Assessing data visualisation, presenting data for decision-making

Data visualisation 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 visualisation. One of the most well-known early examples of data visualisation 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 visualisation encompasses all manners of presenting data visually, from dashboards to reports, statistical graphs, heat maps, plots, infographics, and more.

The business value of data visualisation

Data visualisation helps people analyse 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.

Visualisations 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 visualisation

There are myriad ways of visualising data, but data design agency The Datalabs Agency says there are two basic categories of data visualisation:

• Exploration: Exploration visualisations help you understand what the data is telling you.
• Explanation: Explanation visualisations tell a story to an audience using data.

It is essential to understand which of those two ends a given visualisation is intended to achieve.

Some of the most common specific types of visualisations include:

2D area

These are typically geospatial visualisations. 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.

Temporal

These are one-dimensional linear visualisations 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.

Multi-dimensional

These common visualisations present data with two or more dimensions. Examples include pie charts, histograms, and scatter plots.

Hierarchical

These visualisations show how groups relate to each other. Tree diagrams are an example of a hierarchical visualisation that shows how larger groups encompass sets of smaller groups.

Network

Network visualisations 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 visualisation examples

Tableau has collected what it considers to be 10 of the best data visualisation 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 neighbourhood. 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 visualisations. 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 visualisations 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 visualisations 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 visualisation tools

There are many applications, tools, and scripts available for data visualisation. Some of the most popular include the following:

Domo

Domo is a cloud software company that specialises in business intelligence tools and data visualisation. It focuses on business-user deployed dashboards and ease of use.

Dundas BI

Dundas BI is a BI platform for visualising data, building and sharing dashboards and reports, and embedding analytics.

Infogram

Infogram is a drag-and-drop visualisation tool for creating visualisations 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

Qlik’s Qlik Sense features an “associative” data engine for investigating data and AI-powered recommendations for visualisations. It is continuing to build out its open architecture and multi-cloud capabilities.

Sisense

Sisense is an end-to-end analytics platform best known for embedded analytics. Many customers use it in an OEM form.

Tableau

One of the most popular data visualisation platforms on the market, Tableau is a platform that supports accessing, preparing, analysing, and presenting data.