Societal modeling for enhanced analytics by public policy officials and related personnel

Organizations and officials in the public sector traditionally have analyzed and viewed information in silos without comprehensive examination across subject matter domains. A serious problem results when purportedly standard information categories used across different government sources can actually represent multiple meanings due to differences in group values and terminology. Thus, the ability of organizations to analyze complex information in a timely manner is impaired. The literature suggests that the public sector could especially benefit from increased decision-making efficacy achieved through improved data access, tools, and transparency. Based in knowledge-driven problem solving theories and the theory of cross-case econometrics, the present study tested a conceptual societal logical data model (SLDM) that provided a process-centric tool for information-gathering and analysis. The SLDM was used to explore two research questions that first tested the fundamental effectiveness of the SLDM and graphical data modeling tools; and then assessed the added value of incorporating socioeconomic data from the IMF and other governmental sources to support complex public sector decision making. Results from ANOVA and t test analyses confirmed that the SLDM in concert with graphical tools provided an efficient, robust and flexible framework for socioeconomic data analysis in the public sector; and similar analyses verified the utility of including IMF and other governmental data in decision modeling. This study provides strong implications for social change by demonstrating that SLDM can provide efficient analytical capabilities for public policy and NGO officials in supporting decisions with socioeconomic data. In turn, the ability of these agencies to make more accurate and timely decisions can greatly add to their efficiency in using increasingly scarce resources.