When Is Big Data Too Big? Making Data-Based Models Comprehensible

NEW ROCHELLE, N.Y. - July 11, 2016 - PRLog -- Data-driven mathematical modeling is having an enormous impact on the ability to organize and describe very large data sets, and make inferences and predictions about populations and situations based on sampling data. However, as these models become increasingly complex, the ability of users to understand and apply them represents a growing challenge. The article "A Framework for Considering Comprehensibility in Modeling (http://online.liebertpub.com/doi/full/10.1089/big.2016.0007)," which describes this emerging dilemma and a strategy for developing solutions, is published in Big Data, the highly innovative, peer-reviewed journal from Mary Ann Liebert, Inc., publishers (http://www.liebertpub.com/). The article is available free for download on the Big Data (http://online.liebertpub.com/doi/full/10.1089/big.2016.0007) website until August 8, 2016.

Michael Gleicher, University of Wisconsin-Madison, defines comprehensibility as "the ability of the various stakeholders to understand relevant aspects of the modeling process." He suggests that comprehensibility should be a key goal in model development. However, as models become more sophisticated, tradeoffs may be inevitable--even between understandability and accuracy--in some cases, improving comprehensibility may help achieve other goals in modeling.

"Gleicher provides a holistic framework of comprehensibility that considers what the various stakeholders in a data science project do and don't understand easily and their need for comprehensibility," says Big Data Editor-in-Chief Vasant Dhar, Professor at the Stern School of Business and the Center for Data Science at New York University. "More broadly, the article highlights comprehensibility from a human-centric standpoint, identifying the role and needs of humans in complex data science projects."

About the Journal
Big Data (http://www.liebertpub.com/big), published quarterly online with open access options and in print, 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. Complete tables of content and a sample issue may be viewed on the Big Data (http://www.liebertpub.com/big) website.

About the Publisher
Mary Ann Liebert, Inc., publishers (http://www.liebertpub.com/)  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, GEN (Genetic Engineering & Biotechnology News), 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 (http://www.liebertpub.com/)  website.