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| Data Visualization Series |
Creating data visualizations nowadays became so easy that anybody can do it
with a minimum of effort and knowledge, which on one side is great for the
creators but can be easily become a nightmare for the readers, respectively
users. Just dumping data in visuals can be barely called data visualization,
even if the result is considered as such. The problems of visualization are
multiple – the lack of data culture, the lack of understanding processes, data
and their characteristics, the lack of being able to define and model
problems, the lack of educating the users, the lack of managing the
expectations, etc.
There are many books on data visualization though they seem an expensive
commodity for the ones who want rapid enlightenment, and often the illusion of
knowing proves maybe to be a barrier. It's also true that many sets of data
are so dull, that the lack of information and meaning is compensated by adding
elements that give a kitsch look-and-feel (aka chartjunk), shifting the
attention from the valuable elements to decorations. So, how do we overcome
the various challenges?
Probably, the most important step when visualizing data is to define the
primary purpose of the end product. Is it to inform, to summarize or to
navigate the data, to provide different perspectives at macro and micro level,
to help discovery, to explore, to sharpen the questions, to make people think,
respectively understand, to carry a message, to be artistic or represent
truthfully the reality, or maybe is just a filler or point of attraction in a
textual content?
Clarifying the initial purpose is important because it makes upfront the
motives and expectations explicit, allowing to determine the further
requirements, characteristics, and set maybe some limits in what concern the
time spent and the qualitative and/or qualitative criteria upon which the end
result should be eventually evaluated. Narrowing down such aspects helps in
planning and the further steps performed.
Many of the steps are repetitive and past experience can help reduce the
overall effort. Therefore, professionals in the field, driven by intuition and
experience probably don't always need to go through the full extent of the
process. Conversely, what is learned and done poorly, has high chances of
delivering poor quality.
A visualization can be considered as effective when it serves the intended
purpose(s), when it reveals with minimal effort the patterns, issues or facts
hidden in the data, when it allows people to explore the data, ask questions
and find answers altogether. One can talk also about efficiency, especially
when readers can see at a glance the many aspects encoded in the
visualization. However, the more the discovery process is dependent on data
navigation via filters or other techniques, the more difficult it becomes to
talk about efficiency.
Better criteria to judge visualizations is whether they are meaningful and
useful for the readers, whether the readers understood the authors' intent,
the further intrinsic implication, though multiple characteristics can be
associated with these criteria: clarity, specificity, correctedness,
truthfulness, appropriateness, simplicity, etc. All these are important in
lower or higher degree depending on the broader context of the visualization.
All these must be weighted in the bigger picture when creating visualizations,
though there are probably also exceptions, especially on the artistic side,
where artists can cut corners for creating an artistic effect, though also in
here the authors need to be truthful to the data and make sure that their work
don't distort excessively the facts. Failing to do so might not have an
important impact on the short term considerably, though in time the effects
can ripple with unexpected effects.
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