What are the implications of big data in terms of big impacts? Our research focuses on the question, “How are data analytics involved in policy analysis to create complementary values?” We address this from the perspective of bibliometrics. We initially investigate a set of articles published in Nature and Science, seeking cutting-edge knowledge to sharpen research hypotheses on what data science offers policy analysis. Based on a set of bibliometric models (e.g., topic analysis, scientific evolutionary pathways, and social network analysis), we follow up with studies addressing two aspects: (1) we examine the engagement of data science (including statistical, econometric, and computing approaches) in current policy analyses by analyzing articles published in top-level journals in the areas of political science and public administration; and (2) we examine the development of policy analysis-oriented data analytic models in top-level journals associated with computer science (including both artificial intelligence and information systems). Observations indicate that data science contribution to policy analysis is still an emerging area. Data scientists are moving further than policy analysts, due to technical difficulties in exploiting data analytic models. Integrating artificial intelligence with econometrics is identified as a particularly promising direction.
Author(s): Yi Zhang, Alan L. Porter, Scott Cunningham, Denise Chiavetta, Nils Newman
Organization(s): University of Technology, Georgia Institute of Technology, Delft University of Technology
Source: 2018 Portland International Conference on Management of Engineering and Technology (PICMET)