Relevance of Big Data for Business and Management. Exploratory Insights (Part I)

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Over the last few decades Big Data has impetuously penetrated almost every domain of human interest/action and it has (more or less consciously) become a ubiquitous presence of day to day life. The main questions this exploratory paper seeks to address (throughout its two parts) are the following: What is the (actual) impact of Big Data on Business & Management and How can businesses (through their management) leverage the potential of Big Data to their benefit? A gradual, step by step approach (based on literature review and a variety of secondary data) will guide the paper in search for answers to the abovementioned questions: starting with a concise history of the topic Big Data as reflected in academia and a critical content analysis of the Big Data concept, the paper will then continue by emphasizing some of the most significant realities and trends that characterize the supply-side of the big data industry; the second part of the paper is dedicated to the investigation of the demand-side of the big data industry – by highlighting some evidences (and projections) on the impact of big data analytics on Business & Management (both at aggregate and granular level) and exploring what companies could and should do (through their management) in order to best capitalize on the opportunities of big data and avoid/minimize the impact of its threats.

Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of ‘big data’. McKinsey Quarterly, 4(1), 24-35.

Burris, P. (2018). Wikibon’s 2018 Big Data and Analytics Market Share Report. March 6.

Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS quarterly, 1165-1188.

Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2), 171-209.

Clarivate Analytics. (2018). Web of Science (WoS) Core Collection.

Coleman, S., Göb, R., Manco, G., Pievatolo, A., Tort-Martorell, X., & Reis, M. S. (2016). How can SMEs benefit from big data? Challenges and a path forward. Quality and Reliability Engineering International, 32(6), 2151-2164.

Cumbley, R., & Church, P. (2013). Is “big data” creepy?. Computer Law & Security Review, 29(5), 601-609.

Cuzzocrea, A., Song, I. Y., & Davis, K. C. (2011, October). Analytics over large-scale multidimensional data: the big data revolution!. In Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP (pp. 101-104). ACM.

De Mauro, A., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122-135.

Demchenko, Y., Ngo, C., & Membrey, P. (2013). Architecture framework and components for the big data ecosystem. Journal of System and Network Engineering, 1-31.

Dijcks, J. P. (2012). Oracle: Big data for the enterprise. Oracle white paper, 16.

George, G., Haas, M. R., & Pentland, A. (2014). Big Data and Management. Academy of Management Journal, 57(2).

Katal, A., Wazid, M., & Goudar, R. H. (2013, August). Big data: issues, challenges, tools and good practices. In Contemporary Computing (IC3), 2013 Sixth International Conference on (pp. 404-409). IEEE.

Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage.

Kwon, T. H., Kwak, J. H., & Kim, K. (2015). A study on the establishment of policies for the activation of a big data industry and prioritization of policies: Lessons from Korea. Technological Forecasting and Social Change, 96, 144-152.

Kobielus, J. (2018). Wikibon’s 2018 Big Data Analytics Trends and Forecast. February 28.

Laney, D. (2001). 3D Data Management: Controlling data volume, velocity and variety. META group research note, 6(70), 1.

LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21.

Lee, I. (2017). Big data: Dimensions, evolution, impacts, and challenges. Business Horizons, 60(3), 293-303.

Lohr, S. (2012). The age of big data. New York Times, 11(2012).

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.

Marr, B. (2016). Key Business Analytics. The 60+ business analysis tools every manager needs to know. Pearson Education Limited.

Martin, K. E. (2015). Ethical issues in the big data industry. MIS Quarterly Executive. 14:2, 67-85.

Mayer-Schonberger, V., Cukier, K. (2014). Big data: A revolution that will transform how we live, work, and think. John Murray Publishers.

McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: the Management Revolution. Harvard Business Review, 90(10), 60-68.

Ohlhorst, F. J. (2012). Big data analytics: turning big data into big money. John Wiley & Sons.

Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19(4), 1-34.

Schmarzo, B. (2013). Big Data: Understanding how data powers big business. John Wiley & Sons.

Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., & Tufano, P. (2012). Analytics: the real-world use of big data: How innovative enterprises extract value from uncertain data, Executive Report. IBM Institute for Business Value and Said Business School at the University of Oxford.

Schwardmann, U. (1993). Parallelization of a multigrid solver on the KSR1. Supercomputer, 10(3), 4-12.

Shafer, T. (2017). The 42 V’s of Big Data and Data Science. Elder Research.

Turban, E., King, D., Sharda, R., & Delen, D. (2013). Business intelligence: a managerial perspective on analytics. Prentice Hall, New York.

Vorhies, B. (2014). How Many “V” s in Big Data–The Characteristics that Define Big Data. Data Science Central.

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.

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