“数据”两个字越来越频繁地出现在大家的工作中。一方面，“用数据说话”成为每个互联网从业者必备的“生存技能”；另一方面，一个名为“数据产品经理”的职位成为各大互联网企业的“热招职位”。那么，作为数据产品经理，有了数据应该怎样“用数据说话”？又应该如何让自己具备独特的竞争优势呢？本书内容涵盖了数据产品经理应该知道和掌握的基础知识——从每个优秀的数据产品经理都应当关注的“效率”问题出发，分别从商业知识和技术知识两个角度，针对什么是数据产品、数据产品诉求的产生和类型、数据产品的实现思路与常见技术方案等关键问题进行讲解。本书既是学习指南，又是速查手册，适合具备不同工作背景并正在从事数据产品经理工作的人士阅读，也适合对这一领域感兴趣并希望从事数据产品经理工作的人士阅读。只要你具备求知的热情，本书将为你提供解决问题的思路、方法和工具。

李阳（网名：御豪同学），数据产品经理、PMI-ACP敏捷项目管理师。现任京东数字科技集团高级数据产品经理，负责数据平台搭建工作，专注业务逻辑梳理及数据分析，对于大数据分析项目及平台搭建有深入了解。曾担任 GrowingIO 增长大会嘉宾、腾讯大数据沙龙嘉宾。2018年出版图书《产品增长力》。

目录
第一篇 理解数据产品：确实有些不一样
第1 章 什么是数据产品 ·······································································.2
1.1 数据产品的关注点 ·······························································.3
1.2 什么是数据应用 ··································································.5
1.2.1 数据处理的角度 ··························································.5
1.2.2 数据展现形式的角度 ····················································.7
1.2.3 应用目的的角度 ··························································.9
1.3 什么是效率问题 ································································.12
1.3.1 成本投入项 ······························································.12
1.3.2 价值产出项 ······························································.14
1.3.3 效率的问题 ······························································.15
1.4 本章小结 ·········································································.17
第2 章 数据产品面临的挑战 ································································.18
2.1 为什么要做—师出有名 ····················································.19
2.1.1 支撑数据应用 ···························································.20
2.1.2 “量入为出”的价值管理 ············································.29
2.2 做的是什么—理解业务 ····················································.29
2.2.1 数据的意义 ······························································.30
2.2.2 架起“量化运营”的桥梁 ············································.33
2.3 怎样做到的—理解技术 ····················································.35
2.3.1 理解“究竟能做些什么” ············································.36
2.3.2 思考“怎样做得更高效” ············································.42
2.4 本章小结 ·········································································.45
第二篇 理解业务：“奇怪”的数据需求从哪来
第3 章 业务是什么 ·············································································.48
3.1 业务的目标是什么 ·····························································.50
3.1.1 能力视角 ·································································.50
3.1.2 利润视角 ·································································.52
3.1.3 效能视角 ·································································.52
3.1.4 影响力视角 ······························································.53
3.2 业务的商业模式与“投资”思维 ············································.56
3.2.1 资金投资 ·································································.57
3.2.2 人力投资 ·································································.63
3.2.3 时间投资 ·································································.66
3.2.4 其他投资 ·································································.67
3.3 常用管理模型和营销组合 ····················································.68
3.3.1 常用管理模型及其关系 ···············································.68
3.3.2 常用营销组合及其关系 ···············································.96
3.4 本章小结 ········································································.101
第4 章 业务的数据诉求 ····································································.103
4.1 用户市场研究 ··································································.104
4.1.1 需求分析的目的 ·······················································.105
4.1.2 需求的分层 ·····························································.108
4.1.3 需求的定位 ·····························································.116
4.1.4 需求分析的评价与KANO 模型 ····································.127
4.1.5 需求的传播和贯彻 ····················································.129
4.2 业务及产品形态研究 ·························································.130
4.2.1 评价标准—怎样才是“好” ·····································.131
4.2.2 业务转化与价值归因 ·················································.144
4.2.3 流量管理与实验框架 ·················································.153
4.3 综合能力升级 ··································································.159
4.3.1 分析方法论及其优化 ·················································.160
4.3.2 固化应用系统与赋能业务 ···········································.171
4.3.3 赋能团队合作 ··························································.174
4.4 工具、模型与业务、产品的“日常” ·····································.176
4.5 本章小结 ········································································.179
第5 章 用数据抽象业务 ····································································.180
5.1 需求研究的数据抽象 ·························································.181
5.1.1 需求挖掘—投放与获得新用户 ··································.182
5.1.2 需求鉴别—留存与促进用户活跃 ·······························.189
5.1.3 用户生命周期与“蓄水池”模型 ··································.194
5.1.4 竞争性抽象与建模 ····················································.200
5.2 业务的数据模型 ·······························································.204
5.2.1 用E-R 图抽象实体关系 ··············································.205
5.2.2 用流程图抽象业务过程 ··············································.212
5.2.3 用时序图抽象处理过程 ··············································.219
5.2.4 用财务思维抽象资金流 ··············································.225
5.3 “数据世界观” ·································································.234
5.3.1 数据模型与现实世界的差异 ········································.234
5.3.2 用户行为的事件模型 ·················································.235
5.4 数据仓库建模 ··································································.242
5.4.1 面向分析的数据模型 ·················································.242
5.4.2 通用数据仓库模型 ····················································.244
5.5 本章小结 ········································································.250
第三篇 理解技术：打开数据系统的“黑箱”
第6 章 从业务诉求到技术系统 ···························································.252
6.1 实现业务诉求的方式 ·························································.253
6.1.1 主动反馈与被动反馈 ·················································.254
6.1.2 通用内容与定制内容 ·················································.256
6.1.3 离线分析与在线分析 ·················································.257
6.1.4 全量与抽样数据 ·······················································.258
6.2 业务中的数据形态 ····························································.259
6.2.1 业务理解与元数据 ····················································.259
6.2.2 离线数据与数据集 ····················································.260
6.2.3 实时数据与数据流 ····················································.261
6.3 业务中的技术问题 ····························································.263
6.3.1 数据量激增问题 ·······················································.264
6.3.2 如何处理“陈旧”的内容 ···········································.267
6.3.3 数据安全问题 ··························································.268
6.4 本章小结 ········································································.272
第7 章 必要的技术基础知识 ······························································.274
7.1 产品的技术结构与“技术世界观” ········································.276
7.1.1 Client/Server 结构 ·····················································.277
7.1.2 Browser/Server 结构 ···················································.278
7.1.3 产品的“技术世界观” ··············································.279
7.2 代码理解世界的“做事思路”··············································.280
7.2.1 面向过程 ································································.280
7.2.2 面向对象 ································································.282
7.3 系统的基本模块化 ····························································.283
7.4 本章小结 ········································································.284
第8 章 常见大数据技术框架 ······························································.286
8.1 大数据技术框架的几个关注点··············································.287
8.1.1 多—数据量 ··························································.288
8.1.2 杂—数据结构 ·······················································.290
8.1.3 乱—数据到达 ·······················································.296
8.1.4 急—时效性 ··························································.299
8.2 常见大数据技术框架及基本逻辑 ···········································.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 本章小结 ········································································.316