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Data Science and Management for Large Scale Empirical Applications in Agricultural and Applied Economics Research
Author(s) -
Woodard Joshua D.
Publication year - 2016
Publication title -
applied economic perspectives and policy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.4
H-Index - 49
eISSN - 2040-5804
pISSN - 2040-5790
DOI - 10.1093/aepp/ppw009
Subject(s) - variety (cybernetics) , data science , big data , computer science , scale (ratio) , data warehouse , agriculture , state (computer science) , data management , database , data mining , geography , algorithm , artificial intelligence , cartography , archaeology
The increased availability of high resolution data and computing power has spurred enormous interest in “Big Data”. While analysts typically source data from a wide variety of agencies, even within the USDA no comprehensive data warehouse exists with which researchers can interact. This leads to massive duplication in efforts, inefficient data sourcing, and great potential for error. The purpose of this article is to provide a brief overview of this state of affairs within the community. An overview of a prototype warehouse is also provided, as are thoughts on future directions.