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Advanced data visualization guide: How visualizing data can boost BI

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Data visualization best practices help users envision business success

In a podcast interview, consultant Lee Feinberg discusses the potential benefits of data visualization and details best practices and common missteps.

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In a growing number of organizations, data visualization software is changing the way that business users and data analysts work with corporate information. One key reason: Data visualization tools enable users to view and manipulate data in a more instinctive way than they can with traditional reporting and analytics technologies, according to consultant Lee Feinberg. But during a podcast interview with SearchBusinessAnalytics, he cautioned listeners that project teams need to adhere to data visualization best practices in order to achieve success on deployments.

"The context of why visualization is so powerful is because as people we're really wired to process images much better than the way we've been working, which is just [with] columns and rows of numbers," said Feinberg, president and founder of DecisionViz, a consultancy in Westfield, N.J., that helps companies develop and implement data visualization strategies and processes. He said visualization tools also open the doors to analytical data to more business users in organizations, which can help improve decision making and instill a more data-driven culture internally. 

Getting it right begins with choosing the right software. Feinberg said features and functionality are important considerations when selecting data visualization technology, "but it's really critical now to understand how the tool will be used in the business." He added that if there are existing visualization tools in place in individual business units, project managers have to be clear about the roles they'll play going forward, with help from executive sponsors if needed to get everyone on the same page. "You don't want this to become a battle between different parts of the company," he said.

For companies starting out on data visualization initiatives, the biggest mistake Feinberg cited is thinking that deploying a tool automatically engenders visualization expertise -- an expectation that can lead projects astray. "Because the tools are so easy to work with now, the risk is that you can create higher volumes of better-looking visuals in less time," he said. "But if the visuals really aren't helping you make decisions and drive action, then they're not going to add [business] value -- and they can actually take away from value if they become a distraction."

That becomes even more of a risk when big data and advanced data visualization are part of the equation. Big data systems increase both the amount and types of data available for analysis. And advanced visualization platforms enable users to go beyond operational reporting and directly analyze data to answer exploratory questions. But Feinberg warned against giving business users "a wheelbarrow full of information" and expecting them to make sense of it through the use of data visualization techniques. "Big data challenges us more than ever to really understand what's important to the business and then figure out what data we need to support the business in making their decisions," he said.

In the 15-minute podcast, moderated by SearchBusinessAnalytics executive editor Craig Stedman, Feinberg further discussed the growth of advanced data visualization technologies and his list of data visualization best practices. Listeners will learn the following:

  • the potential benefits of data visualization tools
  • both the distinctions and similarities between mainstream and advanced data visualization technologies
  • how business intelligence and analytics teams can work together to best support data visualization users
  • Feinberg's advice on data visualization best practices to follow and common missteps to avoid

Text by Emma Preslar, editorial assistant. Email us at editor@searchbusinessanalytics.com and follow us on Twitter: @BizAnalyticsTT.

This was first published in December 2013

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Essential Guide

Advanced data visualization guide: How visualizing data can boost BI

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