跳到主要內容

簡易檢索 / 詳目顯示

回結果列表
研究生： 黃文斾
Wen-Pei Huang
論文名稱： 企業數據分析能力評估模式之研究
The Study of Assessment Model For Enterprise Data Analytical Capabilities
指導教授： 陳炫碩
口試委員:
學位類別： 碩士
Master
系所名稱： 管理學院 - 企業管理學系
Department of Business Administration
論文出版年： 2018
畢業學年度： 106
語文別： 中文
論文頁數： 55
中文關鍵詞： 成熟度模型 、數據分析能力
外文關鍵詞： maturity model, data analytical capabilities
相關次數： 點閱：8  下載：0 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 根據Gartner 2018 年所提出的十大科技發展趨勢，其中第一個就是人工智慧
    (Artificial Intelligence)，他認為在2018 年，每個企業都應該接觸AI。然而，AI 的發展需
    要許多基礎和條件，企業在追趕AI 之前，需要先檢查其數據分析能力。
    多數的臺灣的企業，在發展數據分析能力之路並不平坦，其包含很多原因，一是組
    織對其狀態、需求和發展策略不了解，二是組織面對大環境的趨勢仍不改變其舊有模式，
    三是有些企業雖投入了數據分析，但短期之內看不見其成效，而採取觀望的做法，不再
    投入。根據IDC 與IBM 所發表的2017 年臺灣企業數據分析能力調查報告，顯示台灣企
    業的數據分析能力越來越兩極化，不進則退和精益求精是臺灣企業目前的現狀。
    因此，本研究結合前人文獻，藉由成熟度模型，發展企業數據分析能力的階段性指
    導方針，分別以五個維度評估企業數據分析之能力，其分別為人力資源、數據、技術、
    意圖以及流程，以協助企業了解目前數據分析能力的狀態，並提供學習路徑，以達成目
    標；另外，本研究亦發展各階段之評估問卷，並更改舊有文獻之評分方法，加入各維度
    之重要性不同之觀點，以協助評估方法更為完善，用以協助企業評估其數據分析能力的
    成熟度。

    According to Gartner top 10 strategic technology trends for 2018, the trend No.1 is
    artificial intelligence foundation. In 2018, every company should be infused with artificial
    intelligence foundation. However, AI-based capabilities requires many foundations and
    condition. Before companies can catch up with AI, they need to check their data analytical
    capabilities.
    Problems appear to Taiwanese enterprises when it comes to developing data analytical
    skills. One is that organizations do not comprehend their status, needs and development
    strategies. Another is that organizations see no issues to change their inadequate models. The
    other is that organizations didn’t receive performance feedback to keep investing new. The
    survey report of 2017 Taiwanese enterprises data analytical capabilities published by IDC and
    IBM shows that the data analysis capabilities of Taiwanese enterprises have become opposition.
    Recently it even become to be the situation that the bigger the stronger.
    Therefore, this study combines previous literature with the maturity model to develop
    guidelines for enterprise data analytical capabilities. It is a journey that involve building
    ecosystem that includes human resourse, data, technology, intent and process. It helps identify
    and define the enterprise’s goals around the program and creates a learning path to achieve
    them. In addition, this study also developes assessment questionnaires to measure the readiness
    and improve the way of evaluating benchmark score. The study contribution is providing a
    quick way for enterprises to comprehend their maturity in big data analytical capabilities.
    Keywords: data analytical capabilitiy, maturity model

    摘要 ........................................................................................................ i 致謝 ....................................................................................................... ii 目錄 ...................................................................................................... iv 表目錄 .................................................................................................. vi 圖目錄 ................................................................................................. vii 第一章 緒論 ......................................................................................... 1 1.1 研究背景與動機 ................................................................................................... 1 1.2 研究目的 ............................................................................................................... 2 第二章 文獻探討 ................................................................................. 3 2-1 數據分析大數據和數據管理的定義 ................................................................... 3 2-2 成熟度模型(Maturity Model) ............................................................................... 3 2-2-1 TDWI 成熟度模型的五個階段和五個分析維度 .................................. 4 2-2-2 IDC 成熟度模型的五個階段和五個分析維度 ...................................... 6 2-2-3 決策流程(Decision-Making Process) .................................................... 7 2-2-4 數據的特性 ............................................................................................. 8 2-2-5 大數據帶來的巨大影響 ......................................................................... 9 2-2-6 資源基礎論觀點的IT 競爭力 ............................................................. 10 第三章 研究方法 ............................................................................... 12 3-1 研究架構 ............................................................................................................. 12 3-2 各維度之階段性指南 ......................................................................................... 13 3-2-1 人力資源 ............................................................................................... 13 3-2-2 數據 ....................................................................................................... 15 3-2-3 技術 ....................................................................................................... 18 3-2-4 意圖 ....................................................................................................... 20 3-2-5 流程 ....................................................................................................... 23 3-3 成熟度評估方法 ................................................................................................... 24 3-3-1 評估方法之介紹 ................................................................................... 25 3-3-2 AHP 法的進行步驟 ............................................................................... 25 3-3-3 維度的權重之發展辦法 ........................................................................ 29 第四章 研究結果 ............................................................................... 36 4-1 資料處理和統計方法 ........................................................................................... 36 4-1-1 資料處理 ............................................................................................... 36 4-1-2 統計分析 ............................................................................................... 36 4-2 敘述性統計分析 ................................................................................................... 36 4-3 結構模型結果評估 ............................................................................................... 36 第五章 結論與建議 ........................................................................... 38 5-1 研究結論 ............................................................................................................... 38 5-2 建議與未來方向 ................................................................................................... 38 第六章 參考資料 ............................................................................... 39 第七章 附錄 ....................................................................................... 41

    Amit, R., & Schoemaker, P. J. (1993). Strategic assets and organizational rent. Strategic
    management journal, 14(1), 33-46.
    Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of management,
    17(1), 99-120.
    Bharadwaj, A. S. (2000). A resource-based perspective on information technology capability
    and firm performance: an empirical investigation. MIS quarterly, 169-196.
    Brynjolfsson, E., & Hitt, L. (1997). Breaking boundaries. Informationweek(649), 54-58.
    Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big
    data to big impact. MIS quarterly, 1165-1188.
    Conner, K. R. (1991). A historical comparison of resource-based theory and five schools of
    thought within industrial organization economics: do we have a new theory of the firm? Journal
    of management, 17(1), 121-154.
    Cukier, K. (2010). Data, data everywhere: A special report on managing information:
    Economist Newspaper.
    Data, D. B. (2012). A Practical Guide to Transforming the Business of Government.
    TechAmerica Foundation‟ s Federal Big Data Commission.
    Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics.
    International Journal of Information Management, 35(2), 137-144.
    Grant, R. M. (1999). The resource-based theory of competitive advantage: implications for
    strategy formulation. In Knowledge and strategy (pp. 3-23): Elsevier.
    Halper, F., & Krishnan, K. (2013). TDWi big data maturity model guide. Interpreting Your
    Assessment Score. TDWI Research, 1, 16.
    Huang, K.-T., Lee, Y. W., & Wang, R. Y. (1999). Quality information and knowledge
    management. Publisher: Prentice Hall.
    Keen, P. G. (1991). Shaping the future: business design through information technology:
    Harvard Business School Press.
    Keen, P. G. W. (1993). Information technology and the management difference: a fusion map.
    IBM Systems Journal, 32(1), 17-39.
    Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and
    acquisition intention of big data analytics. International Journal of Information Management,
    34(3), 387-394.
    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.
    Mata, F. J., Fuerst, W. L., & Barney, J. B. (1995). Information technology and sustained
    competitive advantage: A resource-based analysis. MIS quarterly, 487-505.
    O’reilly, T. (2005). What is web 2.0. In.
    Rockart, J. F., Earl, M. J., & Ross, J. W. (1996). Eight imperatives for the new IT organization.
    MIT sloan management review, 38(1), 43.
    Russo, M. V., & Fouts, P. A. (1997). A resource-based perspective on corporate environmental
    performance and profitability. Academy of management Journal, 40(3), 534-559.
    Saaty, T. L. (1994). How to make a decision: the analytic hierarchy process. Interfaces, 24(6),
    19-43.
    Sambamurthy, V., & Zmud, R. W. (1992). Managing IT for success: The empowering business
    partnership: Financial Executives Research Foundation.
    Schendel, D. (1994). Introduction to ‘Competitive Organizational Behavior: Toward an
    Organizationally‐Based Theory of Competitive Advantage’. Strategic management journal,
    15(S1), 1-4.
    Schulze, W. S. (1992). THE TWO RESOURCE-BASED MODELS OF THE FIRM:
    DEFINITIONS AND IMPLICATIONS FOR RESEARCH. Paper presented at the Academy of
    Management Proceedings.
    Sharma, R., Mithas, S., & Kankanhalli, A. (2014). Transforming decision-making processes: a
    research agenda for understanding the impact of business analytics on organisations. European
    Journal of Information Systems, 23(4), 433-441.
    Sutanto, J., Kankanhalli, A., Tay, J., Raman, K. S., & Tan, B. C. (2008). Change management
    in interorganizational systems for the public. Journal of Management Information Systems,
    25(3), 133-176.
    Vasset, D., & Xiong, S. (2015). IDC MaturityScape Benchmark: Big Data and Analytics in the
    United States. Framingham: IDC Analyze the Future.
    Vroom, V. H., & Yetton, P. W. (1973). Leadership and decision-making (Vol. 110): University
    of Pittsburgh Pre.
    Weill, P., Broadbent, M., & Butler, C. (1996). Exploring how firms view IT infrastructure:
    Melbourne Business School, the University of Melbourne.
    Analytic Hierarchy Process Theory 層級分析法(AHP)理論與實作 國立東華大學企業管理
    學
    鄧振源、曾國雄(1989)，層級分析法(AHP)的內涵特性與應用(上)
    鄧振源、曾國雄(1989)，層級分析法(AHP)的內涵特性與應用(下)

    QR CODE
    :::