توضیحات
ABSTRACT
The recent advent of remote sensing, mobile technologies, novel transaction systems, and highperformance
computing offers opportunities to understand trends, behaviors, and actions in a manner that has not been previously possible. Researchers can thus leverage “big data” that are generated from a plurality of sources including mobile transactions, wearable technologies, social media, ambient networks, and business transactions.Anearlier Academy of Management Journal (AMJ) editorial explored the potential implications for data science in management
research and highlighted questions for management scholarship as well as the attendant challenges of data
sharing and privacy (George, Haas, & Pentland, 2014). This nascent field is evolving rapidly and at a speed
that leaves scholars and practitioners alike attempting to make sense of the emergent opportunities that big
data hold.With the promise of big data come questions about the analytical value and thus relevance of these
data for theory development—including concerns over the context-specific relevance, its reliability and its validity
To address this challenge, data science is emerging as an interdisciplinary field that combines statistics, data mining, machine learning, and analytics to understand and explainhowwecan generate analytical insights and prediction models from structured and unstructured big data. Data science emphasizes the systematic study of the organization, properties, and analysis of data and their role in inference, including our confidence inthe inference (Dhar, 2013).Whereas both big data and data science terms are often used interchangeably, “big data” refer to large and varied data that can be collected and managed, whereas “data science” develops models that capture, visualize, and analyze the underlyingpatterns inthe data.In this editorial, we address both the collection and handling of big data and the analytical tools provided by data science for management scholars. At the current time, practitioners suggest that data science applications tackle the three core elements of big data: volume, velocity, and variety (McAfee & Brynjolfsson, 2012; Zikopoulos & Eaton, 2011). “Volume” represents the sheer size of the dataset due to the aggregation of a large number of variables and an even larger set of observations for each variable.
Year : 2016
Publisher : Academy of Management Journal
By : FROM THE EDITORS
File Information : English Language /15 Page / Size : 350 K
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سال : 2016
ناشر : Academy of Management Journal
کاری از : FROM THE EDITORS
اطلاعات فایل : زبان انگلیسی / 15 صفحه / حجم : 350 K
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