What is an interactive display? Here is an example

The visual representation of data helps our brain to digest large amounts of information quickly and easily. Interactive visualizations make it easier for non-experts to analyze complex situations represented as data.

As we introduced in our last post on this topic, graphical data visualization is a whole discipline  within the universe of data science. In this new post we want to put the focus on interactive data visualizations. Dynamic visualizations allow the user to interact with data and transform it into graphs, tables and indicators that have the ability to display different information according to the filters set by the user. To a certain extent, interactive visualizations are an evolution of classic visualizations, allowing us to condense much more information in a space similar to the usual reports and presentations. 

The evolution of digital technologies has shifted the focus of visual data analytics to the web and mobile environments. The tools and libraries that allow the generation and conversion of classic or static visualizations into dynamic or interactive ones are countless. However, despite the new formats of representation and generation of visualizations, sometimes there is a risk of forgetting the good practices of design and composition, which must always be present. The ease to condense large amounts of information into interactive visualisations can means that, on many occasions, users try to include a lot of information in a single graph and make even the simplest of reports unreadable. But, let's go back to the positive side of interactive visualizations and analyse some of their most significant advantages.

Benefits of interactive displays

The benefits of interactive data displays are several:

• Web and mobile technologies mainly. Interactive visualizations are designed to be consumed from modern software applications, many of them 100% web and mobile oriented. This makes them easy to read from any device.
• More information in the same space. The interactive displays show different information depending on the filters applied by the user. Thus, if we want to show the monthly evolution of the sales of a company according to the geography, in a classic visualization, we would use a bar chart (months in the horizontal axis and sales in the vertical axis) for each geography. On the contrary, in an interactive visualization, we use a single bar chart with a filter next to it, where we select the geography we want to visualize at each moment.
• Customizations. With interactive visualizations, the same report or dashboard can be customized for each user or groups of users. In this way, using filters as a menu, we can select some data or others, depending on the type and level of the user-consumer.
• Self-service. There are very simple interactive visualization technologies, which allow users to configure their own graphics and panels on demand by simply having the source data accessible. In this way, a non-expert user in visualization, can configure his own report with only dragging and dropping the fields he wants to represent.

Practical example

To illustrate with a practical example the above reasoning we will select a data se available in  datos.gob.es data catalogue. In particular, we have chosen the air quality data of the Madrid City Council for the year 2020. This dataset contains the measurements (hourly granularity) of pollutants collected by the air quality network of the City of Madrid. In this dataset, we have the hourly time series for each pollutant in each measurement station of the Madrid City Council, from January to May 2020. For the interpretation of the dataset, it is also necessary to obtain the interpretation file in pdf format. Both files can be downloaded from the following website (It is also available through datos.gob.es).

Interactive data visualization tools

Thanks to the use of modern data visualization tools (in this case Microsoft Power BI, a free and easily accessible tool) we have been able to download the air quality data for 2020 (approximately half a million records) in just 30 minutes and create an interactive report. In this report, the end user can choose the measuring station, either by using the filter on the left or by selecting the station on the map below. In addition, the user can choose the pollutant he/she is interested in and a range of dates. In this static capture of the report, we have represented all the stations and all the pollutants. The objective is to see the significant reduction of pollution in all pollutants (except ozone due to the suppression of nitrogen oxides) due to the situation of sudden confinement caused by the Covid-19 pandemic since mid-March. To carry out this exercise we could have used other tools such as MS Excel, Qlik, Tableau or interactive visualization packages on programming environments such as R or Python. These tools are perfect for communicating data without the need for programming or coding skills.

In conclusion, the discipline of data visualization (Visual Analytics) is a huge field that is becoming very relevant today thanks to the proliferation of web and mobile interfaces wherever we look. Interactive visualizations empower the end user and democratize access to data analysis with codeless tools, improving transparency and rigor in communication in any aspect of life and society, such as science, politics and education.

Content elaborated by Alejandro Alija, expert in Digital Transformation and Innovation.

Contents and points of view expressed in this publication are the exclusive responsibility of its author.