Why all this stuff is now happening.....now......on the Web (p.s., most
people have NEVER really liked to read...all text garbage....data visualization is now, here, and NOT a part of human's "history"
Fundamentally, our visual system is extremely well built for visual analysis.
There’s a huge amount of data coming into your brain through your eyes; the optic nerve is a very big pipe,
and it sends data to your brain very quickly (one study
estimates the transmission speed of the optic nerve at around 9Mb/sec). Once that data arrives at the brain, it’s rapidly
processed by sophisticated (brain native-resident) software that’s extremely good at tasks such as edge detection, shape
recognition, and pattern matching.
Data visualization is a general term used to describe any technology that lets corporate executives and ot her end
users “see” data in order to help them better understand the information and put it in a business context.
go beyond the standard charts and graphs used in Excel spreadsheets, displaying
data in more sophisticated ways such as dials and gauges, geographic maps, time-series charts, spark lines, heat
maps, tree maps and detailed
bar, pie and fever charts. Patterns, trends and correlations that might go undetected in text-based data can be exposed
and recognized easier with data visualization software.
Visualized data is frequently displayed in business intelligence (BI) dashboards and performance scorecards that provide
users with high-level views of corporate information, metrics and key performance
indicators (KPIs). The images may include interactive
capabilities, enabling users to manipulate them or drill into the data for querying and analysis. Indicators designed to alert
users when data has been updated or predefined conditions occur, can also be included.
Most business intelligence software vendors
embed data visualization tools into their products, either developing the visualization technology themselves or sourcing
it from companies that specialize in visualization.
Data visualization is the study of the visual representation of data, meaning “information which has been abstracted
in some schematic form, including attributes or variables for the units of information”.
According to Friedman (2008)
the “main goal of data visualization is to communicate information clearly and effectively through graphical means.
It doesn’t mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful.
convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather
sparse and complex data set by communicating its key-aspects in a more intuitive way. Yet designers often fail to achieve
a balance between design and function, creating gorgeous data visualizations which fail to serve their main purpose —
to communicate information”.
Data visualization is closely related to Information graphics, Information visualization,
Scientific visualization and Statistical graphics. In the new millennium data visualization has become active area of research,
teaching and development. According to Post et al (2002) it has united the field of scientific and information visualization”. ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Visualization is changing the way we - us humans - see the world, and process that "real time world data"...all
in milliseconds...as opposed to minutes, i.e., the medeival Europe peoples thought timelines....the "grandfathers and
grandmothers" of the creators of the Internet family....Especially the Web!
Dateline: Dullsville. This week the Tate Modern
museum in London unveiled a Damien Hirst retrospective that's about as fresh as one of its featured pieces: "A Thousand
Years" is an actual rotting cow's head. Why are we picking at these carcasses of creativity? We should instead be celebrating
the really new and relevant: the rise of the data visualizers. Their medium is the one with momentum, the one genuinely changing
how we think and feel. And it's about to boom.
At companies and universities, and far beyond, the goal of data-driven digital artists is
clear, not cynical: convey complex concepts quickly and crisply. They want to generate not Art-with-a-capital-A, necessarily,
but understanding. They take stone-cold data—units of information—and turn them into something warmly communicative.
Beautiful, too. So they become a pleasure for us to absorb.
Humans process information 17 times faster using sight than other senses,
according to one Danish physicist. Take Gareth Lloyd's Web-based "A History of the World in 100 Seconds": over 14,000
geo-tagged Wikipedia articles, digitally mapped from 499 B.C. to the present. You can watch the data points pop like fireworks
as they gradually form a glowing map of the continents. Hans Rosling's Gapminder website tracks data on the wealth and health
of nations from 1800 on. You watch as bubbles representing each country expand, contract and bounce along over time—almost
visualization has a history, of course. William Playfair invented four key visualization types in 1786, influencing Florence
Nightingale, among others. She used a graphic polar chart in 1859 to show that soldiers were dying from infections, not wounds.
But big data is today's specific bane. We all battle data obesity—too much information, not enough of it nutritious—and
crave experts to help us sort and savor it. Happily, technology has handed us, and them, the tools. Faster computers, new
"It's not unlike
a microscope—taking something you can't see and bringing it into the scale of perception," Aaron Koblin, 30, told
me at lunch in Google's San Francisco office. He's head of the company's Data Arts Team.
Koblin has temporally mapped text messages in New York. 'You really understand a lot about cities from flows,' he says.”
Mr. Koblin's work sits right on the line
between art and information. The shimmery tiles in eCloud, his installation at San Jose International Airport, change from
opaque to transparent depending on the global weather data they're receiving. His New York Talk Exchange project visualizes
the volume of long-distance telephone and Internet data between New York and other cities, revealing New Yorkers' relationships
with the world.
He has temporally
mapped text messages, too, in Amsterdam and New York. "You really understand a lot about cities from flows, when people
are awake and doing what things at what locations," says Mr. Koblin. "And you can say, people in Brooklyn tend to
get up later than people in Manhattan."
Add to that sonification:
sound reiterating what you see, helping to "storify" information, especially for those who are pitch-sensitive.
"You can turn data into rhythms," says Mr. Koblin, such as cable-box data. "CNBC has a constant rhythm, really
local. But CNN is really event-driven—and you get these crazy spikes."
exhibition space is online, not in a gallery. Still, some of Mr. Koblin's visualizations are part of the permanent collections
at the Museum of Modern Art in New York and the Centre Georges Pompidou in Paris. He was nominated for a Grammy for his Radiohead
video. And he co-created the first made-for-Web music video for Arcade Fire's "The Wilderness Downtown." Plug in
the address where you grew up and the video, using Google Street View, transports you back there. You can almost taste the
and entertainment in such delightfully shocking ways is what the coming age of data arts is all about: a flat screen reaching
out to smack, or sway, you. Talk about fresh. There are no putrefying cow heads here.
Over the last couple months,
I’ve written several posts trying to help those designers create better visuals. However, I’ve realized that I’ve
neglected those who are still somewhat skeptical and are unsure if data visualizations are worth it. So if you are a skeptic,
this post is for you. I want to take a minute and explain why data visualizations matter.
Before I jump immediately into singing the graces of data
visualization I want to take a minute and point out that not all data visualizations are created equal. As I have previously explained, when it comes to data visualization it really boils down to selecting the right
visualization for the job. In an article from O’reilly Radar by Julie Steele,
she writes that when it comes to data visualization they can be “at best confusing, and at worst misleading. But the
good ones are an absolute revelation.”
According to Steele, “The best data visualizations are ones that expose something new about the underlying
patters and relationships contained within the data” and I agree. The real power of data visualization is both interesting
and engaging, so if you can help the reader easily understand the information, while still presenting it in an entertaining
and engaging manner well then; you’ve got a real winner.
This is always easier said than done of course, partially because data visualizations are like “a new set of
languages you can use to communicate…the various kinds of data visualization are a kind of bidirectional encoding that
lets ideas and information be transported from the database into your brain.” Given that visualizations can be complex
and confusing, you need to select the right one for the job.
Explaining vs. Exploring
According to Steele, those designed for explaining
include “infographics and other categories of hand-drawn or custom-made images”. She explains that this type of
image is best when kept clean, pointing out the fact that the “ability to pare down the information to its simplest
form – to strip away the noise entirely – will increase the efficiency with which a decision maker can understand
it.” So, when designing “explaining” type visualizations, it’s good to remember less is more.
This can only be successfully done once you “understand what the data is telling you, and you want to
communicate that to someone else.” These are those visualizations you should see in power point presentations and reports.
visualizations can be more imprecise. Unlike “explaining” visualizations, “exploring” visualizations
are “useful when you’re not exactly sure what the data has to tell you.” They are great for when you’re
trying to get an understanding of the underlying patterns and relationships the data may have. Therefore Steele suggests that
“visualization for exploring is best done in such a way that it can be iterated and experimented upon, so that you can
find the signal within the nose. Software and automation are your friends here.”
Don’t forget about your customers
It’s important not to forget that your customers are
out there making important decisions too. Every day, they are analyzing complex interactive or animated graphics that chart
the differences between your company’s services versus the one your competitors provide. However, Steele rightfully
points out that “so far the tide of popularity has risen more quickly than the tide of visual literacy, and mediocre
efforts abound, in presentations and on the web.”
But as people are exposed to more visualizations and as the general visual literacy rises,
“data visualization will increasingly become a language your customers and collaborators expect you to speak –
and speak well.”
leave it to chance, hire a designer
It’s well worth the investment of hiring an in-house designer. To make sure you get the most out of your visualizations,
doesn’t it seem like a good idea to hire someone who speaks that language? Steele makes the analogy of using Google
Translate is great when you only want a general idea of what the message is about, but you wouldn’t trust it when writing
an important letter. Instead, you hire a translator. Do the same when it comes to data visualizations and you’ll be
much happier, the last thing you want to do is leave it to chance.
professionals have used traditional visualization tools like Bar charts, scatter graphs, and maps to envisage meaningful information.
This is bound to be challenged now. Traditional methods of data display have limited potential in making data easily digestible.
There is a growing need in the industry to have meaningful tools and methodologies that can combine the principles
of visualization with powerful applications and large data sets to create sophisticated images and animations.
Data Visualization is
the visual representation of data and helps with the analysis of information. It presents information in a way that’s
engaging, helps communicate complex ideas quicker and present it in a way that allows viewers to discover patterns that might
otherwise be hard to uncover.
Data visualizations offer ways to find trends and correlations that can lead to important discoveries.
Visualizations allow you to understand and process enormous amounts of information quickly because it is all represented
in a single image or animation. An effective release of data to display the information is a major portion of data analysis.
Formal analytics has given almost no guidance to this exposure and it is unclear how well effective visualization can be fitted
into any of the structures of analytics as applied in modern context. There is rarely a single visualization that answers
Instead, the ability to generate appropriate visualizations quickly is the key.
day examples of efficient data visualization include tag cloud, for instance. It uses text size to indicate the relative frequency
of use of a set of terms. More complex visualizations sometimes generate animations that demonstrate how data change over
time. Hans Rosling’s Trendalyzer software (acquired by Google) turned complex global trends into lively animations,
making decades of data pop. GE used data visualization to simplify the complexity of their huge data and drive a deeper understanding
of the context in which they operate.
More recently, visualizations have been created that reflect creative ways of representing all sorts of data visually,
with virtually no limit to what kind of information can be translated into an image. From the structure of a piece of music
to user relationships on social networks, from relationships between citations in scholarly journals to the possible moves
on a chess board; creative artists and data analysts are collaborating in all spheres of human lives to reveal interesting
stories behind the data. Visual.ly is the world’s largest community for sharing infographics and data visualizations
Some of these collaborations
would lead to new understanding about relationships between a wide range of variables from diverse fields. There would be
a growing reliance of animation tools like Flash and HTML5, allowing end users to control those visualizations real-time.
There are niche courses being designed
for professionals interested in building better visualization tools and systems. These courses address elements of cognitive
science theory, examine appropriate forms for representation and review design considerations. The next decade or so is envisaged
by many as the golden age of Data Visualization wherein dedicated vocation and tutoring would be conceived “to affect
thro’ the Eyes what we fail to convey to the public through their word-proof ears”.
Data visualization would continue to thrive
and designers would be putting more thought into what the data is about and discover much better, profound, creative and absolutely
fascinating ways to visualize it. For analytics professionals, visualization can be an important data-analysis tool, uncovering
key findings that would otherwise be difficult or impossible to perceive.