Estratégia e Ciência de Dados Relacionadas à Vantagem Competitiva – um Ensaio Teórico
DOI:
https://doi.org/10.24023/FutureJournal/2175-5825/2021.v13i3.565Palavras-chave:
Ciência de Dados, Estratégia de Dados, Cultura Orientada por Dados, Governança de Dados, Vantagem CompetitivaResumo
Objetivo: defender a tese de que estratégia, cultura e governança de dados são determinantes no modo como a organização obtém vantagem competitiva por meio da ciência de dados.
Método: este ensaio está fundamentado em uma revisão teórica de estudos empíricos e conceituais para a identificação e definição de construtos e desenvolvimento de proposições, e de um modelo de conceitual.
Originalidade/Relevância: na Era Digital, o Big Data e a Ciência de Dados redefiniram a produtividade, a inovação e a competitividade. Contudo, o sucesso no uso da Ciência de Dados depende do adequado alinhamento entre os fatores estratégicos.
Resultados: considera-se que o modelo organizacional, formado pela estratégia, cultura e governança de dados, beneficia o uso da Ciência de Dados. Conclui-se, então que, para suportar a transformação digital, as organizações precisem formular sua estratégia de dados, além de estabelecer a composição ideal entre cultura e governança, a fim de direcionar suas capacidades analíticas e desbloquear o potencial da Ciência de Dados em prol da vantagem competitiva.
Contribuições Teóricas: o modelo teórico proposto é original por combinar construtos relacionados à gestão estratégica da Ciência de Dados, estabelecendo as bases para a compreensão de suas inter-relações, e descrevendo a relação destes com a vantagem competitiva.
Contribuições para a Gestão: o modelo teórico proposto pode ser utilizado tanto para direcionar a gestão estratégica dos dados, como para balancear o alinhamento estratégico organizacional que influencia no uso da Ciência de Dados, bem como para avaliar o sucesso das iniciativas analíticas e as vantagens competitivas obtidas.
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Referências
Alharthi, A., Krotov, V., & Bowman, M. (2017). Addressing barriers to big data. Business Horizons, 60(3), 285-292.
Aboelmaged, M., & Mouakket, S. (2020). Influencing models and determinants in big data analytics research: A bibliometric analysis. Information Processing & Management, 57(4), 102234.
Abraham, R., Schneider, J., & vom Brocke, J. (2019). Data governance: A conceptual framework, structured review, and research agenda. International Journal of Information Management, 49, 424-438.
Alhassan, I., Sammon, D., & Daly, M. (2018). Data governance activities: A comparison between scientific and practice-oriented literature. Journal of Enterprise Information Management, 31(2), 300-316.
Al-Badi, A., Tarhini, A., & Khan, A. I. (2018). Exploring big data governance frameworks. Procedia Computer Science, 141, 271-277.
Appelbaum, D., Kogan, A., Vasarhelyi, M., & Yan, Z. (2017). Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25, 29-44.
Avery, A., & Cheek, K. (2015). Analytics governance: towards a definition and framework. Twenty-first Americas Conference on Information Systems, Puerto Rico.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management 17: 99–120.
Barton, D., & Court, D. (2012). Making Advanced Analytics Work For You. Harvard Business Review, (October), 78.
Belhadi, A., Zkik, K., Cherrafi, A., & Yusof, M. (2019). Understanding the capabilities of Big Data Analytics for manufacturing process: insights from literature review and multiple case study. Computers & Industrial Engineering, 106099.
Benbasat, I., & Zmud, R. W. (2003). The identity crisis within the IS discipline: Defining and communicating the discipline's core properties. MIS quarterly, 183-194.
Bersin, J., & Zao-Sanders, M. (2020, Fevereiro 12). Boost Your Team’s Data Literacy. Harvard Business Review [Blog]. Recuperado de https://hbr.org/2020/02/boost-your-teams-data-literacy
Cao, G., Duan, Y., Li, G., 2015. Linking business analytics to decision making effectiveness: a path model analysis. IEEE Trans. Eng. Manage. 62, 384–395.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS Quarterly, 1165-1188.
Comuzzi, M., & Patel, A. (2016). How organizations leverage big data: A maturity model. Industrial Management & Data Systems, 116(8), 1468-1492.
Columbus, L. (2014, Outubro 19). 84% of enterprises see big data analytics changing their industries’ competitive landscapes in the next year. Forbes [Blog]. Recuperado de https://www.forbes.com/sites/louiscolumbus/2014/10/19/84-of-enterprises-see-big-data-analytics-changing-their-industries-competitive-landscapes-in-the-next-year/#281811e617de.
Côrte-Real, N., Ruivo, P., Oliveira, T., & Popovič, A. (2019). Unlocking the drivers of big data analytics value in firms. Journal of Business Research, 97, 160-173.
DalleMule, L., & Davenport, T. H. (2017). What’s your data strategy. Harvard Business Review, 95(3), 112-121. Recuperado de https://hbr.org/2017/05/whats-your-data-strategy
Davenport, T. H., & Bean, R. (2018, Fevereiro 15). Big companies are embracing analytics, but most still don’t have a data-driven culture. Harvard Business Review [Blog]. Recuperado de https://hbr.org/2018/02/big-companies-are-embracing-analytics-but-most-still-dont-have-a-data-driven-culture
Davenport, T. H., & Bean, R. (2020, Fevereiro 7). Are You Asking Too Much of Your Chief Data Officer? Harvard Business Review [Blog]. Recuperado de https://hbr.org/2020/02/are-you-asking-too-much-of-your-chief-data-officer
Davenport, T., & Harris, J. (2017). Competing on Analytics: Updated, with a New Introduction: The New Science of Winning. Harvard Business Press.
Duan, Y., Cao, G., & Edwards, J. S. (2020). Understanding the impact of business analytics on innovation. European Journal of Operational Research, 281(3), 673-686.
Fernando, F., & Engel, T. (2018). Big Data and Business Analytic Concepts: A Literature Review. Twenty-fourth Americas Conference on Information Systems, New Orleans, 1-10.
Fiorini, P. D. C, Seles, B. M. R. P., Jabbour, C. J. C., Mariano, E. B., & de Sousa Jabbour, A. B. L. (2018). Management theory and big data literature: From a review to a research agenda. International Journal of Information Management, 43, 112-129.
Fleckenstein, M., & Fellows, L. (2018). Implementing a data strategy. In Fleckenstein, M., & Fellows, L. Modern Data Strategy (pp. 35-54). Cham: Springer.
Frisk, J. E., & Bannister, F. (2017). Improving the use of analytics and big data by changing the decision-making culture. Management Decision, 55(10), 2074-2088.
Ghasemaghaei, M., & Calic, G. (2020). Assessing the impact of big data on firm innovation performance: Big data is not always better data. Journal of Business Research, 108, 147-162.
Gnizy, I. (2018). Big data and its strategic path to value in international firms. International Marketing Review, Vol. 36 No. 3, pp. 318-341.
Grant, R. M. (2010). Contemporary strategy analysis. 6th. Malden, MA: Blackwell Pub, 13(482), 133.
Grossman, R. L. (2018). A framework for evaluating the analytic maturity of an organization. International Journal of Information Management, 38(1), 45-51.
Grover, V., Chiang, R. H., Liang, T. P., & Zhang, D. (2018). Creating strategic business value from big data analytics: A research framework. Journal of Management Information Systems, 35(2), 388-423.
Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049-1064.
Hagiu, A. and Wright, J. (2020, Janeiro), When data creates competitive advantage. Harvard business review [Blog]. Recuperado de https://hbr.org/2020/01/when-data-creates-competitive-advantage.
Hall, J. (2017). Data Governance at State Departments of Transportation. MWAIS 2017 Proceedings. 24.
Harrison, T., F Luna-Reyes, L., Pardo, T., De Paula, N., Najafabadi, M., & Palmer, J. (2019, June). The Data Firehose and AI in Government: Why Data Management is a Key to Value and Ethics. In 20th Annual International Conference on Digital Government Research (pp. 171-176). ACM.
Hoehndorf, R., & Queralt-Rosinach, N. (2017). Data science and symbolic AI: Synergies, challenges and opportunities. Data Science, 1(1-2), 27-38.
International Data Corporation. (2019, Abril 4). IDC forecasts revenues for big data and business analytics solutions will reach $189.1 billion this year with double-digit annual growth through 2022. [Blog]. Recuperado de https://www.idc.com/getdoc.jsp?containerId=prUS44998419
Jha, A.K. and Bose, I. (2016), Innovation research in information systems: A commentary on contemporary trends and issues. Information & Management, 53(3), 297–306.
Kaul, A. (2019), Culture vs strategy: which to precede, which to align?, Journal of Strategy and Management, 12(1), 116-136.
Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148-152.
Kiron, D., Ferguson, R. B., & Prentice, P. K. (2013). From value to vision: Reimagining the possible with data analytics. MIT Sloan Management Review, 54 (3), 1–19.
Kiron, D., & Shockley, R. (2011). Creating Business Value with Analytics. MIT Sloan Management Review, 53(1), 57.
Keywell, B. (2020, November 10). Your Board Needs a Data-Integrity Committee. Harvard Business Review [blog]. Recuperado de: https://hbr.org/2020/10/your-board-needs-a-data-integrity-committee?utm_medium=email&utm_source=newsletter_monthly&utm_campaign=technology_not_activesubs&deliveryName=DM105364
Korotana, A., McLetchie, J., & Ingelgem, K.V. (2019, Agosto 1). O papel de advanced analytics em fusões e aquisições bem-sucedidas. McKinsey & Company [Blog]. Recuperado de https://www.mckinsey.com.br/our-insights/m-and-a-success-powered-by-advanced-analytics
Krishnamoorthi, S., & Mathew, S. K. (2018). Business analytics and business value: A comparative case study. Information & Management, 55(5), 643-666.
Lee, S. U., Zhu, L., & Jeffery, R. (2017). Data governance for platform ecosystems: Critical factors and the state of practice. PACIS 2017 Proceedings. 89.
Lillie, T., & Eybers, S. (2018, August). Identifying the constructs and agile capabilities of data governance and data management: A review of the literature. In Krauss, K., Turpin, M., & Naude, F. Locally Relevant ICT Research (pp. 313-326). Cham: Springer.
Mazzei, M. J., & Noble, D. (2017). Big data dreams: A framework for corporate strategy. Business Horizons, 60(3), 405-414.
Mikalef, P., & Pateli, A. (2017). Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: Findings from PLS-SEM and fsQCA. Journal of Business Research, 70, 1-16.
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261-276.
Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and e-Business Management, 16(3), 547-578.
Moeini, M., Simeonova, B., Galliers, R. D., & Wilson, A. (2020). Theory borrowing in IT-rich contexts: Lessons from IS strategy research. Journal of Information Technology, 0268396220912745
Morris, T. (2018, Junho 5). 6 competitive advantages of data-driven organizations. MicroStrategy [Blog]. Recuperado de https://www.microstrategy.com/us/resources/blog/bi-trends/6-competitive-advantages-of-data-driven-organizati
Müller, O., Fay, M., & vom Brocke, J. (2018). The effect of big data and analytics on firm performance: An econometric analysis considering industry characteristics. Journal of Management Information Systems, 35(2), 488-509.
Newman, R., Chang, V., Walters, R. J., & Wills, G. B. (2016). Model and experimental development for business data science. International Journal of Information Management, 36(4), 607-617.
NewVantage Partners LLC. (2020). Big data and AI executive survey 2020. Data-Driven Business Transformation. Connecting Data/AI Investment to Business Outcomes. Recuperado de http://newvantage.com/wp-content/uploads/2020/01/NewVantage-Partners-Big-Data-and-AI-Executive-Survey-2020-1.pdf
Nielsen, O. B. (2017). A Comprehensive review of data governance literature. Selected Papers of the IRIS, Issue Nr 8 (2017). 3.
Niño, H. A. C., Niño, J. P. C., & Ortega, R. M. (2020). Business intelligence governance framework in a university: Universidad de la costa case study. International Journal of Information Management, 50, 405-412.
Otto, B. (2011). Organizing data governance: Findings from the telecommunications industry and consequences for large service providers. Communications of the Association for Information Systems, 29(1), 3.
Paré, G., Trudel, M. C., Jaana, M., & Kitsiou, S. (2015). Synthesizing information systems knowledge: A typology of literature reviews. Information & Management, 52(2), 183-199.
Porter, M. E., & Millar, V. E. (1985). How information gives you competitive advantage. Harvard Business Review, 63(4), 149–160.
Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51-59.
Pugna, I. B., Duțescu, A., & Stănilă, O. G. (2019). Corporate attitudes towards big data and its impact on performance management: A qualitative study. Sustainability, 11(3), 684.
Rikhardsson, P., & Yigitbasioglu, O. (2018). Business intelligence & analytics in management accounting research: Status and future focus. International Journal of Accounting Information Systems, 29, 37-58.
Rivard, S. (2020). Theory building is neither an art nor a science. It is a craft. Journal of Information Technology, 0268396220911938.
Ross, J. W., Beath, C. M., & Quaadgras, A. (2013). You may not need big data after all. Harvard Business Review, 91(12), 90. Recuperado de https://hbr.org/2013/12/you-may-not-need-big-data-after-all
Sangari, M. S., & Razmi, J. (2015). Business intelligence competence, agile capabilities, and agile performance in supply chain: An empirical study. The International Journal of Logistics Management, 26(2), 356-380.
Shamim, S., Zeng, J., Shariq, S. M., & Khan, Z. (2018). Role of big data management in enhancing big data decision-making capability and quality among chinese firms: A dynamic capabilities view. Information & Management, 56(6), 103-135.
Sharma, R., Mithas, S., & Kankanhalli, A. (2014). Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organizations. European Journal of Information System, 23(4), 433-441.
Schryen, G., Wagner, G., Benlian, A., & Paré, G. (2020). A Knowledge Development Perspective on Literature Reviews: Validation of a new Typology in the IS Field. Communications of the Association for Information Systems, 46, pp-pp.
Suddaby, R. (2010) Editor’s comments: Construct clarity in theories of management and organization. The Academy of Management Review 35: 346–357.
Surbakti, F. P. S., Wang, W., Indulska, M., & Sadiq, S. (2020). Factors influencing effective use of big data: A research framework. Information & Management, 57(1), 103-146.
Sumbal, M. S., Tsui, E., & See-to, E. W. (2017). Interrelationship between big data and knowledge management: an exploratory study in the oil and gas sector. Journal of Knowledge Management, 21(1), 180-196.
Tabesh, P., Mousavidin, E., & Hasani, S. (2019). Implementing big data strategies: A managerial perspective. Business Horizons, 21(1), 347-358.
Tallon, P. P., Ramirez, R. V., & Short, J. E. (2014). The information artifact in IT governance: Toward a theory of information governance. Journal of Management Information Systems, 30(3), 141-178.
Tim, Y., Hallikainen, P., Pan, S. L., & Tamm, T. (2020). Actualizing business analytics for organizational transformation: A case study of Rovio Entertainment. European Journal of Operational Research, 281(3), 642-655.
Torres, R., Sidorova, A., & Jones, M. C. (2018). Enabling firm performance through business intelligence and analytics: A dynamic capabilities perspective. Information & Management, 55(7), 822-839.
Upadhyay, P., & Kumar, A. (2020). The intermediating role of organizational culture and internal analytical knowledge between the capability of big data analytics and a firm’s performance. International Journal of Information Management, 102100.
Urbinati, A., Bogers, M., Chiesa, V., & Frattini, F. (2018). Creating and capturing value from big data: A multiple-case study analysis of provider companies. Technovation, 84, 21-36.
Vassakis, K., Petrakis, E., & Kopanakis, I. (2018). Big data analytics: Applications, prospects and challenges. In Mobile big data (pp. 3-20). Springer, Cham.
Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), 626-639.
Vries, A. d., Chituc, C. M., & Pommeé, F. (2016, July). Towards identifying the business value of big data in a digital business ecosystem: A case study from the financial services industry. In International Conference on Business Information Systems (pp. 28-40). Springer, Cham.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365.
Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84.
Watson, H. J. (2014). Tutorial: Big data analytics: Concepts, technologies, and applications. Communications of the Association for Information Systems, 34, 1247-1268.
Watson, H. J. (2017). Preparing for the Cognitive Generation of Decision Support. MIS Quarterly Executive, 16(3), 153-169.
Xu, Z., Frankwick, G. L., & Ramirez, E. (2016). Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective. Journal of Business Research, 69(5), 1562-1566.
Zarkadakis, G. (2020, November 10). “Data Trusts” Could Be the Key to Better AI. Harvard Business Review [blog], available in: https://hbr.org/2020/11/data-trusts-could-be-the-key-to-better-ai?utm_medium=email&utm_source=newsletter_monthly&utm_campaign=technology_not_activesubs&deliveryName=DM105364
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