The reader and reading data visualisation

Posted on May 11th, 2013 by Chris Twigg

Alberto Manguel writes about reading and readers, shares his Notes Towards a Definition of the Ideal Reader.

I recently thought about stories in data visualisation as being like vignettes, like an entry point to prompt the reader into a journey of interpretation and discovery of a bigger text. So much emphasis seems to be upon what the reader does in data visualisation – whether they are guided through a presentation of data or are free to roam through it to make up a story.

Some of Manguel’s thoughts spur ideas behind reading as an activity impacting on how individuals make meaning out of data visualisation storytelling. What does the reader expect from a text, what is their motive for reading, and how does the reader’s characteristic attitude toward reading impact on how they interpret story? Some ideas and implications:

‘The ideal reader does not reconstruct a story: he recreates it.’

Implies: you are not looking for what has already been added up and verified in a data visualisation but you want to take the source data behind it and model it again to decide if it really stacks up.

‘Robinson Crusoe is not an ideal reader. He reads the Bible to find answers. An ideal reader reads to find questions.’

Implies: you are not looking for an ‘end’ in a data visualisation story so much as a great starting point spurring further conversation and other possible stories. You are comfortable with this uncertainty.

‘The ideal reader is the translator. He is able to dissect the text, peel back the skin, slice down to the marrow, follow each artery and each vein and then set on its feet a whole new sentient being.’

Implies: you are familiar with the source data and the methods used to aggregate and visually encode them, and you have the motives and resources to respond to the visualisation in kind.

‘For the ideal reader all devices are familiar.’

Implies: you’re aware of any device that has gone towards making the data visualised in front of you – e.g. curation, aggregation, rhetoric, encoding.

‘The ideal reader subverts the text. The ideal reader does not take the writer’s word for granted.’

Implies: you are playful and you read a data visualisation from a position of scepticism.

‘Writing on the margins is a sign of the ideal reader.’

Implies: you want to and can (either physically or virtually) write notes and thoughts on a data visualisation – like as an aide memoire or when contributing to a discussion around issues that the text raises.

‘The ideal reader proselytizes.’

Implies: you feel compelled to take a data visualisation as basis for converting a person from one belief to another.

‘The ideal reader is not concerned with anachronism, documentary truth, historical accuracy, topographical exactness. The ideal reader is not an archeologist.’

Implies: you are concerned with something higher than any exactitude presented in a data visualisation – the why question is more important for you than the who what when and where.

‘The ideal reader is never impatient.’

Implies: when you read a data visualisation, if the answer (or even the question) is not obvious, you will persevere.

‘A writer is never his own ideal reader.’

Implies: you derive most pleasure and utility looking at data visualisations other people have created – not so should you want (and be able) to create your own.

 

These implications are not necessarily right or wrong, or indeed what Manguel implies – just a bunch of ideas that throw interesting challenges towards who we assume the reader is or what we assume their motives are when they engage with a data visualisation.

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