Extraction of knowledge from open government data

Abstract

Purpose The purpose of this paper is to establish a new conceptual iterative framework for extracting knowledge from open government data (OGD). OGD is becoming a major source for knowledge and innovation to generate economic value, if properly used. However, currently there are no standards or frameworks for applying knowledge continuum tactics, techniques and procedures (TTPs) to improve elicit knowledge extraction from OGD in a consistent manner. Design/methodology/approach This paper is based on a comprehensive review of literature on both OGD and knowledge management (KM) frameworks. It provides insights into the extraction of knowledge from OGD by using a vast array of phased KM TTPs into the OGD lifecycle phases. Findings The paper proposes a knowledge iterative value network (KIVN) as a new conceptual model that applies the principles of KM on OGD. KIVN operates through applying KM TTPs to transfer and transform discrete data into valuable knowledge. Research limitations/implications This model covers the most important knowledge elicitation steps; however, users who are interested in using KIVN phases may need to slightly customize it based on their environment and OGD policy and procedure. Practical implications After its validation, the model allows facilitating systemic manipulation of OGD for both data-consuming industries and data-producing governments to establish new business models and governance schemes to better make use of OGD. Originality/value This paper offers new perspectives on eliciting knowledge from OGD and discussing crucial, but overlooked area of the OGD arena, namely, knowledge extraction through KM principles.