Estratégia e Ciência de Dados Relacionadas à Vantagem Competitiva – um Ensaio Teórico
DOI:
https://doi.org/10.24023/FutureJournal/2175-5825/2021.v13i3.565Keywords:
Ciência de Dados, Estratégia de Dados, Cultura Orientada por Dados, Governança de Dados, Vantagem CompetitivaAbstract
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.
Downloads
References
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
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Future Studies Research Journal: Trends and Strategies
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Authors who publish in this journal agree to the following terms: the author(s) authorize(s) the publication of the text in the journal;
2. The author(s) ensure(s) that the contribution is original and unpublished and that it is not in the process of evaluation by another journal;
3. The journal is not responsible for the views, ideas and concepts presented in articles, and these are the sole responsibility of the author(s);
4. The publishers reserve the right to make textual adjustments and adapt texts to meet with publication standards.
5. Authors retain copyright and grant the journal the right to first publication, with the work simultaneously licensed under the Creative Commons Atribuição NãoComercial 4.0 internacional, which allows the work to be shared with recognized authorship and initial publication in this journal.
6. Authors are allowed to assume additional contracts separately, for non-exclusive distribution of the version of the work published in this journal (e.g. publish in institutional repository or as a book chapter), with recognition of authorship and initial publication in this journal.
7. Authors are allowed and are encouraged to publish and distribute their work online (e.g. in institutional repositories or on a personal web page) at any point before or during the editorial process, as this can generate positive effects, as well as increase the impact and citations of the published work (see the effect of Free Access) at http://opcit.eprints.org/oacitation-biblio.html
• 8. Authors are able to use ORCID is a system of identification for authors. An ORCID identifier is unique to an individual and acts as a persistent digital identifier to ensure that authors (particularly those with relatively common names) can be distinguished and their work properly attributed.
