| DC 欄位 |
值 |
語言 |
| DC.contributor | 企業管理學系 | zh_TW |
| DC.creator | 黃文斾 | zh_TW |
| DC.creator | Wen-Pei Huang | en_US |
| dc.date.accessioned | 2018-6-27T07:39:07Z | |
| dc.date.available | 2018-6-27T07:39:07Z | |
| dc.date.issued | 2018 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=105421061 | |
| dc.contributor.department | 企業管理學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 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。 | zh_TW |
| dc.description.abstract | 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 | en_US |
| DC.subject | 成熟度模型 | zh_TW |
| DC.subject | 數據分析能力 | zh_TW |
| DC.subject | maturity model | en_US |
| DC.subject | data analytical capabilities | en_US |
| DC.title | 企業數據分析能力評估模式之研究 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | The Study of Assessment Model For Enterprise Data Analytical Capabilities | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |