Big Data & the Future of DataViz

October 7, 2014

Today’s modern society is easily characterized as the era of “big data” – a term for the truly massive amounts of data created and consumed by Internet users. What is “big data”? Data is quite literally everywhere – recorded, stored, and analyzed. IBM has classified big data into four elements: volume, the scale of data; velocity, the analysis of streaming data; variety, the different forms of data; and veracity, the uncertainty of data. Information has never been generated at such a fast pace with no signs of decreasing any time soon. For instance, IBM estimates that 43 trillion gigabytes of data will have been created by 2020, and that 2.3 trillion gigabytes are created each day. From a social media data standpoint, in a month: 30 billion posts are shared on Facebook, over 4 billion hours of video are watched on YouTube, and 400 million tweets are posted on Twitter. In today’s digital world, the act of simply going about one’s day – browsing and searching, buying things, sharing information, and communicating with others – creates immense trails of data, according to the McKinsey Global Institute.

 

Big data is the future, and the future is here. However, without the proper ways to understand all of this data, patterns and insights may go unobserved. As claimed by management author Phil Simon, “it’s data visualization that allows big data to unleash its true impact… the necessary ingredient in bringing the power of big data to the mainstream.” He argues that data visualization, or dataviz, is having its moment – made possible by “the rise of big data and the growing public awareness of its power… a new, data-oriented mindset is permeating the business world.”     

 

Dataviz are critical components of our big-data world. As Carlos Scheidegger, who works on visualization and data analysis for AT&T Research affirms, “well-designed data graphics can be both beautiful and meaningful. As visualizations take center stage in a data-centric world, researchers and developers spend much time understanding and creating better visualizations; but they spend just as much time understanding how tools can help programmers and designers create visualizations faster, more effectively, and more enjoyably.”

One of the best ways to utilize dataviz is through the infographic. The difference between data visualization and an infographic isn’t set in stone, it can be argued that dataviz is a tool used to interactively explore data, whereas an infographic is a visual display with the intent of making a specific point. The use of infographics to display data has been steadily on the rise during the past few years, primarily thanks to social media and the ready availability of data. Infographic design and social media marketing company NowSourcing has determined that the most popular topics for infographics include technology, business, social media, health, and the economy, and that potentially fifteen million individuals can view a single infographic. However, some consider the Buzzfeed-esque nature of today’s dominating social media infographics to be “crappy promotional infographics churned out for viral attention” and that the experimental data visualization infographic is, in fact, declining in use.

 

“Once a playground for independent designers, data visualization has evolved into something more mature, corporate, and honest about its feelings. The quirky, experimental infographics that once peppered the Internet may be disappearing, but that’s only because data visualization, as a medium, has finally grown up and gotten a job,” writes Fast Company Design columnist Mark Wilson. “Years ago, the hardest part of a data visualization designer’s job was explaining what he did and why it was worthwhile. Today, organizations ranging from [U.S.] presidential campaigns to the World Bank seek out data visualization specialists. Business is good. A few years ago, companies engaged in complex financial negotiations to get a visualization done, now it’s a standard, budgeted line item.”

 

Additionally, data visualization has become much simpler to create with the development of software specifically designed to create data visualizations. In the not-so-distant past, designers had to completely custom-code their visualizations from scratch. With the multitude of software options available to organizations, a company doesn’t have to “spend thousands or millions of dollars to get going with dataviz,” author Phil Simon asserts. These new tools have increasingly become more powerful and universal over the last decade. No longer are the days when IT personnel are needed to generate reports for their non-technical co-workers. These programs have made it easier than ever for any employees to quickly discover new exciting ways to display large datasets. Employees are now beginning to interact with their data, and are learning new things about their businesses in the process.

 

Regardless of all the positive aspects of data visualization software, some view this new simplicity as a negative thing, that simplicity sacrifices creativity. “Take D3,” writes Mark Wilson, “It’s a JavaScript library that helps turn information into any number of pre-coded visual frameworks. Most experts I spoke to agree that D3 is a superb tool with a strong community of supporters, including both hardcore statisticians and designers. Publications like the New York Times use D3 in their work daily, but its pre-canned tools have eaten into much of the quirk that’s lacking in many new data visualizations.” Whether infographics have truly sold out to the corporate world and forgone their eccentricity, or the corporate world has simply seen the light regarding the use of dataviz, big data, and their company’s future remains to be seen.

 

“Infographic and visualization design is very much a subjective experience, and tools won’t ever replace careful, thoughtful design,” says Scheidegger, “But the more they [designers of dataviz software] automate the boring, repetitive, and yet invaluable bookkeeping processes of our design workflows, the more time we can spend understanding and exploring the design space, and we will be better designers and data analysts for it.”

 

 

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