Model of the big data use for customer cognition
Abstract
In a customer-oriented market, understanding customer behavior is an important determinant of the success of an organization. An organization that strives to survive and succeed can not ignore increasing amounts of data – big data. Big data is complex data arrays that are difficult to process using traditional data processing applications. Optimal analysis of such data enables organizations for better understanding of its customers, improve the decision-making process and increase its competitive advantage. It is important for the organization to understand how to use big data, which processing tools and models to apply. This article analyzes the concepts and evolution of big data, the risks of exploitation, mining methods and applied models. Applied methods: systematic, logical analysis of information sources, comparison of information, systemization.
Article in Lithuanian.
Didžiųjų duomenų naudojimas klientui pažinti
Santrauka
Į klientus orientuotoje rinkoje klientų elgsenos supratimas yra svarbus veiksnys, lemiantis organizacijos sėkmę. Organizacija, siekianti išlikti ir sėkmingai egzistuoti, negali ignoruoti nuolat didėjančių duomenų kiekių – didžiųjų duomenų. Didieji duomenys – sudėtingi duomenų masyvai, kuriuos sunku apdoroti naudojant tradicines duomenų apdorojimo programas. Optimaliai išanalizuoti tokie duomenys suteikia galimybę geriau pažinti klientus, tobulinti sprendimų priėmimo procesą, didinti konkurencinį pranašumą. Organizacijai svarbu suprasti, kaip panaudoti didžiuosius duomenis, kokias apdorojimo priemones ir modelius taikyti. Šiame straipsnyje analizuojamos didžiųjų duomenų koncepcijos ir raida, naudojimo rizikos, gavybos būdai ir taikomi modeliai. Taikomi šie metodai: mokslinių šaltinių sisteminė, loginė analizė, informacijos sugretinimas, sisteminimas.
Reikšminiai žodžiai: didieji duomenys, kliento pažinimas, didžiųjų duomenų analizė, naudojimo rizikos, duomenų tyryba, duomenų valdymas.
Keyword : Big data, customer cognition, big data analytics, risks of exploitation, big data mining, big data management
How to Cite
Politaitė, S., & Sabaitytė, J. (2018). Model of the big data use for customer cognition. Mokslas – Lietuvos Ateitis / Science – Future of Lithuania, 10. https://doi.org/10.3846/mla.2018.932
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Kaur, K., Kaur, I., Kaur, N., Tanisha, Gurmeen, & Deepi. (2016). Big data management: characteristics, challenges and solutions. International Journal of Computer Science and Technology, 7(4), 54-57.
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Koturwar, P., Girase, S., & Mukhopadhyay, D. (2015). A survey of classification techniques in the area of big data. International Journal of Advance Foundation and Research in Computer, 1(11), 1-7.
Krasnow Waterman, K., & Bruening P. J. (2014). Big Data analytics: risks and responsibilities. International Data Privacy Law, 4(2), 89-95. https://doi.org/10.1093/idpl/ipu002
Laney, D. (2001). 3-D data management: controlling data volume, velocityand variety. Application delivery strategies by META Group Inc. Retrieved from https://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
Langkafel, P. (2016). Big Data in medical science and healthcare management: diagnosis, therapy, side effects (248 p.). Germany: Berlin. ISBN: 978-3-11-044528-2.
Lee, I. (2017). Big Data: dimensions, evolution, impacts, and challenges. Business Horizons, 60(3), 293-303. https://doi.org/10.1016/j.bushor.2017.01.004
Lee, J., Kao, H. A., & Yang, S. (2014). Service innovation and smart analytics for Industry 4.0 and big data environment. Procedia CIRP, 16, 3-8. https://doi.org/10.1016/j.procir.2014.02.001
Makhdoomi, M. (2017). Data mining approach for Big Data Analysis: a theoretical discourse. International Journal of Advanced Research in Computer Science, 8(7), 104-109. https://doi.org/10.26483/ijarcs.v8i7.4032
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McAfee, A., & Brynjolfsson, E. (2012). Big data: the management revolution. Harvard Business Review, 90(10), 60-68.
Miller, S. (2014). Collaborative approaches needed to close the big data skills gap. Journal of Organization Design, 3(1), 26-30. https://doi.org/10.7146/jod.9823
Oussous, A., Benjelloun, F. Z., Lahcen, A. A., & Belfkih, S. (2017). Big Data technologies: a survey. Journal of King Saud University – Computer and Information Sciences. Retrieved from http://www.sciencedirect.com/science/article/pii/S1319157817300034
Pandey, P., Kumar, M., & Srivastava, P. (2016). Classification techniques for Big Data: a survey. Retrieved from http://iee-explore.ieee.org/stamp/stamp.jsp?arnumber=7724938
Panigrahi, P. K. (2012). A comparative study of supervised machine learning techniques for spam e-mail filtering. Fourth International Conference on Computational Intelligence and Communication Networks (pp. 506-512). https://doi.org/10.1109/CICN.2012.14
SAP. (2012). Small and midsize companies look to make big gains with “big data”. Retrieved from http://global.sap.com/corpo-rate-en/news.epx?PressID=19188
Seddon, J. J. J. M., & Currie, W. L. (2017). A model for unpacking big data analytics in high-frequency trading. Journal of Business Research, 70, 300-307. https://doi.org/10.1016/j.jbusres.2016.08.003
Sharma, S. (2015). Rise of Big Data and related issues. IEEE. Retrieved from http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7443346
Simon, P. (2014). The visual organization: data visualization, big data, and the quest for better decisions (28 p.). USA: SAS Institute.
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286. https://doi.org/10.1016/j.jbusres.2016.08.001
Stanton, S. J., Sinnott-Armstrong, W., & Huettel, S. A. (2017). Neuromarketing: ethical implications of its use and po-tential misuse. Journal of Business Ethics, 144(4), 799-811. https://doi.org/10.1007/s10551-016-3059-0
Sunita, B. A., & Lobo, L. M. R. J. (2012). A comparative study for selecting the best unsupervised learning algorithm in e-learning system. International Journal of Computer Applications, 41(3), 27-34. https://doi.org/10.5120/5523-7562
Tole, A. A. (2013). Big Data challenge. Database Systems Journal, 4(3), 31-40.
TRUSTe. (2015). 2015 US IoT Privacy Index. Retrieved from https://www.truste.com/resources/privacy-research/us-internet-of-things-index-2015/
ur Rehman, M. H., Liew, C. S., Abbas, A., Jayaraman, P. P., Wah, T. Y., & Khan, S. U. (2016). Big Data reduction methods: a survey. Data Science and Engineering, 1(4), 265-284. https://doi.org/10.1007/s41019-016-0022-0
Walker, R., ap Cenydd, L., Pop, S., Miles, H. C., Hughes, C., Teahan, W. J., & Roberts, J. C. (2013). Storyboarding for visual analytics, Information Visualization, 14(1), 27-50. https://doi.org/10.1177/1473871613487089
Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016). Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 101(4), 158-168. https://doi.org/10.1016/j.comnet.2015.12.017
Ward, J. S., & Barker, A. (2013). Undefined by data: a survey of big data definitions. Retrieved from http://arxiv.org/abs/1309.5821
Zakir, J. (2015). Big Data analytics. International Association for Computer Information Systems, 16(2), 81-90.
Zhan, Y., Tan, K. H., Li, Y., & Tse, Y. K. (2016). Unlocking the power of big data in new product development. Annals of Operations Research (pp. 1-19). https://doi.org/10.1007/s10479-016-2379-x
Akoka, J., Comyn-Wattiau, I., & Laoufi, N. (2017). Research on Big Data – a systematic mapping study. Computer Standards & Interfaces, 54(2), 105-115. https://doi.org/10.1016/j.csi.2017.01.004
Alharthi, A., Krotov, V., & Bowman, M. (2017). Addressing barriers to big data. Business Horizons, 60(5), 285-292. https://doi.org/10.1016/j.bushor.2017.01.002
Beyer, A. M., & Laney, D. (2012). The Importance of Big Data: a definition. Gartner, Stamford, CT. Retrieved from https://www.gartner.com/doc/2057415/importance-big-data-definition
Boyd, D., & Crawford, K. (2012). Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon, Information. Communication & Society, 15(5), 662-679. https://doi.org/10.1080/1369118X.2012.678878
Boksem, M. A. S., & Smidts, A. (2015). Brain responses to movietrailers predict individual preferences for movies and their population-wide commercial success. Journal of Marketing Research, 52(4), 482-492. https://doi.org/10.1509/jmr.13.0572
Chen, M., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From Big Data to Big Imptact. MIS Quarterly: Management Information Systems, 36(4), 1165-1188.
De Mauro, A., Greco, M., & Grimaldi, M. (2014). What is big data? A consensual definition and a review of key research topics. 4th International Conference on Integrated Information AIP Proceedings. Madrid.
Deloitte. (2015). Opportunities in Telecom Sector: arising from Big Data. Retrieved from https://www2.deloitte.com/content/dam/Deloitte/in/Documents/technology-media-tel-ecommunications/in-tmt-opportunities-in-telecom-sector-noexp.pdf
Diebold, F. X. (2012). A personal perspective on the origin(s) and develop-ment of “big data”: The phenomenon, the term, and the discipline, second version. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2202843
Dippel, A. (2017). Das Big Data Game, NTM Zeitschrift für Geschichte der Wissenschaften. Technik und Medizin, 25(4), 485-517.
Fan, J., Han, F., & Liu, H. (2016). Challenges of big data analysis. National ScienceReview, 1(2), 293-314. https://doi.org/10.1093/nsr/nwt032
Gaber, M. M. (2010). Scientific data mining and knowledge discovery – principles and foundations (397 p.). New York: Springer. ISBN: 97 836 42027871.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of In-formation Management, 35(2), 137-144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
Gartner. (2015). Gartner says business intelligence and analytics leaders must focus on mindsets and culture to kick start ad-vanced analytics. Retrieved from https://www.gartner.com/newsroom/id/3130017
Gartner. (2017). Big data. Retrieved from http://www.gartner.com/it-glossary/big-data/
Gupta, U. G., & Gupta, A. (2016). Vision: a missing key dimension in the 5V Big Data framework. Journal of International Business Research and Marketing, 1(3), 46-52.Hua Tan, K., & Zhan, Y. (2016). Improving new product devel-opment using big data: a case study of an electronics company. R&D Management, 47(4), 570-582. https://doi.org/10.1111/radm.12242
Jinjiang, Y.; Xueling, Z. (2016). The research on China’s current online retail statistics based on Big Data’s Perspective. Innovation, entrepreneurship and strategy in the era of internet (pp. 388-393).
Kaur, K., Kaur, I., Kaur, N., Tanisha, Gurmeen, & Deepi. (2016). Big data management: characteristics, challenges and solutions. International Journal of Computer Science and Technology, 7(4), 54-57.
Khan, N., Yaqoob, I., Hashem, I. A. T., Inayat, Z., Ali, W. K. M., Alam, M., Shiraz, M., & Gani, A. (2014). Big data: survey, technologies, opportunities, and challenges. The Scientific World Journal. Retrieved from https://www.hindawi.com/journals/tswj/2014/712826/
Koturwar, P., Girase, S., & Mukhopadhyay, D. (2015). A survey of classification techniques in the area of big data. International Journal of Advance Foundation and Research in Computer, 1(11), 1-7.
Krasnow Waterman, K., & Bruening P. J. (2014). Big Data analytics: risks and responsibilities. International Data Privacy Law, 4(2), 89-95. https://doi.org/10.1093/idpl/ipu002
Laney, D. (2001). 3-D data management: controlling data volume, velocityand variety. Application delivery strategies by META Group Inc. Retrieved from https://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
Langkafel, P. (2016). Big Data in medical science and healthcare management: diagnosis, therapy, side effects (248 p.). Germany: Berlin. ISBN: 978-3-11-044528-2.
Lee, I. (2017). Big Data: dimensions, evolution, impacts, and challenges. Business Horizons, 60(3), 293-303. https://doi.org/10.1016/j.bushor.2017.01.004
Lee, J., Kao, H. A., & Yang, S. (2014). Service innovation and smart analytics for Industry 4.0 and big data environment. Procedia CIRP, 16, 3-8. https://doi.org/10.1016/j.procir.2014.02.001
Makhdoomi, M. (2017). Data mining approach for Big Data Analysis: a theoretical discourse. International Journal of Advanced Research in Computer Science, 8(7), 104-109. https://doi.org/10.26483/ijarcs.v8i7.4032
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. Retrieved from https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation
Marr, B. (2015). Big Data: 20 mind-boggling facts everyone must read. Retrieved from https://www.forbes.com/sites/bernard-marr/2015/09/30/big-data-20-mind-boggling-facts-everyone-must-read/#5947d77c17b1
McAfee, A., & Brynjolfsson, E. (2012). Big data: the management revolution. Harvard Business Review, 90(10), 60-68.
Miller, S. (2014). Collaborative approaches needed to close the big data skills gap. Journal of Organization Design, 3(1), 26-30. https://doi.org/10.7146/jod.9823
Oussous, A., Benjelloun, F. Z., Lahcen, A. A., & Belfkih, S. (2017). Big Data technologies: a survey. Journal of King Saud University – Computer and Information Sciences. Retrieved from http://www.sciencedirect.com/science/article/pii/S1319157817300034
Pandey, P., Kumar, M., & Srivastava, P. (2016). Classification techniques for Big Data: a survey. Retrieved from http://iee-explore.ieee.org/stamp/stamp.jsp?arnumber=7724938
Panigrahi, P. K. (2012). A comparative study of supervised machine learning techniques for spam e-mail filtering. Fourth International Conference on Computational Intelligence and Communication Networks (pp. 506-512). https://doi.org/10.1109/CICN.2012.14
SAP. (2012). Small and midsize companies look to make big gains with “big data”. Retrieved from http://global.sap.com/corpo-rate-en/news.epx?PressID=19188
Seddon, J. J. J. M., & Currie, W. L. (2017). A model for unpacking big data analytics in high-frequency trading. Journal of Business Research, 70, 300-307. https://doi.org/10.1016/j.jbusres.2016.08.003
Sharma, S. (2015). Rise of Big Data and related issues. IEEE. Retrieved from http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7443346
Simon, P. (2014). The visual organization: data visualization, big data, and the quest for better decisions (28 p.). USA: SAS Institute.
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286. https://doi.org/10.1016/j.jbusres.2016.08.001
Stanton, S. J., Sinnott-Armstrong, W., & Huettel, S. A. (2017). Neuromarketing: ethical implications of its use and po-tential misuse. Journal of Business Ethics, 144(4), 799-811. https://doi.org/10.1007/s10551-016-3059-0
Sunita, B. A., & Lobo, L. M. R. J. (2012). A comparative study for selecting the best unsupervised learning algorithm in e-learning system. International Journal of Computer Applications, 41(3), 27-34. https://doi.org/10.5120/5523-7562
Tole, A. A. (2013). Big Data challenge. Database Systems Journal, 4(3), 31-40.
TRUSTe. (2015). 2015 US IoT Privacy Index. Retrieved from https://www.truste.com/resources/privacy-research/us-internet-of-things-index-2015/
ur Rehman, M. H., Liew, C. S., Abbas, A., Jayaraman, P. P., Wah, T. Y., & Khan, S. U. (2016). Big Data reduction methods: a survey. Data Science and Engineering, 1(4), 265-284. https://doi.org/10.1007/s41019-016-0022-0
Walker, R., ap Cenydd, L., Pop, S., Miles, H. C., Hughes, C., Teahan, W. J., & Roberts, J. C. (2013). Storyboarding for visual analytics, Information Visualization, 14(1), 27-50. https://doi.org/10.1177/1473871613487089
Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016). Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 101(4), 158-168. https://doi.org/10.1016/j.comnet.2015.12.017
Ward, J. S., & Barker, A. (2013). Undefined by data: a survey of big data definitions. Retrieved from http://arxiv.org/abs/1309.5821
Zakir, J. (2015). Big Data analytics. International Association for Computer Information Systems, 16(2), 81-90.
Zhan, Y., Tan, K. H., Li, Y., & Tse, Y. K. (2016). Unlocking the power of big data in new product development. Annals of Operations Research (pp. 1-19). https://doi.org/10.1007/s10479-016-2379-x