大數據是一種新的能力，它結合硬體、軟體、統計模型、專業領域知識、經驗等元素，從資料解析出對決策有用的資訊，讓企業可以達到節省成本、加快速度、改進產品與服務、提升決策品質的目的，進而創造競爭優勢。這種能力以物聯網、雲端計算、Hadoop叢集資料處理等資訊技術為基礎，加上人的專業知識，經過實驗、分析、解讀、調整的循環，逐步改進程序以獲取有意義資訊，形成企業的策略行動。其中最關鍵因素是人的專業知識與判斷，影響大數據行動成敗。
工程專案執行時會產生大量非結構化資料，過去未被有效利用。個案公司期望引進大數據技術，解讀與業主往來的文件，從中分析其對於工程的要求與偏好，並將這些資訊累積形成知識文件，透過經驗的傳承，提高對業主的熟悉度，減少punch發生、縮短工期，達到節約成本與提高工程品質的目的。
本研究採用R語言的文字探勘模組(tm package)，解析工程文件內容，尋找出現頻率最高關鍵字，再透過領域專家解讀，從中了解業主的潛在需求，經由實做過程，熟悉大數據技術的應用，評估是否適合用於解決流程問題，並建議引進的程序與步驟，供有意在公司內部養成大數據分析能力的企業參考。
亞倫•韋伯在《改寫規則的人，獨贏》書中提到「未來將是個人化、隨身化、數位化的世界，主角不是科技本身，而是科技將什麼化為可能，真正的科技是無形的，包括它所建立的連結、因它實現的速度與彈性、它所導致的行為改變，還有它所激發的創新可能等。要見識科技的真正力量，就請注意觀察那些無形的事物，最好多思考科技的用途，而不是科技工具本身，運算要比電腦重要。」

Big Data is a new capability that combines hardware, software, statistical models, domain knowledge, experience and other elements. By parsing out useful information for decision-making, companies can cut cost, speed up, improve products and services, and enhance the quality of decision-making, which thereby create competitive advantages. This capability based on Internet of Things, Cloud Computing, Hadoop cluster data processing and other information technology, coupled with human expertise, through experiment, analysis, interpretation, adjustment cycle, and gradually improvement on procedures to obtain meaningful information forms business strategic action. One of the most critical factors is the human expertise and judgment, which affect the success of Big Data actions.
Engineering project will generate a lot of unstructured data when executed, which is not effectively used in the past. Case company expects to introduce Big Data technologies to interpret business documents, and analyze engineering requirements and preferences from it. These data will accumulate and become knowledge file. Through experience, company will become more and more familiar with owners, which enables them to reduce occurrence of punch, shorten construction period, save costs and improve project quality.
In this study, by using text mining module (tm package) in R language, we analyze project file contents and find the keywords which appear most frequently. Then through the interpretation of experts, we learn about the potential needs of the owners. After some actual practices, we can get familiar with Big Data’s applications, evaluate whether it is suitable for solving procedure problems, and suggest which process or procedure should be introduce. This will be provided as reference for company who intend to cultivate Big Data analysis capability.
Alan M. Weber mentioned in the book 《Rules of Thumb》 that the future will be personal, portable, digital. The protagonist is not the technology itself, but what will become possible via technology. The real technology is intangible, including the link it established, the speed and flexibility it implements, behavioral change it leads, and innovative possibility it stimulates. To behold the true power of technology, please observe the intangible things. Think more about the uses of technology, rather than the technological tools themselves, just like computing is way more important than the computer.

Chen, HSinchun, Roger H. L. Chiang, and Veda C. Storey (2012), “Business Intelligence and Analytics: From Big Data to Big Impact,” MIS Quarterly, Vol. 36 No. 4, (December), 1165-1188.
Dean, Jeffrey, and Sanjay Ghemawat (2008), “MapReduce: Simplified Data Processing on Large Clusters,” Communications of the ACM, Vol.51, No1, (January), 107-113.
Executive Office of the President (2014), “Big Data: Seizing Opportunities, Preserving Values,” Executive Office of the President, (May), 1-79.
Feinerer, Ingo, Kurt Hornik, and David Meyer (2008), “Text Mining Infrastructure in R,” Journal of Statistical Software, (March), 1-54.
Halevy, Alon, Peter Norvig, and Fernando Pereira (2009), “The Unreasonable Effectiveness of Data,” IEEE Intelligent Systems, vol.24, no. 2, (March ), 8-12.
Hilbert, Martin, and Priscila López (2011), “The World’s Technological Capacity to Store, Communicate, and Compute Information,” Science, (February).
Manyika, James, Michael Chui,Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, and Angela Hung Byers (2011), “Big data: The Next Frontier For Innovation, Competition, and Productivity,” Mckinsey Global Institute, (May), 1-143.
Zhao, Yanchange (2012), “R and Data Mining: Examples and Case Studies,” Elsevier, (December), 1-149.
彼得•杜拉克 (2009), “打造與時俱進的策略” , 哈佛商業評論, October, 1-9.
馬可•顏西提, 卡林•拉哈尼 (2014), “無處不數位” , 哈佛商業評論, November, 94-107.
麥可•波特, 詹姆斯•赫普曼 (2014), “波特描繪競爭新版圖”, 哈佛商業評論, November, 62-93.
麥爾荀伯格, 庫基耶 (2013), ”大數據”, 天下文化出版社, March, ISBN 978-986-320-191-5.
曼尼許•高亞爾, 瑪麗安•韓考克, 賀馬永•哈塔米 (2012), “小市場大獲利”, 哈佛商業評論, July, 68-77.
戴文坡, 哈瑞斯 (2008), “魔鬼都在數據裡”, 中國生產力中心, March, ISBN 978-986-7096-75-3.
湯瑪斯•戴文波特 (2009), “決策分析力”, 哈佛商業評論, August, 1-11.
湯瑪斯•戴文波特 (2011), “當顧客的「先知」”, 哈佛商業評論, December, 83-90.
湯瑪斯•戴文波特 (2013), “打造專家級決策”, 哈佛商業評論, August, 76-81.
湯瑪斯•戴文波特 (2013), “巨量資料分析3.0版”, 哈佛商業評論, December, 69-76.
湯瑪斯•戴文波特 (2014), ”大數據@工作力”, 天下文化出版社, November, ISBN 978-986-320-618-7.
湯瑪斯•雷曼 (2013), “盡信資料不如…”, 哈佛商業評論, December, 90-95.
雷徐克•帕瑪, 伊恩•麥肯吉, 大衛•柯恩, 大衛•甘恩 (2014), “啓動新式創新”, 哈佛商業評論, February, 54-63.
羅伯•賽蒙 (2015), “人盡其才大策略”, 哈佛商業評論, February, 74-85.
胡世忠 (2013), “雲端時代的殺手級應用「海量資料分析」”, 天下文化出版社, March, ISBN 978-986-241-673-0.
簡禎富, 許嘉裕 (2014), ”資料挖礦與大數據分析”, 前程文化, September, ISBN 978-986-5774-25-7.
珍•羅斯, 辛西雅•比思, 安•闊格拉斯 (2013), “誰需要巨量資料？”, 哈佛商業評論, December, 96-104.
城田真琴 (2013), ”｢大數據的獲利模式｣(圖解、案例、策略、實戰)”, 經濟新潮社, August, ISBN 978-986-6031-36-6.
史蒂芬•湯克, 吉姆•曼茲 (2013), “以實驗考驗創新”, 哈佛商業評論, December, 90-101.
史蒂芬•柯維 (2013), ”第三選擇：解決人生所有難題的關鍵思維”, 天下文化出版社, January, ISBN 978-986-320-100-7.
史蒂芬•齊克, 阿恩德•胡赫澤麥爾, 蘇格•奈特西 (2014), “歐洲打造解決方案工廠”, 哈佛商業評論, April, 132-137.
安德魯•麥克菲, 艾立克•布林約爾松 (2012), “管理的資訊革命”, 哈佛商業評論, October, 54-62.
亞倫•韋伯 (2009), ”改寫規則的人，獨贏”, 大是文化有限公司, December, ISBN 978-986-6526-44-2.