Analytics in the Pursuit of Knowledge: Adapting the Knowledge Pyramid

Abstract

Advances in storage leading to the Internet of Things (IOT) and Big Data has exponentially increased the Data aspect of the traditional Knowledge Pyramid-Data-Information-Knowledge-Wisdom (DIKW). This paper presents an adaptation of the Knowledge Pyramid as an Analytics Pyramid in which Time is posited to represent Wisdom as the pinnacle achievement when pursuing knowledge. Analogies of the DIKW are presented from the Analytics Pyramid as Description-Aggregation-Modeling-Time. Implementing the premise of the Analytics Pyramid focuses on an interative/repetitive movement of both individuals and organizations through all Description-Aggregation-Modeling-Time stages in order to build and obtain the Wisdom pursued in the traditional Knowledge Pyramid. This model reinforces organizational learning and the importance of adaptability when pursuing knowledge. In addition, the wisdom gained from analytics is only recognized when monitored business processes are longitudinal in nature. Organizational analytics must rely on the recognition of a changing environment (Time) in order to adapt.

Visual Analytics: Transferring, Translating and Transforming Knowledge from Analytics Experts to Non-technical Domain Experts in Multidisciplinary Teams

“…Other researchers, such as (Dilla et al, 2010;Dilla & Raschke, 2015;Huber et al, 2018;Phillips-Wren & McKniff, 2020), found that IDV improves decision-makers' perceptions, decisions and impact in various contexts, from finance to healthcare operations. These studies highlight the importance of experiential knowledge of non-technical decision makers (Freeze, 2018).…”