Isso não é uma pirâmide: revisando o modelo clássico de dado, informação, conhecimento e sabedoria
DOI:
https://doi.org/10.18225/ci.inf.v49i2.5066Palavras-chave:
Gestão do conhecimento, Sistemas de informação, Ciência da informaçãoResumo
Mais de 30 anos se passaram desde que os primeiros rascunhos do modelo clássico de dado-informação-conhecimento-sabedoria (DIKW) apareceram na literatura científica. Depois disso – em uma sociedade cada
vez mais digital e conectada – a pirâmide DIKW tornou-se popular, apresentando variantes contendo apenas
dado, informação e conhecimento, em uma configuração curta de DIK, ou adicionando níveis, como inteligência; ao mesmo tempo em que muita pouca interpretação crítica e validação empírica foram realizadas pelos pesquisadores para esclarecer como esses elementos estão realmente interligados. Isso deixou implicações teóricas do modelo original despercebidas, não validadas empiricamente, tornando-se uma explicação dada como certa, que não compreende totalmente a cadeia de criação de conhecimento, um processo que foi cuidadosamente estudado pelos pesquisadores. Este estudo revisa e analisa sistematicamente artigos relevantes, cobrindo o período de 32 anos de pesquisa, para identificar as principais fragilidades do modelo DIKW e propor um novo, em conformidade com a literatura de gestão do conhecimento, considerando o cenário atual de inteligência artificial e dilemas éticos. O modelo resultante desafia a pirâmide como a melhor maneira de transmitir esse “processo causal” de criação de conhecimento ao público e torna clara a necessidade de estudos empíricos a serem realizados no futuro
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Referências
ACKOFF, R. L. From data to wisdom. Journal of Applied Systems Analysis, v. 16, p. 3-9, 1989.
AL-HAWAMDEH, S. Knowledge management: re-thinking information management and facing the challenge of managing tacit. Information Research, v. 8, n. 1, 2002.
ALAVI, M.; LEIDNER, D. E. Review: knowledge management and knowledge management systems: conceptual foundations and research issues. MIS Quarterly, v. 25, n. 1, p. 107-136, 2001.
ALLEN, G. D. Hierarchy of knowledge – from data to wisdom. International Journal of Current Research in Multidisciplinary (IJCRM), v. 2, n. 1, p. 1-23, 2016.
ARDOLINO, M. et al. The role of digital technologies for the service transformation of industrial companies. International Journal of Production Research, v. 56, n. 6, p. 2116-2132, 2017.
AUKETT, J. The DIKW pathway: a route to effective oral health promotion? British Dental Journal, v. 226, n. 11, p. 897-901, 2019.
AYDIN, B.; OZLEBÇEBICI, Z. Is intelligence cycle still viable? In: INTERNATIONAL CONFERENCE ON MILITARY AND SECURITY STUDIES (ICMSS), 2015, Istanbul. Proceedings... Istanbul: Turkish Army War College, 2015. p. 95-100.
BALESTRIN, A. Uma análise da contribuição de Herbert Simon para as teorias organizacionais. REAd, v. 8, n. 4, 2002.
BAŠKARADA. S.; KORONIOS, A. Data, information, knowledge, wisdom (DIKW): a semiotic theoretical and empirical exploration of the hierarchy and its quality dimension. Australasian Journal of Information Systems, v. 18, n. 1, 2013.
BATRA, S. Big data analytics and its reflections on DIKW hierarchy. Review of Management, v. 4, n. 1/2, 2014.
BHATT. G. D. Management strategies for individual knowledge and organizational knowledge. Journal of Knowledge Management, v. 6, n. 1, p. 31-39, 2002.
BIERLY, P. E.; KESSLER, E. H.; CHRISTENSEN, E. W. Organizational learning, knowledge and wisdom. Journal of Organizational Change Management, v. 13, n. 6, p. 595-618, 2000.
BOSANCIC, B. Information in the knowledge acquisition process. Journal of Documentation, v. 72, n. 5, p. 930-960, 2016.
BRAGANZA, A. Rethinking the data-information-knowledge hierarchy: towards a case-based model. International Journal of Information Management, v. 24, p. 347-356, 2004.
CAR, J. et al. Beyond the hype of big data and artificial intelligence: bulding foundations for knowledge and wisdom. BMC Medicine, v. 17, n. 1, 2019.
CHEN et al. Data, information, and knowledge in visualization. IEEE Computer Graphics and Applications, v. 29, n. 1, p. 12-19, 2009.
COOPER, P. Data, information, knowledge and wisdom. Anaesthesia & Intensive Care Medicine, v. 18, n. 1, p. 55-56, 2017.
DALAL, N.; PAULEEN, D. J. The wisdom nexus: guiding information systems research, practice, and education. Information Systems Journal, v. 29, p. 224-244, 2018.
DAMMANN, O. Data, information, evidence, and knowledge: a proposal for health informatics and data science. Online Journal of Public Health Informatics, v. 10, n. 3, 2019.
DAMMANN, O.; SMART, B. Making population health knowledge. In: ______. Causation in population health informatics and data science. Springer Nature: Cham, 2019, p. 63-77.
DAVENPORT, T. H.; PRUSAK, L. Working knowledge: how organizations manage what they know. Boston: Harvard Business School Press, 1998.
EL HOUARI, M.; RHANOUI, M.; EL ASRI, B. From big data to big knowledge: the art of making big data alive. In: INTERNATIONAL CONFERENCE AND CLOUD TECHNOLOGIES AND APPLICATIONS (CLOUDTECH), 2015, Marrakech. Proceedings... [S.l.]: IEEE, 2015.
ERMINE, J-L. A knowledge value chain for knowledge management. Journal Of Knowledge & Communication Management, v. 3, n. 2, p. 85-101, 2013.
FIORE, S. M. et al. Towards an understanding of macrocognition in teams: developing and defining complex collaborative processes and products. Theoretical Issues in Ergonomics Science, v. 11, n. 4, p. 250-271, 2010.
FIORINI, P. D. et al. Management theory and big data literature: from a review to a research agenda. International Journal of Information Management, v. 43, p. 112-129, 2018.
FRICKÉ, M. The knowledge pyramid: a critique of the DIKW hierarchy. Journal of Information Science, v. 35, n. 2, p. 131-142, 2008.
GANDHI, S. Knowledge management and reference services. The Journal of Academic Librarianship, v. 30, n. 5, 2004.
GARCÍA-MARCO, F-J. La pirâmide de la información revisitada: enriqueciendo el modelo desde la ciencia cognitiva. El profesional de la información, v. 20, n. 1, p. 11-24, 2011.
GOEDE, M. The wise society: beyond the knowledge economy. Foresight, v. 13, n. 1, p. 36-45, 2011.
GONZALEZ, W. J. From intelligence to rationality of minds and machines in contemporary society: the sciences of design and the role of information. Minds & Machines, v. 27, p. 397-424, 2017.
GUOLINAG, L. et al. EASE: an effective 3-in1 keyword search method for unstructured, semi-structured and structured data. In: ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, 8., 2008, Vancouver. Proceedings... [S.l.]: ACM, 2008, p. 903-914.
GUPTA, S. et al. Big data with cognitive computing: a review for the future. International Journal of Infotmation Management, v. 42, p. 78-89, 2018.
HASHEM, I. A. T. et al. (2015). The rise of “big data” on cloud computing: review and open research issues. Information Systems, v. 47, p. 98-115.
HOPPE, A. et al. Wisdom – the blurry top of human cognition in the DIKW-model? In: CONFERENCE OF THE EUROPEAN SOCIETY FOR FUZZY LOGIC AND TECHNOLOGY, 7., 2011. Aix-les-Bains. Proceedings... [S.l.]: Atlantis Press, 2011.
JANSEN, B.; RIEH, S. Y. The seventeen theoretical constructs of information searching and information retrieval. Journal of the American Society for Information Science and Technology, v. 61, n. 80, p. 1517-1534, 2010.
Jennex, M. E. (2017). Big data, the internet of things, and the revised knowledge pyramid. ACM SIGMIS Database: The Database for Advances in Information Systems, 48(4), 69-79.
JENNEX, M. E.; BARTCZAK, S. E. A revised knowledge pyramid. International Journal of Knowledge Management, v. 9, n. 3, p. 19-30, 2013.
LIEW, A. DIKIW: data, information, knowledge, intelligence, wisdom and their interrelationships. Business Management Dynamics, v. 2, n. 10, p. 49-62, 2013.
MA, L. Meanings of information: the assumptions and research consequences of three foundational LIS theories. Journal of the American Society for Information Science and Technology, v. 63, n. 4, p. 716-723, 2012.
MAITY, S. Identifying opportunities for artificial intelligence in the evolution of training and development practices. Journal of Management Development, v. 38, n. 8, p. 651-663, 2019.
MARCH, J. G.; SIMON, H. A. Organizations. New York: Wiley, 1958.
MARTINS, C. S.; SIMÕES, P.; SÁ. J. O. Uma arquitetura moderna de dados: um caso de teste. In: CONFERÊNCIA DA ASSOCIAÇÃO PORTUGUESA DE SISTEMAS DE INFORMAÇÃO (CAPSI), 2014, 14., Évora. Atas... [S.l.]: PTAIS, 2014.
MICHAELS. S.; GOUCHER, N. P.; McCARTHY, D.. Considering knowledge uptake within a cycle of transforming data, information, and knowledge. Review of Policy Research, v. 23, n. 1, p. 267-279, 2006.
MORESI, E. A. D. Delineando o valor do sistema de informação de uma organização. Ciência da Informação, v. 29, n. 1, p. 14-24, 2000.
MUTONGI, C. Revisiting data, information, knowledge and wisdom (DIKW) model and introducing the green leaf model. IOSR Journal of Business and Management (IOSR-JBM), v. 18, n. 7, p. 66-71, 2016.
NAVEGA, S. Informação, conhecimento e data mining. RCA – Revista de Controle e Administração, v. 3, n. 1, p. 49-71, 2007.
NURULIN, Y et al. Role of knowledge in management of innovation. Resoucers, v. 8, n. 87, 2019.
PONCHIROLLI, O.; FIALHO, F. A. P. Gestão estratégica do conhecimento como parte da estratégia empresarial. Revista da FAE, v. 8, n. 1, p. 127-138, 2005.
POURDJAM, M; SIADAT, S-A.; RAJAEEPOURS, S. Structural modeling for the relationship of organizational wisdom and strategic intelligence. Journal of Studies in Education, v. 5, n. 2, 2015.
PRENSKY, M. H. sapiens digital: from digital immigrants and digital natives to digital wisdom. Innovate: Journal of Online Education, v. 5, n. 3, 2009.
PREWITT, V. Wisdom in the workplace. Performance Improvement Quarterly, v. 15, n. 1, p. 84-98, 2002.
RAWSON, T. M. et al. Artificial intelligence can improve decision-making in infection management. Nature, 2019.
REMOR, C. A. M.; FIALHO, F. A. P.; QUEIROZ, M. P. Analisando a hierarquia DIKW. In: CONGRESSO INTERNACIONAL DE CONHECIMENTO E INOVAÇÃO (CIKI), 2017, 7., Foz do Iguaçu/PR. Anais. Florianópolis: UFSC, 2017.
ROWLEY, J. The wisdom hierarchy: representations of the DIKW hierarchy. Journal of Information Science, v. 33, n. 2, p. 163-180, 2007.
SAJJA, P. S.; AKERKAR, R. Knowledge-based systems for development. In: ______ (ed.). Advanced knowledge based systems: model, applications & research. Sudbury, MA: Jones & Bartlett, 2010. v. 1. p. 1-11.
SATO, A.; HUANG, R. A generic formulated KID model for pragmatic processing of data, information and knowledge. In: INTERNATIONAL CONFERENCE ON UBIQUITOUS INTELLIGENCE AND COMPUTING, 12., 2015. Proceedings... [S.l.]: IEEE Computer Society, 2015.
SIMON, H. A. A rational decision making in business organization. American Economic Review, v. 69, p. 493-513, 1979.
______. Making management decisions: the role of intuition and emotion. Academy of Management Executive, v. 1, n. 1, p. 57-64, 1987.
SIMON, J. P. Artificial intelligence: scope, players, markets and geography. Digital Policy, Regulations and Governance, 2019.
STERNBERG, R. J. Wisdom as a form of giftedness. Gifted Child Quarterly, v. 44, n. 4, p. 252-260, 2000.
STOREY, V. C.; SONG, I-Y. Big data technologies and management: what conceptual modeling can do. Data & Knowledge Engineering, v. 108, p. 50-67, 2017.
SUCCI S.; COVENEY, P. V. Big data: the end of scientific method? Philosophical Transactions, v. 377, n. 2142, 2019.
TAKEUCHI, H.; NONAKA, I. Gestão do conhecimento. Porto Alegre: Bookman, 2008.
TANG, V.; YANINE, F.; VALENZUELA, L. Data, information, knowledge and intelligence. International Journal of Innovation Science, v. 8, n. 3, p. 199-216, 2016.
TIAN, X. Big data and knowledge managemet: a case of déjà vu or back to the future? Journal of Knowledge Management, v. 21, n. 1, p. 113-131, 2017.
TUOMI, I. Data is more than knowledge: implications of the reversed knowledge management and organizational memory. Journal of Management Information Systems, v. 16, n. 3, p. 103-117, 1999.
VANDERGRIFF, L. J. Welcome to the intelligence age: an examination of intelligence as a complex ventures emergent behavior. VINE: The Journal of Information and Knowledge Systems, v. 38, n. 4, p. 432-444, 2008.
WAMBA, S. F. et al. Big data analytics and firm performance: effects of dynamic capabilities. Journal of Business Research, v. 70, p. 356-365, 2017.
WAN, K.; ALAGAR, V. Synthesizing data-to-wisdom hierarchy for developing smart. In: International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 11., 2014. Proceedings... [S.l.]: IEEE Computer Society, 2014.
YAO, X.; JIN, H.; Zhang, J. Towards a wisdom manufacturing vision. International Journal of Computer Integrated Manufacturing, v. 28, n. 12, p. 1291-1312, 2014.
YLIKOKI, O.; PORRAS, J. A recipe for big data value creation. Business Process Management Journal, v. 25, n. 5, p. 1085-1100, 2019.
ZELENY, M. Management support systems: towards integrated knowledge management. Human Systems Management, v. 7, n. 1, p. 59-70, 1987.
ZINS, C. Conceptual approaches for defining data, information, and knowledge. Journal of the American Society for Information Science and Tecnhology, v. 58, n. 4, p. 479-493, 2007.
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