Data 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 hopefully.

Data 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 programming languages.

"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.

“Mr. 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."

Tomorrow's prime 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 chocolate milk.

Delivering information 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

Explaining

Exploring

“Exploring” 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.”

Don’t 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.

Sure 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.