Data visualization definition
Data visualization is the presentation of data in a graphical format. It reduces the “noise” of data by presenting it visually, making it easier for decision makers to see and understand trends, outliers, and patterns in data.
Maps and charts were among the earliest forms of data visualization. One of the most well-known early examples of data visualization was a flow map created by French civil engineer Charles Joseph Minard in 1869 to help understand what Napoleon’s troops suffered in the disastrous Russian campaign of 1812. The map used two dimensions to depict the number of troops, distance, temperature, latitude and longitude, direction of travel, and location relative to specific dates.
Today, data visualization encompasses all manners of presenting data visually, from dashboards to reports, statistical graphs, heat maps, plots, infographics, and more.
The business value of data visualization
Data visualization helps people analyze data quickly and efficiently. By providing easy-to-understand visual representations of data, it helps employees make more informed decisions based on that data. Presenting data in visual form can make it easier to comprehend, enable people to obtain insights more quickly. Visualizations can also make it easier to communicate those insights. Visual representations of data can also make it easier to see how independent variables relate to one another. This can help you see trends, understand the frequency of events, and track connections between operations and performance, for example.
Types of data visualization
There are myriad ways of visualizing data, but data design agency The Datalabs Agency says there are two basic categories of data visualization:
- Exploration: Exploration visualizations help you understand what the data is telling you.
- Explanation: Explanation visualizations tell a story to an audience using data.
It is essential to understand which of those two ends a given visualization is intended to achieve.
Some of the most common specific types of visualizations include:
These are typically geospatial visualizations. For example, cartograms use distortions of maps to convey information such as population or travel time. Choropleths use shades or patterns on a map to represent a statistical variable, such as population density by state.
These are one-dimensional linear visualizations that have a start and finish time. Examples include a time series, which presents data like website visits by day or month, and Gantt charts, which illustrate project schedules.
These common visualizations present data with two or more dimensions. Examples include pie charts, histograms, and scatter plots.
These visualizations show how groups relate to each other. Tree diagrams are an example of a hierarchical visualization that shows how larger groups encompass sets of smaller groups.
Network visualizations show how data sets are related to each other in a network. An example is a node-link diagram, also known as a network graph, which uses nodes and link lines to show how things are interconnected.
Data visualization examples
Tableau has collected what it considers to be 10 of the best data visualization examples. Number one on Tableau’s list is Minard’s map of Napoleon’s march to Moscow, mentioned above. Other prominent examples include:
- A dot map created by English physician John Snow in 1854 to understand the cholera outbreak in London that year. The map used bar graphs on city blocks to indicate cholera deaths at each household in a London neighborhood. The map showed that the worst-affected households were all drawing water from the same well, which eventually led to the insight that wells contaminated by sewage had caused the outbreak.
- An animated age and gender demographic breakdown pyramid created by Pew Research Center as part of its The Next America project, published in 2014. The project is filled with innovative data visualizations. This one shows how population demographics have shifted since the 1950s, with a pyramid of many young people at the bottom and very few older people at the top in the 1950s to a rectangular shape in 2060.
- A collection of four visualizations by Hanah Anderson and Matt Daniels of The Pudding that illustrate gender disparity in pop culture by breaking down the scripts of 2,000 movies and tallying spoken lines of dialogue for male and female characters. The visualizations include a breakdown of Disney movies, the overview of 2,000 scripts, a gradient bar with which users can search for specific movies, and a representation of age biases shown toward male and female roles.
Data visualization tools
There are many applications, tools, and scripts available for data visualization. Some of the most popular include the following:
Domo is a cloud software company that specializes in business intelligence tools and data visualization. It focuses on business-user deployed dashboards and ease of use.
Dundas BI is a BI platform for visualizing data, building and sharing dashboards and reports, and embedding analytics.
Infogram is a drag-and-drop visualization tool for creating visualizations for marketing reports, infographics, social media posts, dashboards, and more.
Microsoft Power BI
Microsoft Power BI is a business intelligence platform integrated with Microsoft Office. It has an easy-to-use interface for making dashboards and reports.
Qlik’s Qlik Sense features an “associative” data engine for investigating data and AI-powered recommendations for visualizations. It is continuing to build out its open architecture and multicloud capabilities.
Sisense is an end-to-end analytics platform best known for embedded analytics. Many customers use it in an OEM form.
One of the most popular data visualization platforms on the market, Tableau is a platform that supports accessing, preparing, analyzing, and presenting data.
Data visualization roles and salaries
Here are some of the most popular job titles related to data analytics and the average salary for each position, according to data from PayScale.
- Data analyst: $63K
- Data scientist: $92K
- Data visualization specialist: $75K
- Senior data analyst: $85K
- Senior data scientist: $121K
- BI analyst: $62K
Next read this: