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What is Data Visualization?

What is Data Visualization?

Data visualization is the practice of arranging data sets into static or dynamic graphics that highlight the complex relationships between the data presented. Because visual representation is highly intuitive, information presented this way is easier to understand, and reaching actionable insights is faster.

Data is a valuable asset within the business context which can benefit every team in a business, and not just those directly associated with data collecting, generation, and processing. Top level leadership uses data visualization to understand broad forces acting on the business, and to understand quickly which strategic issues need to be prioritized. Frontline teams may use different aspects of data generated from the same sources but data and visualizations are chosen to help them understand more efficient operations.

In the IT context, data visualizations are attached to some aspect of automated data gathering and representation, which adds real-time representations for immediate business use through dashboards.

Dashboards rely on common archetypes for data visualizations which are easily read and almost universally used. Some of these common chart types:

  • Standard Graphs — The most common data visualization representations include: line graphs, pie charts, bar charts, scatter plots, area charts, and histograms. However, these are somewhat limited and other charts have been devised to help represent more complex data sets.
  • Infographics — Infographics present information graphics, and differ from charts because information does not need to remain numerical. Information can include other diagrams, such as maps, or flow charts.
  • Bubble Cloud Charts — Bubble cloud charts are scatter plots with a third variable included. The independent and dependent variables are plotted on an X/Y axis, while the third variable is represented as a circle (or bubble), with a size indicating its value and the plot point at its center.
  • Bullet Graphs — Bullet graphs represent bar graphs as something like how a thermometer presents temperature gradients. Essentially, bullet graphics are a type of skeuomorphism, a way to retain familiar designs in new forms, like representing the physical radio dials as on screen dials to aid in quick understanding and adoption.
  • Heat Maps — Heat maps represent magnitude on a geographic map, or in abstract, as a two dimensional color spectrum. Sometimes other variables can be introduced, such as a heat map divided into squares of different sizes that represent one variable, while another independent variable is represented as the ‘heat’ color.
  • Fever Charts — Fever charts are exceptionally useful, and common, because they represent a single variable over time. These charts are also referred to as time series

Why is data visualization important?

For businesses, data visualization is an essential tool in rapidly identifying data trends in data volumes that are prohibitively large to analyze using traditional methods. Data visualization software brings the power of graphic representation to big data, helping to uncover and visualize new patterns.

While graphical representations of data are common, the use of technology has allowed for business systems to feed data stores that in turn feed these visual discoveries. These underlying systems of data generating devices are connected in our modern world via the Internet of Things, and pull data from every device, throughout multiple locations, over vast regions—think, for example, of global supply chains connected to vendor supply chains, and ultimately to retail outlets, and now tracking through to clients and back are returns. New visualizations and tools are innovated using advance computer features that are able to compile and present this type of scenario, in real-time.

The enormity of capability in the above scenarios allows enterprises tremendous control over nearly every aspect of their business, with the defining power understand and react to customer demands immediately and effectively. Ultimately, data visualization is important to:

  1. Analyze large volumes of data in better ways
  2. Make faster and more insightful data-driven business decisions
  3. Bring multiple complicated data sets into context

What are the data types in data visualization?

Nearly any form of data can be used in data visualization. The selection rest on the importance of data to its intended use, and the ease of accessing that data for analysis. Within the context of data storage, both unstructured and structured data can be used as a base for data visualizations. What the data represents also varies enormously, including very structured data say from sensors and devices, to data requiring greater qualitative analysis techniques such as social media and pictures. In general terms, data visualizations can be categorized based on their functional use. The table below highlights the various categories of data visualization, their intended use and the range of data charts, maps, plots and diagrams used to represent a range of data types.

Category

Function

Data Visualization

Comparison

Show the differences or similarities between values

Bar Chart, Box & Whisker Plot, Bubble Chart, Bullet Graph, Histogram, Line Graph, Marimekko Chart, Multi-Set Bar Chart, Nightingale Rose Chart, Parallel Coordinates Plot, Population Pyramid, Radar Chart, Radial Bar Chart, Radial Column Chart, Span Chart, Stacked Area Graph, Stacked Bar Graph, Chord Diagram, Choropleth Map, Donut Chart, Dot Matrix Chart Heatmap, Parallel Sets, Pictogram Chart, Pie Chart, Proportional Area Chart, Tally Chart, Treemap, Venn Diagram

Proportions

Use size or area to show differences or similarities between values or for parts to a whole

Bubble Chart, Bubble Map, Circle Packing, Dot Matrix Chart, Nightingale Rose Chart, Proportional Area Chart, Stacked Bar Graph, Word Cloud, Donut Chart Marimekko Chart, Parallel Sets, Pie Chart, Sankey Diagram, Stacked Bar Graph, Treemap

Relationships

Show relationships and connections between the data or show correlations between two or more variables

Heatmap, Marimekko Chart, Parallel Coordinates Plot, Radar Chart, Venn Diagram, Arc Diagram, Brainstorm, Chord Diagram, Connection Map, Network Diagram, Non-Ribbon Chord Diagram, Tree Diagram, Bubble Chart, Heatmap, Scatter Plot

Hierarchy

Show how data or objects are ranked and ordered together in an organization or system

Circle Packing, Sunburst Diagram, Tree Diagram, Treemap

Concepts

Explain and show ideas or concepts

Brainstorm, Flow Chart, Illustration Diagram, Venn Diagram

Location

Show data over geographical regions

Bubble Map, Choropleth Map, Connection Map, Dot Map, Flow Map

Part-To-A-Whole

Show part (or parts) of a variable to its total

Donut Chart, Marimekko Chart, Pie Chart, Stacked Bar Graph, Sunburst Diagram, Treemap

Distribution

Display frequency, how data is spread out over an interval or is grouped

Box & Whisker Plot, Bubble Chart, Density Plot, Dot Matrix Chart, Histogram, Multi-Set Bar Chart, Parallel Sets Pictogram Chart, Stem & Leaf Plot, Tally Chart, Timeline, Violin Plot, Dot Map, Connection Map, Flow Map, Population Pyramid, Word Cloud

How Things Work

Illustrate how an object or system functions

Flow Chart, Illustration Diagram, Sankey Diagram

Process & Methods

Explain processes or methods

Flow Chart, Gantt Chart, Illustration Diagram, Parallel Sets, Sankey Diagram

Movement Or Flow

Show movement data or the flow of data

Connection Map, Flow Map, Parallel Sets, Sankey Diagram

Patterns

Reveal forms or patterns in the data to give it meaning

Arc Diagram, Area Graph, Bar Chart, Box & Whisker Plot, Bubble Chart, Candlestick Chart, Choropleth Map, Connection Map, Density Plot, Dot Map, Dot Matrix Chart, Heatmap, Histogram, Kagi Chart, Line Graph, Multi-Set Bar Chart, Open-High-Low-Close Chart, Parallel Coordinates Plot, Point & Figure Chart, Population Pyramid, Radar Chart, Scatterplot, Spiral Plot, Stacked Area Graph, Stream Graph, Timeline, Violin Plot

Range

Display the variations between upper and lower limits on a scale

Box & Whisker Plot, Bullet Graph, Candlestick Chart, Error Bars, Histogram, Gantt Chart, Kagi Chart, Open-High-Low-Close Chart, Span Chart, Violin Plot 

Data Over Time

Show data over a time period as a way to find trends or changes over time

Area Graph, Bubble Chart, Candlestick Chart, Gantt Chart, Heatmap, Histogram, Line Graph, Nightingale Rose Chart, Open-High-Low-Close Chart, Spiral Plot, Stacked Area Graph, Stream Graph, Calendar, Timeline, Time Table

Analyzing Text

Reveal patterns and insights from a body of text

Word Cloud

Reference

Easily look-up individual data points

Calendar, Gantt Chart, Time Table

Benefits of data visualization?

Data visualization primarily benefits the understanding of huge data sets easily, and can highlight insights that simply are not readily seen in the numbers. Subsequent benefits include:

  • Improve future understanding, planning, and predictions
  • Improve user and audience attention towards data
  • Share informational insight amongst teams and departments who can benefit from insights, but are not experts in specific topics, e.g. visualized website analytics for marketing departments
  • Automated data visualizations reduce data scientist overhead, allowing them to focus on innovation rather than mundane tasks
  • Read actionable insights quicker which lead to performance fixes or improvements

Data visualization tools

Data visualization tools are commonly used today as business intelligence (BI) reporting tools. These tools use automation to collect and process data and then create dashboards that represent company key performance indicators (KPIs) as visuals.

Two qualifications are required to be considered a data visualization tool:

  • Collect and consumer data from multiple sources, file uploads, database queries, and APIs
  • Provide visual representation of key performance indicators

Tools can come as proprietary, with enhanced features, and a support team to help troubleshoot. Popular names in data visualization and big data tools include Microsoft, SAP, IBM, and SAS. For those teams with different needs, open source data visualizations tools exist too, like Redash, D3.js, and Google Charts.

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