“There isn’t any more truth in the world than there was before the Internet or the printing press. Most of the data is just noise, as most of the universe is filled with empty space.”
— Nate Silver, The Signal and the Noise
1 A difference that makes a difference
Information is made of data put together according to the chosen syntax. Well formed, meaningful data provide a semantic content. In fact, the elementary unit of information is ‘a distinction which makes a difference’ (D. McKay), often remembered as ‘a difference that makes a difference’ (G. Bateson).
The definition treated strictly within the theory of information describes distinction as a discrete state, a single datum. If we chose to move above the level of noise into a human context, meaningful bits of information are what we analyse into gems of insight, build reflections, and synthesise into knowledge and, hopefully, love, peace and understanding. Because of this, the value lies in the synthesis of information, not in the data itself.
2 “One needs to see clearly in order to understand.” — Le Corbusier
Information is only useful when it can be understood. Since our ability to harvest exponential amounts of data does not match our evolutionarily stable ability to process and reflect on them, we reach for data visualisations or infographics.
The choice of tools and strategies for presenting data is never logical, rational, objective and neutral at the same time — neither is technology itself. They are selected in an ambitious attempt to satisfy user needs while reflecting conventions, constraints and affordances of the data design. Even with the best intentions, any data display emphasises some things and hides the others, often promoting or demoting particular moral values and norms.
On the visual side, data design balances between high resolution, data dense visualisations that are often complex and multidimensional, and abstracted, simplified notation systems that compress the dimensionality of data into flat displays of bland universality.
3 “«The simpler the form of a letter the simpler its reading» was an obsession of beginning constructivism.” — J. Albers, Interaction of Color
Roughly speaking, if the data presentation aims to enhance understanding or provide credibility of evidence, data dense visualisations should be used. If the goal is to provide actionable insight, using simpler data design makes more sense.
(Obviously, there’s a point of simpleness at which no data design is necessary and using a signal will suffice. If the data design produces blatantly trivial results, perhaps only a verbal synthesis should be used.)
Unfortunately, misunderstood visual efficiency blurs that distinction. The notion that all visual queries should be processed rapidly is simply false. The consequent belief that every design should result in at-a-glance comprehension can only be explained by years of passive-aggressive relationship between graphic design and marketing.
Sadly, the obsession to express every kind of data in layers of standardised charts, diagrams and graphs has become a part of design toolbox and is since used with fierce commitment. Ever since, data design became synonymous with crunching numbers into pictorial statistics rather than devising elegant visual statements.
“What we seek instead (of simpleness of data design) is a rich texture of data, a comparative context, an understanding of complexity with an economy of means” — E. Tufte, Envisioning Information
4 Clarified ignorance
Simpleness of data display does indeed contribute to clarity of reading but it does not influence neither clarity nor quality of understanding. Clarifying complexity is different to simplifying complexity.
Engaging data selectively, picking the parts that seem familiar and ignoring the context puts the user in a state of clarified ignorance, in which they’re given a false assumption on being well informed on matters that contribute little to generating knowledge or consideration.
5 Information and ideas
In The Cult of Information Theodore Roszak suggested that the mind thinks with ideas, not information. Recognising relations takes you from raw data to information, identifying patterns transforms information into knowledge, but it takes understanding principles to convert it to wisdom.
The influx of data will continue to grow, yet “there isn’t any more truth in the world than there was before the Internet or the printing press”. The solution lies perhaps in representing visually information worth presenting, providing narratives than include the subtlety of human context rather than set of highly abstracted charts and most of all, designing tools than explain complexity by encouraging attentive thought. The data is just part of a story.
“In this context of information design, with its characteristic emphasis on users’ requirements, an awareness of the semantic dimension becomes all the more apposite: as a continual reminder that understanding is more than just a reception of messages, but entails a construction of meaning and that this ‘meaning’ is subject to influence from a very large set of factors.” — Robin Kinross, Unjustified texts *
Shneiderman’s OZFD model for visualising large data sets:
Visual displays of data should offer:
- Overviews to establish the scope and categories of a database and provide the context for attributes
- Zooming to access information at varying levels of granularity and keep the user oriented to the relationship between levels
- Filters to limit the kinds of information they zoom in and out based on attributes
- Details on demand to allow users to call up the finest levels of granularity when needed without cluttering the interface (via: Janet H. Murray, Inventing the Medium)