Sinan Aral from Harvard Business Review writes on visualizing big data, allowing for better insight into it.
Although data visualization has produced some of the most captivating artistic displays in recent memory, some of which have found their way into exhibits at the New York Museum of Modern Art and countless art installations around the world, business leaders are asking: is data visualization actionable?
I think so. In my role as the Scholar-in-Residence at The New York Times R&D Lab, I am collaborating with one of the world's most advanced digital R&D teams to figure out how we can draw actionable insights from big data.
How big? Massive: We are documenting every tweet, retweet, and click on every shortened URL from Twitter and Facebook that points back to New York Times content, and then combining that with the browsing logs of what those users do when they land at the Times. This project is a relative of the widely noted Cascade project. Think of it as Cascade 2.0.
We're doing this to understand and predict when an online cascade or conversation will result in a tidal wave of content consumption on the Times, and also when it won't. More importantly we are interested in how the word-of-mouth conversation drives readership, subscriptions, and ad revenue; how the Times can improve their own participation in the conversation to drive engagement; how we can identify truly influential readers who themselves drive engagement; and how the Times can then engage these influential users in a way that complements the users' own needs and interests. Do it, and we can turn that statistical analysis, as you'll see below, into elegant, artistic real time data streams.
Handling the streams, archiving the sessions and storing and manipulating the information are in themselves herculean tasks. But the even bigger challenge is transforming beautiful, big data into actionable, meaningful, decision-relevant knowledge. We've found that visualization is one of the most important guideposts in this search for knowledge, essential to understanding where we should look and what we should look for in our statistical analysis.
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Source: Harvard Business Review
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