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題名 金融業與網路科技業導入巨量資料系統的關鍵因素之個案研究
Case Studies of Key Factors for implementing Big Data system on Financial and Internet Technology Industries
作者 陳冠廷
Chen, Kuan Ting
貢獻者 吳豐祥<br>沈錳坤
Wu, Vincent<br>Shan, Man Kwan
陳冠廷
Chen, Kuan Ting
關鍵詞 金融業
網路科技業
巨量資料
關鍵因素
Financial Industry
Internet Technology
Big Data
Critical Factors
日期 2015
上傳時間 1-Oct-2015 14:29:54 (UTC+8)
摘要 隨著網際網路的普及，智慧型手機與物聯網開始興起，根據資策會的調查，台灣約有49.5%的智慧型手機占有率，大約每2人就有一人擁有智慧型手機，而物聯網的興起，製造了大量的數據與資料，而這些數據與資料透過不同的處理方式，可帶給企業不同的商業智慧與洞見，而傳統產業因此面臨了巨大的轉變與挑戰，優步(Uber)就是改變傳統計程車產業與物聯網的一個例子，顧客不再需要招手才能搭上計程車，靠著網路、手機APP與GPS定位系統即可獲知車輛資訊、到達時間與聯絡司機，而優步可掌握乘客資訊、行車路線與顧客服務。不僅僅是計程車產業，亞馬遜的崛起也代表了傳統零售業的轉變，因此如何面對巨量資料對傳統企業都將是一項挑戰。
巨量資料的導入與分析可以提供企業掌握消費者行為，也可透過數據分析研發新服務與產品，此研究從三方面來探討金融產業與網路科技業導入巨量資料系統的關鍵因素，分別為導入流程、企業本身與巨量資料系統，另外藉由三家個案公司訪談，並輔以文獻所探討的研究架構來進行驗證”金融業與網路科技業導入巨量資料的流程為何?”、”金融業與網路科技業導入巨量資料時的關鍵因素?”、” 金融業與網路科技業導入巨量資料後有何優勢?”
本研究最後發現，金融業與網際網路業導入巨量資料分成三個階段，首先企業會先詮釋自身對巨量資料之定義，定義自身巨量資料之意義後，企業會開始集體研討導入流程，依照自身對巨量資料的詮釋來集體擬定對企業最好的導入流程，此階段通常會是三階段中耗時最長，也需做許多內外部研究、規劃與管理。最後一階段為實做階段，企業會依照集思廣益後所擬定出的計畫來完成巨量資料的導入。而本研究透過個案訪談也發現七項導入巨量資料之關鍵因素，包含，導入隊伍的組成、高層管理者的支持、導入時機、巨量資料系統的選擇、明確的目標與策略、內外部員工訓練與支援。最後企業運用第三方與開放式資料軟體來處理巨量資料使企業更了解顧客需求與運用於新產品研發。
With the popularity of internet, smart phones and Internet of Things begin to emerge. According to Institute for Information Industry, there are approximately 49.5% of smart devices in Taiwan, which mean every two people will own at least one smart device. In addition, more devices are connected to the internet. Therefore, tremendous amount of data is created and increased exponentially. With applicable and correct techniques, Big Data can provide valuable insight and business intelligent. Traditional industries are forced to change. For example, Uber is one innovative idea that changes the ways people ride taxi. Riding taxi become more efficient and effective with Uber.
This research explores critical factors of Big Data implementation on financial and internet technology industries from three perspectives. This includes key processes of the Big Data implementation, enterprises factors, and the Big Data system. Moreover, literature review was conducted to. In addition, three case studies were interviewed and analyzed based on research framework. Lastly, three research questions are answered. First, what are the key process for financial and internet technology industries implementing the Big Data system? Second, what are the critical factors for financial and internet technology industries implementing the Big Data system? Third, what are the potential benefits after the Big Data implementation?
The research findings are primarily categorized into two parts. First, there are three phases of financial institution and internet technology industries implementing the Big Data system. The three phases included defining, brainstorming and implementation phases. The three phases are described below:
1. Defining Phase: Companies will first define their own interpretation of Big Data in order to plan and coordinated their implementation.
2. Brainstorming Phase: Companies averagely spent most of the time in this phase. The implementation team leads must brainstorm to find the best way to enforce and carry out the Big Data project by searching, organizing and surveying internal and externally.
3. Implementation Phase: Companies follow their previous made proposal steps by steps.
This research also concluded and found several critical factors during the Big Data implementation. The critical factors included but not limited to:
1. An implementation team regardless the size to carry out the Big Data project
2. Top management’s commitment on implementation
3. Timing on the implementation
4. Big Data system selection
5. Clear goals and objectives
參考文獻 1. 申燕儒(2002)，「組織結構、資訊系統與流程再造在導入ERP系統之角色探討」，成功大學工業管理科學系碩博士班碩士論文。
2. 資策會. (2013). 2013 臺灣消費者科技應用生活型態研究分析報告.
3. IBM海量資料的淘金術(2011) http://www-07.ibm.com/tw/blueview/2012oct/8.html
4. Ahituv, Niv, Seev Neumann and Moshe Zviran (2002), “A System Development Methodology for ERP Systems.”Journal of Computer Information Systems, Spring 2002, Vol. 42, No. 3, pp. 56-67.
5. Al-Mashari, M. and Zairi, M. (2000), ""Information and business process equality: the case of SAP R/3 implementation’’, Electronic Journal on Information Systems in Developing Countries, Vol. 2 (http://www.unimas.my/fit/roger/EJISDC/EJISDC.htm)
6. Benbasat, I., Goldstein, D. K., & Mead, M. (1987). The case research strategy in studies of information systems. MIS quarterly, 11(3).
7. Blackstone Jr., J.H., Cox, J.F., 2005. APICS Dictionary, 11th ed.
8. D.A. Reed, D.B. Gannon, and J.R. Larus, Imagining the Future: Thoughts on Computing, Computer, vol. 45, no. 1, pp. 25-30, jan. 2012.
9. Davenport, T. H. (1998). Putting the enterprise into the enterprise system.Harvard business review, (76), 121-31.
10. Deloitte Consulting 1998. “ERP’s Second Wave, Maximizing the Value of ERP-enabled Processes”, Deloitte Touche Tohmatsu, http://www.dc.com
11. D. Johnson, R. Johnson, Cooperation and Competition: Theory and Research, Interaction, Edina, MN, 1989.
12. De Sousa, J. M. E. (2004). Definition and analysis of critical success factors for ERP implementation projects (Doctoral dissertation, Universitat Politècnica de Catalunya, Barcelona, Spain)
13. E. Ackerman and E. Guizzo, 5 technologies that will shape the web, Spectrum, IEEE, vol. 48, no. 6, pp. 40-45, June 2011.
14. Ehie, I. C., & Madsen, M. (2005). Identifying critical issues in enterprise resource planning (ERP) implementation. Computers in industry, 56(6), 545-557.
15. Gould, L. (1997). Planning and scheduling today`s automotive enterprises. Automotive Manufacturing & Production, 109(4), 62-66. Retrieved from http://search.proquest.com/docview/217446535?accountid=10067
16. Hassan, Qusay (2011). Demystifying Cloud Computing. The Journal of Defense Software Engineering (CrossTalk) 2011 (Jan/Feb): 16–21. Retrieved 11 December 2014.
17. J. P. Dijcks. Oracle: Big Data for the enterprise. Oracle White Paper, 2012.
18. Kale, V. (2014). Implementing SAP® CRM: The Guide for Business and Technology Managers. CRC Press
19. King, B. (2013). Bank 3.0 why banking is no longer somewhere you go, but something you do. Singapore: John Wiley & Sons Singapore Pte.
20. Mandal, P., & Gunasekaran, A. (2003). Issues in implementing ERP: A case study. European Journal of Operational Research, 146(2), 274-283.
21. Mark Raskino, Jackie Fenn, and Alexander Linden, "Extracting Value From the Massively Connected World of 2015," Gartner Research, Tech. rep. 2005.
22. Manyika J, McKinsey Global Institute, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers AH (2011) Big Data:the next frontier for innovation, competition, and productivity. McKinsey Global Institute
23. Markus M., Tanis C. 2000. “The Enterprise Systems Experience- From Adoption to Success”, In Framing the Domains of IT Research Glimpsing the Future Through the Past, R. W. Zmud (Ed.), Pinnaflex Educational Resources, Cincinnati, OH
24. McAfee, Andrew, et al. "Big Data." The management revolution. Harvard Bus Rev 90.10 (2012): 61-67.
25. M. Earl, Viewpoint: new and old business process redesign, Journal of Strategic Information Systems 3 (1) (1994) 5–22.
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27. Motwani, J., Subramanian, R., & Gopalakrishna, P. (2005). Critical factors for successful ERP implementation: Exploratory findings from four case studies. Computers in Industry, 56(6), 529-544.
28. Paul Zikopoulos. (2012, March) IBM Big Data: What is Big Data Part 1 and 2. [Online]. http://www.youtube.com/watch?v=B27SpLOOhWw [Accessed on: 2012-06-08]
29. P. Bingi, M.K. Sharma, J.K. Godla, Critical issues affecting an ERP implementation, Information Systems Management 16 (Summer (3)) (1999) 7–14.
30. PwC, Capitalizing on the promise of Big Data: How a buzzword morphed into a lasting trend that will transform the way you do business. January 2013, www.pwc.com/us/bigdata.
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32. R. Kilman, M. Saxton, R. Serpa, Issues in understanding and changing culture, California Management Review 28 (2)(1986) 87–94.
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34. S. Guha, V. Grover, W. Kettinger, J. Teng, Business process change and organizational performance: exploring an antecedent model, Journal of Management Information Systems 14(1) (1997) 119–154.
35. Stratman, Jeff K., and Aleda V. Roth. "Enterprise resource planning (ERP) competence constructs: Two-stage multi-item scale development and validation." Decision Sciences 33.4 (2002): 601.
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38. Wagle, D. (1998). The case for ERP systems. McKinsey Quarterly, 130-139.
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40. W. Kettinger, V. Grover, Toward a theory of business process change management, Journal of Management Information Systems 12 (1) (1995) 1–30.
41. Wylie, L., 1990. A vision of the next-generation MRP II. Scenario S-300-339, Gartner Group, April 12, 1990.
42. Yin, R.K. Case Study Research, Design and Methods, Sage Publications, Beverly Hills, California, 1984.
43. Zikopoulos, P., Parasuraman, K., Deutsch, T., Giles, J., & Corrigan, D. (2012).Harness the power of Big Data The IBM Big Data platform. McGraw Hill Professional.
描述 碩士
國立政治大學
科技管理與智慧財產研究所
102364139
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102364139
資料類型 thesis
dc.contributor.advisor 吳豐祥<br>沈錳坤zh_TW
dc.contributor.advisor Wu, Vincent<br>Shan, Man Kwanen_US
dc.contributor.author (Authors) 陳冠廷zh_TW
dc.contributor.author (Authors) Chen, Kuan Tingen_US
dc.creator (作者) 陳冠廷zh_TW
dc.creator (作者) Chen, Kuan Tingen_US
dc.date (日期) 2015en_US
dc.date.accessioned 1-Oct-2015 14:29:54 (UTC+8)-
dc.date.available 1-Oct-2015 14:29:54 (UTC+8)-
dc.date.issued (上傳時間) 1-Oct-2015 14:29:54 (UTC+8)-
dc.identifier (Other Identifiers) G0102364139en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/78813-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 科技管理與智慧財產研究所zh_TW
dc.description (描述) 102364139zh_TW
dc.description.abstract (摘要) 隨著網際網路的普及，智慧型手機與物聯網開始興起，根據資策會的調查，台灣約有49.5%的智慧型手機占有率，大約每2人就有一人擁有智慧型手機，而物聯網的興起，製造了大量的數據與資料，而這些數據與資料透過不同的處理方式，可帶給企業不同的商業智慧與洞見，而傳統產業因此面臨了巨大的轉變與挑戰，優步(Uber)就是改變傳統計程車產業與物聯網的一個例子，顧客不再需要招手才能搭上計程車，靠著網路、手機APP與GPS定位系統即可獲知車輛資訊、到達時間與聯絡司機，而優步可掌握乘客資訊、行車路線與顧客服務。不僅僅是計程車產業，亞馬遜的崛起也代表了傳統零售業的轉變，因此如何面對巨量資料對傳統企業都將是一項挑戰。
巨量資料的導入與分析可以提供企業掌握消費者行為，也可透過數據分析研發新服務與產品，此研究從三方面來探討金融產業與網路科技業導入巨量資料系統的關鍵因素，分別為導入流程、企業本身與巨量資料系統，另外藉由三家個案公司訪談，並輔以文獻所探討的研究架構來進行驗證”金融業與網路科技業導入巨量資料的流程為何?”、”金融業與網路科技業導入巨量資料時的關鍵因素?”、” 金融業與網路科技業導入巨量資料後有何優勢?”
本研究最後發現，金融業與網際網路業導入巨量資料分成三個階段，首先企業會先詮釋自身對巨量資料之定義，定義自身巨量資料之意義後，企業會開始集體研討導入流程，依照自身對巨量資料的詮釋來集體擬定對企業最好的導入流程，此階段通常會是三階段中耗時最長，也需做許多內外部研究、規劃與管理。最後一階段為實做階段，企業會依照集思廣益後所擬定出的計畫來完成巨量資料的導入。而本研究透過個案訪談也發現七項導入巨量資料之關鍵因素，包含，導入隊伍的組成、高層管理者的支持、導入時機、巨量資料系統的選擇、明確的目標與策略、內外部員工訓練與支援。最後企業運用第三方與開放式資料軟體來處理巨量資料使企業更了解顧客需求與運用於新產品研發。		zh_TW
dc.description.abstract (摘要) With the popularity of internet, smart phones and Internet of Things begin to emerge. According to Institute for Information Industry, there are approximately 49.5% of smart devices in Taiwan, which mean every two people will own at least one smart device. In addition, more devices are connected to the internet. Therefore, tremendous amount of data is created and increased exponentially. With applicable and correct techniques, Big Data can provide valuable insight and business intelligent. Traditional industries are forced to change. For example, Uber is one innovative idea that changes the ways people ride taxi. Riding taxi become more efficient and effective with Uber.
This research explores critical factors of Big Data implementation on financial and internet technology industries from three perspectives. This includes key processes of the Big Data implementation, enterprises factors, and the Big Data system. Moreover, literature review was conducted to. In addition, three case studies were interviewed and analyzed based on research framework. Lastly, three research questions are answered. First, what are the key process for financial and internet technology industries implementing the Big Data system? Second, what are the critical factors for financial and internet technology industries implementing the Big Data system? Third, what are the potential benefits after the Big Data implementation?
The research findings are primarily categorized into two parts. First, there are three phases of financial institution and internet technology industries implementing the Big Data system. The three phases included defining, brainstorming and implementation phases. The three phases are described below:
1. Defining Phase: Companies will first define their own interpretation of Big Data in order to plan and coordinated their implementation.
2. Brainstorming Phase: Companies averagely spent most of the time in this phase. The implementation team leads must brainstorm to find the best way to enforce and carry out the Big Data project by searching, organizing and surveying internal and externally.
3. Implementation Phase: Companies follow their previous made proposal steps by steps.
This research also concluded and found several critical factors during the Big Data implementation. The critical factors included but not limited to:
1. An implementation team regardless the size to carry out the Big Data project
2. Top management’s commitment on implementation
3. Timing on the implementation
4. Big Data system selection
5. Clear goals and objectives		en_US
dc.description.tableofcontents ABSTRACT I
摘要 II
致謝 III
TABLE OF CONTENTS IV
LIST OF FIGURES VI
LIST OF TABLES VII
Chapter 1. Introduction 1
1.1 Motivation and Background 1
1.1.1 Big Data, Big Value 2
1.1.2 Implementing Big Data System for Enterprises 3
1.2 Research Questions and Objectives 4
1.3 Research Process 5
Chapter 2. Literature Review 7
2.1 Big Data 7
2.1.1 Definition of the Big Data 8
2.1.2 Big Data Challenges in Management 10
2.1.3 Approach to Big Data 12
2.2 ERP Implementation Process 15
2.2.1 History of ERP and Definition 15
2.2.2 ERP Implementation Motivation and Benefits 17
2.2.3 ERP Implementation Definition and Process 18
2.3 Critical Factors for ERP Implementation 28
Chapter 3. Research Method and Design 34
3.1 Research Framework 34
3.2 Research Method 36
3.3 Research Target 36
3.4 Data Collection Method 37
3.4.1 Interview Design 37
3.4.2 Question Design 38
Chapter 4. Case Study 39
4.1 Case Study of Company A 39
4.1.1 Background: Financial Company A 39
4.1.2 Implementation and Process 41
4.1.3 Enterprise Requirements 44
4.1.4 Benefits after Implementation 46
4.1.5 Big Data System Performance (Company A) 47
4.2 Case Study of Company B 48
4.2.1 Background: Financial Company B 48
4.2.2 Implementation and Process 50
4.2.3 Enterprise Requirements 51
4.2.4 Benefits after Implementation 52
4.2.5 Big Data System Performance (Company B) 53
4.3 Case Study of Company C 56
4.3.1 Background: Company C 56
4.3.2 Implementation and Process 57
4.3.3 Enterprise Requirements 59
4.3.4 Benefits after Implementation 60
Chapter 5. Research Findings and Discussion 63
5.1 Implementation Process 64
5.2 Enterprise Requirements 66
5.3 Performance of the Big Data System 67
5.4 Relationship Discussion between Implementation process, Enterprise requirements and Big Data system 68
Chapter 6. Summary and Recommendation 73
6.1 Research Summary 73
6.2 Research Limitation and Recommendation 74
References 76		zh_TW
dc.format.extent 2093057 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102364139en_US
dc.subject (關鍵詞) 金融業zh_TW
dc.subject (關鍵詞) 網路科技業zh_TW
dc.subject (關鍵詞) 巨量資料zh_TW
dc.subject (關鍵詞) 關鍵因素zh_TW
dc.subject (關鍵詞) Financial Industryen_US
dc.subject (關鍵詞) Internet Technologyen_US
dc.subject (關鍵詞) Big Dataen_US
dc.subject (關鍵詞) Critical Factorsen_US
dc.title (題名) 金融業與網路科技業導入巨量資料系統的關鍵因素之個案研究zh_TW
dc.title (題名) Case Studies of Key Factors for implementing Big Data system on Financial and Internet Technology Industriesen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 1. 申燕儒(2002)，「組織結構、資訊系統與流程再造在導入ERP系統之角色探討」，成功大學工業管理科學系碩博士班碩士論文。
2. 資策會. (2013). 2013 臺灣消費者科技應用生活型態研究分析報告.
3. IBM海量資料的淘金術(2011) http://www-07.ibm.com/tw/blueview/2012oct/8.html
4. Ahituv, Niv, Seev Neumann and Moshe Zviran (2002), “A System Development Methodology for ERP Systems.”Journal of Computer Information Systems, Spring 2002, Vol. 42, No. 3, pp. 56-67.
5. Al-Mashari, M. and Zairi, M. (2000), ""Information and business process equality: the case of SAP R/3 implementation’’, Electronic Journal on Information Systems in Developing Countries, Vol. 2 (http://www.unimas.my/fit/roger/EJISDC/EJISDC.htm)
6. Benbasat, I., Goldstein, D. K., & Mead, M. (1987). The case research strategy in studies of information systems. MIS quarterly, 11(3).
7. Blackstone Jr., J.H., Cox, J.F., 2005. APICS Dictionary, 11th ed.
8. D.A. Reed, D.B. Gannon, and J.R. Larus, Imagining the Future: Thoughts on Computing, Computer, vol. 45, no. 1, pp. 25-30, jan. 2012.
9. Davenport, T. H. (1998). Putting the enterprise into the enterprise system.Harvard business review, (76), 121-31.
10. Deloitte Consulting 1998. “ERP’s Second Wave, Maximizing the Value of ERP-enabled Processes”, Deloitte Touche Tohmatsu, http://www.dc.com
11. D. Johnson, R. Johnson, Cooperation and Competition: Theory and Research, Interaction, Edina, MN, 1989.
12. De Sousa, J. M. E. (2004). Definition and analysis of critical success factors for ERP implementation projects (Doctoral dissertation, Universitat Politècnica de Catalunya, Barcelona, Spain)
13. E. Ackerman and E. Guizzo, 5 technologies that will shape the web, Spectrum, IEEE, vol. 48, no. 6, pp. 40-45, June 2011.
14. Ehie, I. C., & Madsen, M. (2005). Identifying critical issues in enterprise resource planning (ERP) implementation. Computers in industry, 56(6), 545-557.
15. Gould, L. (1997). Planning and scheduling today`s automotive enterprises. Automotive Manufacturing & Production, 109(4), 62-66. Retrieved from http://search.proquest.com/docview/217446535?accountid=10067
16. Hassan, Qusay (2011). Demystifying Cloud Computing. The Journal of Defense Software Engineering (CrossTalk) 2011 (Jan/Feb): 16–21. Retrieved 11 December 2014.
17. J. P. Dijcks. Oracle: Big Data for the enterprise. Oracle White Paper, 2012.
18. Kale, V. (2014). Implementing SAP® CRM: The Guide for Business and Technology Managers. CRC Press
19. King, B. (2013). Bank 3.0 why banking is no longer somewhere you go, but something you do. Singapore: John Wiley & Sons Singapore Pte.
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