Abstract
More and more, society is learning how to live in a digital world that is becoming engulfed in data. Companies and organizations need to manage and deal with their data growth in a way that compliments the data getting bigger, faster and exponentially more voluminous. They must also learn to deal with data in new and different unstructured forms. This phenomenon is called Big Data. This chapter aims to present other definitions for Big Data, as well as technologies, analysis techniques, issues, challenges and trends related to Big Data. It also looks at the role and profile of the Data Scientist, in reference to functionality, academic background and required skills. The result is a global overview of what Big Data is, and how this new form is leading the world towards a new way of social construction, consumption and processes.
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References
A. Affelt, Acting on big data a data scientist role for info pros. Online Search 38(5), 10–14 (2014)
D. Agrawal, Analytics based decision making. J. Indian Bus. Search 6, 332–340 (2014)
A. Alexandrov, R. Bergmann, S. Ewen et al., The Stratosphere platform for big data analytics. VLDB J. 23, 939–964 (2014)
J. Archenaa, E.A. Mary Anita, A survey of big data analytics in healthcare and government. Procedia Comput. Sci. 50, 408–413 (2015)
M.D. Assunção, R.N. Calheiros, S. Bianchia et al., Big Data computing and clouds: trends and future directions. Parallel Distrib. Comput. 80, 03–15 (2013)
P. Barnaghi, A. Sheth, C. Henson, From data to actionable knowledge: big data challenges in the web of things. IEEE Intell. Syst. 28, 06–11 (2013)
R. Buyya, K. Ramamohanarao, C. Leckie et al., Big data analytics-enhanced cloud computing: challenges, architectural elements, and future direction. IEEE (2015). https://doi.org/10.1109/ICPADS.2015.18
A. Cardenas, P.K. Manadhata, S.P. Rajan, Big data analytics for security. IEEE Secur. Priv. 11(6), 74–76 (2013)
H. Chen, R.H.L. Chiang, V.C. Storey et al., Business intelligence and analytics: from big data to big impact. MIS Q 36(4), 1165–1188 (2012)
M. Chen, S. Mao, Y. Liu, Big data: a survey. Mobile Netw. Appl. 19, 171–209 (2014)
J. Chen, Y. Chen, X. Du et al., Big Data challenge: a data management perspective. Environ. Front. Comput. Sci. 7(2), 157–164 (2013)
P. Chen, C.Y. Zhang, Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inform. Sci. 275, 314–347 (2014)
T. Davenport, D.J. Patil, Data scientist: the sexiest job of the 21st century. Harv. Bus. Rev. (2012). https://doi.org/10.1080/01639374.2016.1245231
Dawar N (2016) Use Big Data to create value for customers, not just target them. Harv. Bus. Rev. https://hbr.org/2016/08/use-big-data-to-create-value-for-customers-not-just-target-them. Accessed 8 Sept 2017
S. Giest, Big data for policymaking: fad or fasttrack? Policy Sci. 50(3), 367–382 (2017)
A. Gandomi, M. Raider, Beyond the hype: big data concepts, methods, and analytics. IJIM 35(2), 137–144 (2015)
S. Earley, The digital transformation: staying competitive. IT Prof. 16, 58–60 (2014)
S. Earley, Analytics, machine learning, and the internet of things. IT Prof. 17, 10–13 (2015)
N. Elgendy, A. Elragal, Big data analytics in support of the decision making process. Procedia Comput. Sci. 100, 1071–1084 (2016)
K. Evans, Where in the world is my information? Giving people access to their data. IEEE Secur. Priv. 12(5), 78–81 (2014)
C. Everett, Big data—the future of cyber-security or its latest threat? Comput. Fraud Secur. 9, 14–17 (2015)
A. Gandomi, M. Haider, Beyond the hype: big data concepts, methods, and analytics. Int. J. Inform. Manage. 35(2), 137–144 (2015)
B. Gupta, M. Goul, B. Dinter, Business intelligence and big data in higher education: status of a multi-year model curriculum development effort for business school undergraduates, MS graduates, and MBAs. Commun. Assoc. Inf. Syst. 36, 450–476 (2015)
K. Gillon, S. Aral, C.Y. Lin et al., Business analytics: radical shift or incremental change? Commun. Assoc. Inf. Syst. 34(1), 287–296 (2014)
D. Hand, Statistics and computing: the genesis of data science. Stat. Comput. 25, 705–711 (2015)
P. Helland, If you have too much data, then ‘Good Enough’ is Good Enough. Commun. ACM 54(6), 40–47 (2011)
C. Ishikiriyama, Big data: um panorama global através de análise da literatura e survey. Dissertation, Universidade Federal Fluminense (2016)
N. Kabir, E. Carayannis, Big data, tacit knowledge and organizational competitiveness. J. Intell. Stud. Bus. 3(3), 220–228 (2013)
A. Katal, M. Wazid, R.H. Goudar, Big data: issues, challenges, tools and good practices. Contemp. Comput. (2013). https://doi.org/10.1109/ic3.2013.6612229
N. Khan, I. Yaqoob, I. Abaker et al., Big data: survey, technologies, opportunities, and challenges. Sci. World J. (2014). https://doi.org/10.1155/2014/712826
G.H. Kim, S. Trimi, J.H. Chung, Big-data applications in the government sector. Commun. ACM 57, 78–85 (2014)
H. Koscielniak, A. Puto, Big Data in decision making process of enterprises. Procedia Comput. Sci. 65, 1052–1058 (2015)
T. Kraska, Finding the needle in the big data systems haystack. IEEE Internet Comput. 17(1), 84–86 (2013)
N. Kshetri, Big data’s impact on privacy, security and consumer welfare. Telecommun. Policy 38(11), 1134–1145 (2014)
D. Laney, 3-D data management: controlling data volume velocity and variety (2001), http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf. Accessed 20 Dec 2015
D. Laney, Gartner predicts three big data trends for business intelligence (2015), http://www.forbes.com/sites/gartnergroup/2015/02/12/gartner-predicts-three-big-data-trends-for-business-intelligence/#5cc6fd8366a2. Accessed 20 Dec 2015
P.S.H. Leeflang, P.C. Verhoef, P. Dahlström et al., Challenges and solutions for marketing in a digital era. Eur. Manage. J. 32(1), 01–12 (2014)
J. Lin, Is big data a transient problem? IEEE Internet Comput. 16(5), 86–90 (2015)
S. Liu, W. Cui, Y. Wu et al., A survey on information visualization: recent advances and challenges. Vis. Comput. 30(12), 1373–1393 (2014)
M. Maciejewski, To do more, better, faster and more cheaply: using big data in public administration. Int. Rev. Adm. Sci. 83(1S), 120–135 (2017)
T. Matzner, Why privacy is not enough privacy in the context of and big data. J. Inf. 12(2), 93–106 (2014)
V. Mayer-Schonberger, K. Cukier, Big Data: como extrair volume, variedade, velocidade e valor da avalanche de informação cotidiana. Elsevier (2013)
D.E. O’Leary, Artificial intelligence and big data. IEEE Intell. Syst. 28, 96–99 (2013)
D.J. Power, Using ‘Big Data’ for analytics and decision support. J. Decis. Syst. 23(2), 222–228 (2014)
A. Picciano, The evolution of big data and learning analytics in American higher education. J. Asynchronous Learn. Netw. 16(3), 09–21 (2012)
F. Provost, T. Fawcett, Data Science and Its Relationship to Big Data and Data-Driven Decision Making (Mary Ann Liebert, Inc., 2013). https://doi.org/10.1089/big.2013.1508
J. Reyes, The skinny on big data in education: learning analytics simplified. Techtrends 59(2), 75–80 (2015)
M. Scherman, H. Krcmar, H. Hemsen et al., Big Data: an interdisciplinary opportunity for information systems research. Bus. Inf. Syst. Eng. 6(5), 261–266 (2014)
J.P. Shim, A.M. French, J. Jablonski, Big data and analytics: issues, solutions, and ROI. Commun. Assoc. Inf. Syst. 37, 797–810 (2015)
P. Tambe, Big data investment, skills, and firm value. Manage. Sci. 60(6), 1452–1469 (2014)
J. Tien, Big Data: unleashing information. J. Syst. Sci. Syst. Eng. 22(2), 127–151 (2013)
W.M. To, L. Lai, Data analytics in China: trends, issues, and challenges. IT Prof. 17(4), 49–55 (2015)
S. Vlaene, Data scientists aren’t domain experts. IT Prof. 15(6), 12–17 (2013)
Z. Xiang, J.H. Gerdes, Z. Schwartz et al., What can big data and text analytics tell us about hotel guest experience and satisfaction? Int. J. Hospitality Manage. 44, 120–130 (2015)
Y. Xiao, L.Y.Y. Lu, J.S. Liu et al., Knowledge diffusion path analysis of data quality literature: a main path analysis. J. Informetr. 8(3), 594–605 (2014)
S.F. Wamba, S. Akter, A. Edwards et al., How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study. Int. J. Prod. Econ. 165, 234–246 (2015)
M. Waller, S. Fawcett, Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. J. Bus. Logistics 34(2), 77–84 (2013)
H. Watson, Tutorial: big data analytics: concepts, technologies, and applications. Commun. Assoc. Inf. Syst. 34(65), 1247–1268 (2014)
B. Wixom, T. Ariyachandra, D. Douglas et al., The current state of business intelligence in academia: the arrival of big data. Commun. Assoc. Inf. Syst. 34, 01–13 (2014)
X. Wu, X. Zhu, G.Q. Wu et al., Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014)
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Ishikiriyama, C.S., Gomes, C.F.S. (2019). Big Data: A Global Overview. In: Emrouznejad, A., Charles, V. (eds) Big Data for the Greater Good. Studies in Big Data, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-319-93061-9_3
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DOI: https://doi.org/10.1007/978-3-319-93061-9_3
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