現代企業基本上已轉向以系統數位化作為主要的經營模式。每天的商業活動都產生大量資料，這些資料被儲存在文件檔案或資料庫中。隨著進入AI網路時代，資料量不斷增加且更加多元化。除了銷售資料之外，各種網路行為，例如消費者的購買紀錄、回購次數、市場評價等，都成為資料的一部分。同時，隨著互聯網技術逐漸成熟，未來預計將產生更多細微且瑣碎的巨量數據。這些結構化和非結構化的資料組成了企業經營上所謂的「大數據」。 商業智慧（Business Intelligence，BI）旨在協助企業篩選重要資料，透過自動化整合建立資料倉儲（Data Warehouse）和數據分析模型，隨後利用資訊視覺化（Information Graphics）將數據資料呈現為圖形。最終，將這些圖形化的資料提供給企業的決策管理者作為决策的參考依據。 本研究聚焦於探討個案公司如何透過引入商業智慧實現數位轉型目標，從資料取得與彙整、資訊透明化減少溝通的落差。透過SAP SAC導入，整合多種數據源，透過數據可視化、報告和分析的能力，以更好地支援決策制定和業務優化。也針對個案公司分析導入成功的原因，規劃商業智慧導入時應考慮的方法和各階段需注意的面向。 ;Modern enterprises have largely shifted toward digital systems as their primary mode of operation. Daily business activities generate vast amounts of data, which are stored in documents or databases. As we enter the era of AI and advanced internet technologies, the volume and diversity of data continue to grow. In addition to sales data, various forms of online behavior—such as customer purchase history, repurchase frequency, and market feedback—have also become integral parts of business data. With the continued development of internet technologies, it is anticipated that even more granular and fragmented big data will be generated in the future. These structured and unstructured data together constitute what is commonly referred to as "big data" in business operations. Business Intelligence (BI) aims to help enterprises filter key information by integrating and automating data collection to build data warehouses and analytical models. Subsequently, data is visualized through information graphics, allowing decision-makers to interpret insights more intuitively and use them as references in strategic planning. This study focuses on how the case company achieves its digital transformation goals through the implementation of Business Intelligence. By utilizing SAP Analytics Cloud (SAC), the company integrates multiple data sources and leverages visualization, reporting, and analytical capabilities to enhance decision-making and optimize operations. The research further analyzes the key success factors of the implementation and proposes a methodological framework, including critical considerations across various stages of BI adoption
