“數據”兩個字越來越頻繁地出現在大家的工作中。一方面，“用數據說話”成為每個互聯網從業者必備的“生存技能”；另一方面，一個名為“數據產品經理”的職位成為各大互聯網企業的“熱招職位”。那么，作為數據產品經理，有了數據應該怎樣“用數據說話”？又應該如何讓自己具備獨特的競爭優勢呢？本書內容涵蓋了數據產品經理應該知道和掌握的基礎知識——從每個優秀的數據產品經理都應當關注的“效率”問題出發，分別從商業知識和技術知識兩個角度，針對什么是數據產品、數據產品訴求的產生和類型、數據產品的實現思路與常見技術方案等關鍵問題進行講解。本書既是學習指南，又是速查手冊，適合具備不同工作背景并正在從事數據產品經理工作的人士閱讀，也適合對這一領域感興趣并希望從事數據產品經理工作的人士閱讀。只要你具備求知的熱情，本書將為你提供解決問題的思路、方法和工具。
李陽（網名：御豪同學），數據產品經理、PMI-ACP敏捷項目管理師。現任京東數字科技集團高級數據產品經理，負責數據平臺搭建工作，專注業務邏輯梳理及數據分析，對于大數據分析項目及平臺搭建有深入了解。曾擔任 GrowingIO 增長大會嘉賓、騰訊大數據沙龍嘉賓。2018年出版圖書《產品增長力》。
目錄
第一篇 理解數據產品：確實有些不一樣
第1 章 什么是數據產品 ·······································································.2
1.1 數據產品的關注點 ·······························································.3
1.2 什么是數據應用 ··································································.5
1.2.1 數據處理的角度 ··························································.5
1.2.2 數據展現形式的角度 ····················································.7
1.2.3 應用目的的角度 ··························································.9
1.3 什么是效率問題 ································································.12
1.3.1 成本投入項 ······························································.12
1.3.2 價值產出項 ······························································.14
1.3.3 效率的問題 ······························································.15
1.4 本章小結 ·········································································.17
第2 章 數據產品面臨的挑戰 ································································.18
2.1 為什么要做—師出有名 ····················································.19
2.1.1 支撐數據應用 ···························································.20
2.1.2 “量入為出”的價值管理 ············································.29
2.2 做的是什么—理解業務 ····················································.29
2.2.1 數據的意義 ······························································.30
2.2.2 架起“量化運營”的橋梁 ············································.33
2.3 怎樣做到的—理解技術 ····················································.35
2.3.1 理解“究竟能做些什么” ············································.36
2.3.2 思考“怎樣做得更高效” ············································.42
2.4 本章小結 ·········································································.45
第二篇 理解業務：“奇怪”的數據需求從哪來
第3 章 業務是什么 ·············································································.48
3.1 業務的目標是什么 ·····························································.50
3.1.1 能力視角 ·································································.50
3.1.2 利潤視角 ·································································.52
3.1.3 效能視角 ·································································.52
3.1.4 影響力視角 ······························································.53
3.2 業務的商業模式與“投資”思維 ············································.56
3.2.1 資金投資 ·································································.57
3.2.2 人力投資 ·································································.63
3.2.3 時間投資 ·································································.66
3.2.4 其他投資 ·································································.67
3.3 常用管理模型和營銷組合 ····················································.68
3.3.1 常用管理模型及其關系 ···············································.68
3.3.2 常用營銷組合及其關系 ···············································.96
3.4 本章小結 ········································································.101
第4 章 業務的數據訴求 ····································································.103
4.1 用戶市場研究 ··································································.104
4.1.1 需求分析的目的 ·······················································.105
4.1.2 需求的分層 ·····························································.108
4.1.3 需求的定位 ·····························································.116
4.1.4 需求分析的評價與KANO 模型 ····································.127
4.1.5 需求的傳播和貫徹 ····················································.129
4.2 業務及產品形態研究 ·························································.130
4.2.1 評價標準—怎樣才是“好” ·····································.131
4.2.2 業務轉化與價值歸因 ·················································.144
4.2.3 流量管理與實驗框架 ·················································.153
4.3 綜合能力升級 ··································································.159
4.3.1 分析方法論及其優化 ·················································.160
4.3.2 固化應用系統與賦能業務 ···········································.171
4.3.3 賦能團隊合作 ··························································.174
4.4 工具、模型與業務、產品的“日常” ·····································.176
4.5 本章小結 ········································································.179
第5 章 用數據抽象業務 ····································································.180
5.1 需求研究的數據抽象 ·························································.181
5.1.1 需求挖掘—投放與獲得新用戶 ··································.182
5.1.2 需求鑒別—留存與促進用戶活躍 ·······························.189
5.1.3 用戶生命周期與“蓄水池”模型 ··································.194
5.1.4 競爭性抽象與建模 ····················································.200
5.2 業務的數據模型 ·······························································.204
5.2.1 用E-R 圖抽象實體關系 ··············································.205
5.2.2 用流程圖抽象業務過程 ··············································.212
5.2.3 用時序圖抽象處理過程 ··············································.219
5.2.4 用財務思維抽象資金流 ··············································.225
5.3 “數據世界觀” ·································································.234
5.3.1 數據模型與現實世界的差異 ········································.234
5.3.2 用戶行為的事件模型 ·················································.235
5.4 數據倉庫建模 ··································································.242
5.4.1 面向分析的數據模型 ·················································.242
5.4.2 通用數據倉庫模型 ····················································.244
5.5 本章小結 ········································································.250
第三篇 理解技術：打開數據系統的“黑箱”
第6 章 從業務訴求到技術系統 ···························································.252
6.1 實現業務訴求的方式 ·························································.253
6.1.1 主動反饋與被動反饋 ·················································.254
6.1.2 通用內容與定制內容 ·················································.256
6.1.3 離線分析與在線分析 ·················································.257
6.1.4 全量與抽樣數據 ·······················································.258
6.2 業務中的數據形態 ····························································.259
6.2.1 業務理解與元數據 ····················································.259
6.2.2 離線數據與數據集 ····················································.260
6.2.3 實時數據與數據流 ····················································.261
6.3 業務中的技術問題 ····························································.263
6.3.1 數據量激增問題 ·······················································.264
6.3.2 如何處理“陳舊”的內容 ···········································.267
6.3.3 數據安全問題 ··························································.268
6.4 本章小結 ········································································.272
第7 章 必要的技術基礎知識 ······························································.274
7.1 產品的技術結構與“技術世界觀” ········································.276
7.1.1 Client/Server 結構 ·····················································.277
7.1.2 Browser/Server 結構 ···················································.278
7.1.3 產品的“技術世界觀” ··············································.279
7.2 代碼理解世界的“做事思路”··············································.280
7.2.1 面向過程 ································································.280
7.2.2 面向對象 ································································.282
7.3 系統的基本模塊化 ····························································.283
7.4 本章小結 ········································································.284
第8 章 常見大數據技術框架 ······························································.286
8.1 大數據技術框架的幾個關注點··············································.287
8.1.1 多—數據量 ··························································.288
8.1.2 雜—數據結構 ·······················································.290
8.1.3 亂—數據到達 ·······················································.296
8.1.4 急—時效性 ··························································.299
8.2 常見大數據技術框架及基本邏輯 ···········································.302
8.2.1 Apache Flume 和Apache Kafka ·····································.303
8.2.2 Apache Hadoop ·························································.306
8.2.3 Apache Hive 和Facebook Presto ····································.310
8.2.4 Apache Kylin ···························································.311
8.2.5 Apache Flink 和Apache Storm ······································.312
8.2.6 Apache Spark ···························································.315
8.3 本章小結 ········································································.316
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