Data Visualization - Revision history

User at 23:21, 22 March 2024

2024-03-22T23:21:13Z

User at 18:10, 12 January 2023

2023-01-12T18:10:48Z

User at 15:36, 26 October 2022

2022-10-26T15:36:57Z

User at 15:36, 26 October 2022

2022-10-26T15:36:24Z

User at 07:06, 16 October 2022

2022-10-16T07:06:01Z

User at 23:16, 15 October 2022

2022-10-15T23:16:55Z

User at 23:15, 15 October 2022

2022-10-15T23:15:44Z

User at 14:10, 2 August 2021

2021-08-02T14:10:01Z

User at 14:00, 2 August 2021

2021-08-02T14:00:49Z

User at 13:30, 2 August 2021

2021-08-02T13:30:08Z

@@ Line 46: / Line 46: @@
 ==See Also==
-*[[Management Information System (MIS)]]
+Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. In the context of data science, business intelligence, and information management, it plays a crucial role in translating complex data sets into a form that can be easily interpreted by decision-makers.
 == References ==
 <references />

@@ Line 1: / Line 1: @@
-'''Data Visualization''' is the graphical representation of [[Information|information]] and [[Data|data]]. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions.<ref>Definition - What Does Data Visualization Mean? [https://www.tableau.com/learn/articles/data-visualization Tableau]</ref>
+'''Data Visualization''' is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions.<ref>Definition - What Does Data Visualization Mean? [https://www.tableau.com/learn/articles/data-visualization Tableau]</ref>
@@ Line 46: / Line 46: @@
 ==See Also==
-*[[Data]]
+*[[Management Information System (MIS)]]
 == References ==
 <references />

← Older revision		Revision as of 15:36, 26 October 2022
Line 71:		Line 71:
	*[[Data Visualization]]		*[[Data Visualization]]
	*[[Data Warehouse]]		*[[Data Warehouse]]
		+
		+	== References ==
		+	<references />

← Older revision		Revision as of 15:36, 26 October 2022
Line 44:		Line 44:
	*Choose an effective visual: Specific visuals are designed for specific types of datasets. For instance, scatter plots display the relationship between two variables well, while line graphs display time series data well. Ensure that the visual actually assists the audience in understanding your main takeaway. Misalignment of charts and data can result in the opposite, confusing your audience further versus providing clarity.		*Choose an effective visual: Specific visuals are designed for specific types of datasets. For instance, scatter plots display the relationship between two variables well, while line graphs display time series data well. Ensure that the visual actually assists the audience in understanding your main takeaway. Misalignment of charts and data can result in the opposite, confusing your audience further versus providing clarity.
	*Keep it simple: Data visualization tools can make it easy to add all sorts of information to your visual. However, just because you can, it doesn’t mean that you should! In data visualization, you want to be very deliberate about the additional information that you add to focus user attention. For example, do you need data labels on every bar in your bar chart? Perhaps you only need one or two to help illustrate your point. Do you need a variety of colors to communicate your idea? Are you using colors that are accessible to a wide range of audiences (e.g. accounting for color blind audiences)? Design your data visualization for maximum impact by eliminating information that may distract your target audience.		*Keep it simple: Data visualization tools can make it easy to add all sorts of information to your visual. However, just because you can, it doesn’t mean that you should! In data visualization, you want to be very deliberate about the additional information that you add to focus user attention. For example, do you need data labels on every bar in your bar chart? Perhaps you only need one or two to help illustrate your point. Do you need a variety of colors to communicate your idea? Are you using colors that are accessible to a wide range of audiences (e.g. accounting for color blind audiences)? Design your data visualization for maximum impact by eliminating information that may distract your target audience.
		+
		+	==See Also==
		+	*[[Data]]
		+	*[[Data Access]]
		+	*[[Data Analysis]]
		+	*[[Data Analytics]]
		+	*[[Data Architecture]]
		+	*[[Data Asset Framework (DAF)]]
		+	*[[Data Buffer]]
		+	*[[Data Center]]
		+	*[[Data Center Infrastructure]]
		+	*[[Data Center Infrastructure Management (DCIM)]]
		+	*[[Data Cleansing]]
		+	*[[Data Compatibility]]
		+	*[[Data Governance]]
		+	*[[Data Integration]]
		+	*[[Data Management]]
		+	*[[Data Mining]]
		+	*[[Data Monitoring]]
		+	*[[Data Munging]]
		+	*[[Data Portability]]
		+	*[[Data Quality]]
		+	*[[Data Reference Model (DRM)]]
		+	*[[Data Security]]
		+	*[[Data Transformation]]
		+	*[[Data Visualization]]
		+	*[[Data Warehouse]]

← Older revision		Revision as of 07:06, 16 October 2022
Line 44:		Line 44:
	*Choose an effective visual: Specific visuals are designed for specific types of datasets. For instance, scatter plots display the relationship between two variables well, while line graphs display time series data well. Ensure that the visual actually assists the audience in understanding your main takeaway. Misalignment of charts and data can result in the opposite, confusing your audience further versus providing clarity.		*Choose an effective visual: Specific visuals are designed for specific types of datasets. For instance, scatter plots display the relationship between two variables well, while line graphs display time series data well. Ensure that the visual actually assists the audience in understanding your main takeaway. Misalignment of charts and data can result in the opposite, confusing your audience further versus providing clarity.
	*Keep it simple: Data visualization tools can make it easy to add all sorts of information to your visual. However, just because you can, it doesn’t mean that you should! In data visualization, you want to be very deliberate about the additional information that you add to focus user attention. For example, do you need data labels on every bar in your bar chart? Perhaps you only need one or two to help illustrate your point. Do you need a variety of colors to communicate your idea? Are you using colors that are accessible to a wide range of audiences (e.g. accounting for color blind audiences)? Design your data visualization for maximum impact by eliminating information that may distract your target audience.		*Keep it simple: Data visualization tools can make it easy to add all sorts of information to your visual. However, just because you can, it doesn’t mean that you should! In data visualization, you want to be very deliberate about the additional information that you add to focus user attention. For example, do you need data labels on every bar in your bar chart? Perhaps you only need one or two to help illustrate your point. Do you need a variety of colors to communicate your idea? Are you using colors that are accessible to a wide range of audiences (e.g. accounting for color blind audiences)? Design your data visualization for maximum impact by eliminating information that may distract your target audience.
−
−
−
−	~~== See Also ==~~
−	~~<div style="column-count:2;-moz-column-count:4;-webkit-column-count:4">~~
−	~~[[Data Compatibility]]<br />~~
−	~~[[Data Access]]<br />~~
−	~~[[Data Analysis]]<br />~~
−	~~[[Data Analytics]]<br />~~
−	~~[[Data Architecture]]<br />~~
−	~~[[Data Asset Framework (DAF)]]<br />~~
−	~~[[Data Buffer]]<br />~~
−	~~[[Data Center]]<br />~~
−	~~[[Data Center Infrastructure]]<br />~~
−	~~[[Data Center Infrastructure Management (DCIM)]]<br />~~
−	~~[[Data Cleansing]]<br />~~
−	~~[[Big Data]]<br />~~
−	~~[[Big Data Integration]]<br />~~
−	~~[[Big Data Maturity Model (BDMM)]]<br />~~
−	~~[[Metadata]]<br />~~
−	~~[[Data Collection]]<br />~~
−	~~[[Data Compatibility]]<br />~~
−	~~[[Data Consolidation]]<br />~~
−	~~[[Data Deduplication]]<br />~~
−	~~[[Data Delivery Platform (DDP)]]<br />~~
−	~~[[Data Description (Definition) Language (DDL)]]<br />~~
−	~~[[Data Dictionary]]<br />~~
−	~~[[Data Discovery]]<br />~~
−	~~[[Data Driven Organization]]<br />~~
−	~~[[Data Element]]<br />~~
−	~~[[Data Enrichment]]<br />~~
−	~~[[Data Entry]]<br />~~
−	~~[[Data Federation]]<br />~~
−	~~[[Data Flow Diagram]]<br />~~
−	~~[[Data Governance]]<br />~~
−	~~[[Data Health Check]]<br />~~
−	~~[[Data Hierarchy]]<br />~~
−	~~[[Data Independence]]<br />~~
−	~~[[Data Integration]]<br />~~
−	~~[[Data Integration Framework (DIF)]]<br />~~
−	~~[[Data Integrity]]<br />~~
−	~~[[Data Island]]<br />~~
−	~~[[Data Item]]<br />~~
−	~~[[Data Lake]]<br />~~
−	~~[[Data Life Cycle]]<br />~~
−	~~[[Data Lineage]]<br />~~
−	~~[[Data Loss Prevention (DLP)]]<br />~~
−	~~[[Data Management]]<br />~~
−	~~[[Data Migration]]<br />~~
−	~~[[Data Minimization]]<br />~~
−	~~[[Data Mining]]<br />~~
−	~~[[Data Model]]<br />~~
−	~~[[Data Modeling]]<br />~~
−	~~[[Data Monitoring]]<br />~~
−	~~[[Data Munging]]<br />~~
−	~~[[Data Portability]]<br />~~
−	~~[[Data Preparation]]<br />~~
−	~~[[Data Presentation Architecture]]<br />~~
−	~~[[Data Processing]]<br />~~
−	~~[[Data Profiling]]<br />~~
−	~~[[Data Proliferation]]<br />~~
−	~~[[Data Propagation]]<br />~~
−	~~[[Data Protection Act]]<br />~~
−	~~[[Data Prototyping]]<br />~~
−	~~[[Data Quality]]<br />~~
−	~~[[Data Quality Assessment (DQA)]]<br />~~
−	~~[[Data Quality Dimension]]<br />~~
−	~~[[Data Quality Standard]]<br />~~
−	~~[[Data Reconciliation]]<br />~~
−	~~[[Data Reference Model (DRM)]]<br />~~
−	~~[[Data Science]]<br />~~
−	~~[[Data Security]]<br />~~
−	~~[[Data Stewardship]]<br />~~
−	~~[[Data Structure]]<br />~~
−	~~[[Data Structure Diagram]]<br />~~
−	~~[[Data Suppression]]<br />~~
−	~~[[Data Transformation]]<br />~~
−	~~[[Data Validation]]<br />~~
−	~~[[Data Value Chain]]<br />~~
−	~~[[Data Vault Modeling]]<br />~~
−	~~[[Data Virtualization]]<br />~~
−	~~[[Data Visualization]]<br />~~
−	~~[[Data Warehouse]]<br />~~
−	~~[[Data Wrangling]]<br />~~
−	~~[[Data and Information Reference Model (DRM)]]<br />~~
−	~~[[Data as a Service (DaaS)]]<br />~~
−	~~[[Database (DB)]]<br />~~
−	~~[[Database Design]]<br />~~
−	~~[[Database Design Methodology]]<br />~~
−	~~[[Database Management System (DBMS)]]<br />~~
−	~~[[Database Marketing]]<br />~~
−	~~[[Database Schema]]<br />~~
−	~~[[Database System]]<br />~~
−	~~</div>~~
−
−
−
−	~~== References ==~~
−	~~<references />~~

← Older revision		Revision as of 23:16, 15 October 2022
Line 138:		Line 138:
	[[Database System]]<br />		[[Database System]]<br />
	</div>		</div>
		+
		+
		+
		+	== References ==
		+	<references />

← Older revision		Revision as of 23:15, 15 October 2022
Line 44:		Line 44:
	*Choose an effective visual: Specific visuals are designed for specific types of datasets. For instance, scatter plots display the relationship between two variables well, while line graphs display time series data well. Ensure that the visual actually assists the audience in understanding your main takeaway. Misalignment of charts and data can result in the opposite, confusing your audience further versus providing clarity.		*Choose an effective visual: Specific visuals are designed for specific types of datasets. For instance, scatter plots display the relationship between two variables well, while line graphs display time series data well. Ensure that the visual actually assists the audience in understanding your main takeaway. Misalignment of charts and data can result in the opposite, confusing your audience further versus providing clarity.
	*Keep it simple: Data visualization tools can make it easy to add all sorts of information to your visual. However, just because you can, it doesn’t mean that you should! In data visualization, you want to be very deliberate about the additional information that you add to focus user attention. For example, do you need data labels on every bar in your bar chart? Perhaps you only need one or two to help illustrate your point. Do you need a variety of colors to communicate your idea? Are you using colors that are accessible to a wide range of audiences (e.g. accounting for color blind audiences)? Design your data visualization for maximum impact by eliminating information that may distract your target audience.		*Keep it simple: Data visualization tools can make it easy to add all sorts of information to your visual. However, just because you can, it doesn’t mean that you should! In data visualization, you want to be very deliberate about the additional information that you add to focus user attention. For example, do you need data labels on every bar in your bar chart? Perhaps you only need one or two to help illustrate your point. Do you need a variety of colors to communicate your idea? Are you using colors that are accessible to a wide range of audiences (e.g. accounting for color blind audiences)? Design your data visualization for maximum impact by eliminating information that may distract your target audience.
		+
		+
		+
		+	== See Also ==
		+	<div style="column-count:2;-moz-column-count:4;-webkit-column-count:4">
		+	[[Data Compatibility]]<br />
		+	[[Data Access]]<br />
		+	[[Data Analysis]]<br />
		+	[[Data Analytics]]<br />
		+	[[Data Architecture]]<br />
		+	[[Data Asset Framework (DAF)]]<br />
		+	[[Data Buffer]]<br />
		+	[[Data Center]]<br />
		+	[[Data Center Infrastructure]]<br />
		+	[[Data Center Infrastructure Management (DCIM)]]<br />
		+	[[Data Cleansing]]<br />
		+	[[Big Data]]<br />
		+	[[Big Data Integration]]<br />
		+	[[Big Data Maturity Model (BDMM)]]<br />
		+	[[Metadata]]<br />
		+	[[Data Collection]]<br />
		+	[[Data Compatibility]]<br />
		+	[[Data Consolidation]]<br />
		+	[[Data Deduplication]]<br />
		+	[[Data Delivery Platform (DDP)]]<br />
		+	[[Data Description (Definition) Language (DDL)]]<br />
		+	[[Data Dictionary]]<br />
		+	[[Data Discovery]]<br />
		+	[[Data Driven Organization]]<br />
		+	[[Data Element]]<br />
		+	[[Data Enrichment]]<br />
		+	[[Data Entry]]<br />
		+	[[Data Federation]]<br />
		+	[[Data Flow Diagram]]<br />
		+	[[Data Governance]]<br />
		+	[[Data Health Check]]<br />
		+	[[Data Hierarchy]]<br />
		+	[[Data Independence]]<br />
		+	[[Data Integration]]<br />
		+	[[Data Integration Framework (DIF)]]<br />
		+	[[Data Integrity]]<br />
		+	[[Data Island]]<br />
		+	[[Data Item]]<br />
		+	[[Data Lake]]<br />
		+	[[Data Life Cycle]]<br />
		+	[[Data Lineage]]<br />
		+	[[Data Loss Prevention (DLP)]]<br />
		+	[[Data Management]]<br />
		+	[[Data Migration]]<br />
		+	[[Data Minimization]]<br />
		+	[[Data Mining]]<br />
		+	[[Data Model]]<br />
		+	[[Data Modeling]]<br />
		+	[[Data Monitoring]]<br />
		+	[[Data Munging]]<br />
		+	[[Data Portability]]<br />
		+	[[Data Preparation]]<br />
		+	[[Data Presentation Architecture]]<br />
		+	[[Data Processing]]<br />
		+	[[Data Profiling]]<br />
		+	[[Data Proliferation]]<br />
		+	[[Data Propagation]]<br />
		+	[[Data Protection Act]]<br />
		+	[[Data Prototyping]]<br />
		+	[[Data Quality]]<br />
		+	[[Data Quality Assessment (DQA)]]<br />
		+	[[Data Quality Dimension]]<br />
		+	[[Data Quality Standard]]<br />
		+	[[Data Reconciliation]]<br />
		+	[[Data Reference Model (DRM)]]<br />
		+	[[Data Science]]<br />
		+	[[Data Security]]<br />
		+	[[Data Stewardship]]<br />
		+	[[Data Structure]]<br />
		+	[[Data Structure Diagram]]<br />
		+	[[Data Suppression]]<br />
		+	[[Data Transformation]]<br />
		+	[[Data Validation]]<br />
		+	[[Data Value Chain]]<br />
		+	[[Data Vault Modeling]]<br />
		+	[[Data Virtualization]]<br />
		+	[[Data Visualization]]<br />
		+	[[Data Warehouse]]<br />
		+	[[Data Wrangling]]<br />
		+	[[Data and Information Reference Model (DRM)]]<br />
		+	[[Data as a Service (DaaS)]]<br />
		+	[[Database (DB)]]<br />
		+	[[Database Design]]<br />
		+	[[Database Design Methodology]]<br />
		+	[[Database Management System (DBMS)]]<br />
		+	[[Database Marketing]]<br />
		+	[[Database Schema]]<br />
		+	[[Database System]]<br />
		+	</div>

@@ Line 1: / Line 1: @@
 '''Data Visualization''' is the graphical representation of [[Information|information]] and [[Data|data]]. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions.<ref>Definition - What Does Data Visualization Mean? [https://www.tableau.com/learn/articles/data-visualization Tableau]</ref>
@@ Line 25: / Line 36: @@
 *Big data visualization projects often require involvement from IT, as well as management, since the visualization of big data requires powerful computer hardware, efficient storage systems and even a move to the cloud.
 *The insights provided by big data visualization will only be as accurate as the information being visualized. Therefore, it is essential to have people and processes in place to govern and control the quality of corporate data, metadata and data sources.

@@ Line 2: / Line 2: @@
-''''''The Uses of Data Visualization<ref>How is data visualization used? [https://www.sas.com/en_us/insights/big-data/data-visualization.html SAS]</ref>''''''<br />
+'''The Uses of Data Visualization<ref>How is data visualization used? [https://www.sas.com/en_us/insights/big-data/data-visualization.html SAS]</ref>'''<br />
 Regardless of industry or size, all types of businesses are using data visualization to help make sense of their data.
 *Comprehend information quickly: By using graphical representations of business information, businesses are able to see large amounts of data in clear, cohesive ways – and draw conclusions from that information. And since it’s significantly faster to analyze information in graphical format (as opposed to analyzing information in spreadsheets), businesses can address problems or answer questions in a more timely manner.

@@ Line 2: / Line 2: @@
-'''The Uses of Data Visualization<ref>How is data visualization used? [https://www.import.io/post/what-is-data-visualization/ Import.io]</ref>'''<br />
+''''''The Uses of Data Visualization<ref>How is data visualization used? [https://www.sas.com/en_us/insights/big-data/data-visualization.html SAS]</ref>''''''<br />
-Data visualization has many uses. Each type of data visualization can be used in different ways. We’ll get into the different types in a moment, but for now, here are some of the most common ways data visualization is used.
+Regardless of industry or size, all types of businesses are using data visualization to help make sense of their data.
-*Changes over time: This is perhaps the most basic and common use of data visualization, but that doesn’t mean it’s not valuable. The reason it is the most common is because most data has an element of time involved. Therefore, the first step in a lot of data analyses is to see how the data trends over time.
+*Comprehend information quickly: By using graphical representations of business information, businesses are able to see large amounts of data in clear, cohesive ways – and draw conclusions from that information. And since it’s significantly faster to analyze information in graphical format (as opposed to analyzing information in spreadsheets), businesses can address problems or answer questions in a more timely manner.
-*Determining frequency: Frequency is also a fairly basic use of data visualization because it also applies to data that involves time. If time is involved, it is logical that you should determine how often the relevant events happen over time.
+*Identify relationships and patterns: Even extensive amounts of complicated data start to make sense when presented graphically; businesses can recognize parameters that are highly correlated. Some of the correlations will be obvious, but others won’t. Identifying those relationships helps organizations focus on areas most likely to influence their most important goals.
-*Determining relationships (correlations): Identifying correlations is an extremely valuable use of data visualization. It is extremely difficult to determine the relationship between two variables without a visualization, yet it is important to be aware of relationships in data. This is a great example of the value of data visualization in data analysis.
+*Pinpoint emerging trends: Using data visualization to discover trends – both in the business and in the market – can give businesses an edge over the competition, and ultimately affect the bottom line. It’s easy to spot outliers that affect product quality or customer churn, and address issues before they become bigger problems.
-*Examining a network: An example of examining a network with data visualization can be seen in market research. Marketing professionals need to know which audiences to target with their message, so they analyze the entire market to identify audience clusters, bridges between the clusters, influencers within clusters, and outliers.
+*Communicate the story to others: Once a business has uncovered new insights from visual analytics, the next step is to communicate those insights to others. Using charts, graphs or other visually impactful representations of data is important in this step because it’s engaging and gets the message across quickly.
-*Scheduling: When planning out a schedule or timeline for a complex project, things can get confusing. A Gantt chart solves that issue by clearly illustrating each task within the project and how long it will take to complete.
-*Analyzing value and risk: Determining complex metrics such as value and risk requires many different variables to be factored in, making it almost impossible to see accurately with a plain spreadsheet. Data visualization can be as simple as color-coding a formula to show which opportunities are valuable and which are risky.