Want to keep learning?

This content is taken from the University of Reading & Institute for Environmental Analytics 's online course, Big Data and the Environment. Join the course to learn more.

Visualising data

In an era of data deluge, the field of visualisation and visual analytics provides us with technologies and tools that can be used to support data intelligence in a wide range of disciplines.

Visualisation enables data to be conveyed using a variety of visual representations, allows rapid observation of large volumes of data, reduces cognitive load through external memorisation, and enables effective use of human knowledge in interpreting and understanding data, identifying interesting patterns, and generating and evaluating hypotheses.

Visual analytics integrates visualisation and interaction with statistics and algorithms to deliver cost effective data intelligence processes, especially in applications featuring large volumes of data but lacking reliable automated tools.

What are the benefits of using visualisation

  • Visualisation can save you a lot of time, though the data displayed on the screen typically have much lower precision than the data in a database or spreadsheet.

  • Visualisation can provide an effective overview of data, which would otherwise demand precious cognitive effort to build such an overview mentally.

  • Overview visualisation can stimulate humans’ heuristics in selecting appropriate details for further investigation according to contextual information that computers usually do not have. For example, a specific objective, location, occasion, and any other factors obtainable only through dynamic situation awareness.

  • Representing data visually can facilitate the utilisation of the powerful human visual system for recognising and searching for patterns among complex multivariate visual signals.

  • Visualisation images can reduce the need for humans to use precious cognitive effort to remember a lot of data because visual information can typically be retrieved through a quick glance.

  • Visualisation can enable rapid observation of distribution, association, clusters, anomalies, or correlations, bring unexpected patterns into the spotlight, reveal information that would otherwise remain unseen, stimulate new hypotheses, evaluate hypotheses through rapid observation of factual information, and aid humans to gain new knowledge and insight.

  • Visualisation can enable humans to devote more cognitive effort to intelligent reasoning by minimising the load for memorisation, overview construction, and non-visual pattern analysis.

  • Visualisation can provide engaging, expressive, and illustrative means for communicating complex information, and effective pedagogical tools in training and education.

Are there any drawbacks?

  • Like any category of tools, some visualisation techniques or systems are better designed than others. Poor visualisation can introduce biases and hinder task performance.

  • Some powerful visualisation techniques, such as parallel coordinates plots, demand a fair amount of learning effort.

  • Some powerful visualisation techniques, such as glyph-based visualisation, demand a joint effort from expert designers and end users.

In general, visualisation is one of the four fundamental components of data intelligence processes. The other three components are statistics, algorithms (including machine-learned algorithms), and interaction (including human-computer and human-human interaction). Undervaluing any of the four components is scientifically ignorant.

This article was written by Professor Min Chen, Head of the Oxford e-Research Centre team which leads the field in areas, including theory of visualisation, video visualisation, mission critical visualisation systems, and knowledge-assisted visualisation.

Share this article:

This article is from the free online course:

Big Data and the Environment

University of Reading

Get a taste of this course

Find out what this course is like by previewing some of the course steps before you join: