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第68行： − + − − [查尔斯·约瑟夫·密纳德1869年关于拿破仑法国入侵俄罗斯的图表，信息图表的早期例子] 第78行：
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第217行： − + − 时间序列图表展示了美国联邦政府的开支和收入随着时间的推移的变化趋势 − + − − 散点图说明两个变量(通货膨胀和失业)在时间点上的负相关性 第350行：
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第506行： − + − − | 每周每天的小费条形图 第532行：
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第906行： + + + + + + − * Deliberately [[Minimalism|minimalist]]—with no technical indicia—to communicate intuitively with non-scientists<ref name=Gizmodo_20190617>{{cite news |last1=Kahn |first1=Brian |title=This Striking Climate Change Visualization Is Now Customizable for Any Place on Earth |url=https://earther.gizmodo.com/this-striking-climate-change-visualization-is-now-custo-1835581866 |work=Gizmodo |date=June 17, 2019 |archiveurl=https://web.archive.org/web/20190626030105/https://earther.gizmodo.com/this-striking-climate-change-visualization-is-now-custo-1835581866 |archivedate=June 26, 2019 |url-status=live }} Developed in May 2018 by [[Ed Hawkins (scientist)|Ed Hawkins]], [[University of Reading]].</ref> − * Can be "stacked" to represent plural series ([[:File:20190909_STACKED_country_warming_stripes_AND_global_average_(1901-_).png |example]]) 第942行：
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第975行： − + − + − 关于数据可视化的范围有不同的方法。一个常见的焦点是信息表达，如 Friedman (2008)。《友好》(2008)假定数据可视化有两个主要部分: 统计图形和专题地图。在这一行《数据可视化: 现代方法》(2007)的文章概述了数据可视化的7个主题: 第989行：
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第1,024行： − + − 另一方面，从计算机科学的角度来看，Frits h. Post 在2002年将该领域分为子领域: 第1,020行：
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无编辑摘要
[[File:Data visualization process v1.png|upright=1.5|thumb|Data visualization is one of the steps in analyzing data and presenting it to users.]]
[[File:Data visualization process v1.png|upright=1.5|thumb|Data visualization is one of the steps in analyzing data and presenting it to users.]]
Data visualization is one of the steps in analyzing data and presenting it to users.
Data visualization is one of the steps in analyzing data and presenting it to users.数据可视化是分析数据并将其呈现给用户的步骤之一。
数据可视化是分析数据并将其呈现给用户的步骤之一。
[[File:Minard.png|thumb|upright=2|[[Charles Joseph Minard]]'s 1869 diagram of [[French invasion of Russia|Napoleonic France's invasion of Russia]], an early example of an information graphic]]
[[File:Minard.png|thumb|upright=2|[[Charles Joseph Minard]]'s 1869 diagram of [[French invasion of Russia|Napoleonic France's invasion of Russia]], an early example of an information graphic]]
[[Charles Joseph Minard's 1869 diagram of Napoleonic France's invasion of Russia, an early example of an information graphic]]
[[Charles Joseph Minard's 1869 diagram of Napoleonic France's invasion of Russia, an early example of an information graphic]]查尔斯·约瑟夫·米纳德(Charles Joseph Minard)的1869年拿破仑时期法国入侵俄国的图表，是信息图表的早期例子
{{quote box|width = 300px|quote=The greatest value of a picture is when it forces us to notice what we never expected to see.
{{quote box|width = 300px|quote=The greatest value of a picture is when it forces us to notice what we never expected to see.
{{quote box|width = 300px|quote=The greatest value of a picture is when it forces us to notice what we never expected to see.
{{quote box|width = 300px|quote=The greatest value of a picture is when it forces us to notice what we never expected to see.
{{ quote box | width 300px | quote 图片的最大价值在于它迫使我们注意到我们从未期望看到的东西。
{{ quote box | width 300px | quote 图片的最大价值在于它迫使我们注意到我们从未期望看到的东西——John Tukey
[[File:Total Revenues and Outlays as Percent GDP 2013.png|thumb|upright=1.75|A time series illustrated with a line chart demonstrating trends in U.S. federal spending and revenue over time]]
[[File:Total Revenues and Outlays as Percent GDP 2013.png|thumb|upright=1.75|A time series illustrated with a line chart demonstrating trends in U.S. federal spending and revenue over time]]
A time series illustrated with a line chart demonstrating trends in U.S. federal spending and revenue over time
A time series illustrated with a line chart demonstrating trends in U.S. federal spending and revenue over time这是一个时间序列图，用折线图表示美国联邦政府支出和收入随时间变化的趋势
[[File:U.S. Phillips Curve 2000 to 2013.png|thumb|upright=1.5|A scatterplot illustrating negative correlation between two variables (inflation and unemployment) measured at points in time]]
[[File:U.S. Phillips Curve 2000 to 2013.png|thumb|upright=1.5|A scatterplot illustrating negative correlation between two variables (inflation and unemployment) measured at points in time]]
A scatterplot illustrating negative correlation between two variables (inflation and unemployment) measured at points in time
A scatterplot illustrating negative correlation between two variables (inflation and unemployment) measured at points in time说明在某一时刻测量的两个变量(通货膨胀和失业)之间负相关的散点图
[[File:Mouvement des planètes au cours du temps.png|thumb|upright=1.5|Planetary movements]]
[[File:Mouvement des planètes au cours du temps.png|thumb|upright=1.5|Planetary movements]]
Planetary movements
Planetary movements行星的运动
==Examples of diagrams used for data visualization==
==Examples of diagrams used for data visualization==
用于数据可视化的图表示例
{{See also|Diagram}}
{{See also|Diagram}}
| [[File:Tips-day-barchart.pdf|thumb|Bar chart of tips by day of week]]
| [[File:Tips-day-barchart.pdf|thumb|Bar chart of tips by day of week]]
| Bar chart of tips by day of week
| Bar chart of tips by day of week每周每天小费的条形图
| [[Bar chart]]
| [[Bar chart]]
* length/count
* length/count
长度/数量
* category
* category
分类
* (color)
* (color)
(颜色)
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* Comparison of values, such as sales performance for several persons or businesses in a single time period. For a single variable measured over time (trend) a line chart is preferable.
* Comparison of values, such as sales performance for several persons or businesses in a single time period. For a single variable measured over time (trend) a line chart is preferable.
价值的比较，例如同一时间段内几个人或多个企业的销售业绩。对于随时间(趋势)变化的单个变量，折线图是最好的。
|-
|-
[[File:Housingprice.png|thumb|Histogram of housing prices]]
[[File:Housingprice.png|thumb|Histogram of housing prices]]
Histogram of housing prices
Histogram of housing prices房价直方图
| [[Histogram]]
| [[Histogram]]
* bin limits
* bin limits
--[[用户:吕倩倩|吕倩倩]]（[[用户讨论:吕倩倩|讨论]]）此处翻译存疑
* count/length
* count/length
数量/长度
* (color)
* (color)
（颜色）
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|
* Determining frequency of annual stock market percentage returns within particular ranges (bins) such as 0-10%, 11-20%, etc. The height of the bar represents the number of observations (years) with a return % in the range represented by the bin.
* Determining frequency of annual stock market percentage returns within particular ranges (bins) such as 0-10%, 11-20%, etc. The height of the bar represents the number of observations (years) with a return % in the range represented by the bin.
在特定范围内(如0-10%，11-20%等)确定股票市场年回报率的频率。直方图的高度表示观察的次数(年)，在容器表示的范围内返回%。
|-
|-
[[File:Scatterplot5.pdf|thumb|Basic scatterplot of two variables]]
[[File:Scatterplot5.pdf|thumb|Basic scatterplot of two variables]]
Basic scatterplot of two variables
Basic scatterplot of two variables两个变量的基本散点图
| [[Scatter plot]]
| [[Scatter plot]]
* x position
* x position
* y position
* y position
* (symbol/glyph)
* (symbol/glyph)
* (color)
* (color)
* (size)
* (size)
x位置
y位置
(符号/字形)
(颜色)
(大小)
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* Determining the relationship (e.g., correlation) between unemployment (x) and inflation (y) for multiple time periods.
* Determining the relationship (e.g., correlation) between unemployment (x) and inflation (y) for multiple time periods.
确定多个时期内失业(x)和通货膨胀(y)之间的关系(例如相关性)。
|-
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* color
* color
x位置
y位置
z位置
颜色
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* [[spatialization]]
* [[spatialization]]
节点的大小
节点的颜色
线条的粗细
线条的颜色
空间化
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* Finding clusters in the network (e.g. grouping Facebook friends into different clusters).
* Finding clusters in the network (e.g. grouping Facebook friends into different clusters).
在网络中寻找集群(例如将Facebook上的朋友分组到不同的集群中)。
* Discovering bridges (information brokers or boundary spanners) between clusters in the network
* Discovering bridges (information brokers or boundary spanners) between clusters in the network
发现网络中集群之间的桥梁(信息代理或边界转换器)
* Determining the most influential nodes in the network (e.g. A company wants to target a small group of people on Twitter for a marketing campaign).
* Determining the most influential nodes in the network (e.g. A company wants to target a small group of people on Twitter for a marketing campaign).
确定网络中最有影响力的节点(例如，一家公司想要在Twitter上针对一小群人进行营销活动)。
* Finding outlier actors who do not fit into any cluster or are in the periphery of a network.
* Finding outlier actors who do not fit into any cluster or are in the periphery of a network.
寻找不适合任何集群或处于网络外围的离群演员。
|-
|-
* width
* width
宽度
* color
* color
颜色
* time (flow)
* time (flow)
时间（流）
|-
|-
| Treemap
| Treemap
树图
| Treemap
| Treemap
* size
* size
大小
* color
* color
颜色
|
|
* disk space by location / file type
* disk space by location / file type
按位置/文件类型划分的磁盘空间
|-
|-
* color
* color
颜色
* time (flow)
* time (flow)
时间（流）
|
|
* schedule / progress, e.g. in [[project planning]]
* schedule / progress, e.g. in [[project planning]]
进度/进度，例如项目计划
|-
|-
| Heat map
| Heat map
| 热度图
| 热图
| [[heatmap|Heat map]]
| [[heatmap|Heat map]]
| Heat map
| Heat map
| 热度图
| 热图
|
|
* color
* color
行
列
集群
颜色
|
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* Analyzing risk, with green, yellow and red representing low, medium, and high risk, respectively.
* Analyzing risk, with green, yellow and red representing low, medium, and high risk, respectively.
分析风险，绿色、黄色和红色分别代表低、中、高风险。
|-
|-
* x position
* x position
x位置
* color
* color
颜色
|
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* Portrays a single variable—prototypically ''temperature over time'' to portray [[global warming]]
* Portrays a single variable—prototypically ''temperature over time'' to portray [[global warming]]
描绘一段时间内单一的可变原型温度来描绘全球变暖
* Deliberately [[Minimalism|minimalist]]—with no technical indicia—to communicate intuitively with non-scientists<ref name=Gizmodo_20190617>{{cite news |last1=Kahn |first1=Brian |title=This Striking Climate Change Visualization Is Now Customizable for Any Place on Earth |url=https://earther.gizmodo.com/this-striking-climate-change-visualization-is-now-custo-1835581866 |work=Gizmodo |date=June 17, 2019 |archiveurl=https://web.archive.org/web/20190626030105/https://earther.gizmodo.com/this-striking-climate-change-visualization-is-now-custo-1835581866 |archivedate=June 26, 2019 |url-status=live }} Developed in May 2018 by [[Ed Hawkins (scientist)|Ed Hawkins]], [[University of Reading]].</ref>
刻意的极简主义者——没有任何技术背景——与非科学家进行直观的交流
* Can be "stacked" to represent plural series ([[:File:20190909_STACKED_country_warming_stripes_AND_global_average_(1901-_).png |example]])
可以“堆叠”表示复数级数(示例)
|-
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* color (passing years)
* color (passing years)
径向距离(因变量)
旋转角度(月循环)
颜色(已流逝年份)
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* Portrays a single dependent variable—prototypically ''temperature over time'' to portray [[global warming]]
* Portrays a single dependent variable—prototypically ''temperature over time'' to portray [[global warming]]
描述一个单一的依赖变量-原型温度随时间变化来描述全球变暖
* Dependent variable is progressively plotted along a continuous "spiral" determined as a function of (a) constantly rotating angle (twelve months per revolution) and (b) evolving color (color changes over passing years)<ref name=WashPost_20160511>{{cite news |last1=Mooney |first1=Chris |title=This scientist just changed how we think about climate change with one GIF |url=https://www.washingtonpost.com/news/energy-environment/wp/2016/05/11/this-scientist-just-changed-how-we-think-about-climate-change-with-one-gif/ |work=The Washington Post |date=11 May 2016 |archiveurl=https://web.archive.org/web/20190206213537/https://www.washingtonpost.com/news/energy-environment/wp/2016/05/11/this-scientist-just-changed-how-we-think-about-climate-change-with-one-gif/ |archivedate=6 February 2019 |url-status=live |quote=[[Ed Hawkins (scientist)|Ed Hawkins]] took these monthly temperature data and plotted them in the form of a spiral, so that for each year, there are twelve points, one for each month, around the center of a circle – with warmer temperatures farther outward and colder temperatures nearer inward.}}</ref>
* Dependent variable is progressively plotted along a continuous "spiral" determined as a function of (a) constantly rotating angle (twelve months per revolution) and (b) evolving color (color changes over passing years)<ref name=WashPost_20160511>{{cite news |last1=Mooney |first1=Chris |title=This scientist just changed how we think about climate change with one GIF |url=https://www.washingtonpost.com/news/energy-environment/wp/2016/05/11/this-scientist-just-changed-how-we-think-about-climate-change-with-one-gif/ |work=The Washington Post |date=11 May 2016 |archiveurl=https://web.archive.org/web/20190206213537/https://www.washingtonpost.com/news/energy-environment/wp/2016/05/11/this-scientist-just-changed-how-we-think-about-climate-change-with-one-gif/ |archivedate=6 February 2019 |url-status=live |quote=[[Ed Hawkins (scientist)|Ed Hawkins]] took these monthly temperature data and plotted them in the form of a spiral, so that for each year, there are twelve points, one for each month, around the center of a circle – with warmer temperatures farther outward and colder temperatures nearer inward.}}</ref>
因变量沿着一个连续的“螺旋”逐步绘制，这个“螺旋”由(a)不断旋转的角度(每次旋转12个月)和(b)不断变化的颜色(随着时间的推移颜色会发生变化)的函数确定。
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== Other perspectives ==
== Other perspectives ==
其他视角
There are different approaches on the scope of data visualization. One common focus is on information presentation, such as Friedman (2008). Friendly (2008) presumes two main parts of data visualization: [[statistical graphics]], and [[Thematic map|thematic cartography]].<ref name = "MF08">[[Michael Friendly]] (2008). [http://www.math.yorku.ca/SCS/Gallery/milestone/milestone.pdf "Milestones in the history of thematic cartography, statistical graphics, and data visualization"] {{Webarchive|url=https://web.archive.org/web/20080911042504/http://www.math.yorku.ca/SCS/Gallery/milestone/milestone.pdf |date=2008-09-11 }}.</ref> In this line the "Data Visualization: Modern Approaches" (2007) article gives an overview of seven subjects of data visualization:<ref>[http://www.smashingmagazine.com/2007/08/02/data-visualization-modern-approaches/ "Data Visualization: Modern Approaches"] {{Webarchive|url=https://web.archive.org/web/20080722233419/http://www.smashingmagazine.com/2007/08/02/data-visualization-modern-approaches/ |date=2008-07-22 }}. in: ''Graphics'', August 2nd, 2007</ref>
There are different approaches on the scope of data visualization. One common focus is on information presentation, such as Friedman (2008). Friendly (2008) presumes two main parts of data visualization: [[statistical graphics]], and [[Thematic map|thematic cartography]].<ref name = "MF08">[[Michael Friendly]] (2008). [http://www.math.yorku.ca/SCS/Gallery/milestone/milestone.pdf "Milestones in the history of thematic cartography, statistical graphics, and data visualization"] {{Webarchive|url=https://web.archive.org/web/20080911042504/http://www.math.yorku.ca/SCS/Gallery/milestone/milestone.pdf |date=2008-09-11 }}.</ref> In this line the "Data Visualization: Modern Approaches" (2007) article gives an overview of seven subjects of data visualization:<ref>[http://www.smashingmagazine.com/2007/08/02/data-visualization-modern-approaches/ "Data Visualization: Modern Approaches"] {{Webarchive|url=https://web.archive.org/web/20080722233419/http://www.smashingmagazine.com/2007/08/02/data-visualization-modern-approaches/ |date=2008-07-22 }}. in: ''Graphics'', August 2nd, 2007</ref>
There are different approaches on the scope of data visualization. One common focus is on information presentation, such as Friedman (2008). Friendly (2008) presumes two main parts of data visualization: statistical graphics, and thematic cartography. In this line the "Data Visualization: Modern Approaches" (2007) article gives an overview of seven subjects of data visualization:
There are different approaches on the scope of data visualization. One common focus is on information presentation, such as Friedman (2008). Friendly (2008) presumes two main parts of data visualization: statistical graphics, and thematic cartography. In this line the "Data Visualization: Modern Approaches" (2007) article gives an overview of seven subjects of data visualization:
关于数据可视化的范围有不同的方法。一个常见的关注点是信息的表示，比如Friedman(2008)的《Friendly(2008)》一书假定了数据可视化有两个主要部分:统计图形和专题制图。在这一行中，“数据可视化:现代方法”(2007)文章概述了数据可视化的七个主题:
* [[Article (publishing)|Articles]] & [[resources]]
* [[Article (publishing)|Articles]] & [[resources]]
* Tools and services
* Tools and services
文章和资源
显示连接
显示数据
显示新闻
展示网站
思维导图
工具和服务
All these subjects are closely related to [[graphic design]] and information representation.
All these subjects are closely related to [[graphic design]] and information representation.
所有这些学科都与平面设计和信息表达密切相关。
所有这些学科都与平面设计和信息表达密切相关。
<!-- This is hardly a reliable source and this list should maybe be moved to Information graphics -->
<!-- This is hardly a reliable source and this list should maybe be moved to Information graphics -->
On the other hand, from a computer science perspective, Frits H. Post in 2002 categorized the field into sub-fields:
On the other hand, from a computer science perspective, Frits H. Post in 2002 categorized the field into sub-fields:
另一方面，Frits H. Post在2002年从计算机科学的角度将该领域划分为子领域:
* [[Information visualization]]
* [[Information visualization]]
* [[Volume visualization]]
* [[Volume visualization]]
信息可视化
交互技术和架构
建模技术
多分辨率的方法
可视化算法和技术
体积可视化
== Data presentation architecture ==
== Data presentation architecture ==
数据表示架构
[[File:Kencf0618FacebookNetwork.jpg|right|thumb|A data visualization from [[social media]]]]
[[File:Kencf0618FacebookNetwork.jpg|right|thumb|A data visualization from [[social media]]]]
A data visualization from [[social media]]
A data visualization from [[social media]]来自社交媒体的数据可视化
Data presentation architecture (DPA) is a skill-set that seeks to identify, locate, manipulate, format and present data in such a way as to optimally communicate meaning and proper knowledge.
Data presentation architecture (DPA) is a skill-set that seeks to identify, locate, manipulate, format and present data in such a way as to optimally communicate meaning and proper knowledge.
数据表示体系结构(DPA)是一种技能，它寻求以最佳的方式识别、定位、操作、格式化和表示数据，以便最佳地传递意义和适当的知识。
'''<font color="#ff8000">数据表示体系结构(DPA)</font>'''是一种技能集，旨在识别、定位、操作、格式化和表示数据，以最佳地传达含义和正确的知识。
Historically, the term data presentation architecture is attributed to Kelly Lautt: "Data Presentation Architecture (DPA) is a rarely applied skill set critical for the success and value of Business Intelligence. Data presentation architecture weds the science of numbers, data and statistics in discovering valuable information from data and making it usable, relevant and actionable with the arts of data visualization, communications, organizational psychology and change management in order to provide business intelligence solutions with the data scope, delivery timing, format and visualizations that will most effectively support and drive operational, tactical and strategic behaviour toward understood business (or organizational) goals. DPA is neither an IT nor a business skill set but exists as a separate field of expertise. Often confused with data visualization, data presentation architecture is a much broader skill set that includes determining what data on what schedule and in what exact format is to be presented, not just the best way to present data that has already been chosen. Data visualization skills are one element of DPA."
Historically, the term data presentation architecture is attributed to Kelly Lautt: "Data Presentation Architecture (DPA) is a rarely applied skill set critical for the success and value of Business Intelligence. Data presentation architecture weds the science of numbers, data and statistics in discovering valuable information from data and making it usable, relevant and actionable with the arts of data visualization, communications, organizational psychology and change management in order to provide business intelligence solutions with the data scope, delivery timing, format and visualizations that will most effectively support and drive operational, tactical and strategic behaviour toward understood business (or organizational) goals. DPA is neither an IT nor a business skill set but exists as a separate field of expertise. Often confused with data visualization, data presentation architecture is a much broader skill set that includes determining what data on what schedule and in what exact format is to be presented, not just the best way to present data that has already been chosen. Data visualization skills are one element of DPA."
从历史上看，数据表示体系结构这个术语是由 Kelly Lautt 提出的: “数据表示体系结构(Data Presentation Architecture，DPA)对于商业智能的成功和价值来说是一个很少应用的技能集。数据表示架构将数字、数据和统计的科学结合起来，从数据中发现有价值的信息，并使其可用、相关和可操作，结合数据可视化、通信、工业与组织心理学和变革管理的艺术，以便提供具有数据范围、交付时间、格式和可视化的商业情报解决方案，这将最有效地支持和推动业务、战术和战略行为，实现理解的业务(或组织)。Dpa 既不是一个 IT 也不是一个业务技能集，而是作为一个独立的专业领域存在的。数据表示架构经常与数据可视化 / 服务混淆，它是一个更广泛的技能集合，包括决定什么数据按照什么时间表和以什么确切的格式呈现，而不仅仅是已经选择的最佳数据表示方式。数据可视化技能是 DPA 的一个要素。”
从历史上看，数据表示体系结构这个术语是由 Kelly Lautt 提出的: “数据表示体系结构(Data Presentation Architecture，DPA)对于商业智能的成功和价值来说是一个很少应用的技能集。数据表示架构将数字、数据和统计的科学结合起来，从数据中发现有价值的信息，并使其可用、相关和可操作，结合数据可视化、通信、工业与组织心理学和变革管理的艺术，以便提供具有数据范围、交付时间、格式和可视化的商业情报解决方案，这将最有效地支持和推动业务、战术和战略行为，实现业务(或组织)的理解。DPA既不是IT也不是业务技能集，而是作为一个独立的专业领域存在的。数据表示体系结构经常与数据可视化相混淆，它是一种更广泛的技能集，包括确定什么数据在什么时间表上以及以什么确切的格式表示，而不仅仅是表示已经选择的数据的最佳方式。数据可视化技能是DPA的一个要素。”
=== Objectives ===
=== Objectives ===
目标
DPA has two main objectives:
DPA has two main objectives:
DPA has two main objectives:
DPA has two main objectives:
DPA有两个主要目标:
* To use data to provide knowledge in the most efficient manner possible (minimize noise, complexity, and unnecessary data or detail given each audience's needs and roles)
* To use data to provide knowledge in the most efficient manner possible (minimize noise, complexity, and unnecessary data or detail given each audience's needs and roles)
使用数据以尽可能有效的方式提供知识(根据每个受众的需求和角色，尽量减少噪音、复杂性和不必要的数据或细节)
* To use data to provide knowledge in the most effective manner possible (provide relevant, timely and complete data to each audience member in a clear and understandable manner that conveys important meaning, is actionable and can affect understanding, behavior and decisions)
* To use data to provide knowledge in the most effective manner possible (provide relevant, timely and complete data to each audience member in a clear and understandable manner that conveys important meaning, is actionable and can affect understanding, behavior and decisions)
使用数据尽可能以最有效的方式提供知识(以清晰易懂的方式向每位听众提供相关、及时和完整的数据，传达重要的含义，可操作，并能影响理解、行为和决策)
=== Scope ===
=== Scope ===
范围
With the above objectives in mind, the actual work of data presentation architecture consists of:
With the above objectives in mind, the actual work of data presentation architecture consists of:
* Creating effective delivery mechanisms for each audience member depending on their role, tasks, locations and access to technology
* Creating effective delivery mechanisms for each audience member depending on their role, tasks, locations and access to technology
根据听众的角色、任务、位置和技术获取情况，为每个听众成员创建有效的传递机制
* Defining important meaning (relevant knowledge) that is needed by each audience member in each context
* Defining important meaning (relevant knowledge) that is needed by each audience member in each context
定义每一上下文中每个听众成员需要的重要意义(相关知识)
* Determining the required periodicity of data updates (the currency of the data)
* Determining the required periodicity of data updates (the currency of the data)
确定所需的数据更新周期(数据的货币)
* Determining the right timing for data presentation (when and how often the user needs to see the data)
* Determining the right timing for data presentation (when and how often the user needs to see the data)
确定数据表示的正确时间(何时以及多长时间用户需要查看数据)
* Finding the right data (subject area, historical reach, breadth, level of detail, etc.)
* Finding the right data (subject area, historical reach, breadth, level of detail, etc.)
找到正确的数据(主题领域、历史范围、广度、细节水平等)
* Utilizing appropriate analysis, grouping, visualization, and other presentation formats
* Utilizing appropriate analysis, grouping, visualization, and other presentation formats
利用适当的分析、分组、可视化和其他演示格式
== See also ==
== See also ==
另请参阅
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== Notes ==
== Notes ==
注释
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