Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
4-1-2021 12:00 AM
End Date
9-1-2021 12:00 AM
Description
Despite the growing body of literature on data-driven decision-making (DDDM) and, more recently, big data, empirical analyses on processes and strategies of government agencies toward DDDM are still scarce. To mitigate this gap in the literature, this study identifies and explains opportunities and challenges of data use and analytics found in a case of a U.S. state-government agency that is in charge of water quality management and has started to implement Evidence-Based Policy Making (EBPM). By drawing on four dimensions, data, technology, organization, and institutions, the results show how the organization’s DDDM practices are enabled or constrained by nine types of determinants: data quality/coverage, compatibility/interoperability, external data, information technologies/software, analytical techniques, cooperation, culture, privacy/confidentiality, and public procurement. Overall, the findings imply that either quality data or advanced analytic techniques alone do not guarantee effective DDDM; organizational and institutional support is also needed for successful implementation.
Towards Data-Driven Decision-Making in Government: Identifying Opportunities and Challenges for Data Use and Analytics
Online
Despite the growing body of literature on data-driven decision-making (DDDM) and, more recently, big data, empirical analyses on processes and strategies of government agencies toward DDDM are still scarce. To mitigate this gap in the literature, this study identifies and explains opportunities and challenges of data use and analytics found in a case of a U.S. state-government agency that is in charge of water quality management and has started to implement Evidence-Based Policy Making (EBPM). By drawing on four dimensions, data, technology, organization, and institutions, the results show how the organization’s DDDM practices are enabled or constrained by nine types of determinants: data quality/coverage, compatibility/interoperability, external data, information technologies/software, analytical techniques, cooperation, culture, privacy/confidentiality, and public procurement. Overall, the findings imply that either quality data or advanced analytic techniques alone do not guarantee effective DDDM; organizational and institutional support is also needed for successful implementation.
https://aisel.aisnet.org/hicss-54/dg/digital_transformation/7