Too much information can be overwhelming, but when it comes to certain types of data that are used to build predictive models to guide decision making there is no such thing as too much data, according to an article in Big Data, the highly innovative, peer-reviewed journal from Mary Ann Liebert, Inc., publishers. The article is available on the Big Data website.
To determine whether more data is really better for predictive modeling, Enric Junqué de Fortuny and David Martens, University of Antwerp, Belgium, and Foster Provost, New York University, NY, tested nine different applications in which they built models using a particular type of data called fine-grained data, such as observing an individual's behavior in a certain setting. In the article "Predictive Modeling with Big Data: Is Bigger Really Better?" the authors state that "certain telling behaviors may not be observed in sufficient numbers without massive data."
"The power of any analytic tool is in using it appropriately," says Founding Editor, Edd Dumbill. "Sweeping assumptions such as 'bigger is better' can be dangerous. This paper significantly advances our knowledge of when massive datasets improve decision-making ability."