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Jingdong book

超越可视化：DT时代的大数据沟通与决策

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Jingdong book

超越可视化：DT时代的大数据沟通与决策

Name: 超越可视化：DT时代的大数据沟通与决策
Brand: Jingdong book
SKU: 3110006971
Price: 50.44 USD
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助你实现目标的强大数据。

在组织收集的大量数据以及可能的使用者之间，有一个巨大的割裂。通过聚焦和周到的数据展示工具，包括报告、仪表盘、可视化或关键指标，可以把这个缺口桥接起来。这些工具和功能整合在一起，将会通往数据畅流，这是一种运用数据语言与你的组织交流和探索思想的能力。

1.征服组织在数据报告及沟通方面所面临的不同挑战；

2.找到受众需求和数据正确演示之间的匹配；

3.设计有效的数据演示和工具，包括可视化、布局和样式的选择；

4.塑造鼓励在日常活动中使用数据的组织文化；

5.建立一个生态系统，在这一系统中，优质的数据演示方案，可以通过几何图形、传统图表、示意图、颜色、美术甚至幽默等方式，到达受众。

Content Description

本书主要作者为国际知名数据分析公司Juice Analytics的创始人，基于多年的研究成果和实践经验，他们分享了“数据畅流”的理念、框架及路径。

数据畅流指的是运用数据语言流畅地交换和探索组织重要思想的能力。数据畅流能让组织更有效和顺畅地决策、沟通、协调和学习。数据畅流框架包括4个要素：具备数据素养的消费者，熟练的数据生产者，数据畅流的文化以及数据产品的生态系统。这4个要素的有机整合是组织实现数据畅流的必要条件。当框架中显示的所有要素都存在并协同工作时，组织可以把数据作为通用语言来使用。本书探讨了帮助个人和组织发展数据畅流框架中每个要素技能的方法，进而基于该框架提供了通往数据畅流的实用指南。

本书结合现实案例，提供了大量精美的数据可视化展示，非常实用的数据产品设计指南，个人及组织数据畅流程度的调查清单，以及包括文字及数据格式、图表制作、排版设计等在内的样式指南和示例，实用性非常强。本书面向的读者范围很广，组织领导者、技术领袖、人力资源经理、分析师、数据产品的制作者、数据初学者，以及对大数据感兴趣的社会各界人士，都可以在本书中找到共鸣点。

Author Description

Zach Gemignani，Juice Analytics公司联合创始人，他帮助公司在设计引人入胜的信息体验和提供独特的数据可视化解决方案领域建立了声望。作为公司的CEO，他负责公司的战略方向、思想领导和业务发展。在Juice公司之前，Zach曾在AOL领导报告与分析工作，在Diamond Technology Partners公司担任过咨询顾问，在Booz Allen公司建立了制作精美幻灯片演示的声誉。他毕业于哈弗福德学院，拥有经济学学士学位，并在弗吉尼亚大学达顿商学院获得MBA学位。Zach和他的妻子以及3个孩子住在田纳西州的Nashville。

Chris Gemignani，Juice Analytics公司联合创始人，是公司的技术先行者。Chris曾在信用卡行业从事风险建模和持卡人行为模式分析，积累了数据处理的经验。他凭着对用户界面与交互设计的挑剔眼光，将分析工作经验与将洞察力展现到屏幕上的能力结合在一起。Chris毕业于威廉姆斯学院，拥有计算机科学与经济学学士学位。他在圣路易斯华盛顿大学获得经济学硕士学位。

Richard Galentino博士，战略规划及组织发展咨询公司Stratable的CEO。在创建Stratable公司之前，Richard曾带领国际医疗团队，将数以百计的医生、护士和保健专家团队送往超过27个国家。Richard毕业于哈佛大学（行政、规划和社会政策专业，教育硕士）以及乔治城大学外交学院（经济专业），被选为哈佛国际教育政策研究员。Richard获得了范德堡大学教育领导力与公共政策博士学位。Richard和他的家人住在田纳西州的Nashville。

Patrick Schuermann博士，范德堡大学皮博迪教育学院研究教授。曾担任美国教育部国家教育工作者薪酬改革中心政策与技术支持部主任，以及多个学校领导力与教育技术研究项目的首席研究员。Patrick目前是独立学校领导力硕士学位项目的主任，以及皮博迪职业培训部主席。Patrick和他多才多艺的创作歌手妻子，以及他们的两条狗一起居住于Nashville。

Catalogue

引言\1
I．1数据是新型商业语言\3
I．2数据沟通是社会问题，而非技术问题\3
I．3联系与协同\4
I．4将数据转化为行动\5
I．5可视化仅是方案的一小部分\6
I．6本书为谁而写\7
I．7注释\8
第1章最后一英里的问题\9
1．1信息时代：推动对数据畅流的需求\10
1．2数据畅流：释放组织的数据潜能\12
1．2．1大数据与数据隐喻\13
1．2．2我们的数据畅流框架\15
1．2．3案例研究：数据畅流框架的窗口\16
1．3 注释\25
第2章数据畅流框架\27
2．1数据畅流框架\29
2．1．1个体和组织\30
2．1．2使用数据与展示数据\31
2．1．3要素1：具备数据素养的消费者\31
2．1．4要素2：数据畅流的生产者\32
2．1．5要素3：数据畅流的文化\34
2．1．6要素4：数据产品生态系统\35
2．1．7结缔组织\36
2．2更深入的资源\36
2．3数据畅流组织的好处\37
2．4如何使用这个框架\38
2．5组织如何艰难前行\39
2．6小结\40
2．7注释\40
第3章组织如何为数据畅流而艰难前行\41
3．1通往数据畅流之路的陷阱\43
3．1．1报告激增\43
3．1．2分裂的数据\45
3．1．3数据精英主义\46
3．1．4超级名模\47
3．1．5缺乏对自身的了解\49
3．1．6不关注数据\50
3．1．7指标僵化\52
3．2寻求平衡\54
3．3注释\55
第4章消费者数据理解指南\57
4．1数据产品\59
4．1．1日常数据产品\61
4．1．2使用数据产品的障碍\65
4．2学习数据的语言\70
4．2．1解构数据\70
4．2．2归纳数据\71
4．2．3探索数据表\73
4．2．4理解图表以及可视化\74
4．2．5可理解性\76
4．2．6解剖数据产品\76
4．3结语\89
4．4注释\90
第5章数据作者：数据演示的熟练设计者\91
5．1一种稀有的技能组合\92
5．2你将学到的\93
5．3有指引的会话\94
5．3．1找到你的目标和主题\96
5．3．2信息甄别\101
5．3．3定义有意义和可付诸行动的指标\105
5．3．4创建数据产品的结构和流\108
5．3．5设计有吸引力、容易理解的数据产品\117
5．3．6用数据产品创建会话\128
5．4设计原则\130
5．4．1可视化\130
5．4．2数据产品的设计原则\136
5．5数据产品的作者万岁\140
5．6注释\141
第6章数据畅流的文化\143
6．1领导力、文化以及沟通优先问题\145
6．1．1设定并沟通期望\146
6．1．2设定明确、可测量的指标\146
6．1．3表彰有效的数据使用和成果\147
6．2建立关键指标作为中心\149
6．2．1什么可以成为好的指标\150
6．2．2运用指标推动组织改进\152
6．2．3向员工表明他们的贡献的重要性\153
6．3共识\156
6．3．1将通用词汇和术语与组织专有数据相关联\158
6．3．2理解与奖励可信、可靠的数据来源\159
6．3．3使数据的操作和建模透明化\161
6．3．4定义一组共享的关键指标\163
6．3．5理解数据产品的目的和动机\164
6．4日常行为\165
6．4．1数据消费者\166
6．4．2数据产品\168
6．4．3数据使用\171
6．5数据畅流文化的演进\172
6．6注释\173
第7章数据产品生态系统\175
7．1负责信息传递的数据产品\176
7．2必要条件\177
7．3向苹果应用商店学习\179
7．4需求\181
7．4．1需求排序图\181
7．4．2基层需求\183
7．4．3从哪里开始\183
7．5设计\184
7．5．1目标\184
7．5．2从样式指南开始\186
7．6开发\186
7．7发现\189
7．7．1目标\189
7．7．2出发点：数据产品的集中式库存\190
7．8讨论\190
7．8．1目标\191
7．8．2从哪里开始：创建获取洞见的场所\192
7．9提炼\193
7．10向维基百科学习\193
7．10．1目标\193
7．10．2哪些是你不需要的\194
7．11“只有联系”\195
7．12注释\196
第8章数据畅流之旅\197
8．1为什么要数据畅流？\199
8．2数据消费者：培养在行的受众\201
8．3数据产品生产者：实现有效数据沟通的技能\202
8．4数据畅流文化：建立对数据的共识\202
8．5数据产品生态系统：促进信息顺畅交流的工具和流程\203
8．6开始旅程\204
8．7注释\205
专题数据畅流清单\207
F．1数据畅流清单调查问题\208
F．1．1第1部分：数据消费者的数据素养\209
F．1．2第2部分：数据产品作者的技能\212
F．1．3第3部分：数据畅流的文化\215
F．1．4第4部分：数据产品生态系统\217
F．2DFI小结\219
F．3DFI评分指南（组织）\220
F．3．1问题类型和分值\220
F．3．2组织评分\220
F．3．3第1部分：数据消费者的数据素养\220
F．3．4第2部分：数据产品的作者\221
F．3．5第3部分：数据畅流的文化\222
F．3．6第4部分：数据产品生态系统\223
F．3．7组织评分小结\224
F．4DFI评分指南（个人）\224
F．4．1怎样能衡量个人？\225
F．4．2第1部分：数据消费者（仅含个人）\225
F．4．3第2部分：数据作者（仅含个人）\226
F．4．4个体评分指南小结\226
F．5DFI配套材料\226
F．5．1介绍调查的电子邮件\227
F．5．2数据素养测验\228
附录A 设计数据产品\233
A．1创建数据产品的清单\234
A．2像设计师那样思考\236
A．3设计以致用\238
A．4摆脱仪表盘一页规则\239
A．5仪表盘警报一览表\241
A．5．1场景：用户需要了解如何定义警报以及如何将它放入更大的视图\242
A．5．2说服力：警报系统需要避免造成不必要的报警，同时提供容易理解的信息以便采取行动\243
A．5．3沟通：必须把警报设计得可以有效吸引注意并告知受众\243
A．5．4控制：先进的警报系统应该给予用户定制和管理警报的能力\244
A．6成功实时仪表盘的8个特征\244
A．7注释\247
附录B 样式指南\249
B．1样式指南示例1：字体\251
B．2样式指南示例2：颜色\252
B．3样式指南示例3：日期/数据格式\253
B．4样式指南示例4：条形图\254
B．5样式指南示例5：趋势图\255
B．6样式指南示例6：表格\256
译者后记\257

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