Contact: Sophie Mohin, 914-740-2254, Smohin@liebertpub.com
Bigger (Data) Is Better and Can Improve Decision Making
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 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.”
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
Mary Ann Liebert, Inc., publishers is a privately held, fully integrated media company known
for establishing authoritative medical and biomedical peer-reviewed journals including OMICS: A Journal of Integrative Biology, Journal of Computational Biology, New Space, and 3D Printing and Additive Manufacturing. Its biotechnology trade magazine, Genetic Engineering & Biotechnology News (GEN), was the first in its field and is today the industry’s most widely read publication worldwide. A complete list of the firm’s more than 80 journals, newsmagazines, and books is available on the Mary Ann Liebert, Inc., publishers website.