Data Visualisation is the representation of data/information in a visual form. This method of data presentation is used to clearly communicate the information in a coherent way that is both enticing and easy to understand.
There are five main categories of data visualisation which we will explore below.
Temporal - that which is linear and one dimensional. Examples include line graphs and timelines.
Hierarchical - usually orders groups within larger groups, typically employed if demonstrating streams of data from one original source, such as tree diagrams.
Network - as its name suggests, this method demonstrates how one data set interlinks with another. Examples include word clouds and matrix charts. This form of data visualisation is excellent to avoid wordy explanations of complex connections.
Multidimensional - typically involves two or more variables and is displayed in 3D. This is particularly useful for breaking down a large amount of information into key points. Popular examples include pie charts, venn diagrams and stacked bar charts. Another plus is this data can be creatively presented with colorful elements.
Geospatial applies to physical locations. Examples vary and include maps, such as locations of market penetration. Density and heat maps can show acquisitions over time/location.
Revolut employs excellent use of data visualisation within their mobile app. Indeed it is one of their major selling points. Within the cryptocurrency section of the app, users can browse the rise and fall in value by viewing histograms.
The budget categories are colourfully presented with progress donut charts as seen below:
According to Crooks (2020), “Data visualization allows you to expose patterns, trends, and correlations that may otherwise go undetected”. This is especially true for Revolut, as seen above, their data visualisation tactic enables users to make more informed spending decisions with easy to read charts on their budget progress.
We’ve compiled a list of best practices to consider when examining how to visualise your data.
Context - we can see from the various data types listed above that different methods have different strengths. Therefore a bar chart or pie chart may be popular choices but they are not necessarily suitable in every situation. Explore the broad range of data visualisation options to find the best fit.
Coherency - Above all the data should be displayed so the relevant stakeholders can understand it. Consider the people who will look at this, will they be completely new to the data presented? If so, consider if the relationship between the data types can be understood and contains the relevant labels and flow to demonstrate this.
Colour - use this to your advantage. Colour can make the data interesting and easy to understand. Stick to conventional tropes such as red for negative and green for positive. It can also enhance a boring black and white chart so it is interesting to interpret. Colours can be contrasted also to highlight differences.
Simplicity - data visualisation is employed to make the information more easy to read. Ensure the visualisation method is suitable and is not overloaded with too many categories. Legends can help with any wordy explanations required on your data chart.
During this case study we have focused on identifying the uses of data within organisations. The presentation of this data back to your stakeholders, whether these are internal or your customer, is just as important. Delivering insights in a coherent way allows for more informed decision making and hence better results.
By Laura Mulqueen
#datavisualisation #data #bigdata
References:
Berinato, S. Harvard Business Review (June 2016) ‘Visualizations that really work’ Available at: https://hbr.org/2016/06/visualizations-that-really-work Accessed: 28 March 2021
Crooks, R. Hubspot (2020) ‘The Power of Data Visualization Plus Examples of Good and Bad Visuals’ Available at: https://blog.hubspot.com/marketing/great-data-visualization-examples Accessed: 28 March 2021
Ridley, A.L. and Birchall, C., 2020. 8. Evaluating data visualization: Broadening the measurements of success. Data Visualization in Society, p.127.
