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Data visualisation: truth or interpretation?


Through our Digging the Data Visualisation Challenge we aim to unlock the potential of 360Giving data to help answer key questions facing grantmakers.

Data, and open data in particular, is supposed to represent the truth and objectivity, as well as introduce the facts in an unbiased way. But what does ‘truth’ really mean in the age of the internet where information is everywhere? How can a dataset be objective if someone created it with certain ideas in mind, having chosen a specific set of variables? Methodological choices in data collection and analysis are often crucial to determining the outcome and its important to acknowledge that objectivity in data is not as straightforward as it initially seems. Labelling anything as ‘unbiased’, ‘objective’ or ‘true’ is risky. The limitations of the data selection and interpretation process can lead to different results depending on the person who performs it.

The issues of ‘truth’ were being discussed well before the emergence of the knowledge economy. According to Nietzsche, the 19th century German philosopher, there is no such thing as truth, only perspective and interpretation, motivated by a person’s interests or ‘will to power’. Nietzsche argued that even the statement ‘everything is subjective’ is in itself an interpretation – the ‘subject’ is invented, which provokes a different set of questions such as whether it is necessary to identify the ‘interpreter’ behind the interpretation. Different stakeholders might have different visions of open data and its objectives. This is why we are asking people to share their interpretation of 360Giving data through our Visualisation Challenge.

We are inviting people to tailor and shape the data according to their creative vision. For instance, they can categorise grants using their own criteria, as long as a logical explanation of their reasoning is provided. We hope that these different interpretations from different people will help with overcoming any hidden biases in the data and encourage different uses of 360 data in the future.

We would like our Visualisation Challenge to promote responsible use of data. We advocate for data protection, privacy and conscientious data sharing. In this competition, it is particularly important to reflect on how to support and create visualisations that are well-researched, credible and contain non-sensitive data.

Data visualisation is a great medium. The use of colours, visuals and graphs can convey complex messages. One quick look at a good visualisation enables us to grasp the most important aspects of the data fast and identify patterns that might have been overlooked otherwise. Interactive visualisations take things a step further, allowing the viewer to explore the data in greater detail and shape the results according to different variables. Thoughtful and well-designed data visualisations are modern-day works of art. Over the past few months we have been posting data visualisations we like on Twitter – to inspire people to build their own. Here are three great examples:


  1. Dear Data is a year-long, analog data drawing project by Giorgia Lupi and Stefanie Posavec, two award-winning information designers. By collecting and hand drawing their personal data and sending it to each other in the form of postcards, they became friends. The project has been acquired by MoMa.
  2. “The Stories Behind a Line” by Federica Fragapane and Alex Piacentini is a visual narrative of six asylum seekers’ routes from their hometowns to Italy. Their stories are told through the data and numbers that shaped their personal travel routes. This is a great example of visualising sensitive data in a responsible way.
  3. How the Recession Reshaped the Economy in 255 Charts by Jeremy Ashkenas and Alicia Parlapiano illustrates how in five years since the end of the Great Recession, the US economy has finally regained the nine million jobs it lost. Each line shows how the number of jobs has changed for a particular industry over the past 10 years. This visualisation is a great example of how to make simple line charts more engaging.


How do you think we can make better data visualisations? Share your favourite visualisations with us. Tweet at @360Giving using hashtags #DiggingTheData #dataviz