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To Make Big Data Useful, Visualize

Michael DeVito
October 1, 2015

Big Data is, in a word, big.

Wikibon projects the Big Data market will top $84B in 2026, up from $27.36B in 2014, and an Accenture survey found that 89% of business leaders believe Big Data will revolutionize business operations in the same way the Internet did.

Big Data is booming so much that it even has the potential to change the way data centers operate in some specific cases. All around us, the rise of Big Data is changing the ways we collect, store, analyze, and utilize data in everyday business.

One of the biggest questions about Big Data is what exactly to do with it. That’s a tough question; there are thousands of uses for datasets like the ones many businesses are collecting today. While we expect the use case conversation to continue, sometimes, simpler is better.

As it turns out, a Harvard Business Review article by Jim Stikeleather from 2013 titled “The Three Elements of Successful Data Visualizations” may have value in a Big Data context. In the piece, Stikeleather discusses 3 elements of successful data visualizations that designers most often overlook. In the context of Big Data, these elements do something important: tell us how to show and explain a dataset.

  1. Understand the audience. Who are you targeting with your data visualization? How do they read and interpret the information? What type of information is most useful to them? Is the visualization exploratory or educational? Even when you're dealing with a massive dataset—say, data examining the issue of human-induced climate change—knowing what your audience values will give you hints as to what types of data to highlight, and how.
  2. Establish a clear framework. In order to establish common ground around a visualization, Stikeleather recommends setting up a clear framework of semantics and syntax under which the data information is designed to be interpreted. For example, lines connect, suggesting a relationship, while bars contain and separate. The framework you use for your data will have a significant impact on how it's interpreted. It's also essential to start with clean data.
  3. Tell a story. Finally, notes Stikeleather, visualization is really a dynamic form of persuasion—and few forms of communication are as persuasive as a compelling narrative. No matter what you do, it's essential that your visualization tell a story to the audience. While you certainly don't want to be disingenuous in how you present your information, good storytelling helps viewers gain insight from the data. Are you enabling your viewer to observe and understand your information, or further complicating or skewing an issue?

Knowing how to use the data you have at hand is half the battle. Applying these longstanding principles of data visualization to Big Data can help make it more useful—even if it doesn’t answer broader questions about the efficacy of Big Data in a business environment.

There’s much more to the Big Data equation than just visualizing datasets, but this is at least a good place to start.

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