CMAF FFT: More effective data visualisations for improving communication with decision makers [Video]

This is the first webinar in the Season 5 of “Friday Forecasting Talks”, hosted by the Centre for Marketing Analytics and Forecasting of Lancaster University, UK.
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*Content of this video*
00:00 – Introduction
01:58 – The start of the talk
03:57 – Why develop a data visualisation guide?
06:16 – What’s the aim of the guide?
08:30 – The role of visualisation
11:50 – Bad reputation of pie charts
13:34 – Issues with 3d charts
15:12 – Designing better visualisations
26:41 – The role of colour in data visualisation
31:52 – Key points
32:49 – Examples of good charts
37:21 – Q&A

The abstract: Data visualisation can be a very efficient method of identifying patterns in data, and communicating model outputs and forecasts to broad audiences. Good data visualisation requires appreciation and careful consideration of the technical aspects of data presentation. But it also involves a creative element. Authorial choices are made about the “story” we want to tell, and design decisions are driven by the need to convey that story most effectively to our audience. Software systems use default settings for most graphical elements. However, each visualisation has its own story to tell, and so we must actively consider and choose settings for the visualisation under construction. In July 2023, the Royal Statistical Society (RSS) published a new guide, “Best Practices for Data Visualisation”, containing insights, advice, and examples (with code) to make data outputs more readable, accessible, and impactful. The guide was initially written primarily for contributors to RSS publications but the information and advice within is also of broad relevance and use for any data visualisation task. The guide is open source, and has received contributions from the wider data visualisation and statistics communities. In this talk, Nicola will showcase why you should visualise data and how the RSS guide was developed, present some guidelines for making better charts to improve decision-making, before we discuss examples of good and bad charts.

Speaker: Nicola Rennie

Bio: Nicola Rennie is a Lecturer in Health Data Science based within the Centre for Health Informatics, Computing, and Statistics at Lancaster Medical School. Her research interests lie in statistical modelling of healthcare data, and improving the communication of complex quantitative information in an accessible way. Nicola also has experience in data science consultancy, and collaborates closely with external research partners. Nicola has roles in several organisations including as a committee member of the R-Ladies Global Team, secretary of the RSS Teaching Statistics Section Group, and as co-lead of the Statistics Software Special Interest Group at the RoSE (Researchers of Statistics Education) Network. She also co-authored the Royal Statistical Society’s “Best Practices for Data Visualisation” guidance and sits on the Editorial Board of Significance magazine.