Роль и методы социальной бизнес-аналитики (Social Business Intelligence) в бизнес-проектах (BI projects)

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Тип работы: Эссе
Предмет: Статистика
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  • Добавлена 24.04.2019
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Content

Abstract 3
Introduction 4
1. Research methodology 5
2. Research concepts and theoretical base 6
3. Application of theoretical base 10
4. Information ethics in business projects 11
5. Result of practical application 12
6. Research perspectives 14
7. Future research 15
Conclusion 16
Bibliography 17



Фрагмент для ознакомления

” (Owyang, 2009)The statement quoted above by Jeremiah Owyang, a well-known web strategist aptly captures the value and importance of approaching social media analytics as a measurement framework that incorporates strategic objectives and tactical requirements – i.e. as a business intelligence practice.As organizations get more serious about measuring the effectiveness of their social media initiatives, it is important for them to link their measurement frameworks to highlevel business objectives of revenue generation, cost reduction, or operational excellence. Situating a social media analytics program as part of the overall business intelligence strategy provides a practical approach for organizations to get the most of their investments in such initiatives. As evident from the recommendations in the extant literature and BI orientation for social media analytics has the potential to provide real-time feedback and actionable insights to help organizations in their decision-making processes. Such an orientation would also enable organizations to improve the tactical execution of social media strategies such as building engagement across various channels and platforms, while directly tying social media metrics to their overarching business objectives.To solve the problem in this work, a qualitative analysis of research on the development and definition of the role of social business Analytics in business projects was performed. The analysis showed that social business Analytics is an integral part of the company's development strategy at the present stage. The use of methods and tools of social business Analytics in business projects allows to ensure a high level of competitiveness of the company, to take into account the needs of consumers and trends in the development of industries and market sectors.BibliographyBachmann, P. and Kantorová, K. 2016. From customer orientation to social CRM. New insights from Central Europe. Scientific papers of the University of Pardubice, Series D, Faculty of Economics and Administration, 36/2016.Berlanga, R., García-Moya, L., Nebot, V., Aramburu, M.J., Sanz, I. and Llidó, D.M. 2016. Slod-bi: An open data infrastructure for enabling social business intelligence. Big Data: Concepts, Methodologies, Tools, and Applications, pp. 1784-1813, IGI Global.Boddy, C. (2012). The nominal group technique: An aid to brainstorming ideas in research. Qualitative Market Research: An International Journal, 15(1), 6-18.Bryson, N. (1997). Supporting consensus formation in group support systems using the qualitative discriminant process. Annalsof Operations Research, 71(0), 75-91. doi:10.1023/A:1018983818299Chen, H., Chiang, R.H. and Storey, V.C. 2012. Business intelligence and analytics: From bigdata to big impact. MIS quarterly, 36(4), 1165-1188. ISO 690.Delbecq, A. L., & Van de Ven, A. H. (1971). A group process model for problem identification and program planning. The Journal of Applied Behavioral Science, 7(4), 466-492.Eden, C., & Ackermann, F. (1999). The role of GDSS in scenario development and strategy making. String Processing and Information Retrieval Symposium, 1999 and International Workshop on Groupware, 234-242.Fan, S., Lau, R.Y. and Zhao, J.L. 2015. Demystifying big data analytics for business intelligence through the lens of marketing mix. Big Data Research, 2(1), 28-32.Fisher, T. (2009). ROI in social media: A look at the arguments. Database Marketing &Customer Strategy Management, 16(3), 189-195.Giotia, H, Ponisb, S. T., and Panayiotou, N. Social business intelligence: Review and research directions. Journal of Intelligence Studies in Business Vol. 8, No. 2 (2018) pp. 23-42.Heijnen, J., de Reuver, M., Bouwman, H., Warnier, M., & Horlings, H. (2013). Social media data relevant for measuring key performance indicators? A content analysis approach. Co-created effective, agile, and trusted eServices (pp. 74-84) Springer.Interactive Advertising Bureau. (2009). Social media ad metrics definitions. Retrieved 06/13, 2013, fromhttps://www.netlingo.com/more/IAB_SocialMediaMetricsDefinitionsFinal.pdfJourdan, Z., Rainer, R. K., & Marshall, T. E. (2008). Business intelligence: An analysis of the literature 1. Information SystemsManagement, 25(2), 121-131. doi:10.1080/10580530801941512Kealey, D. and Protheroe, D. (1996). The effectiveness of cross-cultural training for expatriates: An assessment of the literature on the issue. InternationalJournalofInterculturalRelations, 20(2), pp.141-165.Kucher, K., Schamp-Bjerede, T., Kerren, A., Paradis, C. and Sahlgren, M. 2016. Visual analysis of online social media to open up the investigation of stance phenomena. Information Visualization, 15(2),93-116.Kurnia, P. F., Suharjito (2018) Business Intelligence Model to Analyze Social Media Information. Procedia Computer Science, 135, 5–14.Lonnqvist, A., & Pirttimaki, V. (2006). The measurement of business intelligence. Information Systems Management, 23(1), 32.Lotfy, A., El Tazi, N and El Gamal, N. 2016. SCIF: Social-Corporate Data Integration Framework. In: Proceedings of the 20th International Database Engineering & Applications Symposium, June 2016, pp. 328-333, ACM.Luhn, H. P. 1958. A business intelligence system. IBM Journal of Research and Development, 2,14-31Lu, Y., Wang, F. and Maciejewski, R. 2014. Business intelligence from social media: A study from the vast box office challenge. IEEEcomputer graphics and applications, 34(5), 58-69.Luo, J., Pan, X. and Zhu, X. 2015. Identifying digital traces for business marketing through topic probabilistic model. Technology Analysis& Strategic Management, 27(10), 1176-1192.Meredith, R. and O'Donnell, P. A. 2010. A Functional Model of Social Media and its Application to Business Intelligence. In: Proceedings of the 2010 conference on Bridging the Socio-technical Gap in Decision Support Systems: Challenges for the Next Decade, 41 August 2010, pp. 129-140, IOS Press, Netherlands.Murdough, C. (2009). Social media measurement: It's not impossible. Journal ofInteractive Advertising, 10(1), 94-99.Olszak, C.M. 2016. Toward better understanding and use of Business Intelligence in organizations. Information SystemsManagement, 33(2), 105-123.Negash, S. (2004). Business intelligence. Communications of the Association for Information Systems, 13(2004), 177-195.Ponis, S. T., & Christou, I. T. 2013. Competitive intelligence for SMEs: a web-based decision support system. International Journal ofBusiness Information Systems, 12(3), 243-258.Pu, J., Teng, Z., Gong, R., Wen, C. and Xu, Y. 2016. Sci-Fin: Visual Mining Spatial and Temporal Behavior Features from Social Media. Sensors, 16(12), 2194.Ram, J., Zhang, C. and Koronios, A. 2016. The Implications of Big Data Analytics on Business Intelligence: A Qualitative Study in China. Procedia Computer Science, 87, 221-226.Ranjan, J. 2009. Business intelligence: Concepts, components, techniques and benefits. Journalof Theoretical and Applied InformationTechnology, 9(1), 60-70.Rosemann, M., Eggert, M., Voigt, M. and Beverungen, D. 2012. Leveraging social network data for analytical CRM strategies: the introduction of social BI. In: Proceedings of the 20th European Conference on Information Systems (ECIS) 2012, AIS Electronic Library (AISeL).Ruhi, U. 2014. Social Media Analytics as a business intelligence practice: current landscape & future prospects. Journal of Internet Social Networking & VirtualCommunities, 2014.Shroff, G., Agarwal, P. and Dey, L. 2011. Enterprise information fusion for real-time business intelligence. In: Proceedings of the 14th International Conference, Information Fusion (FUSION), pp. 1-8, IEEE.Sigman, B. P., Garr, W., Pongsajapan, R., Selvanadin, M., McWilliams, M. and Bolling, K. 2016. Visualization of Twitter Data in the Classroom. Decision Sciences Journal ofInnovative Education, 14(4), 362-381.Sutton, S. G., & Arnold, V. (2011). Focus group methods: Using interactive and nominal groups to explore emerging technology-driven phenomena in accounting and information systems. InternationalJournal of Accounting Information Systems.Sample, J. A. (1984). Nominal group technique: An alternative to brainstorming. Journal of Extension, 22(2), 1-2.Trieu, V. Getting value from Business Intelligence systems: A review and research agenda. Decision Support Systems, 93, 111–124Tziralis, G., Vagenas, G., & Ponis, S. 2009. Prediction markets, an emerging Web 2.0 business model: towards the competitive intelligent enterprise. In Web 2.0 (pp. 1-21).Springer, Boston, MA.Vedder, R. G., Vanecek, M. T., Guynes, C. S., & Cappel, J. J. (1999). CEO and CIO perspectives on competitive intelligence. Communications of the ACM, 42(8), 108-116.Watson, H. J., & Wixom, B. H. (2007). The current state of business intelligence. Computer, 40(9), 96-99.Wen, C., Teng, Z., Chen, J., Wu, Y., Gong, R. and Pu, J. 2016. SocialRadius: Visual Exploration of User Check-in Behavior Based on Social Media Data. In: Proceedings of the International Conference on Cooperative Design, October 2016, Visualization and Engineering, pp. 300-308, Springer International Publishing.Wongthongtham, P., & Abu-Salih, B. 2015. Ontology and trust based data warehouse in new generation of business intelligence: Stateof-the-art, challenges, and opportunities. In Industrial Informatics (INDIN), 2015 IEEE 13th International Conference on (pp. 476-483). IEEE.Wu, Y., Liu, S., Yan, K., Liu, M. and Wu, F. 2014. Opinionflow: Visual analysis of opinion diffusion on social media. IEEE Transactionson Visualization and ComputerGraphics, 20(12), 1763-1772.Zimmerman, C., & Vatrapu, R. 2015. The Social Newsroom: Visual Analytics for Social Business Intelligence. In: Proceedings of the International Conference on Design Science Research in Information Systems, pp. 386-390, Springer International Publishing.

Bibliography
1. Bachmann, P. and Kantorová, K. 2016. From customer orientation to social CRM. New insights from Central Europe. Scientific papers of the University of Pardubice, Series D, Faculty of Economics and Administration, 36/2016.
2. Berlanga, R., García-Moya, L., Nebot, V., Aramburu, M.J., Sanz, I. and Llidó, D.M. 2016. Slod-bi: An open data infrastructure for enabling social business intelligence. Big Data: Concepts, Methodologies, Tools, and Applications, pp. 1784-1813, IGI Global.
3. Boddy, C. (2012). The nominal group technique: An aid to brainstorming ideas in research. Qualitative Market Research: An International Journal, 15(1), 6-18.
4. Bryson, N. (1997). Supporting consensus formation in group support systems using the qualitative discriminant process. Annals of Operations Research, 71(0), 75-91. doi: 10.1023/A:1018983818299
5. Chen, H., Chiang, R.H. and Storey, V.C. 2012. Business intelligence and analytics: From bigdata to big impact. MIS quarterly, 36(4), 1165-1188. ISO 690.
6. Delbecq, A. L., & Van de Ven, A. H. (1971). A group process model for problem identification and program planning. The Journal of Applied Behavioral Science, 7(4), 466-492.
7. Eden, C., & Ackermann, F. (1999). The role of GDSS in scenario development and strategy making. String Processing and Information Retrieval Symposium, 1999 and International Workshop on Groupware, 234-242.
8. Fan, S., Lau, R.Y. and Zhao, J.L. 2015. Demystifying big data analytics for business intelligence through the lens of marketing mix. Big Data Research, 2(1), 28-32.
9. Fisher, T. (2009). ROI in social media: A look at the arguments. Database Marketing &Customer Strategy Management, 16(3), 189-195.
10. Giotia, H, Ponisb, S. T., and Panayiotou, N. Social business intelligence: Review and research directions. Journal of Intelligence Studies in Business Vol. 8, No. 2 (2018) pp. 23-42.
11. Heijnen, J., de Reuver, M., Bouwman, H., Warnier, M., & Horlings, H. (2013). Social media data relevant for measuring key performance indicators? A content analysis approach. Co-created effective, agile, and trusted eServices (pp. 74-84) Springer.
12. Interactive Advertising Bureau. (2009). Social media ad metrics definitions. Retrieved 06/13, 2013, from https://www.netlingo.com/more/IAB_SocialMediaMetricsDefinitionsFinal.pdf
13. Jourdan, Z., Rainer, R. K., & Marshall, T. E. (2008). Business intelligence: An analysis of the literature 1. Information Systems Management, 25(2), 121-131. doi: 10.1080/10580530801941512
14. Kealey, D. and Protheroe, D. (1996). The effectiveness of cross-cultural training for expatriates: An assessment of the literature on the issue. International Journal of Intercultural Relations, 20(2), pp.141-165.
15. Kucher, K., Schamp-Bjerede, T., Kerren, A., Paradis, C. and Sahlgren, M. 2016. Visual analysis of online social media to open up the investigation of stance phenomena. Information Visualization, 15(2), 93-116.
16. Kurnia, P. F., Suharjito (2018) Business Intelligence Model to Analyze Social Media Information. Procedia Computer Science, 135, 5–14.
17. Lonnqvist, A., & Pirttimaki, V. (2006). The measurement of business intelligence. Information Systems Management, 23(1), 32.
18. Lotfy, A., El Tazi, N and El Gamal, N. 2016. SCIF: Social-Corporate Data Integration Framework. In: Proceedings of the 20th International Database Engineering & Applications Symposium, June 2016, pp. 328-333, ACM.
19. Luhn, H. P. 1958. A business intelligence system. IBM Journal of Research and Development, 2,14-31
20. Lu, Y., Wang, F. and Maciejewski, R. 2014. Business intelligence from social media: A study from the vast box office challenge. IEEE computer graphics and applications, 34(5), 58-69.
21. Luo, J., Pan, X. and Zhu, X. 2015. Identifying digital traces for business marketing through topic probabilistic model. Technology Analysis & Strategic Management, 27(10), 1176-1192.
22. Meredith, R. and O'Donnell, P. A. 2010. A Functional Model of Social Media and its Application to Business Intelligence. In: Proceedings of the 2010 conference on Bridging the Socio-technical Gap in Decision Support Systems: Challenges for the Next Decade, 41 August 2010, pp. 129-140, IOS Press, Netherlands.
23. Murdough, C. (2009). Social media measurement: It's not impossible. Journal of Interactive Advertising, 10(1), 94-99. Olszak, C.M. 2016. Toward better understanding and use of Business Intelligence in organizations. Information Systems Management, 33(2), 105-123.
24. Negash, S. (2004). Business intelligence. Communications of the Association for Information Systems, 13(2004), 177-195.
25. Ponis, S. T., & Christou, I. T. 2013. Competitive intelligence for SMEs: a web-based decision support system. International Journal of Business Information Systems, 12(3), 243-258.
26. Pu, J., Teng, Z., Gong, R., Wen, C. and Xu, Y. 2016. Sci-Fin: Visual Mining Spatial and Temporal Behavior Features from Social Media. Sensors, 16(12), 2194.
27. Ram, J., Zhang, C. and Koronios, A. 2016. The Implications of Big Data Analytics on Business Intelligence: A Qualitative Study in China. Procedia Computer Science, 87, 221-226.
28. Ranjan, J. 2009. Business intelligence: Concepts, components, techniques and benefits. Journal of Theoretical and Applied Information Technology, 9(1), 60-70.
29. Rosemann, M., Eggert, M., Voigt, M. and Beverungen, D. 2012. Leveraging social network data for analytical CRM strategies: the introduction of social BI. In: Proceedings of the 20th European Conference on Information Systems (ECIS) 2012, AIS Electronic Library (AISeL).
30. Ruhi, U. 2014. Social Media Analytics as a business intelligence practice: current landscape & future prospects. Journal of Internet Social Networking & Virtual Communities, 2014.
31. Shroff, G., Agarwal, P. and Dey, L. 2011. Enterprise information fusion for real-time business intelligence. In: Proceedings of the 14th International Conference, Information Fusion (FUSION), pp. 1-8, IEEE.
32. Sigman, B. P., Garr, W., Pongsajapan, R., Selvanadin, M., McWilliams, M. and Bolling, K. 2016. Visualization of Twitter Data in the Classroom. Decision Sciences Journal of Innovative Education, 14(4), 362-381.
33. Sutton, S. G., & Arnold, V. (2011). Focus group methods: Using interactive and nominal groups to explore emerging technology-driven phenomena in accounting and information systems. International Journal of Accounting Information Systems.
34. Sample, J. A. (1984). Nominal group technique: An alternative to brainstorming. Journal of Extension, 22(2), 1-2.
35. Trieu, V. Getting value from Business Intelligence systems: A review and research agenda. Decision Support Systems, 93, 111–124
36. Tziralis, G., Vagenas, G., & Ponis, S. 2009. Prediction markets, an emerging Web 2.0 business model: towards the competitive intelligent enterprise. In Web 2.0 (pp. 1-21). Springer, Boston, MA.
37. Vedder, R. G., Vanecek, M. T., Guynes, C. S., & Cappel, J. J. (1999). CEO and CIO perspectives on competitive intelligence. Communications of the ACM, 42(8), 108-116.
38. Watson, H. J., & Wixom, B. H. (2007). The current state of business intelligence. Computer, 40(9), 96-99.
39. Wen, C., Teng, Z., Chen, J., Wu, Y., Gong, R. and Pu, J. 2016. Social Radius: Visual Exploration of User Check-in Behavior Based on Social Media Data. In: Proceedings of the International Conference on Cooperative Design, October 2016, Visualization and Engineering, pp. 300-308, Springer International Publishing.
40. Wongthongtham, P., & Abu-Salih, B. 2015. Ontology and trust based data warehouse in new generation of business intelligence: Stateof-the-art, challenges, and opportunities. In Industrial Informatics (INDIN), 2015 IEEE 13th International Conference on (pp. 476-483). IEEE.
41. Wu, Y., Liu, S., Yan, K., Liu, M. and Wu, F. 2014. Opinionflow: Visual analysis of opinion diffusion on social media. IEEE Transactions on Visualization and Computer Graphics, 20(12), 1763-1772.
42. Zimmerman, C., & Vatrapu, R. 2015. The Social Newsroom: Visual Analytics for Social Business Intelligence. In: Proceedings of the International Conference on Design Science Research in Information

Вопрос-ответ:

Какая роль играет социальная бизнес аналитика в проектах бизнес интеллекта?

Социальная бизнес аналитика (Social Business Intelligence) играет важную роль в проектах бизнес интеллекта, так как позволяет анализировать и использовать данные, связанные с социальными медиа и сетями, для принятия более эффективных бизнес-решений. Она предоставляет компаниям возможность улучшить свою стратегию в социальных медиа, определить потребности клиентов, анализировать репутацию бренда и оценивать эффективность маркетинговых кампаний.

Какие методы используются в социальной бизнес аналитике?

В социальной бизнес аналитике используются различные методы для анализа данных из социальных медиа и сетей. Это включает в себя мониторинг и анализ текстовых данных (таких как комментарии, отзывы, посты), анализ графовых данных (связи между пользователями), анализ семантики (выделение ключевых слов и тематик) и многие другие. Кроме того, используются методы машинного обучения для автоматической классификации, прогнозирования и выявления закономерностей в данных.

Какую роль играют этические аспекты в проектах социальной бизнес аналитики?

Этические аспекты играют важную роль в проектах социальной бизнес аналитики. В процессе анализа данных из социальных медиа и сетей необходимо учитывать принципы конфиденциальности, безопасности и справедливости. Компании должны быть готовы защищать личную информацию пользователей и использовать данные социальных медиа в соответствии с законодательством и правилами платформы. Также важно обеспечить прозрачность и объяснимость алгоритмов и моделей, используемых в анализе данных.

Какова роль социальной бизнес аналитики в бизнес-проектах?

Роль социальной бизнес аналитики в бизнес-проектах заключается в сборе и анализе данных из социальных медиа и других онлайн-источников с целью выявления трендов, понимания мнения потребителей, оценки эффективности маркетинговых кампаний и принятия взвешенных решений для развития бизнеса.

Какие методы применяются в социальной бизнес аналитике?

Социальная бизнес аналитика использует различные методы, включая сбор и анализ данных из социальных медиа, машинное обучение, статистический анализ, анализ сентимента, а также методы визуализации данных для представления результатов исследования.

Каким образом социальная бизнес аналитика может помочь в разработке бизнес-проектов?

Социальная бизнес аналитика может предоставить ценную информацию о целевой аудитории, потребительских предпочтениях, конкурентной среде и эффективности маркетинговых кампаний. Это позволяет разработчикам бизнес-проектов принимать обоснованные решения, улучшать продукты и услуги, оптимизировать маркетинговые стратегии и повышать уровень удовлетворенности клиентов.

Какая роль этики информации в бизнес-проектах?

Этика информации играет важную роль в бизнес-проектах, особенно при использовании данных из социальных медиа. Важно соблюдать принципы конфиденциальности, защиты персональных данных и справедливого использования информации. Этически правильное обращение с данными повышает доверие клиентов и способствует устойчивому развитию бизнеса.

Каковы перспективы развития социальной бизнес аналитики?

Перспективы развития социальной бизнес аналитики включают более широкое использование искусственного интеллекта и машинного обучения для анализа данных, улучшение алгоритмов прогнозирования и предсказательной аналитики, а также интеграцию социальной бизнес аналитики в различные сферы деятельности, включая маркетинг, продажи, операции и управление клиентским опытом.