A network of the ethos of political policy actors, stakeholders, the citizenry, and professionals with the data capacity of shared knowledge, policy-relevant knowledge, policy analytic data, policy coordination, policy innovation, principled belief systems, mutual expectations, collective action, and shared interpretations.
Published in Chapter:
Public Administration Curriculum-Based Big Data Policy-Analytic Epistemology: Symbolic IoT Action-Learning Solution Model
Emmanuel N. A. Tetteh (Norwich University, USA & Action Learning, Action Research Association, USA)
Copyright: © 2019
|Pages: 22
DOI: 10.4018/978-1-5225-7432-3.ch024
Abstract
The equilibration that underscores the internet of things (IoT) and big data analytics (BDA) cannot be underestimated at the behest of real-life social challenges and significant policy data generated to redress the concerns of epistemic communities, such as political policy actors, stakeholders, and the citizenry. The cognitive balancing of new information gathered by BDA and assimilated across the IoT is at the crossroads of ascertaining how the growing increases of such BDA can be better managed to transition from the big data state of disequilibration to reach a more stable equilibrium of policy data usefulness. In the quest for explicating the equilibration of policy data usefulness, an account of the curriculum-based MPA policy analysis and analytics concentration program at Norwich University is described as a case example of big data policy-analytic epistemology. The case study offers a symbolic ideology of an IoT action-learning solution model as a recommendation for fostering the stable equilibration of policy data usefulness.