Data Visualisation And Why It Matters
Data Visualisation And Why It Matters
Data visualisation is one of the final steps in data analysis, but often one of the most important. Put simply, data visualisation is about the creative or logical choice as to how to display data graphically. The choice depends on the data presenter’s goals, and most often, that goal is to communicate the information as effectively as possible, and help an audience to understand and remember. This can require certain variables, colours, types of graph, and sizes; all to get the best and clearest message out of the data.
The Subtle Power of Effective Data Visualisation
There is a significant quantity of academic study on the importance of visuals when it comes to presenting and recalling information. The University of Minnesota found that the brain processes visual information 60,000 times faster than text, and a Stanford/Wharton School study in 2019 found that written information was 70% more memorable when matched with visuals or actions. These findings highlight the strong bias that our brains have for visual understanding over any other form, and the heavy load that visuals carry when it comes to the recall of ideas, brands, and products.
Modern Trends in Data Visualisation
Data visualisation has been found nearly everywhere for most of the last century, from boardrooms to TV studios to social media. However, the modern boom of technology has gradually refined it, as the use of data becomes more universal, and data sources become more attainable.
One central trend in commercial data visualisation, i.e paid dashboards and visualisations for SaaS companies, has been to keep the user experience “clean” with minimal clutter and as much space as possible while including all useful data. Despite the amount of available data skyrocketing in recent years, businesses that present data have been careful to ensure that data is accurately and effectively presented while remaining navigable and understandable for all. In this context, the fields of data visualisation and UX overlap significantly.
The modern reality of big data is that users need to be able to dig deeper themselves, and find insights from the masses of information available. Automation has brought things forward by leaps and bounds in this space. IBM’s Watson allows users to ask IBM’s platform questions, such as “what drives overall satisfaction?”, recognises the question via natural language processing, finds the answer in the data, and presents it in the most clear and appropriate visualisation it can find. This level of automated data visualisation will only improve over time, to give unprecedented access to analysis of large data sets.
Exploratory vs Explanatory Data
One key distinction within the function of newer forms of data visualisation is exploratory data vs explanatory data.
Exploratory data presents a wide picture of a certain context, often an industry or region, and encourages the viewer to explore the data from a number of viewpoints, and extract a range of insights. Meanwhile, explanatory data presents a narrower view, often with the aim of distilling the message of the data to one or several key insights that the viewer should come away with. Explanatory data is certainly the most common, such as a simple two-column graph to compare two companies’ prices, as it serves up data in a quick and efficient fashion. However, as more interactive tools become available online, exploratory data is being seen more and more, often in the form of interactive maps and dashboards such as this Global Commodities interactive display of commodities markets by country. Exploratory data is like a virtual playground that lets the user change variables, customise their view, and look at the data from as many angles as possible. What started with Google Maps has pervaded many other industries, as data presenters chase user interaction and attention.
Keeping it Simple
In the world of big data, there can be so many numbers, and so many angles to choose from that it becomes very easy to overcomplicate things. While there is a whole subsection of the data visualisation world devoted to the artistic expression of data, most simply use data visualisation as a way to get their message across. Simple rules like the colour green representing positive movements, and red representing negative, can go further than any time-consuming innovation in design.
Furthermore, as data gets “bigger”, attention spans get smaller, and data presenters need to make certain concessions on social media and similar platforms. Take for example, National Geographic’s award-winning “Migration Waves” charts of the shifting patterns of global emigration and migration. Their full charting (see below) is an evocative and educational resource, with clear comparisons of multiple countries’ migration flows. The presentation is well-suited for their magazine and online articles.
However, for posts on social media, they settle for a quick distillation of the data (see below) that jettisons the text and labels in favour of pure visuals that will stand out on a scrolling screen. In the digital age, data visualisation has had to adapt to both the audience it gets presented to, and the medium it gets displayed on.
