"A common mistake is that all visualization must be
simple, but this skips a step. You should actually design graphics that lend
clarity, and that clarity can make a chart 'simple' to read. However, sometimes
a dataset is complex, so the visualization must be complex. The visualization
might still work if it provides useful insights that you wouldn’t get from a
spreadsheet. […] Sometimes a table is better. Sometimes it’s better to show
numbers instead of abstract them with shapes. Sometimes you have a lot of data,
and it makes more sense to visualize a simple aggregate than it does to show
every data point."
"After you visualize your data, there are certain things
to look for […]: increasing, decreasing, outliers, or some mix, and of course,
be sure you’re not mixing up noise for patterns. Also note how much of a change
there is and how prominent the patterns are. How does the difference compare to
the randomness in the data? Observations can stand out because of human or
mechanical error, because of the uncertainty of estimated values, or because
there was a person or thing that stood out from the rest. You should know which
it is."
"Area can also make data seem more tangible or relatable, because physical objects take up space. A circle or a square uses more space than a dot on a screen or paper. There’s less abstraction between visual cue and real world." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)
"Context (information that lends to better understanding
the who, what, when, where, and why of your data) can make the data clearer for
readers and point them in the right direction. At the least, it can remind you
what a graph is about when you come back to it a few months later. […] Context
helps readers relate to and understand the data in a visualization better. It
provides a sense of scale and strengthens the connection between abstract
geometry and colors to the real world."
"Data is an abstraction of real life, and real life can be complicated, but if you gather enough context, you can at least put forth a solid effort to make sense of it."
"Data is more than numbers, and to visualize it, you must
know what it represents. Data represents real life. It’s a snapshot of the
world in the same way that a photograph captures a small moment in time. […] The
connection between data and what it represents is key to visualization that
means something. It is key to thoughtful data analysis. It is key to a deeper
understanding of your data. Computers do a bulk of the work to turn numbers
into shapes and colors, but you must make the connection between data and real
life, so that you or the people you make graphics for extract something of
value."
"Early exploration of a dataset can be overwhelming,
because you don’t know where to start. Ask questions about the data and let your
curiosities guide you. […] Make multiple charts, compare all your variables,
and see if there are interesting bits that are worth a closer look. Look at
your data as a whole and then zoom in on categories and individual data points.
[…] Subcategories, the categories within categories (within categories), are
often more revealing than the main categories. As you drill down, there can be
higher variability and more interesting things to see."
"Good visualization is a winding process that requires statistics and design knowledge. Without the former, the visualization becomes an exercise only in illustration and aesthetics, and without the latter, one of only analyses. On their own, these are fine skills, but they make for incomplete data graphics. Having skills in both provides you with the luxury - which is growing into a necessity - to jump back and forth between data exploration and storytelling."
"It’s tempting to look at data as absolute truth, because we associate numbers with fact, but more often than not, data is an educated guess. Your goal is to use data that doesn’t have large levels of uncertainty attached."
"Most data is linked to time in some way in that it might be a time series, or it’s a snapshot from a specific period. In both cases, you have to know when the data was collected. An estimate made decades ago does not equate to one in the present. This seems obvious, but it’s a common mistake to take old data and pass it off as new because it’s what’s available. Things change, people change, and places change, and so naturally, data changes." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)
"Most important, the range of data literacy and familiarity with your data’s context is much wider when you design graphics for a general audience."
"Numbers seem concrete and absolute, but estimates carry uncertainty with them. Data is an abstraction of what it represents, and the level of exactness varies."
"People often skip methodology because it tends to be
complex and for a technical audience, but it’s worth getting to know the gist
of how the data of interest was collected."
"Put everything together - from understanding data, to
exploration, clarity, and adapting to an audience - and you get a general
process for how to make data graphics."
"Readability in visualization helps people interpret data
and make conclusions about what the data has to say. Embed charts in reports or
surround them with text, and you can explain results in detail. However, take a
visualization out of a report or disconnect it from text that provides context
(as is common when people share graphics online), and the data might lose its
meaning; or worse, others might misinterpret what you tried to show."
"The best way to learn where people are is to show your work to those who don’t know your data. You get an immediate sense of understanding just from first impressions." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)
"The connection between data and what it represents is key to visualization that means something. It is key to thoughtful data analysis. It is key to a deeper understanding of your data. Computers do a bulk of the work to turn numbers into shapes and colors, but you must make the connection between data and real life, so that you or the people you make graphics for extract something of value."
"The data is a simplification - an abstraction - of the
real world. So when you visualize data, you visualize an abstraction of the
world, or at least some tiny facet of it. Visualization is an abstraction of
data, so in the end, you end up with an abstraction of an abstraction, which
creates an interesting challenge. […] Just like what it represents, data can be
complex with variability and uncertainty, but consider it all in the right
context, and it starts to make sense."
"Visualization can be appreciated purely from an aesthetic point of view, but it’s most interesting when it’s about data that’s worth looking at. That’s why you start with data, explore it, and then show results rather than start with a visual and try to squeeze a dataset into it. It’s like trying to use a hammer to bang in a bunch of screws. […] Aesthetics isn’t just a shiny veneer that you slap on at the last minute. It represents the thought you put into a visualization, which is tightly coupled with clarity and affects interpretation."
"Visualization is a medium: a way to explore, present, and
express meaning in data. […] Visualization is often framed as a medium for
storytelling. The numbers are the source material, and the graphs are how you
describe the source. When referring to stories or data narrative, I don’t mean
novels (but great if that’s what you’re after). Rather, I mean statistical
stories […]"
"Visualization is often thought of as an exercise in graphic design or a brute-force computer science problem, but the best work is always rooted in data. To visualize data, you must understand what it is, what it represents in the real world, and in what context you should interpret it in."
"Visualization is what happens when you make the jump from raw data to bar graphs, line charts, and dot plots. […] In its most basic form, visualization is simply mapping data to geometry and color. It works because your brain is wired to find patterns, and you can switch back and forth between the visual and the numbers it represents. This is the important bit. You must make sure that the essence of the data isn’t lost in that back and forth between visual and the value it represents because if you can’t map back to the data, the visualization is just a bunch of shapes."
"What is good visualization? It is a representation of data that helps you see what you otherwise would have been blind to if you looked only at the naked source. It enables you to see trends, patterns, and outliers that tell you about yourself and what surrounds you. The best visualization evokes that moment of bliss when seeing something for the first time, knowing that what you see has been right in front of you, just slightly hidden. Sometimes it is a simple bar graph, and other times the visualization is complex because the data requires it." (Nathan Yau, "Data Points: Visualization That Means Something", 2013)
"Without context, data is useless, and any visualization
you create with it will also be useless. Using data without knowing anything
about it, other than the values themselves, is like hearing an abridged quote
secondhand and then citing it as a main discussion point in an essay. It might
be okay, but you risk finding out later that the speaker meant the opposite of
what you thought."
"You have to know the who, what, when, where, why, and how
- the metadata, or the data about the data - before you can know what the
numbers are actually about. […] Learn all you can about your data before
anything else, and your analysis and visualization will be better for it. You
can then pass what you know on to readers."
"We often think of visualization as a design and programming task, but the process starts further back with the data. You have to understand the data - its trends and patterns, along with its flaws and imperfections - and the rest follows." (Nathan Yau)
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