A Data Science Central Community
Today’s need to leverage unprecedented amounts of available information has resulted in a flood of tools, services and models claiming to surface insights from Big Data. One model in particular, visualization, has received a lot of attention lately because of its abilities to organize and present information. However, visualization is actually one of the biggest barriers to insight because it places the burden of discovery on the user, and any tool that places the burden on the analyst is a game-stopper.
Data visualization is the study of the visual representation of data, meaning information that has been abstracted in some schematic form, including attributes or variables for the units of information. Humans are better equipped to consume visual data than text. As we know, a picture is worth a thousand words.
While visualization tools are interesting, they rely on human evaluation to extract insight and knowledge. The problem with this is that people often see what they are looking for and miss the breakthrough evidence they are actually seeking. It’s human nature: we see what we are conditioned to see and miss the fact that a gorilla just danced through the living room. But that’s just the beginning. The more severe limitation of visualization is it can only represent two or three dimensions before the amount of information is overwhelming. Visualizing a network of 10-100 friends is fine, but what happens when the data approaches one billion? Thus, while it is certainly a good test for small samples, it is not a sustainable method to gain insight into large volumes of shifting data.
In a previous blog, I wrote that given today’s explosion of “Big Data,” companies need more advanced methods for leveraging their data – methods that don’t rely solely on tribal knowledge, personal experience or best guesses. Like data mining, visualization is limited to manual endeavors. Why limit company success to antiquated methods that by design fail to leverage the data for all it’s worth? It’s time to usher in new methods and new technologies for transforming the enterprise from reactive (based on guesstimates, hunches, and flawed insight) to proactive (based on data-driven, actionable insight).