Using (big) data: Making data user-friendly
Making sense of (big) data is our fourth and last stop on the Road to Bern. This briefing note provides background points for our discussion on making use of big data.
Statistics show that 90% of all data was created in the last two years. This should come as no surprise given that today, on average, 2.5 quintillion bytes of data are produced on a daily basis. This stands in sharp contrast to the 5 exabytes of data produced by human civilization up until 2003. Predictions indicate that by 2025, some 463 exabytes of data will be created every day which is equivalent to over 212 million DVDs of content per day.
While data is available more than ever before, estimates suggest that up to 90% of it is unusable due to the absence of relevant context, despite its potential to improve decision-making, policy development or monitoring implementation of SDGs.
What can be done to change this negative trend and make available data more contextual?
Part of the answer to this question lies in the power of data visualisation.
Data visualisation comes to aid
Relying on the brain’s most dominant capacity (i.e. visual processing), on average, the human brain needs some 13 milliseconds to process an image. As such, visualisations are more successful in communicating information than any other form of data representation.
Their basic purpose is to make the rather abstract results of big data analysis more tangible and understandable. Moreover, visualisations help users identify or interpret patterns, have a better understanding of the available or unavailable data, and potentially gain new insights.
However, the benefits of data visualisation come with a caveat. Consumers tend to resort to visualisations as the most convenient data representation method, without first thinking about their purpose and goals – the so-called ‘click and viz’ impulse.
Uses of data visualisation
Data visualisation has become a cornerstone of discussions in the data-driven era, thus highlighting the need to present data in a manner which can aid understanding and facilitate decision-making.
To that end, the Publications Office of the European Union has launched a conference in 2019 entitled EU DataViz that brought together experts and practitioners to address the needs of the community engaged in data visualisation for the public sector in Europe.
On the country level, for example, the State Department has made data visualization part of its diplomacy effort. According to the Open Government Plan, visuals help users explore and track U.S. government foreign assistance investments.
Many international organisations have resorted to data visualisation to communicate priority areas and performance of their member states in areas within the scope of their mandate. To illustrate, the ITUICT-Eye, that compiles the International Telecommunications Union (ITU) data on information technologies, enables users to access and compare the data whilst providing detailed country profiles that include information in question.
When it comes to measuring progress on the attainment of 2030 Agenda for Sustainable Development, the World Health Organization (WHO), for its part, has developed the World Health Statistics data visualizations dashboard, that provides data on health-related targets. Similarly, the International Organisation for Migration has created a migration data portal that allows for monitoring of migration flow trends, migration policy and public opinion on migration.
Grasping the underlying challenges
While communicating data through visuals offers vast opportunities, visualisation tools are not always as straightforward.
Oftentimes, data producers struggle with the need to make their illustrations stand out in a myriad of images emerging on a daily basis. In order to communicate the purpose of data, adequate technical skills to distil the right data before making them visually appealing is key. Another related challenge pertains to translating hundreds of spreadsheet cells into a single mapping that fails to present the entire picture.
In some cases, available data may not be representative of the purpose of the study. In other cases, data may be deliberately manipulated to communicate desired narrative. Deliberately or not, visualisations based on such data frequently lead users to making erroneous conclusions.
Lastly, the lack of digital competences i.e., ability to operate complex data visualisation tools further complicates the task.
Prepared by the GIP team