PUBLIC RELEASE DATE:

21-Jan-2014

Contact: Sophie Mohin smohin@liebertpub.com 914-740-2100 x2254 Mary Ann Liebert, Inc./Genetic Engineering News

New Rochelle, January 21, 2014 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 Junqu 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."

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About the Journal

Big Data, published quarterly in print and online, facilitates and supports the efforts of researchers, analysts, statisticians, business leaders, and policymakers to improve operations, profitability, and communications within their organizations. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address the challenges and discover new breakthroughs and trends living within this information.

About the Publisher

See the rest here:
Bigger (data) is better and can improve decision making

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