Telling Your Big Data Stories with Data Visualisation
From the number of WhatsApp messages we send every hour to our many Google searches each day, many of our seemingly mundane activities online are quantified into massive datasets. Big Data though is voluminous and goes beyond digital footprints, so how do we begin to identify and understand trends and patterns without becoming overwhelmed?
To enhance our work around Big Data for development, data visualisation has become a valuable technique for telling our data stories across a broad audience. Just last month, our data visualisation whiz, Lody Andrian, had the pleasure of facilitating a visual information design workshop at the Urban Social Forum in Bandung. In this blog, we unpack some of the snazzy examples he designed and explain the concepts behind them.
At the Lab, we work with complex datasets — many of which at first glance might seem abstract. It is thus necessary for us to make sense of it all, by translating everything into digestible chunks that can be easily understood. Going from our interactions with everyday visuals like traffic signals to pedestrian crossings, research has shown that the human brain is wired to process a variety of visual information efficiently.
In heavy-reporting sectors like government services and academia these days, visual representations have outpaced the old-school simplicity of colorless bar graphs and two-dimensional pie charts. Instead, they are now designed more interactively with an intended purpose, some of which intend to enrich textual information, understand dense information quickly, showcase a complex methodology, influence decision making, or simply to add a ‘cool factor’.
The one thing all these visualisations have in common though is: they all tell a story.
The importance of storytelling
With data visualisation, storytelling is all about converting data from a passive to an active form. In other words, adding a sort of narrative and voice to data in order to communicate analyses, results and insights that an audience can relate to. How might one achieve this? One way is through framing, a technique in which data is presented according to the intended audience’s perspective or frame of mind.
Take a few seconds to examine the two visuals below:
The image to the left (with the VOC logo centered) attempts to depict the value of the Dutch East India Company in the year 1637. While positioned against an ancient-looking background, the message about its worth may be a bit abstract for some millenials who either cannot fathom the idea of such worth or have never heard of the company. These millennials, however, might be able to better understand the magnitude of the company’s worth if the story (using visual representation) is framed to include the names of a few modern companies that they are familiar with. The differences in box size also allow a rough comparison of each’s worth.
Your data stories though do not need to be limited to framing, other techniques involve exploring the relationships within a dataset and then accentuating certain patterns and trends (see the other examples below). This is a necessary step to undertake before attempting to visualise any dataset, big or small.
Modes of visualisation
Once the ‘angle’ of the story is determined from the dataset, the next step is to select the most effective mode to visualise it. Depending on the particular dataset and storyline, data visualisation can take on different modes, i.e. exploration, communication or observation.
First, the exploration mode assists the audience in finding the story the data is trying to tell. Check out the example below that shows the fluctuation of housing prices across Indonesia.
Covering the span of a few months, the dataset is complex and may not make much sense if presented solely in numbers to the average homebuyer or investor. By visualising the dataset, the intended user can easily compare housing prices in one region with another and explore whether or not a certain region has stable enough prices.
Second, the communication mode entails using data to present a message or idea. This form is often used for infographics in journalism, as seen in the example below.
This infographic visualises different datasets under a common topic, with a view to informing an audience on a particular issue. It showcases data related to special needs schools in Jakarta, more specifically the gap between the number of special needs students and the number of facilities available for them. Using a combination of graphs, charts, statistics and graphics, this infographic aims to communicate the issue — substantiated with multiple visual aids — to relevant stakeholders.
Third, the observation mode as the name suggests typically involves active monitoring of data in real time. Unlike the exploration and communication modes, it does not offer any direct interpretation or conclusion; it presents the data as is to help the audience keep track of updates as they occur. Pulse Lab Jakarta’s CycloMon platform, for instance, provides real-time data visualisations of cyclones around the world to help disaster management units with their preparedness and response efforts.
Bringing it all together
Combining aspects of graphic design, Pulse Lab Jakarta wielded its data vis toolkit at the forum to illustrate how visual representations can complement many ongoing efforts to solve urban issues. Towards the end of the session, participants were divided into groups and given different datasets, including urban datasets on crime, special needs schools and others, to experiment with — their task was to employ data visualisation to help make sense of each dataset. This meant revisiting some of the concepts that they were introduced to in order to conceptualise their data stories (i.e. identify the target audience and the purpose of the visualisation) and to determine the visual language (should it be represented by a pie chart, table, graph, infographic or something completely new?).
Unintended to convert the participants into data vis experts overnight, the overall aim of the workshop was to equip them with useful approaches that can be used to visualise complex information in today’s data rich world.
Pulse Lab Jakarta is grateful for the generous support from the Government of Australia.