企業大數據分析能力現況調查之研究

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陳淑絹(Shu-Jiuan Chen) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

企業大數據分析能力現況調查之研究
(A Survey on Enterprise Big Data Analytical Capability Status in Taiwan)

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摘要(中)

Gartner指出策略性科技趨勢具有快速成長、變動性高且將於未來五年內到達高點的特性。然而，企業的數據基礎是發展策略性科技重要的關鍵，故企業在發展策略性科技之前，需先發展組織的數據分析能力。有許多組織收集愈來愈多的不同數據，但收集的資源超出了他們能夠管理或分析的範圍，加上高階主管忽視了組織缺乏能力或成熟度來解決所涉及的技術、員工、流程和數據的必要範圍。許多學者及專家為幫助組織在大數據分析能力成熟的連續階段，有效的在階段、維度、結果和行動推進，提出大數據分析成熟度模型。但專家學者在提出大數據分析成熟度模型時，尚未考慮各維度重要性不同之觀念，以至於評估組織大數據分析能力時有些許不完善之處。
本研究採用國際數據資訊有限公司（International Data Corporation）所提出的IDC大數據分析成熟度模型，發展「組織與數據分析能力」研究問卷，並更改舊有文獻計算方法，加入各維度之重要性不同之觀點，協助企業評估其大數據分析能力的成熟度，以了解目前組織數據分析能力之狀態。
本研究共收回109份有效問卷，共有16種產業，其中科技業製造業及金融保險業為大宗。而受測對象主要的職位前五名為營運部門主管、IT人員與資料工程師、資料科學家與資料分析人員、人資人員以及資訊長與IT部門主管。受訪公司營收50億以上及50億以下各佔一半左右。所計算出的權重為W_願景=0.31,W_數據=0.23 ,W_技術=0.20,W_員工=0.14,W_流程=0.12。

摘要(英)

Gartner suggested that strategic technologies have the potential to foster opportunities along with significant disruptions that can be observed in many fields of studies and industries within the next five years. However, prior to fully implement strategic technologies within a firm, one should place focus on developing its analytical capability after diving into the field of Big Data. Many organizations nowadays collect all kinds of data but some lack the ability to organize these data. Moreover, because of the fervor of Big Data, people in the management role of the firms which are in the rudimentary stage of analyzing data may oversee obstacles confronted currently such as—skill, people, process and data and may experience a strong friction implementing data analysis concepts alike. In order for an organization to improve its big data, analytical capability and maturity effectively, many scholars and researchers have composed a grading rubric to better assess a firm’s ability to utilize data science in the industry such as Big Data and Analytics Maturity Benchmark. But many of the studies did not place enough weights on the different dimensions of Big Data and Analytics Maturity Scape.
This study is based on IDC′s Big Data and Analytics Maturity Benchmark and a research questionnaire is later composed which blend in the concept of different dimension in the study. As a result, this study should suggest a new measure for assessing enterprise big data and its analytical capability status in Taiwan.
There are 109 valid questionnaires collected in the surveys sent out across 16 different industries. Out of the questionnaires gathered, most of the respondents are from technology industry. Manufacturing industry comes in second and the financial industry comes in third. Overall, the composition of job role of respondents from the most to least are as follows: heads of operations departments, IT and data engineers, data scientists and data analysts, human resources personnel, CIO and heads of IT departments. The dimension weights are as follows：W_vision=0.31,W_data=0.23,W_technology=0.20,W_people=0.14,W_process=0.12。

關鍵字(中)

★ 大數據
★ 大數據分析
★ 成熟度模型

關鍵字(英)

★ Big data
★ Big data analytical
★ Maturity Benchmark

論文目次

摘要 i
Abstract ii
致謝 iv
目錄 v
圖目錄 viii
表目錄 ix
一、緒論 1
1-1 研究背景與動機： 1
1-2 研究目的與問題： 1
二、文獻探討 2
2-1 大數據的定義、管理與價值 2
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
2-3 商業智能和數據分析的發展 9
2-4 層級分析法 (Analytic Hierarchy Process,AHP） 10
三、研究方法 14
3-1 研究架構 14
3-2 成熟度評估方法 19
3-2-1 研究變項定義與衡量 19
3-2-2 各維度重要性評估 20
3-3 抽樣對象 24
3-3-1 產業分析 24
3-3-2 職位分析 25
3-3-3 公司年營收分析 26
3-4 問卷實測與發放 27
3-5 資料分析流程 27
四、研究結果 28
4-1各維度權重 28
4-2 結構模型結果評估 28
4-2-1願景維度 28
4-2-2數據維度 29
4-2-3技術維度 30
4-2-4員工維度 31
4-2-5流程維度 31
4-2-6綜合維度 32
4-2-7 大數據分析成熟度階段分佈情形 33
五、結論與建議 36
5-1 研究結論 36
5-2 建議與方向 36
六、參考文獻 37
七、附錄 40

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指導教授

陳炫碩呂俊德(Shiuann-Shuoh Chen Jun-Der Leu)

審核日期

2019-6-12

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