planning Studies, v. 25, n. 6, p. 941-957, 2017.
HENRICH, B. A. et al. The Use of Energy Models in Local Heating Transition Decision Making: Insights from ten municipalities in the Netherlands. Energies, v. 14, n. 2, p. 423, 2021.
HOLZER, M.; YANG, K. Performance Measurement and Improvement: an Assessment of the State of the Art. International Review of Administrative Sciences, v. 70, n. 1, p. 15-31, 2004.
KANDT, J.; BATTY, M. Smart cities, big data and urban policy: Towards urban analytics for the long run. Cities, v. 109, 2021.
KASSEN, M. Understanding motivations of citizens to reuse open data: open government data as a philanthropic movement. Innovation: organization and management, Graduate School of Public Policy, Nazarbayev University, Nur-Sultan, Kazakhstan, v. 23, n. 1, p. 44-70, 2021.
KELLER, S. et al. Harnessing the power of data to support community‐based research. WIREs Computational Statistics, [S.l.], v. 10, n. 3, 2018.
KELLER, S.; LANCASTER, V.; SHIPP, S. Building Capacity for Data-Driven Governance: Creating a New Foundation for Democracy. Statistics and Public Policy, [S.l.], v. 4, n. 1, p. 1-11, 2017.
KIM, B. et al. A value of civic voices for smart city: a big data analysis of civic queries posed by Seoul citizens. Cities, [S.l.], v. 108, 2021.
KIM, E. S. S.; CHOI, Y.; BYUN, J. Big data analytics in government: Improving decision making for R&D investment in Korean SMEs. Sustainability (Switzerland), Data Analysis Division, Korea Institute of Science and Technology Information, 66 Hoegi-ro, Dongdaemungu, Seoul, 02456, South Korea, v. 12, n. 1, 2020.
LEE, J. W. Big data strategies for government, society and policy-making. Journal of Asian Finance, Economics and Business, [S.l.], v. 7, n. 7, p. 475-487, 2020.
LEVY, Y.; ELLIS, T. J. A systems approach to conduct an effective literature review in support of information systems research. Informing Science, v. 9, n. 1, p. 181-212, 2006.
LI, F. et al. How smart cities transform operations models: a new research agenda for operations management in the digital economy. Production Planning & Control, [S.l.], v. 27, n. 6, p. 514-528, 2016.
LUCIANO, E. M. Information management hits and misses in the COVID19 emergency in Brazil. International Journal of Information Management, [S.l.], v. 55, p. 102194, 2020.
LUTHFI, A.; JANSSEN, M. Open data for evidence-based decision-making: Data-driven government resulting in uncertainty and polarization. International Journal on Advanced Science, Engineering and Information Technology, v. 9, n. 3, p. 1071-1078, 2019.
MATHEUS, R.; JANSSEN, M.; MAHESHWARI, D. Data science empowering the public: Data-driven dashboards for transparent and accountable decision-making in smart cities. Government Information Quarterly, v. 37, n. 3, p. 101284, 2020.
MEES, H. Local governments in the driving seat? A comparative analysis of public and private responsibilities for adaptation to climate change in European and North-American cities. Journal of Environmental Policy & Planning, v. 19, n. 4, p. 374-390, 2017.
MENDES-DA-SILVA, W. Contribuições e limitações de revisões narrativas e revisões sistemáticas na área de negócios. Revista de Administração Contemporânea, v. 23, n. 2, p. 1-11, 2019
MICHENER, G.; RITTER, O. Comparing resistance to open data performance measurement: public education in Brazil and the UK. Public Administration, v. 95, n. 1, p. 4-21, 2017.
MOHER, D.; LIBERATI, A.; TETZLAFF, J.; ALTMAN, D. G; The PRISMA Group. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS MED, v. 6, n. 7, 2009.
MOHER, D.; SHAMSEER, L.; CLARKE, M.; GHERSI, D. et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement, Systematic Reviews, v. 4, n. 1, 2015.
OSMAN, A. M. S. A novel big data analytics framework for smart cities. Future Generation Computer Systems, v. 91, p. 620-633, 2018.
PÉREZ-CHACÓN, R. et al. Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities. Energies, v. 11, n. 3, p. 683, 2018.
PIRES, F. M.; MENDES, L. de S.; QUIÑONEZ, L. L. Integrated system architecture for decision-making and urban planning in smart cities. International Journal of Distributed Sensor Networks, v. 15, n. 8, p. 155014771986782, 2019.
RICHARDS, G. Big Data and Analytics Applications in Government: Current Practices and Future Opportunities. Auerbach Publications, 2017.
SANDERS, C.; SHEPTYCKI, J. Policing, crime and “big data”; towards a critique of the moral economy of stochastic governance. Crime, Law & Social Change, v. 68, n. 1-2, p. 1-15, 2017.
SANGER, M. B. From measurement to management: breaking through the barriers to state and local performance, 2008.
SEREY, J. et al. Methodological proposals for the development of services in a smart city: a literature review. Sustainability, v. 12, n. 24, 2020.
SIMON, P. Too Big to ignore: The business case for Big Data. Wiley Inc, 2013.
ŠPALKOVÁ, D.; ŠPAČEK, D.; NEMEC, J. Performance Management and Performance Appraisal: Czech Self-Governments. NISPAcee Journal of Public Administration and Policy, v. 8, n. 2, p. 69-88, 2015.
SOOMRO, K. et al. Smart city big data analytics: An advanced review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, v. 9, n. 5, 2019.
QUAGLIO, G. Q. S. P. et al. Overview of future EU health systems. An insight into governance, primary care, data collection and citizens’ participation. Journal of Public Health, v. 40, n. 4, p. 891-898, 2018.
RIZK, A.; STÅHLBRÖST, A.; ELRAGAL, A. Data-driven innovation processes within federated networks. European Journal of Innovation Management, 2020.
RICKER, B.; CINNAMON, J.; DIERWECHTER, Y. When open data and data activism meet: An analysis of civic participation in Cape Town, South Africa. The Canadian Geographer/Le Géographe canadien, Copernicus Institute of Sustainable Development, Utrecht University, Netherlands, v. 64, n. 3, p. 359-373, 2020.
SUSHA, I. Establishing and implementing data collaborations for public good: A critical factor analysis to scale up the practice. Information Polity: The International Journal of Government & Democracy in the Information Age, v. 25, n. 1, p. 3-24, 2020.
VAN ROESSEL, I.; REUMANN, M.; BRAND, A. Potentials and challenges of the health data cooperative model. Public Health Genomics, v. 20, n. 6, p. 321-331, 2018.
VALDEZ, A.-M.; COOK, M.; POTTER, S. Roadmaps to utopia: Tales of the smart city. Urban Studies (Sage Publications, Ltd.), v. 55, n. 15, p. 3385-3403, 2018.
VOGL, T. M. et al. Smart Technology and the Emergence of Algorithmic Bureaucracy: Artificial Intelligence UK Local Authorities. Public Administration Review, v. 80, n. 6, p. 946–961, 2020.
WEERAKKODY, V. et al. Open data and its usability: an empirical view from the Citizen’s perspective. Information Systems Frontiers, v. 19, n. 2, p. 285–300, 2016. WHITEHORN, M. Big Data Bites Back: How to Handle Those Unwieldy Digits When You Can’t Just Cram It into Tables. Disponível em Acessado em: Maio 2021.
Published
Issue
Section
License
Copyright (c) 2023 Revista: apenas para a 1ª Publicação.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Autores que publicam na Revista Controle – Doutrina e Artigos concordam com os seguintes termos:
1 A Revista Controle – Doutrina e Artigos não se responsabiliza pelas opiniões, ideias e conceitos emitidos nos textos, por serem de inteira responsabilidade de seu(s) autor(es), não significando necessariamente o posicionamento do Tribunal de Contas do Estado do Ceará e do Instituto Plácido Castelo;
2. O periódico segue o padrão Creative Commons (CC BY NC 4.0), que permite o compartilhamento e adaptação de obras derivadas do original, mas não pode usar o material para fins comerciais. As novas obras devem conter menção ao(s) autor(es) nos créditos;
3 O(s) responsável(is) pela submissão de artigos declara(m), sob as penas da Lei, que a informação sobre a autoria do trabalho é absolutamente completa e verdadeira;
4 O(s) autor(es) garante(m) que o artigo é original e inédito e que não está em processo de avaliação em outros periódicos;
5 A responsabilidade por eventuais plágios nos artigos publicados é de responsabilidade do(s) autor(es);
6 É reservado aos Editores o direito de proceder ajustes textuais e de adequação dos artigos às normas da publicação;
7 A Revista Controle – Doutrina e Artigos não realiza cobrança de nenhuma taxa ou contribuição financeira em razão de submissão de artigos ou de seu processamento;
8 A publicação dos artigos na Revista Controle – Doutrina e Artigos não gerará direito à remuneração de qualquer espécie; e
9 O(s) autor(es) autoriza(m) a publicação do artigo na Revista Controle – Doutrina e Artigos.