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題名 以資料治理與協力觀點探究資料公益專案－以D4SG資料英雄計畫為例
Data projects from the perspective of data governance and collaboration: The case of Data projects from the perspective of data governance and collaboration: The case of D4SG Fellowship Program
作者 游蕙瑜
Yu, Hui-Yu
貢獻者 蕭乃沂
Hsiao, Nai-yi
游蕙瑜
Yu, Hui-Yu
關鍵詞 資料治理
協力
專案
D4SG資料英雄計畫
Data governance
Collaboration
Program
D4SG Fellowship Program
日期 2020
上傳時間 2-Sep-2020 12:34:48 (UTC+8)
摘要 在大數據蓬勃發展的時代，對於政府來說，運用資料可改善行政效率、提供民眾客製化服務甚至精進決策品質降低風險，當政府內部難以運用現有人力與技術，外部領域專家的參與提供另一公私協力的契機，例如D4SG資料英雄計畫以資料科學技術解決社會問題，惟專案歷程中政府機關可能會面對到資料治理相關問題，例如定義關鍵資料、資料詮釋、資料品質等困難。因此，本研究以深度訪談法作為主要研究途徑，藉由資料治理及公私協力雙重觀點，釐清政府機關在資料公益專案之參與動機，理解專案歷程中存在攸關資料治理困難及關鍵成功因素，並探討資料公益專案成果如何在專案結束後發揮其影響力。
研究發現公部門參與資料公益專案，除了現實面可運用外部專業補足人力及技術不足外，專案啟發關鍵變革者對政府資料應用之期待外，亦會受到其他成功案例影響，且專案公益性有助於機關營造正面形象。在資料公益專案中，政府必須設定議題需求、提供關鍵資料及領域知識，並爭取組織支持，惟專案歷程並非順遂，本研究綜整七大攸關資料治理之挑戰，惟「溝通耗時」及「機關欠缺資料科學技能與監督能力」最為棘手，畢竟面臨領域知識不對稱之矛盾。再者，本研究希冀界定資料公益專案之關鍵成功因素，發現互惠性有助於良好協力經驗的建立，並有助於未來資料成果持續在體制內發揮影響力。最後，雖資料公益專案結束，但成果如何在公部門深化及擴散值得探討，本研究發現可運用其他協作關係及經驗分享向外擴散，亦可作為未來政策精進參考及檢討現有作法。
最後，本研究針對公務機關及外部領域專家提出相關實務建議，前者強調資料公益專案後成果如何深化，必須取決於知識移轉程度並建立制度，後者則是提醒外部領域專家須持續與議題單位溝通釐清細節，以確保資料詮釋符合現實情境。另外，本研究建議未來研究可透過量化問卷評價資料治理困難程度及關鍵影響因素，藉由不同利害關係人及應用領域去豐富研究內容等。
The government uses big data to increase administrative efficiency, improve the quality of policy decision-making, and so on. Owing to the lack of data science manpower, the government practically delegates academic research institutes or information technology firms to implement data analysis. Public-private partnership (PPP) provides another approach to use data science to solve particular social problems, such as the D4SG followship program in Taiwan. The project comprises data scientists (fellows), mentors and project partner such as government agencies and non-profit oganizations. However, the project partners may face information governance related difficulties during the programs, for example, they realize it is hard to define the key demand, understand the data science technology, and how to interpretate the result of data analysis.
The methodology is multiple case study of four D4SG programs that have proposed by the public sectors in 2018. The study aims to clarify the information governance challenges and key success factors in the programss of data for social good from the perpective of government officials. Additionally, the study would explore how to motivate the government to join and how to realize the project results in the government system when finished the projects. Results reveal that the projects inspire key changers` expectations for the application of government data and provides external approach of data science manpower to supply. The government must set their agenda issues, provide datas and domain knowledge, and strive for organizational supports. This study summarizes the seven major data governance challenges. For example, they take times to communicate between data scientists and social workers because of the asymmetry in domain knowledge. Furthermore, this study defines five key success factors in the projects. Reciprocity is the most important to establish good collaborative experience and influence governtment officials to take action for data-driven governance. After the programs, the government agencies join other data hackathons to deepen their findings, and share experience and the results of data analysis. Also, they refine the information related administrative practices and policy decisions.
The study emphasizes the project results of data for social good should be realized in the government system. And, it depends on the degree of knowledge transfer from data scientists and domain experts to the government officials during the project. Addictionally, the study suggests the external experts should communicate with the project partners to clarify the details of key issues and data to ensure that the data interpretation is close to the real situation. Finally, this study suggests future studies that conduct quantitative questionnaires to evaluate data governance challenges and key success factors, and research the long-term effects of the projects of data for social good.
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描述 碩士
國立政治大學
公共行政學系
105256019
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105256019
資料類型 thesis
dc.contributor.advisor 蕭乃沂zh_TW
dc.contributor.advisor Hsiao, Nai-yien_US
dc.contributor.author (Authors) 游蕙瑜zh_TW
dc.contributor.author (Authors) Yu, Hui-Yuen_US
dc.creator (作者) 游蕙瑜zh_TW
dc.creator (作者) Yu, Hui-Yuen_US
dc.date (日期) 2020en_US
dc.date.accessioned 2-Sep-2020 12:34:48 (UTC+8)-
dc.date.available 2-Sep-2020 12:34:48 (UTC+8)-
dc.date.issued (上傳時間) 2-Sep-2020 12:34:48 (UTC+8)-
dc.identifier (Other Identifiers) G0105256019en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131731-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 公共行政學系zh_TW
dc.description (描述) 105256019zh_TW
dc.description.abstract (摘要) 在大數據蓬勃發展的時代，對於政府來說，運用資料可改善行政效率、提供民眾客製化服務甚至精進決策品質降低風險，當政府內部難以運用現有人力與技術，外部領域專家的參與提供另一公私協力的契機，例如D4SG資料英雄計畫以資料科學技術解決社會問題，惟專案歷程中政府機關可能會面對到資料治理相關問題，例如定義關鍵資料、資料詮釋、資料品質等困難。因此，本研究以深度訪談法作為主要研究途徑，藉由資料治理及公私協力雙重觀點，釐清政府機關在資料公益專案之參與動機，理解專案歷程中存在攸關資料治理困難及關鍵成功因素，並探討資料公益專案成果如何在專案結束後發揮其影響力。
研究發現公部門參與資料公益專案，除了現實面可運用外部專業補足人力及技術不足外，專案啟發關鍵變革者對政府資料應用之期待外，亦會受到其他成功案例影響，且專案公益性有助於機關營造正面形象。在資料公益專案中，政府必須設定議題需求、提供關鍵資料及領域知識，並爭取組織支持，惟專案歷程並非順遂，本研究綜整七大攸關資料治理之挑戰，惟「溝通耗時」及「機關欠缺資料科學技能與監督能力」最為棘手，畢竟面臨領域知識不對稱之矛盾。再者，本研究希冀界定資料公益專案之關鍵成功因素，發現互惠性有助於良好協力經驗的建立，並有助於未來資料成果持續在體制內發揮影響力。最後，雖資料公益專案結束，但成果如何在公部門深化及擴散值得探討，本研究發現可運用其他協作關係及經驗分享向外擴散，亦可作為未來政策精進參考及檢討現有作法。
最後，本研究針對公務機關及外部領域專家提出相關實務建議，前者強調資料公益專案後成果如何深化，必須取決於知識移轉程度並建立制度，後者則是提醒外部領域專家須持續與議題單位溝通釐清細節，以確保資料詮釋符合現實情境。另外，本研究建議未來研究可透過量化問卷評價資料治理困難程度及關鍵影響因素，藉由不同利害關係人及應用領域去豐富研究內容等。		zh_TW
dc.description.abstract (摘要) The government uses big data to increase administrative efficiency, improve the quality of policy decision-making, and so on. Owing to the lack of data science manpower, the government practically delegates academic research institutes or information technology firms to implement data analysis. Public-private partnership (PPP) provides another approach to use data science to solve particular social problems, such as the D4SG followship program in Taiwan. The project comprises data scientists (fellows), mentors and project partner such as government agencies and non-profit oganizations. However, the project partners may face information governance related difficulties during the programs, for example, they realize it is hard to define the key demand, understand the data science technology, and how to interpretate the result of data analysis.
The methodology is multiple case study of four D4SG programs that have proposed by the public sectors in 2018. The study aims to clarify the information governance challenges and key success factors in the programss of data for social good from the perpective of government officials. Additionally, the study would explore how to motivate the government to join and how to realize the project results in the government system when finished the projects. Results reveal that the projects inspire key changers` expectations for the application of government data and provides external approach of data science manpower to supply. The government must set their agenda issues, provide datas and domain knowledge, and strive for organizational supports. This study summarizes the seven major data governance challenges. For example, they take times to communicate between data scientists and social workers because of the asymmetry in domain knowledge. Furthermore, this study defines five key success factors in the projects. Reciprocity is the most important to establish good collaborative experience and influence governtment officials to take action for data-driven governance. After the programs, the government agencies join other data hackathons to deepen their findings, and share experience and the results of data analysis. Also, they refine the information related administrative practices and policy decisions.
The study emphasizes the project results of data for social good should be realized in the government system. And, it depends on the degree of knowledge transfer from data scientists and domain experts to the government officials during the project. Addictionally, the study suggests the external experts should communicate with the project partners to clarify the details of key issues and data to ensure that the data interpretation is close to the real situation. Finally, this study suggests future studies that conduct quantitative questionnaires to evaluate data governance challenges and key success factors, and research the long-term effects of the projects of data for social good.		en_US
dc.description.tableofcontents 第一章 緒論 1
第一節 研究背景 1
第二節 研究動機與目的 3
第三節 重要名詞解釋 6
第四節 研究流程 7
第二章 文獻回顧 8
第一節 資料運用與資料科學 8
第二節 資料驅動政府治理 15
第三節 資料驅動政府治理的挑戰 23
第四節 公私協力 32
第五節 綜合分析 39
第六節 問題意識 46
第三章 研究設計 47
第一節 研究架構 47
第二節 個案介紹－D4SG資料英雄計畫 49
第三節 研究方法 58
第四節 研究倫理與資料分析品質 65
第四章 研究分析 66
第一節 公私協作的契機－探索資料詮釋意義及期待成果效益 66
第二節 公私協力前的準備－議題設定、資料盤點及爭取組織支持 72
第三節 資料公益專案中的角力－釐清角色定位及資料治理困難 75
第四節 資料公益專案中之認知評價與關鍵成功因素 92
第五節 資料公益專案之後續應用 104
第六節 綜合分析 120
第五章 結論與建議 134
第一節 研究發現 134
第二節 實務建議 137
第三節 研究限制與後續研究建議 140
參考文獻 142
附錄 各類受訪者的訪談大綱 150
壹、政府提案單位 150
貳、專案顧問 151
叁、資料英雄 151		zh_TW
dc.format.extent 5802191 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105256019en_US
dc.subject (關鍵詞) 資料治理zh_TW
dc.subject (關鍵詞) 協力zh_TW
dc.subject (關鍵詞) 專案zh_TW
dc.subject (關鍵詞) D4SG資料英雄計畫zh_TW
dc.subject (關鍵詞) Data governanceen_US
dc.subject (關鍵詞) Collaborationen_US
dc.subject (關鍵詞) Programen_US
dc.subject (關鍵詞) D4SG Fellowship Programen_US
dc.title (題名) 以資料治理與協力觀點探究資料公益專案－以D4SG資料英雄計畫為例zh_TW
dc.title (題名) Data projects from the perspective of data governance and collaboration: The case of Data projects from the perspective of data governance and collaboration: The case of D4SG Fellowship Programen_US
dc.type (資料類型) thesisen_US
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dc.identifier.doi (DOI) 10.6814/NCCU202001253en_US