Big data and the missing links. (17th August 2016)

• Record Type: Journal Article
• Authors: De Veaux, Richard D.; Hoerl, Roger W.; Snee, Ronald D.
• Is Part Of: Statistical analysis and data mining. Volume 9:Number 6(2016)
• Page Start: 411
• Page End: 416
• Publication Date: 2016-08-17
• Subjects: data mining -- statistical engineering -- quality -- data integrity -- data science -- data quality -- ethics; Data mining -- Statistical methods -- Periodicals; 006.312
• DOI: 10.1002/sam.11303
• Languages: English
• ISSNs: 1932-1864

Abstract

Abstract : Although Big Data can have the potential to help researchers in science and industry solve large and complex problems, basic statistical ideas are often ignored in the Big Data literature. It is not true that simply having massive amounts of data renders subject‐matter models and experiments obsolete, alleviates the need to ensure data quality and no longer requires that variables accurately measure what they are supposed to. We refer to these fundamentals as missing links in the Big Data process. In this paper, we illustrate the challenges of making decisions from Big Data through a series of case studies. We offer some strategies to help ensure that projects based on Big Data analyses are successful. © 2016 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2016