Despite actionable insight is widely recognized as the outcome of data analytics, there is a lack of a systematic and commonly-shared definition for the term. More importantly, existing definitions are generally too abstract for informing the design of data analytics systems. This study proposes a definition for actionable insight in data analytics. For this purpose, this study conceptualizes actionable insight as a multi-component concept. The components, namely analytics insight, synergic insight, and prognostic insights are grounded in theories from multiple disciplines. Collectively, the components provide a holistic definition for actionable insight. Based on the multi-component view, a conceptual framework is introduced to actionable insight in detail, at both the concept-level and component-level. Each component's analytical, cognitive, and computational requirements are explained and relevant design considerations are suggested. We hope this study could be a rudimentary step toward the realization of data-centric data analytics that can deliver the promised actionable insight.
@article{arxiv.1606.03510,
title = {Defining and Conceptualizing Actionable Insight: A Conceptual Framework for Decision-centric Analytics},
author = {Shiang-Yen Tan and Taizan Chan},
journal= {arXiv preprint arXiv:1606.03510},
year = {2016}
}
Comments
ISBN# 978-0-646-95337-3 Presented at the Australasian Conference on Information Systems 2015 (arXiv:1605.01032)