数据产品经理宝典:大数据时代如何创造卓越产品9787121386275
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作者李阳
出版社电子工业出版社
ISBN9787121386275
出版时间2020-04
装帧平装
开本16开
定价69元
货号1248764373607503344
上书时间2025-01-20
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作者简介
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不错数据产品经理，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
目录篇 理解数据产品：确实有些不一样 章 什么是数据产品 ·······································································.21.1 数据产品的关注点 ·······························································.31.2 什么是数据应用 ··································································.51.2.1 数据处理的角度 ··························································.51.2.2 数据展现形式的角度 ····················································.71.2.3 应用目的的角度 ··························································.91.3 什么是效率问题 ································································.121.3.1 成本投入项 ······························································.121.3.2 价值产出项 ······························································.141.3.3 效率的问题 ······························································.151.4 本章小结 ·········································································.17第2 章 数据产品面临的挑战 ································································.182.1 为什么要做―师出有名 ····················································.192.1.1 支撑数据应用 ···························································.202.1.2 “量入为出”的价值管理 ············································.292.2 做的是什么―理解业务 ····················································.292.2.1 数据的意义 ······························································.302.2.2 架起“量化运营”的桥梁 ············································.332.3 怎样做到的―理解技术 ····················································.352.3.1 理解“究竟能做些什么” ············································.362.3.2 思考“怎样做得更高效” ············································.422.4 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 ·············································································.483.1 业务的目标是什么 ·····························································.503.1.1 能力视角 ·································································.503.1.2 利润视角 ·································································.523.1.3 效能视角 ·································································.523.1.4 影响力视角 ······························································.533.2 业务的商业模式与“投资”思维 ············································.563.2.1 资金投资 ·································································.573.2.2 人力投资 ·································································.633.2.3 时间投资 ·································································.663.2.4 其他投资 ·································································.673.3 常用管理模型和营销组合 ····················································.683.3.1 常用管理模型及其关系 ···············································.683.3.2 常用营销组合及其关系 ···············································.963.4 本章小结 ········································································.101第4 章 业务的数据诉求 ····································································.1034.1 用户市场研究 ··································································.1044.1.1 需求分析的目的 ·······················································.1054.1.2 需求的分层 ·····························································.1084.1.3 需求的定位 ·····························································.1164.1.4 需求分析的评价与KANO 模型 ····································.1274.1.5 需求的传播和贯彻 ····················································.1294.2 业务及产品形态研究 ·························································.1304.2.1 评价标准―怎样才是“好” ·····································.1314.2.2 业务转化与价值归因 ·················································.1444.2.3 流量管理与实验框架 ·················································.1534.3 综合能力升级 ··································································.1594.3.1 分析方法论及其优化 ·················································.1604.3.2 固化应用系统与赋能业务 ···········································.1714.3.3 赋能团队合作 ··························································.1744.4 工具、模型与业务、产品的“日常” ·····································.1764.5 本章小结 ········································································.179第5 章 用数据抽象业务 ····································································.1805.1 需求研究的数据抽象 ·························································.1815.1.1 需求挖掘―投放与获得新用户 ··································.1825.1.2 需求鉴别―留存与促进用户活跃 ·······························.1895.1.3 用户生命周期与“蓄水池”模型 ··································.1945.1.4 竞争性抽象与建模 ····················································.2005.2 业务的数据模型 ·······························································.2045.2.1 用E-R 图抽象实体关系 ··············································.2055.2.2 用流程图抽象业务过程 ··············································.2125.2.3 用时序图抽象处理过程 ··············································.2195.2.4 用财务思维抽象资金流 ··············································.2255.3 “数据世界观” ·································································.2345.3.1 数据模型与现实世界的差异 ········································.2345.3.2 用户行为的事件模型 ·················································.2355.4 数据仓库建模 ··································································.2425.4.1 面向分析的数据模型 ·················································.2425.4.2 通用数据仓库模型 ····················································.2445.5 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 ···························································.2526.1 实现业务诉求的方式 ·························································.2536.1.1 主动反馈与被动反馈 ·················································.2546.1.2 通用内容与定制内容 ·················································.2566.1.3 离线分析与在线分析 ·················································.2576.1.4 全量与抽样数据 ·······················································.2586.2 业务中的数据形态 ····························································.2596.2.1 业务理解与元数据 ····················································.2596.2.2 离线数据与数据集 ····················································.2606.2.3 实时数据与数据流 ····················································.2616.3 业务中的技术问题 ····························································.2636.3.1 数据量激增问题 ·······················································.2646.3.2 如何处理“陈旧”的内容 ···········································.2676.3.3 数据安全问题 ··························································.2686.4 本章小结 ········································································.272第7 章 必要的技术基础知识 ······························································.2747.1 产品的技术结构与“技术世界观” ········································.2767.1.1 Client/Server 结构 ·····················································.2777.1.2 Browser/Server 结构 ···················································.2787.1.3 产品的“技术世界观” ··············································.2797.2 代码理解世界的“做事思路”··············································.2807.2.1 面向过程 ································································.2807.2.2 面向对象 ································································.2827.3 系统的基本模块化 ····························································.2837.4 本章小结 ········································································.284第8 章 常见大数据技术框架 ······························································.2868.1 大数据技术框架的几个关注点··············································.2878.1.1 多―数据量 ··························································.2888.1.2 杂―数据结构 ·······················································.2908.1.3 乱―数据到达 ·······················································.2968.1.4 急―时效性 ··························································.2998.2 常见大数据技术框架及基本逻辑 ···········································.3028.2.1 Apache Flume 和Apache Kafka ·····································.3038.2.2 Apache Hadoop ·························································.3068.2.3 Apache Hive 和Facebook Presto ····································.3108.2.4 Apache Kylin ···························································.3118.2.5 Apache Flink 和Apache Storm ······································.3128.2.6 Apache Spark ···························································.3158.3 本章小结 ········································································.316
内容摘要
“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推荐的“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――从每个很好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。
主编推荐
数据产品经理是产品经理的一个重要分支，如今也是互联网就业和求职的热点。本书知识体系更加完整，涵盖数据分析思路、数据平台建设、数据仓库、数据治理等重要内容，既适合新人学习和入门使用，又适合从业者作为工具手册，是该领域一本优质图书。
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