THE SCIENCE BEHIND DATA VISUALISATION
Visual perception plays a big role in helping us to understand the world around us; it allows us to quickly process information that may otherwise may be imperceptible. This phenomenon is called visual information processing or visual perception1. Although everyone is familiar with the concept that a picture ‘speaks a thousand words’, not much thought goes into how or why we process visual information much more efficiently; as a result, we tend to miss out on opportunities to understand how we can use graphics to convey complex information as efficiently and effectively as possible, enabling users to focus on actionable insight, rather than understanding information.
A great example of this is how people report on their data; there is a tendency within any organisation to prioritise the tables and charts that are familiar to users, rather than finding graphics that highlight actions. It is much easier to use reporting or technology to replicate the status quo, for example, than it is to use it as a means of highlighting what, how and when key stakeholders need their data.
This means there is natural waste in any organisation’s reporting, with inherent challenges in showcasing the data that provides insight into the health of the company and its projects. This is an opportunity that can be used to form a forward looking view into how improvements in certain areas or functions could lead to greater financial rewards or efficiencies that reduce costs. Looking at huge tables, and cross-referencing with other data sources is a major challenge – and this is where the visual elements come in for us to better depict the data and shine a lens on the right opportunities in the right amount of detail.
We know from a previous blog that the brain processes visual information up to ‘60,000 times faster’2 than it takes to decode text. Recognising this, we can focus on optimising the structure and layout of the visualisations that are required for our teams so that they can focus on deriving actionable insight, rather than processing the data.
The key driver here is to provide targeted visualisations to the right audience, so that they can look for analytical patterns within the data that is presented to them. Successful data visualisation projects need to focus on the needs of the end user, and use this to customise the data visualisations that directly meet their requirements. The next step is to build templates that correctly lay out the information that the end user needs to see, in a format that they recognise and can find useful. Automating the process so that the data within the tool is always up to date is the last step, and ensures that users pick up the tool and continue to use it in the future.