Data visualisation is the most efficient way to view certain data clearly, using graphic representation. It is well suited to the characteristics of the human brain, which is able to interpret information presented in a visual context immediately.
The use of graphics to display statistics obtained via market research or analysis, allows for quite accurate conclusions to be made and forecasts for the future to be calculated. This is particularly true in the case of projects involving the gathering of large amounts of data (big data), which, without graphic representation, would otherwise be almost impossible to decipher.
The advantages of making data accessible
Visual communication has always been appreciated because the interpretation of figures in a graphic context is much easier than having to read a series of numbers and then correctly place them into context relative to each other. However, in order to take full advantage of this instrument, it is essential to be aware of its most important features, as follows:
- Absorbing information, gaining insight and taking decisions;
- Identifying measures to be adopted for optimal organisation;
- Building public interest following presentation of the information;
- Sharing data quickly with those involved;
- Reducing the need for data scientists to interpret complex data;
- Limiting the chance of errors being made while rushing to achieve targets quickly.
In a broader context of the type found in a company, data visualisation helps businesses establish which factors lead their clients to behave in certain ways. Having this information to hand enables companies to understand when and where to market certain products and how to forecast sales volumes etc.
Despite these advantages, it should be noted that obtaining tangible benefits through big data visualisation implies significant investment of resources. It is therefore crucial to employ a visualisation specialist, who will be able to extrapolate the most important data for the company and choose the most suitable presentation style.
IT department involvement is also required, as the acquisition of the values for analysis requires adequate hardware components, reliable storage systems and (in most cases) passage to cloud.
The evolution of data visualisation and applications
Early examples of data visualisation involved the display of data as bar or pie charts, often used in electronic spreadsheets. These methods are still in use, as they are simple and easy to create but, alas, rather limited.
The increase in the range of values to be entered and the need to filter those of greatest impact, have led to the need to introduce new, more elaborate methods of visualisation. Some interesting examples are: infographics, bubbles, bullet charts, thermal-imaging maps and time series graphics.
Today’s data presentation systems feature animations, showing their evolution in real time. They can be used for example, to monitor world population growth (starting from a certain year until the present), in order to see which nations have influenced this figure the most.
Data visualisation plays a decisive role in a wide range of sectors. They include marketing (especially tracking web traffic), politics (to illustrate which parties receive the most votes in elections) and healthcare (with choropleth maps to keep a record of relevant healthcare information) are also used in fields such as scientific research, finance etc.
Translated by Joanne Beckwith