from a communication perspective
European Data Incubator
Bilbao, 9th November 2018
artefact (US artifact) noun
1 An object made by a human being, typically one of cultural or historical interest.‘gold and silver artefacts’
2 Something observed in a scientific investigation or experiment that is not naturally present but occurs as a result of the preparative or investigative procedure.‘the curvature of the surface is an artefact of the wide-angle view’
The Oxford Dictionary of English
Maximum data density is
1:1, and this is not usually the case:
Some strategies to overcome this constraint:
1. Filter observations
2. Split data into multiple charts
Facets, trellis, small multiples.
3. Augmenting visualisations
Adding the time factor:
Require an additional effort for operational intelligence, where immediate decision making could be a requirement.
Source: Aragues 2018
How to communicate nothingness? (Kirk 2014)
How to communicate nothingness?
How to communicate nothingness?
Communicating uncertainty, projections,
and other non-factual data is challenging.
It is required to reduce dimensionality (statistically): PCA, factors, clustering.
A word of caution:
Xenographics: Weird but (sometimes) useful charts
(Lagner, Kister & Dachselt 2019)
virtual / augmented reality
The modern approach to data visualisation is focused on quickly making data visualisation.
Focus on speed affects:
Ultimately, data visualisation is not a technical problem, it’s a design problem and, more than that, a communication problem.
Let’s look at what charts say, mean, and do.
Charts do “show me the data” (but remember that it’s more that they tell the data than actually show it).
Means chosing the right specific chart to use in order to display and query the data.
How to improve: Expose data cleanly and clearly. Accuracy vs. precision.
No chart is an unbiased view of the data, as data visualisation is a manufactured artefact.
All data is transformed to be in a chart, and the inaction of not designing that transformation carries just as strong an implication as the action of transforming it.
The implicit channel of a data visualisation (the title and other framing elements) can be even more powerful than the explicit channel.
How to improve: Style should be intentional, purposeful and thematically appropriate, not the result of defaults or superficial decisions.
[…] all charts display data and all data is a proxy for the systems that created and measured that data.
How to improve: Caution not to reveal an underlying system that is proprietary or confidential.
Unlike the implicit channel, the descriptive channel is active and purposeful (not subconscious).
How to improve: Consider annotations, labels, axis elements as part of the data visualisation.
By being more explicit in our own understanding of what charts say and how we can systematically describe what they say, we can grow more capable of using the channels available in that expression to our advantage.
What does your chart say that you didn’t intend?
The mode and purpose of a chart should be well understood by the chart maker and immediately apparent to the chart reader.
Charts are products of their time.
It is important to provide background about the data sources, to enable checking whether they are still based on relevant priorities, dimensions and metrics.
Charts should be adapted to the culture they will be consumed in (think user-centered design techniques).
Enable removing and adjusting data visualisation elements to reduce complexity, not based on screen size as in responsive data visualisation, but on priority.
Meaning-making may sound too soft to the kind of technical professionals that make and read data visualisation but communication without meaning is just noise.
The most important thing about a chart is its impact.
Identify and emphasize the insights that the readers might expect.
As difficult to measure as it is important.
How have they impacted business decisions? How were they used in presentations? Where they modified (changed colours, cropped, annotated) somehow?
Cause visual literacy
All data visualisation was, at some point, complex data visualisation, until an audience grew comfortable and literate enough to read it.
Cause visual literacy
Create new charts
All communication is evaluated based on content, but persuasive communication, which is all data visualisation unless it is purely decorative, is rightly also evaluated based on effect.
This presentation is available at
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