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Data Visualisation



Visualisations are visual representations of data or information. Typical examples include:

  • Graphs – for example, bar charts, pie charts, scatter plots
  • Diagrams – for example, systems diagrams, flow-charts, venn diagrams
  • Maps – see Geographic Information Systems

Carefully designed visualisations can efficiently communicate information and help facilitate interaction between various actors in the IA process.

Visualisations can:

  • be an efficient way of communicating information that would otherwise be very lengthy or difficult to communicate in words
  • help to organise information so that it is easier to explore, search and analyse
  • help to identify patterns and trends in the information


They can be particularly useful in communicating or exploring multidimensional data - for example, how variables of interest might change over time, across different geographic areas or under different policy scenarios. 

However, poorly designed visualisations can be difficult to understand or interpret, and may lead to confusion or misinterpretation. It is therefore important to carefully consider the design of visualisations. 




Due to the complex array of variables, contexts and communication goals that might be considered by an IA, there are no hard-and-fast rules about what type of visualisation should be used and when. The choice of visualisation should be driven by the intended purpose, or task at hand (Hegarty, 2011).


Tips for effective visualisations

(adapted from Hegarty, 2011; Kosslyn, 2006)


Task and user considerations:

  • Give the visualisation a title that will help to guide the user – it should indicate the question(s) that can be most easily answered by the visualisation.
  • Consider the task the user is expected to use the visualisation for – if information is intended to be compared or contrasted, this should be facilitated by placing the relevant data close together, or by using grouping indicators, such as colour coding.
  • Only show information in the visualisation that is needed by the user for the intended task – too little information makes interpretation of the visualisation difficult, too much information creates clutter and can distract the user.
  • Consider the abilities and prior knowledge of the user – if it takes too much effort to interpret a visualisation, it will not be interpreted correctly. Use symbols that will be easily understood, and explain (or avoid) jargon in the visualisation that the user may not be familiar with.
  • Use mappings and layouts that are intuitive to users – for example, up usually means good, down usually means bad, time scales normally run horizontally (from left to right in Western cultures).


Design and layout considerations:

  • Make the most important information in the visualisation more visually salient than less important information.
  • Make sure the visualisation can be perceived by the user – think about how the user will receive the information in its final form (e.g. on a computer screen, printed in a document, etc.) so that all of the content in the visualisation is legible.
  • Ensure consistency within and between visualisations – changes are usually interpreted as having meaning or relevance – if a change in design is arbitrary, then it might cause confusion.
  • Avoid using 3D effects in graphs and charts – 2D figures are generally more easily interpreted, especially when communicating precise information.
  • Use graphs when communicating relationships between variables, but use a table if you need to communicate precise values.




Due to its widespread availability, Microsoft Excel is typically the most accessible software for most people to create visualisations. For many basic graphs and charts, Excel is perfectly adequate. However, care should be taken when adjusting Excel’s design options and settings, in line with the tips listed above.

For an overview of other visualisation software and tools see the useful links below.




Visualisation Software and Tools:

Visualisation Options: 




Hegarty, M. 2011. The cognitive science of visual-spatial displays: implications for design. Topics in Cognitive Science; 3: 446–74.

Kohlhammer, J., Nazemi, K., Ruppert, T., et al. 2012. Toward visualization in policy modelling. IEEE Computer Graphics and Applications; 32 (5): 84-9.

Kirk, A. 2012. Data Visualization: a successful design process. Packt Publishing, Birmingham, UK.

Kosslyn, S.M. 2006. Graph Design for the Eye and Mind. Oxford University Press, UK.

McInerny, G.J., Chen, M., Freeman, R., et al. 2014. Information visualisation for science and policy: engaging users and avoiding bias. Trends in Ecology and Evolution; 29(3): 148-57.

Tufte, E.R. 1997. Visual Explanations. Graphics Press, Connecticut, USA.

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