大數據是當今討論話題的中心，所有行業的組織都在大量投資大數據行動。大數據分析已經成為學術界和業界的重要研究領域，因為它大大改變了訊息的產生和用於決策的方式。儘管如此，這種新興科技在技術方面過於關注，並且對其他相關組織元素的關注有限。過去以資源基礎論或資訊科技發展為觀點的研究表明，組織要重新整合各項資源，這些資源組合建立了大數據分析能力。然而，基於組織能力的運作必定由組織來進行，產生這個能力的前因，必定和組織本身有關。在組織中，大數據分析團隊會來執行這個任務。團隊的結構、團隊的特徵和組織的慣例會影響組織能力的使用。本研究利用Garner的四層級資料分析作為測量大數據分析的階段，以文獻分析法推導團隊結構和團隊特徵，在組織慣例的運作下，如何影響各階層的大數據分析能力。

Big data is the most intensively discussed topics today, organizations in all industries are large investing in big data initiatives. Big data analytics has become an important area of research in academia and industry because it has dramatically changed the way information are generated and used in decision making. Nevertheless, this emerging technology takes too much attention to technological aspects and has limited focus on other relevant organizational elements. Previous studies based on resource-based theory or information technology development has shown that organizations need to re-integrate resources that combine to build big data analytics. However, the operation based on organizational capabilities must be carried out by the organization, and the antecedent of this ability must be related to the nature of the organization. In the organization, the big data analytics team will perform this task. The structure and characteristics of the team, and the organization routine will influence the use of organizational capabilities. This study (1) uses Garner's four-level data analysis as a stage for measuring big data analysis, (2) identifies team structure, team characteristics and organization routine that in combination build a big data analytical capability.

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