Future Session #5 on data literacy: how to lie with data

Data visualizations have a great potential for influencing a lot of people, but it can be very misleading too. How do you deal with that? 

agenda July 3, 2018

Josje Spierings (Leiden Centre for Innovation) gave insight into how data visualization work and can be misused. In an interactive workshop, participants learned how to critically look at the visualizations and how you can mislead the viewer using visual data. 

Future Sessions are monthly series of inspirational working sessions that The Spindle organizes in collaboration with HumanityX (Centre for Innovation of Leiden University). In these future sessions, participants explore new techniques and methods, to see how it can be of value for NGOs in international development. During this session on the 26th of June, at the Humanity Hub in The Hague, Josje Spierings gave insight in data literacy and showed the participants how misleading data visualization can be when used in a manipulative way.

Data is a very powerful tool to tell a story about reality to an audience, which is exemplified by Hans Rosling in this short clip about Syrian refugees. The message he wants to express is very clear, thanks to the data visualization he used: there are not as many refugees in Europe than we may think. Nowadays we gather a great amount of data in the development sector to test whether the program had the intended result. This is one of the reasons why it is important that you can use data in a correct way and are also able to inform others about it.


Data literacy and data visualizations

The meaning of data literacy itself is really broad: it’s about collecting data, making sense of the data and data visualization. This future session focussed on the visualization part. Data visualization is getting more important because it’s more intuitive than text alone and in light of the great amount of data gathering, it is a great tool to make sense of all that data in one sight.  But there are several questions one can ask when it comes to visualizations. Who collected the data? When did this data collection take place? How representative is the data and what do the colours in the visualization stand for? In this presentation, you can find several examples of misleading visualizations.

During the interactive part of the workshop, the participants learned in real life how to critically look at data visualizations. They experienced how you can make your own story about reality by selectively using different data visualizations. The afternoon resulted in interesting pitches where imaginary program managers were misled with data visualizations in order to get more funding for programs.

The most important insights from this workshop were:

  • If you change the control group with which is compared and thus the mean,  a story can become totally different. If you use, for example, statistics from the world (something way larger) and you compare that with a very small situation like a village, is different than when you compare that village with the region the village is located in.
  • Sometimes great datasets like the one from the UN changes it’s calculation, making trends completely different. This has a great impact, especially because most of the times the data from the year before is deleted and not shown next to the new algorithm. So many choices are made and you need to be open about it, especially when you make use of trends.


How can you prevent to get misled by data visualizations?

  • Be critical: check the axes and check if percentages add up (for example a pie chart that is more than 100%).
  • Think about choices and calculations that have been made (for example: are groups split up into men and women and why?).
  • Make use of metadata* as they can help to verify if the visualization is representative.
  • Work with the data yourself and check what kind of visualizations you can make.


Towards a responsible approach to data
At the end of the meeting, students from Leiden University that participated in the IHL clinic from KGF and OAM consult launched their research on data protection guidelines and principles of INGO’s and UN-organizations: ‘Responsible Approach to Data’. The report will be published soon.


Next session: the Future with Gamification and (serious) games!
During our next Future Session you will get the chance to learn more about gamification and (serious) games and the applications of games in the development sector. How to start using these opportunities for your organization? Sign up here for the fifth session about the Future with Gamification and (serious) games.


*Metadata is structured data: ‘metadata is data about data’. It’s like a catalogue card: it gives you information about the data, it tells about the elements in it, where you can find it and if it’s relevant for you

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