Data Visualisations: Knowledge or not?


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As a visual learner, I can see the immediate appeal of data visualisations. Large quantities of information can be represented in succinct forms, highlighted by colour and emphasised through shapes. This kind of display is engaging and provides a nice alternative for what can sometimes be a monotonous world of stagnant text. However, as with any source these visualisations must be subjected to scrutiny. Without delving too far into the deep realm of epistemology it is important to assess what transforms data into knowledge and where do these visualisations fall on our triangle.

I am not an epistemologist, nor am I an expert in data analysis, but in the study of history we are trained to see that while we may never know the past in absolute certainty we can employ strategies to obtain as much knowledge as is within our power. My aim with this post is to highlight how humanities research techniques can be applied to data visualisations so that we can estimate the place of these visual products both on the triangle and within research.

To truly understand the risks in using data visualisations we need to look at the process behind their fncreation. The first is data collation.  During a recent lecture Dr Vinayak Das Gupta posited to our class that data was fact regardless of our state of knowing. Building on this, if we can accept that data is fact, and therefore non-negotiable, our emphasis must shift to the researcher who gathers this data. For any form of data collation, the researcher must set parameters to define their data by. These parameters decide which data is included and which is left behind. Coming from the humanities perspective this selection process is equal to the choice of selecting primary resources. A good researcher will aim to gather large quantities using clear and unbiased parameters in the same way that historians aim to use a wide variety of primary source material. Inherent in both disciplines is the possibility of biased selections. Therefore, when using data visualisations, as with secondary texts, it is essential to interrogate which how the data was collected and which parameters were applied.

Following on from data source selection is contextual support. In the same way that primary source material must be placed within its historical background so to must data visualisations be placed in context. In his TED talk on the subject David McCandless exhibits how visualisations without context can be dangerous and misleading (McCandless). McCandless begins with a visualisation comparing American military spending to other countries, as expected the visualisation returns a large red sector for America which dominates the screen. However, McCandless follows this nvisualisation with another placing American military spending within the context of American GDP. The new data context reveals a new side to the data in general and alters our view of military spending. Therefore, context is integral to understanding whether these visualisations are providing us with knowledge or a skewed reflection of manipulated data sets.

Therefore, data visualisations are very similar to a historian’s traditional realm of benefits. However, they come with added benefits. Thanks to data processing researchers can assess large quantities of data which would overwhelm if not outreach the traditional research. In addition to bringing a visually pleasing product to the reader visualisations can accomplish tasks that would be near impossible without the technology and allows us as researchers to see trends and patterns that we may have never noticed without this technology.

So where does this leave visualisations on the data-knowledge triangle. Despite their benefits there is no doubt that as with any other source visualisations need to be subjected to scrutiny and placed within context to be of true value. They, in themselves, are not enough to constitute knowledge and we cannot automatically assume that they are justified even though the data may be true. As a consequence, visualisations lie firmly in the information category. With added information and justification, they may assist us in our pursuit of knowledge but they in themselves are not enough to constitute it.

Further Reading:

‘Recorded Crime Offences by Type of Offence and Quarter’ cso.ie. Date accessed: 15 February 2017.

McCandless, David, ‘The Beauty of Data Visualisations’ TED Talks. https://www.youtube.com/watch?v=5Zg-C8AAIGg. Date accessed: 15 February 2017

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