Abstract

Sensemaking is often associated with processing large or complex amounts of data obtained from diverse and distributed sources. Sensemaking is an important process for any business, since it deals with understanding data and facts that relate to unknown or ambiguous situations. To-date, the research base on sensemaking has not moved far from the conceptual realm however. Our vision here is to operationalise sensemaking in order to improve the human decision-making process (ultimately in the context of large data volumes in a business context). This study contributes to the knowledge base by proposing a novel conceptual framework that utilises Data Mining (DM) and Machine Learning (ML) to assist in transforming user interactions with the analytical software that models sensemaking patterns. These patterns reflect people’s experience during the analysis and exploration of the data related to the emergent ambiguous situation.

Recommended Citation

Lycett, M. & Marshan, A. (2016). Capturing Sensemaking Pattern during Data Analysis: A Conceptual Framework. In J. Gołuchowski, M. Pańkowska, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development: Complexity in Information Systems Development (ISD2016 Proceedings). Katowice, Poland: University of Economics in Katowice. ISBN: 978-83-7875-307-0. http://aisel.aisnet.org/isd2014/proceedings2016/ISDMethodologies/4.

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Capturing Sensemaking Pattern during Data Analysis: A Conceptual Framework

Sensemaking is often associated with processing large or complex amounts of data obtained from diverse and distributed sources. Sensemaking is an important process for any business, since it deals with understanding data and facts that relate to unknown or ambiguous situations. To-date, the research base on sensemaking has not moved far from the conceptual realm however. Our vision here is to operationalise sensemaking in order to improve the human decision-making process (ultimately in the context of large data volumes in a business context). This study contributes to the knowledge base by proposing a novel conceptual framework that utilises Data Mining (DM) and Machine Learning (ML) to assist in transforming user interactions with the analytical software that models sensemaking patterns. These patterns reflect people’s experience during the analysis and exploration of the data related to the emergent ambiguous situation.