Data visualization software tools have kept pace with the growth of data and data types. Even better, these tools are getting into the hands of regular business people.
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Without a way to show correlations, trends and outliers, organizations now busy collecting multiple terabytes of data won't be able to pull out actionable insights.
Fortunately, data visualization software tools have been keeping pace with the promiscuous growth of data and data types. Even more promising, these powerful tools are now getting into the hands of regular business people, those without advanced degrees in mathematics, statistics or computer programming.
"Every organization is in some form or fashion leveraging business intelligence, even with Excel spreadsheets," Jennifer Marchi, global product marketing manager for business intelligence at SAS, told InformationWeek in a phone call. "Visualization lets them explore their data and ask questions they didn't before."
SAS's flagship product in this category is Visual Analytics, which can be deployed onsite, in a private cloud, in a public cloud or in the SAS Cloud.
SAS promotes Visual Analytics' "easy-to-use, drag-and-drop interfaces." "In other words," reads the FAQ, "ordinary people can leverage extraordinary analytics -- and see the results in a visual way that reveals the truth in time to take action."
Because it is ad hoc and very interactive, the tool extends SAS's other report-based business intelligence products, and Marchi strongly hinted SAS's traditional analytic products would pick up these features over time.
Visual Analytics, introduced last March, already has more than 500 customers. One, the University of Texas, uses the tool to surface data to anyone with a Web browser about the school's performance, such as its loan-to-debt ratio and student demographics. The visualizations are on UT's System Productivity Dashboard, first launched at the end of 2011.
"As more info becomes available, more people want access to it," Marchi said. "We're not all data scientists, statisticians or quants, so we need to make that info easy to understand and consume."
A similar push to democratize analytics, and get insights into more hands, was behind Tableau Software's announcement in July of Tableau Online, a cloud-based version of its data-visualization-driven business intelligence (BI) platform.
Aimed at corporate users, Tableau Online also simplified the visualization tool pricing model: $500 per user, per year.
"Most of the [cloud BI] business models we've seen are overly complicated, with charges for N number of workbooks and reports, data-transfer and storage fees, and other complexities," Francois Ajenstat, Tableau's director of product management, told InformationWeek at the time.
But visualizations aren't just ways to help communicate a model's output. They are important tools for building that model in the first place.
"We see visualization as an important, integral part of not just communicating insights to decision-makers, but very important to your understanding and analyzing your data," said Michael Rappa, executive director of the Institute for Advanced Analytics and distinguished professor in the department of computer science at North Carolina State University. The Institute was the first place in the U.S. to offer a master of science in analytics (MSA) degree.
For this reason, visualization should be considered early, along with other upstream tasks, such as data capture and cleaning," Rappa said.
Visualization has made big strides in the past 48 months, Woopra CEO Elie Khoury told InformationWeek in a phone call. Instead of making business decision-makers wait "months and months for one visualization" because it takes expert engineers to parse and extract the data, "the whole idea is make use of data in real time, from multiple data sources," Khoury said.
Based in San Francisco, Woopra is a real-time customer analytics platform tracking over 200,000 websites, or 15 billion customer interactions monthly. Among its 3,000 paying customers are Gap, KLM and Ogilvy.
When it comes to using visualization effectively, Khoury said, organizations must aggregate data from multiple sources, import it into a model and make "decisions in minutes rather than months."
Automating data visualization dashboards is key, Lillian Pierson told InformationWeek in an email. "If the process can't be automated and scripted, then it can't be scaled," wrote Pierson, a spatial data scientist and data analytics engineer for Florida's Orange County government. She goes by the Twitter handle @BigDataGal.
While it's possible to manually manipulate data and build dashboards, reports and maps, Pierson asked, "How long will those be useful? How much time and resources will you spend continually updating them?"
Then again, automation isn't a panacea. "Automation is not an excuse for slacking on [quality control]," Pierson wrote. "Things go wrong, source data changes, things happen." Her advice? Automate and use scripts, but make sure to put in place quality-control measures.
"The biggest thing to know is that data visualization is hard. Really difficult to pull off well," David McCandless, data journalist, information designer and author of Information is Beautiful (Collins, 2000), wrote in an email to InformationWeek.
"It requires harmonization of several skills sets and ways of thinking: conceptual, analytic, statistical, graphic design, programmatic, interface-design, story-telling, journalism -- plus a bit of 'gut feel.'"
While the end result is often simple and beautiful, the process is "usually challenging and messy," McCandless wrote.
On the other hand, he said technologies like graphics, gaming, touch screens, interface design, interactive Web, open libraries, cross platforming, live data, APIs, and HTML 5 / JS are at an "evolutionary high point."
"Dataviz is an almost perfect creative use for this tech," McCandless wrote. "In fact, you probably need most of it to pull off a great interactive dataviz."
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