Noah Iliinsky, a data visualization expert at IBM, writes for Information Week about some of the errors to avoid when visualizing big data:
Avoid common visualization mistakes. Here's advice on how to clarify goals and get better results.
There has been a lot of talk about data visualization lately -- almost as much as there has been about big data. We're told that visualization is the best way (or the only way) to understand data, and that if we're not visualizing it, we're missing out.
Visualization is a great way to gain and share insight, but many big data teams are doing it the wrong way. How can it be done wrong? It turns out there are several ways to undermine data visualizations. Let's look at a few of the most common mistakes.
Error 1: Displaying all the data
Despite what you were told in school, most people don't care about seeing your work. They don't care about how much data you can process every day or how big your Hadoop cluster is. Customers and internal users want specific, relevant answers, and the sooner they can get those answers, the better. The closer you can come to giving them exactly what they want, the less effort they have to expend looking for answers. Any irrelevant data on the page makes finding the relevant information more difficult; irrelevant data (no matter how valid) is noise.
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