September 2013
Intermediate to advanced
264 pages
5h
Chinese
Content preview from Bad Data 技術手冊
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第十一章：別讓「完美」成為「好」的敵人：壞資料真的壞嗎？
髒資料是什麼？它有數個定義。第一，就單純是不正確的資料。
X
的值應該要等於 1，
但在你的結構中它等於 2。這就是壞的。這可能是在企業或是處理技術中要將資料放入
資料管線內時，包含了任意數量的壞功能，在一個交易系統中，從輸入感測器到 ETL
（萃取（extract）、轉置（transform）與載入（load））工作都有可能發生。第二，資料
本身的值是正確的，但是相關的元資料不正確，或在許多狀況中，元資料根本不存在。
這代表你擁有值，但你無法準確的知道它代表什麼。第三，將資料用在更廣泛的集合或
是系統中可能會有問題。當你嘗試使用資料處理工具來從資料中抓取出資訊，但又不遵
守程序、演算法的規則或假設就有可能會發生。
從這些，我們看到「髒資料」實際上是個模糊的詞。它擁有能夠影響整個企業或是影響
嘗試在 R 中跑回歸的資料科學家的意義。然而，清理與修復資料的重要性必須要被所有
參與的人瞭解，而跨組織的套用規範則能夠減輕可能會發生的潛在傷害。
政府資料是非常真實的
知道「正確」管理與準備資料方法的概念後，將這些技巧套用在政府行政資料上則是
不切實際的。許多政府擁有的資料集合—如 911 或 311 的電話記錄、犯罪記錄或是許可
證—本質上就不是乾淨的，因為它們代表了都市生態系中的日常生活的資料。
當我進入政府服務時，我擁有關於資料應該要如何被使用的非常高貴的構想。身為一個
電腦科學家，我認為我們能夠在遭遇到任何問題時，將它分解成變數組，套用一個方法
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